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Regression models for the analysis of well-being and income distribution

Wellbeing and income inequality are taking centre stage in policy-making and the measurement of national success. The interests of economists, social scientists, policymakers and others on the relations of well-being, income, demographics, health, education, religion and employment, have generated a vast global literature.

Some of the measurements of these variables are in a bounded range, such as the London ward where the well-being scores are a combined measure based on 12 different indicators. All scores have known bounded ranges between 0 and 1, and over 0 indicate a higher probability that the population, on average, will experience better well-being. Similarly, the Gini coefficient of income distribution is the most commonly-used measure of income inequality that condenses the entire income distribution for a country into a single number between 0 and 1: the higher the number, the greater the degree of income inequality.

Statistical analysis of this type of variables is very complex and requires a great level of care. By analysing the publicly available data, this project aims to develop new regression methods to model the relationships between well-being, income, demographics, education, religion, employment and so on, and therefore provides a useful tool for a clear description of the factors affecting wellbeing and income inequality. This is turn will help suggests concrete actions for policy-making and data collection.

This project answers some of the following questions:

  • Are Londoners or British people happy? Why?
  • Is there income mobility in London, England and even whole UK? And how?
  • Will we be “objectively” better off/happier after Brexit? Why?
  • How well-off are we as a developed country compared to other developed countries?

Meet the Principal Investigator(s) for the project

Related Research Group(s)

Statistics and Data Science

Statistics and Data Science - Strategic growth area of statistical methods and models for data science.

Project last modified 13/07/2021