The OECD produces a very extensive database and this is available at http://stats.oecd.org/
Using data extracted from this database, the purpose of the coursework is to investigate the relationship between GDP per capita and another measure of your choice.
For example, you may be interested in examining whether or not the number of doctors in European countries differs across time or between countries (e.g., OECD versus non-OECD countries). In addition, you may also be interested in establishing a relationship between the number of doctors per 10,000 of the population and GDP per capita for a number of countries in the database and compare this between OECD and non-OECD countries.
As a prelude to any empirical analysis you undertake, there are a number of basic questions you need to ask yourself. For instance, have you measured the variables appropriately? Should you use total GDP or GDP per capita? Should it be the total number of doctors, or doctors per 10,000 of the population? Does it matter that one is per capita and the other is per 10,000? Would it be useful to look at other variables? Perhaps one country has fewer doctors than another, but this is because it employs a lot more nurses or other health care professionals. Could you take account of this in some way?
Go to the tab labelled ‘National Accounts’ under the ‘Data by theme’ heading to the left of the link above (http://stats.oecd.org/). Next, go to the ‘Annual National Accounts’ tab and click on ‘Main aggregates’ and then on ‘Gross Domestic Product (GDP)’. Next, click on the second option (‘GDP per head, US
$, constant prices, constant PPPs, reference year 2010’).
As a starting point, you could calculate the mean, median and standard deviation for the estimates of GDP per capita (i.e. ‘GDP per head, US $, constant prices, constant PPPs, reference year 2010’) for the OECD countries (i.e. all the rows from ‘Australia’ to ‘United States’) for the most recent year (e.g., 2016). What do you notice?
Now do the same exercise for the non-OECD countries in the table (the figures for the non-OECD countries are at the bottom of the table, from ‘Argentina’ to ‘South Africa’) for the same year as you did for the OECD countries (i.e., 2016). What do you notice? How do they compare to the estimates for the OECD countries?
Some hints on using the database
Browse for the variable or variables you intend to use for your investigation. Information such as the definition of the variable is given on the right of the screen.
Choose a convenient year for your analysis – it is best that it is a recent year but with a good range of countries included.
You can draw graphs using the database software itself (but note you have to select carefully the data first). It is much better to download the data (see button above the data table) and do all the organisation and analysis in Excel yourself.
Developing the Project Research
In order to proceed further, you will need to access the database again and choose a measure you want to investigate in conjunction with GDP per capita. Choose something that you find interesting and expect to have a relationship with GDP per capita. Examples might include:
- Numbers of students entering higher education in each country (see Education, Education at a Glance tab). Also the graduation rate may be of interest here. You can get these by gender – are girls more successful academically than boys, for example?
- Is life expectancy related to GDP per capita? Does this differ by gender (i.e., between men and women)?
- Which countries have most road accidents? Is this correlated with GDP? Perhaps there are other variables that might be more relevant?
- Which countries produce the most greenhouse gases? How should you measure this – total emissions or per capita? Do richer countries produce more or less emissions?
Perform the same exercise for your chosen variable (e.g., number of doctors per 10,000 of the population). In other words, calculate the same statistics (the mean, median and standard deviation for both the OECD and non-OECD countries in the table for the most recent year (i.e., 2016). What do you notice? How do the estimates compare between OECD and non-OECD countries?
Once you have chosen your relevant variable(s) and undertaken some summary descriptive analysis, it is a good idea to have a hypothesis to investigate and test. It is always better to start with a sharp and focussed research question than just describing the data. So think clearly about how to phrase the research question.
For example: ‘Is there a stronger correlation between GDP per capita and the number of doctors per 10,000 of the population in OECD compared to non- OECD countries?’
The Empirical Analysis for the Project
The following provides some guidelines on how you should conduct your empirical analysis.
- Comment on the differences in means, medians and standard deviations between OECD and non-OECD countries for both the GDP measure and your chosen variable (or variables).
- Conduct any appropriate tests of differences between the
- Present your results regarding the relationship between GDP and your chosen variable in the form of a scatter chart in the first instance to see if there is some form of relationship of interest for the most recent year This would involve undertaking a separate scatter chart for the OECD countries and then the non-OECD countries. Can you think of reasons or explanations for the form of the relationship you observe? Does it differ between OECD and non-OECD countries?
- Compute and interpret the Pearson correlation coefficient and compare the results between the OECD and non-OECD What do you conclude?
The Write-up for the Project
The following points should be borne in mind when drafting your report.
- Specify clearly the research question you are
- Describe clearly the data you have used and the definitions of the variables.
- Show all relevant calculations to justify any conclusions you have come to and make sure any graphical and tabular information reported is clear and properly labelled and
- Outline the basis of your calculations using one table, as presented in the supplementary notes, and then show how you applied it to the
- Outline your key
- Describe briefly the limitations of your
- Use a concluding section to briefly outline what you have done and restrict this to one
The total report should be about eight or nine short paragraphs in length. The text described above should be focused and comprise a maximum of 600 words in total. The table and graphs comprise the remaining 400 words. Remember the total word limit is 1,000 and submissions that exceed that are subject to the imposition of penalties.
The overall allocation of marks when grading your project will be as follows:
10 marks will be allocated to how you motivate and specify the research question you are investigating;
10 marks will be allocated to how you describe the data, and define the variables used in your empirical analysis;
35 marks will be allocated to how you conduct your computations and present your findings in both graphical and tabular form your results;
35 marks will be allocated to how you write-up and draft your key findings (including conclusions);
10 marks will be allocated for the overall presentational quality of the project.
This research paper will look into and analyze how the income inequality has risen over the years and if there is a correlation between the income and GDP per capita along with the trend for the OECD and the Non-OECD countries. The previous era has seen a restored enthusiasm for the principle factors driving financial development in the OECD nations. A couple of countries – including the United States, the innovation head – have encountered a speeding up in the development of Gross domestic product per capita, yet other real economies have lingered behind, bringing up issues with regards to the job of innovative advancement just as strategy and establishments. This paper goes for revealing some insight into these issues by showing proof on the long haul interfaces between strategy settings, foundations and monetary development in OECD nations while controlling for basic contrasts in innovative advance. Specifically, the center is two-crease: first, on the conceivable impacts of human capital, innovative work action, macroeconomic and auxiliary arrangement settings, exchange approach and money related economic situations on financial productivity; second, on the impacts of a considerable lot of similar factors on the amassing of physical capital.
Pay imbalance has augmented in most OECD part nations amid the previous a few decades. These patterns are all around reported (see references). As indicated by a customary proportion of disparity, the Gini coefficient, pay imbalance ascended by 9% from the mid-1980s to the 2000s, while the proportion of top pay decile to base pay decile achieved its largest amount in 30 years. Nonetheless, between nations, the ascent in salary imbalance has been a long way from uniform, and decay has even been seen in certain nations. By the opinion of Maddison 2001, From the mid-90s until the late 2000s, the OECD territory encountered a kind of “disparity union,” as imbalance expanded in nations, for example, Sweden, Denmark, and Finland, yet fell in nations, for example, Turkey, Mexico, and Chile.
The data is analyzed on the growth of income and the GDP for the countries for both the OECD and the non-OECD countries. Inside nations, markers of disparity, for example, the Gini coefficient, say small regarding who has profited or lost from these patterns. A more critical look at the circumstance of families gives a progressively complete picture and demonstrates that in numerous OECD nations, gains in expendable earnings have missed the mark concerning increments in GDP. This has been especially the situation for less fortunate families: in about all OECD nations for which information are accessible, GDP development was generously higher than families’ salary development in the most reduced quintile. Center pay family units have commonly fared better, even though they likewise linger behind GDP development in an extensive number of nations. There is a developing hole among low-and center pay family units which is especially articulated in Finland, Israel, Sweden, Spain, and the US. According to Park 1995, All the more, by and large, developing salary incongruities between low and center pay families have been more boundless and articulated than the normal, as estimated by the Gini coefficient. A few nations have seen augmenting aberrations in the lower half of salary appropriation, occurring notwithstanding when by and the large disparity has been narrowing– this example is especially striking in Spain. In different nations, for example, Australia, the United Kingdom, and the US, somewhere in the range of 20% and half of the absolute pay gains produced have collected to the top 1% of family units, indicating rising imbalances likewise inside the upper portion of salary dissemination. As OECD nations attempt to empower recuperation, how do development upgrading strategies influence salary imbalance? Recognizing the exchange offs among development and imbalance is no straightforward assignment. Valid, in a greater part of OECD nations, GDP development in the course of the last a few decades has been related to developing pay inconsistencies. Late OECD work has demonstrated that this expansion to a substantial degree reflects ability one-sided mechanical change (OECD @ 100).
Nonetheless, the potential approach drivers of these adjustments in salary distribution– inside and between countries– are less clear. According to Park 1995, To reveal insight into this issue, one ongoing investigation by Causa et al. has researched the long-run sway that auxiliary changes have had on GDP per capita and family unit salary dispersion. Given this investigation, changes that support development can be recognized by whether they increment, decrease or have no effect on discretionary cashflow disparity. It uncovers some intriguing patterns. Without a doubt, a few development upgrading changes added to smaller disparity by conveying more grounded pay gains for family units at the base of the circulation contrasted and the normal family. Such is the situation, for example, of lessening administrative hindrances to residential challenge, exchange, and internal remote direct speculation, just as venturing up the pursuit of employment backing and actuation programs.
For the non-OECD countries, the inequality is much lesser with time than the OECD, and thus the overall economic scenario looks better. By the opinion of Maddison 2001, To investigate the inquiry further, our examination assessed a relationship for GDP for each capita in which an adjustment in pay imbalance was added to standard development drivers, for example, physical and human capital. The thought was to test whether the adjustment in salary disparity after some time has significantly affected GDP per capita by and large crosswise over OECD nations and if this impact varies as per whether the imbalance is estimated in the lower or upper piece of the circulation. The outcomes demonstrate that the effect is perpetually negative and measurably huge: a 1% expansion in disparity brings GDP by 0.6% down to 1.1%. Along these lines, in OECD nations, in any event, larger amounts of imbalance can diminish GDP per capita. Additionally, the extent of the impact is comparative, paying little mind to whether the ascent in disparity happens chiefly in the upper or lower half of the appropriation.
MADDISON, A. (2001),
“The world economy: a millennial perspective”, OECD Development Centre Studies, Paris. Available from OECD online (18th April 2019).
MANKIW, G.N., D. ROMER and D.N. WEIL (1992),
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