Part I: Introduction

The unpredicted rise of the COVID-19 pandemic had a huge impact on the world economy. This pandemic lead to restricted economic activity and affected many countries in varying degrees since the end of 2019. Most countries applied different fiscal and monetary policies to deal with economic fallout resulting from the pandemic. There was a similar response in Canada. A large number of stores and businesses in Canada were forced to close due to the high risk of spreading on Covid-19, which caused an extremely higher unemployment rate about 13.7% (May,2020) and pushed many enterprises into crisis. The total number of cases in Canada reached more than 600,000 cases in January, 2021.

This paper focuses on the business activities during the Covid-19 pandemic by gathering time series data from Statistics Canada. The analysis separates the business activity into opening, closing, continuing and active business, and compares firm activities in different sectors, size classes and provinces. The first part of this paper provides historical context for Canada by looking at business activity from 2001 to 2019. In particular, the historical context uses past business activity data to assess the impact of the 2008 recession and 2014 oil price shock on Canadian economy. Both events affected business activity and provide context to asses the impact of COVID-19 pandemic on business dynamics in the Canadian economy.

The second part uses data for 2020 to investigate the impact on the Covid-19 pandemic on business activity. The analysis will look at the impact on firms in different size and sectors across different provinces and try to find out the main reason that cause these effects. This part will also give a better view on what governments are doing in different period and see how policies work and how people respond. The last part will bring all the data and analysis together and provide some results with some ideas and do a simple preview for the future.

Part II: Background

As a starting point, Figure 1 presents Canadian expenditure-based GDP growth for each quarter from 2007 onwards. The growth rate is normalized to an annualized value. For the recent period, GDP began to fall in the fourth quarter of 2019, and lead to a much lower GDP on quarter 2 of 2020 with a decrease of more than ten percent. The economy experiences some recovery with annualized growth rates of eight percent and two percent in the third and fourth quarters of 2020, respectively. The third quarter in 2020 shows a large boom with a GDP growth rate close to ten percent. This growth shows the impact of the country’s policy, lower restrictions, virus infection rates, and change on people’s expectations on the economy.

If we take a look at the 2008 recession (-2%) and 2014 oil price shock (-1%), the COVID-19 pandemic had a much bigger impact over a relatively shorter time. Recovery occurs relatively quickly in the next two quarters.  Normally, GDP will increase slowly and stable after the recession. In 2008 recession, the growth rate change from positive to -0.02 since quarter four and then back to positive growth again in 2009 quarter three. Similarly in 2014 oil price shock, the GDP growth rate (starts from one percent in 2014 quarter one) slowly becomes negative (-0.55%) in the fourth quarter of 2014, then it gradually moved back to positive growth since the first quarter in 2015.  A sudden drop and increase on GDP (from -11% to 8%) during the COVID-19 pandemic never occurs before, which can be seems as an unpredicted economic situation.

Figure 2a focuses on the annualized GDP growth along with its components across the eight quarters of 2019 and 2020. The figure indicates that growth remains at close to zero percent in 2019 for overall GDP and individual expenditure components. Thus, the Canadian economy is stable and “healthy” in 2019. In 2020, a significant drop happens in Quarter two with a similar size of increase in next quarter and a continuing increase in quarter four. The import of goods and services seems to receive the largest impact to below negative 20 percent decreasing, and the second one is the total investment which is the sum of gross fixed capital and investment on inventories. Compared with other expenditures, investment recovery with a relatively slower speed and least for longer period.

Figure 2b uses the index numbers as 2018 Q4 equals to 100, in order to give a better result and clearly view on the change of GDP during the pandemic. Similarly, as above discussion in Figure 2a, the GDP drop significantly below the normal level compare with 2018 quarter four since Quarter two in 2020 and continuing lower than the original level in Q3 and Q4. However, the gap become smaller and smaller which brings an optimistic point of view. Partially effects on Canadian economy due to the Covid-19 pandemic captured by looking at the data of investment in inventories. The growth rate of inventories remains at around 0 percent in 2019, but in Quarter 2 2020, the number of investments on inventories are twice smaller than 2018 quarter four and have a short move back on the fourth quarter from -29.3 billion to positive 1.7 billion dollars (index number = 11.71 in quarter four 2020). The changing pattern in inventories not really match with the change on total GDP. The slowing down of the negative growth on the inventories do explain some of the drop on GDP in 2020 Q2, but in quarter four, the dramatically increase on inventories does not came up with a higher growth on GDP. This can be explained by looking at Figure 2a. In the second wave, even though total investment grows at a constant speed, the consumption import and export drop dramatically which cause a slowing down on GDP growth. So, decreasing on investment and inventories do become one of the major reasons that cause a drop on GDP in first wave, but not for the second wave. So, this may become the main reason that Canadian economy are hit not harder in the second wave base on more funding support from the government and better policy from the central bank (will discuss more in part 5).

The official measures of business entry and exit rate since 2000 provide valuable historical context for the current state of business dynamics. In Figure 3, the two shadow areas indicate the 2008 recession (Q4 2008 to Q2 2009) and the 2014 oil crisis (Q3 2014 to Q2 2015). From 2000 to 2019, the business entry and exit rate follow a similar decreasing trend with falling gap between each other. The entry rate drops in both 2007-2008 and 2014-2015 periods. During the 2008 recession, net job loss was 400,000 jobs, and the unemployment rate rose from 6.3% to 8.6% (from October 2008 to October 2009) (LaRochelle-Côté and Gilmore (2009)). At first, the world financial crisis and US recession did not greatly affect the Canadian economy. Eventually, the global recession caused a collapse on the oil and exports prices. The business entry rate fell from 14.5 percent to 13 percent from 2008 to 2009. The entry rate falls further in 2011 to 12.5 percent. The data shows that the exit rate continues to decrease slightly into 2010 and remains flat in the 2010-2012 period.

The 2014 oil crisis also caused problems for the Canadian economy. The rise of oil and natural gas prices in early 21st century caused an oil glut and the stronger U.S. dollar in 2013 lead to a further steep drop on the oil and gas prices. As a small open economy, Canadian economy is export based which takes over around 45% of its total gross domestic product, and exportation of oil itself represents 10% of total Canadian exports. (Friedberg direct (2015)) A lower price on oil causes a great increase on unemployment rate especially in Alberta and Newfoundland and Labrador. The purchasing power decrease cause the revenue drop sharply, which bring a sharp decrease on the business entry rate with more that 2 percent (from 12.65% to 10.65%). At the same time, the exit rate increases from 11.5 percent to more than 12 percent between 2014 to 2015 and falls back to trend by the end of 2015 (number of exits businesses increase about 4000 within a year). The business activity provides historical context, and it can make a great contribution for analysis of current and future economic events.

Part III: literature review

Currently, there are many articles that look at what happened for the global economy as a result of the pandemic. Since COVID-19 is spreading fast across globe, the policies restricted the exports which brings a significant loss of income and declining on GDP, especially on developing countries. (Maliszewska, Mattoo, and van der Mensbrugghe (2020)) For consumers, the lockdown forces them to stay at home, and isolate themselves. However, in the positive way, people start to potentially develop new skills and become more familiar using computer and internet. For markets, some of the businesses are thriving, and some are struggling. The COVID-19 pandemic shows that markets are dynamic. The pandemic poses a unique opportunity to study how market evolve in a short period of time. The retailers can minimize their current and future business impacts by addressing some major emergencies and professors should change their traditional way of thinking in order to give better education after COVID-19. (Donthu & Gustafsson (2020))

If we look at the effects of COVID-19 on individual aspects of world economy, there is an unpredicted impact on the manufacturing industry and a delay of research for education especially for master degree students. Tourism and aviation are the hardest hit. Even though the demand for food increased, restaurants and shops are forced to shut down which put firms in food sector under strain. However, the pandemic boosts the pharmaceutical industry and give a further help to research and development sectors.  (Nicola et. Al (2020)) These business failures in different sectors may stem from the mismatch between the organization and its business environment, that is internal and external misfits. Air transport and automotive also face this issue which shows that the collapse in demand during the pandemic brings a disruption of products’ supply. (Amankwah-Amoah. Khan, and Wood (2020))

Small-Medium Enterprises (SMEs) might be at a disadvantage compared with the larger counterparts, based on the limited range of scale and scope (Amankwah-Amoah. Khan, and Wood (2020)). The problems faced by SMEs is not a health crisis but more as an economic crisis. Individual business owners are facing issues such as reduced staff and stay home for safety reason. The business relation and supply chain disruption become an issue affecting the rebound of businesses. More government funding and stimulus funds are expected by SMEs and clearly government measures become necessary. (Al-Fadly (2020)) In the US, small business employs almost 50% of American workers and recent research match the global story that many SMEs likely fail in the absent financial assistance. (W. Bartika et. Al (2020))

In Canada, research shows similar results matching the above. Gu (2020) examines production and employment at firms throughout the pandemic. By using data from Statistics Canada to estimate real GDP of firms and hours worked of employees, the decline was largest for large firms in the goods sector, while small firms had the largest decline in the service sector. On the production side of economy, the micro data gives the idea that small and medium size firms, concentrated in transportation, restaurants, accommodations, arts and entertainment, felt the most impact from the policies associated with the pandemic.

Mo et. Al (2020) also shows that small businesses were hit more seriously and hardly. Because the small business makes up 97.9% of all employer businesses in Canada in 2019. After analysis two datasets (CPSS and CSBC) and comparing, women relative to men, and Canadian-born individuals relative to non-Canadian born individuals, Canadian born are less concerned about losing their jobs or income, while women are more likely to be absent than men. Moreover, the businesses owned by immigrants and women are most likely to request credit to cover costs with the impact of Covid-19. Beland et. Al (2020) note that many small businesses may never recovery or even start. Jones et al. (2020) look at the labor market and analyse the number of job post on job banks. Job vacancies rebounded to 80% after COVID-19 happened for 5 months. LFS data is a great tool to show that there are indeed a sharply decrease on employment rate because of the falling on labour demand, however, most of workers do not search for new jobs but wait for go back to work at their original job. Based on the data, Quebec’s vacancies recovers earlier and employment recovery more rapidly. Even though, there has been an unprecedented rise in temporary layoff unemployment, the marginal group remains “closer” to the labour market and have better employment prospect going forward, which has been seen as a positive sign.

Beland et. Al (2020) indicates that during the Covid-19 pandemic, Arts, education, social, sales and services have the largest decrease on numbers of active businesses, but the number of businesses in the health sector increased by 11.4% in the first five month of 2020. Sun et Al. (2020) analyze data from Flightrader24 by airline to show that impact of COVID-19 pandemic was much larger on international flight than domestic and more concentrated in the Southern hemisphere. Almost every airport loses half of the connections but most reaction seems delay for about two month which lose the chance of preventing the COVID-19 from a local burst to a global pandemic.

Agriculture is remarkably important to Canada’s economy especially in trade with more than four times its share of exports relative to its share of GDP. Currently, the food supply is at or above normal which bring a positive overview, however, the elasticity of different food and change on Canadian dollar exchange rate affect a lot on agriculture sectors. At the same time, the restriction from the government policy on export may become the major reason that cause an increase on price. But there’s more concern on bigger danger on Canadian food exports when the current lockdown is over (Barichello, 2020). Hailu (2020) suggests the COVID-19 pandemic had and will continue to have effect on food processors. A slowing down related activities, but a decline in export and import activity will also continue hurt the economy.

COVID-19 will indeed continue to impact societies in multiple ways. The Canadian government did respond quickly when the pandemic started, but the delay of the process and large difference between provincial actions became key to ease restrictions. Desson et Al. (2020) provide a discussion of the policy response to the pandemic in Canada, France and Belgium. The spread of Covid-19 was slower in Canada than the European countries. The largely provincial decision-making in Canada has allowed certain provinces such as British Columbia to harness strong governance capacity to swiftly tackle the pandemic (specifically in the first wave), while others like Quebec have struggled to flatten the curve, perhaps due in part to a more laissez-faire approach to policy-making.

Part IV: Data

The unexpected COVID-19 virus brings many uncertainties to the public especially to different business activities. The data from Statistic Canada shows clearly about the change pattern and effects under this situation by using experimental estimated data.

This paper major uses the data from table 33-10-0270-01 which gives the information about experiment estimates for business openings and closures by provinces and sectors from 2015 to 2021. It includes six variables and definitions which are showing below in Table 1.

The source of this data is the Longitudinal Employment Analysis Program (LEAP) database. The LEAP database contains annual employment information taken from annual statements of remuneration paid (T4 slips) of Canadian businesses.

Part V: Covid-19 Impact on business activities

5.1 The change on business opening and closure

Before the pandemic, the activities are stable with almost all the growth rates of open and active business is close to zero percent as Figure 4.1a shows. In Figure 4.1b, we index the variables with variable values indexed to 100 in January 2020. Figure 4.1b also shows the similar story by using index numbers. In the Figure 4.1a, opening and closing businesses growth rate shows fluctuations around the zero since 2015 January. These fluctuations continued until the end of 2018 and become relatively flat and stable. However, Figure 4.1b shows that those fluctuation did not have too much impact on the total number of businesses opening and closing.

Figures 4.2a and 4.2b provide variable growth rates and index values matching those in Figures 4.1a and 4.1b except focuses in on the business dynamics for 2020[1]. Since mid-February, closing businesses jump dramatically from 40,709 to around 119,730 (90% increase). Similarly, the number of opening businesses drops by a smaller amount of about 10,000. According to the CBC news, the Covid-19 was well under control at February by blocking the flights and transportations from China. However, the increasing number of cases in Iran and Italian become the key sources of infection. Before March, almost no one in Canada knew what to do to prevent the spread of the virus. Since February, the uncertainty increasing which cause a significant rise on closing businesses shown on Figure 4.2a and Figure 4.2b below, but the growth rate on opening businesses only decreases slightly compare to the growth rate on closing businesses.

From mid-March 2020, almost all the Canadian provinces responded and treated this pandemic as emergency. With the set up about the policy on behavior and relative funding support from the government, the virus infection rate fell, even though the number of cases still rose slowly. The number of opening businesses starts to recover gradually and back to normal on the end of August (from 839,441 on March 2020 to 821,773 on November 2020) (see, Appendix 1 graph 1). Closing business also shows a similar inverse pattern, but with more fluctuation, but it become stable later in September 2020. Figure 4.2b shows that active and continuing businesses remain low, which indicates that many businesses close permanently. Even though the growth rate for these business dynamic measures looks back to normal and suggests a relative stable economy overall, the negative effect on the economy is not reversed in the relatively short period. The Figure 4.1b and figure 4.2b shows the index number of businesses opening and closure by choosing January 2020 as the base month. The figure 4.2b gives a clear view that closing business is still below the original numbers and the opening business is above the original number compare with January 2020.  The Covid-19 pandemic has not only a temporary shock but has some permeant impacts and recovery requires time. Figure 4.2a shows that the opening businesses remain at a higher level and closing businesses remain at a lower level since June 2020 compared to 2019. Increased vaccination and people doing activities a “new way” both provide an optimistic view.

In Table 2, the entry and exit rate was calculated by using numbers of active businesses on denominator and number of entry/exit businesses on numerators. (The formula shows below)

After the calculation, Table 2 shows a similar story as above in Figure 4.1a and 4.1b that entry and exit rate both remain relatively stable at around 0.047 from 2015 January to first quarter 2020. The only large fluctuation starts when the Covid-19 pandemic happens. The exit rate starts to increase largely in March 2020 and reach the peak around 0.139 (13.9%) in April, then it decreases dramatically in next two month and then fall into smaller value around 0.040. However, the entry rate responds later with slower growth since March, reaches a peak at around 0.070 in July and then gradually falls back to around 0.055. These numbers also give the idea that Canadian businesses are currently at a recovery period with a higher entry rate (0.052% > 0.04%) and a lower exit rate (0.041% < 0.045%). The number of active businesses become lower from 906,982 to 882,013. These numbers suggest things have “not reversed” in short period. At the same time, the number of continuing businesses drop from 866,304 in January 2020 to 828,888 in December 2020. This suggests that the short run policy restrictions cause a longer period of damage on businesses which will be discussed later in part six.

“The first wave” seems have a deeper impact to the business activity compare with “the second wave”. Since February, closing businesses growth rate (and the exit rate) increase sharply and last for about 3 months, however, the rate starts to become positive again in June with lower rate but last for a much longer period of time. Similarly, the opening businesses entry rate peaks in June and drops off from July until December. This rise and drop-off might be due to seasonality. The first wave is more like a Temporary shock compare with the second wave with higher changing rate and shorter period of time. More policy support and self isolations may be the main reason that the numbers are more stable with less fluctuations since August. At the same time, some of the businesses may come up with the counterplans and trying to solve problems by borrowing money to support any payments and transform to online working in order to minimize their costs.

I use regression analysis to further examine the Covid-19 pandemic impact on businesses entry and exit in each year, and specifically during first wave and second wave. These regressions are given by the following equations:

In these two regressions, the first wave dummy is equal to 1 from March to August and equal to 0 in all other months. Similarly, the second wave dummy is equal to 1 in September to December and 0 otherwise. For the year dummies, the regression omits the dummy for year 2015. Estimates for the first regression show that first wave and second wave dummy are both statistically significant with p value smaller than 5%. The coefficients indicate that Entry rate increases by 1.47 percent in first wave and increases by 0.94 percent in the second wave. The estimated coefficients on the other variables are statistically insignificant. The result from the second regression is similar. The coefficient on the first wave dummy variables is 0.032 and statistically significant. This implies that the exit rate increase by 3.2 percent during the first wave. The other variables including the second wave dummy variable are not statistically significant.

The reason of running the above regression is major to see and compare the impact on Entry and Exit rate across recent 5 years and between first and second wave. Before the regression, many news and evidences support that the year 2020 dummy will cause a negative impact to the entry rate but a positive impact on the exit rate. At the same time, the first wave will cause a larger effect than the second wave, which can be explained by the discussion above. Before 2020, the economy was stable which means there will be a negative effect on exit rate and a positive effect on entry rate. The entry rate in Regression 1 indeed shows a “larger” influence during 2020 especially in first wave, but both first and second wave shows a positive coefficient which is against the predictions, only 2020 dummy shows a negative coefficient but not statistically significant. The exit rate from the regression better matches the stories which brings a positive effect in 2020 and a large coefficient (0.0316) in the first wave. But a negative coefficient is shown in the second wave with no statistically significant. These errors and bias may because of the omitted variables such as different policies across various provinces in Canada, the natural disasters or even the impact from the stock market. Also, the second wave may not stop at December which means we are lack of data. Moreover, the delay of the policies impact may bring a totally different results when we pick March to August as the first wave and September to December as the second wave.

Statistic Canada also provides data on different types of opening businesses. Reopening businesses are defined as opening businesses that were also active in a previous month. Entrants are the opening businesses that were not active in a previous month. By looking at the difference of these types, they can indirectly show the pattern between permanent and temporary closing businesses. Appendix 1, Graph 2 provides the growth rates of the different type of business openings, while Figure 5.1 provides the actual numbers of business opening across the different types. Graph 2 from Appendix 1 shows that the reopening businesses and entrants seem to have opposing trends in general and especially during Covid-19 pandemic. The entrant’s businesses have a sharp increase from November to December on 2019 with start of the COVID-19 pandemic in China. However, a possible explanation is that a large number of businesses (stores) enter to prepare and produce goods and service in order to make profit during the Christmas. From January to June, there are two significant drops in March (-30%) and May (-20%). Most provinces lockdown started in March. There is an abnormal 10% increase on April to May 2020 in entrant businesses which caused a really small effect on Total opening businesses.

Figure 5.1 shows that the reopening businesses and opening businesses follow similar trends. Both numbers have a smaller decrease in February, then a large rise until June and followed by gradually decrease until the end of 2020. The numbers of entrants have a dramatically decreasing since February from around 43,000 to 13,000 on June. After that it slowly increasing back to higher level. This can be explained on the growth rate above. Since we have a such low numbers, the growth rate will have a larger fluctuation if the numbers are increased. One thing needs to mention is that some businesses may not reopen especially for small businesses, so they may be permanently closed or become part of entrants, such as open a new company. This gives another idea on why entrants jump back to higher level.

Figure 5.2 shows the Opening reopening and entrants’ rate by the following calculations:

It shows a similar pattern with Figure 5.1 which both indicates that opening, reopening and entrant rate (number of businesses) all share the same changing pattern since 2015 to end of 2018. What need to be noticed is that the opening rate start to fall in January 2019 and remain at a negative growth until Covid-19 pandemic happens. Major fall in opening businesses can be explained by a decrease on entrant rate, since the reopening rate are flat until Jan 2020. When the pandemic start, the increase on reopening businesses rate become the major reason that cause a dramatically increase on the opening rate which can also be shown in Figure 5.1. Entrant rate keep falling to the minimum around 0.012 both in March and May and start to move back to original level since June. even though, we are seeing a large increase on opening rate since May 2020, most of the businesses are reopening instead of enter the market. So, it is still a worse situation in recent two years since the entry rate keep falling from 2019 to 2020 which may need more time to recovery, although the rate currently seems back to normal.

5.2 Covid-19 Impact on different size of enterprises

Recession happened in 1981, 1990 and also 2008. Each of these recession periods would have affected business activity, and likely had different effects for firms of different sizes. Large businesses, they may face wider impacts than others, but at the same time have larger resources, such as financial reserves in order to minimize recession effects. Early in recession, firms will reduce employment and engage in other measures to lower their cost. Smaller businesses have more limited resources. Most small business, such as restaurants and shops, normally don’t have any stock and lack of politically connection. Thus, small business (defined as employees lower than 100) likely suffer more during the recession period.

The Covid-19 pandemic impact on the economy was quicker and more serious situation than previous recessions. Figure 6.1 indicates a sharp fall on employment rate about 0.2% for the businesses that have 20 to 99 employees, and then second largest effect happened on businesses that own 5 to 19 employees, decreased about 0.15%. In the first two quarters in 2020, the small businesses reduce their employees about 1.5 million, including all temporary or permanent closing businesses. At the same time, the large businesses (businesses with more than 100 employees) have 0.8 million layoffs. Small businesses have a large increase on their employment change rate since quarter two in 2020. This finding might indicate some business dynamism among small firms. For example, most restaurant started to focus on online deliver and some companies used Zoom working. Employment growth exceeds 0.2 percent, even though the total employment still less than 2019. One thing needs to notice is that, when we calculate the growth rate, small businesses have a small base(denominator). A similar increase on employment for a large company results in a lower growth rate. But for enterprises between 0-4 employees, there are less room to adjust which will shows a relatively small fluctuations in the graph.

By calculating the index numbers (set 2019_Q1 =100), Figure 6.2 shows that the change on the number of employments in different size of businesses. The businesses with 0 to 4 employees and 100+ employees have an index number around 100 before 2020. Numbers suggest, businesses with 5 to 19 employees and 20 to 99 employees have a seasonal pattern to their employment with high numbers in Quarter 3. The index returns to close to 100 for all size groups in Quarter 1 of 2020. As Figure 6.1 shows, the numbers reach the minimum level in Q2 2020 and start to recovery after. In Q4 2020, most numbers trend to move back to the level in 2019 but remain at a lower level. Figure 6.2 indicates that the businesses with 20 to 99 employees have the lowest index value of 95 in Quarter 4 of 2020. Alternatively, large companies already have their employment at a higher level (larger than 100).

Figure 7 compares total business revenue from April 2020 compare to April 2019 across four different employment size classes. Over 75 percent of businesses have their revenues fall (less than 25 percent experience no change) in each size category. Small businesses were hit more than large businesses. There are only 25% of large enterprises (100 or more employees) that lose more than 50% revenue, while 37 percent of firms in the smallest size class (1-4 employees) have revenues fall. Figure 6 shows that businesses that own 20 to 99 employees have the largest drop on their employees. Figure 7 indicates these firms also experience large drops in revenues. As we discuss above, larger companies may face fewer constraints and have funds to solve problems compared to small businesses. Large firms may also find it easier to deal with a fall in revenues by decreasing their employees. Therefore, fewer large enterprises are losing a lot money. The pandemic indeed affects many companies but at least 15% of companies do not have revenues fall. This is especially true for businesses that have 1 to 4 employees. This gives an idea that really small companies will have a relatively smaller risk of being affected by the pandemic. However, if a small firm is affected then, it may face more serious problems and even leaving the market. Overall, large businesses are widely spread over different situations but most small businesses are located in losing revenue categories.

5.3 Impact on different sectors during Covid-19 pandemic.

Before looking at the impact across sectors, this paper will give a clearly view on which sectors are relative more important than others. The real estate has the largest percentage of the share of GDP in Canada around 12% to 14%, and manufacturing is second at around 9% to 10%. Comparing GDP in Canada between January and October of 2020, most sectors increase their proportional contribution during the Covid-19 pandemic. However, there are two sectors whose impact drops: (i) Mining, oil and gas extraction from 7.95% to 7.17%; and (ii) Transportation and warehousing from 4.49% to 3.68%. With less ‘day-to-day’ activity in the Canadian economy due to the pandemic and associated shutdown, demand of oil drops significantly about 300 million barrels a day. Even though the federal government provided $2 billion support for the industries (major for schools), these industries still have a remarkable loss. Canada is an open country that relies heavily on exports. At the same time, the stagnant economy forces businesses to change their strategy by selling more from their inventory. This strategy gives businesses more liquidity and increases their survival prospects. Finally, the food service sector decreases their proportion of the economy from 2.17% to 1.53% which can be easily explained by more closing business on restaurants and small shops. (All numbers above in Statistic Canada, Table 36-10-0434-01)

The Figure 8.1a and 8.1b show the businesses activities for both face-to-face and non-face-to-face industries and using January 2020 as base month to form index numbers. Face-to-face industries include: 72 Accommodation and food services; 44-45 Retail trade; 52 Finance and insurance; 53 Real estate and rental and leasing. Non-face-to-face include: 11 Agriculture, forestry, fishing and hunting; 21 Mining, quarrying, and oil and gas extraction; 23 Construction; 31-33 Manufacturing; 41 Wholesale trade; 48-49 Transportation and warehousing. First, it is clear to see that similar patterns occur for both sets of industries. The figures show a sharp increase on closing businesses. Opening businesses line shows a decreasing trend in the beginning and then remains at higher number (greater than 100) after April (similar to Figure 4.2b above). However, the numbers show that COVID-19 pandemic hits face-to-face industries much more seriously. In April, the closing business number increases to around 350 which is 3.5 times larger than January and remain a little bit lower than 100 after July. Even though there is a smaller impact on the non-face-to-face industries, the index in April still reaches around 2.5 times compared to January. However, the opening businesses number achieves 150 in June and July for face-to-face industries, which shows a stronger and quicker recovery.

In order to get a better view on the economic change in different sectors, I look at employment change by sectors from December 2019 to December 2020. Table 3 shows that the Covid-19 pandemic causes a decrease in employment for all industries by 571.6 thousand people with a -3% change rate. The service-producing sector has a larger total and percentage (-3.5%) drop in total employment than the goods-producing sector (-1.1%). For the goods producing sectors, Construction lost most employees (80.6 thousand). The agriculture sector lost 11.9 thousand employees. For the service sectors, accommodation and food service sector loses 0.3 million jobs which is about -26.7%. This gives a better view on how the Covid-19 hit the food sectors. The fact is that most of the time in the history, restaurants are always influenced the most, even though right now most restaurants successfully change to online delivery services, their employment still is visibly decreasing. The business sector also has a large employment drop of around 9%. Employment in the Transportation decreased about 56.3 thousand employees or 5.5 percent, which matches the change on GDP that last paragraph discussed.

Some sectors increase their total employment. Table 3 shows that the employment number is increasing for the scientific and education sectors, with a total around 0.1 million, the education sectors increase employment by 37,500. In the good producing sector, manufacturing increases employment about 36.9 employment or -2.2 percent. This potentially indicates an increasing demand on goods such as medical tools, masks or even freezers or cars.

In order to understand the reason of changes on employment rate, Figure 8.2 provides data on the Impact of various expenditures due to COVID-19 for quarter 4, 2020. During the pandemic, the major expenditure was on sanitization, cleaning and personal protective equipment and supplies. Around 92% Companies from retail trade increase on expenditure for both reasons. In addition, 80% of companies from Manufacturing, health care and accommodation food service increase expenditure on cleaning and purchasing protective equipment. Almost thirty percent of companies increase their expenditure on technology and equipment for teleworking especially for finance and health care sectors. If we sum up all the percentage from all reasons and compare, it shows that most companies who increase their expenditure the most are concentrate on retail trade, health care and accommodation and food sectors.

The graphs from Appendix 2 shows explanations for how the following three sectors were hit badly by Covid-19 pandemic: Transportation, Commercial real estate and Retail trade. Graph 1 shows data for Canadian airlines. We can see that the number of passengers dropped from 7000 to 200 within one month when the pandemic started. The policy on restrictions of traveling between countries as well as in domestic is likely the main reason. These decreases in passengers bring an almost 0 revenue for the airline companies. A monthly revenue about 2.2 million dollars to almost 0.2 million dollars revenue will cause an unpredictable. In addition, we can see from the graph 1 that this situation remains for over a year. This suggests a reason that transportation has such a dramatic drop on numbers of employees. Even though there is a small decrease in building prices since Q1 2020 (Appendix 2 graph 2). The office and industrial building price move back to normal or even higher within only one quarter, but retail housing price decreases by almost 5% through the whole year in 2020. This gives more evidences on the idea that small businesses really facing a hard time especially for retail trade sectors. The last graph from the appendix 2 gives a better view of the retail trade sector. The graph shows a substantial increase in online sales for retail trade businesses beginning in March 2020. Electronic shopping and mail ordering increased larger compare with data in 2019. Even though this makes retail sector better, they still face many problems. For example, their cost likely increases and some small businesses may need time to transition. During the process, some businesses may leave and permanent exit the market.

5.4 Covid-19 business activities impact in different provinces

Desson et Al. (2020) notes that provincial policies do vary and lead to various results. Figure 9 shows the change on average actual hours worked from January 2020 to January 2021.The reason to compare January 2020 to January 2021 is to control for seasonality. There are some consistencies in this figure. Overall, the average working hours are increasing for most provinces with Saskatchewan and New Brunswick providing exceptions. The increase in hours worked indicates some recovery from the pandemic. The service sector experiences the largest increase in hours worked in most provinces. Five provinces have a drop in average working hours for the goods-producing sector. British Columbia has an increase of -3.2 hours in the goods sector and 1.4 hours overall. Even though this result suggests BC is recovering faster than the other provinces, the level of average working hours (39.6 hours on good sector and 33.8 hours on service sector) are still lower than other provinces in January 2021. One thing needs to mention is that most sectors across different provinces have a larger drop on average hours worked in November 2020 (Statistic Canada, Table:14-10-0036-01). From it we can see that the second wave of the COVID-19 pandemic cause a graver situation. With longer time of lockdown and larger numbers of cases and death, more and more people choose to stay at home rather.

This article also separates the province into 5 groups: (i) Ontario; (ii) Quebec; (iii) British Columbia; (iv) Western Canada (Alberta, Manitoba, and Saskatchewan); and (v) Atlantic Canada (New Brunswick, Newfoundland and Labrador, Nova Scotia, Prince Edward Island). Figure 10.1 shows the closing business rate. Most provinces have a similar trend with an increase on closing businesses in April and a decrease (negative rate) in May. After May, most provinces start to move back to a growth rate of zero. Atlantic Canada has the closing business number increase by over 150. As two provinces that have most cases, Quebec and Ontario follow similar patterns.

Figure 10.2 looks at changes for opening businesses across the geographical groupings. The two lowest decreases for Quebec occur in March and August for opening businesses. The opening businesses rate in Quebec increases a lot on May. Compared to Quebec, Ontario shows a better result with smaller changes for opening businesses but a later recovery period starts on June. Western Canada has the smallest effect with a smaller closing rate and a short period on recovery. British Columbia follows a pattern similar to the rest of Western Canada a smaller effect (60% increase on closing businesses and 5% decrease on opening businesses). What’s more, the pandemic shock started earlier in BC in March compared with other countries and moves back to more normal rates slower in May for closing businesses and in July for opening businesses.

Figure 11 provides the index of businesses activities across Provinces. The active and continuing businesses shows a similar pattern in different provinces. Ontario has the largest decreases at about 15% on numbers of active businesses and 20% on continuing businesses. BC has a relatively large impact on continuing businesses compared with Quebec, Atlantic and Western Canada. Western Provinces have the lowest changing index on active businesses, which shows the smallest impact from Covid-19 pandemic on Businesses activity. Most provinces have a large increase in the number of closing businesses. This is especially true for Atlantic, Ontario and Quebec where the index for closing businesses reaches approximately 315 in April. Similar to above, this figure shows that British Columbia gets hit and recovers earlier with a relatively low and flat change on index number after April. This may because of the earlier response of the Government or the geographical position of closing the sea. Since June, almost all the provinces start to recovery even though; their number of cases and policy may be different. Until December, the active and continuing businesses level trend to move back to origin, even though they are still lower than January. Moreover, the number of opening businesses remains at a higher level and number closing businesses remains at a lower level for more than 6 months.

Part VI: Policies and Effects

When we look back the history in Canada, we have to mention the Spanish Flu happened in 1918. The Spanish Flu is the most damaging pandemic of influenza for Canada and the world with almost 100 million people dead, including 50,000 Canadians. (Dickin er Al. (2020)) Unlike the common flu, the Spanish Flu tended to kill young and hearty rather than elder and weak people. It came with the soldiers who went back from the war and spread quickly in the communities. During that time, the isolation policy was made but unsuccess, and each province create their new law such as force people wearing masks, at the same time, government force to shut down all unnecessary services which cause a decrease on productivity and less profit for firms. After around 100 years, Covid-19 pandemic caused a similar situation with millions of people affected and an irreversible impact on the Canadian economy. However, in 21th century, Canadian government has more mature health care system and more experience with more methods and tools. The following gives an overview on the policy and their effects on business activities during the Covid-19 pandemic.

6.1 Government restrictions and influence

During the pandemic, government restrictions had a major influence on people and firm’s behavior. According to the Statistic Canada, social distancing policy affected around 70% of the businesses across different provinces, sectors and firm sizes. In particular, the policy affected 77.3% of the small firm (5-19 employees). A larger number compared to large firms. Also, this policy affects 90% of the business in Arts, entertainment and recreation and Accommodation and food services, around 87% on education and health care sectors. (Statistic Canada, Table: 33-10-0237-01) For some good and service sectors, supply may exceed demand, which causes them produce less and further decrease wages for their employees. Travel restrictions also plays an important role. The tourism industry was hit the most with around 50% to 70% reduction in GDP. According to Liu (2020), total paid employment in the tourism industry for April and May 2020 declined by almost 60% from 2019 levels. The decline was more than double the decline observed in non-tourism industries.

The dramatic increase on purchasing protection equipment caused an increase on the operational cost, which further influenced decisions by owners. The lockdown decision across different provinces may become the major shock for small businesses. Based on CFIB data, a third of Ontario business report their business will not survive a second lockdown (CFIB (2020)).  As this paper shows above, the small businesses have lower financial support, and ability to survive under pandemic, especially when there are more restrictions with negative impacts. (Watts. AL (2020))

6.2 Fiscal policy: Funding and stimulation

There are two main government policies to provide support for businesses. The first one is the Canadian Emergency Wage Subsidy (CEWS) program. CEWS covers up to 75% of an employee’s wages in order to rehire employees and avoid layoffs. The second policy is the Canada Emergency Business Account (CEBA). CEBA provides businesses with financial support, loans along with access to credit. For small businesses, the Canadian government provides interest-free loans up to $60,000, and provides loan guarantee up to 80% of new operating credit through CEBA. Moreover, the BDC and the financial institutions will co-lend term loans up to $6.25 million to SMEs. At the same time, Canada Emergency Rent Subsidy (CERS) provides money directly to tenants up to 65% of the expansion in order to help businesses overcome the pandemic. (Government of Canada, Covid-19 response plan)

From the Above Figure 11, we can see that CEBA, wage subsidy and CEWS are the three major funding supports for all businesses. Many businesses are getting support in the first wave in May. For the companies with 5 to 19 employees, there were 52% of small businesses get help from CEBA which is much higher than firms in other size categories. However, 31.1% of companies with 20 to 99 employees get help from wage subsidy, and 40.7% of them get support from the CEWS.  At the same time, the CECRA, EDC and BDC major focus was helping small businesses (5 to 19 employees) with 6.6%, 1.4% and 4% respectively. If we compare policies in May, we can see that CEBA and CECRA are major focus for firms with 1 to 99 employees. EDC and BDC are the main subsidy programs for small and medium enterprises. Wage subsidy, CEWS and government programs widely provide support to specific businesses who need help.

Before the second wave start in the third quarter in 2020, more and more companies are approved for most of funding programs. Almost 67% of small enterprises (5 to 19 employees) get support from CEBA and at the same time, 54.5% of medium enterprises (20 to 99 employees) get support from CEWS. One thing needs to mention is that fewer large companies need funding from CEBA and wage subsidy and only less changes for other findings except CEWS. This shows a similar story as above. Larger companies will have more capital reserves and more opportunity to transform. The pandemic creates less uncertainty for large companies. However, large companies may need more support for wages.

6.3 Monetary policy: Cut interest rate

Monetary policy is one of the most important way to control the economy. Interest rate cuts during the recession and pandemic encourage people to save less and consume more. At the same time, there is cheaper borrowing costs, which encourage firms to receive loan in order to invest and spend. The mortgage interest will also be lower and reduce the cost of mortgage repayments. Over last ten years, Canadian government control the inflation by targeting the overnight interest rate. The Figure 12 below shows that since 2011, the economy is relatively stable with only one cut for the 2014 oil price shock from 1 to 0.5 percent, and then gradual increase. This shows a stable healthy economy. However, similarly with other countries, during the Covid-19 pandemic, the overnight interest rate was cut dramatically from 1.75 to 0.25 in Canada in order to stimulate the economy.  At the same time, the restriction policies force many stores temporary closed, more and more SMEs permanently shut down, so it is harder for people to consume. Thus, the major purpose of the interest rate focuses on lowering the debt-surviving costs for borrowers in order to help them overcome the pandemic. (Bank of Canada, our response: Policy actions) In the 2008 recession, the overnight rate was cut by 0.5% (from 3.0% to 2.5%). At that time, the Bank of Canada did not see fit to engage in quantitative easing. (Gordon (2017)) However, during the Covid-19 pandemic, the bank of Canada purchasing bonds and other debt from governments and businesses in order to ensuring access to credit. When the pandemic started, many institutions were more willing to hold money and selling bonds. The Bank of Canada bought bonds to make sure there was enough money in the economy to allow transactions. The Covid-19 pandemic is much worse than 2008 recession which need more support to overcome and longer time of recover for most businesses.

Part VII: Conclusion

This paper gives an overview on Covid-19 pandemic impact on business activities. By looking at the GDP and comparing between different parts, the analysis shows that the pandemic causes a dramatically decrease on GDP especially on investment and net export. Statistics Canada has excellent data on Business activity by separating it to continuing, closing, active and opening businesses. The historical data for business activity shows the evidence on the accuracy and usefulness of the data. During the analysis, the closing businesses have a much larger fluctuation with higher numbers and growth rate.

Different perspectives are considered when examining the impact on Businesses. Part 4.2 compares different enterprises sizes, and finds that the greatest impact is on small businesses who experience a sharp drop in employment and revenue. Part 4.3 looks at different sectors of the economy. There appears to be a larger effect on the service sector than the good sectors. More specifically, the transportation, food and construction sectors are hit the most. Analysis data from airline, commercial rents and retail sectors, we find that travel restrictions cause: (i) a serious problem to the airlines; (ii) e-commerce becomes more popular with consumers; and (iii) retail building price falls. This paper also analyzes differences across provinces. Even though Ontario and Quebec have the most cases, Atlantic Canada seems affected most by Covid-19 pandemic. New Brunswick and Saskatchewan have a negative employment rate on both good and service sectors. BC experience lower impact on businesses activities with a relatively higher employment rate in good sectors.

The last part of the paper discusses policy changes in three parts: (i) restrictions, (ii) fiscal policy(funding) and (iii) monetary policy (cut rate). Most of the restriction hurt the business activity in different degrees. Social distancing restrictions largely affect food and entertainment sectors, and travel restrictions cause the tourism sector to shrink. Most companies spend more on protection equipment, which increases their costs and lower the profits. The government provided different types of funding in order to support the operations of businesses, especially for small firms. During these findings, CEBA and CEWS are widely used. Extra support from EDC and BDC helps SMEs against the pandemic. The bank of Canada lowered their overnight interest rate to 0.25% in order to stimulate the purchasing power and help firms borrow at lower cost. At the same time, the Bank of Canada bought bonds to provide cash flow to stabilize the economy.

The Covid-19 pandemic brings a hard time for most businesses in Canada. Even though the virus causes many people to lose their jobs and forced shut down of their businesses. This still bring people great experience on how to solve the problems during the recession. The pandemic shows the weakness of Canadian economy and gives the idea that our businesses are supported by communications, and connection.

Appendix 1:

Appendix 2:

Reference:

·       Amankwah-Amoah. Khan, and Wood (2020). “COVID-19 and business failures: The paradoxes of experience, scale, and scope for theory and practice”. written by Joseph Amankwah-Amoah, Zaheer Khan, Geoffrey Wood. European Management Journal, 2020.

·       Al-Fadly (2020). “IMPACT OF COVID-19 ON SMES AND EMPLOYMENT”. Ahmad Al-Fadly, College of Business Administration, December 2020.

·       Barichello (2020). “The Covid-19 Pandemic: Anticipating its Effects on Canada’s Agricultural Trade”. written by Richard Barichello, University of British Columbia, 2020.

·       Bank of Canada. “Our response: Policy actions”:

https://www.bankofcanada.ca/2020/05/our-policy-actions-in-the-time-of-covid-19/

·       Beland et. Al (2020). “Short-Term Effect of COVID-19 on Self-Employed Workers in Canada”. written by LOUIS-PHILIPPE BELAND, OLUWATOBI FAKOREDE, AND DEREK MIKOLA, Carleton University.

·       CFIB (2020). “One-third of Ontario businesses report they will not survive the second lockdown”. CFIB, December 21, 2020

·       Desson et Al. (2020). “An analysis of the policy responses to the COVID-19 pandemic in France, Belgium, and Canada”. Zachary Desson, Emmi Weller, Peter McMeekin, Mehdi Ammi, September 2020.

·       Donthu and Gustafsson (2020). “Effects of COVID-19 on business and research”. written by Naveen Donthu and Anders Gustafsson. Journal of Business Research 2020.

·       Dickin er Al. (2020). “1918 Spanish Flu in Canada”. Article by Janice Dickin, Patricia G. Bailey, Erin James-Abra, The Canadian Encyclopedia, March 19, 2020

·       Friedberg direct (2015). “How Does the Price of Oil Affect the Canadian Economy?” Reviewed by FXCM Team - 29 December 2015, 12:49 pm

·       Gu (2020). “Economic Impact of the COVID-19 Pandemic on Canadian Businesses across Firm Size Classes”. by Wulong Gu, August 19, 2020.

·       Government of Canada. “Covid-19 response plan”:

https://www.canada.ca/en/department-finance/economic-response-plan.html#agri-food

·       Gordon (2017). “Recession of 2008–09 in Canada”. written by Stephen Gordon, October 24, 2017.

https://www.thecanadianencyclopedia.ca/en/article/recession-of-200809-in-canada#:~:text=The failure of Lehman Brothers,see Interest Rates in Canada).

·       Hailu (2020). “Economic thoughts on COVID-19 for Canadian food processors”. Getu Hailu, Department of Food, Agricultural & Resource Economics, University of Guelph, Guelph, Ontario, Canada. 2020

·       Jones et Al. (2020). “Waiting for Recovery: The Canadian Labour Market in June 2020”. written by STEPHEN R.G. JONES, FABIAN LANGE, W. CRAIG RIDDELL, CASEY WARMAN, August 2020.

·       LaRochelle-Côté and Gilmore (2009). “Canada’s employment downturn”. Sébastien LaRochelle-Côté and Jason Gilmore, December 2009.

·       Liu (2020). “The Economic Impact of Travel Restrictions on the Canadian Economy due to the COVID-19 Pandemic”. Statistic Canada, October 23, 2020.

·       Maliszewska, Mattoo, and van der Mensbrugghe (2020). “The potential impact of COVID-19 on GDP and Trade”. Maryla Maliszewska, Aaditya Mattoo, Dominique van der Mensbrugghe, World bank group, April 2020

·       Mo et. Al (2020). “Differential Impacts during COVID-19 in Canada: A Look at Diverse Individuals and Their Businesses”. written by: Guangying Mo, Wendy Cukier, Akalya Atputharajah, Miki Itano Boase, Henrique Hon. October 2020, University of Toronto.

·       Nicola et. Al (2020). “The socio-economic implications of the coronavirus pandemic (COVID-19): A review”. written by Maria Nicola, Zaid Alsaf, Catrin Sohrabi, Ahmed Kerwan, Ahmed Al-Jabir, Christos Iosifidis, Maliha Agha, Riaz Agha. International Journal of Surgery 2020.

·       Sun et Al. (2020). “How did COVID-19 impact air transportation? A first peek through the lens of complex networks”. Written by Xiaoqian Sun, Sebastian Wandelt, Anming Zhang. Journal of Air Transport Management, 2020.

·       W. Bartika et. Al (2020). “The impact of COVID-19 on small business outcomes and expectations”. Alexander W. Bartika, Marianne Bertrand, Zoe Cullen, Edward L. Glaeser, Michael Luca and Christopher Stanton, June 23, 2020.

·       **Watts. Al (2020)). “**Provincial governments restrict business operations in the fight against COVID-19”. written by Michael Watts, Lawrence E. Ritchie, James R. Brown, Michael Fekete, Julien Ranger, Susan Newell, Marty Putyra, Mark Longo, Patrick Sullivan, Paula Olexiuk, Carson Wetter, Mar 24, 2020

https://www.osler.com/en/resources/regulations/2020/provincial-governments-restrict-business-operations-in-the-fight-against-covid-19#_ftn20

Resources (data) we have:

·       Experimental estimates for business opening and closures for Canada and provinces and territories 2015-2020 monthly:

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310027001

·       Employment for all employees by enterprise size, By provinces, quarterly 2001-2020:

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410021401

·       Quarterly estimates of business entry and exit only Canada, 2001-2019

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310016501

·       Business Sector employment flow rates, by industry, provinces and the territories, 2001-2018, annual:

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310008901

·       Business Dynamics measures, by industry, per province or territory, 2001-2018, Annual:

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310008701

·       Business revenue from April 2020 compared with April 2019, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310025301

·       Percentage of workforce laid off to adapt to COVID-19, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3310025201

·       Likelihood of various protective measures being implemented once transitioned to on-site work, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310026401

·       Employment by industry, monthly, seasonally adjusted (x 1,000)

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410035502

·       Gross domestic product, expenditure-based, Canada, quarterly (x 1,000,000)

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3610010401

·       Operating and financial statistics for major Canadian airlines, monthly

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=2310007901

·       Commercial rents services price index, quarterly

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1810026001

·       Retail e-commerce sales, unadjusted (x 1,000)

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=2010007201

·       International merchandise trade by commodity, monthly (x 1,000,000)

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1210012101

·       Actual hours worked by industry, monthly, unadjusted for seasonality

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1410003601

·       Approved funding or credit due to COVID-19, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310028401

·       Impacts on various expenditures due to COVID-19, by business characteristic

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310031901

·       Impact of social distancing measures on businesses, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310023701

·       Extent of various impacts experienced by businesses because of COVID-19, by business characteristics

https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3310022901&pickMembers[0]=3.12

·       Funding or credit approved due to COVID-19, by business characteristics, May 2020

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310025501

·       Approved funding or credit due to COVID-19, by business characteristics, third quarter of 2020

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3310028401