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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)

Health Economics (HERG)

Health Economics (HERG) - Our strategic focus is on economic evaluation and systematic reviews of a broad range of clinical and health service technologies by providing high-quality, applied, policy-relevant research, as well as developing and refining methods to increase the rigour and relevance of such studies.

Statistics and Data Science

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


Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.


Project last modified 18/10/2021