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Copy file name to clipboardExpand all lines: lectures/long_run_growth.md
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## Overview
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In this lecture we use Python, Pandas, and Matplotlib to download, organize, and visualize historical data on economic growth.
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In this lecture we use Python, [pandas](https://pandas.pydata.org/), and [Matplotlib](https://matplotlib.org/) to download, organize, and visualize historical data on economic growth.
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In addition to learning how to deploy these tools more generally, we'll use them to describe facts about economic growth experiences across many countries over several centuries.
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In addition to learning how to deploy these tools more generally, we'll use them to describe facts about economic growth experiences across many countries over several centuries.
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Such "growth facts" are interesting for a variety of reasons.
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Such "growth facts" are interesting for a variety of reasons.
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Explaining growth facts is a principal purpose of both "developmenteconomics" and "economic history".
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Explaining growth facts is a principal purpose of both "developmenteconomics" and "economic history".
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And growth facts are important inputs into historians' studies of geopolitical forces and dynamics.
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And growth facts are important inputs into historians' studies of geopolitical forces and dynamics.
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Thus, Adam Tooze's account of the geopolitical precedents and antecedents of World War I begins by describing how Gross Domestic Products (GDP) of European Great Powers had evolved during the 70 years preceding 1914 (see chapter 1 of {cite}`Tooze_2014`).
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Thus, Adam Tooze's account of the geopolitical precedents and antecedents of World War I begins by describing how the Gross Domestic Products (GDP) of European Great Powers had evolved during the 70 years preceding 1914 (see chapter 1 of {cite}`Tooze_2014`).
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Using the very same data that Tooze used to construct his figure, here is our version of his chapter 1 figure.
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Using the very same data that Tooze used to construct his figure (with a slightly longer timeline), here is our version of his chapter 1 figure.
(This is just a copy of our figure {numref}`gdp1`. We desribe how we constructed it later in this lecture.)
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(This is just a copy of our figure {numref}`gdp1`. We describe how we constructed it later in this lecture.)
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Chapter 1 of {cite}`Tooze_2014` used his graph to show how US GDP started the 19th century way behind the GDP of the British Empire.
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Chapter 1 of {cite}`Tooze_2014` used his graph to show how US GDP started the 19th century way behind the GDP of the British Empire.
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By the end of the nineteenth century, US GDP had caught up with GDP of the British Empire, and how during the first half of the 20th century,
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US GDP surpassed that of the British Empire.
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(An impatient reader seeking a hint at the answer might now want to jump ahead and look at figure {numref}`gdp2`.)
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As we'll see, reasoning by analogy, this graph perhaps set the stage for an "XXX (21st) century", where you are free to fill in your guess for country XXX.
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As we'll see, reasoning by analogy, this graph perhaps set the stage for an "XXX (21st) century", where you are free to fill in your guess for country XXX.
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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.
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Since populations differ across countries and vary within a country over time, it will
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be interesting to describe both total GDP and GDP per capita as it evolves within a country.
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First let's import the packages needed to explore what the data says about longrun growth
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First let's import the packages needed to explore what the data says about long-run growth
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```{code-cell} ipython3
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import pandas as pd
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The data can be downloaded from the [Maddison Historical Statistics webpage](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) by clicking on the "Latest Maddison Project Release".
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We are going to read from a QuantEcon GitHub repository.
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We are going to read the data from a QuantEcon GitHub repository.
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Our objective in this section is to produce a convenient `DataFrame` that contains per capita GDP for different countries.
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Our objective in this section is to produce a convenient `DataFrame`instance that contains per capita GDP for different countries.
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Here we read the Maddison data into a pandas dataframe:
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Here we read the Maddison data into a pandas `DataFrame`:
We create a variale`color_mapping` to store a map between country codes and colors for consistency
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We create a variable`color_mapping` to store a map between country codes and colors for consistency
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```{code-cell} ipython3
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:tags: [hide-input]
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## GDP per capita
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In this section we examine GDP per capita over the long run for serveral different countries.
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In this section we examine GDP per capita over the long run for several different countries.
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### United Kingdom
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First we examine United Kingdom GDP growth
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First we examine UK GDP growth
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```{code-cell} ipython3
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---
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```
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:::{note}
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[International dollars](https://en.wikipedia.org/wiki/international_dollar) are a hypothetical unit of currency that has the same purchasing power parity that the U.S. Dollar has in the United States at any given time. They are also known as Geary–Khamis dollars (GK Dollars).
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[International dollars](https://en.wikipedia.org/wiki/international_dollar) are a hypothetical unit of currency that has the same purchasing power parity that the U.S. Dollar has in the United States at a given point in time. They are also known as Geary–Khamis dollars (GK Dollars).
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:::
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We can see that the data is non-continuous for longer periods in the early 250 years of this millennium, so we could choose to interpolate to get a continuous line plot.
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```{code-cell} ipython3
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def draw_interp_plots(series, # pandas series
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country, # contry code
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country, # list of country codes
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ylabel, # label for y-axis
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xlabel, # label for x-axis
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color_mapping, # code-color mapping
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ax.legend(loc='upper left', frameon=False)
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ax.set_ylabel(ylabel)
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ax.set_xlabel(xlabel)
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return ax
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```
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As you can see from this chart, economic growth started in earnest in the 18th century and continued for the next two hundred years.
# Define the parameters for the events and the text
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ylim = ax.get_ylim()[1]
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plt.show()
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```
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The preceding graph of per capita GDP strikingly reveals how the spread of the industrial revolution has over time gradually lifted the living standards of substantial
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The preceding graph of per capita GDP strikingly reveals how the spread of the Industrial Revolution has over time gradually lifted the living standards of substantial
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groups of people
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- most of the growth happened in the past 150 years after the industrial revolution.
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- most of the growth happened in the past 150 years after the Industrial Revolution.
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- per capita GDP in the US and UK rose and diverged from that of China from 1820 to 1940.
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- the gap has closed rapidly after 1950 and especially after the late 1970s.
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- these outcomes reflect complicated combinations of technological and economic-policy factors that students of economic growth try to understand and quantify.
In this section we describe how we have constructed a version of the striking figure from chapter 1 of {cite}`Tooze_2014` that we discussed at the start of this lecture.
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In this section we describe how we have constructed a version of the striking figure from chapter 1 of {cite}`Tooze_2014` that we discussed at the start of this lecture.
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Let's first define a collection of countries that consist of the British Empire (BEM) so we can replicate that series in Tooze's chart.
It is tempting to compare this graph with figure {numref}`gdp1` that showed the US overtaking the UK near the start of the "American Century", a version of the graph featured in chapter 1 of {cite}`Tooze_2014`.
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## Regional analysis
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We often want to study historical experiences of countries outside the club of "World Powers".
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We often want to study the historical experiences of countries outside the club of "World Powers".
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Fortunately, the[Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) dataset also includes regional aggregations
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The[Maddison Historical Statistics](https://www.rug.nl/ggdc/historicaldevelopment/maddison/) dataset also includes regional aggregations
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```{code-cell} ipython3
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data = pd.read_excel(data_url,
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Looking more closely, let's compare the time series for `Western Offshoots` and `Sub-Saharan Africa`and more broadly at a number of different regions around the world.
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Looking more closely, let's compare the time series for `Western Offshoots` and `Sub-Saharan Africa`with a number of different regions around the world.
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Again we see the divergence of the West from the rest of the world after the industrial revolution and the convergence of the world after the 1950s
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Again we see the divergence of the West from the rest of the world after the Industrial Revolution and the convergence of the world after the 1950s
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