Drivers of Economy

Gross Domestic Product (GDP) is an important indicator to measure the wealth of a country.

The year over year percentage change of a country’s GDP helps in understanding several critical social and financial aspects such as unemployment levels.

GDP in simple terms can be defined as the sum of all the income generated in a year or what everyone has spent.

This paper will try to analyse the relationships and interdependencies among several variables, which drive the economy, keeping GDP as the dependent variable.

1.           Historical GDP figures (GDP vs Growth Rate)

    To start with its important to see the state of the current global economy, by looking at the historical GDP figures.

               By plotting the GDP figures from 1990 to 2015 for the top 25 biggest economies on an area chart, it is evident how Unites States dwarfs other countries in terms of the sheer size of the economy. Other visible high peaks knowingly include Japan, China, United Kingdom and Germany.

                       But the measure of the compounded annual growth rate (CAGR) between 2000 and 2015 brings forth an interesting picture. CAGR marked by the bar chart on the second axis showcases the fastest developing major economies. The BRIC nations along with Indonesia, Saudi Arabia and Nigeria are on the top half of the fastest growing economies in the last fifteen years. If these countries can sustain these enviable figures for the next few decades, a change in the world order is definitely on the cards, especially with the current trend of lower GDP growth rates seen within the developed nations.

The above world map showcases the GDP growth between 2013 & 2014 and without any surprises China and India come under the dark green shade – fastest growing major economies. One can also see the devastating effect of the Libyan civil war on the country’s economy with GDP contracting manifolds during the same period.

          2     Labor – Independent variable

Availability of labor force is important for any country to grow and sustain the economy. For the purpose of evaluating total labor force - who meet the International Labour Organization definition of the economically active population have been taken into consideration. As expected China, India and USA feature in the top 5 of this group.

           3.    Human Capital (Education spending) – Independent variable

As skills, knowledge and ideas can drive innovation, productivity and in turn economic growth, government expenditure on education, expressed as a percentage of GDP becomes an important variable to study.

          4.      Life expectancy – Independent variable

The changing trend of the average age human beings are expected to live can indicate the effort and investment put into the health care system by the government. The assumption here being increased life expectancy will positively affect the economy.

(Chart details – Average life expectancy vs average log labor. Color showcase average of education spending %. Size showcase average GDP figures. All figures are between 1990 & 2014)

Putting the above three independent variables on a bubble chart, visually represents their effects on the GDP. It can be seen how some of the most developed economies of the world also possess high average life expectancy figures, with Japan having the best figures-above 80 years.

Intuitively one would expect the developed economies to spend more on education, which exactly is the case here with all the developed economies shown in the color blue and developing economies in color red, meaning more effort is required from the up and coming economies in this direction.

  

Statistical Modelling

To see the relationships among all the independent variables and their effects on the GDP, statistical modelling-regression & multivariate analyses are carrier out.

GDP and labor figures are changed into logarithmic values to better fit on the chart.

As positive correlation, can be seen between Labor, Life expectancy and GDP, as a subsequent step regression analyses is carried out.

Regression Statistics

Actual by Predicted Plot

Summary of Fit

RSquare	0.873785
RSquare Adj	0.871504
Root Mean Square Error	0.334779
Mean of Response	10.45144
Observations (or Sum Wgts)	170

Analysis of Variance

Source	DF	Sum of Squares	Mean Square	F Ratio
Model	3	128.80141	42.9338	383.0732
Error	166	18.60483	0.1121	Prob > F
C. Total	169	147.40624		<.0001*

Parameter Estimates

Term	Estimate	Std Error	t Ratio	Prob>|t|
Intercept	0.1652146	0.314512	0.53	0.6001
Life Exp	0.0565166	0.002803	20.16	<.0001*
Log Labor	0.9721186	0.034714	28.00	<.0001*
Education spending % 2	0.0146473	0.015146	0.97	0.3349

Effect Tests

Source	Nparm	DF	Sum of Squares	F Ratio	Prob > F
Life Exp	1	1	45.572311	406.6151	<.0001*
Log Labor	1	1	87.893706	784.2242	<.0001*
Education spending % 2	1	1	0.104821	0.9353	0.3349

A high R square indicates that 87.3% of the variability of GDP (Log GDP) can be explained by the variability of life expectancy and labor. Education spending % is oddly not coming out as a significant predicting variable with a high Probability value of 0.33, perhaps more specific and direct data is required, but for the purpose of this analyses education spending can be removed.

The equation after performing the analyses again without education spending:

Log GDP = 0.24 + 0.96(Log Labor) + 0.06(Life Expectancy)

For further research, other important driver variables explaining areas such as ease of doing business, innovation, technology level etc. can be taken into consideration to optimize this regression model and enhance its explanatory power.

Though trained labor force has been a critical aspect of the economy and overall growth, which is also highlighted by the above equation, the rise in effectiveness of artificial intelligence and robots could very well change this trend with machines substituting for human capital in the near future becoming a more realistic scenario.