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Machine Learning Analysis of the Impact of Increasing the Minimum Wage on Income Inequality in Spain from 2001 to 2021

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  • المؤلفون: Lacasa-Cazcarra, Marcos
  • المصدر:
    Recent Progress in Science and Engineering ; volume 01, issue 01, page 1-13 ; ISSN 3067-4573
  • نوع التسجيلة:
    article in journal/newspaper
  • اللغة:
    unknown
  • معلومة اضافية
    • بيانات النشر:
      LIDSEN Publishing Inc
    • الموضوع:
      2025
    • نبذة مختصرة :
      The main aim of this work is to analyze the impact of the National Minimum Wage (NMW) on wealth redistribution in Spain during the 2001-2021 period. This research addresses a fundamental question: Does increasing the minimum wage effectively reduce income inequality and contribute to a more equitable wealth distribution without negatively impacting overall economic growth and employment? Using a complete statistical census provided by the Spanish Tax Administration Agency, this study examines how the increase in the NMW—which rose from €505.7/month (2001) to €1,108.3/month (2021)—affected income inequality and other macroeconomic indicators. A distinctive feature of this research is the use of a complete national census database, not a sample or projection, which provides more accurate results and more efficient predictive models that reflect the studied population. Through various machine learning models, it is demonstrated that income inequality has been reduced by raising the minimum wage. Contrary to the predictions of previous economic analyses, the increase in the minimum wage has not led to inflation or increased unemployment. On the contrary, it has been consistent with increased net employment, contained prices, and increased corporate profit margins. The main conclusion is that the increase in the minimum wage during the analyzed period has contributed to an effective redistribution of wealth, simultaneously increasing the country’s prosperity, employment, and business profits under the analyzed conditions.
    • الرقم المعرف:
      10.21926/rpse.2501005
    • الدخول الالكتروني :
      https://doi.org/10.21926/rpse.2501005
      https://www.lidsen.com/journals/rpse/rpse-01-01-005/rpse.2501005.xml
      https://www.lidsen.com/journals/rpse/rpse-01-01-005
    • Rights:
      https://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.9C80726