## 1. The World Bank's international debt data¶

It's not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. The World Bank is the organization that provides debt to countries.

In this notebook, we are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories. We are going to find the answers to questions like:

• What is the total amount of debt that is owed by the countries listed in the dataset?
• Which country owns the maximum amount of debt and what does that amount look like?
• What is the average amount of debt owed by countries across different debt indicators?

The first line of code connects us to the international_debt database where the table international_debt is residing. Let's first SELECT all of the columns from the international_debt table. Also, we'll limit the output to the first ten rows to keep the output clean.

In [34]:
%%sql
postgresql:///international_debt

SELECT *
FROM international_debt
LIMIT 10;

10 rows affected.

Out[34]:
country_name country_code indicator_name indicator_code debt
Afghanistan AFG Disbursements on external debt, long-term (DIS, current US$) DT.DIS.DLXF.CD 72894453.700000003 Afghanistan AFG Interest payments on external debt, long-term (INT, current US$) DT.INT.DLXF.CD 53239440.100000001
Afghanistan AFG PPG, bilateral (AMT, current US$) DT.AMT.BLAT.CD 61739336.899999999 Afghanistan AFG PPG, bilateral (DIS, current US$) DT.DIS.BLAT.CD 49114729.399999999
Afghanistan AFG PPG, bilateral (INT, current US$) DT.INT.BLAT.CD 39903620.100000001 Afghanistan AFG PPG, multilateral (AMT, current US$) DT.AMT.MLAT.CD 39107845
Afghanistan AFG PPG, multilateral (DIS, current US$) DT.DIS.MLAT.CD 23779724.300000001 Afghanistan AFG PPG, multilateral (INT, current US$) DT.INT.MLAT.CD 13335820
Afghanistan AFG PPG, official creditors (AMT, current US$) DT.AMT.OFFT.CD 100847181.900000006 Afghanistan AFG PPG, official creditors (DIS, current US$) DT.DIS.OFFT.CD 72894453.700000003
In [35]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '  country_name country_code                                     indicator_name  indicator_code                 debt\n0  Afghanistan          AFG  Disbursements on external debt, long-term (DIS...  DT.DIS.DLXF.CD   72894453.700000003\n1  Afghanistan          AFG  Interest payments on external debt, long-term ...  DT.INT.DLXF.CD   53239440.100000001\n2  Afghanistan          AFG                  PPG, bilateral (AMT, current US$) DT.AMT.BLAT.CD 61739336.899999999\n3 Afghanistan AFG PPG, bilateral (DIS, current US$)  DT.DIS.BLAT.CD   49114729.399999999\n4  Afghanistan          AFG                  PPG, bilateral (INT, current US$) DT.INT.BLAT.CD 39903620.100000001\n5 Afghanistan AFG PPG, multilateral (AMT, current US$)  DT.AMT.MLAT.CD             39107845\n6  Afghanistan          AFG               PPG, multilateral (DIS, current US$) DT.DIS.MLAT.CD 23779724.300000001\n7 Afghanistan AFG PPG, multilateral (INT, current US$)  DT.INT.MLAT.CD             13335820\n8  Afghanistan          AFG         PPG, official creditors (AMT, current US$) DT.AMT.OFFT.CD 100847181.900000006\n9 Afghanistan AFG PPG, official creditors (DIS, current US$)  DT.DIS.OFFT.CD   72894453.700000003'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[35]:
1/1 tests passed


## 2. Finding the number of distinct countries¶

From the first ten rows, we can see the amount of debt owed by Afghanistan in the different debt indicators. But we do not know the number of different countries we have on the table. There are repetitions in the country names because a country is most likely to have debt in more than one debt indicator.

Without a count of unique countries, we will not be able to perform our statistical analyses holistically. In this section, we are going to extract the number of unique countries present in the table.

In [36]:
%%sql
SELECT
COUNT(DISTINCT country_name) AS total_distinct_countries
FROM international_debt;

 * postgresql:///international_debt
1 rows affected.

Out[36]:
total_distinct_countries
124
In [37]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '   total_distinct_countries\n0                       124'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[37]:
1/1 tests passed


## 3. Finding out the distinct debt indicators¶

We can see there are a total of 124 countries present on the table. As we saw in the first section, there is a column called indicator_name that briefly specifies the purpose of taking the debt. Just beside that column, there is another column called indicator_code which symbolizes the category of these debts. Knowing about these various debt indicators will help us to understand the areas in which a country can possibly be indebted to.

In [38]:
%%sql
SELECT
DISTINCT indicator_code AS distinct_debt_indicators
FROM international_debt
ORDER BY distinct_debt_indicators;

 * postgresql:///international_debt
25 rows affected.

Out[38]:
distinct_debt_indicators
DT.AMT.BLAT.CD
DT.AMT.DLXF.CD
DT.AMT.DPNG.CD
DT.AMT.MLAT.CD
DT.AMT.OFFT.CD
DT.AMT.PBND.CD
DT.AMT.PCBK.CD
DT.AMT.PROP.CD
DT.AMT.PRVT.CD
DT.DIS.BLAT.CD
DT.DIS.DLXF.CD
DT.DIS.MLAT.CD
DT.DIS.OFFT.CD
DT.DIS.PCBK.CD
DT.DIS.PROP.CD
DT.DIS.PRVT.CD
DT.INT.BLAT.CD
DT.INT.DLXF.CD
DT.INT.DPNG.CD
DT.INT.MLAT.CD
DT.INT.OFFT.CD
DT.INT.PBND.CD
DT.INT.PCBK.CD
DT.INT.PROP.CD
DT.INT.PRVT.CD
In [39]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '   distinct_debt_indicators\n0            DT.AMT.BLAT.CD\n1            DT.AMT.DLXF.CD\n2            DT.AMT.DPNG.CD\n3            DT.AMT.MLAT.CD\n4            DT.AMT.OFFT.CD\n5            DT.AMT.PBND.CD\n6            DT.AMT.PCBK.CD\n7            DT.AMT.PROP.CD\n8            DT.AMT.PRVT.CD\n9            DT.DIS.BLAT.CD\n10           DT.DIS.DLXF.CD\n11           DT.DIS.MLAT.CD\n12           DT.DIS.OFFT.CD\n13           DT.DIS.PCBK.CD\n14           DT.DIS.PROP.CD\n15           DT.DIS.PRVT.CD\n16           DT.INT.BLAT.CD\n17           DT.INT.DLXF.CD\n18           DT.INT.DPNG.CD\n19           DT.INT.MLAT.CD\n20           DT.INT.OFFT.CD\n21           DT.INT.PBND.CD\n22           DT.INT.PCBK.CD\n23           DT.INT.PROP.CD\n24           DT.INT.PRVT.CD'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[39]:
1/1 tests passed


## 4. Totaling the amount of debt owed by the countries¶

As mentioned earlier, the financial debt of a particular country represents its economic state. But if we were to project this on an overall global scale, how will we approach it?

Let's switch gears from the debt indicators now and find out the total amount of debt (in USD) that is owed by the different countries. This will give us a sense of how the overall economy of the entire world is holding up.

In [40]:
%%sql
SELECT
ROUND(SUM(debt)/1000000, 2) AS total_debt
FROM international_debt;

 * postgresql:///international_debt
1 rows affected.

Out[40]:
total_debt
3079734.49
In [41]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '   total_debt\n0  3079734.49'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[41]:
1/1 tests passed


## 5. Country with the highest debt¶

"Human beings cannot comprehend very large or very small numbers. It would be useful for us to acknowledge that fact." - Daniel Kahneman. That is more than 3 million million USD, an amount which is really hard for us to fathom.

Now that we have the exact total of the amounts of debt owed by several countries, let's now find out the country that owns the highest amount of debt along with the amount. Note that this debt is the sum of different debts owed by a country across several categories. This will help to understand more about the country in terms of its socio-economic scenarios. We can also find out the category in which the country owns its highest debt. But we will leave that for now.

In [42]:
%%sql
SELECT
country_name,
SUM(debt) AS total_debt
FROM international_debt
GROUP BY country_name
ORDER BY total_debt DESC
LIMIT 1;

 * postgresql:///international_debt
1 rows affected.

Out[42]:
country_name total_debt
China 285793494734.200001568
In [43]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '  country_name              total_debt\n0        China  285793494734.200001568'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[43]:
1/1 tests passed


## 6. Average amount of debt across indicators¶

So, it was China. A more in-depth breakdown of China's debts can be found here.

We now have a brief overview of the dataset and a few of its summary statistics. We already have an idea of the different debt indicators in which the countries owe their debts. We can dig even further to find out on an average how much debt a country owes? This will give us a better sense of the distribution of the amount of debt across different indicators.

In [44]:
%%sql
SELECT
indicator_code AS debt_indicator,
indicator_name,
AVG(debt) AS average_debt
FROM international_debt
GROUP BY debt_indicator, indicator_name
ORDER BY average_debt DESC
LIMIT 10;

 * postgresql:///international_debt
10 rows affected.

Out[44]:
debt_indicator indicator_name average_debt
DT.AMT.DLXF.CD Principal repayments on external debt, long-term (AMT, current US$) 5904868401.499193612 DT.AMT.DPNG.CD Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$) 5161194333.812658349
DT.DIS.DLXF.CD Disbursements on external debt, long-term (DIS, current US$) 2152041216.890243888 DT.DIS.OFFT.CD PPG, official creditors (DIS, current US$) 1958983452.859836046
DT.AMT.PRVT.CD PPG, private creditors (AMT, current US$) 1803694101.963265321 DT.INT.DLXF.CD Interest payments on external debt, long-term (INT, current US$) 1644024067.650806481
DT.DIS.BLAT.CD PPG, bilateral (DIS, current US$) 1223139290.398230108 DT.INT.DPNG.CD Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$) 1220410844.421518983
DT.AMT.OFFT.CD PPG, official creditors (AMT, current US$) 1191187963.083064523 DT.AMT.PBND.CD PPG, bonds (AMT, current US$) 1082623947.653623188
In [45]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '   debt_indicator                                     indicator_name          average_debt\n0  DT.AMT.DLXF.CD  Principal repayments on external debt, long-te...  5904868401.499193612\n1  DT.AMT.DPNG.CD  Principal repayments on external debt, private...  5161194333.812658349\n2  DT.DIS.DLXF.CD  Disbursements on external debt, long-term (DIS...  2152041216.890243888\n3  DT.DIS.OFFT.CD         PPG, official creditors (DIS, current US$) 1958983452.859836046\n4 DT.AMT.PRVT.CD PPG, private creditors (AMT, current US$)  1803694101.963265321\n5  DT.INT.DLXF.CD  Interest payments on external debt, long-term ...  1644024067.650806481\n6  DT.DIS.BLAT.CD                  PPG, bilateral (DIS, current US$) 1223139290.398230108\n7 DT.INT.DPNG.CD Interest payments on external debt, private no... 1220410844.421518983\n8 DT.AMT.OFFT.CD PPG, official creditors (AMT, current US$)  1191187963.083064523\n9  DT.AMT.PBND.CD                      PPG, bonds (AMT, current US$) 1082623947.653623188' try: assert last_output.DataFrame().to_string() == correct_result_string except AttributeError: assert False, "Please ensure a SQL ResultSet is the output of the code cell." except AssertionError: assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."  Out[45]: 1/1 tests passed  ## 7. The highest amount of principal repayments¶ We can see that the indicator DT.AMT.DLXF.CD tops the chart of average debt. This category includes repayment of long term debts. Countries take on long-term debt to acquire immediate capital. More information about this category can be found here. An interesting observation in the above finding is that there is a huge difference in the amounts of the indicators after the second one. This indicates that the first two indicators might be the most severe categories in which the countries owe their debts. We can investigate this a bit more so as to find out which country owes the highest amount of debt in the category of long term debts (DT.AMT.DLXF.CD). Since not all the countries suffer from the same kind of economic disturbances, this finding will allow us to understand that particular country's economic condition a bit more specifically. In [46]: %%sql SELECT country_name, indicator_name FROM international_debt WHERE debt = (SELECT MAX(debt) FROM international_debt WHERE indicator_code='DT.AMT.DLXF.CD');   * postgresql:///international_debt 1 rows affected.  Out[46]: country_name indicator_name China Principal repayments on external debt, long-term (AMT, current US$)
In [47]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '  country_name                                     indicator_name\n0        China  Principal repayments on external debt, long-te...'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[47]:
1/1 tests passed


## 8. The most common debt indicator¶

China has the highest amount of debt in the long-term debt (DT.AMT.DLXF.CD) category. This is verified by The World Bank. It is often a good idea to verify our analyses like this since it validates that our investigations are correct.

We saw that long-term debt is the topmost category when it comes to the average amount of debt. But is it the most common indicator in which the countries owe their debt? Let's find that out.

In [48]:
%%sql
SELECT
indicator_code,
COUNT(indicator_code) AS indicator_count
FROM international_debt
GROUP BY indicator_code
ORDER BY indicator_count DESC, indicator_code DESC
LIMIT 20;

 * postgresql:///international_debt
20 rows affected.

Out[48]:
indicator_code indicator_count
DT.INT.OFFT.CD 124
DT.INT.MLAT.CD 124
DT.INT.DLXF.CD 124
DT.AMT.OFFT.CD 124
DT.AMT.MLAT.CD 124
DT.AMT.DLXF.CD 124
DT.DIS.DLXF.CD 123
DT.INT.BLAT.CD 122
DT.DIS.OFFT.CD 122
DT.AMT.BLAT.CD 122
DT.DIS.MLAT.CD 120
DT.DIS.BLAT.CD 113
DT.INT.PRVT.CD 98
DT.AMT.PRVT.CD 98
DT.INT.PCBK.CD 84
DT.AMT.PCBK.CD 84
DT.INT.DPNG.CD 79
DT.AMT.DPNG.CD 79
DT.INT.PBND.CD 69
DT.AMT.PBND.CD 69
In [49]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '    indicator_code  indicator_count\n0   DT.INT.OFFT.CD              124\n1   DT.INT.MLAT.CD              124\n2   DT.INT.DLXF.CD              124\n3   DT.AMT.OFFT.CD              124\n4   DT.AMT.MLAT.CD              124\n5   DT.AMT.DLXF.CD              124\n6   DT.DIS.DLXF.CD              123\n7   DT.INT.BLAT.CD              122\n8   DT.DIS.OFFT.CD              122\n9   DT.AMT.BLAT.CD              122\n10  DT.DIS.MLAT.CD              120\n11  DT.DIS.BLAT.CD              113\n12  DT.INT.PRVT.CD               98\n13  DT.AMT.PRVT.CD               98\n14  DT.INT.PCBK.CD               84\n15  DT.AMT.PCBK.CD               84\n16  DT.INT.DPNG.CD               79\n17  DT.AMT.DPNG.CD               79\n18  DT.INT.PBND.CD               69\n19  DT.AMT.PBND.CD               69'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[49]:
1/1 tests passed


## 9. Other viable debt issues and conclusion¶

There are a total of six debt indicators in which all the countries listed in our dataset have taken debt. The indicator DT.AMT.DLXF.CD is also there in the list. So, this gives us a clue that all these countries are suffering from a common economic issue. But that is not the end of the story, a part of the story rather.

Let's change tracks from debt_indicators now and focus on the amount of debt again. Let's find out the maximum amount of debt across the indicators along with the respective country names. With this, we will be in a position to identify the other plausible economic issues a country might be going through. By the end of this section, we will have found out the debt indicators in which a country owes its highest debt.

In this notebook, we took a look at debt owed by countries across the globe. We extracted a few summary statistics from the data and unraveled some interesting facts and figures. We also validated our findings to make sure the investigations are correct.

In [50]:
%%sql
SELECT country_name, indicator_code, MAX(debt) AS maximum_debt
FROM international_debt
GROUP BY country_name, indicator_code
ORDER BY maximum_debt DESC
LIMIT 10;

 * postgresql:///international_debt
10 rows affected.

Out[50]:
country_name indicator_code maximum_debt
China DT.AMT.DLXF.CD 96218620835.699996948
Brazil DT.AMT.DLXF.CD 90041840304.100006104
China DT.AMT.DPNG.CD 72392986213.800003052
Russian Federation DT.AMT.DLXF.CD 66589761833.5
Turkey DT.AMT.DLXF.CD 51555031005.800003052
South Asia DT.AMT.DLXF.CD 48756295898.199996948
Brazil DT.AMT.PRVT.CD 43598697498.599998474
Russian Federation DT.AMT.DPNG.CD 42800154974.900001526
Brazil DT.AMT.DPNG.CD 41831444053.300003052
Least developed countries: UN classification DT.DIS.DLXF.CD 40160766261.599998474
In [51]:
%%nose
# %%nose needs to be included at the beginning of every @tests cell

last_output = _

def test_output():
correct_result_string = '                                   country_name  indicator_code           maximum_debt\n0                                         China  DT.AMT.DLXF.CD  96218620835.699996948\n1                                        Brazil  DT.AMT.DLXF.CD  90041840304.100006104\n2                                         China  DT.AMT.DPNG.CD  72392986213.800003052\n3                            Russian Federation  DT.AMT.DLXF.CD          66589761833.5\n4                                        Turkey  DT.AMT.DLXF.CD  51555031005.800003052\n5                                    South Asia  DT.AMT.DLXF.CD  48756295898.199996948\n6                                        Brazil  DT.AMT.PRVT.CD  43598697498.599998474\n7                            Russian Federation  DT.AMT.DPNG.CD  42800154974.900001526\n8                                        Brazil  DT.AMT.DPNG.CD  41831444053.300003052\n9  Least developed countries: UN classification  DT.DIS.DLXF.CD  40160766261.599998474'
try:
assert last_output.DataFrame().to_string() == correct_result_string
except AttributeError:
assert False, "Please ensure a SQL ResultSet is the output of the code cell."
except AssertionError:
assert False, "The results of the query are incorrect. Please review the instructions and check the hint if necessary."

Out[51]:
1/1 tests passed