Trading trends and its effect on world development
Author: Yousef Moterassed September 2022

Trading trends and its effect on world development

Dedicated to:
Arlete Nogueira Vieira Ascarruz
(1971-2022)
Dedicated to Arlete Nogueira Vieira Ascarruz

In memory of my beloved mother, and my guardian angel who thought me to be a better human being and try to help our world to be a better place. She encouraged me to choose the data science path and use it to help humanity with free, public, and open-source projects and studies to mark my footprint in this world.

May she rest in peace...


Abstract

These days it is inevitable to be involved with the economy and politics since it strongly affects our day-to-day life. So it might be helpful to have a realistic and data-driven sight of the actual situation of the global economy and trading exchanges.

My name is?Yousef Moterassed, a Persian borned boy who lives in Samba-Land ????. I am working in the trade and business field for many years and after stepping into the Data Science and Data Analysis road, decided to combine this knowledge to be able to uncover some hidden information behind the curtain of the data, and release some data-driven information publicly in which it might help myself and our society to increase their understanding and common knowledge about what's going on in our world's economy.

This is a long notebook since I tried to cover almost all the details and corners of this dataset. I hope this project which has been done with Python in the Kaggle platform, would help some of you to have a better vision of what's going on in our world's economy and trades.

I also tried to present this project aesthetically pleasant, whether with scripts or through the art of visualizations. Most of the conclusions are derived through graphical figures and plots since it has been proved that most of the time a simple image is worth more than a thousand words.

Use, inspiration, and modification of this project are free of any charges and allowed with attribution since creating contents and conclusion takes a lot of time and effort, which is I kindly ask you to provide attribution to credit the source.

You can find the original notebook with all the codes and scripts here


Who is the audience of this work?

This project has been done in the hope to help to improve?public knowledge?in the first place, however, this is a very long project, some of the analytic sections or visualizations might seem redundant or boring to the public eyes, but it is exactly when that information will be useful for?data sienctists[1], and?economists?in which they will use these analysis, results or visuals and conclusions to use or advance their studies and projects. Although some statistical or mathematical terms and calculations in some contents might seem a bit difficult or boring to understand, I will try to facilitate them with simplified visualizations which will be explained in plain English within the Explanation and Observation section.

I deeply hope that through the ease of using this project some of our statisticians, civil and public servants, politicians and economists, to help the progress of our societies with their deeper and more technical works.


Introduction

This dataset was kindly published by the?United Nations?Statistics Division on the?UN Data????? site. You can find the original dataset?here. This dataset contains about?8,225,870 entries?and around?1.23 GB?of data. Some of the numbers on this dataset are more trustworthy than others. I'd expect that British tea imports are fairly accurate, but doubt that Afghanistan exported exactly 51 sheep in 2016. Can we identify which nations appear to have the most trustworthy data? Which industries?

I will try to reach some useful and reliable conclusions at the end and surely will keep updating this notebook with fresh ideas and new techniques I would learn during my DS journey, and I hope you would not withhold your conclusions and opinions about this analysis.


Columns explanation

This dataset contains 10 major columns and is defined as below:

  1. country_or_area: Name of the country or the area in which we will rename it to "Country" for our analysis
  2. year: This is an annual report, so it is the year in which the trade has taken place
  3. comm_code: Per the World Customs Organization: The Harmonized Commodity Description and Coding System generally referred to as "Harmonized System" or simply "HS" is a multipurpose international product nomenclature developed by the?World Customs Organization?(WCO). It comprises about 5,000 commodity groups; each identified by a six-digit code, arranged in a legal and logical structure, and supported by well-defined rules to achieve uniform classification. For more, see?here.
  4. commodity: The description of a particular commodity code, i.e. "Horses, live pure-bred breeding".
  5. flow: Flow of trade i.e. Export, Import, Re-Import
  6. trade_usd: Value of the trade in US Dollars
  7. weight_kg: The weight of the commodity in the SI unit of mass, Kilograms (Kg)
  8. quantity_name: A description of the quantity measurement type given the type of item (i.e. Number of Items, Weight in Kilograms, etc.)
  9. quantity: Number of the quantities or units of a given item based on the Quantity Name
  10. category: Category to identify commodity i.e "02 Meat and edible meat offal"


License

License?Per the UNData terms of use: all data and metadata[2]?provided on UNdata’s website are available free of charge and may be copied freely, duplicated, and further distributed provided that?UN Data ?????is cited as the reference.


What is the purpose of my project?

I have learned from the most impressive person in my life whose legacy and lessons are in my ears every day and the most important of them is that we came to this world to create, not just to consume. And we have to know some things before creating anything for other human beings to use or consume. So I will repeat my mother's words for you too.

  • Why are we doing this?
  • Whom will it serve?
  • What do you want to deliver with this project?

One of the most difficult parts of the life of a freelance data scientist is when we are working with an open-world project with a large dataset and all the freedom to do what we want. The difficulty is when we want to ask questions of ourselves! How come?

Well when there is no stakeholder to ask you to solve a problem or indicate a solution or root problem finding or any other tasks, then you are your own stakeholder and normally the pickiest one. So it will be a challenge to know where to begin and how to arrange the questions that could lead you to the main outcome.

For me, the goal of this study was to understand global trading trends and flows better than what is being presented through the media and other available sources, since obviously most of them are biased due to political, religious, or other ideological reasons.

So I have listed some important questions from this dataset that I'm going to answer through statistical and explanatory analysis, together easy to interpret visualizations.


Questions to be answered:


  1. What are the countries' ranks in all of the trading flows?
  2. when we had the most trading flows?
  3. Which trading segments have the most activities?
  4. What are the top trading categories?
  5. Which commodities are traded more than the others?
  6. What is the countries' rank in export and import segments?
  7. Which countries exported more than they imported?
  8. Which countries exported the top trading categories?
  9. What are the most imported products?
  10. Which products are the most exported commodities by the top 3 exporters?
  11. Which year had the most and the least total trading flows?
  12. What was the effect of the GFC and Nine Eleven on global trading?
  13. Did trading trends help the countries to develop?
  14. You name it in the comments.
  15. You name it in the comments.


Preparing our dataset


Here is the very first step of our work, we simply import essential python libraries[3]?and call our dataset in a dataframe and then let's have a quick look at this large data set with only the first five rows.

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Descriptive and Exploratory Statistics

(EDA)

This is important to understand that these days the statistics and mathematical calculations behind it is the inseparable part of any serious analysis. Luckily the efforts of prior scientists made our job way easier than before since there are some functions in Python like?describe()?and?info()?to calculate almost all the important statistical tools for us, also to see the data types and entries.

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This function gives us very interesting information and description about this dataset, such as:

  • The number of countries recorded in this dataset is counted as?206 distinct countries.
  • Also, we can see the interval of these records which is between?1988 to 2016.
  • The top country in all segments appears to be?Australia.
  • The import records seem to be more applied than exports.
  • There are 8,139,480 product categories recorded, and the?category code 55?which is?Man-made staple fibre yarns and woven fabrics?are in the highest records of deals. For more information about this category code see?here.
  • The mean value[5]?of trade values are: with the median and standard deviation[6][7]?of:.
  • The mean value of weight and quantities are: and .
  • There are 8,139,480 commodity codes recorded, and the?commodity code 2106.90?which is?Food preparations, n.e.s.?has the highest frequency. For more information about this commodity code see?here.

We will dive deeper into other items later in other sections.


Analyze

Now we are going to ask our questions one by one. First I would like to answer a very common question that would come to anyone's mind about the subject of this study; which countries are in the top ranking of the trading world?

However, I defined this question in the purpose of this project section, because this question is very common and is a big picture of what we have to present to the public attention. What is that, you asked? For answering this question first we need to learn something. Question any presentations and any statistical information or visualizations that you may see in day-to-day life.

The reason is that there are a lot of elements involved in such a big question that must be considered before reaching any conclusion. After that, we want to know what categories, products, and values have been traded in each segment. Then we will group some countries in each tier and compare their data with reasonable values. Then we want to see some historical points that impact the trading trends, such as GFC,[8].


Question 1. Countries rank in all trading flows

When it comes to the global economy the very first question that will be in anyone's mind is what is the ranking of the countries in the trading market. So we can satisfy this curiosity right in the first place, and then move forward with answering the questions we defined in the purpose of the project section.

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All right, we need to interpret this graph. As we can see the top 20 countries in all trading segments including import, export, re-import[9], and re-export[10], and during all time available in this dataset are listed in this figure and indicates that China is in the first place following by Germany, Belgium and Canada, and others.

The interesting point is that the first countries have a significant distance from the rest of the group, and we will verify the reason for that later in another section.


Question 2. When we had the most trading flows?

It is important to know how was the global trading flow[11]?to understand how population growth and technological improvement impacted global trade.

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This figure is telling us a piece of very interesting and important information in such that it is showing us that we apparently are in a downtrend with the trade values. It is interesting because after increasing the value of almost all the goods in the global trading market this downtrend is something interesting that should be investigated further.


Matching our analysis with the world population data

look at this trend, it shows the population growth. This visualization here shows the annual global population increase from 1950 to today and the projection until the end of this century.

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The absolute increase of the population per year peaked in the late 1980s at over 90 million additional people each year. But it stayed high until recently. From now on the UN expects the annual increase to decline by around 1 million every year.


How long did it take for the world population to grow?

The visualization shows how strongly the growth rate of the world population changed over time. In the past the population grew slowly: it took nearly seven centuries for the population to double from 0.25 billion (in the early 9th century) to 0.5 billion in the middle of the 16th century. As the growth rate slowly climbed, the population doubling time fell but remained in the order of centuries into the first half of the 20th century. Things sped up considerably in the middle of the 20th century.

The fastest doubling of the world population happened between 1950 and 1987: a doubling from 2.5 to 5 billion people in just 37 years — the population doubled within a little more than one generation. This period was marked by a peak population growth of 2.1% in 1962.

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Since then, population growth has been slowing, and along with it the doubling time. In this graph, we have used the UN projections[12]?to show how the doubling time is projected to change until the end of this century. By 2100, it will once again have taken approximately 100 years for the population to double to a predicted 10.8 billion.

What we can see is the interval between 1950 and 2100 which is the prediction of the next year's population growth rate by the United Nations. This graph is good to understand what is the whole picture, but to match it with our prior line plot about trading records during the years we need to look at it from another angle. Now let's zoom into the dataset period between exactly 1988 to 2016 and see what's going on in that period.


Now look at this prediction of the population. It can explain the downtrend of the trade values we explored in the first question's figure.

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Question 3. Global trades are mostly in which flow?

One of the most important elements of understanding the economic trend for a country or for the entire planet is knowing that most of the trades have been done in importation or exportation.

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We can see that importation has an slight advantage over exportation. We also can see that how much is the difference between their values:

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Which is almost?3 trillion dollars, and?considering the 28 years of trading records, this number can be considered as an slight difference between the two major categories. We also can show the equation with a simple line of code as below:

fls.iloc[0,1]-fls.iloc[1,1]

Out[13]:
3135303.3900000006        



Question 4. Which are the top?categories?in all trading segments?

Now we want to know which product categories are in the highest trade records. We will analyze both segments of import and export to see the categories and then commodities in the next chapter.

But first, we might need to understand what is the category in the trading world. The economical term for categories is being used by the name of HS Codes.

Harmonized System

From?Wikipedia?encyclopedia

The Harmonized Commodity Description and Coding System, also known as the Harmonized System?(HS)?of?tariff nomenclature?is an internationally standardized system of names and numbers to classify traded products. It came into effect in 1988 and has since been developed and maintained by the?World Customs Organization (WCO)?(formerly the Customs Co-operation Council), an independent intergovernmental organization based in Brussels, Belgium, with over 200 member countries.

Now we will discover which HS Codes are in the top tier trading levels in all segments. For achieving this goal I created a new segment named?Overall Trades?which contains all the trading segments, including import, export, re-import, and re-export.


Overall Trades

Product Categories in?overall trades, ordered by?trade values

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We can see the top 30 products ordered by trade values in US Dollars, with the top three of HS parent codes:

  • 27:?Mineral fuels, Mineral oils, and products of their distillation. See the sub-products?here
  • 30:?Pharmaceutical products. See the sub-products?here
  • 26?Ores, Slag and Ash. See the sub-products?here

Now we let's see the ranks in order of weight and quantities as well.


Product Categories in overall trades, ordered by their weights

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Product Categories in overall trades, ordered by their quantities

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As we can see the way we express our data the results will be different. for instance, someone could say the top traded products are tobacco and cigarettes, well it's not wrong, it's just not defined on which scale this information is being presented.


Export

Product Categories in?exportation, ordered by?trade values

One of the most important indicators of the economic power of a commodity is the amount of exportation on the trade value scale. Here we will investigate this subject but for more informative answers which might help some commercial professionals and economists, we will present the information in weight and quantity scales as well.

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Product Categories in?exportations, ordered by?quantities

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Product Categories in?exportations, ordered by?weight

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Now we have a piece of useful information about the trading categories' ranking, and we can see that almost the same changing pattern for overall trading is repeating here too. Now let's see the same process for the importation segment.


Imports

Product Categories in?importations, ordered by?trade values

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Product Categories in?importations, ordered by?weight

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Product Categories in?importations, ordered by?quantities

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These plots are interesting since they reveal such important information which I can indicate some of below:

  1. Mineral fuels and petroleum is by far the top category in all of the segments and classifications.
  2. The pharmaceutical category seems a highly profitable business since it stands in second place by trade values, however the quantity and weight of the trades are not even close to the top10 categories.
  3. Even though it's been decades since the consuming tobacco is reduced in the whole world, it is still one of the most valuable trading categories.
  4. Most of the imported and exported trade categories appear to be aimed at feeding people.

Can you see the difference between segments and classifications? This is why statistics and correct visualization are so important when we want to deliver correct information to the public. For example, if in TV News, papers, or even political debates someone is talking about the economical stats, trading ranks, or anything similar, it is important to ask: what is the scale you measured this data?

As we can see in each scale such as weight, quantity, and trade values show some different results and ranks. So it is not too simple that we could brief the results in just a sentence and say for example pharmaceutical products are always in the top 3 categories of traded goods.

Now the question here is can we reach a solid conclusion based on the categories only? Do we need further investigation? In what field and on which scale? To answer this question I will investigate further with commodities ranking which is the child directory of these categories to see which products exactly are being traded.


Question 5. What are the top?commodities?in all trading segments?

Now it's time to verify the products in each segment (overall, imports and exports) separately and see which items are in the highest supply and demand ranking. We are going to analyze them exactly like before scaled by their trade values, weight, and quantities and see the differences and reach some conclusions based on data and the visualizations.


Overall Trades

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Super interesting again, we can see every time we receive some new information. However, trust issues are something we all have to absorb when it comes to data, even if it is from such a trustable source as the UN. But I never imagined that the ballpoint pen is one of the highest goods in the trading records, not at all!

We will continue our analysis to be able to reveal a solid conclusion in the end, so let's begin.


Export

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Imports

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Now we have a better and clearer vision of what's inside this dataset. These results are telling us such important points, most importantly is that from trading categories we can't have any kind of conclusion.

Based on the results we can see that most of the trading activity focused on these categories:

  • Energy
  • Transportation and logistics(parts)
  • Gold
  • Iron
  • Diamond
  • Vehicles
  • Human feeding

So interesting, a lot of questions can be answered within the simple question we asked. For example, is feeding human being the most challenging problem? Is it real that mineral and natural resources are close to being ended?

Our actions recorded with data are answering a lot of questions and striking through a lot of myths and lies told by many during past years, but now data is uncovering so many facts about what's going on and how we can manage our world.


Question 6. What are the top?countries?in import and export segments?

Earlier we saw the countries' ranks for their trading activities, but as I mentioned it wasn't a clear picture of what was going on exactly and it was just like what most of the politicians and media show to the public about economic activities. Here I want to take this analysis a step further which is the trade segments for all countries, and then we will take it even under more investigation with the yearly analysis and other factors in other sections.


Export

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Imports

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Question 7. Which countries exported more than their imports?

In the first part of our analysis, we tried to answer the most commonly asked question about trades and economies by the public, which is what are the countries' ranks in trading? As I explained before it is a very relative subject and we need to understand that there isn't a straightforward answer to such questions.

For example, we can classify countries by their exportation values, weights, quantities, or their importations in those scales as well.

Also, we can classify countries when their exportation is higher than their importations, this specific question for answering some political and justicial doubts about governmental actions and even sometimes to uncover corruption inside a governmental system, but that is for another notebook that I am going to dig deep about some countries and release some useful information that might help responsible organizations for verifying the fact of the existence of the corruption in their systems.

But now in this section, we will focus on the top-tier exporters and importers. We want to know those countries with a high value of exportations, how much they paid for the importations in return.

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Interpreting this graph is something that needs a lot of time and effort since this data is aggregated at all times, however we can see those top exporters are not necessarily top importers. But interpreting it is not such a simple process, consuming rate and importation are relevant to so many elements such as population, industrial activities, modernization and so on which is not in the scale of this project.

As an straightforward result, we can say that based on historical data, among top exporters only four countries exported more than their importation. These countries are:

  1. Canada
  2. Belgium
  3. Brazil
  4. United States

Again, this is not an absolute result and needs to be expressed in a more informative manner, such as per year, per commodity, and other factors that should be mentioned due to the question. We asked an overall question based on what we have on hand and tried to answer it in the most simplest way possible. So interpreting this figure as an answer to any specific question is not approved by me and surely is not valid. Now let's continue to the next question.



Question 8. What countries are the exporters of top trading categories?

After identifying the top trading exported categories in question 4 we want to answer which countries exported those categories more than others. Let's focus on the top 5 categories so that the answer won't go too long.

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As a reminder, we plotted the top 10 categories, but now we will focus on the top five items for further analysis. We can clearly see how far is petroleum from the other categories, but does this mean that human beings consume petroleum more than other categories? What about precious stones and metals?

Actually, I think this section could tell us so many things, that maybe men now passed the necessity of feeding problem and survival era! Other than that we can use this plot for answering many more questions, but now let's focus on our main question. Which countries are exporting these categories more than the others?

Does it mean that top countries in exportation such as China or Canada exported these products more than the others? Here we will see that.

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Now you can see that, right? Top exporters are not top exporters anymore! Also if we draw the table for the top exporters you can see that the quantity or weight is so different.

We will plug a table only for the petroleum category just for an example, and show what I mean about the difference:

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Here as we plotted before, we can see the top exporters of the petroleum category. This ranking is based on the trading value, but if you look at the weight or quantity column you can obviously see that for example Australia sold way more products than Canada, but still is in third place.

China isn't even close to the top 10 and also in the other categories we can see the rankings are so different from each other, which will tell us that a country's rank in trading is not defined by an specific category or material; at least not in the top exporters of top importers. A mixture of trading activities is involved to make a country economically powerful and place them in the top rankings. So as I said before, for revealing some information about any complex field, we first need to improve our knowledge about that particular subject. For example, the price variation of petroleum depends on so many factors, such as type, quality, geographical points, logistics, market tensions, and so on. So now let us jump into the next question.



Question 9. What are the most imported products?

The question here is: is it the most imported categories have the same ranking as the most exported ones? If yes, what countries are the most importers of those that are in the top 3 of the ranking?

We already ranked the top imported categories, scaled by their values, weight, or quantities; but as I mentioned for other questions, we will only analyze our question by trade values, so in the end, we could have a unique conclusion. As a reminder, we will plot the top 10 imported commodities, and then will continue our analysis.

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As we can see the petroleum category is still at the high top with a lot of distance from the rest of the categories. Vehicles are the highest demand and traded value after petroleum, and the rest are almost in the same order besides copper and articles which are in the 10th place. Now we want to know who imported the top 5 categories at most.

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Again we gained such wonderful results from these graphs. for example, did you know that the US is both the top exporter and the top importer of aircraft parts? And that Belgium is one of the top exporters and also importers of petroleum? Or who would imagine that Botswana is one of the top importers of precious stones/metals in the world? After knowing such a piece of good information, we will proceed to the next question.


Question 10. Which products are the most exported products by the top 3 exporters?

This particular question was asked by so many people from some of my friends who were wondering, which countries are the top exporters of all time and what products they are exporting at most. So now I'm going to answer this question in the simplest way possible.

We already identified the top exporters in all segments and of all time (recorded in UN), Now we will list the top three exporters by trade values, and observe the top products they sold at most. As a reminder let's see the top 10 countries in a plot.

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I should mention that I will choose the top 3 countries because this study won't be too long and boring to study for the public eye.

So as we can see the top three are China, Canada, and Germany. Now let's see what specific commodity line they mostly exported that made them the top exporters of all time.

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Every single plot is interesting by itself since it says how different those countries are in the trading fields, however they are close in the outcomes. Now we could jump into our next question.


Question 11. Which year has the most and the least trading flows?

There are a couple of simple ways to answer this question, but we will choose two. First, we will see the numerical result in a tabular shape, and then we will see the trend in a graph.

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This is telling us that 1988 and 2013 were the min and max of the trade values, but we can understand that this is not the case, since the minimum value is determined by the beginning of the recorded data in the dataset and there are probably more dates in history with lower values. But for now, let us consider that we need to consider only the interval of this dataset, then can we say the same thing about the years considering the quantities and weights?

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So yes, the inflation and other pricing factors didn't affect the trend that much to change the min and max points in 1988 and 2013.



Question 12. What was the effect of the GFC on global trading?

According to Wikipedia the financial crisis of 2008, or the Global Financial Crisis (GFC), was a severe worldwide economic crisis that occurred in the early 21st century. It was the most serious financial crisis since the Great Depression (1929). Predatory lending targeting low-income homebuyers, excessive risk-taking by global financial institutions, and the bursting of the United States housing bubble culminated in a "perfect storm." Mortgage-backed securities (MBS) tied to American real estate, as well as a vast web of derivatives linked to those MBS, collapsed in value. Financial institutions worldwide suffered severe damage, reaching a climax with the bankruptcy of Lehman Brothers on September 15, 2008, and a subsequent international banking crisis.

The preconditions for the financial crisis were complex and multi-causal. Almost two decades prior, the U.S. Congress had passed legislation encouraging financing for affordable housing. In 1999, parts of the Glass-Steagall legislation were repealed, permitting financial institutions to comingle their commercial (risk-averse) and proprietary trading (risk-taking) operations. Arguably the largest contributor to the conditions necessary for the financial collapse was the rapid development of predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities. This market development went unattended by regulators and thus caught the U.S. government by surprise.

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So however the global financial crisis (GFC) refers to the period of extreme stress in global financial markets and banking systems between mid-2007 and early 2009 in the United States during the GFC, a downturn in the US housing market, but it spread from the United States to the rest of the world through linkages in the global financial system. Many banks around the world incurred large losses and relied on government support to avoid bankruptcy. Millions of people lost their jobs as the major advanced economies experienced their deepest recessions since the Great Depression in the 1930s. Recovery from the crisis was also much slower than in past recessions that were not associated with a financial crisis.



Question 13. Did this trading trend help the countries to develop?

International Development or Global Development is a broad concept denoting the idea that societies and countries have different levels of economic or human development on an international scale. It is the basis for international classifications such as developed country, developing country, and least developed country, and for a field of practice and research that in various ways engages with international development processes. There are, however, many schools of thought and conventions regarding which are the exact features constituting the "development" of a country. Historically, development was largely synonymous with economic development, and especially its convenient but flawed quantification (see the parable of the broken window) through readily gathered (for developed countries) or estimated monetary proxies (estimated for severely undeveloped or isolationist countries) such as?gross domestic product (GDP)[13], often viewed alongside actuarial measures such as life expectancy. More recently, writers and practitioners have begun to discuss development in the more holistic and multi-disciplinary sense of human development. Other related concepts are, for instance, competitiveness, quality of life, or subjective well-being. Now we want to see the Global Development trend based on economical growth.

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We also can see the world map indicating Human Development Index in 2006 by?Wikipedia Commons.

Map based on 2006 stats according to en: List of countries by Human Development Index by Andrew Oakley so that it is distinguishable by people with red-green color vision deficiency. Andrew Oakley 09:41, 26 January 2007 (UTC);

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The?Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The health dimension is assessed by life expectancy at birth, the education dimension is measured by the mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using the geometric mean. Refer to Technical notes for more details.

The HDI can be used to question national policy choices, asking how two countries with the same level of GNI per capita[14]?can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.

The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO[15]?provides other composite indices as a broader proxy for some of the key issues of human development, inequality, gender disparity, and poverty.

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So yes, as a short answer to our question the global trading trend clearly helped our world in many aspects that pushed the GNI of many nations forward and made other elements such as HDI and Knowledge more achievable.




Conclusion

Where does someone want to go with this study? Well, it completely depends on every single person and what they want to learn and what exactly is their objective of looking for specific information.

This dataset however contains so much data about global trading movements, but it has its own limitations such as historical limits and updates. Later in another notebook, I will use the world bank dataset which is more specific and has a wider historical range. I will update the project link in this notebook and will investigate more specific problems in it with dedicated data for that subject.

For this project we observed so many factors and presented them in the most simple and practical manner, we learned how different would be to express economical situations with the scale we can use. We also saw countries rank in the global trading trends and the most traded products and categories scaled by values, weight, and quantities.


Global economic growth and trading trends

Now we want to know what is the effect of global economic growth on trading trends. But first, we need to learn how we can define economic growth.

Growth in an economy is measured by the change in the volume of its output or in the real expenditure or income of its residents. The 2008?System of National Accounts?offers three approaches for estimating GDP: a production approach to finding gross value added; an expenditure approach; and an income approach (no data in the WDI database use the income approach). In theory, all should give the same estimate.


What are the basic indicators of economic activity?

Measuring economic activity in a country or region provides insights into the economic well-being of its residents.?Gross Domestic Product (GDP), a widely used indicator, refers to the total gross value added by all resident producers in the economy. Growth in the economy is measured by the change in GDP at a constant price. Many WDI indicators use GDP or GDP per capita as a denominator to enable cross-country comparisons of socioeconomic and other data.

Also widely used in assessing a country’s wealth and capacity to provide for its people is?Gross National Income (GNI) per capita?- the sum of total domestic and foreign value added by residents divided by the total population. Furthermore, GNI per capita is normally in U.S. dollars, converted from local currency using the Atlas method, is used to classify countries for operational purposes - lending eligibility and repayment terms. It is also used to classify economies into four main income groups for analytical purposes: low-income, lower-middle-income, upper-middle-income, and high-income. Further information on the operational and analytical classifications is available here.

GNI per capita data are published every year in July for the previous year—data for 2017 will be published during the July 2018 update of the World Development Indicators (WDI) database in World Bank which is the primary World Bank collection of development indicators, compiled from officially recognized international sources.

However, some national data do not become available until later in the year. Major updates for national accounts data occur every July and December.


Prices and Terms of Trade effects

Price inflation is the average rise in the price of goods and services within an economy. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. Data on prices are collected at periodic intervals and appropriate weights are derived from household expenditure surveys. The Laspeyres formula[16]?for calculating price indices is generally used which measures the basket of goods at current prices using base year quantities.

Other price indices include the export price index which interprets the change in prices received by exporters, and the import price index which shows the change in prices paid by the importers. The terms of trade index measure the relative prices of a country’s exports and imports.


Labor Productivity

Labor productivity assesses a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn minimize vulnerable employment (contributing family workers and own-account workers) and working poverty.

Productivity increases do not guarantee these improvements, but without them—and the economic growth they bring—improvements are highly unlikely.


Output per worker

Productivity is often measured by output per unit of input. One indicator of labor productivity is GDP per person. The growth rate is used to achieve higher levels of economic productivity through diversification, technological upgrading, and innovation, including through a focus on high value-added and labor-intensive sectors. The World Development Indicators database also presents value added per worker by sector (agriculture, industry, and services). Visit?the People section?of the World Bank for more information on labor statistics.




References

  • [1].?Data Scientist: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data, and apply knowledge from data across a broad range of application domains. and Data Scientist is a person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making.
  • [2].?Metadata: Metadata is "data that provides information about other data",[1] but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including descriptive metadata, structural metadata, administrative metadata, reference metadata, and legal metadata.
  • [3].?Python libraries: Python Libraries are a set of useful functions that eliminate the need for writing codes from scratch. There are over 137,000 python libraries present today, and they play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications, and more.
  • [4].?Null values: A null value in a relational database is used when the value in a column is unknown or missing. A null is neither an empty string (for character or DateTime data types) nor a zero value (for numeric data types).
  • [5].?Mean value: The mean is probably the most commonly used statistic in all social science research. The mean is simply the arithmetic average of a distribution of scores, and researchers like it because it provides a single, simple number that gives a rough summary of the distribution. The mean is the average or the most common value in a collection of numbers. In statistics, it is a measure of the central tendency of a probability distribution along median and mode. It is also referred to as an expected value for predicting outcomes from samples in more advanced statistics. The mathematical equation of mean values can be written as:?(m=sum of the values ÷ length of the values)
  • [6].?Median: The median is the score in the distribution that marks the 50th percentile. That is 50% of the scores in the distribution fall above the median and 50% fall below it. Researchers often use the median when they want to divide their distribution scores into two equal groups (called a median split). The median is also a useful statistic to examine when the scores in a distribution are skewed or when there are a few extreme scores at the high end or the low end of the distribution.
  • [7].?Standard deviation: The best way to understand a standard deviation is to consider what the two words mean. Deviation, in this case, refers to the difference between an individual score in a distribution and the average score for the distribution. So if the average score for distribution is 10 (as in our previous example), and an individual child has a score of 12, the deviation is 2. The other word in the term standard deviation is standard. In this case, standard means typical, or average. So a standard deviation is the typical, or average, the deviation between individual scores in a distribution and the mean for the distribution.1 This is a very useful statistic because it provides a handy measure of how spread out the scores is in the distribution. When combined, the mean and standard deviation provides a pretty good picture of what the distribution of scores is like.
  • [8].?GFC: The global financial crisis (GFC) refers to the period of extreme stress in global financial markets and banking systems between mid-2007 and early 2009. During the GFC, a downturn in the US housing market was a catalyst for a financial crisis that spread from the United States to the rest of the world through linkages in the global financial system. Many banks around the world incurred large losses and relied on government support to avoid bankruptcy. Millions of people lost their jobs as the major advanced economies experienced their deepest recessions since the Great Depression in the 1930s. Recovery from the crisis was also much slower than in past recessions that were not associated with a financial crisis.
  • [9].?Re-Import: Reimportation is the importation of goods into a country that had previously been exported from that country. A number of legal issues arise with the re-importation of goods, particularly where the goods were not designed for sale in the country from which they were initially exported. Because prices differ from one country to another, a re-importer may purchase goods in another country where they are sold at a low price and re-import them in order to undercut the price at which the goods are being sold in the country to which they are imported. Such re-imported goods may constitute grey market goods.
  • Re-importation occurs often when excise taxes are high on a commodity, such as alcohol. Buyers who desire certain domestic products, but do not wish to pay the high excise tax, can buy them from another country where the excise tax is lower. This occurs, for example, when re-importing Koskenkorva Viina, a Finnish product, from Estonia to Finland.
  • [10].?Re-Export: Re-exportation, also called entrepot trade, is a form of international trade in which a country exports goods that it previously imported without altering them. One such example could be when one member of a free trade agreement charges lower tariffs to external nations to win the trade, and then re-exports the same product to another partner in the trade agreement, but tariff-free. Re-exportation can be used to avoid sanctions by other nations.
  • [11].?Trading flow: Trade flow means the flow of imports and exports, their components, and their direction. Trade flow analysis helps to examine patterns of trade, the trend of flow, concentration, or the extent.
  • [12].?UN Projection: It is projecting future levels of the population by measuring fertility and mortality, probabilistic methods were used to reflect the uncertainty of the projections based on the historical variability of changes in each variable. The method takes into account the past experience of each country, while also reflecting uncertainty about future changes based on the past experience of other countries under similar conditions. The medium scenario projection corresponds to the median of several thousand distinct trajectories of each demographic component derived using the probabilistic model of the variability in changes over time. Prediction intervals reflect the spread in the distribution of outcomes across the projected trajectories and thus provide an assessment of the uncertainty inherent in the medium scenario projection.
  • [13].?GDP: Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced and sold (not resold) in a specific time period by countries. Due to its complex and subjective nature, this measure is often revised before being considered a reliable indicator. GDP (nominal) per capita does not, however, reflect differences in the cost of living and the inflation rates of the countries; therefore, using a basis of GDP per capita at purchasing power parity (PPP) may be more useful when comparing living standards between nations, while nominal GDP is more useful comparing national economies on the international market. Total GDP can also be broken down into the contribution of each industry or sector of the economy. The ratio of GDP to the total population of the region is the per capita GDP (also called the Mean Standard of Living).
  • GDP definitions are maintained by a number of national and international economic organizations. The Organisation for Economic Co-operation and Development (OECD) defines GDP as "an aggregate measure of production equal to the sum of the gross values added of all resident and institutional units engaged in production and services (plus any taxes, and minus any subsidies, on products not included in the value of their outputs)". An IMF publication states that, "GDP measures the monetary value of final goods and services—that are bought by the final user—produced in a country in a given period of time (say a quarter or a year).
  • [14].?GNI per capita: The gross national income (GNI), previously known as the gross national product (GNP), is the total domestic and foreign output claimed by residents of a country, consisting of gross domestic product (GDP), plus factor incomes earned by foreign residents, minus income earned in the domestic economy by non-residents. Comparing GNI to GDP shows the degree to which a nation's GDP represents domestic or international activity. GNI has gradually replaced GNP in international statistics. While being conceptually identical, it is calculated differently. GNI is the basis of the calculation of the largest part of contributions to the budget of the European Union.
  • [15].?HDRO: The mission of the Human Development Report Office (HDRO) is to advance human development. The goal is to contribute toward the expansion of opportunities, choice, and freedom. The office works towards this goal by promoting innovative new ideas, advocating practical policy changes, and constructively challenging policies and approaches that constrain human development. The office works with others to achieve change through writing and research, data analysis and presentation, support for national and regional analysis, and outreach and advocacy work.
  • [16].?Laspey formula: A number of different formulas, more than a hundred, have been proposed as means of calculating price indexes. While price index formulae all use price and possibly quantity data, they aggregate these in different ways. A price index aggregates various combinations of base period prices (p0), later period prices (pt), base period quantities (q0), and later period quantities (qt). Price index numbers are usually defined either in terms of (actual or hypothetical) expenditures (expenditure = price × quantity) or as different weighted averages of price relatives (ptp0). These tell the relative change of the price in question. Two of the most commonly used price index formulae were defined by German economists and statisticians étienne Laspeyres and Hermann Paasche, both around 1875 when investigating price changes in Germany. This equation was developed in 1871 by étienne Laspeyres, the mathematical equation can be expressed as PL=∑(pt?q0) ÷ ∑(p0?q0) which compares the total cost of the same basket of final goods?q0?at the old and new prices.



Resources



This project has been done by?Yousef Moterassed?towards helping to improve public understanding about a small part of the global economy and commodity distribution through trading segments, countries' rankings, and yearly movement of the trading segments. This project is available through?Kaggle,?GitHub,?my website,?and?LinkedIn.

Please participate in this project with your work, opinions, comments, questions, and sharing. Use, inspire, and modify this project are free of any charges and?allowed with attribution?since creating content and conclusion takes a lot of time and effort, which is why I kindly ask you to provide attribution to credit the source.


With all the love and respect

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Yousef Moterassed

Data Scientist from Brazil

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