Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2333-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2333-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 May 2024
Data description paper |
| 14 May 2024
| 14 May 2024
A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015
Kanishka B. Narayan
CORRESPONDING AUTHOR
Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL), College Park, Maryland, USA
Brian C. O'Neill
Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL), College Park, Maryland, USA
Stephanie Waldhoff
Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL), College Park, Maryland, USA
Claudia Tebaldi
Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL), College Park, Maryland, USA
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Cited articles
Babones, S. J. and Alvarez-Rivadulla, M. J.: Standardized income inequality data for use in cross-national research, Sociol. Inq., 77, 3–22, 2007.
Badel, A., Huggett, M., and Luo, W.: Taxing top earners: a human capital perspective, Econ. J., 130, 1200–1225, 2020.
Bank, W.: PovcalNet, https://data.worldbank.org/indicator/SI.POV.DDAY (last access: 5 May 2024), 2015.
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.
Chancel, L. and Piketty, T.: Global income inequality, 1820–2020: the persistence and mutation of extreme inequality, J. European Econ. A., 19, 3025–3062, 2021.
Chotikapanich, D.: Modeling income distributions and Lorenz curves, Springer Science & Business Media, ISBN 9780387727561, 2008.
Deaton, A. and Zaidi, S.: Guidelines for constructing consumption aggregates for welfare analysis (English), Living standards measurement study (LSMS) working paper, no. LSM 135 Washington, D.C., World Bank Group, http://documents.worldbank.org/curated/en/206561468781153320/ Guidelines-for-constructing-consumption-aggregates-for-welfare-analysis (last access: 7 May 2024), 2002.
Frank, M. W.: Inequality and growth in the United States: Evidence from a new state-level panel of income inequality measures, Econ. Inq., 47, 55–68, 2009.
Fujimori, S., Hasegawa, T., and Oshiro, K.: An assessment of the potential of using carbon tax revenue to tackle poverty, Environ. Res. Lett., 15, 114063, https://doi.org/10.1088/1748-9326/abb55d, 2020.
G. Ferreira, F. H., Lustig, N., and Teles, D.: Appraising cross-national income inequality databases: An introduction, J. Econ. Inequal., 13, 497–526, 2015.
Hallegatte, S. and Rozenberg, J.: Climate change through a poverty lens, Nat. Clim. Change, 7, 250–256, https://doi.org/10.1038/nclimate3253, 2017.
Hughes, B., Irfanm, M. T., Khan, H., Kumar, K. B., Rothman, D. S., and Solorzano, J. R.: Patterns of Potential Human Progress: Reducing Global Poverty, 1, ISBN 9781594516405, 2009.
Hughes, B. B.: International futures: Building and using global models, Academic Press, ISBN 978-0128042717, 2019.
Jafino, B. A., Walsh, B., Rozenberg, J., and Hallegatte, S.: Revised estimates of the impact of climate change on extreme poverty by 2030, 2020.
Lakner, C. and Milanovic, B.: Global income distribution: from the fall of the Berlin Wall to the Great Recession, World Bank Econ. Rev., 30, 203–232, 2016.
Narayan, K., Casper, K., O'Neill, B. C., Tebaldi, C., and Waldhoff, S.: JGCRI/pridr: software package that can represent and project income distributions dynamically in R (v0.1.0), Zenodo [software], https://doi.org/10.5281/zenodo.7468250, 2022a.
Narayan, K. B., O'Neill, B. C., Waldhoff, S., and Tebaldi, C.: A consistent dataset for net income deciles for 190 countries, aggregated to 32 geographical regions and the world from 1958-2015 (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7093997, 2022b.
Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., and Tebaldi, C.: Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways, Environ. Res. Lett., 18, 044013, https://doi.org/10.1088/1748-9326/acbdb0, 2023.
Pachauri, P.: WIDER Working Paper 2020/65-Explaining income inequality trends: an integrated approach, United Nations University World Institute for Development Economics Research. Finland, https://policycommons.net/artifacts/1908953/wider-working-paper-202065-explaining-income-inequality-trends/2660129/ (last access: 8 May 2024), CID: 20.500.12592/kmjp4x, 2020.
Piketty, T. and Saez, E.: Income inequality in the United States, 1913–1998, Q. J. Econ., 118, 1–41, 2003.
Rao, N. D. and Min, J.: Less global inequality can improve climate outcomes, Wires Clim. Change, 9, e513, https://doi.org/10.1002/wcc.513, 2018.
Rao, N. D., Sauer, P., Gidden, M., and Riahi, K.: Income inequality projections for the Shared Socioeconomic Pathways (SSPs), Futures, 105, 27–39, https://doi.org/10.1016/j.futures.2018.07.001, 2019.
Ravallion, M.: The Luxembourg Income Study, J. Econ. Inequal., 13, 527–547, https://doi.org/10.1007/s10888-015-9298-y, 2015.
Reid, C. D.: World development indicators 2011, Reference Reviews, 26, 26–27, 2012.
Shorrocks, A. and Wan, G.: Chap. 22, Ungrouping Income Distributions: synthesizing samples for inequality and poverty analysis, in: Arguments for a Better World: Essays in Honor of Amartya Sen, edited by: Basu, K. and Kanbur, R., Volume I, Ethics, Welfare, and Measurement, Oxford, Oxford Academic, https://doi.org/10.1093/acprof:oso/9780199239115.003.0023, 2008.
Smeeding, T. and Latner, J. P.: PovcalNet, WDI and “All the Ginis”: a critical review, J. Econ. Inequal., 13, 603–628, 2015.
Smeeding, T. M. and Grodner, A.: Changing Income Inequality in OECD Countries: Updated Results from the Luxembourg Income Study (LIS), Springer Berlin Heidelberg, 205–224, https://doi.org/10.1007/978-3-642-57232-6_10, 2000.
Soergel, B., Kriegler, E., Bodirsky, B. L., Bauer, N., Leimbach, M., and Popp, A.: Combining ambitious climate policies with efforts to eradicate poverty, Nat. Commun., 12, 22315-9, https://doi.org/10.1038/s41467-021-22315-9, 2021.
Solt, F.: Measuring income inequality across countries and over time: The standardized world income inequality database, Soc. Sci. Quart., 101, 1183–1199, 2020.
UNECE (United Nations Economic Commission For Europe): Canberra group handbook on household income statistics, https://unece.org/fileadmin/DAM/stats/groups/cgh/Canbera_Handbook_2011_WEB.pdf (last access: 7 May 2024), 2011.
Van der Mensbrugghe, D.: Shared socio-economic pathways and global income distribution, 2015 Conference Paper, presented at the 18th Annual Conference on Global Economic Analysis, Melbourne, Australia, https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4790 (last access: 7 May 2024), 2015.
Wider, U.: World Income Inequality Database, User Guide and data Sources, https://www.wider.unu.edu/sites/default/files/WIID/WIID-User-Guide-31MAY2021.pdf (last access: 7 May 2024), 2008.
Short summary
Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. We also present another version of this dataset aggregated from the country level to 32 geographical regions.
Here, we present a consistent dataset of income distributions across 190 countries from 1958 to...
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