3. Business Cycles#

3.1. Overview#

In this lecture we review some empirical aspects of business cycles.

Business cycles are fluctuations in economic activity over time.

These include expansions (also called booms) and contractions (also called recessions).

For our study, we will use economic indicators from the World Bank and FRED.

In addition to the packages already installed by Anaconda, this lecture requires

!pip install wbgapi
!pip install pandas-datareader
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We use the following imports

import matplotlib.pyplot as plt
import pandas as pd
import datetime
import wbgapi as wb
import pandas_datareader.data as web

Here’s some minor code to help with colors in our plots.

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# Set graphical parameters
cycler = plt.cycler(linestyle=['-', '-.', '--', ':'], 
        color=['#377eb8', '#ff7f00', '#4daf4a', '#ff334f'])
plt.rc('axes', prop_cycle=cycler)

3.2. Data acquisition#

We will use the World Bank’s data API wbgapi and pandas_datareader to retrieve data.

We can use wb.series.info with the argument q to query available data from the World Bank.

For example, let’s retrieve the GDP growth data ID to query GDP growth data.

wb.series.info(q='GDP growth')
id value
NY.GDP.MKTP.KD.ZGGDP growth (annual %)
1 elements

Now we use this series ID to obtain the data.

gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
            ['USA', 'ARG', 'GBR', 'GRC', 'JPN'], 
            labels=True)
gdp_growth
Country YR1960 YR1961 YR1962 YR1963 YR1964 YR1965 YR1966 YR1967 YR1968 ... YR2014 YR2015 YR2016 YR2017 YR2018 YR2019 YR2020 YR2021 YR2022 YR2023
economy
JPN Japan NaN 12.043536 8.908973 8.473642 11.676708 5.819708 10.638562 11.082142 12.882468 ... 0.296206 1.560627 0.753827 1.675332 0.643391 -0.402169 -4.147119 2.559320 0.954737 1.923056
GRC Greece NaN 13.203841 0.364811 11.844867 9.409678 10.768010 6.494501 5.669486 7.203719 ... 0.475696 -0.196088 -0.487173 1.092149 1.668429 1.879490 -9.316436 8.379944 5.557287 2.011521
GBR United Kingdom NaN 2.701314 1.098696 4.859545 5.594811 2.130333 1.567450 2.775738 5.472693 ... 3.195782 2.220258 1.921063 2.655070 1.403792 1.641611 -10.359901 8.674904 4.345229 0.104018
ARG Argentina NaN 5.427843 -0.852022 -5.308197 10.130298 10.569433 -0.659726 3.191997 4.822501 ... -2.512615 2.731160 -2.080328 2.818503 -2.617396 -2.000861 -9.900485 10.718010 4.956370 -1.550502
USA United States NaN 2.300000 6.100000 4.400000 5.800000 6.400000 6.500000 2.500000 4.800000 ... 2.523820 2.945550 1.819451 2.457622 2.966505 2.467038 -2.213469 5.800206 1.935496 2.542700

5 rows × 65 columns

We can look at the series’ metadata to learn more about the series (click to expand).

wb.series.metadata.get('NY.GDP.MKTP.KD.ZG')
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Series: NY.GDP.MKTP.KD.ZG

Field Value
Aggregationmethod Weighted average
Developmentrelevance An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions.
IndicatorName GDP growth (annual %)
License_Type CC BY-4.0
License_URL https://datacatalog.worldbank.org/public-licenses#cc-by
Limitationsandexceptions Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs. In many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult. Moreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation. Informal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians. Rebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful. To obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate.
Longdefinition Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
Periodicity Annual
Source World Bank national accounts data, and OECD National Accounts data files.
StatisticalconceptandmethodologyGross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices. When value added is measured at producer prices. Growth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price in U.S. dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year.
Topic Economic Policy & Debt: National accounts: Growth rates

3.3. GDP growth rate#

First we look at GDP growth.

Let’s source our data from the World Bank and clean it.

# Use the series ID retrieved before
gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
            ['USA', 'ARG', 'GBR', 'GRC', 'JPN'], 
            labels=True)
gdp_growth = gdp_growth.set_index('Country')
gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)

Here’s a first look at the data

gdp_growth
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ... 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Country
Japan NaN 12.043536 8.908973 8.473642 11.676708 5.819708 10.638562 11.082142 12.882468 12.477895 ... 0.296206 1.560627 0.753827 1.675332 0.643391 -0.402169 -4.147119 2.559320 0.954737 1.923056
Greece NaN 13.203841 0.364811 11.844867 9.409678 10.768010 6.494501 5.669486 7.203719 11.563668 ... 0.475696 -0.196088 -0.487173 1.092149 1.668429 1.879490 -9.316436 8.379944 5.557287 2.011521
United Kingdom NaN 2.701314 1.098696 4.859545 5.594811 2.130333 1.567450 2.775738 5.472693 1.939138 ... 3.195782 2.220258 1.921063 2.655070 1.403792 1.641611 -10.359901 8.674904 4.345229 0.104018
Argentina NaN 5.427843 -0.852022 -5.308197 10.130298 10.569433 -0.659726 3.191997 4.822501 9.679526 ... -2.512615 2.731160 -2.080328 2.818503 -2.617396 -2.000861 -9.900485 10.718010 4.956370 -1.550502
United States NaN 2.300000 6.100000 4.400000 5.800000 6.400000 6.500000 2.500000 4.800000 3.100000 ... 2.523820 2.945550 1.819451 2.457622 2.966505 2.467038 -2.213469 5.800206 1.935496 2.542700

5 rows × 64 columns

We write a function to generate plots for individual countries taking into account the recessions.

Hide code cell source
def plot_series(data, country, ylabel, 
                txt_pos, ax, g_params,
                b_params, t_params, ylim=15, baseline=0):
    """
    Plots a time series with recessions highlighted. 

    Parameters
    ----------
    data : pd.DataFrame
        Data to plot
    country : str
        Name of the country to plot
    ylabel : str
        Label of the y-axis
    txt_pos : float
        Position of the recession labels
    y_lim : float
        Limit of the y-axis
    ax : matplotlib.axes._subplots.AxesSubplot
        Axes to plot on
    g_params : dict
        Parameters for the line
    b_params : dict
        Parameters for the recession highlights
    t_params : dict
        Parameters for the recession labels
    baseline : float, optional
        Dashed baseline on the plot, by default 0
    
    Returns
    -------
    ax : matplotlib.axes.Axes
        Axes with the plot.
    """

    ax.plot(data.loc[country], label=country, **g_params)
    
    # Highlight recessions
    ax.axvspan(1973, 1975, **b_params)
    ax.axvspan(1990, 1992, **b_params)
    ax.axvspan(2007, 2009, **b_params)
    ax.axvspan(2019, 2021, **b_params)
    if ylim != None:
        ax.set_ylim([-ylim, ylim])
    else:
        ylim = ax.get_ylim()[1]
    ax.text(1974, ylim + ylim*txt_pos,
            'Oil Crisis\n(1974)', **t_params) 
    ax.text(1991, ylim + ylim*txt_pos,
            '1990s recession\n(1991)', **t_params) 
    ax.text(2008, ylim + ylim*txt_pos,
            'GFC\n(2008)', **t_params) 
    ax.text(2020, ylim + ylim*txt_pos,
            'Covid-19\n(2020)', **t_params)

    # Add a baseline for reference
    if baseline != None:
        ax.axhline(y=baseline, 
                   color='black', 
                   linestyle='--')
    ax.set_ylabel(ylabel)
    ax.legend()
    return ax

# Define graphical parameters 
g_params = {'alpha': 0.7}
b_params = {'color':'grey', 'alpha': 0.2}
t_params = {'color':'grey', 'fontsize': 9, 
            'va':'center', 'ha':'center'}

Let’s start with the United States.

fig, ax = plt.subplots()

country = 'United States'
ylabel = 'GDP growth rate (%)'
plot_series(gdp_growth, country, 
            ylabel, 0.1, ax, 
            g_params, b_params, t_params)
plt.show()
_images/12c50a6937a600b4f4ff698437996b4cfe2e8eeb85dd86090a2aac8d3d599975.png

Fig. 3.1 United States (GDP growth rate %)#

GDP growth is positive on average and trending slightly downward over time.

We also see fluctuations over GDP growth over time, some of which are quite large.

Let’s look at a few more countries to get a basis for comparison.

The United Kingdom (UK) has a similar pattern to the US, with a slow decline in the growth rate and significant fluctuations.

Notice the very large dip during the Covid-19 pandemic.

fig, ax = plt.subplots()

country = 'United Kingdom'
plot_series(gdp_growth, country, 
            ylabel, 0.1, ax, 
            g_params, b_params, t_params)
plt.show()
_images/e31e6ad2fe3f5bff0a77976b05954fbab2dd0f171d6226aa10c3d58f73b4091e.png

Fig. 3.2 United Kingdom (GDP growth rate %)#

Now let’s consider Japan, which experienced rapid growth in the 1960s and 1970s, followed by slowed expansion in the past two decades.

Major dips in the growth rate coincided with the Oil Crisis of the 1970s, the Global Financial Crisis (GFC) and the Covid-19 pandemic.

fig, ax = plt.subplots()

country = 'Japan'
plot_series(gdp_growth, country, 
            ylabel, 0.1, ax, 
            g_params, b_params, t_params)
plt.show()
_images/21a14269efc6ebb46db1ea04900a5718ce18e047b9298df0e50da999031655fd.png

Fig. 3.3 Japan (GDP growth rate %)#

Now let’s study Greece.

fig, ax = plt.subplots()

country = 'Greece'
plot_series(gdp_growth, country, 
            ylabel, 0.1, ax, 
            g_params, b_params, t_params)
plt.show()
_images/2bdd42b219fe60aa596e6daad55dc8d7e3bbb2c3f9cc0a8bff6b9a932f4de854.png

Fig. 3.4 Greece (GDP growth rate %)#

Greece experienced a very large drop in GDP growth around 2010-2011, during the peak of the Greek debt crisis.

Next let’s consider Argentina.

fig, ax = plt.subplots()

country = 'Argentina'
plot_series(gdp_growth, country, 
            ylabel, 0.1, ax, 
            g_params, b_params, t_params)
plt.show()
_images/31c83a208039c217670755c9cd8aec838811a8597c266b77c11a1c8a15da815d.png

Fig. 3.5 Argentina (GDP growth rate %)#

Notice that Argentina has experienced far more volatile cycles than the economies examined above.

At the same time, Argentina’s growth rate did not fall during the two developed economy recessions in the 1970s and 1990s.

3.4. Unemployment#

Another important measure of business cycles is the unemployment rate.

We study unemployment using rate data from FRED spanning from 1929-1942 to 1948-2022, combined unemployment rate data over 1942-1948 estimated by the Census Bureau.

Hide code cell source
start_date = datetime.datetime(1929, 1, 1)
end_date = datetime.datetime(1942, 6, 1)

unrate_history = web.DataReader('M0892AUSM156SNBR', 
                    'fred', start_date,end_date)
unrate_history.rename(columns={'M0892AUSM156SNBR': 'UNRATE'}, 
                inplace=True)

start_date = datetime.datetime(1948, 1, 1)
end_date = datetime.datetime(2022, 12, 31)

unrate = web.DataReader('UNRATE', 'fred', 
                    start_date, end_date)

Let’s plot the unemployment rate in the US from 1929 to 2022 with recessions defined by the NBER.

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# We use the census bureau's estimate for the unemployment rate 
# between 1942 and 1948
years = [datetime.datetime(year, 6, 1) for year in range(1942, 1948)]
unrate_census = [4.7, 1.9, 1.2, 1.9, 3.9, 3.9]

unrate_census = {'DATE': years, 'UNRATE': unrate_census}
unrate_census = pd.DataFrame(unrate_census)
unrate_census.set_index('DATE', inplace=True)

# Obtain the NBER-defined recession periods
start_date = datetime.datetime(1929, 1, 1)
end_date = datetime.datetime(2022, 12, 31)

nber = web.DataReader('USREC', 'fred', start_date, end_date)

fig, ax = plt.subplots()

ax.plot(unrate_history, **g_params, 
        color='#377eb8', 
        linestyle='-', linewidth=2)
ax.plot(unrate_census, **g_params, 
        color='black', linestyle='--', 
        label='Census estimates', linewidth=2)
ax.plot(unrate, **g_params, color='#377eb8', 
        linestyle='-', linewidth=2)

# Draw gray boxes according to NBER recession indicators
ax.fill_between(nber.index, 0, 1,
                where=nber['USREC']==1, 
                color='grey', edgecolor='none',
                alpha=0.3, 
                transform=ax.get_xaxis_transform(), 
                label='NBER recession indicators')
ax.set_ylim([0, ax.get_ylim()[1]])
ax.legend(loc='upper center', 
          bbox_to_anchor=(0.5, 1.1),
          ncol=3, fancybox=True, shadow=True)
ax.set_ylabel('unemployment rate (%)')

plt.show()
_images/efb5c11163a618b00856b910e5c2ae41039793d80fea458716cfb41a2263a510.png

Fig. 3.6 Long-run unemployment rate, US (%)#

The plot shows that

  • expansions and contractions of the labor market have been highly correlated with recessions.

  • cycles are, in general, asymmetric: sharp rises in unemployment are followed by slow recoveries.

It also shows us how unique labor market conditions were in the US during the post-pandemic recovery.

The labor market recovered at an unprecedented rate after the shock in 2020-2021.

3.5. Synchronization#

In our previous discussion, we found that developed economies have had relatively synchronized periods of recession.

At the same time, this synchronization did not appear in Argentina until the 2000s.

Let’s examine this trend further.

With slight modifications, we can use our previous function to draw a plot that includes multiple countries.

Hide code cell source
def plot_comparison(data, countries, 
                        ylabel, txt_pos, y_lim, ax, 
                        g_params, b_params, t_params, 
                        baseline=0):
    """
    Plot multiple series on the same graph

    Parameters
    ----------
    data : pd.DataFrame
        Data to plot
    countries : list
        List of countries to plot
    ylabel : str
        Label of the y-axis
    txt_pos : float
        Position of the recession labels
    y_lim : float
        Limit of the y-axis
    ax : matplotlib.axes._subplots.AxesSubplot
        Axes to plot on
    g_params : dict
        Parameters for the lines
    b_params : dict
        Parameters for the recession highlights
    t_params : dict
        Parameters for the recession labels
    baseline : float, optional
        Dashed baseline on the plot, by default 0
    
    Returns
    -------
    ax : matplotlib.axes.Axes
        Axes with the plot.
    """
    
    # Allow the function to go through more than one series
    for country in countries:
        ax.plot(data.loc[country], label=country, **g_params)
    
    # Highlight recessions
    ax.axvspan(1973, 1975, **b_params)
    ax.axvspan(1990, 1992, **b_params)
    ax.axvspan(2007, 2009, **b_params)
    ax.axvspan(2019, 2021, **b_params)
    if y_lim != None:
        ax.set_ylim([-y_lim, y_lim])
    ylim = ax.get_ylim()[1]
    ax.text(1974, ylim + ylim*txt_pos, 
            'Oil Crisis\n(1974)', **t_params) 
    ax.text(1991, ylim + ylim*txt_pos, 
            '1990s recession\n(1991)', **t_params) 
    ax.text(2008, ylim + ylim*txt_pos, 
            'GFC\n(2008)', **t_params) 
    ax.text(2020, ylim + ylim*txt_pos, 
            'Covid-19\n(2020)', **t_params) 
    if baseline != None:
        ax.hlines(y=baseline, xmin=ax.get_xlim()[0], 
                  xmax=ax.get_xlim()[1], color='black', 
                  linestyle='--')
    ax.set_ylabel(ylabel)
    ax.legend()
    return ax

# Define graphical parameters 
g_params = {'alpha': 0.7}
b_params = {'color':'grey', 'alpha': 0.2}
t_params = {'color':'grey', 'fontsize': 9, 
            'va':'center', 'ha':'center'}

Here we compare the GDP growth rate of developed economies and developing economies.

Hide code cell source
# Obtain GDP growth rate for a list of countries
gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
            ['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'JPN', 'MEX'], 
            labels=True)
gdp_growth = gdp_growth.set_index('Country')
gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)

We use the United Kingdom, United States, Germany, and Japan as examples of developed economies.

Hide code cell source
fig, ax = plt.subplots()
countries = ['United Kingdom', 'United States', 'Germany', 'Japan']
ylabel = 'GDP growth rate (%)'
plot_comparison(gdp_growth.loc[countries, 1962:], 
                countries, ylabel,
                0.1, 20, ax, 
                g_params, b_params, t_params)
plt.show()
_images/83ac21889601f3312c67309941cd324d320ca2fb2bcd4d1502b8e65ef03e4515.png

Fig. 3.7 Developed economies (GDP growth rate %)#

We choose Brazil, China, Argentina, and Mexico as representative developing economies.

Hide code cell source
fig, ax = plt.subplots()
countries = ['Brazil', 'China', 'Argentina', 'Mexico']
plot_comparison(gdp_growth.loc[countries, 1962:], 
                countries, ylabel, 
                0.1, 20, ax, 
                g_params, b_params, t_params)
plt.show()
_images/5ca65b058a7c93cae736d8ec13ffa403774e483fb5172722ddf9ad170cc016e1.png

Fig. 3.8 Developing economies (GDP growth rate %)#

The comparison of GDP growth rates above suggests that business cycles are becoming more synchronized in 21st-century recessions.

However, emerging and less developed economies often experience more volatile changes throughout the economic cycles.

Despite the synchronization in GDP growth, the experience of individual countries during the recession often differs.

We use the unemployment rate and the recovery of labor market conditions as another example.

Here we compare the unemployment rate of the United States, the United Kingdom, Japan, and France.

Hide code cell source
unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',
    ['USA', 'FRA', 'GBR', 'JPN'], labels=True)
unempl_rate = unempl_rate.set_index('Country')
unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)

fig, ax = plt.subplots()

countries = ['United Kingdom', 'United States', 'Japan', 'France']
ylabel = 'unemployment rate (national estimate) (%)'
plot_comparison(unempl_rate, countries, 
                ylabel, 0.05, None, ax, g_params, 
                b_params, t_params, baseline=None)
plt.show()
_images/5f49da0b9a513ffed822dd221c5c09c8bac1bf9894b3870d9cfa4cad07074ecb.png

Fig. 3.9 Developed economies (unemployment rate %)#

We see that France, with its strong labor unions, typically experiences relatively slow labor market recoveries after negative shocks.

We also notice that Japan has a history of very low and stable unemployment rates.

3.6. Leading indicators and correlated factors#

Examining leading indicators and correlated factors helps policymakers to understand the causes and results of business cycles.

We will discuss potential leading indicators and correlated factors from three perspectives: consumption, production, and credit level.

3.6.1. Consumption#

Consumption depends on consumers’ confidence towards their income and the overall performance of the economy in the future.

One widely cited indicator for consumer confidence is the consumer sentiment index published by the University of Michigan.

Here we plot the University of Michigan Consumer Sentiment Index and year-on-year core consumer price index (CPI) change from 1978-2022 in the US.

Hide code cell source
start_date = datetime.datetime(1978, 1, 1)
end_date = datetime.datetime(2022, 12, 31)

# Limit the plot to a specific range
start_date_graph = datetime.datetime(1977, 1, 1)
end_date_graph = datetime.datetime(2023, 12, 31)

nber = web.DataReader('USREC', 'fred', start_date, end_date)
consumer_confidence = web.DataReader('UMCSENT', 'fred', 
                                start_date, end_date)

fig, ax = plt.subplots()
ax.plot(consumer_confidence, **g_params, 
        color='#377eb8', linestyle='-', 
        linewidth=2)
ax.fill_between(nber.index, 0, 1, 
            where=nber['USREC']==1, 
            color='grey', edgecolor='none',
            alpha=0.3, 
            transform=ax.get_xaxis_transform(), 
            label='NBER recession indicators')
ax.set_ylim([0, ax.get_ylim()[1]])
ax.set_ylabel('consumer sentiment index')

# Plot CPI on another y-axis
ax_t = ax.twinx()
inflation = web.DataReader('CPILFESL', 'fred', 
                start_date, end_date).pct_change(12)*100

# Add CPI on the legend without drawing the line again
ax_t.plot(2020, 0, **g_params, linestyle='-', 
          linewidth=2, label='consumer sentiment index')
ax_t.plot(inflation, **g_params, 
          color='#ff7f00', linestyle='--', 
          linewidth=2, label='CPI YoY change (%)')

ax_t.fill_between(nber.index, 0, 1,
                  where=nber['USREC']==1, 
                  color='grey', edgecolor='none',
                  alpha=0.3, 
                  transform=ax.get_xaxis_transform(), 
                  label='NBER recession indicators')
ax_t.set_ylim([0, ax_t.get_ylim()[1]])
ax_t.set_xlim([start_date_graph, end_date_graph])
ax_t.legend(loc='upper center',
            bbox_to_anchor=(0.5, 1.1),
            ncol=3, fontsize=9)
ax_t.set_ylabel('CPI YoY change (%)')
plt.show()
_images/4581bf9488edbc275b5e69edc940f6df0fb43ddcf27b23722843094f21329a9a.png

Fig. 3.10 Consumer sentiment index and YoY CPI change, US#

We see that

  • consumer sentiment often remains high during expansions and drops before recessions.

  • there is a clear negative correlation between consumer sentiment and the CPI.

When the price of consumer commodities rises, consumer confidence diminishes.

This trend is more significant during stagflation.

3.6.2. Production#

Real industrial output is highly correlated with recessions in the economy.

However, it is not a leading indicator, as the peak of contraction in production is delayed relative to consumer confidence and inflation.

We plot the real industrial output change from the previous year from 1919 to 2022 in the US to show this trend.

Hide code cell source
start_date = datetime.datetime(1919, 1, 1)
end_date = datetime.datetime(2022, 12, 31)

nber = web.DataReader('USREC', 'fred', 
                    start_date, end_date)
industrial_output = web.DataReader('INDPRO', 'fred', 
                    start_date, end_date).pct_change(12)*100

fig, ax = plt.subplots()
ax.plot(industrial_output, **g_params, 
        color='#377eb8', linestyle='-', 
        linewidth=2, label='Industrial production index')
ax.fill_between(nber.index, 0, 1,
                where=nber['USREC']==1, 
                color='grey', edgecolor='none',
                alpha=0.3, 
                transform=ax.get_xaxis_transform(), 
                label='NBER recession indicators')
ax.set_ylim([ax.get_ylim()[0], ax.get_ylim()[1]])
ax.set_ylabel('YoY real output change (%)')
plt.show()
_images/5f9fa0f687db4d27a848b8b16312c6ec7ab32399eecff677bef61c4e8c9c0b0f.png

Fig. 3.11 YoY real output change, US (%)#

We observe the delayed contraction in the plot across recessions.

3.6.3. Credit level#

Credit contractions often occur during recessions, as lenders become more cautious and borrowers become more hesitant to take on additional debt.

This is due to factors such as a decrease in overall economic activity and gloomy expectations for the future.

One example is domestic credit to the private sector by banks in the UK.

The following graph shows the domestic credit to the private sector as a percentage of GDP by banks from 1970 to 2022 in the UK.

Hide code cell source
private_credit = wb.data.DataFrame('FS.AST.PRVT.GD.ZS', 
                ['GBR'], labels=True)
private_credit = private_credit.set_index('Country')
private_credit.columns = private_credit.columns.str.replace('YR', '').astype(int)

fig, ax = plt.subplots()

countries = 'United Kingdom'
ylabel = 'credit level (% of GDP)'
ax = plot_series(private_credit, countries, 
                 ylabel, 0.05, ax, g_params, b_params, 
                 t_params, ylim=None, baseline=None)
plt.show()
_images/0ce1632825930f95104e0eb9f04412511758f8b3c93d9d0fe2627420995c0b6d.png

Fig. 3.12 Domestic credit to private sector by banks (% of GDP)#

Note that the credit rises during economic expansions and stagnates or even contracts after recessions.