Mapping Well Being Around the World

DATA MISSION:

There is more to life than the cold numbers of GDP and economic statistics. I will evaluate data on well-being produced by the the Organization for Economic Cooperation & Development (OECD).

HAPPINESS FACTORS? 

This data will compare well-being across OECD member countries. The OECD has identified 11 factors as essential for quality of life.

 

  • Housing
  • Income
  • Jobs
  • Community
  • Civic Engagement
  • Health
  • Safety
  • Work-Life Balance
  • Environment
  • Education

I will use the OECD factors to build a model that will determine the top Factors for an increased Better Life Index.

Breaking Down Well-Being:

 The Participating Countries

FACTOR 1:

The Better Life Index

*Note about the Better Life Index Indicators moving forward

Each factor has 1-4 Indicators to analyze.

The overall F Stat is inherently low due to the fact this data is about human behavior as compared to a physical process.

CONCLUSION

Model Used For Final Conclusion: 

  • I built a multiple linear regression model with the indicators.
  • I used backwards elimination to come up with strongest indicators of the Better Life Index

There are 3 factors that play a significant role for increased well-being: Health, Jobs & Housing.

I was able to narrow down the data to show these 4 key indicators. Leadership in the bottom 74% of  the countries can focus on the following:

  1. Countries should focus on maintaining and promoting a high standard of health for residents. They should strive for Self Reported Health to be above 79%. (FACTOR: Health)
  2. Countries should focus on job security as well as mitigating job loss with effective unemployment insurance to attain a goal of less than 3.3% for Labor Market Insecurity. (FACTOR: Jobs)
  3. Countries should focus on reducing the overall number the homes without basic facilities to less than 0.7% for their residents. (FACTOR: Housing)
  4. Countries should focus on increasing Life Expectancy to 82 years old or higher. (FACTOR: Health)

Here’s the analysis of the logistical regressions for each indicator in Factors 2 – 11:



 

 

 

 

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