HDI as a Measure of Human Development

Question: Discuss about the HDI as a Measure of Human Development? Answer: 1. Gross Domestic Product is known as GDP which is referred to as the money value of the total finished services as well as products within the territory of a country in a particular period of time (Blanchard and Johnson, 2013). GDP is calculated in quarterly basis as well as on yearly basis. The most important measure of standard of living is the measure of real per capita GDP. It can be said that the increased per capita GDP might be at the increased pollutions costs which will lead to a decline in the living standards of the rise in GDP. GDP is used to measure the Purchasing Power Parity. Thus, it assists to measure the real costs of living. Thus, it can be possible for the economists to compare the PPP of different countries (Goodhew, 2013). GDP is generally measured in dollars. Japanese measure GDP in Yen. Thus, the Japanese will convert the GDP to dollars to compare it with Yen. The conversion process will be done by Yen/Dollar. 2. GDP per capita may not be a good measure especially in the case of measuring living standards and the economic welfare. It is said to be an average and hence it fails to capture the picture of inequality, poverty and other economic activities (Gordon, 2012). It cannot measure the leisure value and also the longevity value. As an example, it can be said that a traffic jam will raise the GDP due to an increasing utilisation of gasoline but the quality of life or the living standards will be hampered by this. GDP per capita failed to capture this. The HDI can be used to measure the standard of living or countrys well-being (Ram and Ural, 2013). Table 1: Top ten countries (GDP level- 2010) It fails to determine the living standards and the well-being of the countries. Thus, to measure well-being and the living standards, it is better to use HDI, HPI etc. (Al-Hilani, 2012). Figure 1: Country-wise GDP Source: (Author) 3. To do a comparative analysis, the researcher can take India as developing country and US as developed country. Here, HDI is taken to show the standard of living trends between two countries. Table 2: UNDP report on HDI Source: (HumanDevelopmentReports, 2016) From table 2, it can be said that in the US, the HDI value had risen marginally in 2014 from 2013. Thus, a very high HDI can be seen in the US according to the UNDP report. In the case of India, the value is also increasing and in 2014, the value is 0.609. The rank is 131 according to the report of UNDP. It will imply that a medium HDI can be seen in India. Table 3: HDI growth (Dept, 2014) From table 3, it can be seen that the rate of HDI growth diminishes in 2010-2014. In the case of US, the rate has changed marginally but in the case of India, the rate has declined from 1.67 to 0.97. Figure 2: HDI in US and India Figure 2 has also shown the upward trend from the last 7 years. Table 4: Components (HumanDevelopmen ReportsComponents. 2016) There are some components include in the HDI Life expectancy, expected schooling years and mean schooling years, GNI per capita (Mankiw, 2012). HDI is said to be a composite index which measures the average achievement in basic 3 dimensions of development in human, these are a healthy as well as long life, standard of living and knowledge. Life expectancy is referred to as the number of period an infant newborn can expect to live. For knowledge, expected schooling years and mean schooling years will be taken. The first one is referred to as the entrance age of schooling which can be expected if age particularity enrolment is persisted. Mean schooling years refers to the average years of education which an individual can receive. GNI per capita is an economys aggregate income that is generated from its production and factors of productions ownership. It can be converted to global dollar value by utilising PPP divided by the population of midyear. Thus, by visualising table 4, it can be said that it will take more than 20 years to be doubled if there are no economic fluctuations in both of the countries. 4. The standard of living is referred to as the wealth level, level of comforts, necessities and material goods which are available in a general socioeconomic class under a specific geographical territory. The standards of living in the countries of NMSs have converged in a rapid manner towards the mean living standard of Europe in the first 10 years of this new century. In this region, many countries like Bulgaria, Baltic countries etc., per capita GDP has been adjusted for PPP. It has assisted in comparing the standard of living of different countries. It had raised greater than 100 percent in the year 1999-2008, but in case of euro areas Member States, the similar indicator had increased only by 30% on an average. However, the convergence rates of the standards of living of the NMS were not at all similar from 1999 to 2008. The countries were divided into two parts on the basis of convergence path. In the first group, Hungary, Poland and the Czech Republic were there and the highest standard of living was seen. In the second group, the rate of convergence was too slower over the 10 years. The countries present in the second group were Romania, Bulgaria, Baltic countries. The declination size of PPP-adjusted per capita GDP of the second groups countries in 1999 and many problems of them in the year 2009 has returned the convergence before the crisis had raised a question of imbalances. Latvia and Estonia had a CAD of 10% of the GDP levels whereas Bulgaria was present at 9% of the GDP level. The countries except Hungary belonging from the first group stood at CAD less than 5% of GDP. Thus, convergence and divergence had also arisen due to CAD effects in GDP (Moukarzel, 2013). The PPP adjusted per capita GDP extrapolation in 2010 had shown that living standard had resumed its increasing trend in most of the countries of NMSs in the year 2009-2010. However, it had faced a slower pace in between 1999 to 2008. This had confirmed that there were a lasting de-convergence did not occur. However, it can be said in this context that the global financial crisis adversely affected the process of convergence (Kokkoris, 2014). In 2010, there was another slowdown in the standard of living increment in comparison with the past decade which was relatively high pronounced for the countries belonging from the second group. The slowdown had arisen in the second group due to some macroeconomic imbalances. Thus, the countries belonging from the second group should perceive sustainable strategies of growth (Alonazi and Thomas, 2014). References Al-Hilani, H. (2012). HDI as a Measure of Human Development: A Better Index than the Income Approach?. IOSR Journal of Business and Management, 2(5), pp.24-28. Alonazi, W. and Thomas, S. (2014). Quality of Care and Quality of Life: Convergence or Divergence?. Health Services Insights, p.1. Blanchard, O. and Johnson, D. (2013). Macroeconomics. Boston (Mass.): Pearson. ConvergencePDF, (2016). [online] Tresor.economie.gouv.fr. Available at: https://www.tresor.economie.gouv.fr/file/326916 Dept, I. (2014). India. Washington: International Monetary Fund. Goodhew, P. (2013). Growth of what: GDP or quality of life?. European Journal of Engineering Education, 38(2), pp.119-120. Gordon, R. (2012). Macroeconomics. Boston: Addison-Wesley. HumanDevelopmen ReportsComponents. (2016). [online] Hdr.undp.org. Available at: https://hdr.undp.org/en/composite/HDI HumanDevelopmentReports. (2016). [online] Hdr.undp.org. Available at: https://hdr.undp.org/en/composite/trends Kokkoris, I. (2014). Introduction: EU and U.S. Competition Enforcement--Convergence or Divergence. The Antitrust Bulletin, 59(1), pp.1-8. Mankiw, N. (2012). Macroeconomics. New York: Worth. Moukarzel, C. (2013). Per-capita GDP and nonequilibrium wealth-concentration in a model for trade. J. Phys.: Conf. Ser., 475, p.012011. Ram, R. and Ural, S. (2013). Comparison of GDP Per Capita Data in Penn World Table and World Development Indicators. Soc Indic Res, 116(2), pp.639-646. Ryser, L. and Halseth, G. (2011). Informal Support Networks of Low-Income Senior Women Living Alone: Evidence from Fort St. John, BC. Journal of Women Aging, 23(3), pp.185-202.