in Math/Stats 660 Words

Happy Planet Index vs Human Development Index

With my post on “Everything is Connected” I thought I’d investigate a bridge between happiness and the level of development in a country…

The Happy Planet Index (HPI)

“The HPI is an innovative measure that shows the ecological efficiency with which human well-being is delivered around the world. It is the first ever index to combine environmental impact with well-being to measure the environmental efficiency with which country by country, people live long and happy lives.”

The Human Development Index (HDI)

“The first Human Development Report (1990) introduced a new way of measuring development by combining indicators of life expectancy, educational attainment and income into a composite human development index, the HDI. The breakthrough for the HDI was the creation of a single statistic which was to serve as a frame of reference for both social and economic development. The HDI sets a minimum and a maximum for each dimension, called goalposts, and then shows where each country stands in relation to these goalposts, expressed as a value between 0 and 1.”

Thoughts and Hypotheses

There are two relationships we will want to consider:

  • Correlation: Is there any direct relationship (positive or negative) between the values of the HDI and HPI?
  • Clustering: By region (or other characteristic field) can we find any clusters in the data?

Since these are composite indices of several weighted variable inputs, hopefully this top-level approach can identify some possible matches and mismatches between underlying data fields too. Related to the HDI, I bet the UN’s HPI (Human Poverty Index) has a bridge to happiness… or most likely, unhappiness.

Data/Discussion

  • There seems to be a connection between deviations in the data. When there exists a large deviation, for a specific region, for the HDI, there seems to also be a large deviation of values for the HPI. Notice that Africa, Australasia, and the Middle East all have similar double-digit deviations. What does this tell us about the range of development and happiness within a specific region? Perhaps this could be tested across many country-level metrics to see if the similar deviations occur more frequently.
  • As with the above note, since we have these metrics on a same scale/range, let’s combine them to see who has the highest composite score. In alphabetical order we have: 84, 125, 138, 137, 133, 134, 126, 134, 119, 117, 119. There seem to be three groups here: High (>130), Medium (100-130), Low (<100). Depending on a user need, algorithms can be created to join metrics to provide a big picture representation of economic, political, sociological, etc metrics, and flexibility can be built to dig into the weeds on the underlying data. This would be a nice comprehensive framework for understanding how countries (and regions as a whole) change over time.


  • Looking at the scatter plot, it is clear that some clusters may exist, for example with Africa (blue). Caribbean (orange), Europe (green), and Russia and Central Asia (purple) also show some quick visual clustering, while the Middle East (red) shows the opposite. What could this mean? That regional trade, policy, weather, etc are good supplementary foundations for providing happiness and development?
  • We could add trend lines and quickly check for any linear (or logarithmic) relationships. If any relationship does exist as a whole or with a region, it is certainly not a directly proportional or inversely proportional one. This was expected as these metrics are quite different (despite the overlap in life expectancy as an input dimension).

Moving forward, the methodologies and underlying dimensions (with their sources) should be compared. Data is always good, but with good data one still must be careful. That being said, this is a good start for a much larger investigation into the connections between different country-level metrics, especially if they are to be used in international and national policy.

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