Over the last few decades, income inequality has grown dramatically in the United States, with the top twenty percent earning more than the bottom eighty percent combined. The studies about the income inequality is becoming more and more important in the economics and politics area. The research of this paper combined the computer technology and the economic problem solving together. The project applied Deep Learning technology for creating a model to predict the most important indicator of income inequality, Gini coefficient in the future based on the historical relevant data, so that observe the ever widening gap between the rich and the poor. It also found the major elements that governed this widening behavior through analyzing the impacts of the related attributes. This study obtained and analyzed substantial amount of data, which contain information on the income, expenses and the financial footprints of families in the United States, to draw empirical conclusions. The results may help the public and the economic research society for their decision making. Using Deep Learning algorithms, the data analysis was far more efficient, and by generating the Deep Learning multi-layer Neural Networks, the prediction was quite accurate. This study has obtained some promising results. It showed an encouraging direction on the prediction of Gini coefficient, through applying Deep Learning models.
| Published in | Science Frontiers (Volume 3, Issue 2) |
| DOI | 10.11648/j.sf.20220302.12 |
| Page(s) | 66-73 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Deep-Learning, Neural Networks, Feature Correlation, Prediction, Income Inequality, Gini Coefficient
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APA Style
Ruijian Zhang. (2022). Applying Deep Learning Technology on Prediction of Gini Coefficient. Science Frontiers, 3(2), 66-73. https://doi.org/10.11648/j.sf.20220302.12
ACS Style
Ruijian Zhang. Applying Deep Learning Technology on Prediction of Gini Coefficient. Sci. Front. 2022, 3(2), 66-73. doi: 10.11648/j.sf.20220302.12
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Download@article{10.11648/j.sf.20220302.12,
author = {Ruijian Zhang},
title = {Applying Deep Learning Technology on Prediction of Gini Coefficient},
journal = {Science Frontiers},
volume = {3},
number = {2},
pages = {66-73},
doi = {10.11648/j.sf.20220302.12},
url = {https://doi.org/10.11648/j.sf.20220302.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sf.20220302.12},
abstract = {Over the last few decades, income inequality has grown dramatically in the United States, with the top twenty percent earning more than the bottom eighty percent combined. The studies about the income inequality is becoming more and more important in the economics and politics area. The research of this paper combined the computer technology and the economic problem solving together. The project applied Deep Learning technology for creating a model to predict the most important indicator of income inequality, Gini coefficient in the future based on the historical relevant data, so that observe the ever widening gap between the rich and the poor. It also found the major elements that governed this widening behavior through analyzing the impacts of the related attributes. This study obtained and analyzed substantial amount of data, which contain information on the income, expenses and the financial footprints of families in the United States, to draw empirical conclusions. The results may help the public and the economic research society for their decision making. Using Deep Learning algorithms, the data analysis was far more efficient, and by generating the Deep Learning multi-layer Neural Networks, the prediction was quite accurate. This study has obtained some promising results. It showed an encouraging direction on the prediction of Gini coefficient, through applying Deep Learning models.},
year = {2022}
}
TY - JOUR T1 - Applying Deep Learning Technology on Prediction of Gini Coefficient AU - Ruijian Zhang Y1 - 2022/05/31 PY - 2022 N1 - https://doi.org/10.11648/j.sf.20220302.12 DO - 10.11648/j.sf.20220302.12 T2 - Science Frontiers JF - Science Frontiers JO - Science Frontiers SP - 66 EP - 73 PB - Science Publishing Group SN - 2994-7030 UR - https://doi.org/10.11648/j.sf.20220302.12 AB - Over the last few decades, income inequality has grown dramatically in the United States, with the top twenty percent earning more than the bottom eighty percent combined. The studies about the income inequality is becoming more and more important in the economics and politics area. The research of this paper combined the computer technology and the economic problem solving together. The project applied Deep Learning technology for creating a model to predict the most important indicator of income inequality, Gini coefficient in the future based on the historical relevant data, so that observe the ever widening gap between the rich and the poor. It also found the major elements that governed this widening behavior through analyzing the impacts of the related attributes. This study obtained and analyzed substantial amount of data, which contain information on the income, expenses and the financial footprints of families in the United States, to draw empirical conclusions. The results may help the public and the economic research society for their decision making. Using Deep Learning algorithms, the data analysis was far more efficient, and by generating the Deep Learning multi-layer Neural Networks, the prediction was quite accurate. This study has obtained some promising results. It showed an encouraging direction on the prediction of Gini coefficient, through applying Deep Learning models. VL - 3 IS - 2 ER -
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