Table 1: Distribution of income
Country |
Gini, % |
Year |
Argentina |
48.3 |
2006 |
Australia |
35.2 |
1994 |
Belgium |
33.0 |
2000 |
El Salvador |
52.4 |
2002 |
European Union |
31.6 |
2003 |
Finland |
26.9 |
2000 |
France |
26.7 |
2002 |
Germany |
28.3 |
2000 |
India |
36.8 |
2004 |
Japan |
38.1 |
2002 |
Morocco |
40.0 |
2005 |
Nigeria |
43.7 |
2003 |
Russia |
40.5 |
2005 |
United Kingdom |
36.0 |
1999 |
United States |
45.0 |
2004 |
Yemen |
33.4 |
1998 |
Source: CIA World Factbook
Which country is the most equal and least equal in terms of income distribution?
Using two countries from Table 1, discuss whether a wealthy (wealth as measured by per capita GDP) society is more equal than a less wealthy society.
Q1.
Gini coefficient measures the distribution of income/wealth of an economy. The higher the Gini coefficient, the higher are the income inequalities in the economy. i.e., If the Gini coefficient is higher for an economy, more inequalities exist in the economy and the society is less equal in nature.
Among the given nations, the Gini coefficient is the highest for El Salvador. Therefore, El Salvador is the least equal nation among the given nations. The Gini coefficient is the least for France. Therefore, France is the most equal nation among the given nations
Q2.
Let us compare two nations: The USA and India
The per capita GDP of the USA is many times higher compared to the per capita GDP of India. However, it is observed that the Gini coefficient of the USA is higher than that of India. The income inequalities are higher in the USA compared to India. Therefore, it can be concluded that the statement wealthy society is a more equal society than a less wealthy society is not always true. The inequalities might be more pronounced in both wealthy as well as less wealthy societies based on the conditions of the respective societies.
Table 1: Distribution of income Country Gini, % Year Argentina 48.3 2006 Australia 35.2 1994 Belgium...
22 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country. 23 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country and size by pop. Looking for the answers in R command codes. 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 142 142 142 142 142 142 142 142 142 142 142 142 > table(gapminder$country) Afghanistan Albania Algeria 12 12 12 Angola Argentina Australia 12...