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Price or quantity effect? The impacts of the pandemic on Canadian trade

PDF version (1.60 MB)

Colin Scarffe
January 2022

Key points

  1. As of October 2021, the value of Canada’s merchandise exports were 13% above the 2019 average (pre-pandemic) level. However, this is the product of two offsetting trends:
    • The first is that export prices surged in 2021 and are 21% above their pre-pandemic level.
    • The second is that export quantities sagged in 2021 and are 6.2% below their pre-pandemic level.
    • This narrative only emerged in 2021, after prices and quantities had recovered from the pandemic dip.
  2. As of October 2021, Canadian merchandise imports are 5.7% above pre-pandemic levels and are a muted version of the export side. Import prices are 5.4% above their pre-pandemic level while import quantities are 0.2% above their pre-pandemic level.
  3. Canadian merchandise export prices move nearly one-for-one with Canadian industrial prices—both industrial prices (excluding energy) and export prices (excluding oil) have increased close to 15% year-over-year in October 2021.
  4. In October, Canadian merchandise imports (excluding oil) had increased a more modest 5% year-over-year while the price of consumer goods (excluding energy) increased 3% year-over-year. While import prices are correlated with consumer and industrial prices, the correlation is somewhat weak suggesting that other domestic factors may be more important than import prices.
  5. As export prices have risen faster than import prices, Canada has seen its terms of trade appreciate. In general, this rising terms of trade indicates the price changes have been a net-benefit to the Canadian economy as Canadian exporters are receiving greater returns for exports relative to the increased costs to importers.

1. Introduction

Largely due to the COVID-19 pandemic, Canada’s merchandise trade —and merchandise trade around the world—has been volatile in the last two years. Business closures to control the spread of the virus, shifts in spending patterns, volatile commodity prices, and lingering supply chain issues have all contributed to the disruptions in trade. Between February 2020 and May 2020, Canadian imports and exports both fell 29%. As pandemic restrictions gradually eased, monetary policy become more accommodating, fiscal supports materialized, and Canadians transitioned to working online, Canadian trade rebounded. As of October 2021, Canadian merchandise imports were 5.7% above 2019 average (pre-pandemic) levels, while Canadian merchandise exports were 13% above pre-pandemic levels. However, the fall and subsequent recovery of Canadian trade is more complicated than the simple narrative that things have returned to normal. In general, two components determine the value of Canadian trade: the quantity of goods traded and the price paid for those goods. Examining the quantity and price changes over the last two years provides more nuance on what actually changed during the pandemic, and contributes to a clearer narrative of the emerging trends in merchandise trade.

2. Data and methodology

The data in this paper covers only merchandise trade and comes from Statistics Canada. Table 12-10-0121-01 provides monthly Canadian trade values by the North American Product Classification System (NAPCS) commodities (101 commodities at the most detailed level).Footnote 1 Table 12-10-0128-01 provides monthly price and volume (hereafter quantity) information for Canadian merchandise trade by NAPCS commodities.Footnote 2 There are two choices to make with the data: whether to seasonally adjust the data, and whether to use the data on a Customs or Balance of Payments (BoP) basis. Given the examination period is measured in months rather than years, seasonally adjusted data is the natural choice; balance of payments data was chosen as it is more commonly reported by Statistics Canada. A manual adjustment was made to the quantity and price data to adjust the base year to 2019. Details of the adjustment, as well as details of other index number calculations can be found in the second appendix. The latest data available at the time of writing was October 2021.

For many series, a counter-factual level is used for comparison. There are several ways to generate a counter-factual; the first would be to use a benchmark level, such as the 2019 average as the “normal” level. One problem is that this simple benchmark ignores the fact that trade generally grows over time, and thus what is “normal” for the end of 2021 should be above the 2019 average. The second option is to use a previous trend to make a linear extrapolation for 2020 and 2021. A third option is to use univariate ARIMAFootnote 3 estimation to generate a prediction for 2020 and 2021. While ARIMA estimation isn’t ex-ante constrained to be linear, in this paper all of the ARIMA estimates produced a linear trend—thus the linear extrapolations and the ARIMA estimates are similar. Both the 2019 average level and the ARIMA estimation will be used as benchmarks in this report. Details of the specific ARIMA procedure can be found in section 9, the third appendix.

3. Exports

The value of Canadian merchandise exports decreased close to 34% between February and April 2020; however, the trough was short-lived with exports recovering half of their value by June 2020 and were above 2019 average levels by January 2021. As seen in figure 1 below, exports have seen steady growth regardless of the starting point. Even if no leniency is granted for the pandemic, Canadian merchandise exports grew close to 15% between October 2019 and October 2021. This is equivalent to an annual growth rate of 7.2% which compares favourably to an annual growth rate of exports between 2010 and 2019 of only 4.3%.

Figure 1: The monthly value of Canadian merchandise exports

figure 1
Text version
DateActual2019 average
Jan-201846068.949630.3
Feb-201846806.149630.3
Mar-201848208.049630.3
Apr-201849008.949630.3
May-201849014.349630.3
Jun-201851684.649630.3
Jul-201851481.649630.3
Aug-201850585.949630.3
Sep-201850664.249630.3
Oct-201850226.349630.3
Nov-201847003.349630.3
Dec-201845412.549630.3
Jan-201947989.649630.3
Feb-201948254.649630.3
Mar-201950298.649630.3
Apr-201950852.149630.3
May-201952341.949630.3
Jun-201949752.049630.3
Jul-201949431.149630.3
Aug-201950070.949630.3
Sep-201949530.449630.3
Oct-201948881.349630.3
Nov-201948837.749630.3
Dec-201949323.549630.3
Jan-202046883.449630.3
Feb-202047381.149630.3
Mar-202043944.349630.3
Apr-202031639.949630.3
May-202034227.449630.3
Jun-202041326.949630.3
Jul-202045248.649630.3
Aug-202045024.049630.3
Sep-202045902.049630.3
Oct-202046423.849630.3
Nov-202046834.749630.3
Dec-202047346.349630.3
Jan-202151292.249630.3
Feb-202150060.849630.3
Mar-202150557.149630.3
Apr-202150274.349630.3
May-202149528.449630.3
Jun-202153268.149630.3
Jul-202153821.049630.3
Aug-202153947.549630.3
Sep-202152801.649630.3
Oct-202156183.149630.3
Nov-2021  
Dec-2021  

However, examining the export value by itself does not tell the full story. Figure 2 decomposes the change in exports (from the 2019 average level) into changes in export quantity and changes in export prices. Figure 2 shows that all of the export growth in 2021 has been due to increasing prices, while the quantity of exported goods has actually acted as a drag on growth.

Figure 2: Price and quantity contribution to the growth of merchandise export value compared to 2019Footnote 4

figure 2
Text version
DateValuePricesQuantities
Jan-2019-0.035-0.029-0.006
Feb-2019-0.0290.005-0.035
Mar-20190.0150.025-0.010
Apr-20190.0250.030-0.005
May-20190.0530.0210.032
Jun-20190.004-0.0120.016
Jul-2019-0.001-0.0170.015
Aug-20190.006-0.0130.019
Sep-2019-0.006-0.005-0.002
Oct-2019-0.019-0.007-0.011
Nov-2019-0.019-0.003-0.016
Dec-2019-0.008-0.0100.002
Jan-2020-0.059-0.020-0.039
Feb-2020-0.047-0.0470.000
Mar-2020-0.120-0.082-0.038
Apr-2020-0.450-0.180-0.270
May-2020-0.373-0.126-0.247
Jun-2020-0.182-0.057-0.124
Jul-2020-0.087-0.038-0.049
Aug-2020-0.100-0.026-0.074
Sep-2020-0.084-0.028-0.056
Oct-2020-0.070-0.013-0.056
Nov-2020-0.060-0.014-0.046
Dec-2020-0.0480.002-0.050
Jan-20210.0310.033-0.002
Feb-20210.0060.070-0.064
Mar-20210.0170.074-0.057
Apr-20210.0110.098-0.087
May-2021-0.0020.113-0.116
Jun-20210.0710.143-0.072
Jul-20210.0810.150-0.069
Aug-20210.0840.141-0.058
Sep-20210.0610.161-0.100
Oct-20210.1270.195-0.068
Nov-20210.1650.199-0.034

Of note is that quantities were above the 2019 average level in January 2021. While the recent sluggishness of quantities could still be pandemic driven, this is a new trend which is distinct from the initial drop in April 2020. Had quantities stayed below 2019 levels the entire time, the narrative could be that they’re slow to recover, or perhaps Canada has lost that capacity entirely. However, given quantities did exceed the 2019 level, neither of these descriptions fit the data.

In order to investigate the trends further, the price and quantity growth is decomposed into the 101 NAPCS components. By decomposing the aggregates into the components, its possible to infer whether the observed trend is the result of a single component—and therefore not reflective of the broader economy—or whether it is broad based—and therefore reflective of the economic conditions. Table 3 & 4 in the first appendix have the 10 components that have the highest contribution to growth—both negative and positive—for export prices and quantities. Table 1 has key summary statistics for the individual components for various periods throughout the pandemic.

Table 1: Summary statistics of components throughout the pandemicFootnote 5

QuantitiesDatesUnweighted mean changeMedian changeNumber of prod. that declinedNumber of prod. that increasedHHI of CTGFootnote 6
Pandemic decline2019 – May-20-15.0%-15.0%77220.14
Pandemic reboundMay-20 – Jan-2133.1%16.3%22770.11
2021Jan-21 – Oct-21-3.2%-3.8%65320.10
full period2019 – Oct-21-3.9%-4.3%64350.12
Prices
Pandemic decline2019 – May-20-3.3%1.1%42570.58
Pandemic reboundMay-20 – Jan-218.8%0.9%41580.33
2021Jan-21 – Oct-2115.8%7.0%22750.17
Full period2019 – Oct-2117.4%12.3%18810.08

The dynamics in export quantities and prices since 2019 can be described as follows:

Pandemic decline (2019 average – May 2020):

Pandemic rebound (May 2020 – January 2021):

2021 (January 2021 – October 2021):

Total pandemic period (2019 average – October 2021):

To summarize the 2021 period in words, there is no single export, or group of exports, that is responsible for the price increase or the quantity decrease. The increase in prices is reflective of the fact that prices around the world for most goods have increased, and Canadian exporters are selling their goods for higher prices. Likewise, no single export was responsible for the lower export quantity. Canadian exporters are struggling to export their goods. This isn’t to say that Canadian exporters have become lousy at exporting; rather, there are supply constraints that are limiting Canadian businesses. The fact that both the higher prices and lower quantities are broad based means that no single cause—such as a semi-conductor shortage—can explain these events; higher prices and lower quantities are simply a feature of the broader economic conditions.

The final piece of the analysis on the export side is to examine how the current level of the data compares to the counter-factual estimates. The reason why a comparison to counter-factual is necessary is that it provides context for the above facts. There’s no questioning that prices have driven export growth while quantities have held back export growth, but if export prices are high while quantities are more normal, then having a lower export quantity would not be a significant issue. Conversely, if quantities are low while prices are normal, this changes the narrative as perhaps the shock isn’t as extensive as thought. Similar to above, before examining prices and quantities, figure 3 has the value of exports compared to its counter-factual.

Figure 3: The value of exports and the counter-factualFootnote 7

figure 3
Text version
DateActualPoint EstimatesLower bound of CIUpper bound of CI
Jan-201032347.0   
Feb-201033103.1   
Mar-201032714.8   
Apr-201032888.5   
May-201033749.6   
Jun-201033387.2   
Jul-201033044.1   
Aug-201033945.2   
Sep-201033280.8   
Oct-201034208.7   
Nov-201034743.7   
Dec-201036554.0   
Jan-201137537.7   
Feb-201135660.6   
Mar-201136331.9   
Apr-201136806.3   
May-201136652.1   
Jun-201136308.3   
Jul-201137664.6   
Aug-201138730.1   
Sep-201139881.5   
Oct-201139087.2   
Nov-201140149.1   
Dec-201141803.1   
Jan-201239670.6   
Feb-201239609.3   
Mar-201238336.5   
Apr-201238587.5   
May-201238523.7   
Jun-201238187.8   
Jul-201237517.6   
Aug-201237988.8   
Sep-201237852.8   
Oct-201238287.8   
Nov-201238471.9   
Dec-201238476.9   
Jan-201338594.1   
Feb-201339491.2   
Mar-201340031.8   
Apr-201340565.4   
May-201339631.3   
Jun-201339535.4   
Jul-201338657.9   
Aug-201340671.8   
Sep-201341044.8   
Oct-201340318.5   
Nov-201340155.0   
Dec-201340527.5   
Jan-201440424.8   
Feb-201443191.7   
Mar-201444432.5   
Apr-201443696.7   
May-201445246.7   
Jun-201445124.9   
Jul-201445317.8   
Aug-201444760.4   
Sep-201444952.0   
Oct-201445009.7   
Nov-201443634.1   
Dec-201443542.3   
Jan-201542271.9   
Feb-201542834.5   
Mar-201543147.5   
Apr-201542892.2   
May-201542493.5   
Jun-201544835.4   
Jul-201545566.1   
Aug-201544755.3   
Sep-201544345.5   
Oct-201543348.8   
Nov-201543055.7   
Dec-201544499.7   
Jan-201645518.4   
Feb-201643349.7   
Mar-201641507.4   
Apr-201641552.4   
May-201640944.8   
Jun-201641120.5   
Jul-201643174.4   
Aug-201644151.3   
Sep-201644001.0   
Oct-201644407.9   
Nov-201646456.4   
Dec-201646116.7   
Jan-201746977.1   
Feb-201746110.4   
Mar-201746769.3   
Apr-201747390.3   
May-201747988.4   
Jun-201745712.0   
Jul-201743807.0   
Aug-201743560.1   
Sep-201743763.8   
Oct-201744638.0   
Nov-201746728.9   
Dec-201747079.4   
Jan-201846068.9   
Feb-201846806.1   
Mar-201848208.0   
Apr-201849008.9   
May-201849014.3   
Jun-201851684.6   
Jul-201851481.6   
Aug-201850585.9   
Sep-201850664.2   
Oct-201850226.3   
Nov-201847003.3   
Dec-201845412.5   
Jan-201947989.6   
Feb-201948254.6   
Mar-201950298.6   
Apr-201950852.1   
May-201952341.9   
Jun-201949752.0   
Jul-201949431.1   
Aug-201950070.9   
Sep-201949530.4   
Oct-201948881.3   
Nov-201948837.7   
Dec-201949323.5   
Jan-202046883.449498.6747473.351610.46
Feb-202047381.149674.4746824.5452697.85
Mar-202043944.349850.8946370.9853591.94
Apr-202031639.950027.9346017.6354387.72
May-202034227.450205.645727.8455121.84
Jun-202041326.950383.9145482.8855813.05
Jul-202045248.650562.8545271.6856472.42
Aug-202045024.050742.4245087.0557107.16
Sep-202045902.050922.634492457722.25
Oct-202046423.851103.4844778.9358321.32
Nov-202046834.751284.9844649.1258907.08
Dec-202047346.351467.1244532.4659481.65
Jan-202151226.251649.944427.360046.7
Feb-202149969.151833.3444332.2760603.59
Mar-202150530.252017.4244246.2961153.42
Apr-202150221.152202.1644168.4361697.14
May-202149562.752387.5644097.962235.52
Jun-202153332.852573.6144034.0662769.24
Jul-202153854.452760.3343976.3363298.87
Aug-202154011.652947.7143924.2263824.91
Sep-202152782.653135.7543877.364347.8
Oct-20215642053324.4643835.1964867.93
Nov-2021 53513.8443797.5665385.64
Dec-2021 53703.943764.165901.24

This simple counter-factual does not attempt to provide an accurate forecast—perhaps Canadian exports should be much higher or much lower than they are currently, given the economic conditions. A better forecast would require a more powerful model which is out of the scope of this work. Instead, the counter-factual can be interpreted as Canadian exports being approximately back to (or above) the same growth path they were prior to the pandemic. Exports surpassed the counter-factual (the trend growth rate between January 2010 and December 2019) by June 2021. As of October 2021, exports were $2.8 billion (or roughly 5%) above the counter-factual estimate.Footnote 8 Next, figure 4 has the export quantity and export prices compared to their respective counter-factuals.

Figure 4: Monthly quantity, price, and counter-factual estimates for exports

figure 4
Text version
DateQuantitiesPoint EstimatesLower bound of CIUpper bound of CI
Jan-201077.34   
Feb-201078.52   
Mar-201079.42   
Apr-201081.01   
May-201081.04   
Jun-201080.86   
Jul-201080.51   
Aug-201081.96   
Sep-201080.52   
Oct-201082.36   
Nov-201082.48   
Dec-201085.77   
Jan-201182.99   
Feb-201179.93   
Mar-201180.15   
Apr-201179.60   
May-201179.93   
Jun-201178.52   
Jul-201184.93   
Aug-201185.67   
Sep-201185.93   
Oct-201184.24   
Nov-201184.99   
Dec-201189.53   
Jan-201286.42   
Feb-201285.62   
Mar-201283.31   
Apr-201284.90   
May-201285.17   
Jun-201284.56   
Jul-201284.15   
Aug-201284.80   
Sep-201283.91   
Oct-201283.21   
Nov-201284.40   
Dec-201283.96   
Jan-201384.02   
Feb-201385.49   
Mar-201385.98   
Apr-201387.72   
May-201386.53   
Jun-201386.59   
Jul-201382.99   
Aug-201386.34   
Sep-201387.85   
Oct-201387.00   
Nov-201387.63   
Dec-201387.05   
Jan-201483.31   
Feb-201486.67   
Mar-201489.23   
Apr-201490.17   
May-201493.60   
Jun-201493.64   
Jul-201494.69   
Aug-201493.57   
Sep-201494.13   
Oct-201495.26   
Nov-201492.53   
Dec-201495.43   
Jan-201592.92   
Feb-201592.26   
Mar-201593.85   
Apr-201593.47   
May-201592.48   
Jun-201596.53   
Jul-201597.10   
Aug-201596.10   
Sep-201596.00   
Oct-201594.29   
Nov-201594.00   
Dec-201597.69   
Jan-201699.75   
Feb-201698.19   
Mar-201694.95   
Apr-201694.72   
May-201691.73   
Jun-201691.24   
Jul-201694.07   
Aug-201697.07   
Sep-201695.38   
Oct-201694.57   
Nov-201698.20   
Dec-201696.54   
Jan-201797.75   
Feb-201796.14   
Mar-201797.23   
Apr-201797.96   
May-201798.81   
Jun-201796.25   
Jul-201795.23   
Aug-201793.95   
Sep-201794.79   
Oct-201795.06   
Nov-201797.00   
Dec-201796.93   
Jan-201895.21   
Feb-201897.16   
Mar-201898.79   
Apr-201899.67   
May-201897.77   
Jun-2018101.29   
Jul-2018100.70   
Aug-201899.62   
Sep-2018100.67   
Oct-2018100.26   
Nov-2018100.09   
Dec-201898.90   
Jan-201999.35   
Feb-201996.59   
Mar-201998.97   
Apr-201999.46   
May-2019103.26   
Jun-2019101.57   
Jul-2019101.55   
Aug-2019101.90   
Sep-201999.83   
Oct-201998.88   
Nov-201998.41   
Dec-2019100.23   
Jan-202096.20101.04697.76703104.4349
Feb-2020100.04101.528997.48892105.7363
Mar-202096.25101.657597.291106.22
Apr-202076.35101.86697.4009106.5358
May-202078.08102.074997.51273106.8505
Jun-202088.31102.284397.62639107.1643
Jul-202095.21102.49497.74178107.4773
Aug-202092.87102.704297.85884107.7895
Sep-202094.59102.914897.97748108.101
Oct-202094.51103.125998.09765108.4119
Nov-202095.52103.337498.21927108.7223
Dec-202095.16103.549398.34229109.0321
Jan-202199.80103.761798.46665109.3415
Feb-202193.80103.974598.59232109.6505
Mar-202194.42104.187798.71924109.9592
Apr-202191.67104.401498.84738110.2675
May-202189.09104.615598.97669110.5756
Jun-202193.01104.830199.10714110.8835
Jul-202193.34105.045199.2387111.1911
Aug-202194.40105.260599.37133111.4987
Sep-202190.46105.476499.50501111.8061
Oct-202193.39648105.692799.6397112.1134
Nov-2021 105.909499.77539112.4206
Dec-2021 106.126699.91205112.7278
figure 4.2
Text version
DatePricesPoint EstimatesLower bound of CIUpper bound of CI
Jan-201084.22   
Feb-201084.90   
Mar-201082.94   
Apr-201081.74   
May-201083.85   
Jun-201083.13   
Jul-201082,62   
Aug-201083.40   
Sep-201083.22   
Oct-201083.61   
Nov-201084.83   
Dec-201085.81   
Jan-201191.08   
Feb-201189.83   
Mar-201191.25   
Apr-201193.09   
May-201192.32   
Jun-201193.09   
Jul-201189.26   
Aug-201190.96   
Sep-201193.43   
Oct-201193.43   
Nov-201195.04   
Dec-201194.01   
Jan-201292.42   
Feb-201293.16   
Mar-201292.63   
Apr-201291.51   
May-201291.08   
Jun-201290.93   
Jul-201289.76   
Aug-201290.20   
Sep-201290.83   
Oct-201292.64   
Nov-201291.75   
Dec-201292.25   
Jan-201392.47   
Feb-201393.00   
Mar-201393.74   
Apr-201393.11   
May-201392.19   
Jun-201391.91   
Jul-201393.78   
Aug-201394.85   
Sep-201394.08   
Oct-201393.33   
Nov-201392.25   
Dec-201393.74   
Jan-201497.68   
Feb-2014100.32   
Mar-2014100.25   
Apr-201497.60   
May-201497.33   
Jun-201496.98   
Jul-201496.35   
Aug-201496.31   
Sep-201496.06   
Oct-201495.04   
Nov-201494.95   
Dec-201491.89   
Jan-201591.55   
Feb-201593.50   
Mar-201592.52   
Apr-201592.30   
May-201592.72   
Jun-201593.65   
Jul-201594.57   
Aug-201593.66   
Sep-201592.83   
Oct-201592.53   
Nov-201592.21   
Dec-201591.71   
Jan-201691.91   
Feb-201688.90   
Mar-201688.03   
Apr-201688.33   
May-201689.86   
Jun-201690.75   
Jul-201692.40   
Aug-201691.60   
Sep-201692.89   
Oct-201694.57   
Nov-201695.29   
Dec-201696.18   
Jan-201796.77   
Feb-201796.60   
Mar-201796.84   
Apr-201797.38   
May-201797.78   
Jun-201795.64   
Jul-201792.61   
Aug-201793.38   
Sep-201792.94   
Oct-201794.55   
Nov-201796.99   
Dec-201797.81   
Jan-201897.43   
Feb-201896.98   
Mar-201898.27   
Apr-201898.99   
May-2018100.94   
Jun-2018102.74   
Jul-2018102.93   
Aug-2018102.15   
Sep-2018101.31   
Oct-2018100.87   
Nov-201894.54   
Dec-201892.45   
Jan-201997.18   
Feb-2019100.53   
Mar-2019102.53   
Apr-2019103.06   
May-2019102.13   
Jun-201998.85   
Jul-201998.35   
Aug-201998.75   
Sep-201999.53   
Oct-201999.28   
Nov-201999.74   
Dec-201999.01   
Jan-202098.0398.6648896.01716101.3856
Feb-202095.3699.0601194.87569103.4291
Mar-202092.1299.04693.97394104.3918
Apr-202083.5599.2596493.33769105.5573
May-202088.2099.3470492.73922106.4257
Jun-202094.4499.5048692.25211107.3278
Jul-202096.2899.6238491.79559108.1197
Aug-202097.4599.7646791.39467108.9012
Sep-202097.2099.8936391.02036109.6319
Oct-202098.68100.029590.67888110.3443
Nov-202098.57100.161890.35912111.0279
Dec-2020100.16100.296390.06159111.6941
Jan-2021103.32100.429989.78149112.3412
Feb-2021107.24100.564389.51789112.9737
Mar-2021107.73100.698589.26837113.5921
Apr-2021110.29100.833189.03184114.1985
May-2021111.99100.967788.80687114.7938
Jun-2021115.42101.102688.59252115.3793
Jul-2021116.16101.237788.38783115.9556
Aug-2021115.18101.372988.19204116.5237
Sep-2021117.48101.508388.00446117.0843
Oct-2021121.5766101.643987.8245117.6379
Nov-2021 101.779787.6516118.1851
Dec-2021 101.915787.48531118.7263

Perhaps unsurprising given the decomposition in figure 2, the value of exports sitting in the middle of the “normal” range is the product of two offsetting abnormal occurrences. Export prices are 18.4% above the counter-factual trend, while export quantities are 11.2% below the counter-factual trend. Both of these events fit into the common narrative emerging in the aftermath of the COVID-19 pandemic. A shift towards consuming more goods causes an increase in demand, putting upward pressure on prices, while constrained supply keeps actual quantities traded down. The result ends up being a close to a wash in terms of value, but the composition is higher prices paid for goods and lower quantities exported.

One final point on exports: reinforcing the narrative from the decomposition in figure 2, these trends have emerged entirely in 2021. In January 2021, export prices were right in line with the point estimate and just slightly above 100—the 2019 average level. Likewise, quantities were below the point estimate, but were above the lower bound of the confidence interval and slightly above 100. Thus for Canadian merchandise exports, the run-up in prices, and the sagging of quantities is a pandemic narrative that is disparate from the initial drop and is a distinct trend for 2021.

4. Imports

Throughout the pandemic, the import side of Canadian merchandise trade has exhibited similar behaviour as the export side; however, it is more muted and the trends in value, prices, and quantities are less distinct. Both merchandise exports and imports fell 29% between February and May 2020, with April being the trough for exports and May being the trough for imports. Figure 5 has the monthly value of Canadian merchandise imports and the 2019 average.

Figure 5: The monthly value of Canadian merchandise imports

figure 5
Text version
DateActual2019 average
Jan-201847927.051164.4
Feb-201848910.151164.4
Mar-201851677.251164.4
Apr-201850496.351164.4
May-201851479.951164.4
Jun-201851933.351164.4
Jul-201851547.051164.4
Aug-201851086.751164.4
Sep-201851191.151164.4
Oct-201850427.951164.4
Nov-201850126.351164.4
Dec-201851167.551164.4
Jan-201951934.451164.4
Feb-201951430.051164.4
Mar-201952688.551164.4
Apr-201951821.051164.4
May-201952622.651164.4
Jun-201951035.651164.4
Jul-201950972.351164.4
Aug-201951703.551164.4
Sep-201950404.551164.4
Oct-201950395.851164.4
Nov-201949475.251164.4
Dec-201949489.351164.4
Jan-202049744.151164.4
Feb-202050191.551164.4
Mar-202048579.851164.4
Apr-202037139.551164.4
May-202036007.051164.4
Jun-202043437.051164.4
Jul-202048482.951164.4
Aug-202048557.351164.4
Sep-202049654.051164.4
Oct-202050544.551164.4
Nov-202050236.251164.4
Dec-202049433.951164.4
Jan-202150073.551164.4
Feb-202148975.851164.4
Mar-202151991.551164.4
Apr-202150121.251164.4
May-202151537.851164.4
Jun-202151388.851164.4
Jul-202153623.151164.4
Aug-202153079.351164.4
Sep-202151610.851164.4
Oct-202154156.251164.4
Nov-2021 51164.4
Dec-2021 51164.4

Next, figure 6 below decomposes the change in imports into price and quantity growth. There are features of figure 6 that are similar to the export decomposition in figure 2—namely the big dip at the beginning of the pandemic and the recent increase in import prices. However, this does not mean that imports have had the same narrative as exports. Prices played a smaller role in the initial decline for imports (albeit on the export side the price decline was entirely due to oil) and accordingly quantities played a larger role on the import side. The second difference is that as late as May 2021, import prices were below the 2019 average level. This is noticeably different from the export side where prices have been above the 2019 average level since December 2020—6 months earlier than imports. Lastly, import quantities were slightly above the 2019 average level in October 2021—albeit essentially no different from the pre-pandemic level. This still differs markedly from the export side where quantities were a significant drag on growth.

Figure 6: Price and quantity contribution to the growth of import value compared to 2019

figure 6
Text version
DateValuePricesQuantities
Jan-2018-0.065-0.048-0.019
Feb-2018-0.045-0.038-0.008
Mar-20180.010-0.0250.035
Apr-2018-0.013-0.0270.013
May-20180.006-0.0230.028
Jun-20180.0150.0000.014
Jul-20180.0070.0060.001
Aug-2018-0.0020.007-0.010
Sep-20180.0010.009-0.009
Oct-2018-0.014-0.001-0.014
Nov-2018-0.0200.004-0.025
Dec-20180.0000.015-0.016
Jan-20190.0150.0100.002
Feb-20190.005-0.0050.011
Mar-20190.0290.0040.026
Apr-20190.0130.0100.004
May-20190.0280.0080.019
Jun-2019-0.0030.002-0.009
Jul-2019-0.004-0.0090.002
Aug-20190.010-0.0020.012
Sep-2019-0.015-0.005-0.011
Oct-2019-0.015-0.005-0.012
Nov-2019-0.034-0.012-0.023
Dec-2019-0.033-0.010-0.023
Jan-2020-0.028-0.002-0.027
Feb-2020-0.0190.000-0.016
Mar-2020-0.0520.023-0.073
Apr-2020-0.320-0.020-0.301
May-2020-0.351-0.010-0.346
Jun-2020-0.164-0.028-0.141
Jul-2020-0.054-0.008-0.049
Aug-2020-0.052-0.013-0.039
Sep-2020-0.030-0.008-0.022
Oct-2020-0.012-0.004-0.009
Nov-2020-0.018-0.011-0.008
Dec-2020-0.034-0.020-0.014
Jan-2021-0.022-0.013-0.009
Feb-2021-0.044-0.007-0.037
Mar-20210.016-0.0200.035
Apr-2021-0.021-0.001-0.020
May-20210.007-0.0100.016
Jun-20210.0040.006-0.002
Jul-20210.0470.0340.012
Aug-20210.0370.047-0.011
Sep-20210.0090.061-0.053
Oct-20210.0570.0550.001
Nov-20210.0800.0730.006

Below, table 2 has the summary statistics for the individual components throughout the pandemic. For consistency, the same time periods are used, but based off of the contributions to import growth in figure 6, their isn’t a clear change in trend that emerges after January 2021.

Table 2: Summary statistics of components throughout the pandemic

QuantitiesDatesUnweighted mean changeMedian changeNumber of prod. that declinedNumber of prod.that increasedHHI of CTGs
Pandemic decline2019—May-20-14.1%-19.3%77220.10
Pandemic recoveryMay-20—Jan-2177.0%22.7%19800.10
2021Jan-21—Oct-2120.0%0.8%46530.08
Full pandemic period2019—Oct-211.9%3.7%38610.05
Prices
Pandemic decline2019—May-200.4%2.6%37620.24
Pandemic recoveryMay-20—Jan-213.2%-2.9%60390.18
2021Jan-21—Oct-2112.2%6.4%23760.05
Full pandemic period2019—Oct-2113.8%4.7%33660.05

In general, the import price and quantity contributions to growth have been broad-based throughout the pandemic. During the pandemic decline and subsequent recovery, import price contributions were slightly concentrated, but given the relatively small change in prices, this number is less important to the narrative. One period to note is that the decline in quantities in August and September 2021 (the two purple bars below 0 towards the right hand side in figure 6) was entirely due to a decline in autos and auto-parts. Otherwise, all other movements have been largely broad-based.

Next, figure 7 has the three series—the value, quantities, and prices—plotted with their respective counter-factuals. One aspect that these figures show is that it is clear that prices are above trend and quantities are below trend—albeit only slightly. Thus, while the path taken to get to October 2021 is different than exports, imports has the similar—though milder—narrative to the export side that prices are up while quantities are down.

Figure 7: The value, quantity, and price of imports and their counter-factuals

figure 7
Text version
DateActualPoint EstimatesLower bound of CIUpper bound of CI
Jan-201031953.0   
Feb-201033041.9   
Mar-201033970.6   
Apr-201032778.9   
May-201034609.8   
Jun-201034783.4   
Jul-201035115.1   
Aug-201035716.7   
Sep-201035614.6   
Oct-201035391.2   
Nov-201035256.5   
Dec-201035438.3   
Jan-201136793.6   
Feb-201135709.1   
Mar-201137331.4   
Apr-201137046.0   
May-201137738.1   
Jun-201137422.8   
Jul-201137515.8   
Aug-201138521.3   
Sep-201138783.7   
Oct-201139927.3   
Nov-201139573.8   
Dec-201139682.0   
Jan-201239705.3   
Feb-201239610.3   
Mar-201239498.6   
Apr-201239328.4   
May-201239910.3   
Jun-201240525.6   
Jul-201240559.6   
Aug-201239065.1   
Sep-201238903.9   
Oct-201238667.1   
Nov-201240145.2   
Dec-201238880.9   
Jan-201339476.7   
Feb-201340181.6   
Mar-201340214.9   
Apr-201340718.5   
May-201340451.7   
Jun-201339979.4   
Jul-201340314.4   
Aug-201341469.4   
Sep-201341079.9   
Oct-201340690.2   
Nov-201341422.6   
Dec-201341370.8   
Jan-201441301.2   
Feb-201442617.2   
Mar-201442909.2   
Apr-201443374.2   
May-201444679.2   
Jun-201443496.6   
Jul-201443622.9   
Aug-201443837.6   
Sep-201444366.5   
Oct-201445201.0   
Nov-201444430.3   
Dec-201444824.8   
Jan-201544695.3   
Feb-201544556.4   
Mar-201546561.0   
Apr-201545273.1   
May-201545144.3   
Jun-201545253.0   
Jul-201546025.6   
Aug-201546363.1   
Sep-201546353.1   
Oct-201545762.2   
Nov-201546205.6   
Dec-201546489.2   
Jan-201646825.3   
Feb-201646555.0   
Mar-201644744.3   
Apr-201644638.7   
May-201644529.7   
Jun-201644896.4   
Jul-201644611.1   
Aug-201645670.0   
Sep-201648036.7   
Oct-201645793.6   
Nov-201645383.2   
Dec-201645665.2   
Jan-201747072.3   
Feb-201747370.9   
Mar-201747440.7   
Apr-201748440.8   
May-201749569.3   
Jun-201749270.4   
Jul-201746754.8   
Aug-201746485.6   
Sep-201746931.0   
Oct-201746322.4   
Nov-201749354.5   
Dec-201750011.6   
Jan-201847927.0   
Feb-201848910.1   
Mar-201851677.2   
Apr-201850496.3   
May-201851479.9   
Jun-201851933.3   
Jul-201851547.0   
Aug-201851086.7   
Sep-201851191.1   
Oct-201850427.9   
Nov-201850126.3   
Dec-201851167.5   
Jan-201951934.4   
Feb-201951430.0   
Mar-201952688.5   
Apr-201951821.0   
May-201952622.6   
Jun-201951035.6   
Jul-201950972.3   
Aug-201951703.5   
Sep-201950404.5   
Oct-201950395.8   
Nov-201949475.2   
Dec-201949489.3   
Jan-202049744.150018.2348341.4451753.18
Feb-202050191.550201.5248211.4852273.7
Mar-202048579.850385.4848123.752753.56
Apr-202037139.550570.1248064.3253206.55
May-202036007.050755.4348025.7253640.3
Jun-202043437.050941.4348003.1854059.52
Jul-202048482.951128.147993.5554467.38
Aug-202048557.351315.4647994.5854866.12
Sep-202049654.051503.5148004.6255257.41
Oct-202050544.551692.2448022.4155642.52
Nov-202050236.251881.6748046.9656022.43
Dec-202049433.952071.7948077.4856397.94
Jan-202150073.552262.6148113.3356769.71
Feb-202148975.852454.1248153.9957138.26
Mar-202151991.552646.3448198.9957504.05
Apr-202150121.252839.2648247.9757867.46
May-202151537.853032.8948300.6158228.82
Jun-202151388.853227.2348356.6358588.41
Jul-202153623.153422.2848415.7858946.49
Aug-202153079.353618.0548477.8659303.27
Sep-202151610.853814.5348542.6759658.93
Oct-202154156.254011.7448610.0660013.66
Nov-2021 54209.6648679.8760367.61
Dec-2021 54408.3148751.9760720.92
figure 7.2
Text version
DateQuantitiesPoint EstimatesLower bound of CIUpper bound of CI
Jan-201076.04   
Feb-201077.94   
Mar-201082.70   
Apr-201080.34   
May-201082.37   
Jun-201083.21   
Jul-201083.75   
Aug-201084.67   
Sep-201084.56   
Oct-201084.08   
Nov-201083.66   
Dec-201084.18   
Jan-201186.73   
Feb-201183.94   
Mar-201187.24   
Apr-201186.68   
May-201187.17   
Jun-201186.27   
Jul-201187.70   
Aug-201188.15   
Sep-201187.12   
Oct-201189.20   
Nov-201187.73   
Dec-201189.49   
Jan-201289.16   
Feb-201289.41   
Mar-201290.23   
Apr-201289.35   
May-201289.74   
Jun-201290.72   
Jul-201292.08   
Aug-201289.69   
Sep-201289.86   
Oct-201288.71   
Nov-201291.84   
Dec-201290.01   
Jan-201390.82   
Feb-201391.43   
Mar-201391.12   
Apr-201393.16   
May-201392.73   
Jun-201390.86   
Jul-201390.89   
Aug-201392.91   
Sep-201391.67   
Oct-201390.86   
Nov-201393.32   
Dec-201391.94   
Jan-201490.19   
Feb-201492.19   
Mar-201491.77   
Apr-201492.77   
May-201495.99   
Jun-201494.09   
Jul-201494.34   
Aug-201494.72   
Sep-201495.31   
Oct-201496.81   
Nov-201494.81   
Dec-201495.77   
Jan-201594.21   
Feb-201593.88   
Mar-201597.07   
Apr-201594.12   
May-201594.58   
Jun-201594.63   
Jul-201593.36   
Aug-201594.19   
Sep-201592.64   
Oct-201593.28   
Nov-201593.80   
Dec-201593.29   
Jan-201693.38   
Feb-201694.53   
Mar-201694.00   
Apr-201694.42   
May-201693.50   
Jun-201693.59   
Jul-201692.18   
Aug-201693.94   
Sep-201699.10   
Oct-201693.08   
Nov-201692.23   
Dec-201693.17   
Jan-201797.16   
Feb-201797.07   
Mar-201795.83   
Apr-201796.86   
May-201798.78   
Jun-201798.81   
Jul-201796.39   
Aug-201795.83   
Sep-201798.24   
Oct-201796.00   
Nov-2017100.39   
Dec-2017101.73   
Jan-201898.14   
Feb-201899.24   
Mar-2018103.52   
Apr-2018101.33   
May-2018102.84   
Jun-2018101.44   
Jul-2018100.10   
Aug-201899.02   
Sep-201899.10   
Oct-201898.56   
Nov-201897.52   
Dec-201898.36   
Jan-2019100.21   
Feb-2019101.12   
Mar-2019102.60   
Apr-2019100.39   
May-2019101.88   
Jun-201999.12   
Jul-2019100.24   
Aug-2019101.23   
Sep-201998.89   
Oct-201998.85   
Nov-201997.77   
Dec-201997.70   
Jan-202097.2998.8834795.99835101.8553
Feb-202098.3799.0834395.90724102.3648
Mar-202093.0099.283895.84008102.8513
Apr-202074.0399.4845895.79166103.3199
May-202070.7999.6857695.75842103.7742
Jun-202086.8599.8873595.73782104.2167
Jul-202095.26100.089395.72795104.6494
Aug-202096.15100.291795.72734105.0738
Sep-202097.82100.494695.73482105.4909
Oct-202099.08100.697895.74946105.9018
Nov-202099.16100.901495.7705106.3072
Dec-202098.63101.105595.7973106.7077
Jan-202199.12101.309995.82932107.104
Feb-202196.35101.514895.86611107.4963
Mar-2021103.60101.720195.90727107.8852
Apr-202197.98101.925895.95248108.2709
May-2021101.64102.131996.00142108.6538
Jun-202199.76102.338496.05385109.0342
Jul-2021101.22102.545496.10953109.4122
Aug-202198.86102.752796.16825109.7881
Sep-202194.83102.960596.22983110.162
Oct-2021100.1214103.168796.29412110.5342
Nov-2021 103.377496.36095110.9047
Dec-2021 103.586496.4302111.2737
figure 7.3
Text version
DatePricesPoint EstimatesLower bound of CIUpper bound of CI
Jan-201082.07   
Feb-201082.80   
Mar-201080.22   
Apr-201079.68   
May-201082.06   
Jun-201081.64   
Jul-201081.89   
Aug-201082.37   
Sep-201082.26   
Oct-201082.21   
Nov-201082.31   
Dec-201082.23   
Jan-201182.85   
Feb-201183.08   
Mar-201183.59   
Apr-201183.47   
May-201184.57   
Jun-201184.73   
Jul-201183.56   
Aug-201185.33   
Sep-201186.95   
Oct-201187.42   
Nov-201188.10   
Dec-201186.61   
Jan-201286.98   
Feb-201286.53   
Mar-201285.51   
Apr-201285.98   
May-201286.85   
Jun-201287.25   
Jul-201286.04   
Aug-201285.07   
Sep-201284.55   
Oct-201285.14   
Nov-201285.38   
Dec-201284.38   
Jan-201384.89   
Feb-201385.83   
Mar-201386.20   
Apr-201385.37   
May-201385.20   
Jun-201385.93   
Jul-201386.63   
Aug-201387.19   
Sep-201387.53   
Oct-201387.47   
Nov-201386.69   
Dec-201387.89   
Jan-201489.44   
Feb-201490.29   
Mar-201491.32   
Apr-201491.32   
May-201490.91   
Jun-201490.28   
Jul-201490.31   
Aug-201490.40   
Sep-201490.93   
Oct-201491.21   
Nov-201491.54   
Dec-201491.44   
Jan-201592.69   
Feb-201592.67   
Mar-201593.74   
Apr-201593.98   
May-201593.29   
Jun-201593.45   
Jul-201596.30   
Aug-201596.25   
Sep-201597.65   
Oct-201595.78   
Nov-201596.17   
Dec-201597.33   
Jan-201697.95   
Feb-201696.19   
Mar-201692.99   
Apr-201692.35   
May-201693.04   
Jun-201693.71   
Jul-201694.52   
Aug-201694.95   
Sep-201694.69   
Oct-201696.12   
Nov-201696.12   
Dec-201695.75   
Jan-201794.66   
Feb-201795.34   
Mar-201796.71   
Apr-201797.69   
May-201798.03   
Jun-201797.38   
Jul-201794.71   
Aug-201794.73   
Sep-201793.28   
Oct-201794.24   
Nov-201796.01   
Dec-201796.01   
Jan-201895.35   
Feb-201896.24   
Mar-201897.48   
Apr-201897.32   
May-201897.76   
Jun-201899.98   
Jul-2018100.56   
Aug-2018100.74   
Sep-2018100.88   
Oct-201899.90   
Nov-2018100.38   
Dec-2018101.56   
Jan-2019101.04   
Feb-201999.53   
Mar-2019100.43   
Apr-2019100.98   
May-2019100.80   
Jun-2019100.24   
Jul-201999.14   
Aug-201999.78   
Sep-201999.50   
Oct-201999.52   
Nov-201998.78   
Dec-201999.02   
Jan-202099.7999.0660597.40049100.7601
Feb-202099.9999.3391496.83456101.9085
Mar-2020102.3099.4588996.55188102.4534
Apr-202098.0399.5811296.36548102.9041
May-202099.0399.7426596.21003103.405
Jun-202097.2299.9087996.07086103.9
Jul-202099.20100.065995.95098104.3572
Aug-202098.67100.220995.84985104.7913
Sep-202099.25100.378395.76369105.2153
Oct-202099.64100.536795.68922105.6298
Nov-202098.93100.69595.62477106.0341
Dec-202098.04100.853395.56929106.4294
Jan-202198.67101.011995.52182106.8174
Feb-202199.27101.170895.48145107.1991
Mar-202198.00101.329995.44742107.575
Apr-202199.92101.489395.41915107.9456
May-202199.03101.648995.39613108.3116
Jun-2021100.60101.808895.37791108.6734
Jul-2021103.47101.96995.36413109.0313
Aug-2021104.86102.129495.35444109.3856
Sep-2021106.29102.2995.34854109.7368
Oct-2021105.6278102.450995.34619110.085
Nov-2021 102.612195.34714110.4305
Dec-2021 102.773595.3512110.7735

5. Impact of export and import prices on the Canadian economy

There are two reasons why export and import prices are important for the Canadian economy. The first is that both export and import prices are eventually passed through to domestic prices. If Canadian exporters are price takers then the mechanism is relatively straightforward. When the global price increases, then it is more profitable for exporters to sell their goods on the global market rather than the domestic market. Thereafter, the domestic market adjusts to the new world price. Likewise, when the global price falls, goods should become cheaper in the domestic market. The mechanisms are more complicated if Canadian exporters are price setters rather than price takers, but in general, higher export prices are associated with higher domestic prices. While there is not much of a correlation between export prices and consumer prices, figure 8 shows that industrial prices, as measured by the industrial producer price index (IPPI), and export prices move nearly one-for-one. Consumer price inflation has made headlines recently by being at their highest point since the 1990’s and close to 5%. However, industrial price inflation is actually much higher and as industrial prices rose 12.4% year-over-year as of October.

Figure 8: Export prices excluding oil and IPPI excluding energy Footnote 9

figure 8
Text version
DateIPPI ex. EnergyExport prices ex. Oil
01-Jan0.0323370.073956
01-Feb0.0373720.062336
01-Mar0.0481130.09608
01-Apr0.0486210.115927
01-May0.0354440.064719
01-Jun0.0460790.090292
01-Jul0.0437750.058898
01-Aug0.0506860.077013
01-Sep0.0489760.100538
01-Oct0.033380.085583
01-Nov0.0318140.086362
01-Dec0.0203960.067284
01-Jan0.018324-0.02101
01-Feb0.016130.004434
01-Mar0.010207-0.00195
01-Apr0.006479-0.0069
01-May0.0080240.000667
01-Jun0.007828-0.01298
01-Jul0.0029930.020879
01-Aug-0.01344-0.01431
01-Sep-0.01132-0.03324
01-Oct-0.001-0.01156
01-Nov0.000571-0.01245
01-Dec0.0065460.01121
01-Jan0.0028240.031327
01-Feb0.0021560.018563
01-Mar0.0046030.021487
01-Apr0.0005260.019035
01-May-0.000920.010431
01-Jun-0.00329-0.00266
01-Jul-0.000940.00789
01-Aug0.0083290.00919
01-Sep-0.0010.000881
01-Oct-0.00212-0.00575
01-Nov-0.00221-0.00213
01-Dec0.0018180.012904
01-Jan0.0144650.052626
01-Feb0.0184230.056524
01-Mar0.0246650.04285
01-Apr0.027160.025311
01-May0.025060.031247
01-Jun0.0245890.033929
01-Jul0.0310260.02369
01-Aug0.0310550.036979
01-Sep0.0340590.043136
01-Oct0.0366520.034666
01-Nov0.0346760.03975
01-Dec0.0340080.022883
01-Jan0.0356780.005345
01-Feb0.0354720.017576
01-Mar0.028610.012355
01-Apr0.0263930.019901
01-May0.0295460.016403
01-Jun0.0310870.029152
01-Jul0.0377490.054847
01-Aug0.0415830.060305
01-Sep0.0417970.063792
01-Oct0.0361120.054744
01-Nov0.03130.039568
01-Dec0.0346220.048624
01-Jan0.035930.045251
01-Feb0.020489-0.00423
01-Mar0.007761-0.01032
01-Apr0.00173-0.01007
01-May0.0076130.003973
01-Jun0.0073060.004624
01-Jul0.0085630.005421
01-Aug0.001468-0.01295
01-Sep0.003243-0.00039
01-Oct0.0095950.017075
01-Nov0.0210550.028373
01-Dec0.0112450.02159
01-Jan0.0010010.009772
01-Feb0.0111820.028114
01-Mar0.0330790.056465
01-Apr0.0452820.081872
01-May0.0450050.077956
01-Jun0.0345910.05359
01-Jul0.0096110.000726
01-Aug0.0126830.012087
01-Sep-0.00081-0.01318
01-Oct0.006987-0.00576
01-Nov0.006715-0.0007
01-Dec0.010585-0.00368
01-Jan0.009693-0.0062
01-Feb0.012140.006266
01-Mar0.0083650.004731
01-Apr0.004108-0.0065
01-May0.004045-0.00541
01-Jun0.0197980.027616
01-Jul0.0361070.061456
01-Aug0.0298130.047907
01-Sep0.0412010.051174
01-Oct0.0307240.040203
01-Nov0.0268110.037448
01-Dec0.0303950.039435
01-Jan0.0279850.035066
01-Feb0.020650.034475
01-Mar0.0161070.029625
01-Apr0.0185820.020392
01-May0.0084210.011917
01-Jun-0.00526-0.01897
01-Jul-0.0066-0.02366
01-Aug0.004773-0.01129
01-Sep0.003339-0.00013
01-Oct0.000348-0.00514
01-Nov0.000263-0.00917
01-Dec-0.00124-0.01249
01-Jan0.002694-0.00853
01-Feb0.010501-0.0146
01-Mar0.01599-0.00964
01-Apr0.012276-0.022
01-May0.018802-0.00556
01-Jun0.017510.010841
01-Jul0.0297690.022005
01-Aug0.0337120.022994
01-Sep0.0483540.030075
01-Oct0.048910.036844
01-Nov0.0431550.039241
01-Dec0.0504510.044442
01-Jan0.0642610.074103
01-Feb0.0816190.106844
01-Mar0.0856330.085457
01-Apr0.1097910.133768
01-May0.1354620.12901
01-Jun0.1461450.154218
01-Jul0.1365840.153767
01-Aug0.1189420.134708
01-Sep0.1179670.134987
01-Oct0.1244070.14851

Import prices also have a positive correlation with Canadian domestic prices. When the global price of imports increase, Canadians and Canadian firms either have to pay the new global price for those goods, substitute to a domestic supplier, or use a substitute good. With only mild economic assumptions, in each case the price paid for the goods increases. As seen in figure 9, compared to export prices and industrial prices, import prices and consumer prices have had a much milder increase in the past year.

Figure 9: Import prices excluding oil, the IPPI excluding energy, and CPI goods excluding energy

figure 9
Text version
DateCPI goods ex. energyIPPI ex. EnergyImport prices ex. Oil
Jan-10   
Feb-10   
Mar-10   
Apr-10   
May-10   
Jun-10   
Jul-10   
Aug-10   
Sep-10   
Oct-10   
Nov-10   
Dec-10   
Jan-11-0.002310.0323370.00209973
Feb-11-0.004840.037372-0.005618758
Mar-110.0074910.0481130.023432698
Apr-11-0.001880.0486210.027871389
May-11-0.00520.0354440.011042676
Jun-11-0.011960.0460790.022128025
Jul-11-0.006190.0437750.003177648
Aug-11-0.004690.0506860.023898616
Sep-110.0008930.0489760.041706874
Oct-11-0.000490.033380.049476264
Nov-110.0023620.0318140.056510712
Dec-11-0.000620.0203960.046825204
Jan-120.003270.0183240.040833546
Feb-120.0018680.016130.031982464
Mar-12-0.010150.0102070.021863593
Apr-120.0023640.0064790.032925135
May-120.0090910.0080240.027745535
Jun-120.011840.0078280.032739116
Jul-120.0087120.0029930.033703405
Aug-120.007021-0.01344-0.008420738
Sep-12-0.00287-0.01132-0.029122541
Oct-124.73E-05-0.001-0.028720773
Nov-120.0013590.000571-0.030081709
Dec-120.0029140.006546-0.030705783
Jan-13-0.001170.002824-0.024800532
Feb-130.0291270.002156-0.00463225
Mar-130.0282960.0046030.013332442
Apr-130.0234620.000526-0.000660226
May-130.020418-0.00092-0.008489108
Jun-130.022354-0.00329-0.015195764
Jul-130.02041-0.000940.010456263
Aug-130.0216420.0083290.025398993
Sep-130.024819-0.0010.031132973
Oct-130.025475-0.002120.032184087
Nov-130.019521-0.002210.02409486
Dec-130.0209210.0018180.049281399
Jan-140.0207880.0144650.064682983
Feb-14-0.001390.0184230.058846086
Mar-140.0028890.0246650.064824056
Apr-140.0029470.027160.07699807
May-140.00640.025060.066316472
Jun-140.0132260.0245890.052604537
Jul-140.0115180.0310260.037208105
Aug-140.0126880.0310550.0428672
Sep-140.0162040.0340590.04972508
Oct-140.0163970.0366520.051940676
Nov-140.0245460.0346760.059638632
Dec-140.0310060.0340080.0530432
Jan-150.0453660.0356780.044322832
Feb-150.0376620.0354720.043052472
Mar-150.0410480.028610.040990719
Apr-150.0423770.0263930.03419227
May-150.0433420.0295460.031211744
Jun-150.0388460.0310870.044023892
Jul-150.0392650.0377490.077366657
Aug-150.0386570.0415830.07493135
Sep-150.0403520.0417970.084808322
Oct-150.0411440.0361120.056745986
Nov-150.0378230.03130.058489394
Dec-150.033970.0346220.069554117
Jan-160.0289680.035930.067172267
Feb-160.0323570.0204890.040210413
Mar-160.0305350.007761-0.001796885
Apr-160.029660.00173-0.007732983
May-160.0250480.0076130.010135265
Jun-160.0241940.0073060.010443443
Jul-160.0235780.008563-0.010958929
Aug-160.0206060.001468-0.010154847
Sep-160.0148880.003243-0.028353195
Oct-160.0064690.0095950.004823953
Nov-160.0030950.021055-0.000976779
Dec-160.0011370.011245-0.018667016
Jan-17-0.001590.001001-0.038587388
Feb-17-0.00250.011182-0.018021613
Mar-17-0.005160.0330790.030757471
Apr-17-0.009010.0452820.046312477
May-17-0.001830.0450050.047080116
Jun-170.0007630.0345910.034341925
Jul-170.0037420.009611-0.001997459
Aug-170.0033890.012683-0.002432888
Sep-170.003066-0.00081-0.01615926
Oct-170.0074630.006987-0.018738864
Nov-170.0100040.006715-0.000961319
Dec-170.0147560.0105850.000674066
Jan-180.0063570.0096930.003235314
Feb-180.0067940.012140.012112864
Mar-180.0041050.0083650.00939606
Apr-180.0111160.004108-0.000244247
May-18-0.000160.004045-0.004937968
Jun-180.003740.0197980.026792605
Jul-180.000470.0361070.062285132
Aug-180.0041690.0298130.053184237
Sep-180.0051020.0412010.076763802
Oct-180.0078740.0307240.057596604
Nov-180.0097170.0268110.043266161
Dec-180.0115460.0303950.060053334
Jan-190.0186950.0279850.062568672
Feb-190.0254190.020650.036289237
Mar-190.0269630.0161070.026518107
Apr-190.0212360.0185820.036764239
May-190.0296830.0084210.032699283
Jun-190.026912-0.005260.001637127
Jul-190.029328-0.0066-0.015175071
Aug-190.0277510.004773-0.002925828
Sep-190.032580.003339-0.010784879
Oct-190.0286730.000348-0.005590265
Nov-190.0290880.000263-0.014790508
Dec-190.021897-0.00124-0.026887322
Jan-200.0217370.002694-0.011087787
Feb-200.0149980.0105010.004852979
Mar-200.0211080.015990.025059057
Apr-200.023840.012276-0.017882062
May-200.0177310.018802-0.001459828
Jun-200.0191010.01751-0.008269946
Jul-200.0166470.0297690.009364022
Aug-200.0124370.033712-0.006301414
Sep-200.0075770.0483540.006318304
Oct-200.0132330.048910.010005463
Nov-200.0122880.0431550.008537364
Dec-200.0084220.050451-0.003844373
Jan-210.0070730.064261-0.007921717
Feb-210.0069980.081619-0.00590183
Mar-210.0021850.085633-0.039846703
Apr-210.0052360.1097910.006775308
May-210.0164470.135462-0.01764766
Jun-210.0181410.1461450.012188456
Jul-210.0242950.1365840.03085261
Aug-210.03130.1189420.059006894
Sep-210.035110.1179670.059795652
Oct-210.0305880.1244070.049242417

Import prices appear to only have a middling correlation with both consumer prices (as measured by the consumer price index) and industrial prices. Importantly, this does not mean the import prices do not affect domestic prices. Import prices must eventually be passed through to domestic prices. What it means is that there are factors other than import prices that go into determining domestic prices. While this statement may seem trivial, it highlights the tightness of the correlation between industrial prices and export prices in figure 8. Not only do export prices and domestic industrial prices move together, but there doesn’t appear to be much room for other factors to play a role in determining industrial prices. Perhaps the tight correlation is expected as merchandise exports are a subset of industrial production whereas only about 25% of CPI goods are imported.Footnote 10 Footnote 11 While two graphs is insufficient evidence to make any broad claims about the price determination in the Canadian economy, they are suggestive of a tight mechanism.

The second reason why trade prices matter is the price of exports relative to the price of imports, known as the terms of trade. If the price of exports increases faster than the price of imports, a terms of trade appreciation, then Canadians are able to consume more imports for the same quantity of exports. In other words, holding export quantities constant, a terms of trade appreciation leads to an increase in utility for Canadians. Conversely, if the price of imports increases faster than the price of exports, a terms of trade depreciation, then Canadians must reduce consumption of imports—resulting in a decrease in utility—or export a higher quantity to compensate for the lower prices. Figure 10 has the official terms of trade from the National accounts (at a quarterly basis) as well as the terms of trade implied by the monthly merchandise price series used in this paper. Footnote 12

Figure 10: Canada’s terms of trade

figure 10
Text version
DateCalculated terms of tradeOfficial ToT (quarterly)
Jan-2010102.6084605Q1 2010105.5202
Feb-2010102.5266556Q2 2010103.8217
Mar-2010103.3777394Q3 2010102.6539
Apr-2010102.5714133Q4 2010105.2017
May-2010102.1698326Q1 2011107.8556
Jun-2010101.8111939Q2 2011108.5987
Jul-2010100.8823874Q3 2011107.1125
Aug-2010101.2401155Q4 2011107.5372
Sep-2010101.1594304Q1 2012107.0064
Oct-2010101.6974705Q2 2012104.5648
Nov-2010103.0580837Q3 2012105.5202
Dec-2010104.3430892Q4 2012107.5372
Jan-2011109.9154822Q1 2013107.6433
Feb-2011108.1125773Q2 2013107.2187
Mar-2011109.1540581Q3 2013106.9002
Apr-2011111.5032254Q4 2013105.6263
May-2011109.1491724Q1 2014107.7495
Jun-2011109.8496733Q2 2014106.1571
Jul-2011106.8172873Q3 2014105.5202
Aug-2011106.5730348Q4 2014102.1231
Sep-2011107.4393116Q1 201599.15074
Oct-2011106.8629414Q2 201599.36306
Nov-2011107.8709812Q3 201597.23992
Dec-2011108.5271943Q4 201596.07219
Jan-2012106.2391332Q1 201694.26752
Feb-2012107.64315Q2 201696.60297
Mar-2012108.3111291Q3 201697.55839
Apr-2012106.4236037Q4 201699.04459
May-2012104.8577221Q1 2017100.7431
Jun-2012104.2082841Q2 201799.36306
Jul-2012104.3104594Q3 201799.78769
Aug-2012106.0135112Q4 2017101.6985
Sep-2012107.4196258Q1 2018101.8047
Oct-2012108.7953552Q2 2018102.7601
Nov-2012107.4485133Q3 2018101.8047
Dec-2012109.3187695Q4 201896.70913
Jan-2013108.9123463Q1 201999.89384
Feb-2013108.3427073Q2 2019100.4246
Mar-2013108.7354772Q3 201999.46921
Apr-2013109.0458128Q4 2019100.2123
May-2013108.1950688Q1 202095.32909
Jun-2013106.9508189Q2 202092.46285
Jul-2013108.2483464Q3 202098.30149
Aug-2013108.771687Q4 2020100.7431
Sep-2013107.4709872Q1 2021107.2187
Oct-2013106.689331Q2 2021111.9958
Nov-2013106.4055635Q3 2021110.1911
Dec-2013106.6416713  
Jan-2014109.2040264  
Feb-2014111.0978875  
Mar-2014109.7677631  
Apr-2014106.8677313  
May-2014107.0447854  
Jun-2014107.4096525  
Jul-2014106.6748814  
Aug-2014106.5243941  
Sep-2014105.6277926  
Oct-2014104.1863318  
Nov-2014103.7074594  
Dec-2014100.4835757  
Jan-201598.76261438  
Feb-2015100.8780622  
Mar-201598.68947548  
Apr-201598.20285151  
May-201599.3761239  
Jun-2015100.2006757  
Jul-201598.19545517  
Aug-201597.29803137  
Sep-201595.05497485  
Oct-201596.59632497  
Nov-201595.862869  
Dec-201594.21541538  
Jan-201693.8220919  
Feb-201692.40591594  
Mar-201694.6627222  
Apr-201695.63216477  
May-201696.57379556  
Jun-201696.83280051  
Jul-201697.74825623  
Aug-201696.45556779  
Sep-201698.08438267  
Oct-201698.37345203  
Nov-201699.11566843  
Dec-2016100.446543  
Jan-2017102.223504  
Feb-2017101.3068401  
Mar-2017100.1183875  
Apr-201799.6681891  
May-201799.73744835  
Jun-201798.20048942  
Jul-201797.76805062  
Aug-201798.56491591  
Sep-201799.62255216  
Oct-2017100.3173625  
Nov-2017101.0067552  
Dec-2017101.8605562  
Jan-2018102.1689234  
Feb-2018100.7492933  
Mar-2018100.793429  
Apr-2018101.7082464  
May-2018103.2398301  
Jun-2018102.7478125  
Jul-2018102.3474946  
Aug-2018101.3804811  
Sep-2018100.4109412  
Oct-2018100.9598324  
Nov-201894.17145074  
Dec-201891.02036832  
Jan-201996.16237256  
Feb-2019100.9940961  
Mar-2019102.0696188  
Apr-2019102.0441386  
May-2019101.3052071  
Jun-201998.59993793  
Jul-201999.19730081  
Aug-201998.94831663  
Sep-2019100.0163991  
Oct-201999.7403787  
Nov-2019100.9614184  
Dec-201999.97066559  
Jan-202098.09248584  
Feb-202095.08811014  
Mar-202089.68444919  
Apr-202084.83798657  
May-202088.95966382  
Jun-202097.57221371  
Jul-202097.35314651  
Aug-202098.87474568  
Sep-202097.95153497  
Oct-202099.02315018  
Nov-202099.70569841  
Dec-2020102.0682229  
Jan-2021104.5650931  
Feb-2021107.8979315  
Mar-2021109.6975632  
Apr-2021110.3775797  
May-2021113.0938645  
Jun-2021114.5436261  
Jul-2021112.1069196  
Aug-2021109.2621882  
Sep-2021109.8103979  
Oct-2021114.3018284  
Nov-2021   
Dec-2021   

The calculated and official data tell identical stories. Canada’s terms of trade deteriorated half-way through the 2010’s, corresponding to the decrease in the price of oil, before partially rebounding in the latter half of the decade. During the pandemic, export prices fell more than import prices, before export prices came roaring back. According to the official statistics, the terms of trade in the second quarter of 2021 was at the highest level since the second quarter in 2008, and the second highest level ever. Complicating the narrative is that export quantities have not stayed constant; regardless, the current trade prices by themselves are good for Canadians—each unit of exports is able to buy more imports.

6. Conclusion

This paper has explored how trade prices and quantities have changed over the course of the COVID-19 pandemic. In value terms, merchandise exports have rebounded from the pandemic dip experienced at the start of the pandemic and are 13% above pre-pandemic levels. However, this is the product of export prices being 21% above their pre-pandemic level and export quantities being 6% below their pre-pandemic level. The increase in export prices and decrease in export quantities is not the consequence of any good in particular, but rather are broad based. Importantly, the high prices and low quantities are not a continuance of the initial pandemic drop—both of these trends only emerged in 2021. The import side is milder, prices are 5% above pre-pandemic levels, while import quantities are roughly equal to their pre-pandemic level.

Export prices have increased more than import prices, leading to a terms of trade appreciation and one of the highest terms of trade levels ever. This is a net benefit as Canadians can consume more imports while producing the same amount of exports. Increasing import prices raises the price for Canadian consumers and businesses—however, the correlation is only mild suggesting that factors other than import prices play important roles for determining domestic prices. Export prices, on the other hand, have a tight correlation with industrial prices suggesting that when world prices for Canadian exports increase, Canadian industrial prices increase almost the same amount.

7. Appendix 1: Largest contributions to growth

Table 3: Largest contribution to growth for export quantities

2019-May 2020May 2020-October 20212019-October 2021
AmountProductAmountProductAmountProduct
-22%total21%total-6.2%total
Largest positive contributions
0.3 p.p.pharmaceutical6.2 p.p.cars and light trucks0.8 p.p.crude oil/bitumen
0.3 p.p.wheat3.4 p.p.auto parts0.6 p.p.iron & steel products
0.2 p.p.fruit/nuts/vegetables/pulses3.2 p.p.crude oil and bitumen0.4 p.p.miscellaneous goods and supplies
0.2 p.p.other crop products1.3 p.p.iron & steel products0.4 p.p.pharmaceutical
0.1 p.p.intermediate food products0.7 p.p.potash0.3 p.p.iron ores and concentrates
0.1 p.p.canola0.7 p.p.other machinery0.3 p.p.other food products
0.1 p.p.electricity0.6 p.p.iron ores and concentrates0.3 p.p.potash
0.1 p.p.asphalt0.5 p.p.other food products0.3 p.p.other crop products
0.1 p.p.copper ores0.5 p.p.medium/heavy trucks0.3 p.p.plastic resins
0.1 p.p.animal feed0.5 p.p.miscellaneous goods and supplies0.2 p.p.electronic and electrical parts
Largest negative contributions
-8.3 p.p.cars and light trucks-1.0 p.p.wheat-3.5 p.p.cars and light trucks
-2.7 p.p.auto parts-0.9 p.p.precious metals-0.9 p.p.precious metals
-1.7 p.p.crude oil and bitumen-0.7 p.p.refined energy products-0.7 p.p.refined energy products
-0.9 p.p.other machinery-0.4 p.p.fruit/nuts/vegetables/pulses-0.5 p.p.aircraft parts
-0.7 p.p.aircraft parts-0.3 p.p.electricity-0.5 p.p.wheat
-0.7 p.p.medium/heavy trucks-0.3 p.p.intermediate food products-0.4 p.p.other machinery
-0.6 p.p.balance of payments adjustments-0.2 p.p.copper ores-0.3 p.p.balance of payments adjustments
-0.4 p.p.lubricants/other petroleum products-0.2 p.p.nickel and nickel alloys-0.3 p.p.medium/heavy trucks
-0.4 p.p.boats/transportation products-0.1 p.p.special transactions trade-0.3 p.p.boats/transportation products
-0.4 p.p.furniture and fixtures-0.1 p.p.machinery and equipment-0.3 p.p.aircraft

Table 4: Largest contribution to growth for export prices

2019-May 2020May 2020-October 20212019-October 2021
AmountProductAmountProductAmountProduct
-12%total37%total21%total
Largest positive contributions
1.3 p.p.precious metals19 p.p.crude oil/bitumen5.0 p.p.crude oil/bitumen
0.4 p.p.meat products2.0 p.p.natural gas1.4 p.p.lumber and other sawmill products
0.2 p.p.fruit/nuts/vegetables1.8 p.p.coal1.4 p.p.natural gas
0.2 p.p.balance of payments adjustments1.5 p.p.lumber and other sawmill products1.3 p.p.coal
0.2 p.p.wheat1.2 p.p.refined energy products0.9 p.p.aluminum and aluminum alloys
0.2 p.p.other food products1.2 p.p.aluminum/alloys0.7 p.p.precious metals
0.2 p.p.miscellaneous goods and supplies0.8 p.p.iron & steel products0.7 p.p.iron & steel products
0.1 p.p.lumber and other sawmill products0.8 p.p.natural gas liquids0.6 p.p.intermediate food products
0.1 p.p.iron ores and concentrates0.7 p.p.intermediate food products0.5 p.p.waste and scrap of metal
0.1 p.p.aircraft0.7 p.p.waste and scrap of metal0.5 p.p.balance of payments adjustment
Largest negative contributions
-12.1 p.p.crude oil/bitumen-0.7 p.p.precious metals-0.2 p.p.pharmaceutical
-0.9 p.p.refined energy products-0.3 p.p.pharmaceutical-0.1 p.p.electronic and electrical parts
-0.4 p.p.lubricants/other petroleum products-0.1 p.p.aircraft parts-0.1 p.p.auto parts
-0.4 p.p.natural gas-0.1 p.p.aircraft-0.1 p.p.fabric, fibre/ yarn/leather
-0.2 p.p.coal-0.1 p.p.medical machinery0.0 p.p.computers and computer parts
-0.2 p.p.pulp and paper-0.1 p.p.auto parts0.0 p.p.aircraft parts
-0.2 p.p.asphalt-0.1 p.p.other food products0.0 p.p.aircraft
-0.2 p.p.plastic resins-0.1 p.p.electronic and electrical parts0.0 p.p.medical machinery
-0.2 p.p.natural gas liquids-0.1 p.p.fabric, fibre/ yarn/leather0.0 p.p.alcoholic beverages
-0.2 p.p.aluminum/alloys-0.1 p.p.computers/parts0.0 p.p.tires

Table 5: Largest contribution to growth for import quantities

2019-May 2020May 2020-October 20212019-October 2021
AmountProductAmountProductAmountProduct
-29%total41%total0.2%total
Largest positive contributions
2.2 p.p.precious metals12.3 p.p.cars and light trucks0.9 p.p.miscellaneous goods and supplies
0.6 p.p.carpets, other textile prod4.9 p.p.auto parts0.8 p.p.special transactions trade
0.4 p.p.other metal ores and concentrates2.4 p.p.clothing, footwear and accessories0.5 p.p.precious metals
0.4 p.p.semi-finished non-ferrous metals1.7 p.p.audio & video equipment0.5 p.p.computers and parts
0.2 p.p.pharmaceutical1.7 p.p.medium/heavy trucks0.5 p.p.pharmaceutical
0.1 p.p.parts of rail roll stock1.5 p.p.refined energy products0.3 p.p.electrical components
0.1 p.p.computers and parts1.3 p.p.miscellaneous goods and supplies0.3 p.p.cars and light trucks
0.1 p.p.nuclear fuel and other energy products1.3 p.p.parts for machinery & equipment0.3 p.p.electronic and electrical parts
0.1 p.p.fertilizers, pesticides & other chemicals1.2 p.p.special transactions trade0.3 p.p.appliances
0.1 p.p.alcoholic beverages1.2 p.p.electrical components0.3 p.p.medical equipment
Largest negative contributions
-8.4 p.p.cars and light trucks-2.4 p.p.precious metals-2.0 p.p.auto parts
-5.4 p.p.auto parts-0.8 p.p.other metal ores and concentrates-1.0 p.p.crude oil and bitumen
-1.5 p.p.medium/heavy trucks-0.7 p.p.carpets, other textile-0.8 p.p.aircraft parts
-1.4 p.p.clothing, footwear and accessories-0.5 p.p.semi-finished non-ferrous metals-0.4 p.p.other machinery
-1.4 p.p.refined energy products-0.3 p.p.parts of rail roll stock-0.3 p.p.iron & steel products
-1.2 p.p.other machinery-0.3 p.p.fertilizers, other chemicals-0.3 p.p.refined energy products
-1.2 p.p.aircraft parts-0.1 p.p.other food products-0.3 p.p.medium/heavy trucks
-1.2 p.p.audio/video equipment-0.1 p.p.crude oil/bitumen-0.3 p.p.aircraft
-0.9 p.p.crude oil and bitumen-0.1 p.p.alcoholic beverages-0.3 p.p.lubricants/other petroleum products
-0.8 p.p.parts for machinery & equipment-0.1 p.p.dyes and pigments, and petrochemicals-0.3 p.p.balance of payment adjustments

Table 6: Largest contributions to growth for import prices

2019-May 2020May 2020-October 20212019-October 2021
AmountProductAmountProductAmountProduct
-1.2%total6.7%total5.4%total
Largest positive contributions
0.3 p.p.other metal ores and concentrates2.2 p.p.crude oil and bitumen1.1 p.p.iron & steel products
0.3 p.p.waste and scrap of metal1.2 p.p.iron & steel products0.6 p.p.other metal ores and concentrates
0.2 p.p.cleaning products0.8 p.p.plastic resins0.6 p.p.plastic resins
0.1 p.p.computers and parts0.7 p.p.refined energy products0.4 p.p.refined energy products
0.1 p.p.meat products0.7 p.p.lubricants/other petroleum prod0.4 p.p.precious metals
0.1 p.p.aircraft parts0.5 p.p.semi-finished  nonferrous metals0.3 p.p.lubricants/other petroleum products
0.1 p.p.fruit/nuts/vegetables/pulses0.5 p.p.precious metals0.3 p.p.balance of payments adjustments
0.1 p.p.auto parts0.5 p.p.fertilizers, other chemical products0.3 p.p.waste and scrap of metal
0.1 p.p.pharmaceutical0.4 p.p.natural gas0.3 p.p.fertilizers, chemicals
0.1 p.p.other machinery0.4 p.p.other metal ores and concentrates0.3 p.p.natural gas
Largest negative contributions
-1.9 p.p.crude oil/bitumen-0.5 p.p.cars & light trucks-0.4 p.p.cars and light trucks
-0.4 p.p.semi-finished  nonferrous metals-0.3 p.p.medical equipment-0.3 p.p.audio/video equipment
-0.3 p.p.lubricants/other petroleum products-0.3 p.p.computers and parts-0.2 p.p.medical equipment
-0.3 p.p.refined energy products-0.3 p.p.audio & video equipment-0.2 p.p.electrical components
-0.2 p.p.plastic resins-0.3 p.p.electrical components-0.2 p.p.miscellaneous goods and supplies
-0.2 p.p.natural gas-0.2 p.p.miscellaneous goods and supplies-0.2 p.p.computers and computer peripherals
-0.2 p.p.fertilizers, other chemicals-0.2 p.p.pharmaceutical-0.1 p.p.electronic and electrical parts
-0.1 p.p.precious metals-0.2 p.p.fruits/nuts/vegetable-0.1 p.p.non-metal minerals
-0.1 p.p.iron & steel products-0.1 p.p.electronic and electrical parts-0.1 p.p.pharmaceutical
-0.1 p.p.dyes and pigments, and petrochemicals-0.1 p.p.non-metallic mineral products-0.1 p.p.appliances

8. Appendix 2: Index numbers

A brief description of the methodology of price and quantity indexes will be given as they are necessary to understand some of the arguments put forth in this paper. The basic index number problem is decomposing the change in value, between period t-1 and period t, into a change in quantity and a change in price. Formally:

The inner product of the price vector and the quantity vector at time t, divided by the same inner product at time t-1 is equal to a price index and a quantity index, both of which are functions of prices at time t-1 and t, and quantities at t-1 a t.

Where pt .qt is the inner product of the price and quantity vector and is equivalent to Σni pit .qit. A functional form is then needed for either the price or quantity formula, and then the other is determined implicitly. Statistics Canada chooses to use the Laspeyres formula, with a base year of 2012, for the quantity index, and a Paasche formula for the price index. Formally:

The Laspeyres quantity index is equal to the inner product of the price vector in 2012 and the quantity vector at time t, divided by the inner product of the price and quantity vector in 2012

The Paasche price index is equal to the inner product to the price and quantity vector at time t, divided by the inner product of the price vector in 2012 and the quantity vector at time t.

The problem with using the Laspeyres and Paasche indexes is that they ignore the substitution effect. The further away is the base year, the larger the effect tends to be. To see why this is an issue, the Laspeyres quantity index can be re-written as a share weighted average of quantity relatives:

The Laspeyres quantity index is equal to the sum from I to N of prices in 2012 and quantities at time t, divided by the sum of prices and quantities in 2012.

The Laspeyres quantity index is equal to the sum from I to N of prices and quantities in 2012 multiplied by the quantity at time t divided by the quantity in 2012, divided by the sum of prices and quantities in 2012.

Which is equal to the sum from I to N of the value share in 2012 multiplied by the quantity at time t divided by the quantity in 2012.

Using 2012 shares of trade to calculate trade effects in 2021 could lead to misleading results. For example, crude oil and bitumen had a 15.6% share of Canadian exports in 2012; by 2019, the share had dropped to 14.1%. By using the 2012 share, the index would be overstating the effect crude oil played on quantities, while understating other items. Likewise the Paasche price index can be written as the current period share weighted harmonic mean of price relatives:

The Paasche price index is equal to the sum from I to N of price multiplied by quantity at time t, divided by the sum from I to n of prices in 2012 multiplied by quantities at time t.

The Paasche price index is equal to the inverse of the sum from I to N of prices in 2012 multiplied by quantity at time t, divided by the sum from I to n of prices multiplied by quantities at time t.

The Paasche price index is equal to the inverse of the sum from I to N of prices in 2012 divided by prices at time t multiplied by price and quantity at time t, divided by the sum from I to n of prices multiplied by quantities at time t.

The Paasche price index is equal to the inverse of the sum from I to N of prices in 2012 divided by prices at time t multiplied by the share of value at time t.

In order to provide more relevant results to 2020, the base year was changed from 2012 to 2019. Compared to the regular price and quantity indexes, the rebased values are different, although highly correlated as expected, as seen in figure 11. One lingering issue is that the series are not completely rebased to 2019. In the construction of price and quantity indices there are two stages of aggregation. The first is taking the raw data (that is, the actual prices and actual quantities sold of various goods) and aggregating using an elementary index that does not use basket weights. We do not have access to this data. This is not an issue in and of itself. However, the issues arises once weights are introduced. For the first series, the most detailed category available is “live animals”. This by itself would require some preliminary aggregation from the elementary indexes. For example, some combination of all live animal exports combine to make this series. This is the problem, the weights to create the most detailed level available are not available and thus cannot be updated to 2019. So the underlying components will still be using outdated shares. Regardless, updating the weights where possible is likely still an improvement from the default Statistics Canada calculations.

Figure 11: Comparison of different base year in the export series

figure 11
Text version
DateBase year 2012Base year 2019
Jan-1073.0498977.00008
Feb-1074.2783178.17253
Mar-1075.1791579.07119
Apr-1076.1618880.6594
May-1077.390380.68829
Jun-1076.7351480.50824
Jul-1076.1618880.159
Aug-1077.5540881.60075
Sep-1075.9161980.17148
Oct-1078.1273481.99995
Nov-1078.2911482.11975
Dec-1080.8298685.39749
Jan-1180.1747182.62731
Feb-1177.0627279.58449
Mar-1177.4721979.80108
Apr-1176.8170379.24738
May-1176.8170379.57921
Jun-1176.1618878.17482
Jul-1180.8298684.56251
Aug-1182.0582885.28949
Sep-1182.9591285.55649
Oct-1181.2393383.87154
Nov-1181.9763984.61758
Dec-1185.8254389.14231
Jan-1282.7953386.03919
Feb-1283.1229185.24192
Mar-1280.9117682.94383
Apr-1282.3039684.53242
May-1282.3039684.7924
Jun-1281.8944984.19062
Jul-1281.5669283.77701
Aug-1282.2220784.43224
Sep-1281.4850283.53867
Oct-1280.4203982.84447
Nov-1281.7307184.03428
Dec-1282.0582883.59658
Jan-1382.0582883.65614
Feb-1383.4504985.11671
Mar-1383.8599685.6065
Apr-1385.4159587.33574
May-1384.5151186.15511
Jun-1384.0237586.21367
Jul-1380.8298682.63206
Aug-1383.9418685.96471
Sep-1385.3340687.46877
Oct-1384.842786.61558
Nov-1385.0064887.24218
Dec-1384.678986.66904
Jan-1481.4031282.94767
Feb-1484.3513386.29182
Mar-1486.9719588.83881
Apr-1487.4633289.77487
May-1491.2304693.18629
Jun-1491.4761593.23033
Jul-1492.4588994.28082
Aug-1491.4761593.16352
Sep-1491.7218393.71795
Oct-1492.5407894.84592
Nov-1490.0839492.12994
Dec-1493.1959395.0117
Jan-1590.739192.51473
Feb-1590.0839491.85238
Mar-1591.6399493.44329
Apr-1591.7218393.06494
May-1590.4115292.0725
Jun-1594.2605696.10883
Jul-1594.7519396.67393
Aug-1594.1786795.67551
Sep-1594.4243595.58212
Oct-1592.131393.87379
Nov-1591.9675193.59132
Dec-1595.3251997.2585
Jan-1697.6182399.31249
Feb-1695.7346697.75781
Mar-1692.7045794.53584
Apr-1692.131394.30445
May-1689.1831191.32475
Jun-1688.6917390.84039
Jul-1691.3942693.65966
Aug-1695.0795196.64381
Sep-1693.2778294.96364
Oct-1692.213294.15369
Nov-1696.3079297.76821
Dec-1694.7519396.11182
Jan-1795.9803597.32255
Feb-1794.6700495.71606
Mar-1795.6527796.80349
Apr-1796.553697.53388
May-1797.4544498.37907
Jun-1794.5881495.8271
Jul-1794.0967794.81239
Aug-1792.4588993.53874
Sep-1793.687394.37807
Oct-1794.2605694.64249
Nov-1795.5708796.58002
Dec-1795.2432996.50542
Jan-1894.2605694.43024
Feb-1895.4889896.23798
Mar-1898.109698.70609
Apr-1899.2561399.81286
May-1897.6182398.14173
Jun-18100.157100.8021
Jul-1899.74749100.3468
Aug-1899.4199199.54447
Sep-18100.3208100.4122
Oct-18100.4026100.3565
Nov-18100.7302100.2861
Dec-1898.8466598.55183
Jan-1999.7474999.80656
Feb-1995.9803596.16789
Mar-1999.3380299.39081
Apr-1999.5018199.38548
May-19103.8422104.0745
Jun-19101.9586101.4748
Jul-19101.6311101.3116
Aug-19101.5492101.3921
Sep-1998.8466599.27639
Oct-1998.8466599.02639
Nov-1998.7647698.86588
Dec-1999.9931799.82893
Jan-2096.7173996.35257
Feb-20101.713100.8414
Mar-2098.2733996.82265
Apr-2079.8471376.07655
May-2079.6014577.3713
Jun-2088.2822687.70528
Jul-2094.9976195.14706
Aug-2092.5407892.52689
Sep-2094.8338294.35945
Oct-2094.3424594.16061
Nov-2096.0622495.1461
Dec-2096.1441395.20124
Jan-21100.238999.89643
Feb-2194.9157293.74117
Mar-2195.0795194.40615
Apr-2191.8037391.61529
May-2189.1012188.82305
Jun-2193.4416192.76961
Jul-2193.9329893.20233
Aug-2195.8165694.78934
Sep-2192.9502591.17814
figure 11.2
Text version
DateBase year 2012Base year 2019
Jan-1088.8226184.23203
Feb-1089.3877684.90898
Mar-1087.2213582.95088
Apr-1086.5620181.74815
May-1087.5039283.85664
Jun-1087.2213583.13692
Jul-1087.0329782.63609
Aug-1087.786583.41308
Sep-1087.9748883.23167
Oct-1087.8806983.62605
Nov-1089.0109984.84257
Dec-1090.7064485.81757
Jan-1193.9089591.08422
Feb-1192.7786589.84064
Mar-1194.0973391.26144
Apr-1196.0753693.10031
May-1195.6985992.32743
Jun-1195.6043993.10323
Jul-1193.4379989.27469
Aug-1194.6624890.96827
Sep-1196.3579393.44349
Oct-1196.4521293.44425
Nov-1198.2417695.05479
Dec-1197.6766194.0207
Jan-1296.0753692.43363
Feb-1295.6043993.16949
Mar-1295.0392592.64573
Apr-1294.0031491.52039
May-1293.9089591.09437
Jun-1293.5321890.94125
Jul-1292.3076989.77571
Aug-1292.6844690.21114
Sep-1293.2496190.84753
Oct-1295.510292.65663
Nov-1294.3799191.76189
Dec-1294.0031492.26453
Jan-1394.3799192.47607
Feb-1394.9450593.01065
Mar-1395.7927893.75332
Apr-1395.2276393.11463
May-1394.0031492.20354
Jun-1394.3799191.92175
Jul-1395.9811693.79208
Aug-1397.2056594.85629
Sep-1396.4521294.08826
Oct-1395.3218293.33903
Nov-1394.7566792.26173
Dec-1395.9811693.75328
Jan-1499.5604497.6972
Feb-14102.6688100.3356
Mar-14102.4804100.26
Apr-14100.219897.61324
May-1499.4662597.33898
Jun-1498.901196.99484
Jul-1498.3359596.36244
Aug-1498.1475796.31829
Sep-1498.2417696.06644
Oct-1497.5824295.04571
Nov-1497.1114694.95757
Dec-1493.7205791.89987
Jan-1593.4379991.56508
Feb-1595.3218293.5069
Mar-1594.3799192.52945
Apr-1593.7205792.30933
May-1594.474192.73167
Jun-1595.6043993.65559
Jul-1596.5463194.5828
Aug-1595.2276393.66759
Sep-1594.0031492.84323
Oct-1594.2857192.54594
Nov-1593.9089592.21719
Dec-1593.6263791.72495
Jan-1693.5321891.91919
Feb-1690.8006388.90779
Mar-1689.7645288.04482
Apr-1690.4238688.33822
May-1692.1193189.87126
Jun-1692.9670390.76282
Jul-1694.7566792.40923
Aug-1693.1554291.60742
Sep-1694.5682992.90143
Oct-1696.640594.57906
Nov-1696.734795.29566
Dec-1697.5824296.1993
Jan-1798.1475796.78525
Feb-1797.6766196.60749
Mar-1798.0533896.85031
Apr-1798.5243397.38842
May-1798.8069197.79156
Jun-1796.9230895.64968
Jul-1793.343892.62218
Aug-1794.474193.39288
Sep-1793.7205792.95296
Oct-1794.9450594.55989
Nov-1798.0533897.00011
Dec-1799.0894997.81999
Jan-1898.1475797.94234
Feb-1898.5243397.66914
Mar-1898.901198.29392
Apr-1899.7488399.08158
May-18101.2559100.5879
Jun-18102.763102.1238
Jul-18103.4223102.7639
Aug-18102.292102.0885
Sep-18101.2559101.1108
Oct-18100.5965100.5876
Nov-1894.3799194.71335
Dec-1892.5902692.85962
Jan-1997.6766197.53745
Feb-19101.6327101.3371
Mar-19103.0455102.9003
Apr-19103.0455103.0581
May-19101.5385101.1887
Jun-1997.5824298.03711
Jul-1998.3359598.61741
Aug-1999.0894999.18784
Sep-19100.219899.74002
Oct-1999.1836798.93613
Nov-1999.6546399.50801
Dec-1998.9952999.09396
Jan-2097.5824297.88359
Feb-2094.2857195.04701
Mar-2090.2354891.52081
Apr-2079.4034583.30407
May-2085.6200988.06623
Jun-2094.0031494.55288
Jul-2097.0172796.79357
Aug-2097.770897.77126
Sep-2096.9230897.36909
Oct-2098.7127298.83796
Nov-2097.9591898.80437
Dec-2099.18367100.1064
Jan-21103.0455103.3287
Feb-21106.0597107.2799
Mar-21107.0016107.6626
Apr-21110.2041110.3642
May-21111.8995112.1187
Jun-21114.5369115.3654
Jul-21115.2904116.1571
Aug-21113.5008114.7204
Sep-21114.3485116.5155

9. Appendix 3: Generating the counter-factuals

As mentioned in the body of the paper, the counter-factuals and confidence intervals were generated using an ARIMA model over the time period January 2010-December 2019. The procedure to select the appropriate ARIMA closely followed the Hyndman-Khandakar algorithm.Footnote 13 First, all series were converted transformed by their natural log. Next, using both the augmented Dickey-Fuller test and the KPSS test, all series were determined to be stationary after the first-difference.Footnote 14 Footnote 15 Footnote 16 Next 16 ARIMA estimations were run for each series, all specified using I(1) data. Allowing the auto-regressive term to vary between 0 and 3, and allowing the moving-average component to vary between 0 and 3, and all combinations thereof. The models were compared using Akaike's Information Criterion with small-sample correction (or AICc).Footnote 17 Next, the parameter of each model was examined for unit roots. If any of the AR terms, the sum of the AR terms, any of the MA terms, or the sum of the MA terms, was exactly equal to 1 or -1, the model was rejected as having a unit-root, and the next best model (based on the AICc) was selected. That model was then checked for unit roots. This proceeded until one of the models didn’t show any signs of unit roots. The linear forecast and confidence interval was then generated using the forecast package in R on the appropriate ARIMA model.Footnote 18 Footnote 19 The last step was to de-log and graph the series and confidence intervals.

The following ARIMA models were selected:

Export values: (0,1,0)

Export prices: (1,1,1)

Export quantities: (0,1,3)

Import values: (0,1,1)

Import prices: (2,1,0)

Import quantities: (0,1,1)

10. References

Brouillette, D., & Savoie-Chabot, L. (2017). Global Factors and Inflation in Canada (No. 2017-17). Bank of Canada.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

Hurvich, C. M., & Tsai, C. L. (1993). A corrected Akaike information criterion for vector autoregressive model selection. Journal of time series analysis, 14(3), 271-279.

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2021). forecast: Forecasting functions for time series and linear models. R package version 8.15, https://pkg.robjhyndman.com/forecast/.

Hyndman RJ, Khandakar Y (2008). “Automatic time series forecasting: the forecast package for R.” Journal of Statistical Software, 26(3), 1–22. .

Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159-178.

Scarffe, C. (2019). Canada’s Geographic Export Diversity, 抖淫视频.

Statistics Canada. Table 12-10-0121-01 International merchandise trade by commodity, monthly (x 1,000,000)

Statistics Canada. Table 12-10-0128-01 International merchandise trade, by commodity, price and volume indexes, monthly

Statistics Canada. Table 18-10-0004-01 Consumer Price Index, monthly, not seasonally adjusted

Statistics Canada. Table 18-10-0265-01 Industrial product price index, by major product group, monthly

Statistics Canada. Table 36-10-0105-01 Gross national income and gross domestic income, indexes and related statistics, quarterly

 
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