Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways
Understanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/acbdb0 |
| _version_ | 1797747358684938240 |
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| author | Kanishka B Narayan Brian C O’Neill Stephanie T Waldhoff Claudia Tebaldi |
| author_facet | Kanishka B Narayan Brian C O’Neill Stephanie T Waldhoff Claudia Tebaldi |
| author_sort | Kanishka B Narayan |
| collection | DOAJ |
| description | Understanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only the Gini coefficient (a summary statistic of the distribution) or utilize the Gini along with the assumption of a lognormal distribution. We test the lognormal assumption and find that it typically underestimates income in the highest deciles and over-estimates it in others. We find that a new model based on two principal components of national time series data for income distribution provides a better fit to the data for all deciles, especially for the highest and lowest. We also construct a projection model in which the first principal component is driven by the Gini coefficient and the second captures deviations from this relationship. We use the model to project income distribution by decile for all countries for the five shared socioeconomic pathways. We find that inequality is consistently higher than projections based on the Gini and the lognormal functional form, with some countries reaching ratios of the highest to lowest income deciles that are almost three times their value using the lognormal assumption. |
| first_indexed | 2024-03-12T15:50:36Z |
| format | Article |
| id | doaj.art-1d548ab74eff42928af1639529b3775a |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:50:36Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-1d548ab74eff42928af1639529b3775a2023-08-09T15:13:02ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-0118404401310.1088/1748-9326/acbdb0Non-parametric projections of national income distribution consistent with the Shared Socioeconomic PathwaysKanishka B Narayan0https://orcid.org/0000-0001-8483-6216Brian C O’Neill1https://orcid.org/0000-0001-7505-8897Stephanie T Waldhoff2https://orcid.org/0000-0002-8073-0868Claudia Tebaldi3https://orcid.org/0000-0001-9233-8903Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL) , Washington, D.C., United States of AmericaJoint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL) , Washington, D.C., United States of AmericaJoint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL) , Washington, D.C., United States of AmericaJoint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL) , Washington, D.C., United States of AmericaUnderstanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only the Gini coefficient (a summary statistic of the distribution) or utilize the Gini along with the assumption of a lognormal distribution. We test the lognormal assumption and find that it typically underestimates income in the highest deciles and over-estimates it in others. We find that a new model based on two principal components of national time series data for income distribution provides a better fit to the data for all deciles, especially for the highest and lowest. We also construct a projection model in which the first principal component is driven by the Gini coefficient and the second captures deviations from this relationship. We use the model to project income distribution by decile for all countries for the five shared socioeconomic pathways. We find that inequality is consistently higher than projections based on the Gini and the lognormal functional form, with some countries reaching ratios of the highest to lowest income deciles that are almost three times their value using the lognormal assumption.https://doi.org/10.1088/1748-9326/acbdb0SSPsincome distributionsincome inequality |
| spellingShingle | Kanishka B Narayan Brian C O’Neill Stephanie T Waldhoff Claudia Tebaldi Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways Environmental Research Letters SSPs income distributions income inequality |
| title | Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways |
| title_full | Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways |
| title_fullStr | Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways |
| title_full_unstemmed | Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways |
| title_short | Non-parametric projections of national income distribution consistent with the Shared Socioeconomic Pathways |
| title_sort | non parametric projections of national income distribution consistent with the shared socioeconomic pathways |
| topic | SSPs income distributions income inequality |
| url | https://doi.org/10.1088/1748-9326/acbdb0 |
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