Analyzing Differences in Development Between Countries and Within Countries

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Figure 1 shows 10 countries with three developmental indicators showing the level of development in the regions. The figure shows countries like Norway, Australia, Switzerland, Japan, the United States, Austria, Israel, Chile, Uruguay, and Egypt. It is to be noted that not more than three countries belong to a continent – Norway, Switzerland, and Austria are from Europe, the United States from North America, Japan, and Israel from Asia, Chile, and Uruguay from South America, Australia from Oceania, and Egypt from Africa. The three developmental indicators taken are income/expenditure share of the richest 10% of the population in percentage, Annual immigration growth rate in percentage, and annual growth rate of GDP per capita in percentage.

Figure 1 shows that the annual rate of growth of GDP per capita is highest for Chile at 3.7%. This indicates the share of the growth per capita of the population. The other countries that have a relatively high measure of the indicator are Norway at 2.6%, Egypt at 2.5% and Australia at 2.4%. Income /expenditure share of the richest 10% of the population actually indicates the income inequality of the country. This indicator is highest for the Japan, Norway, and Egypt at 4.8%, 3.9%, and 3.9% respectively. This shows that in Japan, 4.8% of the income/expenditure can be accrued to the richest 10% of the population, indicating a higher income gap between the rich and the poor, thus increasing income inequality. The annual immigration growth rate in percent has been highest in Norway at 4%, then in Australia at 2.1%, Japan at 2.4%, and Israel at 1.8%. Immigration growth is found to be negative in Uruguay at -1.8%. The above figure shows that the income inequality is highest in countries, which have relatively higher rate of immigration. These countries as in case of Japan have a low annual GDP per capita growth rate. The figure shows that in countries where there is a higher degree of annual immigration has a low rate of growth of GDP per capita. In certain countries like Uruguay, immigration flow is negative indicating there is more migration than inflow of immigrants in the country. Thus, in these countries there is a greater flow of people leaving the country. The reason for this trend may be observed from the lower rate of growth of GDP and income/expenditure share of the richest 10% of the population.

Figure 2 shows a scatter plot for two indicators i.e. GDP rate per capita and education index for the countries. 20 countries were randomly chosen and figures for these indicators were taken for the year 1980, 1985, 1990, 1995, 2000, 2005, 2006, and 2007⁴. The data demonstrates that most of the countries selected form a cluster around the low GDP per capita growth and high education index zone. The scatter graph presents a trend line for all the countries and their correlations. The graph shows that the correlation between GDP per capita and education index. Education index is plotted on the y-axis and GDP per capita on y-axis. The figure also shows the trend lines for the countries under consideration. The trend lines actually indicate the best ft line between the two indicators representing the correlation between the two.⁵ The figure shows that the best fit line indicating that the closest possible point between the clusters of points indicating a relation between the two indicators. All the countries for the period under study shows a positive relation between GDP per capita and Education index, indicating that when there is a rise in GDP per capita, there is a rise in education index i.e. education in the country. However, there are differences in the degree of strength of these correlations. For instance, Liberia shows that the r-square value of the trend line is 0.48 indicating a weak positive correlation between GDP per capita and education index. Similarly for countries like Iran, Bahrain, Rwanda, Benin where the r-square is less than 0.7 indicating a weak positive correlation between GDP per capita and education index. Strong correlation is indicated by countries like Australia, Norway, Canada, Ireland, Iceland, Thailand, Netherlands etc. were the r-square is found to be exceeding 0.7. Therefore, this correlation analysis using scatter plots allows demarcating the developed, developing countries, and the less developed countries. The developing and the developed countries have a strong correlation between GDP per capita and education cess. However, for the less developed countries, the correlation, though positive, is weak. There are three distinct clusters or groups formed on the graph in figure 2. On the northeastern side of the graph, the developed countries are clustered. The data indicates that the developed counties have a high level of GDP per capita with high on education index. In this section, the countries have a high GDP per capita and have a high score on the education index. Then there is a cluster on the northwestern part of the graph. This section represents countries with moderately low GDP per capita, but high score on education index. Then is the third section, which indicates countries on the southwestern part of the graph. These countries have a very low GDP per capita and low of education index. These countries can be indicated as the less developed countries and the countries clustered on the northeast section of the graph indicate developed countries. On the northwest section is the cluster of developing countries.

From figure 2 three countries can be identified which have a contrasting positions and r-square value. The three countries thus chosen are Liberia⁶, Canada⁷, and Thailand⁸. Clearly, Liberia and Thailand are less developed countries (LEDC) and Canada is a more economic developed country (MEDC). The major economic and social differences in the three countries will be discussed. Table 1 shows five social and five economic indicators that demonstrate the difference between a MEDC and LEDC. First, the economic indicators are discussed. GDP growth rate is highest in Liberia and lowest in Canada indicating that the growth of the economy is low in MEDCs as the economy is already developed and mature. In Thailand, the GDP growth rate is 2.5 indicating a growing economy. In terms of inflation, it is highest in LEDCs, as in Liberia. It is low in Canada and Thailand with inflation rate of 3.9 and 3.8. Export as a percentage of GDP is found to be highest for Thailand, which is a developing country and lowest for Liberia, which is a LEDC. For Canada, it is 35%. The unemployment rate is found to be highest in Canada at 6.1%, while it is lowest in Thailand at 1.4 percent. In Liberia, the unemployment rate is 5.6%. Gross capital formation as a percentage of GDP is found to be highest in Thailand, then Canada and then Liberia. The economic indicators show that they are better for a country, which is developing; as there are better, economic activates occurring in the region. Economic activities are low in LEDCs and MEDCs.

Table 1

In terms of social indicators, overall, Canada is in a better position than the other two countries. Life expectancy ratio is found to be highest in Canada at 80.6%. The ratio is low for Thailand at 78% while it is 57.6% in Liberia. HDI trend developed by UNDP shows that Canada is in the best social position then Thailand and in the end is Liberia. Therefore, in terms of HDI trend, Canada has better human development possibilities and situation than what it is in Liberia and Thailand. Urban development in Canada is 100%, while that in Liberia, it is 49% and in Thailand, it is 95%. Therefore, in case of LEDCs a large amount of the population still lives in villages as is indicated through urban development in Liberia. Gross enrolment ratio is schools are found to be highest in Canada at 99.3%, then in Thailand at 78% and then in Liberia at 57.6%. Therefore, the enrolment for education is more in MEDCs than in LEDCs. Income inequality is higher in LEDCs than in MEDCs. On reviewing, the income share of the highest 20% population is found to be 49% and 45% in Thailand and Liberia respectively, while it is lower in Canada at 39.9%.

From the above analysis of the economic and social factors of three countries Canada, Liberia and Thailand shows that MEDCs have a lower growth rate of GDP. However, they have low inflation, which is higher in LEDCs. Lower in gross capital formation, and export in MEDCs and higher in LEDCs. Therefore, in terms of economic development it is found that LEDCs, which have a high growth rate, are at par or better in economic indicators as in case of Thailand. However, LEDCs, with very low GDP, have lower other economic indicators lower than that of the MEDCs. Francis G. Adams has mentioned that there has been an inherent difference in the economic and social development of the LEDCs and the MEDCs.⁹ Higher level of inflation in Liberia indicates that in LEDCs, the level of money spent is higher than that in MEDCs, and the foreign exchange rate for LEDCs is always lower than that of the MEDCs. Further, more people have the benefit of education in MEDCs than in LEDCs. In Liberia, there are a lot of people in the country who are not even enrolled in schools indicating that there education system in the country is not adequate. Further urban development in Liberia is less than 50%, indicating a mostly rural economy. Further the social data also indicates that there is high degree of income inequality in LEDCs with less people have share of the largest 20% of the income. This finding supports the findings of Sundrum who states that income distribution in LEDCs is less even than in MEDCs.¹⁰ Thus, MEDCs have higher social development than in LEDCs.

Figure 3 shows the economic activity map of Thailand. The map shows that the economic activity of the region. Before beginning a discussion on economic activities in the region, a brief background of the country is provided. Thailand is a Southeast Asian country, and has a Malay Muslim population.¹¹ The country has it neighbours in Laos, Burma, Malaysia, and Cambodia.¹² There economic activity as demonstrated in the graph in figures 3 shows that a large part of the country is under agricultural production. The largest produced crop is rice and the area under rise production is 10,320,000.¹³ According to the World Bank data, 38% of the land is under agriculture as in 2008.¹⁴ Teak and rubber are the other crops, which forms a large chunk of the economy, which forms the other agricultural crop. There are dams in the country mainly located on the North and Northeastern side of the country. Agriculture forms 11.8% of the total GDP in Thailand.¹⁵ The main dams of the country are Bhumibol Dam, Sirikit Dam, Mae Ngat Sombunchon Dam, Kiu Lom Dam, Mae Kuang Dam, Lam Pao Dam, Lam Takong Dam, Lam Phra Ploeng Dam, Nam Un Dam, Huai Luang Dam, Lam Nang Rong Dam, Pran Buri Dam, and Thap Salao Dam.¹⁶ Thailand has a large industrial sector with 44.8% contribution to national GDP.¹⁷ Petroleum refinery are found in Phuket, Shatahip, and another location close to Bangkok. The reason for this distribution is the availability of the petroleum in these regions.¹⁸ Thailand is rich in minerals found in the country.¹⁹ The main mining areas in the country in northern and southern part of the country. The northern region is abundant in iron while the southern part is abundant in tin. The iron production of the country increased by 41% in 2003.²⁰

References

Adams, F. G., Macroeconomics for business and society: a developed/developing country, London, World Scientific, 2002.

Bradley, T., Essential statistics for economics, business and management, West Sussex, John Wiley and Sons, 2007.

GeoFRED, Total Gross Domestic Product by State (Millions of Dollar), 2008. Web.

LePoer, B. L., Thailand: A Country Study, 1987. Web.

Ministry of Industry, Information Center of the Ministry of Industry, 2010. Web.

NationMaster, Thailand, 2010. Web.

RID GIS, Important Storage Dams of Thailand, 2010. Web.

Sundrum, R. M., Income distribution in less developed countries, Bristol: Routledge, 1999.

UNDP, Annual rate of growth in international migrant stocks, 2009. Web.

UNDP, Earned income (estimated), ratio of female to male, 2009. Web.

UNDP, Gender-related development index and its components, 2009. Web.

UNDP, Human Development Report 2009 – Economy and inequality, 2009. Web.

UNDP, Human Development Reports, 2009. Web.

UNDP, Human movement: snapshots and trends, 2009. Web.

UNDP, Income/expenditure share of the richest 10% of the population, 2009. Web.

UNDP, Population living below $1.25 a day, 2009. Web.

World Bank, Canada, 2010. Web.

World Bank, Liberia, 2010. Web.

World Bank, Thailand, 2010. Web.

Wu, J. C.,The Mineral Industry of Thailand, 2003. Web.

Footnotes

1. UNDP, Earned income (estimated), ratio of female to male, 2009. Web.
2. UNDP, Human Development Report 2009 – Economy and inequality, 2009. Web.
3. UNDP, Income/expenditure share of the richest 10% of the population, 2009. Web.
4. UNDP, Human Development Reports, 2009. Web.
5. T. Bradley, Essential statistics for economics, business and management, West Sussex, John Wiley and Sons, 2007, p. 118.
6. World Bank, Liberia, 2010. Web.
7. World Bank, Canada, 2010. Web.
8. World Bank, Thailand, 2010. Web.
9. F. G.Adams, Macroeconomics for business and society: a developed/developing country, London, World Scientific, 2002, p. 52.
10. R. M. Sundrum, Income distribution in less developed countries, Bristol: Routledge, 1999, p. -252.
11. NationMaster, Thailand, 2010. Web.
12. NationMaster, Thailand.
13. NationMaster, Thailand.
14. World Bank, Thailand.
15. World Bank,Thailand.
16. RID GIS. 2002, Important Storage Dams of Thailand, Flood monitoring 2002 & MWMS Project:GIS. Web.
17. World Bank,Thailand.
18. Ministry of Industry, Information Center of the Ministry of Industry, 2010. Web.
19. B. L. LePoer, Thailand: A Country Study, 1987. Web.
20. J. C. Wu, The Mineral Industry of Thailand, 2003. Web.

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