Tom's non-edit of cagan_ree

Author: thomassargent30

lectures/long_run_growth.md

@@ -4,7 +4,7 @@ jupytext:
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@@ -40,7 +40,6 @@ import numpy as np
 from matplotlib.lines import Line2D
 ```
 
-
 A project initiated by [Angus Maddison](https://en.wikipedia.org/wiki/Angus_Maddison) has collected many historical time series that study economic growth. 
 
 We can use the [Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) to look at many different countries, including some countries dating back to the first century. 
@@ -230,7 +229,6 @@ data['gdp'] = data['gdppc'] * data['pop']
 gdp = data['gdp'].unstack('countrycode')
 ```
 
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 ### Early Industralization (1820 to 1940)
 
 
@@ -356,7 +354,6 @@ ax.legend(handles=legend_elements, loc='lower center', ncol=3, bbox_to_anchor=[0
 plt.show()
 ```
 
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 ## Other Interesting Plots
 
 Here are a collection of interesting plots that could be linked to interesting stories
@@ -369,7 +366,6 @@ gdppc['CHN'].loc[1500:1980].interpolate().plot(ax=fig.gca())
 plt.show()
 ```
 
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 China (CHN) then followed a very similar growth story from the 1980s through to current day China.
 
 ```{code-cell} ipython3
@@ -378,7 +374,6 @@ gdppc[['CHN', 'GBR']].interpolate().plot(ax = fig.gca())
 plt.show()
 ```
 
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 ## Regional Analysis
 
 The [Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) dataset also includes regional aggregations
@@ -388,22 +383,19 @@ data = pd.read_excel("datasets/mpd2020.xlsx", sheet_name='Regional data', header
 data.columns = data.columns.droplevel(level=2)
 ```
 
-
 We can save the raw data in a more convenient format to build a single table of regional GDP per capita
 
 ```{code-cell} ipython3
 regionalgdppc = data['gdppc_2011'].copy()
 regionalgdppc.index = pd.to_datetime(regionalgdppc.index, format='%Y')
 ```
 
-
 Let us interpolate based on time to fill in any gaps in the dataset for the purpose of plotting
 
 ```{code-cell} ipython3
 regionalgdppc.interpolate(method='time', inplace=True)
 ```
 
-
 and record a dataset of world GDP per capita
 
 ```{code-cell} ipython3
@@ -421,7 +413,6 @@ ax = worldgdppc.plot(
 )
 ```
 
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 Looking more closely, let us compare the time series for `Western Offshoots` and `Sub-Saharan Africa`
 
 ```{code-cell} ipython3
@@ -432,7 +423,6 @@ ax.legend(loc='lower center', ncol=2, bbox_to_anchor=[0.5, -0.26])
 plt.show()
 ```
 
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 and more broadly at a number of different regions around the world
 
 ```{code-cell} ipython3