Merge branch 'update-long-run' of https://github.com/QuantEcon/lecture-python-intro into update-long-run

Author: HumphreyYang

lectures/_static/quant-econ.bib

@@ -3,6 +3,17 @@
 Note: Extended Information (like abstracts, doi, url's etc.) can be found in quant-econ-extendedinfo.bib file in _static/
 ###
 
+
+@misc{Tooze_2014,
+  title={The Deluge: The Great War, America and the Remaking of the Global Order, 1916--1931},
+  author = {Adam Tooze},
+  address = {New York},
+  publisher = {Viking},
+  year = {2014}
+}
+
+
+
 @article{chambers1996theory,
 	title={A theory of commodity price fluctuations},
 	author={Chambers, Marcus J and Bailey, Roy E},

lectures/long_run_growth.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.5
+    jupytext_version: 1.14.4
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -13,11 +13,34 @@ kernelspec:
 
 +++ {"user_expressions": []}
 
-# Long Run Growth
+# Economic Growth Evidence
 
 ## Overview
 
-This lecture looks at different growth trajectories across countries over the long term. 
+Adam Tooze's account of the geopolitical precedents and antecedents of World War I  includes a comparison of how  National Gross National Products of European Great Powers had evolved during the 70 years preceding 1914 (see chapter 1 of {cite}`Tooze_2014`).
+
+We report a version of Tooze's graph later in this lecture.
+
+Looking at his graph and how it set the geopolitical stage for "the American (20th) century" naturally 
+tempts one to want a counterpart to his graph for 2014 or later.
+
+As we'll see, reasoning just by analogy, this graph perhaps set the stage for an "XXX (21st) century", where you get to fill in a country for our XXX.
+
+As we gather data to construct those two graphs, we'll also study growth experiences for a number of countries for time horizons extending as far back as possible.
+
+These graphs will portray how the "Industrial Revolution" began in Britain in the late 18th century, then migrated to one country after another.  
+
+In a nutshell, this  lecture records  growth trajectories of various  countries over long time periods. 
+
+While some countries have experienced long term rapid growth across that has lasted a hundred years, others have not. 
+
+
+Since populations differ across country and within a country vary over time, it will
+be interesting to describe both total GNP and GNP per capita as it evolves within a country.
+
+First let's import the packages needed to explore what the data says about long run growth.
+
+This lecture  growth trajectories of various  countries over long time periods. 
 
 While some countries have experienced long term rapid growth across that has lasted a hundred years, others have not. 
 
@@ -185,7 +208,7 @@ plt.show()
 We can now put this into a function to generate plots for a list of countries
 
 ```{code-cell} ipython3
-def draw_interp_plots(series, xlabel, ylabel, color_mapping, code_to_name, lw, logscale, ax):
+def draw_interp_plots(series, ylabel, xlabel, color_mapping, code_to_name, lw, logscale, ax):
 
     for i, c in enumerate(cntry):
         # Get the interpolated data
@@ -212,8 +235,8 @@ def draw_interp_plots(series, xlabel, ylabel, color_mapping, code_to_name, lw, l
     
     # Draw the legend outside the plot
     ax.legend(loc='lower center', ncol=5, bbox_to_anchor=[0.5, -0.25])
-    ax.set_xlabel(xlabel)
     ax.set_ylabel(ylabel)
+    ax.set_xlabel(xlabel)
     
     return ax
 ```
@@ -237,7 +260,6 @@ mystnb:
     caption: GDP per Capita (China, UK, USA)
     name: gdppc_comparison
 ---
-
 # Define the namedtuple for the events
 Event = namedtuple('Event', ['year_range', 'y_text', 'text', 'color', 'ymax'])
 
@@ -298,24 +320,26 @@ plt.show()
 
 +++ {"user_expressions": []}
 
-(TODO: Finalize trend)
-We can see some interesting trends:
+The preceding graph of percapita GDP strikingly reveals how the spread of the industrial revolution has over time gradually lifted the living standards of substantial
+groups of people  
 
 - Most of the growth happened in the past 150 years after the industrial revolution.
-- There was a divergence between the West and the East during the process of industrialization (from 1820 to 1940).
-- The gap is rapidly closing in the modern era.
-- The shift in the paradigm in policy is usually intertwined with the technological and political.
+- Percapita GDP's in the  UK and the US, on the one hand, and in China, on the other, diverged  from 1820 to 1940.
+- The gap has closed  rapidly after 1950 and especially after the late 1970s.
+- These outcomes reflect complicated combinations of technological and economic-policy factors that students of economic growth try to understand and quantify
 
 +++ {"user_expressions": []}
 
-Looking at China's GDP per capita levels from 1500 through to the 1970s showed a long period of declining GDP per capital levels from the 1700s to the early 20th century.
+It is fascinating to see  China's GDP per capita levels from 1500 through to the 1970s.
+
+Notice the long period of declining GDP per capital levels from the 1700s until the early 20th century.
 
-(TODO: Finalize trend)
-Trends to note:
-- Period of economic downturn after the Closed-door Policy by the Qing government
-- Missing out on the industrial revolution
-- Self-Strengthening Movement may help the growth but in a very mild way
-- Modern Chinese economic policies and the growth after the founding of the PRC (political stability) and after the Reform and Opening-up
+Thus, the graph indicates 
+
+- A long  economic downturn and stagnation after the Closed-door Policy by the Qing government
+- China's very different experience than the UK's after the onset of   the industrial revolution in the UK
+- How the Self-Strengthening Movement seemed mostly to help China to grow
+- How stunning have been the growth achievements of Modern Chinese economic policies by the PRC that culminated with its late 1970s Reform and Opening-up
 
 ```{code-cell} ipython3
 ---
@@ -324,7 +348,6 @@ mystnb:
     caption: GDP per Capita (China)
     name: gdppc_china
 ---
-
 fig, ax = plt.subplots(dpi=300, figsize=(10, 6))
 
 cntry = ['CHN']
@@ -368,12 +391,12 @@ plt.show()
 
 +++ {"user_expressions": []}
 
-(TODO: Finalize trend)
-Trends to note:
-- The impact of trade policy (Navigation Act)
-- The productivity change created by the industrial revolution
-- US surpasses UK -- any specific event?
-- Wars and business cycles (link to business cycles lecture)
+In the following graph, please watch for 
+- impact of trade policy (Navigation Act)
+- productivity changes brought by the industrial revolution
+- how the US gradually approaches and then  surpasses the UK, setting the stage for the ``American Century''
+- the  often unanticipated consequenes of Wars 
+- interruptions and scars left by business cycle recessions and depressions
 
 ```{code-cell} ipython3
 ---
@@ -382,8 +405,6 @@ mystnb:
     caption: GDP per Capita (UK and US)
     name: gdppc_ukus
 ---
-
-
 fig, ax = plt.subplots(dpi=300, figsize=(10, 6))
 
 cntry = ['GBR', 'USA']
@@ -430,9 +451,9 @@ plt.show()
 
 ## The industrialized world
 
-(TODO: Write description for this section)
+Now we'll construct some graphs of interest to geopolitical historians like Adam Tooze.
 
-Now we can look at total Gross Domestic Product (GDP) rather than focusing on GDP per capita (as a proxy for living standards).
+We'll focus on  total Gross Domestic Product (GDP)  (as a proxy for ``national geopolitical-military power'') rather than focusing on GDP per capita (as a proxy for living standards).
 
 ```{code-cell} ipython3
 data = pd.read_excel("datasets/mpd2020.xlsx", sheet_name='Full data')
@@ -528,10 +549,11 @@ ax = draw_interp_plots(gdppc[cntry].loc[start_year:end_year],
 
 +++ {"user_expressions": []}
 
-## Regional analysis
-(TODO: Write descriptions for this section)
+## Regional Analysis
+
+We often want to study historical experiences of countries outside the club of "World Powers".
 
-The [Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) dataset also includes regional aggregations
+Fortunately, the [Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) dataset also includes regional aggregations
 
 ```{code-cell} ipython3
 data = pd.read_excel("datasets/mpd2020.xlsx", sheet_name='Regional data', header=(0,1,2), index_col=0)