[Business Cycles] In-work Lecture on Business Cycle

in-work/ business_cycle.md

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+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
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+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Business Cycle
+
+## Overview
+
+This lecture is about illustrateing business cycles in different countries and period.
+
+The business cycle refers to the fluctuations in economic activity over time. These fluctuations can be observed in the form of expansions, contractions, recessions, and recoveries in the economy.
+
+In this lecture, we will see expensions and contractions of economies from 1960s to the recent pandemic using [World Bank API](https://documents.worldbank.org/en/publication/documents-reports/api).
+
+In addition to what's in Anaconda, this lecture will need the following libraries to get World bank data
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install wbgapi
+!pip install imfpy
+```
+
+We use the following imports
+
+```{code-cell} ipython3
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import scipy.stats as st
+import wbgapi as wb
+from imfpy.retrievals import dots
+```
+
+## Data Acquaisition
+
+We will use `wbgapi` and `imfpy` to retrieve data from the World Bank and IMF API throughout this lecture.
+
+So let's explore how to query data  together.
+
+We can use `wb.series.info` with parameter `q` to query available data from the World Bank (`imfpy. searches.database_codes()` in `imfpy`)
+
+For example, GDP growth is a key indicator to show the expension and contraction of level of economic activities.
+
+Let's retrive GDP growth data together
+
+```{code-cell} ipython3
+wb.series.info(q='GDP growth')
+```
+
+In a similar fashion, we can also retrieve data that covers different aspects of economic activities.
+
+We can cover indicators of 
+
+- labour market
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+wb.series.info(q='unemployment')
+```
+
+- credit level
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+wb.series.info(q='credit')
+```
+
+- consumer price index
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+wb.series.info(q='consumer')
+```
+
+- consumption level
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+wb.series.info(q='consumption')
+```
+
+```{code-cell} ipython3
+wb.series.info(q='capital account') # TODO: Check if it is to be plotted
+```
+
+- international trade volumn
+
++++
+
+These indicators will give us an overview of variations in economic activities across time.
+
+## GDP Growth Rate and Unemployment
+
+First we look at the GDP growth rate and unemployment rate.
+
+Let's source our data from the World Bank and clean the data
+
+```{code-cell} ipython3
+gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'MEX', 'CHL', 'COL', 'SLV', 'HTI'], labels=True)
+gdp_growth = gdp_growth.set_index('Country')
+gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+gdp_growth
+```
+
+```{code-cell} ipython3
+cycler = plt.cycler(linestyle=['-', '-.', '--'], color=['#377eb8', '#ff7f00', '#4daf4a'])
+plt.rc('axes', prop_cycle=cycler)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+ax.set_xticks([i for i in range(1960, 2021, 10)], minor=False)
+plt.locator_params(axis='x', nbins=10)
+
+def plot_comparison(data, countries, title, ylabel, title_pos, ax, g_params, b_params, t_params):
+    for country in countries:
+        ax.plot(data.loc[country], label=country, **g_params)
+    ax.axvspan(1973, 1975, **b_params)
+    ax.axvspan(1990, 1992, **b_params)
+    ax.axvspan(2007, 2009, **b_params)
+    ax.axvspan(2019, 2021, **b_params)
+    ylim = ax.get_ylim()[1]
+    ax.text(1974, ylim + ylim * title_pos, 'Oil Crisis\n(1974)', **t_params) 
+    ax.text(1991, ylim + ylim * title_pos, '1990s recession\n(1991)', **t_params) 
+    ax.text(2008, ylim + ylim * title_pos, 'GFC\n(2008)', **t_params) 
+    ax.text(2020, ylim + ylim * title_pos, 'Covid-19\n(2020)', **t_params) 
+    ax.set_title(title, pad=40)
+    ax.set_ylabel(ylabel)
+    ax.legend()
+    return ax
+
+g_params = {'alpha': 0.7}
+b_params = {'color':'grey', 'alpha': 0.2}
+t_params = {'color':'grey', 'fontsize': 9, 'va':'center', 'ha':'center'}
+countries = ['United Kingdom', 'United States', 'Germany']
+title = 'United Kingdom, United States, and Germany (GDP Growth Rate %)'
+ylabel = 'GDP Growth Rate (%)'
+title_height = 0.1
+ax = plot_comparison(gdp_growth, countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Argentina']
+title = 'Argentina (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Mexico']
+title = 'Mexico (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Chile']
+title = 'Chile (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Colombia']
+title = 'Colombia (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['El Salvador']
+title = 'El Salvador (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Haiti']
+title = 'Haiti (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'MEX', 'CHL', 'COL', 'SLV', 'HTI'], labels=True)
+unempl_rate = unempl_rate.set_index('Country')
+unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['United Kingdom', 'United States', 'Germany']
+title = 'United Kingdom, United States, and Germany (Unemployment Rate %)'
+ylabel = 'Unemployment Rate (National Estimate) (%)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.03, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil']
+title = 'Brazil (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Chile']
+title = 'Chile (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Colombia']
+title = 'Colombia (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['El Salvador']
+title = 'El Salvador (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+## Credit Level
+
+```{code-cell} ipython3
+private_credit = wb.data.DataFrame('FD.AST.PRVT.GD.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
+private_credit = private_credit.set_index('Country')
+private_credit.columns = private_credit.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['United Kingdom', 'United States', 'Germany']
+title = 'United Kingdom, United States, and Germany \n Domestic credit to private sector by banks (% of GDP)'
+ylabel = '% of GDP'
+ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
+ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['United Kingdom', 'China']
+title = 'United Kingdom and China \n Domestic credit to private sector by banks (% of GDP)'
+ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+## Inflation
+
+```{code-cell} ipython3
+cpi = wb.data.DataFrame('FP.CPI.TOTL.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
+cpi = cpi.set_index('Country')
+cpi.columns = cpi.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['United Kingdom', 'United States', 'Germany']
+title = 'United Kingdom, United States, and Germany \n Inflation, consumer prices (annual %)'
+ylabel = 'annual %'
+ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+## International Trade
+
+```{code-cell} ipython3
+trade_us = dots('US','W00', 1960, 2020, freq='A')
+trade_us['Period'] = trade_us['Period'].astype('int')
+```
+
+```{code-cell} ipython3
+def plot_trade(data, title, ylabel, title_pos, ax, g_params, b_params, t_params):
+    ax.plot(data['Period'], data['Twoway Trade'], **g_params)
+    ax.axvspan(1973, 1975, **b_params)
+    ax.axvspan(1990, 1992, **b_params)
+    ax.axvspan(2007, 2009, **b_params)
+    ax.axvspan(2019, 2021, **b_params)
+    ylim = ax.get_ylim()[1]
+    ax.text(1974, ylim + ylim * title_pos, 'Oil Crisis\n(1974)', **t_params) 
+    ax.text(1991, ylim + ylim * title_pos, '1990s recession\n(1991)', **t_params) 
+    ax.text(2008, ylim + ylim * title_pos, 'GFC\n(2008)', **t_params) 
+    ax.text(2020, ylim + ylim * title_pos, 'Covid-19\n(2020)', **t_params) 
+    ax.set_title(title, pad=40)
+    ax.set_ylabel(ylabel)
+    return ax
+
+
+fig, ax = plt.subplots()
+title = 'United States (International Trade Volumn)'
+ylabel = 'US Dollars, Millions'
+plot_UStrade = plot_trade(trade_us[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+trade_cn = dots('CN','W00', 1960, 2020, freq='A')
+
+trade_cn['Period'] = trade_cn['Period'].astype('int')
+title = 'China (International Trade Volumn)'
+ylabel = 'US Dollars, Millions'
+plot_trade_cn = plot_trade(trade_cn[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+trade_mx = dots('MX','W00', 1960, 2020, freq='A')
+
+trade_mx['Period'] = trade_mx['Period'].astype('int')
+title = 'Mexico (International Trade Volumn)'
+ylabel = 'US Dollars, Millions'
+plot_trade_mx = plot_trade(trade_mx[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+trade_ar = dots('AR','W00', 1960, 2020, freq='A')
+
+trade_ar['Period'] = trade_ar['Period'].astype('int')
+title = 'Argentina (International Trade Volumn)'
+ylabel = 'US Dollars, Millions'
+plot_trade_ar = plot_trade(trade_ar[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```