content changes

Author: AakashGfude

lectures/long_run_growth.md

@@ -64,35 +64,37 @@ wbi = pd.read_csv("datasets/GDP_per_capita_world_bank.csv")
 
 ## Comparison of GDP between different Income Groups
 
-Countries taken into account
-
-**High income** 
-- USA
-- Canada
-- Australia
-- Japan
-
-**Upper middle income**
-- China
-- Brazil
-- Fiji
-- Jamaica
-
-**Lower middle income**
-- India
-- Pakistan
-- Bangladesh
-- Vietnam
-
-**Low income**
-- Congo
-- Uganda
-- Yemen
-- Afghanistan
-
+A few countries from different Income Groups where chosen to compare the GDP at a more targeted level. GDP of countries from all income groups were compared. 
+
+```{list-table}
+:header-rows: 1
+
+* - High Income
+  - Upper middle income
+  - Lower middle income
+  - Low income
+* - USA
+  - China
+  - India
+  - Congo
+* - Canada
+  - Brazil
+  - Pakistan
+  - Uganda
+* - Australia
+  - Fiji
+  - Bangladesh
+  - Yemen
+* - Japan
+  - Jamaica
+  - Vietnam
+  - Afghanistan
+```
 
 ### Plot for all countries
 
+We compare time series graphs of all the countries in the list. The clear separation between high income countries and other groups are clearly seen. It seems at first glance that other income groups probably have similar economies. Let's look at that further in the following sections.
+
 ```{code-cell} ipython3
 # USA, Canada, Australia, Japan, China, Brazil, Fiji, Jamaica, India, Pakistan, Bangladesh, Vietnam, Congo, Uganda, Yemen, Afghanistan
 country_list = ['USA', 'CAN', 'AUS', 'JPN', 'CHN', 'BRA', 'FJI', 'JAM', 'IND', 'PAK', 'BGD', 'VNM', 'COD', 'UGA', 'YEM', 'AFG']
@@ -110,47 +112,67 @@ def filter_country_list_data(dataframe, country_list):
 
 ```{code-cell} ipython3
 wbi_country_filtered = filter_country_list_data(wbi, country_list)
-wbi_country_filtered.plot()
+ax = wbi_country_filtered.plot()
+ax.set_xlabel("year")
+ax.set_ylabel("GDP per capita (current US$) ")
 ```
 
 ### Plot for Upper middle and lower middle income groups
 
+Now, we compare the time-series graphs of GDP per capita for  upper middle and lower middle income group countries, taking one country from each group. China and Pakistan was chosen as they are from the same region. 
+On analysing the graph, the difference is quite striking from 90s onwards. But also expected, as during that time China opened up for trade and labour. 
+It can be concluded that, further inspection reveals the economies are vastly different in the present time, unlike what the previous graph was suggesting. 
+
 ```{code-cell} ipython3
 # China, Pakistan (Upper middle income and lower middle income)
 country_list_umi_lmi = ['CHN', 'PAK']
 wbi_filtered_umi_lmi = filter_country_list_data(wbi, country_list_umi_lmi)
-wbi_filtered_umi_lmi.plot()
+ax = wbi_filtered_umi_lmi.plot()
+ax.set_xlabel("year")
+ax.set_ylabel("GDP per capita (current US$) ")
 ```
 
-Here is a plot for lower middle income countries.
+### Plot for lower middle income
+
+Here, we compare the time-series graphs of GDP per capita for two lower middle income group countries. Keeping Pakistan fixed in our set, we chose Vietnam as the second country. Apart from its turbulent past, its comeback from it and a steady growing economy qualifies it to be in this set. 
+From the graph, we can see that Vietnam has done quite well from around 1990 onwards, and has quite surpassed Pakistan. We can also conclude that countries in the same income groups can be also be quite different.
 
 ```{code-cell} ipython3
 # Vietnam, Pakistan (Lower middle income)
 country_list_lmi = ['VNM', 'PAK']
 wbi_filtered_lmi = filter_country_list_data(wbi, country_list_lmi)
-wbi_filtered_lmi.plot()
+ax = wbi_filtered_lmi.plot()
+ax.set_xlabel("year")
+ax.set_ylabel("GDP per capita (current US$) ")
 ```
 
-Here is a plot for lower middle income and low income
+### Plot for lower middle income and low income
+
+Finally, we compare time-series graphs of GDP per capita between a lower middle income country and a low income country. Again, keeping Pakistan fixed in our set as a lower middle income country, we choose Democratic Republic of Congo as our second country from a low income group. Congo is chosen for no particular reason apart from its unstable political atmoshpere and a dwindling economy. 
+On comapring we see quite a bit of difference between these countries. With Pakistan's GDP per capita being almost four times as much. Further strengthning our assumption that countries from different income groups can be quite different.
 
 ```{code-cell} ipython3
 # Pakistan, Congo (Lower middle income, low income)
 country_list_lmi_li = ['PAK', 'COD']
 wbi_filtered_lmi = filter_country_list_data(wbi, country_list_lmi_li)
-wbi_filtered_lmi.plot()
+ax = wbi_filtered_lmi.plot()
+ax.set_xlabel("year")
+ax.set_ylabel("GDP per capita (current US$) ")
 ```
 
 
 
 ## Histogram comparison between 1960, 1990, 2020
 
+We compare histograms of the **log** of GDP per capita for the years 1960, 1990 and 2020 for around 170 countries. The years have been chosen to give sufficient time gap between the histograms. We see that the overall plot is shifting towards right, denoting the upward trend in GDP per capita worldwide. And also, the overall distribution is becoming more Gaussian. Which indicates that the economies have gotten more uniform over the years. Economic disparities are getting lesser possibly because of globalisation, technological advancements, better use of resources etc. 
+
 ```{code-cell} ipython3
 def get_log_hist(data, years):
     filtered_data = data.filter(items=['Country Code', years[0], years[1], years[2]])
     log_gdp = filtered_data.iloc[:,1:].transform(lambda x: np.log(x))
     max_log_gdp = log_gdp.max(numeric_only=True).max()
     min_log_gdp = log_gdp.min(numeric_only=True).min()
-    log_gdp.hist(bins=16, range=[min_log_gdp, max_log_gdp], log=True)
+    log_gdp.hist(bins=16, range=[min_log_gdp, max_log_gdp])
 ```
 
 ```{code-cell} ipython3