Per Capita GDP and HDI Relationship | Human Development Index | Interesting Application of Correlation

In the present scenario, we are frequently talking about GDP growth and economic development. There are many parameters which are responsible directly or indirectly for economic changes of any county. Factors are like population, export, import, GDP, per capita GDP, human development index (HDI), National income and many more. There are many factors which are the deciding factors for a country to be with the tag of developed country or developing country. Those factors are Per capita Income/ per capita GDP, Human development index (HDI), GDP, Standard living/ quality of life, Level of Industrialization. Among all other economic factor, population is directly connected to all the other economic factors.

In this article, we’ll discuss and check about per capita GDP and HDI relationship. Also, application of correlation will be discussed on economic parameters like per capita GDP and HDI.

Parameters to calculate GDP & Per Capita GDP:

There are few parameters which are being considered to calculate GDP.

1. Private consumption

2. Gross Investment of the country

3. Government Investment

4. Spending of Government

5. Net export: export – import

So, each of the factors are important as a contributor to the nation’s economy or GDP.

To get per capita GDP, total GDP of country should be divided by total population of the country. In other words, per capita GDP is an economic indicator of per person’s economic output. Basically, per capita GDP is more indicative of country’s economic condition.

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Human Development Index (HDI):

Human development index is a measure of people’s quality of life, health, and education of a country. To measure long and healthy life, life expectancy of birth is being measured. Knowledge/ education index parameter is evaluated by average schooling year and expected schooling year. On the other hand, people’s standard of leaving is evaluated through gross national income (GNI) index or through GNI per capita.

All the countries are being divided into four parts as type of HDI country.

Very High HDI– 64 countries (33%); Average HDI score- 0.878

High HDI– 55 countries (29%); Average HDI score- 0.745

Medium HDI– 36 countries (19%); Average HDI score- 0.620

Low HDI– 36 countries (19%); Average HDI score- 0.480

Every year HDI ranking released to measure countries development in terms of economic and social development. In the below map, country wise HDI has been shown as visual representation. As shown in the map chart that highest HDI of a country is 0.961 and lowest HDI is 0.377. Around 33% countries are falling under very high HDI bucket which is high among all four HDI bucket.

Per Capita GDP & HDI relationship:

The objective is to find correlation between human development index and per capita GDP. So, country wise data has been collected to check the correlation (data source world bank website). Here 2018 data has been used.

For the working, we have taken Per capita GDP as on 2018 and human development data as on 2018 and used ‘CORREL’ function to find out correlation among these two factors.

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As an output of correlation, 0.66 depicted as strong positive correlation.

Application of correlation

For more understanding and further deep dive into it, checked the correlation based on the type of HDI countries. So, each type of HDI bucket and its characteristics will be explored in terms of correlation and regression.

HDI TypeCorrelation
Very High HDI0.70
High HDI0.46
Medium HDI0.26
Low HDI0.30
Correlation between Per Capita GDP & HDI as per HDI bucketing
Very High HDI countries:

Comment- For very high HDI countries, per capita GDP and HDI are strong and positively correlated and points are near to the trendline. Most of the developed countries are falling in this list. Also, regression line is showing good fit with the data points.

High HDI countries:

Comment- For high HDI countries, per capita GDP and HDI are decent and positively correlated, points are scattered and not near to the trendline. So, for high HDI countries, there is good correlation between HDI with per capita GDP. Regression line is not showing good fit with the points.

Medium HDI countries:

Comment- For medium HDI countries, there is week correlation between per capita GDP and HDI, correlation is 0.26. R square value is not depicted as good fit as it is near to zero.

Low HDI countries:

Comment- For low HDI countries, per capita GDP and HDI are weakly and positively correlated and points are scattered not near to the trendline. So, for low HDI countries, there is no good correlation between HDI with per capita GDP. R square value is near to zero, so not concluded as good fit with data points.

Conclusion:

It can be concluded that with the overall increase in per capita income, human development index is also increasing of a country. In other words, quality of life, education and health has a direct correlation with per capita GDP.

The strong positive correlation between HDI and per capita GDP is not always true for all types of HDI countries. We found in the result that strong positive correlation between Per capita GDP and HDI is only applicable for Very high HDI and high HDI countries like Norway, Monaco, Switzerland, USA, UK, China, Mexico etc.

For other types of HDI countries apart from very high HDI, per capita GDP is not strongly correlated with human development index. As the income for those countries are not high as compared to very high and high HDI countries. Also because of high population, govt may not spent much on the HDI parameters.

If we look at the HDI bucket and correlation simultaneously, it can be observed that with the decreasing HDI bucket, correlation is also decreasing. So, it can be concluded from the pattern that correlation of the two parameters are increasing with the increase in HDI scores of countries.

HDI TypeCorrelationR2
Very High HDI0.700.49
High HDI0.460.21
Medium HDI0.260.06
Low HDI0.300.08

Though low and medium HDI bucket has week correlation but R2 or regression line is not showing good fit with the data points.

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