1500 word essay
GDP measures output with no regard for the quality of such, therefore rendering its value as a mark of economic welfare substantially flawed. Critically discuss this statement.
How is GDP is measured – 3 approaches What does GDP not take account of?
Gini coefficient – Lorenz curve
Good and bad output eg. Accidents increase GDP, prostitution, illegal drugs
Environment – Pollution – Working hours or other examples
Gross Domestic Income (GDI) – World Bank measure
Physical Quality of Life Index (PQLI)
How to measure? How to compare. Total or per capita
EU – beyond GDP
GDP MEASURES OUTPUT WITH NO REGARD FOR THE QUALITY OF SUCH
GDP Measures Output With No Regard for the Quality of Such
Gross Domestic Product is a measure of the monetary value that a country adds to the economy through the production of various goods and services. It encompasses public and private consumption, investments, government outlays, as well as exports minus imports occurring within a country. In other words, it is a broad measure of the overall economic activity taking place in a country. In recent times, GDP has become an increasingly popular indicator of economic health in the modern world. It is being widely relied on to determine the standard of living in specific countries. Countries with a higher GDP are considered more productive than those with a lower GDP. Economists have determined that GDP figures are a fairly accurate measure of the annual economic performance of a country relative to that of another country, especially after adjustments have been made to take into consideration the level of inflation.
One major disadvantage of GDP, though, is that it measures output with no regard for the quality of such. For instance, it does not draw a distinction between good and bad output. Examples of bad output include illegal drugs, prostitution, and human trafficking. It also fails to take into consideration the negative effects of economic growth such as environmental pollution. Critics also lament the inability by GDP to highlight the existing inequalities in the distribution of wealth in virtually all countries. In this paper, a critical discussion of these disadvantages is presented.
How GDP Is Measured and What It Does Not Take Account of
There are three approaches for measuring GDP; they include the production estimate, the expenditure estimate, and the income estimate. The production estimate focuses on the final output’s value within the economy less the value of inputs that were used up during the production process. A product such as a car is an example of a final output, while intermediate products such as car tires, advertising, and electricity are inputs used during the production other goods and services. This method avoids overestimation of the GDP due to double counting by calculating and aggregating the value that is added at each stage during the production process. The resulting gross value added is further adjusted for subsidies and taxes to yield the GDP estimate.
The expenditure estimate measures the GDP based on the value of total expenditure on all goods and services produced in a country during a specific period. Intermediate products are excluded from this estimate. In this case, focus is on spending by consumers, corporations, government departments, and overseas purchasers on the goods and services that have been produced in a country. Data for this GDP measure come from a wide range of expenditure surveys of both businesses and households as well as government expenditure data.
The income estimate is based on the incomes of individuals (for example, wages and salaries) and corporations (for example, profits) that result directly from the production of goods and services. Data for this approach comes from employer surveys, weekly earnings surveys, quarterly operating profits, as well as administrative data from customs and revenue departments.
Unfortunately, GDP does not take account of certain aspects of the economy. To begin with, it does not take into consideration changes in the quality of products as well as the inclusion of newer products. Yet newer and/or higher quality products tend to replace older products in the market. For example, many of the cars that were being produced during the 1980s were of a lower quality than the ones being produced today. Similarly, technological advancements have led to the emergence of new electronic items that did not exist three decades ago. GDP’s inability to take into account these factors means that real income changes could be understated.
GDP statistics also fail to capture aspects of the underground economy, which comprises of cash and barter transaction that occur outside of formally recorded marketplaces. Some of these activities are illegal, for example, prostitution, illegal drug trade, human trafficking, and organ trade. Others are legal but are undertaken in the underground to avoid taxes. At the same time, GDP estimates do not address the harm side effects of the process of economic production such as pollution (Mankiw 2007). Market transactions that relate to efforts geared towards correcting the negative effects are always added to GDP estimates while no subtractions are made to these estimates to account for the harmful effects of the production process. The estimates also fail to take into account certain aspects of life that have a profound impact on the level of outputs such as leisure time and accidents. Moreover, non-market production tends to be excluded from GDP statistics. This form of production encompasses goods and services that have been produced but have not been exchanged for money despite them having value. For example, subsistence agriculture tends to be excluded from GDP estimates yet it is a major source of livelihood for millions of households in many countries.
GDP and Economic Inequality: Lorenz Curve and the Gini Coefficient
Although GDP estimates give an indication on the wealth that is being created in a country, it does not give a clear picture of the way the income is distributed in the economy. Thus, it is difficult to identify the top and bottom income earners. This is a major shortcoming because it creates a situation where changes in inequality cannot be monitored. Efforts to use average and median measures to get a flavor of changes that are taking place in terms of income distribution tend not to take into account all distributional issues (Bourguignon & Morrisson 2002). This limitation has created the need for other approaches that can portray a greater level of distributional sensitivity in measuring national income.
A crucial source of insight into the need to address the issue of inequality while measuring a country’s wealth is the Lorenz curve. The Lorenz curve is a tool for measuring income distribution and inequality within a population (Zoli 1999). The graph contains a straight diagonal line that represents perfect equality in the distribution of wealth. Beneath this line is the Lorenz curve that shows the reality in terms of wealth distribution. The difference between the curved line and the straight line reflects the level of inequality in the way wealth is distributed within an economy, and it described using the Gini coefficient.
Gross Domestic Income (GDI) and the World Bank Measure
The concept of Gross Domestic Income (GDI) has also been widely used to measure productivity. The main difference between GDI and GDP is that the former includes the current account (net foreign income) as opposed to the balance of trade (net exports) (Choe 2003). Thus, in comparison to GDP, GNP contains an additional component which is the net foreign investment income. This essentially means that most of the limitations that are inherent in GDP are also found in GDI.
In the provision of GDP estimates, the World also faces the problem of low quality statistics particularly in developing countries (Sender 1999). In many of these countries, the resources allocated to national statistical offices tend to be inadequate. This situation has greatly contributed to significant differences in “official” GDP estimates in many developing countries. In poor countries, the challenge of statistical capacity is exacerbated by data loss or distortion due to corruption, political instability, wars, and unrecorded informal sectors.
Physical Quality of Life Index (PQLI)
Dissatisfaction with GDP as an economic indicator has led to efforts to develop alternative measures, one of which is the Physical Quality of Life Index (PQLI). The PQLI was developed by the Overseas Development Council (ODC) as a social indicator that is based on a single index combining life expectancy, infant mortality, and literacy for each country (Larson & Wilford 1979). As one may expect, countries with lower per capita GNP (Gross National Product) have tended to have low PQLIs while those with higher per capita GNP have tended to have higher PQLIs (Fasolo, Galetto & Turina 2011). However, these correlations are not substantially close, such that some countries with low per capital GNP have been found to have high PQLIs while other countries reporting high per capital GNP have been found to have low PQLIs (Mitsuhiko 2008). These findings suggest that having a higher GNP does not guarantee a better quality of life. Conversely, it shows that it is possible for the quality of life to be improved even before an increase in per capital GNP has occurred.
Per Capita GDP
The idea of using per capital income as a way of addressing the shortcomings of GDP has also been proposed. GDP per capital is obtained by simply dividing GDP by the population. In this approach, GDP estimates are normally adjusted to take into account current market prices. A variation of this indicator is sometimes derived by dividing the percentage change in GDP by the population to determine growth in GDP per capita (Bishop & Gripaios 2005). The objective of the per capital income approach is to assess the changes that may have occurred in terms of the overall wellbeing of a country’s population (Islam and Clarke 2002; Carrion-i-Silvestre, Barrio-Castro & López-Bazo 2005). The per capita method is a useful approach but it still does not provide a direct measure of sustainable development (Bregar, Rovan & Pavsic 2008).
One of the most recent initiatives to address the limitations of the GDP measure is the Beyond GDP initiative. This initiative combines the clear and appealing aspects of GDP with greater inclusion of social and environmental aspects of progress (European Commission 2015). It encourages the sharing of knowledge among policy makers, researchers, and civil society on how to develop alternative approaches to the measurement of development and wellbeing (Boarini & D’Ercole 2013). Some of the suggested dimensions include economic performance, trends relating to material welfare, households’ net disposable income measures in terms of real GDP and per capita GDP, and life satisfaction levels. Other crucial dimensions include life satisfaction levels, changes in the use of renewable energy, level of carbon emissions, and environmental problems arising from prevailing levels of energy and land use (Hobijn & Steindel 2009). Some of the proposed components of an alternative index that captures both economic and social-environmental aspects include economy; health; safety and security; governance, entrepreneurship and security; education, social capital, and personal freedom (European Commission 2015).
In conclusion, GDP is a very useful measure of the monetary value that a country adds to the economy through the production of various goods and services. However, it has a number of shortcomings that render it incapable of capturing the reality in terms of the overall wellbeing of a country’s citizens. It measures output with no regard for the quality of such, thereby leading to a flawed assessment of changes in economic welfare. For instance, GDP does not account for the underground economy, subsistence production, economic inequality, and environmental impact of production. This explains why several alternative approaches for measuring economic development have been proposed, including Physical Quality of Life Index (PQLI), per capita GDP, and most recently the “Beyond GDP” initiative.
Bishop, P & Gripaios, P (2005) ‘Patterns of persistence and mobility in GDP per head across GB counties’, Tijdschrift Voor Economische En Sociale Geografie, vol. 96, no. 5, pp. 529–540.
Boarini, R & D’Ercole, M (2013) ‘Going beyond GDP: an OECD perspective,’ Fiscal Studies, vol. 34, no. 3, pp. 289–314.
Bourguignon, F & Morrisson, C (2002) ‘Inequality among World Citizens: 1820-1992’, The American Economic Review, vol. 92, no. 4, pp. 727-744.
Bregar, L, Rovan, J & Pavsic, M (2008) ‘Validity of GDP per capita for international development comparisons’, Economic and Business Review for Central and South-Eastern Europe, vol. 10, no. 3, 181-195.
Carrion-i-Silvestre, J, Barrio-Castro, T & López-Bazo, E (2005) ‘Breaking the panels: An application to the GDP per capita’, The Econometrics Journal, vol. 8, no. 2, pp. 159–175.
Choe, J (2003) ‘Do foreign direct investment and gross domestic investment promote economic growth?’, Review of Development Economics, vol. 7, no. 1, 44–57.
European Commission (2015) What is the ‘Beyond GDP’ initiative, Web.
Fasolo, L, Galetto, M & Turina, E (2011) ‘A pragmatic approach to evaluate alternative indicators to GDP’, Quality & Quantity, vol. 47, no. 2, pp. 633-657.
Hobijn, B & Steindel, C (2009) Do Alternative measures of GDP affect its interpretation? Current Issues in Economics and Finance, vol. 15, no. 7, pp. 104-119.
Islam, S & Clarke, M. (2002) ‘The relationship between economic development and social welfare: a new adjusted GDP measure of welfare’, Social Indicators Research, vol. 57, no. 2, pp. 201-229.
Larson, D & Wilford, W (1979) ‘The physical quality of life index: A useful social indicator?’, World Development, vol. 7, no. 6, pp. 581-584.
Mankiw, N (2007) Principles of Economics, Volume 1, 4th Edition, Thomson South-Western, Mason.
Mitsuhiko, I (2008) ‘Towards a high quality of life society: GDP, welfare and happiness’, St. Andrew’s University Economic and Business Review, vol. 49, no. 4, pp. 123-138.
Sender, J (1999) ‘Africa’s economic performance: limitations of the current consensus’, The Journal of Economic Perspectives, vol. 13, no. 3, pp. 89-114.
Zoli, C (1999) ‘Intersecting generalized Lorenz curves and the Gini index’, Social Choice and Welfare, vol. 16, no. 2, pp. 183-196.