Volume-Related Composite Price Index (VRCPI) forecasting model review: Consultation paper

Introduction

The Agency annually sets the Volume-Related Composite Price Index (VRCPI) for each of the Canadian National Railway Company (CN) and the Canadian Pacific Railway Company (carrying on business as Canadian Pacific Kansas City [CPKC]). The VRCPI is an inflation index that reflects forecasted price changes of railway costs for labour, fuel, material, cost of capital and depreciation. It also includes an adjustment to reflect the costs associated with the acquisition of hopper cars for the movement of Western grain.

The purpose of this document is to consult interested Canadians on the current forecasting models used by the Canadian Transportation Agency (Agency) in determining certain price changes for the VRCPI. Going forward, the Agency intends to hold regular consultations on the VRCPI as a means of monitoring the effectiveness and viability of the models. While this consultation will focus on the labour, fuel and material price models only, future consultations may include topics such as historical (actual) price index development and investment (cost of capital and depreciation) index development.

The Agency is seeking input from industry stakeholders on how they view the effectiveness of the current models and whether or not they have suggestions for improvement (that is, is there a specific change that may increase the effectiveness of the model and hence, reduce the variance between the forecasted price change and the actual price change), or is there a defensible alternative methodology that stakeholders might feel would be a superior approach.

The specific questions of interest, and instructions on how to participate in the consultation are provided below. We invite railway companies, shippers, producers and other interested Canadians to submit comments by August 29^th, 2025.

Background

The Agency and its predecessors^{Footnote 1} have been determining forecasted indices for railway input prices since the early 1980s. In that time various forms of consultative processes were conducted with industry stakeholders.

The latest process was established in 2010. The new process established clear deadlines and introduced well-defined parameters for the submission, by all industry stakeholders, of any new methodological/interpretive proposals. Once received, the Agency would consider the proposal and decide if it merited change or required further consultation. That process remains in place today.

The plan going forward is to run annual consultative processes on a rotating basis where in:

• year 1, the forecasting models for the labour, fuel and material components of the VRCPI would be examined;
• year 2, comments on the development of the historical (actual) price indices would be sought; and
• year 3, the investment index components of the VRCPI would be examined.

Key Issues and Questions

1. Labour Price Forecasting Model

The labour price index (LPI) reflects annual changes in the price of labour for the railways. Canadian freight railways’ labour workforce is typically comprised of 79 occupational jobs. The LPI calculations take into account salaries and wages, wage-related benefit payments to employees for work done, and fringe benefits.

The following are examples of what is included in each of these categories:

Salary and Wage Items

Regular pay; overtime pay; vacation pay (if applicable); pay while on training, or holidays; pay while on other leave (sick leave, bereavement, jury duty, etc.).

Wage-Related Items

Management bonuses; signing bonuses; training costs; amounts for share purchase plans, or for gain-sharing; amounts for stock-based compensation plans.

Fringe-Benefit Items

Government pensions (CPP, QPP); company pensions; pension asset^{Footnote 2}; employment insurance; health & welfare (including dental plans).

Current Agency Forecast Methods for Labour

The current Agency methodology for forecasting labour starts with forecasts of the sub-components. For salaries and wages, the forecasted wage amount is based on wage increments from negotiated labour contracts.

For wage-related benefits, fringe and stock-based compensation benefits, the forecasted amounts are five-year moving averages of these sub-components. For pension benefits, the forecasted amounts are ten-year moving average of the sub-components.

The forecasted component amounts are then weighted based on the proportions of each component in the annual total labour cost, to arrive at a forecast of the LPI.

Annex A provides more details regarding the approach employed for forecasting labour.

Questions

1. How do you view the effectiveness of labour forecast methodology?
2. Do you have specific suggestions on how to address variance between forecasted and actual price changes?
3. Are there clearly superior alternative methodologies that can provide reasonable accurate forecast results?

Please give reasons in support of your answers.

2. Fuel Price Forecasting Model

Diesel fuel is used extensively in railway operations to transport goods to domestic and international markets. Crude oil is a key input in the refining process of diesel fuel and is estimated to account for an average of 40-45 percent of retail diesel price. Retail diesel price comprises of four main components: crude oil; refiner’s margins; taxes; and distribution/marketing cost.

There is a generally accepted view that a linear relationship exists between the price of crude oil and the price of products refined from it, including diesel fuel. Retail gasoline and diesel prices tend to correlate with global crude oil prices. This implies that diesel prices tend to follow fluctuations in crude oil prices to a large extent.

Current Agency Forecast Methods for Fuel

The Agency forecasts the price of fuel using a regression model that tracks the relationship between the price of diesel fuel used for railway operations and global crude oil prices.

The Agency’s model requires forecasts for changes in West Texas Intermediate (WTI) crude oil prices, a formula relating these changes to railway fuel purchase costs and forecasts for the federal fuel excise tax, provincial fuel sales taxes, corporate carbon taxes^{Footnote 3} and refiner’s margin forecasts^{Footnote 4} to derive its forecast Fuel Price Index. The Agency relies on third-party forecasts to calculate the fuel component of the VRCPI.

Further details of the Agency’s approach are found in attached Annex B.

Questions

1. How do you view the effectiveness of fuel forecast methodology?
2. Do you have specific suggestions on how to address variances between forecasted and actual price changes?
3. Are there clearly superior alternative methodologies that can provide reasonable accurate forecast results?

Please give reasons in support of your answers.

3. Material Price Forecasting Model

Railway companies purchase thousands of material items to maintain, upgrade and modernize their rail infrastructure. Material items include brakes, wheel sets, engine parts, nuts, bolts, railway ties, etc. Given their vast number, it is not possible for the Agency to determine forecasted price changes for each of these items individually. The Agency therefore employs a more universal approach in estimating changes in the annual average price of basket of railway materials.

Current Agency Forecast Methods for Material

The Agency’s material forecast models measure the relationship between the railway material price index and selected sub-components of the Industrial Product Price Index (IPPI)^{Footnote 5}. The IPPI sub-components employed in the Material Price Index (MPI) forecast model are fabricated metals, primary steel, and refined petroleum and coal indices.

The Agency’s material forecast model comprises of four regression equations that measure the effect of material price changes on a railways' MPI. Three of the models rely heavily on fabricated metals as a reasonable representation of significant railway material input^{Footnote 6}. The Agency obtains third-party forecasts for these parameters in recognition of the parties’ subject matter expertise. Third-party forecasts are incorporated into the results of the regression models to generate forecasts for the railway MPI. These results are then adjusted to better reflect the impact of petroleum products^{Footnote 7} and the Canadian dollar/American dollar exchange rate to arrive at the final material forecast^{Footnote 8}.

Further details of the Agency’s approach are found in attached Annex C.

Questions

1. How do you view the effectiveness of the material forecast methodology?
2. Do you have specific suggestions on how to address variances between forecasted and actual price changes?
3. Are there clearly superior alternative methodologies that can provide reasonable accurate forecast results?

Please give reasons in support of your answer.

Providing your feedback

Thank you for participating in this consultation. Please send your feedback to grain@otc-cta.gc.ca.

Schedule

You are invited to submit your input to the questions posed in this discussion paper by August 29, 2025. You can respond to all questions, or simply those questions that are of interest to you or your organization. Your answers will help the Agency decide whether to propose any changes to its forecasting models/approaches.

Initial public submissions will be posted on the Agency’s Consultation page by September 15, 2025, in the official language in which they were sent, along with your name or that of the organization represented.

Stakeholders can provide responses to these initial submissions by October 17, 2025.

Public versions of those responses will be posted here by October 31, 2025.

Submissions by mail can also be sent to:

Secretariat, Canadian Transportation Agency
60 Laval Street, Unit 1-117
Gatineau, Quebec
Canada J8X 3G9

Please note that these must be postdated no later than August 29, 2025, for the first round and October 17, 2025, for the second round of consultations.

If you wish to submit a video due to accessibility issues, please send an email to grain@otc-cta.gc.ca with the subject line “Video“. We will contact you to coordinate your submission.

Who can see your feedback

Your feedback is public information

Your feedback will appear on the Agency website in the official language you used to write it, along with your name. If you believe part, or all, of your feedback should be treated as confidential, follow the steps below.

Confidential Information

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1. One copy (the public version) from which the confidential information has been blacked out.
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   • you highlight, or otherwise show, on each page the confidential information that was blacked out in the first copy.

The Agency will post the public version on its website and keep the confidential one for internal use only.

However, all feedback is subject to the Access to Information Act and Privacy Act. The Agency will protect the confidentiality of your information subject to the provisions of those acts, but the Agency can be required to release information if someone requests it and if it doesn't fall within the legislated exceptions.

Annex A: Labour components

While in accordance with Decision 97-R-2012, the Agency’s determination of annual historical labour price components is done on a single year basis, the general methodology employed by the Agency to forecast CN and CPKC labour prices involves single-year and multi-year averaging formulas for the various labour components as follows:

• A. Single-year Salary and wages

  Table

  Labour Components	Method
  Salary and Wages	Forecast wage increases from labour contracts are applied to actual single-year salary and wage amounts

  1. About 70 percent of CN and CPKC employees are members of various union groups, whose wage and benefits agreements are negotiated with the railways. Collective wage settlement contracts typically cover a period of three or more years. Where ratified wage increases are known, they are applied to the LPI wage only. If a settlement has expired, or will expire before or during the forecast periods, the Agency may consider using railway-submitted forecasted amounts or publicly available general wage increase statistics. An analysis of existing ratified wage settlement information extending into the forecast years are submitted by the railways.
  2. For remaining non-union executive employees, the Agency historically applies railways’ internal or third-party wage increases estimates.

• B. Multi-year moving averages – Wage related and Fringe Benefits

Forecasts for wage-related and fringe benefit items are derived using recent five-year moving averages of historical data with the exception of pension amounts that are derived using ten-year moving averages.

Table

Labour Components	Method
Stock-Based Compensation Bonus Fringe	Five-year moving average
Pension Cash Other pension related plans	Ten-year moving average
Amortization / Pension Asset	Accounting depreciation

Annex A-1: LPI multi-year averaging technique

Below is an illustration of a moving average for wage-related and pension - components of the Agency’s labour forecast. Single-year amounts for the five most recent historical years are used to derive a five-year average where:

• The 2024 value [A] = Average[2020-2024]
• The 2025 value [B] = Average [2021-2024, A]
• The 2026 value [C] = Average [2022-2024, A, B]

For pension amounts, the same approach is used but with ten years of data.

Annex B: Fuel Forecast model

The Agency fuel forecast model is a simple linear relationship between the price per litre of diesel fuel, purchased by the railways and the price of the referenced North American crude oil benchmark West Texas Intermediate (WTI). The model regresses the railways’ monthly railway diesel cost per litre against the cost per litre of WTI (lagged by one month). The time series data runs from 1988 to current.

Railways' fuel cost is regressed against WTI (represented as X) prices in linear regression model:

$Y{t}_{}=B_0+B_{1}X{t}_{}$

where;

• Y represents Fuel cost measured in $CAD per litre
• X represents West Texas Intermediate converted to $CAD per litre
• B₀ represents constant
• B₁ represents correlation coefficient.

MODEL DATA	RAILWAY FUEL COST	WTI	WTI	REGRESSION EQUATION	TOTAL FUEL COSTS
Monthly		CRUDE OIL	CRUDE OIL		REGRESSED FUEL COSTS
1988-2024	$ CAN/L	$ CAN/L	+ 1 MO.		+ TAXES
			$X{t}_{}$	$Y{t}_{}=B_0+B_{1}X{t}_{}$	+ REFINER'S MARGINS

TAXES – Average forecast taxes are based on weighted forecast diesel consumption.

Annex C: Material Forecasting Models

Below is a table setting out the various material forecasting models that track the relationship between the railway material price index and various sub-components of the Industrial Product Price Index (IPPI).

Table of Model data and Estimated Regression Coefficients

MODEL DATA	B0 CONSTANT	B1 CPI	B2 PSII	B3 IPPI	B4 RPCI	B5 FMPI	B6 RMPI(t-1)
1 1973-2024	0.0	X1				X5
2 1973-2024	0.0			X3		X5
3 1973-2024	0.0	X1	X2		X4
4 1973-2024	0.0					X5	X6

where:

• RMPI is Railway Material Price Index (historical)
• CPI is Consumer Price Index
• FMPI is Fabricated Metal Product Index
• IPPI is Industrial Product Price Index
• PSII is Primary Steel Industries Index
• RCPI is Refined Petroleum and Coal Industrial Index

Independent variables data are sourced from Statistics Canada’s CPI, IPPI and RMPI tables.

LINEAR REGRESSION EQUATIONS

• $\text{Model I:} \mathrm{RMPI}{t}_{}=B_0+B_1\mathrm{CPI}{t}_{}+B_2\mathrm{FMPI}{t}_{}$
• $\text{Model II:} \mathrm{RMPI}{t}_{}=B_0+B_1\mathrm{IPPI}{t}_{}+B_2\mathrm{FMPI}{t}_{}$
• $\text{Model III:} \mathrm{RMPI}{t}_{}=B_0+B_1\mathrm{CPI}{t}_{}+B_2\mathrm{PSII}{t}_{}+B_3\mathrm{RPCI}{t}_{}$
• $\text{Model IV:} \mathrm{RMPI}{t}_{}=B_0+B_1\mathrm{FMPI}{t}_{}+B_2\mathrm{RMPI}{t}_{-}$