Tracking the Canadian Economy Through COVID-19-Insights from Customer Transactions  

Overview

1. Key Insights from Customer Transactions
2. COVID-19, the Canadian Economy and Scotiabank’s Transactions Data
3. Business Transactions Data Details
4. Retail Transactions Data Details
5. Other High-Frequency Indicators of Activity
6. Caveats

1. Key Insights from Canadian Customer Transactions

This presentation is part of the weekly series intended to draw insights about the state of the Canadian economy from the flow of Scotiabank’s retail and non-retail transactions data.

Key takeaways from the payments data in this week’s publication:

• Updated to January 16^th, y/y growth in business sector transactions picked up over the past week:

  • The financial industry saw a strong increase in y/y growth in incoming payments, and manufacturing firms received payments at the rate that was similar to that of a year ago.

  • Growth in overall outgoing payments, while it slowed somewhat since the introduction of new restrictions across various provinces in late December, remained positive. Rent payments, in contrast to payroll deposits, continued to run significantly below last year’s levels in January.

• Following the conclusion of the holiday shopping season, consumer transactions continued to average close to the levels seen a year ago, pushing up y/y growth close to zero as of January 17^th. The resilience in spending seen recently in January is perhaps a sign that the restrictions had their largest impact during the usually busy post-Christmas shopping season. The impact so far in January seems much more muted.

  • The improvement in growth seen nationally is also evident at the provincial level, with Ontario, Quebec, Alberta, BC, Saskatchewan and Manitoba all showing growth having bottomed out in early January.

  • Among the sectors we are tracking, activity picked up at online retailers, hardware and grocery stores in early January. 

2. COVID-19 and the Canadian Economy: Scotiabank Transactions Data

• We present data on retail and non-retail transactions, which capture distinct but related aspects of economic activity in Canada.

• The data comprises actual observed daily transactions going through debit or credit card payment networks in the retail space, and automated funds transfers (AFTs) in the non-retail space.

  • The transactions are anonymized and aggregated to protect the privacy of Scotiabank’s clients.

• The AFT payments show bill payments to/from companies in Canada.

  • Incoming payments can be associated with company revenue, and outgoing payments can be associated with costs.

• Debit and credit card payments can be used to measure the evolution of retail spending at various types of establishments.

  • The transactions can serve as a measure of economy-wide retail spending, and of the extent to which households are resuming pre-COVID levels of activity.

  • Note that the use of electronic payments has increased because of COVID-19, so comparisons to year-ago levels can be misleading. These data are best used to observe directional movements rather than to make specific assessments on the level of activity.

• In the current circumstances, comparing the dollar volumes of transactions in the current year to its level of a year ago should help track growth through the re-opening phase. 

3. Business Transactions Data: Growth in Payments Jumped Higher in Mid-January

• Updated to January 16^th, y/y growth in business sector transactions picked up over the past week (chart 1):

  • The financial industry saw a strong increase in y/y growth in incoming payments, and manufacturing firms received payments at the rate that was similar to that of a year ago (charts 2-6).

• Automated Funds Transfers (AFT) are used for:

  • rent and mortgage payments;

  • payroll deposits; and

  • other bills. 

3. Business Transactions Data: Retail and Wholesale Payments Stabilized

3. Business Transactions Data: Manufacturing Payments Level with A Year Ago

3. Business Transactions Data: Growth in Bill Payments Remains Positive

• Growth in overall outgoing payments, while it slowed somewhat since the introduction of new restrictions across provinces in late December, remained positive (Chart 7).

• Rent payments, in contrast to payroll deposits, continued to run significantly below last year’s levels in January (Charts 8 and 9).

• Rent payment slowdown is likely an indication of the impact of new restrictions on the ability of customer-facing businesses to pay rent. The recently launched Canada Emergency Rent Subsidy program will likely help to boost the flow of rent payments as the program’s take-up increases. 

3. Business Transactions Data: Rent Payments Still Below Early-2020 Levels

4. Retail Transactions Data: Spending Growth Pushes Higher Recently

• Following the conclusion of the holiday shopping season, consumer transactions averaged close to the levels seen a year ago, pushing up y/y growth close to zero as of January 17^th. The resilience in spending so far in January is perhaps a signal that the restrictions had their largest impact during the usually busy post-Christmas shopping season. The impact so far in January seems much more muted (Chart 10).

  • Most of the negative impact on growth in December came from lower credit card spending. 

4. Retail Transactions Data: Card Spending Bottoming Out Across Provinces

• The improvement in growth seen nationally is also evident at the provincial level, with Ontario, Quebec, Alberta, BC, Saskatchewan and Manitoba all showing growth having bottomed out in early January (Charts 11-14).

• Growth in the Atlantic provinces continued to outperform the national average in the last few weeks. 

4. Retail Transactions Data: Spending Growth Improves Across Sectors

• At the end of December growth decelerated in large part because of reduced spending on travel, clothes and restaurants, among other sectors (Chart 15).

• The recent pickup was driven by stronger y/y growth in sectors outside of travel. 

4. Retail Transactions Data: Hardware Stores See Stronger Spending Growth

• Since the start if the year, growth bottomed out in several hard-hit sectors, such as clothing and entertainment. In addition, consumers continued to spend at hardware stores which saw y/y growth rise in mid-January (Chart 16). The latter is likely the result of continued strength in home renovations after a holiday-related pause. 

4. Retail Transactions Data: Online Spending Picks Up Further in January

• All merchants that saw a strong increase in demand over the course of 2020 saw a further rise in growth in the new year (Chart 17).

• Spending continues to migrate increasingly online as the pandemic accelerated a pre-existing trend towards online shopping and on-demand delivery services.

5. Other High-Frequency Indicators Of Activity

Charts 18-31 Google Searches, Various Keywords, Dining and Transportation Data

6. Caveats

• Note that the mapping from the volume of transactions to measures of economic activity (e.g. GDP) is imperfect, and so care must be taken when drawing the implications.

• The data is observed at daily frequency and embeds different types of seasonal patterns.

  • For retail payments, the volume and types of payments are different depending on the day of the week and the season.

  • For non-retail payments, both the day of the week and the season are important. In addition, some payments are tied to the calendar date (e.g. rent payments are made on the first day of each month), some payments have a bi-weekly schedule, etc.

  • To smooth out most of the day-to-day seasonality we use a 14-day moving average of the dollar volume of transactions, taking a y/y% change to remove any remaining seasonal patterns related to the calendar date.

• In addition to seasonality, there is normal payment volatility related to the random nature of the transactions process and the impact of regional and economy-wide events (weather, labour strikes, etc.).

  • The volatility of this nature may or may not be related to economic activity as measured by GDP and so, as mentioned above, care must be taken in drawing inference.

• For business transactions, which are inherently more lumpy compared to retail spending, data towards the end of the sample can be revised as some AFT payments are recorded with a lag. As a result we exclude the last few days of data of business transactions only. 

Nikita Perevalov* (Scotiabank Economics)

Taha Jaffer, Jason Liang (Data Science and Analytics)
Roland Merbis, Artur Motruk (Customer Insights & Analytics)

*  Director of Economic Forecasting, 437.775.5137,  nikita.perevalov@scotiabank.com

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