Question:
Describe the GDP Sturcture and Its Phases?
GDP is the market value of all final goods and services produced within the domestic territory of the country in an accounting year.
If we sum the gross value added of all the firms of the economy in a year, we get a measure of the value if aggregate amount of goods and services produced by the economy in a year. Such estimates called Gross Domestic Product (GDP)
There are just two periods in the world history during which there was a sustained growth in living standards. The first occurred in China between the eighth and the twelfth centuries, where a modest rate of growth took living standards to a level not attained in Europe until the eighteenth century. The Chinese experience demonstrates that the initiation of a growth process does not entail its continuation indefinitely, in spite of its early experience of growth, China entered the post-Second World War period as one of the world’s poorest countries. (Sultan Ayoub Meo, 2013)
The second phase of sustained growth is a very recent phenomenon and began in Europe. A sustained increase in living standards started in Europe sometime after 1500, but it was initially very slow. During the first two centuries it averaged only about 0.1 per cent per annum, which translates into a 22 per cent increase in income per capita over the entire period.
Economic progress slowly gathered momentum, averaging 0.2 per cent per annum during the 1700-1820 periods, while from the early nineteenth century it began to grow by about 1 per cent per annum, allowing for a doubling in the standard of living in seventy years. Growth rates that consistently reached above 1 per cent per annum were only recorded after 1870. Yet the century and a quarter of sustained growth since then has had spectacular effects, transforming life for people in the countries that have stepped on to the growth elevator and creating a yawning gap between the ‘haves’ and the ‘have not’s.
The dramatic increase in world inequality is illustrated by the fact that in 1900, average income per head in Western Europe, the USA, and Japan was about five times higher than in Africa; now it is fifteen times higher. (Gregg Marland, 2014)
Fig. 1.6 uses the Penn World Tables 6.1 square to look at the distribution of average per capita income across countries in the world in 1960 and 2000. GDP per head of the population is measured at constant prices and at purchasing power parity adjusted exchange rates to enable average living standards to be compared across countries. In fig. 1.6, country averages of GDP per capita are measured relative to the United States, i.e. the USA = 1. In 1960 the world’s poorest country, Tanzania, had an average per capita GDP level of $382 per annum, while China’s was $ 682 and the United States had per capita GDP of $ 12,273. Today, Tanzania is still the world’s poorest country at $ 482 per capita GDP while the United States enjoys $ 33,293 per capita GDP. (Sheedy, 2010)
Average growth rate in the United States was about 1.8 per cent per annum from 1870 to 2000. If average growth rate in the USA had been on percentage point from lower over that period, and thus comparable to those achieved by India or Pakistan (over most of that period), its per capita GDP in 2000 would have only reached about $ 9,000 which would have a meant a current level of economic performance roughly similar to that of Mexico or Poland. If on the other hand the USA had enjoyed growth rates only one percentage point higher than the actual one and thus comparable to the average growth rate of Japan or Taiwan for most of that period, it’s GDP per capita levels would have been almost four times higher than they are now. These examples illustrate how small differences in growth rates produce large effects in terms of the standard of living when they persist for long periods of time. (Arthur, 2013)
Fig. 1.6 shows on the left the frequency distributions of the average GDP per capita of countries of the world in 1960 and 2000, where average GDP is measured relative to the USA (=1). It is immediately obvious that the vast bulk of countries are to be found with levels of average income way below that of the USA. The so called kernel distribution in the right-hand panel is different method of using the same information presented in the histograms on the left. Looking at the right hand panel, we can see the way the world cross-country income distribution has changed between 1960 and 2000. First we notice a bimodal pattern, meaning that it now looks as if it has two peaks at the ends, while in 1960 it had only one. We also notice that the distribution in 2000 is slightly wider than it was in 1960. These are signs that over time as some countries have become richer and others poorer, the world has become increasingly more clustered between rich and poor countries. (Zhu, 2013)
A different question is what happened to the distribution of income across the peoples of the world, rather than across countries. In the past twenty years, some of the most populous countries such as China and India have grown faster than the rich countries and this has had the effect of pulling large numbers of people out of poverty. (Powers, 2012) This hints at powerful changes that occurred over the past four decades as the world witnessed both growth miracles and growth disasters, coupled with a profound transformation of the political landscape and the collapse of many African economies under the burden of the AIDS epidemic. The extent of economic inequality between today’s world economies becomes even more striking when we are reminded that in the developing world over 790 million people do not have enough food to eat and 1.3 billion people do not have access to safe drinking water. Almost half of the world’s population survives on less than $2 a day. In Asia the number of people living in poverty, on less than $ 1 a day, fell from 420 million to around 280 million even when taking into account the financial meltdown of the late 1990s. In Eastern Europe on the other hand the number of people living on less than $ 1 a day has increased by a factor of twenty. (Downie, 2011)
Today most of the OECD countries together with some of the Asian economies find themselves at the top of the world’s income distribution. In 1960 however we witness Latin American countries like Argentina, Uruguay and Venezuela in the top 25 countries, whereas none are in the top 25 in 2000. Similarly, some Asian countries like China, India, Indonesia and Pakistan that were in the bottom 25 countries in 1960 experienced sufficient growth to move well outside this group. Differences in economic growth rates have ranged from 6 per cent per annum for Taiwan to -1.8 per cent per annum for Zambia and have dictated the winners and losers of the last few decades. (Lancieri, 2009)
In fig. 1.7 we plot the relative per capita incomes of economies in 1960 and 2000 against the 45 degree line. Points that lie relatively close to the diagonal represent countries that have seen very little change in relative living standards over the past few decades compared to the USA. Points that lie above the diagonal represent countries that have experienced positive relative rates of economic growth. The plot also shows that within the cluster of points in the lower left corner, representing the poor countries of the world, many have experienced deterioration in their relative position. Only very few countries that have had relatively low incomes per capita in 1960 have seen a significant improvement in their relative living standards, and can thus be identified as growth miracles. These form the loose cluster of points to the left of the 45 degree line and include most of the Asian economies but also Botswana, Mauritius, Cyprus and Romania. Countries represented by points to the extreme right of the 45 degree line correspond to economies that have seen a deterioration of their relative position over the past few decades and are thus labeled as growth disasters. Notable examples include Chad, Iraq and Venezuela.
Given the varied performance of countries as shown in fig. 1.7, we naturally face the question of what the prospects are for the evolution of the income distribution in the future. Specifically, is there any hope that the world’s poorest economies will catch up with the world’s richest ones? We construct fig. 1.8 by plotting the growth rate in per capita GDP over the period 1960 to 2000 against the log value of per capita GDP in 1960. This plot is a simple example of an attempt to explore the concept of economic convergence and corresponds to an old economic hypothesis that countries which start off poor ought to grow faster and thus catch up with the richer ones. If the countries that are initially poor are to catch up, there should be a negative relationship in the graph, with countries on the left hand side ( poor at the outset ) having the high growth rates ( located at the top ) and vice versa for countries that are initially rich. We also have plotted the best fit line corresponding to the sample regression and we observe a small positive slope. However, we should note that the points are widely dispersed, and indeed if we perform a statistical test we obtain the result that the slope parameter is not significantly different from zero. Thus, we cannot confidently conclude that there is divergence on the basis of this data, but there is clearly no sign of convergence across countries. (Zimmermann, 2009)
However, if we were to perform the same analysis on sub samples of the data set, such that the countries that we include are relatively similar in terms of their economic, social, political or historical experience we will obtain a strong negative relationship between the growth rate of income and the initial starting position, thus confirming the convergence hypothesis.
Employment rates are lower for women and for older people: this is the outcome of a combination of lower participation rates and higher unemployment rates. This is a common feature across the OECD countries. Nevertheless, there are interesting cross-country variations, the shortfall of the women’s employment rate below that of men is least in the Nordic countries at around 10 percentage points or less; the shortfall is highest in the Catholic Southern European countries and Ireland at nearly 30 per cent points or more. For older people aged 55-64, the emolument rate shortfall as compared with those between the ages of 25 and 54 is lowest in Japan and Switzerland and highest in the continental economies of Austria, Belgium and the Netherlands
In the light of stylized empirical facts of economic growth we can turn to the theoretical models. One place to begin is to focus on what determines the level of output per worker. In our discussion of the short and medium run, we focused on a single factor of production, labor. We assumed that the amount of capital requirement available to the worker was fixed. When we move to the long run, we would expect differences in levels of output per worker across countries to depend on the amount of capital equipment available. As we shall see, Human capital should also be included. Human capital refers to the abilities and skills that people can acquire. The term human capital is used to highlight the analogy with physical capital: investment only takes place if current consumption is sacrificed and resources are devoted to acquiring capital goods instead. Similarly, resources and time must be devoted to the accumulation of human capital through education, training or learning on the job. In addition to the quantity of factors of production available per worker, both technology and efficiency will matter as well
GDP is the market value of all final goods and services produced within the domestic territory of the country in an accounting year.
If we sum the gross value added of all the firms of the economy in a year, we get a measure of the value if aggregate amount of goods and services produced by the economy in a year. Such estimates called Gross Domestic Product (GDP)
GDP = sum total of gross value added of all the firms in the economy.
GDP includes the market value of all the items produced in an economy and sold legally through the markets. It measures the market value of bananas, onions, potatoes, grapes, movies, health care, haircuts, etc.
Steady state growth is characterized by a constant output ratio and constant wage and profit shares in GDP. There is no growth of per capita GDP. A rise in the savings or investments rate or fall in the population growth rate leads to a period during which the GDP per capita grows as a consequence of the increasing capital intensity of a production, but growth dies out because of the role of diminishing return to the capital. The level of output per capita is higher in the steady state is higher, the higher is the savings or investment share, and the lower, the rate of population growth. If the government is able to keep raising the savings or investments rate, there will come a point at which this reduces welfare in the economy, where this is measured by the steady-state level of consumption per head. The savings rate that maximizes consumption per head is known as the golden rule rate. (Landefeld, 2008)
The Solow-Swan model must be modified if it is to be consistent with the stylized fact of steady growth in per capita GDP. This is accomplished by including Harrods-neutral technological progress in the model: i.e. it is assumed that the productivity of labor is enhanced by improvements in technology on the existing capital stock that take place at a constant exogenously given rate. This modification allows for balances growth with rising GDP per capita. (ichikawa, 1992)
Conclusion
Growth rate is measured by the GDP growth rate. In the 1990s, the rate of growth was around average 3.5 per cent. The rate was slightly higher than in the year 1980s, the home country growth rate was below the OECD average during the year 1950s and in the year 1960s. As with the US, the home country suffered physical and various other destruction during the World War II, so as the result of war, the post war economic growth of the country was not so high rather it was too slow. By the early 1990s, the GDP had fallen by around 8 per cent which was below the OECD average. In the early 1990s, due to various reform measures, there was a rapid economic growth which was accompanied with the low level of inflation and decline in the level of unemployment. Reform measures included the reduction in the tariff level and various other trade barriers, improving the remuneration and employment condition of employees. These reforms increase the level of productivity in the year 1990s, unlike the US, the accelerate rate in the home country’s productivity growth was very little in comparison to the growth of information technologies goods, in spite of the fact that the home country is an intensive user of information technologies goods. The growth rate after 1990s showed a relative increase during the 2000s (Hannesson, 2009)
References:
Arthur, J. (2013). Advance Statistics of GDP by State for 2013 by NAICS Sector. Bureau of Economics Analysis , 1-6.
Downie, J. (2011). Measuring GDP. World Economics , 2.
Gregg Marland, T. L. (2014). GDP: No one metric can rule them all. nature , 1-9.
Hannesson, R. (2009). “Energy and GDP growth. International Journal of Energy Sector Management , 1-2.
ichikawa, y. (1992). Efficient chemical synthesis of GDP-fucose. The journal of organic chemistry , 1-5.
Lancieri, E. (2009). omparison of GDP. World Economics , 2-6.
Landefeld. (2008). Taking the Pulse of the Economy: Measuring GDP. Journal of Economic Perspectives , 22.
Powers, W. (2012). Growth of GDP. World Economics , 2.
Sheedy, K. D. (2010). Debt and Incomplete Financial Markets: A Case for Nominal GDP Targeting. IDEAS , 1-4.
Sultan Ayoub Meo, 1. A. (2013). Impact of GDP, Spending on R&D, Number of Universities and Scientific Journals on Research Publications among Asian Countries. PMC , 1-3.
Zhu, J. Z. (2013). The generally held belief that China’s consumption is too low is a myth based on inadequate theory, a misreading of official statistics. World Economics , 1-10.
Zimmermann, H. (2009). Multi colored GDP. World Economics , 1-15.
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