Project 1

I have been thinking about how to name my project for a while now. Some possible names that came to mind are “How fast can you eradicate poverty?” or “How can economy change in 10 years?” or “Things to keep in mind when you govern.” However, I will refer to it as “The Income Fluctuations Project” for reasons it will become obvious as you read. My project addresses the issue of economic positioning throughout the years, by looking at countries’ GDP for a period of ten years (2006-2015). This post is intended at giving a description of the project, describing the process of creating it, detailing the hardships that needed to overcome, results and reflections on the projects’ outcome.

A. The beginning

In order to discuss the variation in income at such a large scale I had to find trustful data sets comprising the GDP information for every country during the specified period of time, and the values of what was considered (every year) to be a low, medium, and high level income. This was easy said and done as World Bank Data has an incredible amount of reliable and verified data sets.

When dealing with the data set, however, I noticed that some of the countries had incomplete data. For example, there was no information for the Democratic People’s Republic of Korea for any of the years (1960-2015), very little available for Somalia, for example, and almost no data during communist Romania (up until 1987). I decided to remove all the incomplete data and so, on the maps provided later, the reader can notice that some countries will always remain unmarked.

Now, I was faced with an important decision: should I look at the economic growth for the entire period (55 years) that the data set provided and simply exclude all countries with incomplete data? Or should I restrict my time period to some recent years in which the data available would be enough for most states? There were two factors that influenced my decision to follow the latter option:

  • first, the historical factor. It was in my favor to explore a period of relative political democratic stability around the world. Starting with the new millennium, due to globalization, open borders, changes in trade policies, the apparition of World Wide Web, the world has become involved in a process of homogenization of economic, social, political, and cultural standards. Most nations involved in this unofficial process are dedicating their resources and efforts into achieving these standards of living (note: in my view, standards of living do not only cover economic aspects, but all the four mentioned before). For example, in order for Romania to become a member of the European Union, the Romanian government had to make sure certain standards from all four categories are met. Therefore, I decided that a recent time frame would be the most useful to look at.
  • second, the political factor. This factor is very tightly related to the promises made by politicians, governments, NGOs, and many other figures and institutions. Most of the presidential and parliamentary offices are held for 4-5 years with the possibility of re-election (once or several times) for the same period of time. In such cases, politicians develop their programs for 4-5 up to 10 years, promising socio-economic and cultural changes. One such example I can give is when the newly re-elect president of Romania, Traian Basescu, declared in 2009 that “the last thing he would do was to borrow money from the International Monetary Fund.” Later, the promise was broken because the economic situation (during the 2007-2009 economic crisis) was impossible to overcome in other ways. By looking at the GDP and income fluctuations in this period of economic insecurities, we can also determine how the actions of past or current political leaders affected the evolution of a nation. Thus, since I see the connection between the political and the economic factors to be of such importance, the project follows a rather short, but significant time frame.

Next, I created a map with different layers using CARTO. The visualization and details can be found in section B.

B. Maps, Layers, Colors, and SQL

The Map was created from a .csv file containing the GDP for all the countries (with complete data according to the specifications made in section A) from 2006 to 2015. In order to display and interact with it I created different layers for each year. A mention I must make is that the finalized map only contains 8 out of 10 years because CARTO does not allow for more than 8 layers. Thus, the visualization only covers the period starting with 2008. Before accessing it, one must make sure they have a (free) account created with CARTO!

How was it made?

For each layer representing an year I used SQL to filter data based on what was declared to be an average low income (GDP – income for the country) during that respective year. For example, for the year 2008 what was representative for a low GDP was $254721906161.606. The SQL for filtering the data accordingly is in the image below:

Data selection panel using SQL

Each layer has a different color so that we can differentiate between them. I repeated this step for every one of the eight allowed layer and exported the resulted map. Before continuing to the next question please make sure you have a CARTO account and open the map (click here). You should be able to select between the layers and see the changes on the map. After doing so, you can resume reading the post.

C. Some results

At first I did not expect fascinating results, but once I interacted with the map more I came to realize some very interesting aspects of the GDP evolution throughout the mentioned period. It was very intriguing to see the dynamics. Some facts that can be seen from the map:

  • by selecting layer 2008 and layer 2015 we can see that more and more countries began to have what was considered for that year a low GDP**: Norway, Finland, Denmark, Greece, Ireland, Portugal, Austria, the UAE and South Africa.
  • The UAE fluctuations: in 2008 was above the low income, in 2009 was below it until 2011, and remained so until 2014 when it fell below the low income again. (When looking at these results one should have in mind the fact that the global average GDP is increasing every year and has done so from $177 billion in 2008 to more than $394 in 2015 which is an incredible high rate).
  • Finland has only been above the line in 2008.
  • None of the countries in Eastern Europe have ever passed the low income line.
  • The above point is valid for almost all countries in Africa as well.
  • The U.S., Canada, China, Russia, and Australia have never fell under the low income line.
  • The East Coast of South America has a less fruitful situation than the West Coast.
  • Oceania has always remained before the low income line which is also true for the western part of Asia.

A lot of other similar conclusions can be drawn from interacting with the map. Some generalities that can be said about the evolution of GDP are:

  • most rich countries in 2008 have not fell below the line until the present.
  • some countries have fluctuating positions relative to the low income line and the fluctuations can occur for two reasons: either the country is struggling economically or the growth rate of their GDP is lower than the growth rate of the global GDP.
  • there are countries who have never raised above he line since 2008 and until last year (and which most certainly continue to do so).
  • There are obvious areas of influence and there are countries which oscillate between the two: e.g. Austria oscillates between the rich Western Europe and the less rich Eastern Europe.

It is important, however, not to forget that some of the countries are not included on the map. So, when we look at it and see countries such as Somalia not highlighted it does not mean that Somalia’s GDP was above the low income line. It means that the necessary data was not provided by the government to the creators of the data set and, given my decision, it was removed from the data set* prepared for the analysis. This, however, can tell us as much about the countries as having the data would probably have done. Most of the countries (e.g. Venezuela, Somalia) with incomplete data have already very unstable economic, social, and political situations so it is almost certain that that would have been the outcome on the map.

Similarly important is to notice where countries succeed to go above the line as those countries’ efforts towards the global economic standards seem to be successful. A richer analysis, perhaps beginning sometimes before the 1990s, could show the evolution even more, as well as the regression of different nations.

* I created a data set (.csv file) in excel comprising all the information I have already mentioned. See below:

.CSV file with the names of the countries and their respective GDP values for every year between 2008-2015

 

** The change could occur in two ways: either the country’s economy was affected and thus the GDP decreased OR the worldwide low income has increased and the country has maintained its economic level.

D. Reflections, obstacles or “some other results”

There are many things you learn from doing a digital humanist project, both about the software you are using the the research you are doing. I have started out on this project  several times with different intentions. I have learnt the limitations of not conducting your own research, but borrowing data sets from other organisations. I also had to understand that even though those organizations might be the best in the world at what they are doing, they still cannot obtain perfect results. But, at the same time, I understood that the lack of results also means something.

 

I have learnt a very useful trick: if your data set is not geo-referenced, CARTO can do it for you and all you need to do after that is to export the new .csv file created and import that one – and you are good to go!

I have learnt, throughout the semester, how maps can both provide more rapid information, but also misinformation. On one hand, looking straight at the map I created instead of a data set immediately gives you the answers. You no longer need to compare and contrast so many of the cells of a .csv file which is a tedious work! Instead, you simply click on different layers and interpret what it is shown. On the other hand, however, maps can mean falsified data and one who sees the map I created without reading the notices regarding the excluded countries would simply imply, for example, that Somalia’s economic situation is a very good one. Which, with a simple search on google, one can find that is obviously not true.

Looking back on when I started the project (when I thought I would create a map on the Romanian Revolution of 1848 and then on the battles in Romanian history, or even on the ski jumpers network – which were all failures I would be happy to talk about in a future post), the core thing I learnt is definitely the importance of having a data set readily available when what you want to do involves quite a large amount of data. Otherwise, finding everything you need could be an exhausting and unsuccessful experience. And, with this main valuable lesson in mind, I will want to conclude that obstacles can be called “collateral results.”