Pricing schedule

Meat & fish (fresh and frozen)

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be grocery stores, super stores or similar. If grocery stores are available, please do not provide prices from specialty retailers like butchers, or fish mongers.

Beef

Std. Quantity

Std. UM

Quantity

UM

Price

Blade or Chuck roast; boneless

1

kg

       

Rump roast; boneless

1

kg

       

Rib steak

1

kg

     

or

Sirloin steak

1

kg

 

Ground beef

1

kg

       

Chicken

Std. Quantity

Std. UM

Quantity

UM

Price

Whole chicken

1

kg

       

Breast; bone in

1

kg

     

or

Breast; boneless skinless

1

kg

 

Drumsticks

1

kg

     

or

Thighs; bone in

1

kg

or

Thighs; boneless skinless

1

kg

 

Cured & Processed Meat

Std. Quantity

Std. UM

Quantity

UM

Price

Sliced side bacon

375

g

       

Sliced ham; pre-packaged

175

g

       

Beef hot dogs

450

g

       

Fish Fillets

Std. Quantity

Std. UM

Quantity

UM

Price

Cod fillets; fresh or thawed

1

kg

     

or

Cod fillets; frozen

400

g

or

Salmon fillets; fresh or thawed

1

kg

or

Salmon fillets; frozen

400

g

 

Lamb

Std. Quantity

Std. UM

Quantity

UM

Price

Leg roast; bone in

1

kg

     

or

Loin chops; bone in

1

kg

 

Pork

Std. Quantity

Std. UM

Quantity

UM

Price

Ground pork

1

kg

       

Tenderloin

1

kg

       

Dairy and refrigerated products

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be grocery stores, super stores or similar.

Butter

Std. Quantity

Std. UM

Quantity

UM

Price

Butter; salted or unsalted

454

g

       

Cheddar Cheese

Std. Quantity

Std. UM

Quantity

UM

Price

Cheddar cheese block; medium

400

g

     

or

Cheddar cheese block; mild

400

g

or

Cheddar cheese block; old

400

g

 

Eggs

Std. Quantity

Std. UM

Quantity

UM

Price

Eggs; extra large

1

doz

     

or

Eggs; large

1

doz

 

Fruit Juice

Std. Quantity

Std. UM

Quantity

UM

Price

Orange juice; 1.5L

1.5

l

     

or

Orange juice; 1L

1

l

 

Apple juice; 1L

1

l

       

Milk

Std. Quantity

Std. UM

Quantity

UM

Price

2% Milk; 2L

2

l

     

or

2% Milk; 4L

4

l

 

Other Cheese

Std. Quantity

Std. UM

Quantity

UM

Price

Mozzarella cheese; block

400

g

     

or

Mozzarella cheese; shredded

320

g

or

Philadelphia cream cheese; soft tub

227

g

 

Yogurt

Std. Quantity

Std. UM

Quantity

UM

Price

Plain yogurt

750

g

       

Bakery and pantry items

Outlet details - important
 

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:
Enter percentage (0 to 100)

Instructions
Outlet information:
Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:
Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:
Stores selected should be grocery stores, super stores or similar. If grocery stores are available, please do not provide prices from specialty retailers.

Baby food

Std. Quantity

Std. UM

Quantity

UM

Price

Baby food; fruit or vegetable puree; jars or pouches

128

g

       
Bread

Std. Quantity

Std. UM

Quantity

UM

Price

White sliced

675

g

     

or

Whole wheat sliced

675

g

 
Canned Fish

Std. Quantity

Std. UM

Quantity

UM

Price

Canned tuna

170

g

       
Canned Fruit

Std. Quantity

Std. UM

Quantity

UM

Price

Peaches

389

ml

       

Pineapple

389

ml

       
Canned Vegetables

Std. Quantity

Std. UM

Quantity

UM

Price

Corn

341

ml

     

or

Peas

398

ml

 

Baked beans

398

ml

       

Tomatoes

796

ml

       
Coffee, beans or ground

Std. Quantity

Std. UM

Quantity

UM

Price

Coffee; beans or ground; Illy

250

g

     

or

Coffee; beans or ground; Lavazza

250

g

or

Coffee; beans or ground; Starbucks

340

g

or

Coffee; beans or ground; other brand

500

g

 
Cooking Oil

Std. Quantity

Std. UM

Quantity

UM

Price

Canola oil

946

ml

       
Dry pasta

Std. Quantity

Std. UM

Quantity

UM

Price

Spaghetti; Barilla

454

g

     

or

Spaghetti; other brand

454

g

 
Mayonnaise

Std. Quantity

Std. UM

Quantity

UM

Price

Mayonnaise

890

ml

       
Nut spreads

Std. Quantity

Std. UM

Quantity

UM

Price

Nutella; 1kg

1

kg

     

or

Nutella; 375g

375

g

or

Nutella; 725g

725

g

or

Peanut butter

500

g

 
Rice

Std. Quantity

Std. UM

Quantity

UM

Price

Basmati or Jasmine rice

900

g

       
Seasonings

Std. Quantity

Std. UM

Quantity

UM

Price

Table salt; exclude pink salt

1

kg

       
Sweeteners

Std. Quantity

Std. UM

Quantity

UM

Price

Sugar; white granulated

2

kg

       

Honey

500

g

       
Wheat flour

Std. Quantity

Std. UM

Quantity

UM

Price

Wheat flour; 1kg

1

kg

     

or

Wheat flour; 2.5kg

2.5

kg

 

Confectionary

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be grocery stores, super stores or similar. If grocery stores are available, please do not provide prices from specialty retailers.

Chocolate

Std. Quantity

Std. UM

Quantity

UM

Price

Ferrero Rocher

200

g

     

or

Mars or Snickers

52

g

or

Milka

100

g

or

Other brand

50

g

or

Ritter Sport

100

g

or

Toblerone

360

g

 

Cookies

Std. Quantity

Std. UM

Quantity

UM

Price

Chocolate chip

500

g

     

or

McVitie's digestives

400

g

or

Shortbread or butter

368

g

 

Potato Chips

Std. Quantity

Std. UM

Quantity

UM

Price

Doritos

213

g

     

or

Kettle cooked

200

g

or

Other brand

200

g

or

Pringles

203

g

 

Soft Drinks

Std. Quantity

Std. UM

Quantity

UM

Price

Soft drinks bottle; 1L

1

l

     

or

Soft drinks bottle; 2L

2

l

 

Soft drinks can; 355mL

355

ml

     

or

Soft drinks case of cans; 12 x 355mL=4.26L

4.26

l

 

Frozen food

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be grocery stores, super stores or similar. If grocery stores are available, please do not provide prices from specialty retailers.

Frozen Prepared Foods

Std. Quantity

Std. UM

Quantity

UM

Price

French fries

800

g

       
Frozen Produce

Std. Quantity

Std. UM

Quantity

UM

Price

Corn

750

g

     

or

Mixed vegetables

750

g

or

Peas

750

g

or

Spinach

500

g

or

Strawberries

500

g

 

Ice Cream

Std. Quantity

Std. UM

Quantity

UM

Price

Ben & Jerry's; tub

473

ml

     

or

Haagen Dazs; tub

450

ml

 

Fresh fruits and vegetables

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be grocery stores, super stores or similar. If grocery stores are available, please do not provide prices from specialty retailers similar to produce markets or farmer's markets.

Fruits

Std. Quantity

Std. UM

Quantity

UM

Price

Oranges

1

kg

       

Lemons; sold by count

1

ea

     

or

Lemons; sold by weight

1

kg

or

Limes; sold by count

1

ea

or

Limes; sold by weight

1

kg

 

Bananas

1

kg

       

Granny Smith apples

1

kg

     

or

Red or Golden Delicious apples

1

kg

 

Peaches

1

kg

       

Blueberries

1

kg

     

or

Raspberries

1

kg

or

Strawberries

1

kg

 

Grapes; any colour

1

kg

       

Vegetables

Std. Quantity

Std. UM

Quantity

UM

Price

Yellow potatoes; sold by bag

2.27

kg

     

or

Yellow potatoes; sold individually (loose)

1

kg

 

Carrots

1

kg

       

Cooking onions

1

kg

       

Green cabbage

1

kg

       

Broccoli; sold by count

1

ea

     

or

Broccoli; sold by weight

1

kg

 

Mushrooms

1

kg

       

Coloured peppers

1

kg

     

or

Green peppers

1

kg

 

Personal care products

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be similar to drug stores, pharmacy aisles of supermarkets, or similar.

Body Wash

Std. Quantity

Std. UM

Quantity

UM

Price

Dove

500

ml

     

or

Nivea

500

ml

or

Other brand

500

ml

 
Deodorant

Std. Quantity

Std. UM

Quantity

UM

Price

Deodorant; solid stick; mens

70

g

     

or

Deodorant; solid stick; womens

70

g

 

Hand Soap

Std. Quantity

Std. UM

Quantity

UM

Price

Bar hand soap; Dove

125

g

     

or

Bar hand soap; other brand

140

g

 
Mouthwash

Std. Quantity

Std. UM

Quantity

UM

Price

Listerine

1

l

     

or

Other brand

1

l

 

Shampoo

Std. Quantity

Std. UM

Quantity

UM

Price

Dove

355

ml

     

or

Head & Shoulders

350

ml

or

Herbal Essence

346

ml

or

L'Oreal

385

ml

or

Other brand

350

ml

or

Pantene

285

ml

 

Toothpaste

Std. Quantity

Std. UM

Quantity

UM

Price

Colgate

100

ml

     

or

Other brand

120

ml

or

Sensodyne

100

ml

 

Personal care products

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be similar to drug stores, pharmacy aisles of supermarkets, or similar. Please provide information for the counts within a package.

Baby Diapers

Std. Quantity

Std. UM

Quantity

UM

Price

Huggies; size 2 (4-8kg)

80

ea

     

or

Huggies; size 3 (6-11kg)

80

ea

or

Pampers; size 2 (4-8kg)

80

ea

or

Pampers; size 3 (6-11kg)

80

ea

 

Menstrual pads

Std. Quantity

Std. UM

Quantity

UM

Price

Always; regular absorbency

18

ea

     

or

Other brand; regular absorbency

18

ea

 

Pain Killers, regular strength

Std. Quantity

Std. UM

Quantity

UM

Price

Aspirin; 325mg; 100 pills

100

ea

     

or

Aspirin; 325mg; 24 pills

24

ea

or

Aspirin; 325mg; 50 pills

50

ea

 

Tylenol (paracetamol or acetaminophen); 325mg; 100 pills

100

ea

     

or

Tylenol (paracetamol or acetaminophen); 325mg; 24 pills

24

ea

 

Advil (ibuprofen); 200mg; 100 pills

100

ea

     

or

Advil (ibuprofen); 200mg; 24 pills

24

ea

 
Tampons

Std. Quantity

Std. UM

Quantity

UM

Price

Other brand; regular absorbency

18

ea

     

or

Tampax; regular absorbency

18

ea

 

Household supplies

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Dish Soap

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid dish soap

591

ml

       
Dishwasher Detergent

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid

1.6

l

     

or

Powder

1.8

kg

or

Tablets

50

ea

 

Facial tissues

Std. Quantity

Std. UM

Quantity

UM

Price

101+ tissues per box

1

ea

     

or

50-100 tissues per box

1

ea

 

Household Operations

Std. Quantity

Std. UM

Quantity

UM

Price

Household insecticide (e.g. Raid)

500

g

       

Packet of planting seeds (e.g. vegetable seeds, flower seeds)

1

ea

       

Laundry Detergent

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid

1.86

l

     

or

Pods

30

ea

or

Powder

4.7

kg

 

Light Bulb

Std. Quantity

Std. UM

Quantity

UM

Price

LED light bulb; A19, 60W equivalent

1

ea

       
Specialty Cleaners

Std. Quantity

Std. UM

Quantity

UM

Price

Glass cleaner

765

ml

     

or

Toilet bowl cleaner

710

ml

 

Household supplies

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Dish Soap

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid dish soap

591

ml

       
Dishwasher Detergent

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid

1.6

l

     

or

Powder

1.8

kg

or

Tablets

50

ea

 

Facial tissues

Std. Quantity

Std. UM

Quantity

UM

Price

101+ tissues per box

1

ea

     

or

50-100 tissues per box

1

ea

 

Household Operations

Std. Quantity

Std. UM

Quantity

UM

Price

Household insecticide (e.g. Raid)

500

g

       

Packet of planting seeds (e.g. vegetable seeds, flower seeds)

1

ea

       

Laundry Detergent

Std. Quantity

Std. UM

Quantity

UM

Price

Liquid

1.86

l

     

or

Pods

30

ea

or

Powder

4.7

kg

 

Light Bulb

Std. Quantity

Std. UM

Quantity

UM

Price

LED light bulb; A19, 60W equivalent

1

ea

       
Specialty Cleaners

Std. Quantity

Std. UM

Quantity

UM

Price

Glass cleaner

765

ml

     

or

Toilet bowl cleaner

710

ml

 

Alcoholic beverages and cigarettes

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:
Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Please do not price alcoholic beverages purchased at restaurants, bars, or nightclubs.

Beer

Std. Quantity

Std. UM

Quantity

UM

Price

Asahi

473

ml

     

or

Budweiser

473

ml

or

Carlsberg

473

ml

or

Corona

473

ml

or

Guiness

473

ml

or

Heineken

473

ml

or

Hoegaarden

473

ml

or

Kronenbourg

473

ml

or

Miller

473

ml

or

Modelo

473

ml

or

Moosehead

473

ml

or

Other brand

473

ml

or

Peroni

473

ml

or

Sapporo

473

ml

or

Stella Artois

473

ml

or

Tsingtao

473

ml

or

Tuborg

473

ml

 

Cigarettes

Std. Quantity

Std. UM

Quantity

UM

Price

Pack of 20

1

ea

     

or

Pack of 25

1

ea

 

Red Wine

Std. Quantity

Std. UM

Quantity

UM

Price

Campo Viejo

750

ml

     

or

Jacob's Creek

750

ml

or

Masi

750

ml

or

Wolf Blass

750

ml

or

Yellowtail

750

ml

 

Scotch, Rye and Whisky

Std. Quantity

Std. UM

Quantity

UM

Price

Canadian Club

750

ml

     

or

Crown Royal

750

ml

or

Makers Mark

750

ml

or

Wild Turkey

750

ml

 

Buchanans

750

ml

     

or

Jack Daniels

750

ml

or

Jameson

750

ml

or

Johnny Walker Black

750

ml

or

Johnny Walker Red

750

ml

 

Vodka

Std. Quantity

Std. UM

Quantity

UM

Price

Absolut

750

ml

     

or

Belvedere

750

ml

or

Finlandia

750

ml

or

Grey Goose

750

ml

or

Ketel One

750

ml

or

Skyy

750

ml

or

Smirnoff

750

ml

or

Tito's

750

ml

 

Transportation - gasoline

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

If you receive VAT rebates or instant rebates through a gas card program at your post, please provide an estimated price per litre after applying the tax reduction.

Gasoline

Std. Quantity

Std. UM

Quantity

UM

Price

Gasoline; regular (e.g. 87-90 octane)

1

l

       

Gasoline; premium (e.g. 91+ octane)

1

l

       

Tax-reduced Gasoline

Std. Quantity

Std. UM

Quantity

UM

Price

Tax- reduced gasoline; regular (e.g. 87-90 octane)

1

l

       

Tax-reduced gasoline; premium (e.g. 91+ octane)

1

l

       

Transportation - service and parts

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Oil Change

Std. Quantity

Std. UM

Quantity

UM

Price

Oil change including filter; 4 litres of oil and labour, Regular oil

1

ea

     

or

Oil change including filter; 4 litres of oil and labour, Synthetic oil

1

ea

 

Tires

Std. Quantity

Std. UM

Quantity

UM

Price

Tire 195/65 R 15; not including installation, All season

1

ea

     

or

Tire 195/65 R 15; not including installation, Winter

1

ea

 

Transportation - miscellaneous

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Parking

Std. Quantity

Std. UM

Quantity

UM

Price

Parking at a lot or garage located centrally in the city; 1 hour

1

ea

     

or

Parking at a lot or garage located centrally in the city; Daily rate

1

ea

 

Taxi

Std. Quantity

Std. UM

Quantity

UM

Price

Taxi fare; price for 1KM + basic fee

1

km

       

Vehicle Licensing and Registration

Std. Quantity

Std. UM

Quantity

UM

Price

Vehicle licensing and registration; gas powered passenger vehicle <3000lbs

1

ea

       

Recreation equipment

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Gaming Consoles

Std. Quantity

Std. UM

Quantity

UM

Price

Playstation 5 Pro

1

ea

     

or

Playstation 5; slim

1

ea

or

Playstation 5; slim digital

1

ea

or

Xbox Series S; 1TB

1

ea

or

Xbox Series S; 512GB

1

ea

or

Xbox Series X digital; 1TB

1

ea

or

Xbox Series X; 1TB

1

ea

 

iPad

Std. Quantity

Std. UM

Quantity

UM

Price

10th generation; 256GB, WiFi Only

1

ea

     

or

10th generation; 64 GB, WiFi Only

1

ea

or

Air 13-in (M2 chip); 512GB, WiFi Only

1

ea

or

Air 13-in (M2 chip); 1TB, WiFi Only

1

ea

or

Air 13-in (M2 chip); 256GB, WiFi Only

1

ea

or

Pro 13-in (M4 chip); 1T, WiFi Only

1

ea

or

Pro 13-in (M4 chip); 256GB, WiFi Only

1

ea

or

Pro 13-in (M4 chip); 512GB, WiFi Only

1

ea

 

Clothing

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Stores selected should be mid-range prices similar to H&M or Zara. If you include several stores, you can add them to the box separated by semi-colons.

Children's Clothing, ages 6 to 13

Std. Quantity

Std. UM

Quantity

UM

Price

Children's jeans; straight leg, cotton blend

1

ea

       

Children's T-shirt, cotton blend

1

ea

     

or

Children's T-shirt; 100% cotton

1

ea

or

Children's hoodie; heavyweight, cotton-polyester blend

1

ea

 

Children's socks; crew, multipack

6

ea

       

Children's sleepwear; 2-pc set, 100% cotton

1

ea

       

Men's Clothing

Std. Quantity

Std. UM

Quantity

UM

Price

Men's dress pants; classic fit, straight

1

ea

     

or

Men's jeans; straight leg, 100% cotton

1

ea

or

Men's jeans; straight leg, cotton-blend

1

ea

 

Men's socks; crew, multipack

4

ea

       

Men's boxer briefs; multipack, cotton-blend

3

ea

       

Men's T-shirt; 100% cotton

1

ea

     

or

Men's T-shirt; cotton blend

1

ea

or

Men's polo; cotton-polyester blend

1

ea

or

Men's white button up shirt; classic/slim

1

ea

 

Men's crewneck; heavyweight, cotton polyester blend

1

ea

       

Women's Clothing

Std. Quantity

Std. UM

Quantity

UM

Price

Women's socks; crew cut, multipack

3

ea

       

Women's briefs; 100% cotton, multipack

5

ea

       

Women's T-shirt; 100% cotton

1

ea

     

or

Women's T-shirt; cotton blend

1

ea

or

Women's white button up shirt; classic or slim fit

1

ea

 

Women's dress pants; classic fit, straight leg

1

ea

     

or

Women's jeans, straight leg; 100% cotton

1

ea

or

Women's jeans, straight leg; cotton-blend

1

ea

 

Women's crewneck, heavyweight; cotton polyester blend

1

ea

       

Fitness equipment

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Dumbbell Weights

Std. Quantity

Std. UM

Quantity

UM

Price

10lbs x 2

2

ea

     

or

2lbs x 2

2

ea

or

5lbs x 2

2

ea

 

Running Shoes

Std. Quantity

Std. UM

Quantity

UM

Price

Women's Adidas

1

ea

     

or

Women's New Balance

1

ea

or

Women's Nike

1

ea

or

Women's other brand

1

ea

 

Men's Adidas

1

ea

     

or

Men's New Balance

1

ea

or

Men's Nike

1

ea

or

Men's other brand

1

ea

 

Children's Adidas

1

ea

     

or

Children's New Balance

1

ea

or

Children's Nike

1

ea

or

Children's other brand

1

ea

 

Small appliances & housewares

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Bath Towel

Std. Quantity

Std. UM

Quantity

UM

Price

Bath towel; 100% cotton; 69cm x 132cm to 76cm x 137 cm

1

ea

       

Electric Appliances

Std. Quantity

Std. UM

Quantity

UM

Price

Iron; Black+Decker

1

ea

     

or

Iron; Conair

1

ea

or

Iron; T-fal

1

ea

 

Hair dryer; Conair

1

ea

     

or

Hair dryer; Dyson Supersonic

1

ea

 

Stand Mixer; Cuisinart; 5.5qt

1

ea

     

or

Stand Mixer; Kitchenaid; 5qt; tilt head

1

ea

 

Household services

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

If you include several providers, you can add them to the box separated by semi-colons. Do not add discretionary tips to the prices reported below.

Banking Services

Std. Quantity

Std. UM

Quantity

UM

Price

Personal chequing account; monthly fee; basic account

1

ea

     

or

Personal chequing account; monthly fee; standard account

1

ea

 

ATM cash withdrawal fee at an out-of-network bank (i.e. not home bank)

1

ea

     

or

Certified cheque fee

1

ea

 

Domestic Services

Std. Quantity

Std. UM

Quantity

UM

Price

Home cleaning services; fee for 1 hour

1

ea

       

Babysitting services; fee for 1 hour

1

ea

       

Hair Services

Std. Quantity

Std. UM

Quantity

UM

Price

Men's barber cut

1

ea

       

Women's shampoo, cut & dry

1

ea

       

Children's hair cut

1

ea

       

Household Operations

Std. Quantity

Std. UM

Quantity

UM

Price

Veterinarian visit; annual exam and vaccinations for a dog

1

ea

     

or

Veterinarian visit; spaying for a cat

1

ea

 

Entertainment services

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

If a service provider charges in a non-local currency, please create a separate copy of the sheet and enter those prices separately. For example, if a subscription service is only available in USD but your local book store charges in EUR, you should complete two versions of page 19—one with products priced in USD and one with products priced in EUR.

Admissions

Std. Quantity

Std. UM

Quantity

UM

Price

Movie admission; standard adult. Exclude IMAX, VIP, 3D tickets.

1

ea

       

Museum admission; adult

1

ea

       
Music Streaming Services

Std. Quantity

Std. UM

Quantity

UM

Price

Apple Music, annual subscription. Exclude family plans.

1

ann

     

or

Apple Music; monthly subscription. Exclude family plans.

1

ea

or

Spotify individual plan; monthly subscription. Exclude family plans.

1

ea

 

Novels

Std. Quantity

Std. UM

Quantity

UM

Price

Recent release; electronic

1

ea

     

or

Recent release; hardcover

1

ea

or

Recent release; paperback

1

ea

 

Streaming and Broadcast Services

Std. Quantity

Std. UM

Quantity

UM

Price

Amazon PrimeTV; annual

1

ann

     

or

Amazon PrimeTV; monthly

1

ea

or

AppleTV subscription; annual

1

ann

or

AppleTV subscription; monthly

1

ea

or

DisneyPlus Subscription; annual

1

ann

or

DisneyPlus subscription; monthly

1

ea

or

Netflix subscription; monthly

1

ea

 

Communications services

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Cell phone and internet prices should be from local providers.

Monthly Cellphone Plan; 1 line. Exclude device cost.

Std. Quantity

Std. UM

Quantity

UM

Price

100-unlimited GB data

1

ea

     

or

31-99 GB data

1

ea

or

Up to 30 GB data

1

ea

 

Monthly Home Internet

Std. Quantity

Std. UM

Quantity

UM

Price

Minimum download speed 1.5GB

1

ea

     

or

Minimum download speed 150MB

1

ea

or

Starlink at home

1

ea

 

Restaurant meals - table service

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions
Price as many items as possible from the menus of sit-down restaurants. These restaurants should offer table service and be frequented by members at the post.

Do not include:

  1. hotel restaurants
  2. room service
  3. fast food
  4. counter service
  5. fine dining
  6. Michelin-rated or Michelin-starred establishments.

At the top of the page, please provide restaurant names, the currency used, and any mandatory service charges or taxes not reflected in the menu prices. Do not add discretionary tips or service fees to the prices reported below.

Additional note:

For this exercise, do not use prices for vegetarian dishes as substitutes for meat dishes. They are not comparable.

Beverages

Std. Quantity

Std. UM

Quantity

UM

Price

Latte; regular size

1

ea

       

Single espresso; black

1

ea

       

Orange juice; regular sized bottle or one glass

1

ea

       

Fountain soda; regular size (eg. Pepsi, soda water)

1

ea

       

Main course. Exclude sharing or family style dishes.

Std. Quantity

Std. UM

Quantity

UM

Price

Sweet breakfast (e.g. pancakes, waffles, crepes)

1

ea

       

Western-style breakfast with eggs, meat and bread/potatoes

1

ea

       

Salad; non-vegetarian (e.g. chicken caesar)

1

ea

       

Noodle soup; non-vegetarian (e.g. pho, ramen)

1

ea

       

Italian style pasta; non-vegetarian (e.g. spaghetti bolognese, fettucini alfredo)

1

ea

       

Stir fry noodles; non-vegetarian (e.g. pad thai, chow mein)

1

ea

       

Curry or stew with rice; non-vegetarian

1

ea

       

Hamburger, including a personal side

1

ea

       

Sirloin steak (approx. 200g / 7 oz portion), including a personal side

1

ea

       

Grilled or baked chicken, including a personal side

1

ea

       

Grilled or baked salmon fillet, including a personal side

1

ea

       

Communications services

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an outlet most frequented by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Products and prices:

Select and price one regular priced product per category, according to the "OR" groups within outlined boxes. For each product, record the quantity, unit of measure (UM), and regular price. Choose items that closely match the description and standard measure provided. Only price "Other" products if no suitable match is available. Each section includes a "Completed?" percentage. If it shows less than 100%, review the section and complete any missing information.

Additional note:

Cell phone and internet prices should be from local providers.

Monthly Cellphone Plan; 1 line. Exclude device cost.

Std. Quantity

Std. UM

Quantity

UM

Price

100-unlimited GB data

1

ea

     

or

31-99 GB data

1

ea

or

Up to 30 GB data

1

ea

 

Monthly Home Internet

Std. Quantity

Std. UM

Quantity

UM

Price

Minimum download speed 1.5GB

1

ea

     

or

Minimum download speed 150MB

1

ea

or

Starlink at home

1

ea

 

Restaurant meals - table service

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions
Price as many items as possible from the menus of sit-down restaurants. These restaurants should offer table service and be frequented by members at the post.

Do not include:

  1. hotel restaurants
  2. room service
  3. fast food
  4. counter service
  5. fine dining
  6. Michelin-rated or Michelin-starred establishments.

At the top of the page, please provide restaurant names, the currency used, and any mandatory service charges or taxes not reflected in the menu prices. Do not add discretionary tips or service fees to the prices reported below.

Additional note:

For this exercise, do not use prices for vegetarian dishes as substitutes for meat dishes. They are not comparable.

Beverages

Std. Quantity

Std. UM

Quantity

UM

Price

Latte; regular size

1

ea

       

Single espresso; black

1

ea

       

Orange juice; regular sized bottle or one glass

1

ea

       

Fountain soda; regular size (eg. Pepsi, soda water)

1

ea

       

Main course. Exclude sharing or family style dishes.

Std. Quantity

Std. UM

Quantity

UM

Price

Sweet breakfast (e.g. pancakes, waffles, crepes)

1

ea

       

Western-style breakfast with eggs, meat and bread/potatoes

1

ea

       

Salad; non-vegetarian (e.g. chicken caesar)

1

ea

       

Noodle soup; non-vegetarian (e.g. pho, ramen)

1

ea

       

Italian style pasta; non-vegetarian (e.g. spaghetti bolognese, fettucini alfredo)

1

ea

       

Stir fry noodles; non-vegetarian (e.g. pad thai, chow mein)

1

ea

       

Curry or stew with rice; non-vegetarian

1

ea

       

Hamburger, including a personal side

1

ea

       

Sirloin steak (approx. 200g / 7 oz portion), including a personal side

1

ea

       

Grilled or baked chicken, including a personal side

1

ea

       

Grilled or baked salmon fillet, including a personal side

1

ea

       

Tenant insurance

Outlet details - important

Outlet(s) used to purchase the products below:

Currency used to purchase the products below:

Enter name

Enter currency

Total additional fees, surcharges, or sales taxes not included in the listed price:

Enter percentage (0 to 100)

Instructions

Outlet information:

Select an insurance provider commonly used by Canadian staff. At the top of the page, record the outlet name and include any additional taxes or fees not reflected in the original/listed price. Select the currency from the dropdown. Do not include products priced in different currencies on the same sheet, make a copy of the workbook.

Additional information:

Obtain annual tenant insurance premium quotes for the following types of dwellings:

  1. there are two people living in the unit
  2. the policy includes $1,000,000 (CAD or equivalent) liability
  3. there is replacement cost coverage of personal belongings of $30,000 (CAD or equivalent)
  4. there have been no prior insurance claims in the past 5 years

When obtaining a quote for the condo or apartment, if prompted, please specify there are 6 units in the building.

Tenant insurance

Std. Quantity

Std. UM

Quantity

UM

Price

Total annual insurance premium for townhouse

1

ann

       

Total annual insurance premium for condo or apartment

1

ann

       

Monthly Survey of Manufacturing: National Level CVs by Characteristic - September 2025

National Level CVs by Characteristic, August 2025
Table summary
This table displays the results of Monthly Survey of Manufacturing: National Level CVs by Characteristic. The information is grouped by Month (appearing as row headers), and Sales of goods manufactured, Raw materials and components inventories, Goods / work in process inventories, Finished goods manufactured inventories and Unfilled Orders, calculated in percentage (appearing as column headers).
Month Sales of goods manufactured Raw materials and components inventories Goods / work in process inventories Finished goods manufactured inventories Unfilled Orders
%
September 2024 0.73 1.12 1.95 1.30 1.53
October 2024 0.76 1.11 1.87 1.25 1.52
November 2024 0.70 1.11 1.81 1.25 1.64
December 2024 0.63 1.06 1.89 1.26 1.45
January 2025 0.67 1.11 1.71 1.25 1.45
February 2025 0.72 1.14 1.85 1.33 1.46
March 2025 0.72 1.18 1.77 1.38 1.49
April 2025 0.75 1.16 1.78 1.41 1.52
May 2025 0.78 1.20 1.87 1.45 1.51
June 2025 0.81 1.19 1.77 1.43 1.43
July 2025 0.74 1.21 1.82 1.41 1.46
August 2025 0.77 1.23 1.84 1.37 1.42
September 2025 0.78 1.29 1.91 1.46 1.37

National Weighted Rates by Source and Characteristic - September 2025

National Weighted Rates by Source and Characteristic, September 2025
  Data source
Response or edited Imputed
%
Sales of goods manufactured 88.2 11.8
Raw materials and components 77.6 22.4
Goods / work in process 80.1 19.9
Finished goods manufactured 76.9 23.1
Unfilled Orders 88.2 11.8
Capacity utilization rates 64.2 35.8

Annual Greenhouse, Sod and Nursery Survey - 2025

Why are we conducting this survey?

This survey collects up-to-date information on the production and value of greenhouse plants and vegetables, and on the production of nursery stock and sod in Canada.

Agriculture and Agri-Food Canada, producer associations, and provincial agriculture departments use the data to perform market trend analysis and to study domestic production and imports. The data are also used to calculate farm cash receipts.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Your participation in this survey is required under the authority of the Statistics Act.

Other important information

Authorization to collect this information

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Confidentiality

By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.

Record linkages

To enhance the data from this survey and to reduce the response burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.

Data sharing agreements

To reduce the respondent burden, Statistics Canada has entered into data sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia and the Yukon. The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

You may also contact us by email at infostats@statcan.gc.ca or by fax at 1-514-496-4879.

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, Northwest Territories and Nunavut, as well as with provincial and territorial ministries of agriculture.

For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Business or organization and contact information

1. Verify or provide the business or organization's legal and operating name, and correct information if needed.

Note: Legal name should only be modified to correct a spelling error or typo.

Legal Name

Name of a corporation as determined by its instrument of incorporation. The legal name of the entity is that which is recognized by law and is, therefore, the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name set by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name, which is different from the legal name, is a name the business or organization is commonly known as for day-to-day activities, and which is used to advertise and promote itself. The operating name is synonymous with trade name.

  • Legal name
  • Operating name (if applicable)

2. Verify or provide the contact information for the designated contact person for the business or organization, and correct information if needed. 

Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.

  • First name
  • Last name
  • Title
  • Preferred language of communication
    • English
    • French
  • Mailing address (number and street)
  • City
  • Province, territory or state
  • Postal code or ZIP code
  • Country
    • Canada
    • United States
  • Email address
  • Telephone number (including area code)
  • Extension number (if applicable)
  • Fax number (including area code)

3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.

  • Operational
  • Not currently operational
  • e.g., temporarily or permanently closed, change of ownership
    Why is this business or organization not currently operational?
  • Seasonal operations
    • When did this business or organization close for the season?
      Date
    • When does this business or organization expect to resume operations?
      Date
  • Ceased operations
    • When did this business or organization cease operations?
      Date
    • Why did this business or organization cease operations?
      • Bankruptcy
      • Liquidation
      • Dissolution
      • Other
        Specify the other reasons why the operations ceased
  • Sold operations
    • When was this business or organization sold?
      Date
    • What is the legal name of the buyer?
  • Amalgamated with other businesses or organizations
    • When did this business or organization amalgamate?
      Date
    • What is the legal name of the resulting or continuing business or organization?
    • What are the legal names of the other amalgamated businesses or organizations?
  • Temporarily inactive, but expected to re-open
    • When did this business or organization become temporarily inactive?
      Date
    • When does this business or organization expect to resume operations?
      Date
    • Why is this business or organization temporarily inactive?
  • No longer operating because of other reasons
    • When did this business or organization cease operations?
      Date
    • Why did this business or organization cease operations?

4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.

Note: The described activity was assigned using the North American Industry Classification System (NAICS).

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System ( NAICS ). The NAICS is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS , are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

The following is the detailed description including any applicable examples or exclusions for the classification currently associated with this business or organization.

Description and examples

  • This is the current main activity
  • This is not the current main activity

Provide a brief but precise description of this business or organization's main activity
e.g., breakfast cereal manufacturing, shoe store, software development

Main activity

5. You indicated that (Dynamic fill of description entered at Question 4) is not the current main activity.
Was this business or organization's main activity ever classified as: (Dynamic fill of description)?

  • Yes
    When did the main activity change?
    Date
  • No

6. Search and select the industry activity classification that best corresponds to this business or organization's main activity.

Select this business or organization's activity sector (optional)

  • Farming or logging operation
  • Construction company or general contractor
  • Manufacturer
  • Wholesaler
  • Retailer
  • Provider of passenger or freight transportation
  • Provider of investment, savings or insurance products
  • Real estate agency, real estate brokerage or leasing company
  • Provider of professional, scientific or technical services
  • Provider of health care or social services
  • Restaurant, bar, hotel, motel or other lodging establishment
  • Other sector

Type of production

1. Which of the following products did you grow for sale in 2025?

Please report Canadian production only.

Select all that apply.

Greenhouse products

Seedlings, potted plants, bedding plants, cuttings and other propagating material, vegetables and fruit grown for sale in a permanent, artificially heated enclosed structure made of plastic, plexiglass, poly-film or glass.

Any plants that you start cultivating in a greenhouse but are finished before sales in a nursery should be considered a nursery product.

Nursery products

A diverse range of non-edible, living plant material grown 'in field' or in containers outdoors and sold with their root system intact. Plants range from tree seedlings to full-grown trees.

Include annual and perennial plants.

Exclude field-grown cut flowers from this category.

Field-grown cut flowers should be reported in its own category only, not in the 'nursery products' category. Cut flowers produced in, and sold from, a greenhouse should be reported in the 'greenhouse products' category.

Christmas trees

Include only the Christmas trees that were cut during the year.

Exclude Christmas trees that were grown in a container with their root systems intact.

Sod

Grass or turf, which has its roots intact. Sod is grown 'in field' and sold as a single product.

  • Greenhouse products
    Include vegetables, fruits, flowers and plants grown in heated structures.
  • Exclude vegetables and fruit grown outdoors or in non-heated covering tunnels or cold frames and all cannabis production.
  • Christmas trees
  • Field-grown cut flowers
  • Nursery products
    e.g, trees, shrubs and plants
  • Sod
    OR
  • Did not grow any products for sale in 2025.

Greenhouse area - unit of measure

2. What unit of measure will be used to report your greenhouse area?

  • Square feet
  • Square metres
  • Acres
  • Hectares

Greenhouse area

3. What was your greenhouse area under the following materials in 2025?

Exclude non-heated covering tunnels, cold frames or any area surrounding a greenhouse.

What was your greenhouse area under the following materials in 2025?

What was your greenhouse area under the following materials in 2025?
  Unit of measure
a. Under glass  
b. Poly-film  
c. Rigid plastic, fibreglass or other enclosed area  
Total greenhouse area Total value

Greenhouse products - number of months in operation

4. How many months was your greenhouse in operation in 2025?

Report the number of months this operation was growing plants in a greenhouse.

Months

Greenhouse products

5. Which of the following greenhouse products were grown for sale in 2025?

Select all that apply.

For this survey, we are only interested in flowers, plants, vegetables, fruits, tree seedlings and bedding plants grown in, and sold from, the greenhouse. Production of vegetables and fruits covered by cold frames or covering tunnels should not be included in the greenhouse section of the survey.

Potted herbs

Plants that will be maintained in a pot by the consumer after purchase should be reported inside the 'potted plants' section. Herb plants sold in a package ready to be consumed should be reported inside the vegetable section.

Cut flowers

Include only cut flowers produced in, and sold from, a greenhouse.

Exclude field-grown cut flowers and dried cut flowers.

Fruit and Vegetables

Include products grown to completion in a greenhouse and sold from the greenhouse.

Exclude greenhouse vegetables and/or fruit that are transplanted for field crops. Bedding plants (transplants) grown in a greenhouse that will be planted in your own fields so that they can be sold as fully grown harvested vegetables at a later date should be excluded; they are reported in Statistics Canada's annual Fruit and Vegetable Survey.

Potted Plants - indoor and outdoor

Any plants grown and sold in a pot from the greenhouse.

Exclude Christmas trees sold in pots. Pots take many forms and sizes, such as baskets (wicker), peat pots, moss pots and plastic pots or ceramic pots.

Cuttings and tree seedlings

Plants (or sections of a plant) capable of developing into a greater number of plants or spreading out and affecting a greater area. Examples include Chrysanthemums, Poinsettias, Begonias, Petunias and shrubs.

Exclude tree seedlings for reforestation.

Bedding plants, also known as transplants

Young plants that are bought and then transplanted into a garden, field, container or basket by the purchaser. These include ornamental bedding plants and vegetable bedding plants. For this survey, the term "ornamental" refers to flowers or plants cultivated for their beauty rather than use.

  • Fruits and vegetables
  • Potted plants — indoor or outdoor
    Include any prefinished or finished plants grown and sold in a pot.
  • Cuttings and tree seedlings
    Exclude tree seedlings for reforestation.
  • Bedding plants, transplants or plugs – ornamental or vegetable
    Include plants sold in cell packs or trays that are ready for transplanting by the purchaser.
  • Cut flowers
    Exclude dried cut flowers.

Greenhouse products

6. What was your greenhouse area for the following products in 2025?

((Special Note: Q6 - Only answer this question when the survey is collected in a Census year.))

What area of your greenhouse was used to produce the following fruits and vegetables in 2025?
  Unit of measure
a. Fruits and vegetables  
b. Potted plants – indoor or outdoor  
c. Cuttings and tree seedlings  
d. Bedding plants, transplants or plugs  
e. Cut flowers  
Total greenhouse area used to grow greenhouse products Total value

Greenhouse products – fruits and vegetables

7. What area of your greenhouse was used to produce the following fruits and vegetables in 2025?

For any multiple plantings of the same fruit or vegetable, count the area only once.

Greenhouse vegetables and fruits are edible and ready to eat at the time of sale. They were grown into sellable products in a greenhouse, not in a field; and sold from the greenhouse by the producer. Field vegetable and fruit farmers should report their production in the Fruit and Vegetable Survey.

Exclude tobacco, ginseng, asparagus, mushrooms, ornamental and vegetable bedding plants (young plants that are bought and transplanted into a garden, field, container or basket by the purchaser; also known as transplants).

A number of greenhouses are expanding to the United States. For this survey, report Canadian production only.

If you produced a multiple crop of the same greenhouse vegetable or fruit in the same greenhouse space, report the area only once. For example, if 1,000 square feet were used for the first tomato crop planting and then the same space was later used for the second tomato crop planting, you would report 1,000 square feet (not 2,000 square feet).

If you produced two or more different types of vegetables or fruit in the same greenhouse space, you would count that area for each type of crop produced.

For example, if you used 2,000 square feet to grow tomatoes for your first crop planting, and then switched to growing cucumbers in that same space half-way through the summer, you would report a total area of 4,000 square feet (2,000 square feet for growing tomatoes, plus 2,000 square feet for growing cucumbers).

What area of your greenhouse was used to produce the following fruits and vegetables in 2025?

  Unit of measure
Greenhouse tomatoes  
a. Beefsteak tomatoes  
b. Large tomatoes on the vine  
c. Cherry and grape tomatoes  
d. Other tomatoes  
Specify other tomatoes  
Total greenhouse tomatoes Total value
Greenhouse cucumbers  
e. English cucumbers  
f. Mini cucumbers  
g. Other cucumbers  
Specify other cucumbers  
Total greenhouse cucumbers Total value
Other greenhouse fruits and vegetables  
h. Greenhouse eggplants  
i. Greenhouse Chinese vegetables  
j. Greenhouse herbs
Exclude sprouts and microgreens.
 
k. Sprouts grown in a controlled environment
Include vegetables, legumes, pulse and herb sprouts.
 
l. Greenhouse microgreens and shoots
Include all microgreens, vegetables and herbs.
 
m. Greenhouse peppers  
n. Greenhouse lettuce  
o. Greenhouse beans (green and wax)  
p. Greenhouse strawberry  
q. Other greenhouse fruit or vegetable 1  
Specify other greenhouse fruit or vegetable 1  
r. Other greenhouse fruit or vegetable 2  
Specify other greenhouse fruit or vegetable 2  
s. Other greenhouse fruit or vegetable 3  
Specify other greenhouse fruit or vegetable 3  
Total area of fruits and vegetables  Total value

Greenhouse products – fruits and vegetables

8. For the following fruits and vegetables, what were the quantity sold (i.e. , marketed production) and sales in 2025?

  Quantity sold Unit of measure Total Sales
Greenhouse tomatoes      
a. Beefsteak tomatoes      
b. Large tomatoes on the vine      
c. Cherry and grape tomatoes      
Total greenhouse tomatoes      
Greenhouse cucumbers      
e. English cucumbers      
f. Mini cucumbers      
g. Other cucumbers      
Total greenhouse cucumbers     Total value
Other greenhouse fruits and vegetables      
h. Greenhouse eggplants      
i. Greenhouse Chinese vegetables      
j. Greenhouse herbs
Exclude sprouts and microgreens.
     
k. Sprouts grown in a controlled environment
Include vegetables, legumes, pulse and herb sprouts.
     
l. Greenhouse microgreens and shoots
Include all microgreens, vegetables and herbs.
     
m. Greenhouse peppers      
n. Greenhouse lettuce      
o. Greenhouse beans (green and wax)      
p. Greenhouse strawberry      
q. Other greenhouse fruit or vegetable 1      
Specify other greenhouse fruit or vegetable 1      
r. Other greenhouse fruit or vegetable 2      
Specify other greenhouse fruit or vegetable 2      
s. Other greenhouse fruit or vegetable 3      
Specify other greenhouse fruit or vegetable 3      
Total gross sales of fruits and vegetables     Total value

Greenhouse products – fruits and vegetables

9. Of the total gross sales reported at question 8, please provide the percentage breakdown of greenhouse fruits and vegetables sales across the following distribution channels.

Sales distribution of greenhouse vegetables and fruit (total gross sales)

The sales of greenhouse vegetables and fruit that the operation produced and sold.

Please report the value of greenhouse fruit and vegetable sales in a percentage (%). The sum of different markets should be equal to 100% of the value reported in in question 8.

Wholesaler

The organization primarily engaged as the intermediary in the distribution of merchandise. Meaning that a wholesaler is a reseller of manufactured goods in whole (without transformation, and rendering services incidental to the sale of merchandise).

A wholesaler provides the warehousing and trade abilities the manufacturer does not want to provide. It also prefers to sell batches, truckloads, pallets, etc. of goods. Often offers discounts as quantity increases. As a result, many wholesalers are therefore organized to sell merchandise in large quantities to retailers, and business and institutional clients.

In addition, wholesalers may frequently perform one of the following related functions; breaking bulk, providing delivery services to customers, or operating warehouse facilities for storage of goods they sell, or marketing and support services such as packaging and labelling, inventory management, shipping, handling of warranty claims, in-store or co-op promotions and training.

Of the total gross sales reported at question 8, please provide the percentage breakdown of your greenhouse fruits and vegetables sales across the following distribution channels.

  Percentage of total sales
a. Sales to domestic wholesalers  
b. Sales to mass market chain stores  
c. Sales to other greenhouses  
d. Sales of exports directly from your operation  
e. Sales to the public from your greenhouse, roadside stand or other outlets  
f. Sales through all other distribution channels
e.g., restaurants, food chains, co-operatives
 
Total sales of fruits and vegetables Total value

Greenhouse products - indoor and outdoor potted plants

10. For the following indoor and outdoor potted plants, how many pots did this greenhouse produce and sell in 2025?

Include only prefinished and finished potted plants grown and sold by this greenhouse operation.

Exclude:

  • bedding plants or plugs sold in cell packs, flats or trays for transplanting
  • nursery-grown stock, such as potted shrubs or fall mums
  • Christmas trees sold in pots
  • plants purchased or imported by this operation for immediate resale.

Include all ornamental potted plants (annuals, biennials and perennials) and all potted vegetable, fruit and herb plants that were produced and sold from your greenhouse in Canada.

Plants grown in containers outdoors should be reported in the 'nursery products' category.

Exclude anything produced outside Canada.

Exclude Christmas trees sold in pots; bedding plants or plugs sold in cell packs, flats or trays; and other nursery stock (non-edible, living plant material grown outdoors 'in field' or in containers outdoors and sold with their root system intact).

Any plant grown in a pot from the greenhouse with the intention of selling to the final consumer can be classified as a finished potted plant (including hanging potted plants, such as baskets (wicker), peat pots, moss pots and plastic pots or ceramic pots). Any plant sold in a pot before it has fully matured or is intended to be grown to maturity at another facility can be classified as a prefinished potted plant.

For the following indoor and outdoor potted plants, how many pots did this greenhouse produce and sell in 2025?

For the following indoor and outdoor potted plants, how many pots did this greenhouse produce and sell in 2025?
Indoor Potted Plants Number of pots produced and sold
a. Azaleas  
b. Lilies  
c. Poinsettias  
d. African Violets  
e. Tropical foliage and green plants
 Include ferns.
Exclude hanging pots.
 
f. Gerberas  
g. Miniature Roses  
h. Orchids  
i. Kalanchoes  
j. Chrysanthemums or Potted Mums  
k. Primulas  
l. Cyclamens  
m. Tulips  
n. Indoor hanging pots  
o. Other indoor potted plants  
Outdoor potted plants  
p. Begonias  
q. Chrysanthemums, garden  
r. Geraniums, in pots only  
s. New Guinea or Hawker impatiens  
t. Petunias  
u. Herbaceous perennials  
v. Argyranthemums  
w. Outdoor hanging pots  
x. Calibrachoas  
y. Dahlias  
z. Pansies  
aa. Rudbeckias  
ab. Heliopsis  
ac. Verbenas  
ad. Zinnias  
ae. Potted herb plants  
ad. Potted vegetable plants  
ag. Other outdoor potted plants
e.g., daisies, gardenias.
 
Total number of pots, indoor and outdoor, produced and sold Total value

11. What were the total gross sales of prefinished and finished potted plants in 2025?

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

Greenhouse products - cuttings and tree seedlings

12. For the following cuttings, what was the total number of cuttings produced and sold in 2025?

Include only cuttings produced by this greenhouse operation.

Cuttings are sections of a plant stem capable of developing into a whole plant. Examples of species that may be sold as cuttings include murrayas, grevilleas, fuchsias, and gardenias.

Exclude ornamental and vegetable bedding plants, also known as transplants, which are young plants that are bought and then transplanted into a garden, field, container or basket by the purchaser.

For the following cuttings, what was the total number of cuttings produced and sold in 2025?

For the following cuttings, what was the total number of cuttings produced and sold in 2025?
  Total number of cuttings produced and sold
a. Chrysanthemum  
b. Poinsettia  
c. Geranium  
d. Impatien
Include only double and New Guinea.
 
e. Other cuttings not listed  
Total number of cuttings produced and sold Total value

13. What were the total gross sales of cuttings in 2025?

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

14. What was the total number of tree seedlings produced and sold in 2025?

Include only tree seedlings produced by this greenhouse operation.

Exclude:

  • nursery products grown in a cold-frame or non-heated tunnel
  • tree seedlings for reforestation.

A tree seedling is a young tree grown from a seed in a nursery or greenhouse for transplanting typically at one or two years of age.

Include tree seedlings produced only inside a greenhouse. Do not report tree seedlings produced in cold frames or covering tunnels.

Number of seedlings

15. What were the total gross sales of tree seedlings in 2025?

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

Greenhouse products – ornamental and vegetable bedding plants, transplants or plugs 

16. What were the number and total gross sales of bedding plants, transplants or plugs produced and sold in 2025?

Include plants ready for transplanting by the purchaser into gardens, fields, containers and baskets.

Report the number of individual plants. If the number is unknown, please estimate it by multiplying the number of trays by the average number of plants per tray.

Bedding plants, also known as transplants, are young plants that are bought and then transplanted into a garden, field, container or basket by the purchaser. Ornamental bedding plants are cultivated for their flowers and beauty, rather than their use. Vegetable bedding plants are not yet edible at the time of sale from your greenhouse.

Bedding plants may be sold in various containers, including plugs, cell packs, flats or trays. Report the number of individual plants. If this number is unknown, please estimate it by multiplying the number of trays by the average number of plants per tray.

Exclude vegetable and herb plants not sold directly from the greenhouse (for example, plants being transplanted from the greenhouse to the field by the producer).

What were the number and total gross sales of bedding plants, transplants or plugs produced and sold in 2025?

What were the number and total gross sales of bedding plants, transplants or plugs produced and sold in 2025?
  Number of plants Total gross sales ($)
a. Ornamental bedding plants    
b. Vegetable bedding plants    

Greenhouse products - cut flowers

17. For the following cut flowers, what was the total number of stems produced and sold in 2025?

Exclude:

  • dried cut flowers
  • field-grown flowers (these will be reported in question 2025)
  • flowers grown by another operation.

Include only cut flowers that were produced in, and sold from, a greenhouse in Canada.

Exclude cut flowers that were initially cultivated in a greenhouse but then grown into sellable products in a field; these should be reported in the 'field-grown cut flowers' section, which is its own category in this survey. Some operators may start seeds in their greenhouse but transplant the flowers in the field in May or June and cut and dry them in August.

Exclude any cut flowers you purchased from other growers to re-sell from your own operation within a short period of time with minimal maintenance work (watering).

For the following cut flowers, what was the total number of stems produced and sold in 2025?

For the following cut flowers, what was the total number of stems produced and sold in 2025?
  Number of stems produced and sold
a. Alstroemerias  
b. Chrysanthemums
Include standard and sprays.
 
c. Daffodils  
d. Freesias  
e. Gerberas  
f. Irises  
g. Lilies  
h. Roses  
i. Snapdragons  
j. Tulips  
k. Lisianthus  
l. Other cut flowers not listed  
Total number of stems produced and sold Total value

18. What were the total gross sales of cut flowers grown by this greenhouse operation in 2025?

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

Greenhouse products - flowers and plants

19. What were your total gross sales of flowers and plants purchased from other greenhouses for immediate resale in 2025?

Total gross sales

OR

  • Did not purchase and re-sell any flowers or plants

Summary - flowers and plants

20. This is a summary of your total gross sales of greenhouse flowers and plants in 2025?

This is a summary of your total gross sales of greenhouse flowers and plants in 2025?
  Sales
a. Total gross sales of potted plants Static summary value
b. Total gross sales of cuttings Static summary value
c. Total gross sales of tree seedlings Static summary value
d. Total gross sales of ornamental bedding plants, transplants or plugs Static summary value
e. Total gross sales of vegetable bedding plants, transplants or plugs Static summary value
f. Total gross sales of cut flowers Static summary value
Total sales of flowers and plants produced in your greenhouse Static summary value
Total gross sales of flowers and plants purchased from other greenhouses for immediate resale Static summary value
Total gross sales of greenhouse flowers and plants Static summary value

Greenhouse products - flowers and plants

21. Of your total gross sales [amount]$ reported, please provide the percentage breakdown of greenhouse flowers and plants sales across the following distribution channels.

Sales distribution of greenhouse flowers and plants (total gross sales)

The sales of greenhouse flowers and plants that the operation produced and purchased for immediate resales.

Please report the value of greenhouse flower and plant sales in percentage (%). The sum of different markets should be equal to 100%.

Wholesaler: the organization primarily engaged as the intermediary in the distribution of merchandise. Meaning that a wholesaler is a reseller of manufactured goods in whole (without transformation, and rendering services incidental to the sale of merchandise).

A wholesaler provides the warehousing and trade abilities the manufacturer does not want to provide. It also prefers to sell batches, truckloads, pallets, etc. of goods. Often offers discounts as quantity increases. As a result, many wholesalers are therefore organized to sell merchandise in large quantities to retailers, and business and institutional clients.

In addition, wholesalers may frequently perform one of the following related functions; breaking bulk, providing delivery services to customers, or operating warehouse facilities for storage of goods they sell, or marketing and support services such as packaging and labelling, inventory management, shipping, handling of warranty claims, in-store or co-op promotions and training.

Of your total gross sales [amount]$ reported, please provide the percentage breakdown of greenhouse flowers and plants sales across the following distribution channels.

Of your total gross sales [amount]$ reported, please provide the percentage breakdown of greenhouse flowers and plants sales across the following distribution channels.
  Percentage of total sales (%)
a. Sales to retail florists
e.g., flower shops, garden centres
 
b. Sales to domestic wholesalers
Include Dutch Auction Clock System.
 
c. Sales to mass market chain stores  
d. Sales to other greenhouses  
e. Export sales made directly by your firm  
f. Sales made directly to the public from your greenhouse or roadside stands  
g. Sales to the government and other public institutions  
h. Other methods of sales not listed  
Total sales of flowers and plants  

Christmas trees

22. Please enter the total area used to grow Christmas trees, the number of trees produced and cut, and the total gross sales of trees in 2025?

Include only the Christmas trees that were cut during the year.

Exclude Christmas trees that were grown in a container with their root systems intact.

When reporting the area, include the total area used to grow Christmas trees, regardless of whether the trees were cut or not. Include naturally established or planted areas, regardless of stage of growth, that are pruned or managed with the use of fertilizer or pesticides.

When reporting the number of cut trees, exclude any Christmas trees that were grown in a container with their root systems intact.

Conversions

  • 1 arpent = 0.9986 acres
  • 1 acre = 1.0014 arpent
  • 1 acre = 0.41 hectares
  • 1 hectare = 2.47 acres

Total area

Unit of measure

  • acres
  • hectares
  • arpents

Number of cut trees

Total gross sales

Field-grown cut flowers

23. Please report the total area used to grow field-grown flowers, the number of cut stems produced and sold, and the total gross sales of field-grown cut flowers in 2025?

Include field-grown fresh and dried flowers, and any plant part used for floral or decorative purposes, such as seed heads, stalks and woody cuts.

Exclude cut flowers grown in a greenhouse from start to finish.

Total area

Unit of measure

  • acres
  • hectares
  • arpents

Number of cut stems

Total gross sales

Nursery products - nursery area

24. What was the total nursery area used for growing nursery stock in 2025?

What was the total nursery area used for growing nursery stock in 2025?
  Nursery area Unit of measure (Acres, Hectares or Arpents)
a. Field area used for growing nursery stock    
b. Container area used for growing nursery stock    
Total nursery area    

Nursery products - nursery stock

25. How many field-grown and container-grown plants did this operation produce and sell in 2025?

Exclude:

  • stock purchased for immediate resale
  • Christmas trees without the root system intact
  • heated greenhouse production and unsold inventory.

A tree seedling is a young tree grown from a seed in a nursery for transplanting typically at one or two years of age.

Include only tree seedlings produced in a nursery.

Exclude tree seedlings produced in and sold from a greenhouse.

Exclude tree seedlings for reforestation.

Note: tree seedlings may be reported as nursery products if they were conditioned outside for part of the production cycle, after having been cared for inside the greenhouse first.

How many field-grown and container-grown plants did this operation produce and sell in 2025?

How many field-grown and container-grown plants did this operation produce and sell in 2025?
  Number of field-grown plants produced and sold Number of container-grown plants produced and sold
a. Trees — conifer    
b. Trees — fruit    
c. Trees — shade or ornamental    
d. Shrubs — evergreen and conifer    
e. Shrubs — evergreen and broadleaf    
f. Shrubs — deciduous
Include roses.
   
g. Vines    
h. Perennials and annuals    
i. Small fruit bushes
e.g., raspberry bush
   
j. Tree seedlings
Exclude tree seedlings for reforestation.
   
k. Other type of plants    
Total number of field and container grown nursery stock Total value Total value

26. What were the total gross sales of field-grown and container-grown nursery stock in 2025?

Exclude sales of stock purchased for immediate resale and revenue from landscaping activities.

Exclude:

  • any nursery stock that was purchased for immediate resale
  • Christmas trees without the root system intact
  • any greenhouse production
  • unsold inventory
  • value received for landscaping services.

Field-grown includes all bailed and burlapped, bare root field potted stock.

Container-grown includes all containers sizes of less than one gallon; one gallon; two gallons; and greater than two gallons.

Balled and burlapped is a method of transplanting that minimizes root disturbance. The tree is dug with a ball of soil around it and wrapped in burlap (method generally used for evergreens and deciduous plants in leaf).

Bare root describes plants dug up, with the soil shaken off (method generally used for deciduous plants in a dormant condition).

Field-potted describes stock which is grown in the field and placed into a pot when dug up for sale. Please report stock that was potted up from the field for a maximum of one full growing season; if potted up for more than one growing season, report under container.

Container-grown is nursery stock grown in a container for a minimum of one growing season before time of sale.

What were the total gross sales of field-grown and container-grown nursery stock in 2025?

What were the total gross sales of field-grown and container-grown nursery stock in 2025?
  Total Gross Sales
a. Total gross sales of field-grown stock  
b. Total gross sales of container-grown stock  
Total gross sales of stock grown by this nursery operation Total value

27. What were the total gross sales of nursery stock purchased for immediate resale in 2025?

Nursery stock for immediate resale is any nursery stock you purchased from other growers to re-sell from your own operation within a short period of time with minimal maintenance e.g., watering. Please enter your total sales of the nursery stock you purchased from other operations.

Examples of stock that may be ready for immediate resale:
Plants, flowers, bulbs, trees, shrubs, etc.

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

OR

Did not purchase and re-sell any nursery stock.

28. This is a summary of your total gross sales of nursery stock in 2025.

This is a summary of your total gross sales of nursery stock in 2025.
  Sales
a. Total gross sales of stock grown by this nursery operation Summed value
b. Total gross sales of stock purchased for resale Summed value
Total sales of nursery stock Summed value

29. Of the total gross sales [amount]$ reported, please provide the percentage breakdown of nursery stock sales across the following distribution channels.

Sales distribution of nursery stocks (total gross sales)

The sales of nursery stocks that the operation produced and purchased for immediate resales.

Please report the value of nursery stock sales in percentage (%). The sum of different markets should be equal to 100%.

Of the total gross sales [amount]$ reported, please provide the percentage breakdown of nursery stock sales across the following distribution channels.

Of the total gross sales [amount]$ reported, please provide the percentage breakdown of nursery stock sales across the following distribution channels.
  Percentage of total sales (%)
a. Sales to the public  
b. Sales to fruit growers  
c. Sales to landscape contractors  
d. Sales to garden centres  
e. Sales to mass merchandisers
e.g., chain stores
 
f. Sales to other growers  
g. Export sales made directly by your operation  
h. Sales to public agencies  
i. Sales through other channels
e.g., wholesalers, brokers, forestry firms
 
Total sales of nursery products Summed value

Labour

Special note: Starting in calendar year 2025, respondents are not required to answer the four Labour questions: Q30, Q31, Q38, and Q39.

In future, these four questions may be reinstated.

Operating expenses

32. In 2025, what were your operating expenses?

Please provide your greenhouse and nursery expenses separately.
If you do not track these expenses separately, please provide the total in the third column.

Growing on is a term used by operators when stock is cultivated in the greenhouse or the nursery for the purpose of growing it to greater proportions. The operators will plant a seed or seedling in their greenhouse and care for it, by maintaining it (transplanting, fertilizing, etc. ) until it becomes a sellable product.

Exclude any plant materials you may have purchased from other growers for immediate resale from your own operation (please report these purchases in row c).

In 2025, what were your operating expenses?

  Greenhouse expenses Nursery expenses Total expenses
Plant material      
a. Purchases of plant material for growing on
Include flowers, cuttings, seedlings, seeds, bulbs, bedding plants, young trees or nursery stock etc.
     
b. Percentage of a. purchased from within your province      
c. Purchases of plant material for immediate resale      
Total plant material purchases Total value Total value Total value
Payroll      
d. Payroll
Include:
  • payroll of employees, owners and family members
  • paid benefits, such as medical insurance, workers' compensation, employment insurance and pension plans.
Exclude wages and benefits paid to employees who provide retail or clerical help, and contract work, e.g., truck driving or landscaping.
     
Fuel expenses      
e. Natural gas      
f. Heating oil      
g. Other types of heating fuel
e.g., coal or wood chips
     
Total fuel expenses Total value Total value Total value
Other expenses      
h. Electricity expenses
Include lighting, airflow fans and heating.
     
i. Other crop expenses
Include fertilizer, pesticides, pollination, irrigation, containers, packaging, bioprograms, and growing mediums such as soil, peat moss, vermiculite, perlite, sand, styrofoam and sawdust.
     
J. Other operating expenses
e.g., Interest, land taxes, insurance, advertising, repairs to farm buildings, machinery, agricultural equipment and vehicles, contract work, and telephone and telecommunications services.
     
Total operating expenses Total value Total value Total value

Sod operations - area and sales

33. What was the total sod area grown in 2025?

Conversions

  • 1 arpent = 0.9986 acres
  • 1 acre = 1.0014 arpent
  • 1 acre = 0.41 hectares
  • 1 hectare = 2.47 acres

Sod is grass or turf, which has its roots intact at the time of sale. Sod is grown in field and sold as a single product.

Report all the area of land used for growing and maintaining sod.

Include any sod grown that was not intended for sale within the survey year (the past calendar year).

Area

Unit of measure

  • acres
  • hectares
  • arpents

34. Of the total sod area, how much was grown for sale in 2025?

Report the area of sod intended to be sold within the survey year (the past calendar year).

The area of sod grown for sale may be less than or equal to the total area of sod reported in the previous question.

Area

35. What were the total gross sales of sod grown on your operation in 2025?

Exclude revenue from laying sod or reselling sod purchased from others.

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

36. What were the total gross sales of sod purchased for immediate resale?

Total gross sales

Report dollar amounts in thousands of Canadian dollars.

OR

Did not purchase and re-sell any sod.

Summary - total sales of sod

37. This is a summary of the total sales of sod in 2025.

This is a summary of the total sales of sod in 2025.
  Sales
a. Total gross sales of sod grown on your operation Static summed value
b. Total gross sales of sod purchased for immediate resale Static summed value
Total sales of sod Static summed value

Sod operations – labour

Special note: The respondent is not required to answer Q38 or Q39 at this time.

Sod operations - expenses

40. Please provide your sod operating expenses in 2025.

  Sod operating expenses ($)
a. Purchases of sod for immediate resale  
b. Percentage of a. purchased from within your province  
C. Payroll
Include:
  • payroll of employees, owners and family members
  • paid benefits, such as medical insurance, workers' compensation, employment insurance and pension plans.
Exclude wages and benefits paid to employees who provide retail or clerical help, and contract work, e.g., truck driving, landscaping or laying sod.
 
d. Other sod operating expenses
Include fertilizer, pesticides, land taxes, interest, insurance, advertising, repairs, fuel, electricity, irrigation expenses, and telephone and other telecommunication services.
 
Total sod operating expenses in 2025 Total value

Agricultural production

41. Which of the following agricultural products are currently being produced on this operation?

  • Field crops
  • Hay
  • Summerfallow
  • Potatoes
  • Fruit, berries and nuts
  • Vegetables
  • Sod
  • Nursery products
  • Greenhouse products
  • Cattle and calves
  • Include beef or dairy.
  • Pigs
  • Sheep and lambs
  • Mink
  • Fox
  • Hens and chickens
  • Turkeys
  • Maple taps
  • Honey bees
  • Mushrooms
  • Other
    Specify agricultural products
  • Not producing agricultural products

Area in crops

42. What area of this operation is used for the following crops? 

Report the areas only once, even if used for more than one crop type.

Exclude land used by others.

What area of this operation is used for the following crops?

What area of this operation is used for the following crops?
  Area Unit of measure
Field crops    
Hay    
Summerfallow    
Potatoes    
Fruit, berries and nuts    
Vegetables    
Sod    
Nursery products    

Greenhouse area

43. What is the total area under glass, plastic or other protection used for growing plants?

Total area

Unit of measure

  • square feet
  • square metres

Livestock (excluding birds)

44. How many of the following animals are on this operation?

Include all animals on this operation, regardless of ownership, including those that are boarded, custom-fed or fed under contract.

Exclude animals owned but kept on a farm, ranch or feedlot operated by someone else.

How many of the following animals are on this operation?

How many of the following animals are on this operation?
  Number
Cattle and calves  
Pigs  
Sheep and lambs  
Mink  
Fox  

Birds

45. How many of the following birds are on this operation?

Report all poultry on this operation, regardless of ownership, including those grown under contract.

Include poultry for sale and poultry for personal use.

Exclude poultry owned but kept on an operation operated by someone else.

How many of the following birds are on this operation?

How many of the following birds are on this operation?
  Number
Hens and chickens  
Turkeys  

Maple taps

46. What was the total number of taps made on maple trees last spring?

Total number of taps

Honey bees

47. How many live colonies of honey bees (used for honey production or pollination) are owned by this operation?

Include bees owned, regardless of location.

Number of colonies

Mushrooms

48. What is the total mushroom growing area (standing footage) on this operation?

Include mushrooms grown using beds, trays, tunnels or logs.

Total area

Unit of measure

  • square feet
  • square metres

Changes or events

49. Indicate any changes or events that affected the reported values for this business or organization, compared with the last reporting period.

Select all that apply.

  • Strike or lock-out
  • Exchange rate impact
  • Price changes in goods or services sold
  • Contracting out
  • Organizational change
  • Price changes in labour or raw materials
  • Natural disaster
  • Recession
  • Change in product line
  • Sold business or business units
  • Expansion
  • New or lost contract
  • Plant closures
  • Acquisition of business or business units
  • Other
    Specify the other changes or events:
  • No changes or events

Contact person

50. Statistics Canada may need to contact the person who completed this questionnaire for further information.
Is the provided given names and the provided family name the best person to contact?

  • Yes
  • No

Who is the best person to contact about this questionnaire?

  • First name:
  • Last name:
  • Title:
  • Email address:
  • Telephone number (including area code):
  • Extension number (if applicable):
    The maximum number of characters is 5.
  • Fax number (including area code):

Feedback

51. How long did it take to complete this questionnaire?

Include the time spent gathering the necessary information.

  • Hours:
  • Minutes:

52. Do you have any comments about this questionnaire?

Meal Rate Survey

Meal rates

30) Please provide copies of menus from a minimum of 12 restaurants typically frequented by post personnel or visitors to the mission, that meet the meal descriptions included below. A menu copy can either be a scanned copy of the menu, or a link to the online menu. If the menu provided is not in English or French, please provide a translation.

Include

  • take-out menus only if the prices are the same as those on the dine-in menu
  • translations for menus that are not in English nor French

Exclude

  • room service menus
  • catering menus
  • fast food restaurant menus
Meal type Restaurant type Meal components or characteristics
Breakfast Hotel and stand-alone restaurants
  • North American (ex: eggs, bacon, toast, etc.)
  • Continental
  • Buffet
Lunch Hotel and stand-alone restaurants
  • Soup of the day or juice;
  • Sandwich, hamburger, pizza, pasta or other typical lunch selection;
  • Dessert;
  • and coffee or tea.
Dinner Hotel and stand-alone restaurants
  • Light appetizer such as soup of the day or small green salad;
  • Main course of meat, chicken, or fish;
  • Dessert;
  • and coffee or tea.

A minimum of 6 breakfast, 12 lunch and 12 dinner menus are required for this location.

Provide menus from a combination of standalone restaurants and hotel restaurants typically frequented by personnel or visitors to the mission. Include supporting details in the table below.

If fewer than the minimum required number of menus are submitted for each meal type, please provide a justification in the space provided below. (e.g., hotel-provided breakfast, unavailability of local breakfast options)

If fewer than 3 hotels are represented for each meal type, please provide an explanation in the space provided below. (e.g., there are only two approved hotels at this location)

Name of Restaurant Restaurant Type (Standalone / Hotel) Currency of Menu Prices Taxes (%) NOT included in menu prices (percentage) Gratuities or Service Charges (%) NOT included in menu prices (percentage) For each restaurant, please paste the web address for the menu in the box below or indicate that a scanned copy is attached.
Example restaurant Standalone USD 10 20 www.restaurant.com/menu
           
           
           
           
           
           
           
           
           
           
           

Statistics Canada’s Direct Health Measures Program – Consultative Summary Report

Consultative engagement objectives

In Statistics Canada, the Centre for Health Data Integration and Direct Measures (CHDIDM) is responsible for the production of statistics on health indicators that require direct measurements of physical health at the population level across several areas, such as chronic conditions, oral health, infectious disease, and environmental contaminants. As the CHDIDM envisions the future of their programs, they will, for instance, review and update survey designs, collection infrastructures, survey content, and analytical plans.

Statistics Canada (StatCan) is committed to the provision of quality data. As part of the Direct Health Measures Programs, the CHDIDM launched a series of engagement sessions with key stakeholders, such as government health entities, pan-Canadian organizations, provincial and territorial Statistical Focal Points, academic researchers, and non-government organizations.

These perspectives will help StatCan to plan the development of existing programs such as the Canadian Health Measures Survey (CHMS), as well as to design and implement new programs to meet stakeholder needs. The goal was to identify data needs and gaps, raise awareness of StatCan data holdings, learn from stakeholder’s subject matter expertise and explore potential collaboration.

Consultative engagement methods

Consultations on the Direct Health Measures Program were conducted virtually through information sessions that included group discussions with stakeholders from the broader Direct Health Measures community. Input was received from 58 different organisations including non-government organisations (NGOs), government organisations, and academic organisations. These sessions occurred during February 2025 and were publicized on Statistics Canada’s Consulting Canadians web page. Individual stakeholders were sent email invitations to participate and encouraged to circulate within their networks. In addition to the virtual sessions, interested individuals were invited to provide feedback through electronic forms or submit written responses. Overall, Statistics Canada moderated 16 sessions in both official languages and received feedback from 98 individuals representing 58 organisations from both the public and private sector.

What we heard from stakeholders

Our consultations revealed a desire on behalf of the participants to establish formal collaboration agreements with Statistics Canada, with clearly defined roles, responsibilities, and expectations.

Participants expressed the importance of Statistics Canada’s data in their work but identified easier access to the data and more timeliness in its dissemination as potential areas of improvement. Participants identified that the most-used direct measures data sources were accelerometry (physical activity data), anthropometry, biomarkers, disease and health condition, sociodemographic information, drug toxicity and substance use, and chemical exposure data. They also identified needs for additional data on environmental health, nutrition, biomarkers, covariates, and specialized health topics and physical measures. Consultations confirmed that cross-sectional population-based surveys with national representation is sufficient to meet most current data needs. However, participants also confirmed the need for, additional geographic and variable data disaggregation. Current data gaps include data on the three territories, and longitudinal data as there are no sources of longitudinal direct health measure data within Statistics Canada’s data repository.

Statistics Canada thanks all participants for their participation and feedback in this consultative engagement initiative. We will continue to engage with specific stakeholders on specific surveys and topics to collaboratively work to address some of the feedback provided throughout the sessions.

Driving Donations: Analytics & ML Modelling for Enhancing Food Drive Operations

By: Uchenna Mgbaja, Nazmus Sakeef, Kendrick Moreno, Catrina Llamas, and Roe Alincastre; NorQuest College

Introduction

The Edmonton Food Drive (EFD) Project is a collaborative effort between NorQuest College, LDS Church, etc. to improve the logistics of one of Alberta’s largest community food donation initiatives. The current food donation management system faces challenges in coordinating drop-off locations, pick-up processes, and route planning. There is a need to automate and enhance these processes to ensure timely collection of donations and minimize logistical complexities.

This multi-stakeholder project supports over 40,000 people monthly by distributing over 400,000 meals to people in need. These figures show the significant demand within the community and highlight the critical role of collaborative efforts in combating food insecurity.

The objective of this project was to develop a machine learning solution to enhance the management of food donation activities in Alberta. The project aims to increase the efficiency and effectiveness of drop-off and pick-up processes, streamline route planning, and improve resource allocation.

Proposed Solutions

A key component of the Edmonton Food Drive is the role played by Wards and Stakes, organizational units within The Church of Jesus Christ of Latter-day Saints (LDS), which facilitate volunteer participation and logistical coordination.

In the LDS Church, a ward is a local congregation that serves a specific geographic area, while a stake is a larger administrative unit composed of multiple Wards. In the context of the EFD Project, Stakes oversee multiple Wards, providing organizational support and resources, while Wards coordinate volunteer efforts, donation collection, and route management within their respective areas.

Building on the objectives of the project, the following solutions were proposed and developed to tackle the identified challenges:

  • Data Collection Improvements:

Create data-acquisition forms to collect data from Wards via structured surveys, enabling volunteers to answer questions as quickly and efficiently as possible.

  • Trend Analysis:

Use data visualization and statistical techniques to perform a year-over-year analysis, revealing critical trends and performance indicators.

  • Interactive Dashboards:

Create user-friendly, interactive dashboards that allow stakeholders to easily explore and compare data, facilitating more informed decision-making.

  • Predictive Modeling:

Implement machine learning techniques to develop a predictive model that forecasts donation patterns and identifies emerging trends.

  • Efficiency Forecasting:

Build a predictive model to estimate which Wards or Stakes will have the greatest impact in terms of efficiency for 2025.

  • Route Mapping Application:

Develop a route digitization application that automatically generates digitized maps for volunteers, improving operational efficiency. Create a route mapping application that generates interactive maps for volunteers, focusing on high-demand or hot-zone addresses for long-term operational efficiency.

These proposed solutions aimed to streamline operational processes, enhance stakeholder engagement, and leverage predictive insights to improve the planning and execution of future food drives.

Methodology

Data Collection

Data on donation volumes, routes, and volunteer participation were gathered during the Edmonton Food Drive in September 2023 & September 2024.  Data was collected from 6 assigned Stakes and 27 Wards. This data was systematically collected from designated drop-off centers, as assigned by client representatives, ensuring accurate coverage of specific routes and regions. While comprehensive within the assigned scope, the data did not represent all collection points across Edmonton, limiting its full-city applicability.

Datasets:

We started our analysis on data collected in 2023 from Wards. In 2024, we added data validation rules to mitigate the risks of wrong data entries while ensuring that the time required for volunteers to complete the form remains as short as before.

Data Acquisition Form for Edmonton Food Drive 2024
Figure 1: Data Acquisition Form for Edmonton Food Drive 2024 Description: This dataset includes 653 samples and 31 features, gathered through a Microsoft Form completed by volunteers. The form was used to record details related to the logistics of claimed donation bags during the 2024 Edmonton Food Drive, providing valuable data for analysis and resource optimization.

 

The data collected in 2023 focused on essential information related to donation collection, volunteers, and routes. While it provided a solid foundation, it was limited in terms of data validation and feature richness. The dataset consisted of 13 features and 454 samples.

Column Name Description
Date The date of the food drive activity took place.
Location The specific area or neighborhood where the food drive was conducted.
Stake The organization or group responsible for managing the volunteers in the area.
# of Adult Volunteers The number of adult volunteers who participated in the activity.
# of Youth Volunteers The number of youth volunteers who participated in the activity.
Donation Bags Collected The total number of donation bags collected during the activity.
Time to Complete (min) The total time (in minutes) taken to complete the assigned route(s).
Completed More Than One Route Indicates whether more than one route was completed (e.g., Yes/No).
Ward The municipal ward where the food drive activity occurred.
Routes Completed The total number of routes were completed by the volunteers.
Doors in Route The total number of doors covered within the assigned route.
Route Number/Name.1 The identifier or name of the route assigned to the volunteers.
Time Spent The total duration volunteers spent during the food drive activity.
# of Adult Volunteers The number of adult volunteers who participated in the activity.
Table 1: Feature Information of EFD 2023 dataset

Description: This dataset comprises data collected via a Google Form during the Edmonton Food Drive 2023. Number of features: 13; Number of samples: 454

Column Name Description
ID A unique identifier assigned to each form submission.
Start time The time the volunteer began filling out the form.
Completion time The time the volunteer completed the form.
Email The email address provided by the volunteer.
Name The name of the volunteer.
How did you receive the form? The method through which the volunteer received the form (e.g., email, link).
Email address The contact email address for further communication.
Drop Off Location The primary location where donations were dropped off.
Other Drop-off Locations Additional locations where donations were dropped off.
Stake The specific stake responsible for organizing the volunteer's participation.
Bonnie Doon Stake Indicates involvement with the Bonnie Doon Stake.
Edmonton North Stake Indicates involvement with the Edmonton North Stake.
Gateway Stake Indicates involvement with the Gateway Stake.
Riverbend Stake Indicates involvement with the Riverbend Stake.
Sherwood Park Stake Indicates involvement with the Sherwood Park Stake.
YSA Stake Indicates involvement with the Young Single Adults (YSA) Stake.
Route Number/Name The identifier or name of the donation collection route.
Time Spent Collecting Donations The total time spent collecting donations for the route.
# of Adult Volunteers who participated in this route The number of adult volunteers involved in this specific route.
# of Youth Volunteers who participated in this route The number of youth volunteers involved in this specific route.
# of Doors in Route The total number of doors covered within the route.
# of Donation Bags Collected The total number of donation bags collected from the route.
Did you complete more than 1 route? Indicates whether the volunteer completed more than one route (e.g., Yes/No).
How many routes did you complete? The total number of routes completed by the volunteer.
Additional Routes completed (2 routes) Details about a second additional route completed, if applicable.
Additional routes completed (3 routes) Details about a third additional route completed, if applicable.
Additional routes completed (3 routes)2 Details about another third route completed, if applicable.
Additional routes completed (More than 3 Routes) Details about additional routes completed beyond three, if applicable.
Additional routes completed (More than 3 Routes)2 Further details about routes completed beyond three, if applicable.
Additional routes completed (More than 3 Routes)3 Further details about routes completed beyond three, if applicable.
Comments or Feedback Any additional comments, suggestions, or feedback provided by the volunteer.
Table 2: Feature Information of EFD 2024 dataset

Description: This dataset comprises data collected via a Microsoft Form during the Edmonton Food Drive 2023. Number of features: 31; Number of samples: 653

Geographical Information Extraction: City of Edmonton Neighborhood Dataset

To complement the food drive data, the City of Edmonton Neighborhood Dataset [Link] was integrated into the analysis. This dataset provided geographic coordinates and neighborhood names, enabling a geospatial analysis of donation trends and route efficiency.

Geographical information was extracted from the Property Assessments dataset and merged to the Food Drive Data using the unique Neighborhood Names. This data was then used to generate maps that provide visual insights into neighborhood-level donation patterns and trends. The columns shown in Table 3 were specifically extracted for this purpose:

Column Name Description
Neighborhood Name The official name of the neighborhood in the City of Edmonton.
Latitude The geographic coordinate specifying the north-south position of the neighborhood.
Longitude The geographic coordinate specifying the east-west position of the neighborhood.
Table 3: Feature Information of City of Edmonton Neighborhood dataset

Description: The City of Edmonton Neighborhood Geographical coordinates data provides comprehensive information about neighborhood boundaries, demographics, land use, and other characteristics for urban planning and analysis. Number of features: 3; Number of samples: 427

This information was crucial for creating interactive geospatial visualizations and digitized route mapping for the Edmonton Food Drive

Exploratory Data Analysis

The collected data was cleaned and prepared for analysis to ensure accuracy and consistency. Key visualizations were generated to provide comparative insights, focusing on identifying trends and patterns in donation volumes, volunteer allocation, and route efficiency. Insights were limited to the data collected from the assigned drop-off centers, emphasizing the need for a more comprehensive data collection strategy in future drives. Our Exploratory Data Analysis strategy involved examining each feature individually and performing detailed analyses for each.

We conducted a comprehensive analysis of the Edmonton Food Drive data, focusing on uncovering patterns and relationships to improve the understanding of key variables and enhance future efforts. The analysis began by examining the frequency and distribution of drop-off locations, exploring their relationship with variables such as the number of donation bags collected, and the number of volunteers involved. The frequency of different "Stake" values was assessed, and their impact on numerical features, including the number of doors and donation bags, was closely analyzed.

Further, we explored time-related aspects, analyzing the frequency of various time categories and investigating how the time spent differed across "Stakes" and "Wards". The distribution of data across Wards was another area of focus, examining how specific Wards influenced other variables, such as the number of donation bags and routes. Volunteer participation was also analyzed, with particular attention given to the correlation between adult volunteers and other numerical features, as well as the overall distribution of volunteers across different areas.

The distribution of the number of doors was assessed in relation to categorical variables, and the average number of doors by "Stake" was calculated. Additionally, the relationship between donation bags and the number of routes was analyzed, comparing variations in donation bags across locations and Wards. Yearly trends were also explored, identifying changes in donation volumes and total volunteer numbers over time.

Through this analysis, we uncovered valuable insights into the relationships between drop-off locations, volunteers, and donation trends.

Data Refinement:

For the EFD 2024 dataset, we identified the following issues and applied the respective methods to address them.

Issues Detected Refining Method
Too long column names Rename column names for clarity
Inconsistent string formats Removed leading and trailing spaces
Converted to title format
Removed unnecessary characters
Incorrect and inconsistent data types Converted variables to the correct data types
Detected null values Numeric Variables: Performed mean imputation to replace null values, preserving the dataset's distribution by using the feature's average.
Categorical Variables: No null values detected
Detected empty values Tagged empty categorical fields with placeholders (e.g., "Unknown Routes")
Duplicated values Dropped duplicated values and columns
Too many irrelevant data Dropped irrelevant columns
Identified outliers Detected using IQR method and imputed using mean
Table 4: Identified Issues in the EFD 2024 Dataset and Their Respective Solutions

After performing data refining on the EFD 2024 dataset, we merged it with the EFD 2023 dataset and the City of Edmonton Neighborhood dataset. We used our final cleaned dataset for further analysis.

Data Visualization:

We created interactive visualizations using Tableau to make our EDA findings easy to understand. These visualizations allow users to explore the data and gain insights through dynamic charts and maps. The dashboard includes various charts and maps that present the key aspects of our analysis in a simple and clear way. Figure 2 shows the visualizations included in the dashboard that help support our overall analysis.

Interactive Dashboard of the Edmonton Food Drive 2024 Visualized Using Tableau
Figure 2: Interactive Dashboard of the Edmonton Food Drive 2024 Visualized Using Tableau Description: This dashboard provides an overview of key metrics related to the Edmonton Food Drive, including donation trends, distribution data, and community engagement. Using Tableau's interactive features, users can explore the data to gain insights into the food drive's impact and performance throughout 2024.

Key features of the dashboard include:

  • KPI Card for Key Features: Displays the total number of donation bags, houses, routes, volunteers, and average time spent, based on the selected criteria.
  • Total Number of Donation Bags by Ward: This map of Edmonton shows the distribution of donation bags across different wards, providing a clear comparison of how they are spread throughout the city.
  • Leading 10 Wards in Efficiency: Highlights the top 10 wards with the highest efficiency, showcasing their performance across key metrics.
  • Overall Volunteer Count: A bar chart comparing volunteer counts over different years, offering insights into trends and changes over time.
  • Contribution Leaders by Ward: A heatmap showing the contributions from each ward, using color gradients to highlight the areas with the highest and lowest contributions.
  • Donation Bags vs. Time Spent Chart: A visualization comparing the number of donation bags to the time spent, providing insights into the efficiency of the donation process.

Machine Learning

Before developing and evaluating machine learning models, we performed several data preparation steps to ensure high-quality inputs

Feature Engineering

To enhance the dataset, we introduced three new features:

  • Total Volunteers: The sum of Total Adult Volunteers and Total Youth Volunteers.
  • Donation Bags per Door: The number of donation bags divided by the number of doors.
  • Donation Bags per Route: The number of donation bags divided by the number of routes.

Additionally, we applied one-hot encoding to the Wards feature to handle categorical data and ensure all variables were properly formatted for modeling.

Data Splitting and Normalization

We split the data into training and testing sets, using 2023 data for training and 2024 data for testing. This approach allowed us to validate model performance on unseen data. To maintain consistency across numerical features, we applied normalization, ensuring all values were on a comparable scale before feeding them into the models.

Model Development and Evaluation

Following data preparation, we implemented and tested six different machine learning models for two prediction tasks:

  • Total number of donation bags.
  • Time spent for each ward.

Each model was evaluated to identify the most accurate one for each prediction task. The results below summarize their performance and effectiveness.

Model MSE RMSE MAE Adjusted R²
Linear Regression 3393.986256 58.257929 26.828851 -0.100185 -0.168338
Polynomial Regression 49.838645 7.059649 2.388835 0.983844 1.146869
Decision Tree Regression 2356.665557 48.545500 8.232945 0.236070 0.188747
Random Forest Regression 1990.524740 44.615297 8.457754 0.354757 0.314786
Gradient Boosting Regression 2144.987415 46.314009 8.164502 0.304687 0.261615
K-Nearest Neighbors Regression 3092.228686 55.607811 17.474875 -0.002368 -0.064461
Table 5: Performance Metrics for Models Predicting Total Donation Bags

Based on the results, the best model for predicting total donation bags is Polynomial Regression, as it achieves the lowest RMSE (7.059649) and MAE (2.388835) while attaining the highest R² score (0.983844), indicating a strong fit and high predictive performance.

Model MSE RMSE MAE Adjusted R²
Linear Regression 1.583989 1.258566 0.917151 0.075887 2.771216
Polynomial Regression 0.708581 0.841772 0.634814 0.586608 1.014787
Decision Tree Regression 0.192435 0.438674 0.356527 0.887732 1.215181
Random Forest Regression 0.216073 0.464836 0.377927 0.873941 1.241613
Gradient Boosting Regression 0.256885 0.506838 0.391840 0.850131 1.287249
K-Nearest Neighbors Regression 0.278344 0.527583 0.394887 0.837612 1.311244
Table 6: Performance Metrics for Models Predicting Time Spent

For predicting time spent, the Decision Tree Regression model stands out as the best among the listed options. It achieves the lowest RMSE (0.438674) and MAE (0.356527), coupled with a high positive R² (0.887732) and Adjusted R² (1.215181), indicating superior accuracy and a strong fit to the data compared to the other models.

Model Optimization:

For the Polynomial Regression model used to predict total donation bags, we opted not to perform additional tuning to avoid the risk of overfitting. Since the metrics were already acceptable, with an R² score of 0.98, further increasing model complexity could lead to diminished generalization and overfitting the training data.

Advanced Analysis:

We used the Polynomial Regression and Decision Tree models to predict the number of donation bags and time spent per Ward for 2025. Below are some key insights based on the predicted values.

Projected Total Number of Predicted Donation Bags for 2025
Figure 3: Projected Total Number of Predicted Donation Bags for 2025 Description: This figure visualizes the estimated number of donation bags for 2025 based on the best-performing predictive model. It provides insights into expected donation trends, helping to anticipate resource needs and optimize collection efforts.

The predicted number of donation bags for next year shows a steady increase. Starting at 14,817 in 2023 and 14,751 in 2024, the total number of donation bags is expected to grow, reaching 16,600 in 2025.

12-Month Outlook of Donation Bags: Top and Bottom 3 Stakes
Figure 4: 12-Month Outlook of Donation Bags: Top and Bottom 3 Stakes Description: This figure presents the projected donation bag counts over the next 12 months, highlighting the top three and bottom three Sakes based on expected contributions. It helps identify areas with the highest and lowest predicted donations, supporting targeted outreach and resource allocation.

The 12-month outlook for donation bags reveals the top and bottom-performing Stakes. The top three Stakes, which are expected to contribute the most to donation bags, are Gateway, Bonnie Doon, and Riverbend. On the other hand, the bottom three Stakes, contributing fewer donation bags, are YSA, Edmonton North, and Riverbend.

12-Month Outlook of Donation Bags: Top and Bottom 10 Wards
Figure 5: 12-Month Outlook of Donation Bags: Top and Bottom 10 Wards Description: This figure displays the projected donation bag counts over the next 12 months, identifying the top 10 and bottom 10 Wards based on predicted contributions. These insights help prioritize support and optimize donation collection efforts across different areas.

The 12-month outlook for donation bags reveals the top and bottom-performing Wards. The top 10 Wards expected to contribute the most donation bags are Lee Ridge, Crawford Plains, Silver Berry, Clareview, Blackmud Creek, Griesbach, Londonderry, Griesbach, Ellerslie, Rabbit Hill and Terwillegar. On the other hand, the bottom 10 Wards, which are projected to contribute fewer donation bags, include Mill Creek YSA, Lago Lindo, Onoway, Whitemud Creek YSA, Devon, Beaumont, Wild Rose, Wainwright, Windsor Park, and Pioneer. These insights show a notable variation in donation contributions across different Wards.

12-Month Outlook of Effectiveness: Top and Bottom 3 Stakes
Figure 6: 12-Month Outlook of Effectiveness: Top and Bottom 3 Stakes Description: This figure illustrates the projected effectiveness of donation collection efforts over the next 12 months, highlighting the top three and bottom three Stakes based on performance metrics. It provides a comparison of areas with the highest and lowest expected impact, helping to focus resources where they are most needed.

The top 3 Stakes with the highest effectiveness (i.e., they are expected to generate the most donation bags per unit of time spent) are Gateway, Riverbend and Bonnie Doon. On the other hand, the bottom 3 Stakes with the lowest effectiveness, meaning they are expected to have the least donation bags per unit of time spent, are YSA, Edmonton North, and Riverbend.

12-Month Outlook of Effectiveness: Top and Bottom 10 Wards
Figure 7: 12-Month Outlook of Effectiveness: Top and Bottom 10 Wards Description: This figure showcases the projected effectiveness of donation collection efforts over the next 12 months, highlighting the top 10 and bottom 10 Wards based on performance metrics. It offers valuable insights into where donation collection efforts are expected to be most and least effective, guiding targeted strategies.

The top 10 Wards with the highest effectiveness, meaning they are expected to generate the most donation bags per unit of time spent, are Lee Ridge, Silver Berry, Clareview, Rio Vista, Woodbend, Coronation Park, Londonderry, Greenfield, Clareview, Blackmud Creek and Griesbach. These Wards are predicted to be more efficient in converting time spent into donation bags.

In contrast, the bottom 10 Wards with the lowest effectiveness, meaning they are expected to have the least donation bags per unit of time spent, include Mill Creek YSA, Lago Lindo Branch, Onoway, Whitemud Creek YSA, Devon, Beaumont, Strathcona Married Student, Wild Rose, Namao and Forest Heights. These Wards are projected to require more time to achieve similar numbers of donation bags, reflecting a lower efficiency in their donation efforts.

Deployment

The final application was divided into six sections: the Information Page, Dashboard Page, Trends Page, Donation Bags Prediction Page, Time Spent Prediction Page, and Route Mapping Application Page. Each page has a distinct feature designed to deliver specific insights and valuable information to its users, ensuring a comprehensive experience. Together, these sections allow users to easily navigate through different functionalities, making data-driven decisions more accessible and efficient. Figure 8 shows the application’s dashboard page.

Interactive Dashboard of Deployed Edmonton Food Drive Application
Figure 8: Interactive Dashboard of Deployed Edmonton Food Drive Application Description: This figure showcases the interactive interface of the Edmonton Food Drive Application, developed to enhance food donation logistics in Edmonton. The application integrates machine learning and user-friendly tools, empowering stakeholders to optimize donation collection and volunteer coordination.

The application was deployed on Tableau, where interactive visualizations were created to represent donation trends, volunteer participation, and route mapping insights.

  • Route mapping was further enhanced using Hugging Face's Gradio, which allowed users to interactively explore donation routes.
  • A chatbot, also embedded using Gradio, provided users with quick responses to queries related to routes and donation processes.

Route Mapping Application:

The Route Mapping Application was developed in response to the client's recurring challenges with generating accurate and efficient maps for volunteer routes. The previous process involved manually printing portions of the Edmonton map, highlighting routes by hand, and then distributing the maps to volunteers, which was time-consuming and prone to errors. This manual approach not only slowed down operations but also increased the risk of mistakes that could affect the efficiency of the donation collection process. Our application simplifies and automates route generation and visualization, enhancing overall efficiency, accuracy, and ease of use for volunteers. Below are images of the manually printed maps that were previously used, highlighting the need for this more efficient solution.

Example of Manually Printed Maps Used for Volunteer Allocation
Figure 9: Example of Manually Printed Maps Used for Volunteer Allocation Description: This figure presents an example of the manually printed maps utilized for volunteer allocation during the Edmonton Food Drive. Annotated with route boundaries and key landmarks, these maps were created to guide volunteers in navigating their assigned areas efficiently. These manually marked maps emphasize the need for clear route planning and highlight the potential improvements that can be made through automated map generation tools.
Before and After: Map Generation Comparison Using Fixed Mode.
Figure 10: Before and After: Map Generation Comparison Using Fixed Mode. Description: Fixed Mode, in contrast to Custom Mode, is designed for route mapping by focusing on specific predefined routes. The process involves identifying hot zone addresses, inputting the required parameters into the application, generating the map, downloading it, and distributing it to the volunteers. Hot zone addresses refer to homes that consistently donate bags, making them crucial for streamlining the donation collection process and optimizing volunteer efforts.

The application offers two modes: Fixed Mode and Custom Mode. The Fixed Mode aims to digitize the map generation process for our client, streamlining their workflow. Custom Mode, on the other hand, is designed for long-term planning, generating maps based on identified hot zones to enhance route efficiency.

To generate maps in Fixed Mode, the client only needs to select the desired ward and route, click "Submit," download the generated map, and then easily email it to the volunteers. This streamlined process eliminates the need for manual map creation, saving time and effort. The provided image shows the before and after results of generating maps using Fixed Mode, highlighting the efficiency and ease of the new approach.

Before and After: Map Generation Comparison Using Custom Mode
Figure 11: Before and After: Map Generation Comparison Using Custom Mode Description: The image compares the manual and automated map generation processes. The pins represent the hot zone addresses from Routes 1, 2, and 3. Previously, the client had to manually input these six addresses, but now the application calculates the optimal route order based on the distance between them. This ensures that volunteers follow the most efficient path, saving time. Volunteers no longer need to cover all three routes; instead, they can focus on specific portions of each route, significantly improving efficiency and streamlining the donation collection process.

This methodology not only highlights the strengths of the analysis but also shows areas for improvement in data collection and coverage to enhance future decision-making processes.

Results & Findings

The Edmonton Food Drive Project yielded several valuable insights and practical outcomes through the analysis and modeling of the collected data. These findings are categorized into key areas of operational improvement: Data collection, Data Analysis, Predictive Modelling, and Application Deployment.

Data Collection

Key Observations:

The data revealed notable year-over-year trends, with some Wards exhibiting consistent donation patterns, while others showed significant variability in donation volumes.

Belmead Ward, despite being the focus of detailed analysis, highlighted limitations in data completeness, as not all routes were accounted for due to the granularity of volunteer-reported data.

Data Analysis

2024 vs 2023 EFD Highlights
Figure 12: 2024 vs 2023 EFD Highlights Description: This figure compares key metrics and outcomes from the 2024 and 2023 Edmonton Food Drive, highlighting the improvements and differences in donation collection and volunteer coordination between the two years. The comparison provides insights into the effectiveness of new strategies and tools implemented in 2024.

Compared to the 2023 food drive, the 2024 results showed a decrease in several key metrics: the number of donation bags, number of volunteers, number of houses, and average time spent per route decreased by 0.4%, 38.17%, 38.17%, and 6.67%, respectively.

Top and Bottom Three Stakes of 2023 and 2024
Figure 13: Top and Bottom Three Stakes of 2023 and 2024 Description: This figure compares the top and bottom three Stakes for the Edmonton Food Drive in 2023 and 2024, showcasing changes in donation levels and performance across different stakes. The comparison helps identify areas of improvement and highlights the impact of any new strategies implemented in 2024.

The top Stakes in 2024 remained largely consistent with 2023, with Gateway, Bonnie Doon, Riverbend, Edmonton North, and YSA leading the rankings. However, Riverbend and Bonnie Doon swapped positions, indicating a slight shift in their relative performance between the two years.

Top and Bottom Five Wards of 2023 and 2024
Figure 14: Top and Bottom Five Wards of 2023 and 2024 Description: This figure compares the top and bottom five Wards for the Edmonton Food Drive in 2023 and 2024, highlighting shifts in donation patterns and volunteer efforts across different areas. The analysis provides insights into which Wards saw the most significant improvements and where additional attention may be needed.

In 2024, Crawford Plains stayed in the top 5, just like in 2023. Some new Wards, like Terwillegar Park and Griesbach, joined the top ranks. On the other hand, Wards like Coronation Park, Drayton Valley, and Pioneer moved into the bottom 5 in 2024, replacing last year's bottom Wards like Devon and Mill Creek YSA.

Predictive Modeling

Prediction of Total Donation Bags

We developed six machine learning models to predict the total number of donation bags for each ward for 2025. Key insights from the model evaluation are summarized below:

Best Model: Polynomial Regression emerged as the most effective model, achieving the lowest RMSE (7.0596) and MAE (2.3888), coupled with the highest R² score (0.9838). This indicates excellent accuracy and consistency in predicting donation volumes.

Key Observations: Polynomial Regression outperformed other models, such as Random Forest and Gradient Boosting, due to its ability to capture non-linear relationships in the data effectively.

Prediction of Time Spent
For predicting the time required to complete donation routes, six models were evaluated. The following insights were observed:

Best Model: Decision Tree Regression provided the most accurate predictions, achieving the lowest RMSE (0.4387) and MAE (0.3565), along with a high R² score (0.8877) and Adjusted R² (1.2152). This model effectively balanced simplicity and performance.

Key Observations: Decision Tree Regression outperformed Polynomial Regression and Gradient Boosting for this task due to its flexibility in handling variations in the data, such as route complexities and volunteer differences.

Additionally, a geospatial analysis was integrated to design digitized donation route maps, identifying areas with the highest potential for donations. This task aimed to streamline logistics and maximize resource allocation in future drives.

We performed hyperparameter tuning on the Decision Regression model for predicting time spent, but it did not result in significant improvements. The tuned model achieved a Mean Squared Error (MSE) of 0.2041, Root Mean Squared Error (RMSE) of 0.4517, Mean Absolute Error (MAE) of 0.3652, R-squared (R²) of 0.8810, and Adjusted R-squared of 1.2282.

Visualizing the Behavior of Polynomial Regression
Figure 15: Visualizing the Behavior of Polynomial Regression Description: This figure presents key visualizations from the machine learning model evaluation process used in predicting donation volumes for the Edmonton Food Drive. The graphs provide insights into the model's performance, residual behavior, and training progress. Residual Plot (Top Left): Depicts the residuals (differences between actual and predicted values) against predicted values; Actual vs. Predicted Values (Top Right): Compares the predicted donation volumes to the actual values. Most predictions align closely with the actual values along the diagonal line, indicating good model performance, except for a few outliers. Distribution of Residuals (Bottom Left): Shows the distribution of residuals to assess their normality. Learning Curve (Bottom Right): Displays the training and cross-validation scores as a function of training size. The rapid convergence of training and cross-validation scores with minimal error suggests the model is well-trained with low variance.

These visualizations from the model evaluation highlight the model's strengths, such as its low error rates and high predictive performance for most predictions, while also identifying areas, like residual biases, that could be optimized for better results.

The models successfully predicted both donation volumes and time spent, enabling stakeholders to make informed decisions for future food drives.

Application Deployment

The application was deployed to provide stakeholders with an interactive, user-friendly platform for predicting donation outcomes and enhancing logistics. The best performing model was deployed on Hugging Face's Gradio and embedded in Tableau to aid decision-making for future food drives.

User Interface of the Donation Bags Prediction Module
Figure 16: User Interface of the Donation Bags Prediction Module Description: This figure represents the user interface of the Edmonton Food Drive application, an interactive tool designed to predict donation outcomes based on specific input parameters. The application provides an accessible platform for stakeholders to forecast donation volumes, enabling more efficient resource allocation and improved decision-making.

Application Input Parameters for Prediction:

Ward: Selects the specific ward for which predictions are needed.
Time Spent (Minutes): Captures the estimated time volunteers spend completing routes.
Number of Doors: Inputs the total number of doors covered in the selected ward.
Number of Routes: Allows users to specify the number of routes included in the analysis.
Year: Enables predictions for future food drives, ranging from 2025 to 2030.
Total Volunteers: Specifies the number of volunteers assigned to the task.

The application uses the provided inputs to generate a Predicted Total Donation Bags value. This prediction helps stakeholders gauge the effectiveness of their planning and resource allocation for upcoming drives.

Challenges Faced:

The Edmonton Food Drive Project encountered several challenges that impacted data collection, analysis, and prediction accuracy. These challenges, though significant, provided valuable insights for improving future food drives.

Data Collection Limitations:

Due to resource constraints, data was collected only from select drop-off locations in Bearspaw, Londonderry, Riverbend, Gateway, and Bonnie Doon. This limited coverage resulted in incomplete datasets that did not fully represent all participating areas in Edmonton.

Multiple volunteers managing the same route and dropping off large numbers of donation bags led to incomplete or duplicate data entries, further complicating the accuracy of the collected data.

Inconsistencies in Dataset Structures:

The datasets for 2023 and 2024 contained discrepancies due to adaptations made in the new form to improve user entries. While these changes aimed to enhance usability, they introduced differences in feature structures, requiring significant effort to reconcile and standardize the data for analysis. Additionally, the absence of uniform data entry standards across Wards contributed to inconsistencies, creating additional challenges during preprocessing.

Prediction Discrepancies:

Predicted donation growth figures based on the collected data did not align with the client’s internal reports, which indicated an overall increase in donations in 2024 compared to 2023.

To address this discrepancy, data refilling was performed to adjust the 2024 figures and bring them closer to actual trends.

Operational Challenges:

The granularity of route information made it difficult to standardize data inputs across multiple Wards.
The lack of a centralized system for data entry led to variations in how data was recorded and submitted, further complicating the analysis.

Conclusions & Recommendations:

To enhance overall effectiveness, a more balanced allocation of volunteers should be considered, with a focus on both improving the performance of lower-performing areas and maintaining the momentum in top-performing wards and stakes. The following recommendations are proposed:

  • Polynomial Regression is recommended for forecasting donation volumes, particularly when capturing complex patterns in historical data.
  • Decision Tree Regression is ideal for predicting time spent, providing actionable insights for route optimization and volunteer allocation.

These predictions can guide planning and resource allocation by Identifying wards expected to generate the highest donation volumes and estimating the time required for volunteers to complete routes efficiently, improving logistical coordination.

Continued improvement in data collection processes (e.g., standardizing volunteer data and digitizing route information) will further enhance prediction accuracy and the utility of these models.

By combining data insights and predictive modeling, this project provides actionable recommendations for improving the logistics of Edmonton's Food Drive initiative.

The project successfully achieved its goals of recommending improvements in the food donation process in the Edmonton Food Drive. Tools to predict donation trends and time requirements were introduced, helping volunteers and organizers plan better. The route mapping application simplifies volunteer coordination and saves significant effort compared to the traditional manual processes. Additionally, interactive dashboards make it easier for stakeholders to understand and analyze the data, leading to better decision-making. Overall, the project streamlines operations and contributes to a more effective and efficient food donation drive.

References

Edmonton's Food Bank Fundraising Efforts. (n.d.). Edmonton Journal.
https://edmontonjournal.com/news/local-news/edmontons-food-bank-fundraising

Where to Build Food Banks: A Machine Learning Approach. (n.d.). Purdue University.
https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1661&context=jpur

Edmonton’s Food Bank. (2024) Winter Gleanings 2024.
https://www.edmontonsfoodbank.com/documents/267/Winter_Gleanings_2024_-_Final.pdf

Food Bank Operations Web-Based Software. (n.d.). Gao Group, Cornell University.
https://gao.cee.cornell.edu/software-2/food-bank-operations-web-based-software/

Researchers Use Machine Learning to Assist State Food Pantries with Distribution. (2022). Auburn University Newsroom.
https://ocm.auburn.edu/newsroom/news_articles/2022/10/070927-researchers-machine-assists-food-pantries.php

Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach. (n.d.). arXiv.
https://arxiv.org/pdf/2410.15420

Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation. (2024). arXiv. https://arxiv.org/abs/2406.06363

Edmonton Food Drive Dashboard. (2024). Tableau Public.
https://public.tableau.com/app/profile/kendrick.kent.moreno/viz/EFD2024Dashboard/EFDDashboard-Main

Government of Alberta. (n.d.). Property assessments: Edmonton region. Alberta Regional Dashboard. https://regionaldashboard.alberta.ca/region/edmonton/property-assessments/#/?from=2018&to=2022

Filling in the Blanks: Understanding Missing Data

In this talk, we explore the role of survey weights and replicate weights in analyzing complex survey data. Using analogies and accessible language, we highlight the key intuitive ideas behind why these tools are essential for drawing sound statistical conclusions from survey data. To bridge theory and practice, we also review software options for working with survey data and demonstrate how to apply different types of weights—including cross-sectional, longitudinal, normalized (or standardized), and bootstrap weights.

Presenter: Claude Girard, Senior Methodologist, Data Analysis Resource Centre

To register for the English webinar, fill out the following form:

To register for the French webinar, fill out the following form:

Summary of the Evaluation of the Disaggregated Data Action Plan

The Disaggregated Data Action Plan (DDAP), established through Budget 2021, is a whole-of-government approach led by Statistics Canada to collect, analyze, and disseminate disaggregated data pertaining to the four employment equity (EE) groups: women, Indigenous Peoples, racialized populations, and persons with disabilities. Where relevant and possible, it also covers lower levels of geography and other equity- and rights-seeking groups (e.g., Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people and those who use other terms related to gender or sexual diversity [2SLGBTQI+ population]; immigrants; low-income populations; children; and seniors). The DDAP aims to support governmental and societal efforts to address known inequalities by integrating fairness and inclusion considerations into decision making. Budget 2021 allocated $172 million to support the DDAP's first five years, and another $36.3 million was allocated to sustain ongoing related activities.

The DDAP governance structure included agency-level and interdepartmental governance bodies. Within Statistics Canada, the Assistant Chief Statisticians (ACS) Steering Committee is supported by the Director General Governance Committee and its related working groups. External bodies include the Assistant Deputy Minister Federal Advisory Committee on Disaggregated Data and the Federal Advisory Committee on the Disaggregation of Justice and Community Safety Statistics. Administrative and logistical support is provided by the DDAP Secretariat.

The DDAP is expected to contribute to building a more equitable Canada by advancing more representative data collection, enhancing statistics on diverse populations, addressing systemic racism and gender discrimination, and integrating fairness and inclusion into decision-making processes to achieve the intended impact. The DDAP included various projects aimed at enhancing the agency's capacity to disaggregate data. Agency leadership also promoted a cultural shift toward prioritizing disaggregated data and analysis. Although the majority (66%) of funding was allocated to the Social, Health and Labour Statistics Field (Field 8), almost all fields benefited from DDAP initiatives.

Since its inception, the DDAP has supported a variety of projects and activities through core funding and targeted calls for applications. Ten core-funded projects formed the foundational of the DDAP's commitment, receiving continuous funding across multiple fiscal years or until project completion. Projects funded through the call for applications process were selected based on criteria aligned with the DDAP's goals and were intended to support early-stage or ongoing work. By 2024/2025, the DDAP supported 74 projects and activities. As the initiative moves into the final year of its five-year funding, the $172 million has been fully allocated.

The objective of this evaluation is to provide credible and neutral information on the relevance, design and delivery, and performance of the DDAP. The scope of the evaluation covered the DDAP's design and delivery, including its governance structure; the sustainability of new activities within the agency's ongoing operations; and progress toward its intended short-, medium-, and long-term results.

Key findings and recommendations

The DDAP is supporting Canada's information needs and aligns with government-wide priorities, as well as Statistics Canada's strategic priorities and core responsibilities. DDAP projects and activities align with the federal government's data priorities and needs outlined in recent federal budgets, strategies, and reports, as well as agency-wide priorities for more disaggregated data, leading-edge methods and data integration described in mandate letters and strategic documents.

The DDAP governance structure and mechanisms facilitate oversight across the federal government and the agency, but they focus primarily on monitoring implementation rather than tracking progress toward intended outcomes. While the governance structure provides some elements of accountability, gaps remain, and the emphasis on implementation over strategic direction limits its ability to facilitate the achievement of medium- and long-term outcomes.

Consultative efforts informed some DDAP priorities and projects; however, concerns remain that some disaggregated data products are released without sufficient consultation, risking the advancement of a deficit narrative. While DDAP activities built upon, and in some cases improved, existing infrastructure, resources and capabilities, initiatives dependent on oversampling are unsustainable. Efforts to sustain new DDAP-funded activities by integrating them within the agency's ongoing operations were limited. Cost-recovery agreements could help, but the current fiscal climate limits this option.

Although the DDAP is making progress toward its intended results, some areas require further attention. Awareness-raising and training activities have helped participants better understand data disaggregation, yet some barriers to applying and acquiring disaggregated data knowledge were identified. The DDAP is helping to address information gaps, but the reliance on oversampling to produce disaggregated data from flagship surveys was identified as a challenge to sustainably doing so. The action plan is improving certain aspects of data quality; however, some issues with relevance and accessibility persist.

The DDAP is supporting and reinforcing a cultural shift that prioritizes disaggregated data and intersectional analysis by expanding the availability of disaggregated data and related products. However, further progress will require greater emphasis on awareness and training. Some early evidence suggests that the DDAP is positively impacting data users, but these impacts are limited in part by data users' and decision makers' capacities to understand and use disaggregated data and analytical products. There is also some concern that the DDAP's long-term outcome is outside Statistics Canada's influence and mandate.

In light of these findings, the following recommendations are proposed.

Recommendation 1

The ACS of Field 8 should ensure that the DDAP governance is strengthened to directly support the initiative in achieving its intermediate and ultimate outcomes by:

  1. updating DDAP oversight committees' mandates to clearly articulate accountabilities, particularly around the provision of strategic direction and the achievement of the DDAP's medium- and long-term objectives
  2. focusing future projects and activities on sustainability
  3. establishing effective communication (e.g., leveraging proposed governance tables) to ensure that priorities, expected outcomes, and performance measures are well understood by staff, key partners and stakeholders (e.g., on a regular basis, using two-way dialogue).

Recommendation 2

The ACS of Field 8 should ensure that outreach and engagement efforts are comprehensive and consistent, so that the ongoing needs of data users, including those from EE groups, are understood and being met by the DDAP's disaggregated data and analytical products.

Evaluation of the Disaggregated Data Action Plan

Evaluation Division
July 2025

Contents

The report in short

The Disaggregated Data Action Plan (DDAP), established through Budget 2021, is a whole-of-government approach led by Statistics Canada to collect, analyze, and disseminate disaggregated data pertaining to the four employment equity (EE) groups: women, Indigenous Peoples, racialized populations, and persons with disabilities. Where relevant and possible, it also covers lower levels of geography and other equity- and rights-seeking groups (e.g., Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people and those who use other terms related to gender or sexual diversity [2SLGBTQI+ population]; immigrants; low-income populations; children; and seniors). The DDAP aims to support governmental and societal efforts to address known inequalities by integrating fairness and inclusion considerations into decision making. Budget 2021 allocated $172 million to support the DDAP's first five years, and another $36.3 million was allocated to sustain ongoing related activities.

The DDAP governance structure included agency-level and interdepartmental governance bodies. Within Statistics Canada, the Assistant Chief Statisticians (ACS) Steering Committee is supported by the Director General Governance Committee and its related working groups. External bodies include the Assistant Deputy Minister Federal Advisory Committee on Disaggregated Data and the Federal Advisory Committee on the Disaggregation of Justice and Community Safety Statistics. Administrative and logistical support is provided by the DDAP Secretariat.

The DDAP is expected to contribute to building a more equitable Canada by advancing more representative data collection, enhancing statistics on diverse populations, addressing systemic racism and gender discrimination, and integrating fairness and inclusion into decision-making processes to achieve the intended impact. The DDAP included various projects aimed at enhancing the agency's capacity to disaggregate data. Agency leadership also promoted a cultural shift toward prioritizing disaggregated data and analysis. Although the majority (66%) of funding was allocated to the Social, Health and Labour Statistics Field (Field 8), almost all fields benefited from DDAP initiatives.

Since its inception, the DDAP has supported a variety of projects and activities through core funding and targeted calls for applications. Ten core-funded projects formed the foundational of the DDAP's commitment, receiving continuous funding across multiple fiscal years or until project completion. Projects funded through the call for applications process were selected based on criteria aligned with the DDAP's goals and were intended to support early-stage or ongoing work. By 2024/2025, the DDAP supported 74 projects and activities. As the initiative moves into the final year of its five-year funding, the $172 million has been fully allocated.

The objective of this evaluation is to provide credible and neutral information on the relevance, design and delivery, and performance of the DDAP. The scope of the evaluation covered the DDAP's design and delivery, including its governance structure; the sustainability of new activities within the agency's ongoing operations; and progress toward its intended short-, medium-, and long-term results.

Key findings and recommendations

The DDAP is supporting Canada's information needs and aligns with government-wide priorities, as well as Statistics Canada's strategic priorities and core responsibilities. DDAP projects and activities align with the federal government's data priorities and needs outlined in recent federal budgets, strategies, and reports, as well as agency-wide priorities for more disaggregated data, leading-edge methods and data integration described in mandate letters and strategic documents.

The DDAP governance structure and mechanisms facilitate oversight across the federal government and the agency, but they focus primarily on monitoring implementation rather than tracking progress toward intended outcomes. While the governance structure provides some elements of accountability, gaps remain, and the emphasis on implementation over strategic direction limits its ability to facilitate the achievement of medium- and long-term outcomes.

Consultative efforts informed some DDAP priorities and projects; however, concerns remain that some disaggregated data products are released without sufficient consultation, risking the advancement of a deficit narrative. While DDAP activities built upon, and in some cases improved, existing infrastructure, resources and capabilities, initiatives dependent on oversampling are unsustainable. Efforts to sustain new DDAP-funded activities by integrating them within the agency's ongoing operations were limited. Cost-recovery agreements could help, but the current fiscal climate limits this option.

Although the DDAP is making progress toward its intended results, some areas require further attention. Awareness-raising and training activities have helped participants better understand data disaggregation, yet some barriers to applying and acquiring disaggregated data knowledge were identified. The DDAP is helping to address information gaps, but the reliance on oversampling to produce disaggregated data from flagship surveys was identified as a challenge to sustainably doing so. The action plan is improving certain aspects of data quality; however, some issues with relevance and accessibility persist.

The DDAP is supporting and reinforcing a cultural shift that prioritizes disaggregated data and intersectional analysis by expanding the availability of disaggregated data and related products. However, further progress will require greater emphasis on awareness and training. Some early evidence suggests that the DDAP is positively impacting data users, but these impacts are limited in part by data users' and decision makers' capacities to understand and use disaggregated data and analytical products. There is also some concern that the DDAP's long-term outcome is outside Statistics Canada's influence and mandate.

In light of these findings, the following recommendations are proposed.

Recommendation 1

The ACS of Field 8 should ensure that the DDAP governance is strengthened to directly support the initiative in achieving its intermediate and ultimate outcomes by:

  1. updating DDAP oversight committees' mandates to clearly articulate accountabilities, particularly around the provision of strategic direction and the achievement of the DDAP's medium- and long-term objectives
  2. focusing future projects and activities on sustainability
  3. establishing effective communication (e.g., leveraging proposed governance tables) to ensure that priorities, expected outcomes, and performance measures are well understood by staff, key partners and stakeholders (e.g., on a regular basis, using two-way dialogue).

Recommendation 2

The ACS of Field 8 should ensure that outreach and engagement efforts are comprehensive and consistent, so that the ongoing needs of data users, including those from EE groups, are understood and being met by the DDAP's disaggregated data and analytical products.

Acronyms and abbreviations

2SLGBTQI+
Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people and those who use other terms related to gender or sexual diversity
ACS
Assistant chief statistician
ADM
Assistant deputy minister
CSDS
Centre for Statistical and Data Standards
CSPS
Canada School of Public Service
DDAP
Disaggregated Data Action Plan
DG
Director general
EDI
Equity, diversity, and inclusion
EE
Employment equity
Field 6
Strategic Data Management, Methods and Analysis Field
Field 7
Census, Regional Services and Operations Field
Field 8
Social, Health and Labour Statistics Field
GBA Plus
Gender-based Analysis Plus
GC
Government of Canada
OGD
Other government department
SMC
Strategic Management Committee
UCASS
University and College Academic Staff System

What is covered

Background

Statistics Canada's mandate is to produce objective, high-quality data to help Canadians better understand their social, economic and environmental conditions to inform the development and evaluation of public policies and programs and improve decision making. However, as demonstrated by the COVID-19 pandemic, which resulted in uneven social and economic realities for various groups, Canadians' social, economic and environmental conditions are not universal. To address these disparities, more detailed data that are broken down—or disaggregated—into sub-categories such as gender, ethnocultural characteristics, age, sexual orientation, disability, and geography, are needed.

The Disaggregated Data Action Plan (DDAP), established through Budget 2021, is a whole-of-government approach led by Statistics Canada to collect, analyze, and disseminate disaggregated data pertaining to the four employment equity (EE) groups: women, Indigenous Peoples, racialized populations and persons with disabilities. Where relevant and possible, it also covers lower levels of geography and other equity- and rights-seeking groups (e.g., Two-Spirit, lesbian, gay, bisexual, transgender, queer and intersex people and those who use other terms related to gender or sexual diversity [2SLGBTQI+ population]; immigrants; low-income populations; children; and seniors). The DDAP aims to support governmental and societal efforts to address known inequalities by integrating fairness and inclusion considerations into decision making. Budget 2021 allocated $172 million to support the DDAP's first five years, and another $36.3 million was allocated to sustain ongoing related activities.

The DDAP is guided by the following four principles:

  1. Data and analyses should be disaggregated at the lowest possible level of population detail while including Gender-based Analysis Plus (GBA Plus) considerations and respecting quality and confidentiality.
  2. Analysis should focus on intersectionality (e.g., young, Black, women), as opposed to binary interactions. A GBA Plus lens should be applied to data analysis.
  3. Statistics Canada's approved standards should be used for disaggregation across all programs.
  4. Data should be released at the lowest possible level of geography.

The DDAP activities are organized into five pillars outlined in Table 1.

Table 1. Pillars of the Disaggregated Data Action PlanFootnote 1
Expanding disaggregated data assets Increasing intersectional and longitudinal insights Access to enhanced disaggregated data Statistical standards Enhanced engagement and communication
To provide more information on populations at various levels of geography To shed light on inequities and promote fairness and inclusion To give access for the public, all levels of government, and other data users To review, develop and promote statistical standards to enable data comparisons over time and across jurisdictions To better reflect the experiences of population groups and meet the needs of data users

Governance

The DDAP's governance structure included agency-level and interdepartmental governance bodies (see Figure 1).

Within Statistics Canada, the Strategic Management Committee (SMC), the agency's senior governance body chaired by the chief statistician and composed of assistant chief statisticians (ACSs), provides broad strategic direction for the agency, including the DDAP.

The ACS Steering Committee makes high-level decisions and is responsible for coordinating, facilitating, and monitoring the implementation of the DDAP within Statistics Canada. This committee is co-chaired by the ACS of the Social, Health, and Labour Statistics Field (Field 8) and the ACS of the Census, Regional Services and Operations Field (Field 7).

The Directors General (DG) Governance Committee is responsible for making recommendations to the ACS Steering Committee for decisions and supporting the monitoring of the DDAP projects. The DG Governance Committee is co-chaired by the DGs in the Strategic Data Management, Methods and Analysis Field (Field 6) and Field 8.

In the first two fiscal years of the DDAP, six agency-level working groups were established to address its five pillars: Engagement and Communication, Data Standards, Data Development and Acquisition, Access and Dissemination, Analytical Insights, and Statistical Infrastructure. These groups reported to the DG Governance Committee and were responsible for making recommendations to support DDAP activities.

The DDAP Secretariat, in the Centre for Population and Social Statistics of Field 8, provides administrative and logistical support to the agency and external DDAP committees, except the SMC.

The external Assistant Deputy Minister (ADM) Federal Advisory Committee on Disaggregated Data advises Statistics Canada on DDAP implementation and is responsible for promoting collaboration and coordination among federal departments; identifying and addressing data needs; and promoting program-level disaggregated data collection strategies, including the use of national data standards. The committee is co-chaired by the ACS of Field 8 and the Assistant Secretary to the Cabinet, Priorities and Planning and Results and Delivery Unit, of the Privy Council Office. Membership includes ADMs from across the federal government.Footnote 2 Four interdepartmental working groups established in 2023 reported to the ADM committee: Engagement and Collaboration, Access to Disaggregated Data, Privacy and Confidentiality, and Expanding Data Assets.

The external Federal Advisory Committee on the Disaggregation of Justice and Community Safety Statistics is leveraged to provide advice specific to justice-related DDAP projects.

Figure 1. Disaggregated Data Action Plan governance structure
Figure 1. Disaggregated Data Action Plan governance structure
Description - Figure 1. Disaggregated Data Action Plan governance structure

Figure 1 depicts an organizational chart of the Disaggregated Data Action Plan (DDAP) governance structure.

Within Statistics Canada, there are the following committees:

  • The Strategic Management Committee (SMC) chaired by the chief statistician and composed of assistant chief statisticians (ACSs).
  • The ACS Steering Committee co-chaired by the ACS of the Social, Health, and Labour Statistics Field (Field 8) and the ACS of the Census, Regional Services and Operations Field (Field 7).
  • The Directors General (DG) Governance Committee co-chaired by the DGs in the Strategic Data Management, Methods and Analysis Field (Field 6) and Field 8. The DG Governance Committee is supported by six working groups, each co-led by the directors of Field 6 and Field 7:
    1. Engagement and Communication
    2. Data Standards
    3. Data Development and Acquisition
    4. Access and Dissemination
    5. Analytical Insights
    6. Statistical Infrastructure.

The DDAP Secretariat, located in Field 8, provides administrative and logistical support to the agency and external DDAP committees, except the SMC.

Outside of Statistics Canada, there are the two following committees:

  • The external Assistant Deputy Minister (ADM) Federal Advisory Committee on Disaggregated Data, which is co-chaired by the ACS of Field 8.
  • The external Federal Advisory Committee on the Disaggregation of Justice and Community Safety Statistics, which is co-chaired by the DG of Field 8.

Expected outcomes

The DDAP is expected to contribute to building a more equitable Canada by advancing more representative data collection, enhancing statistics on diverse populations, addressing systemic racism and gender discrimination, and integrating fairness and inclusion into decision making processes to achieve the intended impact. The DDAP's expected immediate, intermediate, and long-term outcomes are summarized in Table 2.

Table 2. Disaggregated Data Action Plan outcomesFootnote 3
Timeline Outcomes Associated performance indicators
Immediate outcomes (1 to 3 years)
  • An increased awareness and understanding of the need for disaggregated data and Gender-based Analysis Plus.
  • Increased data quality and decreased information gaps.
  • Increased and enhanced access to disaggregated data and detailed statistical information.
Proportion of indicators disaggregated for employment equity group (women, Indigenous Peoples, racialized populations, and persons with disabilities)
Intermediate outcome (3 to 5 years)
  • A change in culture that prioritizes the use and collection of disaggregated data, and—where possible—intersectional analyses to meet policy makers' and other data users' needs.
Not applicable
Long-term outcome (5 years and more)
  • Increasingly fair and inclusive policy, program, and legislation development across all levels of government and within society.
Not applicable

To achieve these outcomes, Statistics Canada has pursued and will continue to pursue the following core activities:

  1. administrative data development
  2. disaggregation of labour market indicators
  3. disaggregation of social indicators
  4. disaggregation of population health indicators
  5. disaggregation of web panel surveys
  6. longitudinal social data development program
  7. universal crime reporting expansion
  8. survey on not-for-profit board diversity
  9. survey on business conditions
  10. enhancements to the Centre for Gender, Diversity and Inclusion Statistics.

Funded projects and activities

The DDAP included various projects aimed at enhancing the agency's capacity to disaggregate data. Agency leadership also promoted a cultural shift towards prioritizing disaggregated data and analysis. Although the majority (66%) of funding was allocated to Field 8, almost all fields benefited from DDAP initiatives.

Since its inception, the DDAP has supported a variety of projects and activities through core funding and targeted calls for applications. Ten core-funded projects, which formed the foundation of the DDAP's commitment, and other specific activities for communication, engagement and training to support consultation and promotion for the DDAP received continuous funding across multiple fiscal years or until project completion. Projects funded through the call for applications process were selected based on criteria aligned with the DDAP's goals. These projects were reviewed and selected by the DG Governance Committee and were intended to support early-stage or ongoing work. For fiscal years 2022/2023 and 2023/2024, projects funded through the call for applications process received annual or multi-year funding. However, the call for applications in 2023/2024 was not as widely advertised as in 2022/2023, and only a few projects selected in Field 8 were chosen.

In 2022/2023, the DDAP's projects faced budgetary constraints as a result of the broader financial context within the agency, which affected project leads' ability to fully implement activities as planned. As new initiatives were funded, uncertainty arose about whether some projects could meet staffing requirements. To address this, additional funding amounts beyond the original allocations were planned for 2022/2023, and corrective actions were taken midway, including delaying some projects, reducing overall project budgets, and realigning funds to prioritize projects directly aligned with the DDAP's core objectives. By 2024/2025, the DDAP supported 74 projects and activities. As the initiative moves into the final year of its five-year funding, the $172 million has been fully allocated.

About the evaluation

Authority

The evaluation was conducted in accordance with the Treasury Board Policy on Results and Statistics Canada's Risk-Based Audit and Evaluation Plan (2024/2025 to 2028/2029).

Objective and scope

The objective of the evaluation is to provide credible and neutral information on the relevance, design and delivery, and performance of the DDAP.

The scope of the evaluation covered the design and delivery of the DDAP, including its governance structure; sustainability of new activities within the agency's ongoing operations; and progress towards its intended short-, medium-, and long-term results. The scope was established in collaboration with the office of primary interest, and the evaluation was conducted from November 2024 to February 2025. It followed a pre-consultation process with senior managers and project leads and included the development of a logic model during the planning phase.

Approach and methodology

The following three evaluation questions were identified:

  1. To what extent is the DDAP relevant in supporting federal needs and priorities, including Statistics Canada's?
  2. To what extent are the design and delivery of the DDAP facilitating the achievement of its intended outcomes?
  3. To what extent is the DDAP progressing towards its intended outcomes in the short, medium, and long term?

More information about the evaluation questions and related indicators can be found in Appendix A.

The data collection methods outlined in Figure 2 were used to inform the evaluation. The findings outlined in this report are based on the triangulation of these data collection methods.

Figure 2. Data collection methods
Figure 2. Data collection methods
Description - Figure 2. Data collection methods

Figure 2 outlines the methods used by the evaluation for data collection.

  • Internal interviews: Semi-structured interviews were conducted with members of the DG and ACS governance committees and project leads. 27 internal interviews were conducted with 33 people.
  • External interviews: Semi-structured interviews were conducted with stakeholders and data users from federal, provincial, and territorial governments; academia; and other organizations. 21 external interviews were conducted with 22 people.
  • Case studies: Three case studies were carried out, using a mixed methodology focusing on the DDAP Administrative Data Fund, statistical standards and internal training. Data collection methods for the case studies included:
    • a file review
    • a survey of Statistics Canada's Training participants. The sample size was 23 with a response rate of 31%.
    • 8 internal and external interviews
  • External survey: A survey of data users from federal, provincial, and territorial government, academia, and other organizations was conducted. The sample size was 370 with a response rate of 29%.
  • Document Review: A review of Statistics Canada's files and documents was carried out.

Two main limitations were identified, and mitigation strategies were employed, as outlined in Table 3.

Table 3. Limitations and mitigation strategies
Limitations Mitigation strategies
The perspectives gathered through external interviews may not be fully representative, as disaggregated data funded by the Disaggregated Data Action Plan (DDAP) are not always distinguishable as Statistics Canada data or as disaggregated data. Interviewees were selected using specific criteria to maximize strategic reach. Multiple recruitment strategies were used. Evaluators were able to find consistent overall patterns.
Detailed financial information on DDAP spending and its allocation across the agency was limited. The evaluation attempted to fill these gaps, to the extent possible, through the other lines of evidence, including interviews with key informants.

What we learned

Relevance

To what extent is the Disaggregated Data Action Plan relevant in supporting federal needs and priorities, including Statistics Canada's?

The Disaggregated Data Action Plan (DDAP) is supporting Canada's information needs and aligns with government-wide priorities, as well as Statistics Canada's strategic priorities and core responsibilities. DDAP projects and activities align with the federal government's data priorities and needs outlined in recent federal budgets, strategies, and reports, as well as agency-wide priorities for more disaggregated data, leading-edge methods and data integration described in mandate letters and strategic documents.

DDAP projects and activities align with the federal government's data priorities and needs outlined in recent federal budgets, strategies, and reports.

DDAP projects and activities that contribute to expanding disaggregated data assets, increasing intersectional analysis and enhancing access to disaggregated data align with several federal priorities and strategies seeking to understand social, economic and environmental issues; advance equity; address issues affecting vulnerable communities; and support evidence-based policy making.

  • In 2018, the Government of Canada (GC) called for greater data disaggregated by gender and other intersecting identify factors to support the implementation of its Gender Results Framework and its Women Entrepreneurship Strategy.
  • Disaggregated data broken down by race or ethnocultural origins and intersecting identities were collected and analyzed to support Building a Foundation for Change: Canada's Anti-Racism Strategy 2019–2022 and Changing Systems, Transforming Lives: Canada's Anti-Racism Strategy 2024–2028.
  • Budget 2021 prioritized the collection and use of disaggregated data to modernize Canada's justice system, strengthen evidence-based policy making and support the Quality of Life Framework to inform federal decision making and budgeting. Also in 2021, Canada's Federal Implementation Plan for the 2030 Agenda identified the need for federal departments to identify gaps in disaggregated data that inform the Sustainable Development Goals and to work in partnership with Statistics Canada to address data gaps.

The DDAP is well aligned with the 2023–2026 Data Strategy for the Federal Public Service, which calls on federal organizations to develop a whole-of-government approach to data standards to generate greater insights, reduce data duplication and enable interoperability. Both initiatives share a common vision and emphasize the importance of developing and integrating statistical standards and building capacity within the public service.

In addition to aligning with federal strategies and priorities, the DDAP's whole-of-government approach and pillars align with several recommendations from the Office of the Auditor General calling for

  • greater collaboration amongst federal departments, including Statistics Canada, to identify and prioritize disaggregated data needs to inform the Sustainable Development Goals
  • increased collection and use of disaggregated data to support GBA Plus to inform and evaluate policies and programs.

DDAP projects and activities are also aligned with agency-wide priorities and departmental strategies.

DDAP projects and activities are reflected in the agency's 2021 mandate letter, which called on Statistics Canada to support a whole-of-government approach to improving the collection, analysis and availability of disaggregated data. The DDAP's focus on enhanced access, data standards, and engagement and collaboration aligns with Statistics Canada's modernization strategy, which emphasizes user-centric service delivery, leading-edge methods and data integration, statistical capacity building and leadership, and sharing and collaboration. It also aligns with the Statistics Canada Data Strategy—specifically data discovery and interoperability.

Design and delivery

To what extent are the design and delivery of the Disaggregated Data Action Plan facilitating the achievement of its intended outcomes?

The Disaggregated Data Action Plan (DDAP) governance structure and mechanisms facilitate oversight across the federal government and the agency, but they focus primarily on monitoring implementation rather than tracking progress toward intended outcomes. While the governance structure provides some elements of accountability, gaps remain, and the emphasis on implementation over strategic direction limits its ability to facilitate the achievement of medium- and long-term outcomes.

Consultative efforts informed some DDAP priorities and projects; however, concerns remain that some disaggregated data products are released without sufficient consultation, risking the advancement of a deficit narrative. While DDAP activities built on, and in some cases improved, existing infrastructure, resources and capabilities, initiatives dependent on oversampling are unsustainable. Efforts to sustain new DDAP-funded activities by integrating them within the agency's ongoing operations were limited. Cost-recovery agreements could help, but the current fiscal climate limits this option.

The DDAP governance structure and mechanisms facilitate oversight across the federal government and the agency, supporting the achievement of DDAP project and activity deliverables and the management of DDAP funds.

The external ADM Federal Advisory Committee on Disaggregated Data provided sufficient oversight to ensure the work plan priorities pursued by the four interdepartmental working groups were on track to achieve their expected deliverables. The ADM committee's quarterly meetings, supported by the DDAP Secretariat, served to monitor progress on the working groups' deliverables through regularly scheduled presentations from the working group co-leads. The oversight efforts of the ADM committee supported the timely completion of working group deliverables. According to ADM committee meeting minutes, all expected deliverables, including the launch of the Disaggregated Data Resource Hub, were on track to be completed by early 2025. However, the evaluation evidence does not clearly indicate the extent to which the ADM committee is prepared to monitor the deployment and uptake of the work plan deliverables across the federal government.

The DG Governance Committee, with support from the DDAP Secretariat, provided sufficient oversight to ensure that the deliverables of agency-led projects and activities were on track or revised to reflect changing circumstances. Every quarter, the DG Governance Committee reviewed the project dashboard reportFootnote 4 to determine whether any steps were required to address projects that were off track, delayed or cancelled. The quarterly project reports enabled the DG Governance Committee to anticipate projects expecting a budget surplus and quickly identify others that could use the funding.Footnote 5 The DG Governance Committee also provided sufficient monitoring to support the achievement of agency-level working group deliverables via presentations by the working group co-leads. Before disbanding, each working group presented its recommendations and any associated tools or strategies to the DG Governance Committee. However, the extent to which the committee's oversight supported the effectiveness of the working groups and their deliverables was unclear. Some agency representatives reported that the working groups primarily functioned as discussion groups and lacked clear direction on the issues they were aiming to resolve.

The DG and ACS committees provided financial oversight to manage the allocation of DDAP funding. Financial oversight was informed through the quarterly project trackers and project-level and strategic-level costing workbooks. The financial oversight efforts of the ACS Steering Committee resulted in a reduction of project funding to address the planned funding overallocation in 2022/2023.

Oversight of some performance indicators provided by DDAP governance helped achieve some short-term outcomes, but the limited number of performance indicators restricted the extent to which DDAP governance could monitor progress on expected results.

The DG Governance Committee provided some oversight of progress in addressing information gaps via the DDAP Secretariat's annual reports on the proportion of indicators disaggregated by the four EE groups. This oversight highlighted that the proportion of indicators disaggregated by racialized populations and persons with disabilities was falling short of its target. To address this, committee members were asked to work with their respective teams to increase indicators for racialized populations. The committee determined that no further action was required for persons with disabilities because the release of the five-year Canadian Survey on Disability was expected to fill this gap. The absence of additional developed performance indicators limited the ability of DDAP governance to oversee progress toward expected results.

Recent changes to oversight efforts within the DG Governance Committee are intended to facilitate monitoring project outcomes to better understand how they contribute to achieving intended results.

In 2024/2025, the DG Governance Committee extended its oversight of DDAP projects and activities to include progress towards intended project outcomes to improve its ability to monitor the alignment of DDAP projects with expected results. To support this expanded oversight, the DG Governance Committee implemented additional efforts. These included returning to monthly meetings (instead of quarterly); revising quarterly project reports to integrate more variables around risks—specifically, the capacity to drive progress and the degree of challenge in achieving the milestones; and inviting project teams to present on their progress, including how the DDAP funds were used.

DDAP governance provides some elements of accountability, and steps were taken to improve governance accountability to ensure projects align with DDAP goals.

Evidence suggests that mechanisms were established to uphold governance accountability. These mechanisms included governance mandates and terms of reference for the DDAP Secretariat, the DG Governance Committee, the ACS Steering Committee, the ADM Federal Advisory Committee on Disaggregated Data, and agency-level committees and working groups that outline their purpose, roles and responsibilities, and reporting structure. The mechanisms also included a formalized evaluation approach to inform the selection and funding of projects through calls for proposals, regular project-level reporting, and a few established performance indicators. However, challenges in governance accountability contributed to issues with the recommendation and approval of DDAP projects.

DDAP projects funded through the call for applications process were selected by the DG Governance Committee and approved by the ACS Steering Committee. The DDAP Secretariat used an evaluation matrix to ensure the initial list of potential projects brought to the DG Governance Committee aligned with the DDAP's core principles. However, neither committee appeared to have additional mechanisms to verify that the recommendation and approval of funded projects aligned with the DDAP's expected results. It was reported that having very little time to deliberate on funding recommendations further limited the ACS Steering Committee's accountability for approving recommended projects. The gaps in project recommendation and approval accountability created a risk that some DDAP-funded projects could be misaligned with the expected outcomes. In 2022/2023, the ACS Steering Committee issued a directive to strengthen accountability in project selection by requiring all projects to align with core DDAP commitments. As a result, some projects were scaled down or defunded to complement the financial corrective actions noted above.

Internal interviewees noted a lack of transparency and communication in the call for applications and project selection processes. Better communication of expectations throughout these processes would have improved the alignment of proposals, although some mechanisms existed to provide support. During the first year of implementation, some projects also faced initial challenges because of unclear expectations and undefined reporting mechanisms, which were eventually resolved. The timing of funding decisions was another concern, as it did not align with project planning, leaving project teams with little time to plan activities and secure resources.

Gaps in governance accountability potentially impede the achievement of medium- and long-term outcomes.

Working group deliverables were intended to advance the culture change within the agency and across the federal government. Gaps in accountability for the implementation of working group deliverables were noted for the DG and the ADM committees. The DG Governance Committee did not have a clear plan or dedicated resources to implement and monitor the agency working groups' recommendations, hindering their full execution. The ADM Federal Advisory Committee on Disaggregated Data does not appear to have a plan to support the uptake and application of the resources generated by the interdepartmental working groups and made available through the Disaggregated Data Resources Hub. While the data hub is part of the Canada School of Public Service (CSPS) platform, it is unclear whether the ADM committee is accountable for monitoring and updating it.

The investment in oversampling flagship surveys was used to enable the timely production of disaggregated data to support the achievement of short-term outcomes. However, the DG and the ACS committees recognized that this oversampling was an unsustainable means of producing disaggregated data given the relatively high costs and declining response rates. While some noted steps were taken to address the reliance on oversampling within the DG Governance Committee,Footnote 6 the evaluation found little evidence to indicate that either committee was accountable for advancing the development or application of alternative methods. This accountability gap in directing efforts to address oversampling challenges hinders the achievement of medium- and long-term outcomes. Without viable alternatives, key interviewees reported that surveys are expected to revert to more aggregated datasets.

Strategic direction to support the achievement of intended outcomes was limited because of inadequate foundational documents, and a short-term focus over a long-term vision.

The extent to which DDAP governance could provide strategic direction was limited by its absence from the mandates and priorities of the DG, ACS and ADM committees. At the agency-level, the priorities of the DG and ACS committees focused on allocating funding and implementing projects and activities rather than providing strategic guidance. The external ADM committee prioritized implementing the work plan to address barriers to the collection and use of disaggregated data, but there was limited evidence of additional efforts to strategically plan for a whole-of-government approach to achieving the medium- and long-term expected outcomes.

The priorities and direction of the DDAP were largely reported to be informed by foundational documents. However, these documents did not include a logic model that clearly articulated the expected outcomes, performance indicators and measurement strategy specific to the DDAP. Instead, they included a more generic logic model aligned with the agency's departmental results. Including a DDAP-specific logic model in the foundational documents could have facilitated more strategic thinking by DDAP governance to guide the development, monitoring and adjustments of the action plan. The evaluation found no evidence that performance indicators of medium- and long-term outcomes were being measured, monitored or reported by DDAP governance, indicating limitations in its ability to identify performance gaps and strategically adjust the plan.

Various consultative efforts informed some DDAP projects and activities to meet the needs of data users.

DDAP consultation efforts included online questionnaires; targeted engagement with partners, stakeholders, underrepresented and marginalized groups, provinces and territories, and academic subject-matter experts; and internal (agency and GC) discussions. The extent to which all DDAP projects and activities incorporated stakeholder consultations could not be determined through the evaluation. However, according to some internal documents, over one-third of DDAP projects reported planned or completed consultation activities. In addition to formal stakeholder consultations, standing stakeholder meetings with data users (e.g., provincial and territorial focal points or the Federation of Canadian Municipalities) were reported to inform some projects.

Consultation feedback is an important component of accountability and helps clarify and confirm stakeholders' needs to ensure that the disaggregated data and analytical products are relevant. According to the surveyed data users who reported participating in disaggregated data consultations, 63%Footnote 7 (n=177) reported receiving a summary or updates on the feedback provided to Statistics Canada, and 90% were satisfied with Statistics Canada's efforts to understand their disaggregated data needs and perspectives. A few data users reported that improvements to Statistics Canada consultations over the last five years, particularly with Indigenous Peoples, had resulted in greater satisfaction with disaggregated data and analytical products.

Consultations improved the relevance of some DDAP projects. However, there were some concerns that disaggregated data and associated products released without sufficient consultation risked reinforcing a deficit narrative.

Input from consultations was reported to have influenced some projects. Project leads reported several improvements to their projects as a result of stakeholder consultations, helping to ensure that the needs of data users were being met. Examples include:

  • revisions to survey content to better meet the needs of data users and align with DDAP priorities (e.g., labour market indicators and the Diversity of Charity and Non-profit Boards crowdsourcing questionnaire)
  • the development of a guidelines document and analytical framework and the establishment of a special purpose committee under the Canadian Association of Chiefs of Police to support jurisdictions' development of a data collection approach for police-reported Indigenous and racialized identity data
  • adjustments to the scope of the project related to environmental, social and governance Indigenous indicators to focus on creating a guideline for developing Indigenous indicators, following a deeper understanding of the complexities involved in their creation.

Some concerns were raised that consultations on various DDAP project-related data or analytical products did not sufficiently involve representatives of the population group before release. Releasing data without sufficient input from these representatives can inadvertently support deficit narratives, as important contextual factors may be overlooked, resulting in data that do not meet stakeholders' needs (e.g., Indigenous Peoples). While some DDAP projects engaged with communities to inform DDAP deliverables (e.g., portraits for selected racialized population groups), some key interviewees recommend that the agency spend more time engaging with external stakeholders to inform the messaging of disaggregated data products.

Some DDAP projects are delivered efficiently through existing infrastructure, improved methodological and sampling approaches, and internal expertise. A few projects reported advancing Statistics Canada's capabilities and resources.

Most of the project leads reported that they had sufficient internal skills and partnerships to meet the needs of their project and that DDAP funding helped secure staff to address any outstanding knowledge or resource gaps. There is evidence that some DDAP projects helped minimize additional costs by using existing infrastructure, resources, and capabilities, such as:

  • using corporate survey infrastructure already in place for collecting, processing, and disseminating data
  • using data from other surveys or linked databases (e.g., Uniform Crime Reporting Survey, census, Longitudinal Immigration Database) to support data disaggregation
  • applying current methodological approaches (e.g., small area estimation) to generate greater data granularity in the Labour Force Survey
  • leveraging the work of other government department (OGD) initiatives to advance DDAP outcomes, such as the partnership with the CSPS to advance disaggregated data training programs and resources
  • creating survey samples targeting specific population groups (e.g., racialized people) from the census and associated administrative data sources to increase response rates and reduce sample sizes
  • using the expertise and experience within Statistics Canada's communications team and the Centre for Indigenous Statistics and Partnerships to support consultation and communication activities.

A few DDAP initiatives reported advancing Statistics Canada capabilities and resources. For example, one project focused on advancing methodological capabilities by working with the data science team to apply machine learning to address survey non-response. The working groups produced several resources to support agency employees' and other federal employees' disaggregated data collection, analysis and communication needs, including a disaggregated data hub with resources and links to OGD- or agency-developed disaggregated data resources, guides and training materials. However, the extent to which these resources have improved skills or knowledge within the agency is unclear.

Some DDAP activities were designed to support ongoing data disaggregation beyond the funding timeline, but efforts to sustain DDAP-funded activities were not adequately prioritized.

Some DDAP activities were designed to support outcomes long after the DDAP funding envelope concludes. Standards and data acquisition activities aim to fill information gaps and avoid additional surveys, while the development of accessible resources and a disaggregated data hub for federal staff is intended to increase awareness and understanding of the need for disaggregated data and foster a culture that prioritizes them. DG Governance Committee representatives described the funding provided through the call for applications process as seed money to help projects get off the ground and demonstrate their value to stakeholders. However, efforts to sustain project activities were not reported to be consistently prioritized, and there is an expectation that continued funding is necessary for ongoing data disaggregation. This reliance on DDAP financial support underscores the need for a more strategic approach to ensure the long-term viability of the DDAP's outcomes.

Oversampling is an unsustainable practice, yet relatively little focus was devoted to exploring or adopting alternative methodological approaches.

When the DDAP was developed, oversampling was used to produce disaggregated data. Most agency staff involved in projects relying on ongoing oversampling stated that no alternative method could produce similar data results. Agency staff recognized the high costs of oversampling, but given the ongoing challenges of declining response rates, any reduction in oversampling funding was reported to limit the ability of flagship surveys to produce disaggregated data.

Evidence suggests that DDAP funding contributed to methodological efforts to support efficient sampling;Footnote 8 improve understanding of non-response and develop guidelines for synthetic data. However, there was limited evidence that efforts to advance methodological solutions to oversampling were strategically prioritized early in the initiative. Some agency representatives expect the Methodological Acceleration initiative to identify emerging techniques for application to the DDAP, but the extent of the progress being made in this regard remains unclear.

Disaggregated Data Action Plan Administrative Data Fund

The Disaggregated Data Action Plan (DDAP) Administrative Data Fund aims to provide external partners with an opportunity to enhance their disaggregated administrative data holdings by providing funding to cover direct costs. Divisions that have identified partners are invited to submit proposals through a formal application process. Currently, five projects from various sectors have been funded or are ongoing. Collaboration with external partners, government departments and Statistics Canada across multiple projects has been essential in advancing data modernization efforts.

For example, the University and College Academic Staff System (UCASS) Modernization project demonstrates how the DDAP supported innovative problem-solving to sustainably address compound challenges, such as response burden, while advancing its priorities. By adding variables to staff surveys and combining them with data from other sources (e.g., the Census of Population), the pilot project provided valuable insights for equity, diversity and inclusion (EDI) initiatives and identified areas for improvement within the universities, without compromising survey respondents' privacy. The UCASS Modernization project has since been expanded to include data on groups previously unrepresented in the system. There are plans to add UCASS data to Statistics Canada's Social Data Linkage Environment, and this could provide a robust framework for EDI analysis across various demographic indicators. External partners are optimistic about the project's future and its continued positive impact, but, as with other DDAP projects, securing future funding sources remains a priority to ensure ongoing success.

Cost-recovery agreements can help sustain some DDAP projects, but the current fiscal climate significantly limits the viability of this option.

Key interviewees reported that the more limited ongoing DDAP funding will be used to support mission-critical projects, such as the Labour Force Survey, while other projects can expect funding reductions. To address project sustainability, DDAP governance is discussing the potential for some DDAP initiatives to transition into cost-recovery projects.

At least one project (the Indigenous satellite account) transitioned to a cost-recovery agreement with Indigenous Services Canada to continue for a second year, and a few other projects reported exploring cost-recovery options with federal (e.g., Canadian Heritage, Employment and Social Development Canada) and provincial and territorial partners. However, many interviewees noted that while the DDAP is generating data and analysis that meet the needs of data users, cost-recovery opportunities are limited and more challenging given the current fiscal climate. Furthermore, some interviewees reported that data users are less inclined to use their own funds, as Statistics Canada received substantial funding to expand and enhance disaggregated data and analysis.

Performance

To what extent is the Disaggregated Data Action Plan progressing towards its intended outcomes in the short, medium, and long term?

Although the Disaggregated Data Action Plan (DDAP) is making progress toward its intended results, some areas require further attention. Awareness-raising and training activities have helped participants better understand data disaggregation, yet some barriers to applying and acquiring disaggregated data knowledge were identified. The DDAP is helping to address information gaps, but the reliance on oversampling to produce disaggregated data from flagship surveys was identified as a challenge to sustainably doing so. The action plan is improving certain aspects of data quality; however, some issues with relevance and accessibility persist.

The DDAP is supporting and reinforcing a cultural shift that prioritizes disaggregated data and intersectional analysis by expanding the availability of disaggregated data and related products. However, further progress will require greater emphasis on awareness and training. Some early evidence suggests that the DDAP is positively impacting data users, though these impacts are limited in part by data users' and decision makers' capacities to understand and use disaggregated data and analytical products. There is also some concern that the DDAP's long-term outcome is outside Statistics Canada's influence and mandate.

DDAP awareness-raising and training activities are contributing to, and sustaining, strong awareness and understanding of the importance of disaggregated data and GBA Plus.

Numerous activities have been implemented to increase awareness of the need for disaggregated data and GBA Plus within and outside Statistics Canada. By the end of 2022/2023, the DDAP Secretariat delivered over 100 presentations across the agency, to OGDs and to external stakeholdersFootnote 9 to raise awareness of the importance of disaggregated data and the DDAP's priorities. Other internal and external awareness-raising efforts include a dedicated agency intranet webpage; a government-wide disaggregated data hub; presentations at the agency's research forum; social media posts; podcasts; conference and workshop presentations; and a two-part introduction to disaggregated data video, which received over 850 views in its first five months. The majority (79%) of surveyed data users (n=370) reported that they were familiar with the concept of disaggregated data.

In response to an identified need for training to support stakeholders' understanding of disaggregated data and GBA Plus, the DDAP funded six training programsFootnote 10 for agency and other federal government staff. The courses covered a variety of key areas related to the use and importance of disaggregated data (e.g., privacy, confidentiality, accessibility, utility, ethics, small area estimation, data types and formats, frameworks, guidelines, application, strategies, case studies). From 2022 to 2024, over 350 federal (agency and non-agency) staff participated in the synchronous programs.

Most highly knowledgeable, long-time users of Statistics Canada data reported that their awareness and understanding of the need for disaggregated data and GBA Plus have remained unchanged over the past four years, as they were already at a high level. While a few data users reported a greater awareness and importance attributed to disaggregated data among their stakeholders, the increase could not be attributed to the DDAP.

DDAP training activities are contributing to participants' understanding of data disaggregation, but some barriers to applying and acquiring disaggregated data knowledge were identified.

Statistics Canada participants reported high satisfaction with DDAP training courses. According to the case study survey, the initial Disaggregated Data Analytical Workshop positively influenced participants' knowledge and awareness of the DDAP and its components, including ethical considerations, confidentiality practices and data standards. The workshop effectively enhanced the application of knowledge for most participants. Furthermore, the training courses strengthened and expanded the community network of disaggregated data users, as participants learned about and established new contacts they could later reach out to during their work. However, participants also noted some barriers to fully using the skills they learned during training. These included insufficient disaggregated data to perform the required level of analysis, the need for additional analytical training, a lack of resources (budget, staff or technology), and limited time to apply what they learned.

Outside Statistics Canada, some evidence suggests that the disaggregated data training programs could benefit from additional awareness-raising efforts. Just over half (53%) of surveyed data users who were federal government employees (n=231) reported being unaware of the training, while 15% had participated in at least some of the training. Of those who participated, the most commonly reported outcome was an increased understanding of disaggregated data and how they can inform policy making.

The DDAP is helping to address information gaps by increasing the proportion of disaggregated indicators for the four EE groups, but few data users reported specific impacts from the increase in disaggregated data.

Since 2020/2021, the percentage of socioeconomic indicators disaggregated for gender, racialized populations, Indigenous Peoples and persons with disabilities has increased. By 2023/2024, gender indicators exceeded the target by 17 percentage points, indicators for racialized populations were 1 percentage point away from reaching their target, and indicators for Indigenous Peoples and persons with disabilities increased but remained 6 and 8 percentage points, respectively, from their targets (see Figure 3).

Figure 3. Percentage of statistical indicators regularly produced by the Socio-economic Statistics program that relate to people and are disaggregated by employment equity group, 2020/2021 to 2023/2024
Figure 3. Percentage of statistical indicators regularly produced by the Socio-economic Statistics program that relate to people and are disaggregated by employment equity group, 2020/2021 to 2023/2024
Description - Figure 3. Percentage of statistical indicators regularly produced by the Socio-economic Statistics program that relate to people and are disaggregated by employment equity group, 2020/2021 to 2023/2024

Figure 3 is a chart with columns that shows the percentage of statistical indicators regularly produced by the Socio-economic Statistics program that relate to people and are disaggregated by employment equity group for the fiscal years 2020/2021 to 2023/2024.

For gender, the target is 80% and the percentage for each fiscal year was as follows:

  • 2020/2021: 64%
  • 2021/2022: 65%
  • 2022/2023: 80%
  • 2023/2024: 97%

For Indigenous Peoples, the target is 70% and the percentage for each fiscal year was as follows:

  • 2020/2021: 47%
  • 2021/2022: 48%
  • 2022/2023: 49%
  • 2023/2024: 64%

For racialized populations, the target is 70% and the percentage for each fiscal year was as follows:

  • 2020/2021: 43%
  • 2021/2022: 49%
  • 2022/2023: 60%
  • 2023/2024: 69%

For persons with a disability, the target is 50% and the percentage for each fiscal year was as follows:

  • 2020/2021: 23%
  • 2021/2022: 26%
  • 2022/2023: 19%
  • 2023/2024: 42%

Approximately 65% of surveyed data users who responded to the question regarding the extent to which Statistics Canada's disaggregated data are filling information gaps since 2021 (n=342) reported that they are indeed filling information gaps, with 23% reporting that gaps were filled to a large or very large extent. Data users' perception of Statistics Canada's effectiveness in producing data to better understand EE groups is highest for women (55%), followed by racialized populations (44%), Indigenous Peoples (42%), and persons with a disability (34%). However, few interviewees could report whether, and how, the increase in disaggregated data was contributing to decision making, research or policy debates. Data users who could speak to the contribution of the DDAP on their organization reported that the increase in disaggregated data is informing the kinds of research questions members of their organization are pursuing, providing the evidence to advocate for funding and investment revisions, and informing organizational research and data collection activities. 

As already stated, the sustainability of oversampling to produce disaggregated data from flagship surveys was identified as a challenge to addressing information gaps. However, it is unclear to what extent the agency has increased its capacity to avoid or reduce its dependency on oversampling by advancing methodologies that help address the declining response rates.

The DDAP is enhancing some dimensions of data quality, but a few relevance and accessibility gaps were noted.

The DDAP is producing data that many data users reported to be meeting their needs. However, key interviewees and surveyed data users also reported a need for greater disaggregation beyond the four EE groups, especially for the 2SLGBTQI+ population and at lower geographic levels.Footnote 11 Although disaggregation for the 2SLGBTQI+ population and at smaller geographic levels is included in the DDAP's priorities, projects prioritizing populations outside of the four EE groups have reportedly not been prioritized. Data users recommended that Statistics Canada focus on developing appropriate estimation or sampling methods to address privacy or confidentiality concerns that contribute to the ongoing gaps.

DDAP standards are contributing to data coherence and comparability. The DDAP supported the approval and adoption of two disaggregated data standardsFootnote 12 within the federal government and the approval and adoption of sexual orientation and gig employment standards within the agency. Efforts to improve the usability of the standards include providing a dedicated web page and making DDAP standards accessible through the Reference Data as a Service application programming interface. However, the development of standards is a complex and lengthy process, and adoption is required outside of Statistics Canada to ensure interoperability across government and non-government agencies.

Statistical standards

To address the inconsistent use of statistical standards for disaggregated data across Statistics Canada and the Government of Canada (GC), and to meet the need for more user-centric products, services, and communications, Statistics Canada's Centre for Statistical and Data Standards (CSDS) launched several initiatives in support of the Disaggregated Data Action Plan. These initiatives provide guidance on survey methodologies, improve interoperability, and engage extensively with internal and external stakeholders. The CSDS has also focused on expanding the accessibility of standards through an enhanced website and consultations with key groups. Additionally, the team has developed training resources to support analysts and survey managers in effectively applying these standards.

The CSDS used established processes, tools and governance structures, along with data standards repositories and consultation networks, to help set key statistical standards across the GC. For instance, it developed a proof of concept using some quality of life variables to disseminate disaggregated data at the lowest level possible while still respecting data confidentiality and quality. Furthermore, consultations with over 300 groups and individuals have informed the development of inclusive data collection frameworks. This has helped harmonize data on gender and sexual orientation across various surveys and refine these standards.

Looking forward, the focus is on securing sustainable funding, integrating new standards into legacy systems and educating stakeholders on the value of standardization, as adapting survey questions across jurisdictions requires careful attention to underlying concepts to ensure consistency.

The DDAP also supported greater accessibility to disaggregated data and analytical products by updating Statistics Canada's Gender, Diversity and Inclusion Statistics Hub and launching the Centre for Municipal and Local Data; releasing more analytical publications and data tables online and through Statistics Canada's official release bulletin, The Daily; and promoting new disaggregated data and analysis through social media. Some data users reported that the DDAP has increased access to disaggregated data for more organizations because data that used to be accessible only through cost-recovery report requests are now accessible through the Statistics Canada website at no additional cost. However, less than half (35%) of surveyed data users (n=370) reported that it was somewhat easy or better to access Statistics Canada's disaggregated data. Barriers included website navigation issues, limited hub value for advanced needs, limited data linking with external sources and users' limited internal capacity.

Priorities related to data sovereignty were identified as a potential challenge to enhancing access to disaggregated data by collecting and integrating data from other sources. Key interviewees recognized that the agency is still developing its approach to supporting data sovereignty for various jurisdictions and populations, but expressed ongoing concern that sharing data with the agency would result in losing control of the data and how they are used. To address this concern, key interviewees recommended that the agency continue to work with other data stewards to determine how the data can be shared in a way that respects the needs and commitments of those involved. 

The DDAP is reinforcing a cultural shift that prioritizes disaggregated data and intersectional analysis by expanding the availability of disaggregated data and analytical products. To advance the cultural shift outside the agency, greater emphasis should be placed on awareness and training efforts.

Interviewees within the agency reported that the DDAP is supporting a culture change. This can be seen by the increased attention to disaggregated statistics and greater intersectional analysis in branches outside Field 8, such as those in the Economic Statistics Field. They also indicated that considering disaggregated data earlier in the process has increased since the implementation of the DDAP.

Results from the case study survey align with this, as 78.3% of respondents indicated that the agency fosters an environment that promotes horizontality for disaggregated data work, as well as the importance of intersectional analysis and longitudinal insights. However, a slightly lower proportion (61.7%) of participants who responded to the question reported an emphasis on applying the GBA Plus lens to all projects and initiatives.

Evidence of a cultural change within the federal government included an increased demand for disaggregated data among federal data users. However, a few interviewees noted that the cultural change is not progressing at the same pace within every department. A department's data literacy, its mandate for research and its leadership's commitment appear to have a greater influence on its use of disaggregated data.

Interviewed data users reported that the DDAP supports a cultural shift toward prioritizing the collection and use of disaggregated data and GBA Plus. However, many noted that this shift started before the implementation of the DDAP. Data users also reported that because Statistics Canada is perceived as a data leader, the increased availability of disaggregated data and analytical products is helping to spread and solidify this cultural change. To further advance a cultural change that prioritizes the collection and analysis of disaggregated data, some data users recommended that greater awareness and training efforts be directed to non-federal data users to help them better understand what information is available and how it can be used in their work.

Some early evidence suggests that the DDAP is positively impacting data users' decision making, research and policy debates, but these impacts are limited by data users' and decision-makers' capacity to understand and use disaggregated data.

As the DDAP enters the final year of its five-year funding, some signs of progress toward achieving its long-term outcome can be seen. Among surveyed data users who responded to the question about whether Statistics Canada's disaggregated data are enhancing their research, decision making or policy debates (n=340), 61% reported they were having a positive impact. However, evidence also shows that some data users are still determining the overall impact of Statistics Canada's disaggregated data, given that just under one-quarter (23%) of respondents reported not knowing whether the data were enhancing their work. Specific benefits of Statistics Canada's disaggregated data include filling or identifying information gaps, highlighting specific trends or inequalities, informing interventions or policies, and targeting resources or recommendations.

As noted, few key interviewees could report any tangible DDAP-related impacts on their decision making, research or policy debates. Disaggregated data had a positive impact on the implementation and effectiveness of GBA Plus in federal budgets. The long-term outcome of increasingly fair and inclusive policy, program or legislation development is limited by data users' and decision-makers' capacities to understand and use disaggregated data and corresponding analytical products. Key interviewees reported that smaller jurisdictions, non-governmental organizations and government staff often lack the capacity or resources to understand and use disaggregated data, limiting the impact of the DDAP on policy and program development. Suggestions to address these capacity gaps include sustained awareness and training efforts within and outside the federal government, particularly at the federal leadership levels.

There is some concern that the DDAP's long-term outcome is outside Statistics Canada's influence and mandate.

Some concerns were reported that the DDAP's long-term outcome of increasingly fair and inclusive policy, programs and legislation is outside Statistics Canada's mandate. While DDAP activities are primarily focused on increasing disaggregated data and analytical products, Statistics Canada has limited influence on whether, and how, the data are considered in program and policy development. Achieving the DDAP's long-term outcome is considered more the collective responsibility of leaders across the federal government. However, doubts have been raised about whether the current ADM Federal Advisory Committee on Disaggregated Data is effectively designed to advance the federal leadership's commitment to applying disaggregated data and intersectional analysis.

How to improve the program

Recommendation 1

The ACS of Field 8 should ensure that the DDAP governance is strengthened to directly support the initiative in achieving its intermediate and ultimate outcomes by:

  1. updating DDAP oversight committees' mandates to clearly articulate accountabilities, particularly around the provision of strategic direction and the achievement of the DDAP's medium- and long-term objectives
  2. focusing future projects and activities on sustainability
  3. establishing effective communication (e.g., leveraging proposed governance tables) to ensure that priorities, expected outcomes, and performance measures are well understood by staff, key partners, and stakeholders (e.g., on a regular basis, using two-way dialogue).

Recommendation 2

The ACS of Field 8 should ensure that outreach and engagement efforts are comprehensive and consistent, so that the ongoing needs of data users, including those from the EE groups, are understood and being met by DDAP's disaggregated data and analytical products.

Management response and action plan

Recommendation 1

The ACS of Field 8 should ensure that the DDAP governance is strengthened to directly support the initiative in achieving its intermediate and ultimate outcomes by:

  1. updating DDAP oversight committees' mandates to clearly articulate accountabilities, particularly around the provision of strategic direction and the achievement of the DDAP's medium- and long-term objectives
  2. focusing future projects and activities on sustainability
  3. establishing effective communication (e.g., leveraging proposed governance tables) to ensure that priorities, expected outcomes, and performance measures are well understood by staff, key partners, and stakeholders (e.g., on a regular basis, using two-way dialogue).

Management response

Management agrees with the recommendation.

  1. The terms of reference for the DG, ACS and ADM committees will be revised to include accountability pertaining to the provision and communication of strategic direction and around the achievement of the DDAP's medium- and long-term objectives.
    1. An integrated annual plan will be developed, incorporating Statistics Canada's strategic direction for the DDAP and disaggregation of data as a key principle, with a detailed workplan to guide implementation and monitoring. The integrated annual plan will integrate considerations of sustainability (e.g., alternative methodological approaches beyond oversampling) and alignment with the DDAP's medium- and long-term outcomes. The DG Governance Committee will recommend the integrated annual plan to the ACS Steering Committee for approval.
    2. The ADM Committee will track DDAP outcomes and capture how disaggregated data have informed OGDs' policy formulation and program implementation/evaluation. These outcomes will form part of the 2024/2025 annual achievements report released by Statistics Canada.
  2. Existing communication and dissemination opportunities (e.g., the CSPS collaboration space, OGDs' dissemination mechanisms) will be leveraged to promote, share and showcase disaggregated data outcomes, best practices, and training opportunities, and to raise their awareness.

Deliverables and timelines

  1. Revised and approved terms of references for the DG, ACS, and ADM committees. (September 2025)
    1. Approved integrated annual plan (October 2025, ongoing afterwards) that incorporates a detailed work plan and logic framework approach, which includes:
      • the DDAP's medium- and long-term objectives
      • measurable indicators to be used to monitor progress against these objectives
      • an approach that will be used to monitor progress against these objectives
      • clear articulation on how priorities will be determined with a focus on sustainability.
    2. Annual achievements report for 2024/2025 will include for the first time outcomes/impacts shared by OGDs. (December 2025).
  2. Robust and updated CSPS collaborative space to showcase best practices, leverage CSPS training opportunities and raise awareness. (November 2025).

Recommendation 2

The ACS of Field 8 should ensure that outreach and engagement efforts are comprehensive and consistent, so that the ongoing needs of data users, including those from the EE groups, are understood and being met by DDAP's disaggregated data and analytical products.

Management response

Management agrees with the recommendation.

An outreach and engagement plan will be developed to establish communication with DDAP partners and stakeholders, including emphasis on leveraging external networks at the ADM and ACS levels (internal and external to GC).

The plan may include the organization of engagement sessions or surveys among EE groups (and those beyond the four EE groups, as needed and as possible).

Deliverables and timelines

Outreach and engagement plan approved by the ADM Federal Advisory Committee on Disaggregated Data (April 2026).

Appendix A: Evaluation questions and indicators

Evaluation questions and indicators
Evaluation questions Evaluation indicators

1. To what extent is the Disaggregated Data Action Plan (DDAP) relevant in supporting federal needs and priorities, including Statistics Canada's?

1.1 Extent of the alignment between Statistics Canada's DDAP projects and activities and (a) the agency's strategic priorities and (b) government-wide DDAP priorities.

2. To what extent are the design and delivery of the DDAP facilitating the achievement of its intended outcomes?

2.1 Extent to which the DDAP's governance provides effective strategic direction, oversight, and accountability across all levels (federal government, agency, program, and project levels).

  1. Extent to which the governance structure ensures effective communication, coordination and collaboration across the different levels.
  2. Extent to which mechanisms are in place to ensure effective priority setting, oversight, accountability and timely course corrections across all levels.

2.2 Extent to which consultation was conducted to inform priorities and projects.

  1. Number of consultations, by format, by type of intended users and fiscal year.
  2. Extent to which the processes for analyzing and reporting on needs following consultations are effective.
  3. Extent to which input from consultations informed DDAP priorities and projects.

2.3 Extent to which DDAP activities built on or enhanced existing infrastructure, resources and capabilities (skills, partnerships), minimizing additional cost and effort.

2.4 Extent to which measures have been taken to ensure the sustainability of the new activities within the agency's ongoing operations.

3. To what extent is the DDAP progressing towards its intended outcomes in the short, medium and long term?

3.1 Extent to which internal and external barriers to achieving expected results were identified and mitigated.

3.2 Extent of progress toward short-term results.

  1. Extent to which the DDAP has contributed to an increased awareness and understanding of the need for disaggregated data and Gender-based Analysis Plus.
  2. Extent to which the DDAP has contributed to enhancing data quality and filling information gaps.
  3. Evidence of greater or enhanced access to disaggregated data
  4. Evidence of greater or enhanced planning for, and the collection, analysis, production and dissemination of, disaggregated data, from 2020/2021 to 2024/2025 (partial year).

3.3 Extent of progress toward medium-term results.

  1. Extent to which the DDAP has contributed to a change in culture that prioritizes the use of existing, and the collection of new, disaggregated data and—where possible—intersectional analyses to meet policy makers' and other data users' needs.

3.4 Extent of progress toward long-term outcomes.

  1. Extent to which the DDAP has contributed to date to enhanced decision making, research or policy debate by providing a more detailed representation of various populations and their experience and environment.
  2. Extent to which the DDAP has contributed to the establishment of standards specific to disaggregated data.

Appendix B: Interview quantification scale

Interview responses are quantified and categorized in this report using the scale shown in the table below.

Interview quantification scale
Term Definition
One One is used when one participant provided the answer.
Few Few is used when 4% to 15% of participants responded with similar answers. The sentiment of the response was articulated by these participants but not by other participants.
Some Some is used when 16% to 45% of participants responded with similar answers.
About half About half is used when 46% to 55% of participants responded with similar answers.
Most or a majority Most, or a majority, is used when 56% to 89% of participants responded with similar answers.
Almost all Almost all is used when 90% to 99% of participants responded with similar answers.
All All is used when 100% of participants responded with similar answers.