Applying Deep Learning Technology on Prediction of Gini Coefficient

Ruijian Zhang

doi:doi:10.11648/j.sf.20220302.12

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Applying Deep Learning Technology on Prediction of Gini Coefficient

Ruijian Zhang

Published in Science Frontiers (Volume 3, Issue 2)

Received: 2 May 2022 Accepted: 18 May 2022 Published: 31 May 2022

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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.

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), 2024. Published by Science Publishing Group

Keywords

Deep-Learning, Neural Networks, Feature Correlation, Prediction, Income Inequality, Gini Coefficient

References

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[2]	Lisa Smith, ‘The Gini Index: Measuring Income Distribution’. August, 2021.
[3]	Gini, Corrado (1936). "On the Measure of Concentration with Special Reference to Income and Statistics", Colorado College Publication, General Series No. 208, 73–79.
[4]	R. P. Cysne, W. L. Maldonado, and P. K. Monteiro, “Inflation and income inequality: a shopping-time approach”, Journal of Development Economics, 78, pp. 516-528, 2005.
[5]	Aleš Bulíř, “Income Inequality: Does Inflation Matter?” IMF Staff Papers, vol. 48, no. 1, pp. 139–159. 2001. JSTOR.
[6]	Federal Reserve Economic Data (FRED), from Research Department at the Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/
[7]	Data from Organization for Economic Cooperation and Development. https://data.oecd.org/
[8]	Institute for Social Research, Survey Research Center, University of Michigan: “Panel Study for income Dynamics.” https://psidonline.isr.umich.edu/default.aspx
[9]	Katherine A McGonagle, Robert F Schoeni, Narayan Sastry, Vicki A Freedman, (Institute for Social Research, University of Michigan): “The Panel Study of Income Dynamics: Overview, Recent Innovations, and Potential for Life Course Research”. https://psidonline.isr.umich.edu/llcs2012.pdf
[10]	U.S. population’s income and economic data, from United States Census’ Current Population Survey. https://www.census.gov/data.html
[11]	World Bank Open Data. https://data.worldbank.org/
[12]	“Trends in the Distribution of Household Income”, from the US Congressional Budget Office. https://www.cbo.gov/publication
[13]	Hubert Ooghe, Heidi Claus, Nathalie Sierens and Jan Camerlynck “International Comparison of Failure Prediction Models From Different Countries: An Empirical Analysis”, September 1999.
[14]	Hans Risselada, Peter C. Verhoef, Tammo H. A. Bijmolt, “Staying Power of Churn Prediction Models”, Journal of Interactive Marketing, Volume 24, Issue 3, August 2010, Pages 198-208.
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[16]	Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi, “Learning Activation Functions to Improve Deep Neural Networks”, Proceeding of the International Conference on Learning Representations (ICLR) 2015.
[17]	Valerie Sarge, Shashank Verma, Ben Barsdell, James Sohn, Hao Wu, and Vartika Singh, “Improved TensorFlow 2.7 Operations for Faster Recommenders with NVIDIA”, from NVIDIA.
[18]	Imran Khan Mohd Jais, Amelia Ritahani Ismail, Syed Qamrun Nisa, “Adam Optimization Algorithm for Wide and Deep Neural Network”, Journal of Knowledge Engineering and Data Science, Vol 2, No 1, 2019.
[19]	Ruijian Zhang, “Applying Parallel Programming and High Performance Computing to Speed up Data Mining Processing”, Proceedings of the 16^th IEEE International Conference on Computer and Information Science, May 2017.
[20]	Ruijian Zhang, "Applying Data Mining Technology on Inflation Prediction in the United States", IEEE Proceedings of the Eighth International Conference on Computational Science, Intelligence & Applied Informatics (CSII 2021), September 2021.

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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

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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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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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@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}
}

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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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Author Information

Ruijian Zhang

Department of Computer Science, Purdue University, Hammond, USA

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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

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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

Copy | Download

AMA 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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@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}
}

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