Employment¶

Due to the limited geographic granularity of the datasets available to us (through the public facing Statistics Canada web portal and the Odesi data portal through the Ontario Tech University library service), we only have access to a version of the monthly labour force survey data at the major metropolitan areas.

lfs_toronto = labourforce.dataframe(cma="Toronto")

def monthly_employment_by_industry(industry):
    if industry is None:
        df = lfs_toronto
    else:
        index = lfs_toronto['naics_21'].str.lower().str.contains(industry)
        df = lfs_toronto[index]
    df = df[['date', 'lfsstat']] \
        .groupby(['date', 'lfsstat']) \
        .size() \
        .reset_index(name='count') \
        .set_index(['date'])
    
    df = df.pivot(columns='lfsstat')

    df.columns = [x[1] for x in df.columns.values]
    return df

#
# analysis of unemployment numbers
#
def unemployment_rolling_mean(df):
    df = df['Unemployed'] / df.sum(axis=1) * 100
    df = df.rolling(5).mean().dropna()
    
    return df

#
# plot the pre and post
#
def plot1(df, industry):
    pre = df[df.index <= '2020-03-01']
    covid = df[(df.index >= '2020-03-01') & (df.index <= '2021-03-01')]
    post = df[df.index >= '2021-03-01']

    pl.figure(figsize=(10,6))
    ax = pl.gca()
    
    pd.Series(pre['2019-10-01'], index=pre.index).plot.line(ax=ax, style='--')
    pre.plot.line(ax=ax, color='green')
    
    covid.plot.line(ax=ax, color='red')
    
    pd.Series(post.iloc[-1], index=post.index).plot.line(ax=ax, style='--')
    post.plot.line(ax=ax, color='blue')
    
    pl.ylabel('%')
    pl.title('Unemployment (%s)' % industry)

Unemployment Numbers¶

df = monthly_employment_by_industry(None)
df = unemployment_rolling_mean(df)
plot1(df, 'All')

df = monthly_employment_by_industry('retail')
df = unemployment_rolling_mean(df)
plot1(df, 'Retail')

Here is a list of the top industries included in the labour force survey.

#
# top industries
#

top_industries = lfs_toronto['naics_21'].value_counts().index[1:].values.tolist()
pd.DataFrame(top_industries, columns=['Top Industries'])

	Top Industries
0	Professional, scientific and technical services
1	Retail trade
2	Health care and social assistance
3	Finance and insurance
4	Educational services
5	Construction
6	Transportation and warehousing
7	Accommodation and food services
8	Information, culture and recreation
9	Manufacturing - non-durable goods
10	Manufacturing - durable goods
11	Business, building and other support services
12	Wholesale trade
13	Other services (except public administration)
14	Public administration
15	Real estate and rental and leasing
16	Utilities
17	Agriculture
18	Mining, quarrying, and oil and gas extraction
19	Forestry and logging and support activities fo...
20	Fishing, hunting and trapping

This is the pre- vs post-COVID lockdown unemployement rates for various industries. The entries are sorted from the most negatively impacted industries to the best recovered industries.

#
# recovery_by_industry
#

df = lfs_toronto.groupby(['date', 'naics_21', 'lfsstat']).size().reset_index(name='count').set_index(['date', 'naics_21'])
df = df.pivot(columns='lfsstat').dropna()
df.columns = [x[1] for x in df.columns.values]
df = df['Unemployed'] / df.sum(axis=1) * 100
df = df.reset_index(level=1).pivot(columns='naics_21').rolling(5).mean().iloc[4:]
df.columns = [x[1] for x in df.columns.values]

df = df.loc[['2019-10-01', '2021-10-01']].transpose()
df.columns = ['pre', 'post']
df['recovery'] = df['pre'] - df['post']
df.sort_values(by='recovery', inplace=True)
df.dropna(inplace=True)

recovery = df['recovery']

df

	pre	post	recovery
Information, culture and recreation	3.153645	6.204308	-3.050663
Manufacturing - non-durable goods	2.267684	4.645276	-2.377592
Other services (except public administration)	3.168398	4.912130	-1.743732
Business, building and other support services	4.731357	5.928790	-1.197433
Construction	2.574257	3.509540	-0.935282
Health care and social assistance	1.911522	2.488857	-0.577335
Manufacturing - durable goods	2.531799	3.022837	-0.491038
Retail trade	3.678571	4.086203	-0.407632
Transportation and warehousing	2.493183	2.845258	-0.352075
Accommodation and food services	5.606982	5.761452	-0.154470
Educational services	5.594402	5.537084	0.057317
Professional, scientific and technical services	2.244297	1.962104	0.282193
Finance and insurance	2.566738	1.639034	0.927704

color = pd.Series('', index=recovery.index)
color[recovery < 0] = 'red'
color[recovery >=0] = 'blue'
pl.figure(figsize=(10, 5))
recovery.plot.bar(color=color);