Household Expenditures Research Paper Series
User Guide for the Survey of Household Spending, 2019

Release date: January 22, 2021

1. Introduction

This guide is a source of information for users of data from the 2019 Survey of Household Spending (SHS). It includes survey term and variable definitions as well as details on the survey methodology and data quality. The guide also has a section that includes various examples of estimates that can be drawn from the survey data.

The SHS is conducted in the 10 provinces and the 3 territorial capitals. It is conducted every 2 years starting with the 2017 reference year. Until 2017, it was conducted annually in the provinces. It has been carried out in the territorial capitals every 2 years since 2015.

The SHS collects household spending information using a questionnaire (administered through a personal interview) and a daily expenditure diary. The questionnaire is used to collect information on larger or less frequent expenditures using varying recall periods based on the type of expenditure (last month, last 3 months, last 12 months or last payment), while the diary is used to collect information on more detailed or frequent expenditures. After completing the questionnaire, all households are asked to complete a diary in which they record their household’s expenditures over a 1-week period in the provinces or over a 2-week period in the territorial capitals.

Data collection is continuous throughout the year to account for seasonal variations in spending. The 2019 SHS, which was conducted from January 2019 to December 2019, used a sample of 17,491 households in the 10 provinces and 937 households in the 3 territorial capitals. The data collected include detailed household expenditures, as well as information on dwelling characteristics, household demographics and household equipment.

Since 2015, the SHS’s coverage in the North has been limited to the 3 territorial capitals (Whitehorse, Yellowknife and Iqaluit). Also, the new design of the SHS, which was implemented for the provinces starting in 2010, has been used for the territorial capitals starting with 2015. The differences in sampling and estimation methodology between the territories and provinces require that the estimates for Whitehorse, Yellowknife and Iqaluit be interpreted with caution and not be directly compared to the provincial estimates. These differences are noted throughout this guide whenever applicable.

It is important to note that data at the national level include the 10 provinces only.

Household expenditure estimates for the 10 provinces are available at the national level, by province, as well as by household tenure, age of reference person, size of area of residence, type of household, and household income quintile. Detailed estimates of food expenditures are also produced at the national and provincial levels.

Household expenditure estimates are available for each of the three territorial capitals (Whitehorse, Yellowknife and Iqaluit). These estimates are not published by household tenure, age of the reference person, size of area of residence, household type and by household income quintile due to the small sample sizes in the territorial capitals. For custom tabulations or more information on the Survey of Household Spending, please contact us (toll-free 1-800-263-1136; 514-283-8300; STATCAN.infostats-infostats.STATCAN@canada.ca).

2. Definitions

2.1 General concepts

Expenditures: The net cost of all goods and services received for private use within a given period (e.g., 1, 3 or 12 months), whether or not the goods or services were paid for during that period, and regardless of whether these expenditures were incurred in Canada or abroad. Business expenditures are excluded.

Gifts: Expenditures may include gifts given to persons outside the household.

Household: A person or group of persons occupying one dwelling unit. The number of households, therefore, equals the number of occupied dwellings.

Household member: A person usually residing in the dwelling unit at the time of the interview.

Insurance settlements: Where an insurance settlement was used to repair or replace property, the survey includes only the deductible amount paid.

Principal residence: The main living quarters of the household at the time of the interview.

Reference person: The household member being interviewed chooses which household member should be listed as the reference person after hearing the following definition: “The household reference person is the member of the household mainly responsible for its financial maintenance (e.g., pays the rent, mortgage, property taxes, and electricity). When members of the household share the responsibility equally, choose one of these members to be shown as the reference person.” This person must be a member of the household at the time of the interview.

Reference year of the survey: Corresponds to the data collection year, from January 1 to December 31.

Secondary residence: Any dwelling used by the household as secondary living quarters (e.g., cottages, hobby farms and summer residences). Includes time-shares and properties outside Canada. Does not include moveable vacation homes (e.g., trailers and motor homes).

Taxes included: All expenditures include, where applicable: the Harmonized Sales Tax, the Goods and Services Tax, provincial retail sales taxes, tips, customs duties and any other additional charges or taxes.

Trade-ins: Where a trade-in is used to lower the price of an item, most commonly a vehicle, the expenditure amount is the total cost after the trade-in. Real estate transactions are an exception.

2.2 Household characteristics

Age of reference person: Corresponds to the age of the reference person at the time of the interview.

Estimated number of households: The estimated number of households in the survey’s target population during the reference year.

Homeowner: Household living in a dwelling owned (with or without a mortgage) by a member of the household at the time of the interview.

Household income before tax: Corresponds to the total income before tax received by the household the year prior to the reference year of the survey. It comprises income from all sources, including government transfers: wages and salaries before deductions, farm self-employment net income, non-farm self-employment net income, Old Age Security (OAS) pension, Canada Pension Plan and Quebec Pension Plan (CPP and QPP) benefits, federal child benefits, provincial or territorial child tax credits or benefits, employment insurance (EI) benefits, social assistance, workers’ compensation benefits, federal goods and services/harmonized sales tax (GST/HST) credit, provincial tax credits, other government transfers, private retirement pensions, support payments received, scholarships, bursaries, and fellowships, as well as other taxable income including income from a Registered Disability Savings Plan (RDSP) and investment income.

Household size: The number of persons in the household at the time of the interview.

Interview respondents: Corresponds to the number of eligible sampled households minus households that interviewers were unable to contact, households that refused to participate and households whose interview questionnaires were rejected due to a lack of sufficient information.

2.3 Selected household expenditures

Accommodation away from home: Includes all expenses for accommodation while travelling. It also includes accommodation expenses for household members while temporarily away at school or working away from home. Excludes expenditures for accommodation that were part of a package trip.

Alcoholic beverages: Includes alcoholic beverages purchased from stores and restaurants. Expenditures for supplies and fees for self-made beer, wine or liquor are also included.

Cannabis: “Cannabis for medical use” refers to cannabis products prescribed by a doctor. “Cannabis for non-medical use” refers to cannabis products not prescribed by a doctor.

Discounts and refunds: Presented in the data tables as “negative expenditures” since they represent a flow of money into the household instead of out of it.

Food purchased from restaurants: “Restaurants” includes full-service restaurants, fast-food outlets and cafeterias, as well as refreshments stands, snack bars, vending machines, mobile canteens, caterers and chip wagons. These expenditures include tips and do not include expenditures for alcoholic beverages.

Food purchased from stores: “Stores” include all establishments where food can be bought, such as grocery stores, specialty food stores, department stores, warehouse-type stores and convenience stores, as well as frozen food suppliers, outdoor farmers’ markets and stands and all other non-service establishments. The expenditures are net of cash premium vouchers or rebates at the cash register and include deposits paid for at the time of purchase. Reimbursements on deposits are excluded from total expenditures and are shown as negative expenditures (flow of money in) in the “Miscellaneous expenditures” category (outside of the “Food purchased from stores” category).

Games of chance: Expenditures for all types of games of chance. The expenditures are not net of the winnings from these games.

Health care: Includes direct (out-of-pocket) costs paid by the household, net of the expenditures reimbursed, as well as private health insurance premiums. Since 2019, this includes out-of-pocket spending on cannabis for medical use.

Household appliances: The net purchase price after deducting the trade-in allowance and any other discount. Excludes appliances included in the purchase of a home.

Income taxes: The sum of federal and provincial income taxes payable for the taxation year prior to the reference year of the survey. Taxes on income, capital gains and RRSP withdrawals are included, after exemptions, deductions, non-refundable tax credits and the refundable Quebec abatement are taken into account. Provincial health insurance premiums are also included.

Package trips: A package trip always includes at least two components. One of them is always transportation. The other(s) could be one or more of accommodation, meals, sightseeing, etc.

Property and school taxes, water and sewage charges for owned vacation homes and other secondary residences: The amount billed, excluding any rebates. Special service charges (e.g., garbage collection and sewers), local improvements, school taxes, and water charges are included if these are part of the property tax bill.

Purchase of automobiles, vans and trucks: The net purchase price, including extra equipment, accessories, and warranties bought when the vehicle was purchased, after deducting any trade-in allowance or the value of a separate sale. A separate sale occurs when a vehicle is sold independently by the owner (i.e., not traded in when purchasing or leasing another vehicle).

Rent: Net rent, excluding rent charged against business income or rooms rented out. Includes additional amounts paid to the landlord (e.g. security deposits).

Repairs and maintenance (owned living quarters): Covers expenditures for labour and materials for all types of repairs and maintenance, including expenditures to repair and maintain built-in equipment, appliances and fixtures. Expenditures related to alterations and improvements are excluded as they are considered an increase in assets (investment) rather than an expense.

Shelter: Principal accommodation (either owned or rented) and all other accommodation (such as vacation homes or accommodation while travelling).

Tenants’/Homeowners’ insurance premiums: Premiums paid for fire and comprehensive policies.

Tobacco products and smokers’ supplies: Includes cigarettes, tobacco, cigars, electronic cigarettes, matches, pipes, lighters, ashtrays, cigarette papers and tubes, and other smokers’ supplies.

Total current consumption: The sum of current expenditures for food, shelter, household operations, household furnishings and equipment, clothing and accessories, transportation, health care, personal care, recreation, education, reading materials and other printed matter, tobacco products, alcoholic beverages and cannabis for non-medical use, games of chance, and miscellaneous expenditures.

Total expenditures: The sum of total current consumption, income taxes, personal insurance payments, pension contributions, gifts of money, alimony and contributions to charity.

Water, fuel and electricity (for principal accommodation): Expenditures for services related to water and sewers, electricity, and natural gas and other fuel for the principal accommodation, whether rented or owned by a member of the household.

2.4 Dwelling characteristics

Repairs needed: Indicates the respondent’s perception of the repairs the dwelling needed at the time of the interview to restore it to its original condition. Renovations, additions, conversions or energy-saving improvements that would upgrade the dwelling over and above its original condition are not included.

  • Major repairs include serious deficiencies in the structural condition of the dwelling, as well as in the plumbing, electrical or heating systems. Examples of such deficiencies include corroded pipes, damaged electrical wiring, sagging floors, bulging walls, damp ceilings and crumbling foundations.
  • Minor repairs include deficiencies in the surface or covering materials of the dwelling and less serious deficiencies in the plumbing, electrical or heating systems. Examples of such deficiencies include small cracks in interior walls and ceilings, broken light fixtures and switches, cracked or broken window panes, leaking sinks, missing shingles or siding, and peeling paint.

Tenure: The housing status of the household at the time of the interview.

  • Owned with mortgage indicates that the dwelling was owned by a household member and that there was a mortgage at the time of the interview.
  • Owned without mortgage indicates that the dwelling was owned by a household member and that there was no mortgage at the time of the interview.
  • Rented indicates that the dwelling was rented by the household or occupied rent-free at the time of the interview.

Type of dwelling: Type of dwelling in which the household resided at the time of interview. A dwelling is a structurally separate set of living premises with a private entrance from outside the building or from a common hall or stairway.

  • A single detached dwelling contains only one dwelling unit and is completely separated by open space on all sides from any other structure, with the exception of its own garage or shed.
  • A single attached dwelling is a double or semi-detached house or a row house.
  • An apartment includes units in duplexes (two dwellings, situated one above the other), triplexes, quadruplexes and apartment buildings.
  • Other dwellings include mobile homes, motor homes, tents, railroad cars or boats (including floating homes and houseboats) that are used as permanent residences and are capable of being moved on short notice.

2.5 Household equipment

Cellular telephone: Includes cellular telephones and handheld text messaging devices with cell phone capability.

Computer: Excludes computers used exclusively for business purposes.

Internet use from home: Indicates whether the household has access to the Internet at home.

Landline telephone service: Includes landline telephone services used for business if the business is conducted in the dwelling.

Owned vehicles: Number of vehicles (automobiles, trucks and vans) owned by members of the household at the end of the month prior to the time of the interview.

2.6 Classification categories

Age of reference person: Households are grouped according to the age of the reference person as follows:

  • Less than 30 years
  • 30 to 39 years
  • 40 to 54 years
  • 55 to 64 years
  • 65 years and over

Before-tax household income quintile (national): Income groupings are obtained by ranking the households who responded to the interview in ascending order by total household income before tax, then partitioning the households into five groups of similar size. The estimated number of households in each group should be the same in principle, but differences may occur due to the weight of the household at the boundary of two quintiles, since this household must lie in either one or the other of these quintiles. Moreover, the specific methodology of the survey (with a set of weights for the interview and another for the diary) implies that the estimated number of households will be the same for the interview as for the diary only if the quintiles are defined at the provincial level. For the national quintiles, the estimated number of households may differ depending on whether the estimate uses interview weights or diary weights (see Section 5).

Canada: Canada-level data include the 10 provinces only.

Household type: Households are divided into the following types:

  • One-person households are the households where the dwelling is occupied by only one person at the time of the interview.
  • Couple households are households where the married or common-law spouse of the reference person is a member of the household at the time of the interview. This household type may be further broken down into couple households without children (without additional persons), with children (without additional persons), and with additional persons. “Children” are never-married sons, daughters or foster children of the reference person and may be any age. “Additional persons” are sons, daughters and foster children whose marital status is other than “single, never-married”, other relatives by birth or marriage, and unrelated persons.
  • Lone-parent households are households where the reference person has no spouse at the time of the interview and there is at least one never-married child (son, daughter or foster child of the reference person). The lone-parent households for which data are presented do not include any additional persons.
  • Other households are households composed of relatives only or households with at least one household member who is unrelated to the reference person (e.g., lodger, roommate, employee). Relatives are the
    • son, daughter, or foster child of the reference person whose marital status is other than “single, never-married”
    • relatives of the reference person by birth or marriage (not the spouse, son, daughter or foster child).

Housing tenure: Indicates whether a household member owned or rented the dwelling in which the household lived at the time of the interview.

  • Owners refers to all households living in a dwelling owned (with or without a mortgage) by a household member at the time of the interview:
    • owners with a mortgage owned the dwelling with a mortgage at the time of the interview
    • owners without a mortgage owned the dwelling without a mortgage at the time of the interview.
  • Renters rented a dwelling at the time of the interview (as a tenant paying rent, or rent free)

Population centre: Area with a population of 1,000 or more and a density of 400 or more people per square kilometre. Population centres are classified as defined below:

  • Small population centre: 1,000 to 29,999
  • Medium population centre: 30,000 to 99,999
  • Large urban population centre: 100,000 and over

Rural area: All areas outside population centres are considered rural areas. Together, population centres and rural areas cover all of Canada.

Size of area of residence: Sampled dwellings are assigned to the following groups depending on the area in which they are located according to the 2016 Census boundaries and population size.

  • Population centres:
    • 1,000,000 and over
    • 500,000 to 999,999
    • 250,000 to 499,999
    • 100,000 to 249,999
    • 30,000 to 99,999
    • 1,000 to 29,999
  • Rural area

Territorial Capitals: These are the three capitals of the northern territories: Whitehorse, Yellowknife and Iqaluit (based on the 2016 Census subdivision concept).

3. Survey methodology

3.1 Target population

The target population of the 2019 SHS is the population of Canada’s 10 provinces plus the 3 territorial capitals (Whitehorse, Yellowknife and Iqaluit). Residents of institutions and members of the Canadian Forces living in military camps are excluded as well as people living on Indian reserves. These exclusions account for about 2% of the population.

For operational reasons, people living in areas where the rate of vacant dwellings is very high and where the collection costs would be exorbitant are excluded from collection. Also excluded are people living in other types of collective dwellings such as:

  • people living in residences for dependent seniors
  • people living permanently in school residences and work camps
  • members of religious and other communal colonies.

Collection exclusions represent less than 0.5% of the target population. However, these people are included in the population estimates to which the SHS estimates are adjusted (see Section 3.6).

3.2 Survey content and reference periods

The SHS primarily collects detailed information on household expenditures. It also collects information on household demographic characteristics, certain dwelling characteristics (e.g., type, age and tenure), as well as certain information on household equipment (e.g., electronics and communications equipment). In addition, income information from personal income tax data is combined with the survey data.

For expenditure information collected through the questionnaire, the length of the reference period varies depending on the recall period specified in the question (e.g., the past month, the past 3 months or the past 12 months). The reference period also varies in relation to the collection month. For example, for households in the January 2019 sample, “the past 12 months” corresponds to the period from January 2018 to December 2018, while for households in the December 2019 sample, it corresponds to the months from December 2018 to November 2019. Expenditures collected in the expenditure diary are reported for a period of one week in the provinces and two weeks in the territorial capitals.

In general, longer reference periods are used to collect expenditures for goods and services that are more expensive or purchased infrequently or irregularly. In contrast, shorter reference periods are used for goods and services that are of lesser value or that are purchased frequently or at regular intervals.

For demographic characteristics, dwelling characteristics and household equipment, the reference period is the interview date. The reference period for income is the calendar year preceding the survey year (i.e. 2018 income for the 2019 SHS).

3.3 Sample design

The 2019 SHS sample consists of 17,491 households throughout the 10 provinces and 937 households in the three territorial capitals (Whitehorse, Yellowknife and Iqaluit).

3.3.1 Sample design in the provinces

A stratified two-stage sampling design was used to select the sample in the 10 provinces. The first stage involves selecting is a sample of geographic areas (referred to as clusters). Next, a list of all the dwellings in the selected clusters is prepared and a sample of dwellings is selected within each cluster. The selected dwellings that are inhabited by members of the target population constitute the survey’s sample of households. The SHS uses a number of components of the Labour Force Survey’s (LFS) sample design to minimize operating costs, although the dwellings selected for the SHS are different than those selected for the LFS.

The national sample is first divided among the provinces, taking the variability of total household expenditures and, to a lesser extent, the number of households in each province, into account. The goal is to obtain estimates of similar quality across all provinces. Provincial sample sizes are shown in Table 1a (Section 4). The sample is then divided into strata defined by grouping clusters with similar characteristics based on various sociodemographic variables. Some strata were defined to target specific subpopulations such as high-income households. To improve the quality of the estimates, the high-income household strata are allocated a larger share of the sample than the allocation proportional to stratum size that is used in other strata.

Since data are collected monthly, the sample is divided into 12 similar-sized subsamples. The geographic concepts used for the 2019 SHS sample are those of the 2016 Census.

3.3.2 Sample design in the three territorial capitals

A one-stage sampling design is used to select the sample in the three territorial capitals. The first step of the sample allocation is to determine the number of dwellings to be sampled in each city. The overall sample is allocated to each city by taking into account the size of the city and the quality of the estimates obtained from previous cycles of the SHS in the North. The sample sizes for the territorial capitals are shown in Table 1b (Section 4).

The sample is divided into 12 monthly subsamples of similar sizes, and the geographic concepts used for the 2019 SHS samples in the territorial capitals are those of the 2016 Census.

3.4 Data collection

The SHS is a voluntary cross-sectional survey that combines an interview and an expenditure diary. Collection is carried out on a continuous monthly basis from January to December of the survey year. The sample of households is distributed over 12 monthly samples. Each household is part of one of the monthly samples and is interviewed only once.

Households in the sample are asked to first complete an interview conducted in person using a questionnaire on a laptop. The interview mainly collects regular expenditures (such as rent and electricity) and less frequent expenditures (such as furniture and dwelling repairs) for a recall period that varies in length depending on the type of expenditure. For regular expenditures such as rent, the amount of the last payment and the period it covers are typically collected. For other types of expenditures collected in the interview, recall periods of 1 month, 3 months or 12 months are used. The recall periods are defined in terms of months preceding the month of the interview. For example, for a household in the June 2019 sample, a reference period of the last three months corresponds to the period from March 1 to May 31, 2019. Demographic characteristics, dwelling characteristics and household equipment information, which are also collected in the interview, refer to the household’s situation at the time of the interview.

Since 2013, respondents are informed that the survey data will be combined with tax data to obtain selected variables related to personal income for household members aged 16 and over on December 31 of the calendar year preceding the survey year. The reference period for personal income tax data is the calendar year prior to the survey year.

Following the interview, all respondents are asked to complete a diary in which they record the expenditures of all household members for a specified reporting period starting the day after their interview. The reporting period for the diary is one week for households in the provinces and two weeks for households in the territorial capitals. Households are required to include spending on all items except for certain types of expenditures such as rent, utilities payments, and real estate and vehicle purchases. Households have the option of providing receipts for purchases made during their diary reporting period to reduce the amount of information manually recorded in the diary. However, they are asked to add information on the receipt if the description of the item appearing on it is incomplete.

A telephone follow-up is carried out a few days after the interview to address any questions the respondent may have and to reiterate how important it is to complete the diary. At the end of the diary reporting period, the interviewer returns to the respondent’s residence to pick up the diary and ask a few additional questions to ensure that the respondent reports any expenditures that they may have overlooked.

The diaries and all receipts supplied by respondents are scanned and captured at Statistics Canada’s head office. An expenditure classification code is assigned to each item from a list of over 650 different codes.

3.5 Data processing and quality control

The computer-assisted questionnaire contains features designed to maximize the quality of the collected data. Many controls are built into the questionnaire to identify unusual values and detect logical inconsistencies. When a response is rejected by the control, an interviewer is prompted to correct the information (with the respondent’s help, if necessary). Once the data are transmitted to the head office, a detailed verification of each questionnaire is completed through a comprehensive series of processing steps. Invalid responses are corrected or flagged for imputation.

The diaries are also subject to a number of verifications when they are received at head office, as well as during data capture and coding steps. For example, checks are carried out to ensure that the start and end dates of the reference period of the diary are indicated, that the reported expenditures were incurred during the specified reference period, and that no items appear in both the data written in the diary and on the receipts provided by the respondent. After validation, capture and coding, quality control procedures are applied to the diary data. In addition, a sample of diaries is selected for a comprehensive reverification to ensure that the diaries were captured and coded as specified in the procedures.

Next, a series of detailed verifications is performed on all diaries, and invalid responses are corrected or flagged for imputation. The final step is to assess whether the information reported in the diaries is of sufficient quality using parameters that are based on household characteristics. The reported expenditures and number of items are compared with minimum thresholds estimated by geographic area (Atlantic provinces, Quebec, Ontario, Prairie provinces, British Columbia, and the three territorial capitals combined), household income class and household size. Diaries that satisfy the conditions are deemed usable. The remaining diaries are examined, and deemed usable if they include notes providing justification for their low expenditures or their small number of reported items (e.g., a person living alone who had few expenses to report while on a business trip during the diary reporting period). Diaries that do not meet the usability criteria are treated as non-response diaries and are excluded from the estimates. It should be noted that some of the usable diaries are incomplete and may have non-responded days.

To solve problems of missing or invalid information in the interview questions, donor imputation using the nearest neighbour method is generally applied. Using this approach, data from a respondent with similar characteristics (the donor) is used to impute missing or invalid data for another respondent. Imputation is done on one group of variables at a time. These groups are formed taking into account relationships that exist among the variables to be imputed. The characteristics used to identify donors are selected such that they are correlated with the variables to be imputed. Household income, dwelling type, and the number of adults and children are commonly used characteristics.

Donor imputation is also used when information is missing from the expenditure diary. For instance, a respondent may have reported a particular expenditure item without its cost or given the total amount spent (e.g., on groceries) without listing the individual items. Imputation is also used to enhance the level of detail in the coding of the items reported. For example, the information provided by the respondent may simply indicate that a bakery product was purchased, but a more detailed code is required to meet the survey’s needs. In this case, donor imputation is used to impute the type of bakery product (e.g., bread, crackers, cookies, cakes and other pastries). Diary imputation is carried out at the reported item level, and the characteristics frequently used to identify a donor are cost, available partial item code, household income and household size. Imputation is done by province and by survey year quarter to control for provincial differences and the seasonality of expenditures.

Starting in 2012, the imputation method was refined to use supplementary information on the type of store where the purchases were made in order to produce detailed expenditures when a respondent has only provided a total amount in their diary. This method takes into account the increasing amount of grocery products sold in large chain stores that do not specialize in groceries.

For income tax data, donor imputation is also used for missing or invalid data. Income and expenditure imputation is performed primarily with Statistics Canada’s Canadian Census Edit and Imputation System (CANCEIS).

After imputation, taxes are added to the diary items that are reported with taxes excluded. To reduce the burden on respondents, instructions are provided to respondents indicating when to include or exclude taxes from reported expenses in the diary. The goods and services tax (GST), provincial sales tax (PST), and harmonized sales tax (HST) are added to the diary items according to the appropriate federal and provincial taxation rates.

3.6 Weighting and estimation

Estimation of population characteristics from a sample survey is based on the premise that each sampled household represents a certain number of other households in the target population in addition to itself. This number is referred to as the survey weight.

Two different sets of survey weights are necessary for the SHS: one set for the interview and another set for the diary. Although all households in the SHS 2019 sample were selected to complete the diary, it is possible that they only completed the interview. Therefore, only a portion of the respondents to the interview also completed the diary.

3.6.1 Initial weights and non-response adjustments

There are a number of steps involved in the process of computing the weight assigned to each household. First, each household in the sample is given an initial weight equal to the inverse of its probability of being selected from the target population. A few adjustments are later applied to the interview weights and the diary weights.

The interview weights are first adjusted to take into account the households that did not respond to the questionnaire. They are also adjusted so that selected survey estimates are coherent with aggregates or estimates from auxiliary sources. This process is called weight calibration. The three data sources used for weight calibration are described in the next section.

The diary weights are also adjusted to take into account households that did not complete the diary. One factor adjusts for non-response to the questionnaire, while another factor adjusts for non-response to the diary among interview respondents. The diary weights then go through the calibration process, as explained in the next section.

3.6.2 Weight calibration

3.6.2.1 Weight calibration in the provinces

First the interview weights in the provinces are adjusted or calibrated using the number of persons by age group and the number of households by household size from population estimates produced by Statistics Canada’s Demography Division. These estimates are derived from 2016 Census data as well as administrative data. Annual estimates of the number of persons in nine age groups (0 to 6, 7 to 17, 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, and 75 and over) are used at the provincial level, and estimates for two age groups (0 to 17 years and 18 years and over) are used at the census metropolitan area level. For the number of households, weights are adjusted so that they sum up to the annual provincial estimates for each of three household size categories (1, 2, and 3 or more persons). An adjustment is also made to ensure that each quarter of the survey year is adequately represented in terms of the total number of households.

The second source used for interview weight calibration is the Statement of Remuneration Paid (T4) from the Canada Revenue Agency (CRA). The T4 data are used to ensure that the survey’s weighted distribution of income (based on wages and salaries) is consistent with the income distribution of the Canadian population. Interview weights are calibrated so that they sum up to the total T4 counts of the number of persons per province by six categories of wages and salaries. These categories are defined based on provincial percentiles (0 to 25th, 25th to 50th, 50th to 65th, 65th to 75th, 75th to 95th, and 95th to 100th).

Starting with the 2012 SHS, a third source used to adjust the interview weights is the personal income tax data (T1) from the CRA. The interview weights are adjusted to reflect the number of persons in each of the three highest personal income classes (based on the 95.5th, 97th and 98.5th percentiles) for each province except Prince Edward Island. In the latter case, only one income class is used. This adjustment compensates for the underrepresentation of the higher income groups among survey respondents.

The diary weights are adjusted so that they sum up to total demographic estimates in a manner similar to that used for the interview weights. The demographic estimates of the number of persons at the provincial level are the same for the diary as for the interview, with the exception of Prince Edward Island. In the latter case, only six age groups (0 to 17, 18 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 and older) are used due to the smaller sample size for this province. For weight calibration at the census metropolitan area level, the two age groups of 0 to 17 years and 18 years and over are  only used for Montréal, Toronto and Vancouver. For the remaining metropolitan areas, only the total number of persons is considered. Like the interview weights, the diary weights are adjusted to sum up to the annual provincial estimates for the three household size categories (1, 2, and 3 or more persons). For the diary, no adjustments are made to match the estimates by survey year quarter.

The diary weights are also adjusted according to income. Instead of adjusting on wages and salaries (T4), the weights are adjusted to sum up to the estimated number of households by provincial income quintile (0 to 20th, 20th to 40th, 40th to 60th, 60th to 80th, and 80th to 100th percentile) calculated using the interview data. This adjustment using the interview estimates ensures that the weighted income distribution of diary-respondent households is consistent with the weighted income distribution of interview-respondent households. The diary weights are also adjusted for the number of high-income individuals according to personal income tax data, using a single income class based on the 95.5th percentile. This last adjustment is not applied to Prince Edward Island.

3.6.2.2 Weight calibration in the three territorial capitals

In the three territorial capitals, only 5 control totals are used in the interview weight calibration process due to the small sample sizes for these cities. These weights are adjusted to control for only two age groups (the number of persons under 18 years of age and the number of persons aged 18 years and older) and to control for the number of households consisting of one, two, and three or more persons.

In the three territorial capitals, the same demographic control totals used to calibrate the interview weights are used for the diary weights.

3.6.3 Annualization and other adjustments

All expenditure amounts collected with the interview using a recall period of less than 12 months, as well as those collected with  the diary, are converted to annual amounts (annualized). For this purpose, the reported expenditures are multiplied by a factor based on the recall period. For example, amounts collected with a 3-month recall period are multiplied by 4 to annualize them. Some expenditure data are also corrected by an adjustment factor if they have been identified as influential or extreme values. For the diary, another adjustment factor is also applied to compensate for non-responded days. All official SHS expenditure estimates are based on the annualized and adjusted amounts.

3.7 Reference period of the estimates

With continuous monthly collection, the reference period of the collected data differs from one month to the next, as illustrated in Figure 1. For example, for an expenditure item with a three-month recall period, the data from the July sample include expenditures incurred between April 1 and June 30, whereas the data from the December sample include expenditures incurred between September 1 and November 30.

Figure 1 Monthly sample reference periods of three different lengths

Description for Figure 1

This figure shows the sample reference periods of three different lengths for each of the twelve monthly collection periods from January to December.

For each monthly collection period, expenditures with a one-month reference period cover the month preceding the month of the collection period, expenditures with a three-month reference period cover the three months preceding the month of the collection period, and expenditures with a twelve-month reference period cover the twelve months preceding the month of the collection period. The following examples are based on the collection periods of January and December.

For the collection period of January of the survey year, expenditures with a one-month reference period cover the month of December of the year prior to the survey year, expenditures with a three-month reference period cover the period from October to December of the year prior to the survey year, and expenditures with a twelve-month reference period cover the period from January to December of the year prior to the survey year.

For the collection period of December of the survey year, expenditures with a one-month reference period cover the month of November of the survey year, expenditures with a three-month reference period cover the period from September to November of the survey year, and expenditures with a twelve-month reference period cover the period from December of the year prior to the survey year to November of the survey year.

Collected expenditures with a reference period of less than 12 months are annualized so that all expenditure amounts cover a period of 12 months. SHS estimates are produced by combining the data from the 12 monthly samples.

Annualized data from the 12 monthly samples are combined to generate annual expenditure estimates. For expenditures with a recall period of 3 months or less, most of these expenditures were incurred during the survey reference year. This is also true for all expenditure data collected with the diary.

For expenditure items with a 12-month recall period, the collected expenses occurred between January of the year before the survey year and November of the survey year, depending on the collection month. For example, expenses collected in January cover the period from January to December of the year before the survey year, while expenses collected in December occurred between December of the year before the survey year and November of the survey year. For the estimates produced to represent a single 12-month period when the data from 12 monthly samples are combined, it must be assumed that expenditures incurred during the survey year are similar to those incurred during the previous year. This must also be considered when making comparisons between estimates based on a 12-month recall period and those based on shorter periods.

The limits of this collection model for producing expenditure estimates covering the same period (or the same year) are known, since the majority of countries use this methodology. Despite these limitations, continuous collection with reference periods adapted to the respondent’s ability to provide information is considered preferable to obtain data that reflect households’ true expenditures.

3.8 Historical revisions

The 2019 SHS estimates were computed with weights calibrated to 2019 demographic population estimates. These population estimates are based on 2016 Census data, as well as more recent information from administrative sources such as birth, death and migration registers. In order to make 2019 SHS estimates comparable to those for 2017, the 2017 SHS estimates have been revised using population projections based on the 2016 Census. These estimates were previously computed with weights calibrated to population estimates projected from the 2011 Census.

This historical revision of SHS 2017 also reflects improvements that were introduced with the 2019 SHS to the method for imputing grocery items and the method for adjusting influential values. These improvements are described in Section 3.9.

The SHS 2010 to 2016 estimates are still based on weights calibrated to 2011 Census population projections. As geographic concepts may have changed between the 2011 and 2016 Censuses, users should be careful when comparing estimates from the SHS 2010 to SHS 2016 series with those from SHS 2017 and 2019.

SHS estimates for years prior to 2010 (2001 to 2009) are based on weights adjusted to population projections from the 2001 Census. No revision (based on more recent Census data) is planned for these estimates due to the break in the data series starting with SHS 2010 (see Section 3.9).

3.9 Comparability over time

The SHS has been conducted annually since 1997. This survey includes most of the content of its predecessors (the periodic Survey of Family Expenditures and the Household Facilities and Equipment Survey). Prior to 2010, the SHS was primarily based on an interview conducted during the first quarter of the year in which households reported expenditures incurred in the preceding calendar year, although some changes to the methodology and definitions were made between 1997 and 2009.

A new methodology, which combines a questionnaire and a diary to collect household expenditures, was introduced in the 10 provinces starting with the 2010 SHS. The recall periods were shortened for several expenditure items and collection became continuous throughout the year. Although the expenditure categories in the redesigned SHS are similar to those of previous years, the changes to data collection, processing and estimation methods have created a break in the data series. As a result, users are advised not to compare SHS data from 2010 onward with data prior to 2010, unless indicated otherwise.

The redesigned SHS incorporates a significant amount of content that was previously collected through the Food Expenditure Survey (FES), last conducted in 2001. Although there are some differences between the SHS and FES methodologies, food expenditure data in both surveys have been collected using an expenditure diary that households are asked to fill in for a period of one or two weeks. The content of the SHS diary is slightly less detailed than that of the FES diary (e.g., the weight and quantity of food items are not collected) in order to limit the SHS respondent’s burden.

The content of the SHS was also reviewed in 2010 to reduce the time required for the interview. A number of components regarding household equipment and dwelling characteristics, as well as most of the questions regarding changes in household assets and liabilities have been dropped. Some definitions have also changed. As well, starting with the 2010 survey, the data related to household income and income tax come mainly from personal income tax data.

The new SHS design (applied in the 10 provinces since 2010) was introduced in the territories for the first time in the 2015 reference year. In prior years, coverage in the territories was near-complete and only remote communities were excluded. Starting in 2015, coverage in the three territories is limited to the capital cities due to operational and budget constraints as a result of adopting the new SHS design. As such, users are advised not to compare data for the territories from 2015 and later with those from previous years, unless otherwise noted.

The 2019 SHS questionnaire was streamlined to reduce respondent burden. As a result, some data series are no longer available or have been combined to create new series starting with reference year 2019. As well, estimates for some expenditure categories in 2019 may no longer be directly comparable to those from previous years due to changes to the questionnaire and/or to the mode used to collect certain expenditures. Additional information on the impact of these changes is available upon request. Also starting with SHS 2019, the length of the reporting period for the diary was reduced from two weeks to one week in the provinces. To mitigate the effect of the shorter reporting period on data quality, the size of the sample of households selected for the diary in the provinces was increased from 50% to 100% of the size of the interview sample.

Over time, it has been observed that a growing number of households report grocery totals in the diary instead of detailed expenses for individual grocery items. For the 2019 SHS, a new method was implemented with the aim of improving the process of distributing these grocery totals among grocery items. The previous method used the list of daily grocery expenses made by a donor household to redistribute another household’s grocery total. Analysis of the results from this method showed that an insufficient number of food items were imputed when the total to be redistributed was lower. The new method uses the detailed grocery stores receipts (with at least one food item) obtained from other respondents to distribute grocery totals reported by certain households. Totals associated to convenience stores were imputed with detailed receipts from these types of stores. In order to ensure comparability between the 2019 SHS estimates of expenditures for food purchased from stores with those of the 2017 SHS, the 2017 SHS data have been revised using the new method. This was done within the historical revision framework.

In 2019, a new method of influential value detection called the conditional bias was introduced to identify influential values in the expenditure data. These influential values are weighted expenditure amounts for a given household and a given item that are much larger or smaller than the weighted amounts of other households for that same item in a given geographic area.  Adjustments are made to the most extreme influential expenditure estimates. While adjustments were made in previous years, this new method has two advantages in that it reduces mean squared error (combination of bias and variance) and removes some of the subjectivity in identifying and adjusting these extreme values. The conditional bias method corrects a larger number of influential values but applies smaller adjustments than the previous method. For this reason, microdata users may observe a greater number of higher values, especially for asymmetric distributions. In order to ensure comparability between the 2019 SHS estimates with those of the 2017 SHS, the 2017 SHS data have been revised using this new method. This was done within the historical revision framework.

4. Data quality

The SHS is subject to error despite all the precautions taken in each step of the survey process to prevent them or reduce their impact. There are two types of errors: sampling and non-sampling.

4.1 Sampling errors

Sampling errors occur because inferences about the entire population are based on information obtained from only a sample of the population. The sample design, estimation method, sample size and data variability determine the size of the sampling error. The data variability for an expenditure item refers to the differences between members of the population in spending on that item. In general, the greater the differences between households, the larger the sampling error will be.

A common measure of sampling error is the standard error. The standard error is the degree of variation in the estimates that results from selecting one particular sample over another. The standard error expressed as a percentage of the estimate is called the coefficient of variation (CV). The CV is used to indicate the degree of uncertainty associated with an estimate. For example, if the estimated number of households with a given dwelling characteristic is 10,000 with a CV of 5%, then the actual number is between 9,500 and 10,500 households 68% of the time, and between 9,000 and 11,000 households 95% of the time.

The standard errors for the SHS are estimated using the bootstrap method (see reference [1] in Section 7). CVs are available for the national and provincial estimates, as well as for the estimates by household type, age of reference person, household income quintile, household tenure and size of area of residence. For the northern territories, CVs are available for the estimates for the three capitals.

4.2 Data suppression

To ensure accuracy, estimates with a CV greater than or equal to 35% have been suppressed in published tables. Suppressed estimates still contribute to summary-level estimates. For example, if the expenditure estimate for a particular item of clothing were suppressed, this amount would still be included in the total estimate for clothing expenditure.

4.3 Non-sampling errors

Non-sampling errors occur because certain factors make it difficult to obtain accurate responses and to ensure that these responses retain their accuracy throughout processing. Unlike sampling errors, non-sampling errors are not easily quantified. Four sources of non-sampling errors can be identified: coverage errors, response errors, non-response errors and processing errors.

4.3.1 Coverage errors

Coverage errors arise when sampling frame units do not adequately represent the target population. Such errors may occur during sample design or selection, during data collection or during data processing.

4.3.2 Response errors

Response errors occur when respondents provide inaccurate information. Such errors may be due to many factors, including flawed design of the questionnaire, misinterpretation of questions by interviewers or respondents, or faulty reporting by respondents.

Response errors are the most difficult aspect of data quality to measure. In general, the accuracy of SHS data depends largely on the respondent’s ability to remember (recall) household expenditures and their willingness to consult records.

4.3.3 Non-response errors

Errors due to non-response occur when potential respondents do not provide the required information or when the information they provide is unusable. The main impact of non-response on data quality is that it can cause a bias in the estimates if the characteristics of non-respondents differ from those of respondents in a way that impacts the expenditures studied. While response rates can be calculated, they provide only an indication of data quality, since they do not measure the degree of bias present in the estimates. The magnitude of non-response can be considered a simple indicator of the risks of bias in the estimates.

At the national level (10 provinces only), the response rate for the 2019 SHS interview is 62.3%. The provincial response rates are shown in Table 1a. The table also shows the number of non-responding households grouped according to reason for non-response. Reasons include the inability to contact the household, the household’s refusal to participate in the survey and the inability to conduct an interview because of special circumstances (e.g., the respondent speaks neither official language or has a physical condition that precludes an interview). Respondents in the latter category are referred to as residual non-respondents.


Table 1a
Interview response rates, CanadaTable 1a Note 1 and provinces, 2019
Table summary
This table displays the results of Interview response rates Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable 1a Note 2
number percentage
Canada 17,491 1,400 4,589 612 10,890 62.3
Atlantic provinces 5,689 272 1,382 208 3,827 67.3
Newfoundland and Labrador 1,522 94 320 42 1,066 70.0
Prince Edward Island 848 40 244 51 513 60.5
Nova Scotia 1,683 51 426 60 1,146 68.1
New Brunswick 1,636 87 392 55 1,102 67.4
Quebec 2,205 94 492 60 1,559 70.7
Ontario 2,411 216 758 120 1,317 54.6
Prairie provinces 5,053 584 1,367 142 2,960 58.6
Manitoba 1,609 137 427 70 975 60.6
Saskatchewan 1,564 165 437 29 933 59.7
Alberta 1,880 282 503 43 1,052 56.0
British Columbia 2,133 234 590 82 1,227 57.5

Some households do not complete the diary or provide a diary that is considered unusable under the criteria outlined in Section 3.5. For the 2019 SHS, the diary response rate among the households who responded to the interview is 69.5% (at the national level, which includes the provinces only). Provincial rates are provided in Table A1 of Appendix A. The final diary response rate (defined as the percentage of usable diaries relative to the number of households in the sample) is 43.3% at the national level, and provincial rates are shown in Table 2a.


Table 2a
Diary response rates, CanadaTable 2a Note 1 and provinces, 2019
Table summary
This table displays the results of Diary response rates Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled householdsTable 2a Note 2 Interview non-respondentsTable 2a Note 3 DiariesTable 2a Note 4 Response rateTable 2a Note 6
RefusalTable 2a Note 5 Unusable Usable
number percentage
Canada 17,491 6,601 2,903 421 7,566 43.3
Atlantic provinces 5,689 1,862 908 180 2,739 48.1
Newfoundland and Labrador 1,522 456 211 71 784 51.5
Prince Edward Island 848 335 162 19 332 39.2
Nova Scotia 1,683 537 315 55 776 46.1
New Brunswick 1,636 534 220 35 847 51.8
Quebec 2,205 646 537 54 968 43.9
Ontario 2,411 1,094 362 37 918 38.1
Prairie provinces 5,053 2,093 727 116 2,117 41.9
Manitoba 1,609 634 190 32 753 46.8
Saskatchewan 1,564 631 257 38 638 40.8
Alberta 1,880 828 280 46 726 38.6
British Columbia 2,133 906 369 34 824 38.6

The response rates vary from month to month. For the 10 provinces, monthly response rates for the interview and diary can be found in tables B1 and B2 of Appendix B. Interview and diary response rates by size of area of residence and dwelling type are shown in tables C1, C2, C3 and C4 of Appendix C, respectively.

The diary response rates of interview respondents can be found in tables D1, D2, D3 and D4 of Appendix D, disaggregated by various household characteristics, including household type, household tenure, age of the reference person and before-tax income quintile for the 10 provinces.

The interview response rates in the three territorial capitals are given in Table 1b below. Altogether, the three territorial capitals have an interview response rate equal to 63.0% for SHS 2019.


Table 1b
Interview response rates, three territorial capitals, 2019
Table summary
This table displays the results of Interview response rates Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable 1b Note 1
number percentage
Territorial capitals 937 105 218 24 590 63.0
Whitehorse 456 30 132 14 280 61.4
Yellowknife 296 46 45 9 196 66.2
Iqaluit 185 29 41 1 114 61.6

In the three territorial capitals, like in the provinces, some households do not complete or provide a diary that is considered unusable under the criteria outlined in Section 3.5. For the 2019 SHS, 61.7% of the households who responded to the interview in the territorial capitals also completed the diary. The final diary response rate in the northern capitals is 38.8%, as shown in table 2b.


Table 2b
Diary response rates, three territorial capitals, 2019
Table summary
This table displays the results of Diary response rates Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households Interview non-respondentsTable 2b Note 1 DiariesTable 2b Note 2 Response rateTable 2b Note 4
RefusalTable 2b Note 3 Unusable Usable
number percentage
Territorial capitals 937 347 222 4 364 38.8
Whitehorse 456 176 134 2 144 31.6
Yellowknife 296 100 38 1 157 53.0
Iqaluit 185 71 50 1 63 34.1

For the territorial capitals, the diary response rates among interview respondents are given in Table H1 of Appendix H. The interview and diary response rates by quarter are provided in Tables H2 and H3 of Appendix H.

For all selected households (provinces and territorial capitals), cases for which the respondent fails to answer some of the questions are referred to as partial non-response. Imputing missing values compensates for this partial non-response. Various imputation rates are shown in Section 4.3.5. There are also cases in which a household fails to enter data in the diary for each day in their diary reporting period as required. Adjustment factors are calculated to take these non-responded days into consideration and are applied to estimates derived from the diary data.

4.3.4 Processing errors

Processing errors may occur in any of the data processing stages, including data entry, coding, editing, imputation of partial non-response, weighting and tabulation. Steps taken to reduce processing errors are described in Section 3.5.

4.3.5 Imputation of partial non-responses

The residual bias remaining after the imputation of partial non-responses is difficult to measure. Its magnitude depends on the imputation method’s ability to produce unbiased estimates. The imputation rates provide an indication of the magnitude of partial non-response.

Partial interview non-response may result from a lack of information or from an invalid response to a question. The national and provincial percentages of households for which certain expenditure categories required imputation due to partial interview non-response are shown in Table 3a. These percentages are shown for the three territorial capitals in Table 3b. These percentages are presented by number of imputed expenditure variables per household (out of all consumer expenditure data collected during the interview). Each of these tables contains two series of results: one series including expenditures for communication services (telephone, cell phone and Internet), television services (via cable, a satellite dish or a phone line), satellite radio services, and home security services, and the other excluding these expenses. This distinction has been made because these services are increasingly being purchased as a package. Households are often billed for bundled services, making it difficult or impossible for them to provide separate expenditure amounts for each service. Therefore, the total amount paid for the package is allocated to individual services through imputation, which significantly increases the number of households for which expenditures must be imputed.


Table 3a
Percentage of households requiring imputation for consumer expenses collected during the interview, CanadaTable 3a Note 1 and provinces, 2019
Table summary
This table displays the results of Percentage of households requiring imputation for consumer expenses collected during the interview Number of variables imputed
(out of 160), Number of variables imputed
(out of 165), 1, 2 to 9, 10 or more and Total, calculated using percentage units of measure (appearing as column headers).
Number of variables imputedTable 3a Note 2
(out of 160)
Number of variables imputedTable 3a Note 3
(out of 165)
1 2 to 9 10 or more Total 1 2 to 9 10 or more Total
percentage
Canada 19.9 34.4 1.2 55.5 8.7 68.7 2.7 80.1
Newfoundland and Labrador 17.4 29.3 0.8 47.6 5.3 78.3 2.3 85.8
Prince Edward Island 25.3 31.0 0.4 56.7 10.5 71.0 1.4 82.8
Nova Scotia 21.2 33.2 1.5 55.8 7.6 70.3 2.8 80.7
New Brunswick 19.4 27.4 0.6 47.5 6.8 75.4 1.9 84.1
Quebec 21.1 34.6 1.3 57.0 7.3 72.5 3.5 83.3
Ontario 24.1 30.8 1.1 56.0 11.7 61.9 2.1 75.6
Manitoba 15.0 54.8 1.4 71.2 9.6 67.7 4.0 81.3
Saskatchewan 19.8 36.5 0.8 57.1 12.1 63.3 1.7 77.2
Alberta 12.4 48.3 2.7 63.3 8.1 63.6 4.9 76.6
British Columbia 23.0 21.9 0.8 45.7 9.5 63.4 1.5 74.5

Table 3b
Percentage of households requiring imputation for consumer expenses collected during the interview, three territorial capitals, 2019
Table summary
This table displays the results of Percentage of households requiring imputation for consumer expenses collected during the interview Number of variables imputed
(out of 160), Number of variables imputed
(out of 165), 1, 2 to 9, 10 or more and Total, calculated using percentage units of measure (appearing as column headers).
Number of variables imputedTable 3b Note 1
(out of 160)
Number of variables imputedTable 3b Note 2
(out of 165)
1 2 to 9 10 or more Total 1 2 to 9 10 or more Total
percentage
Territorial capitals 20.7 43.4 2.0 66.1 13.9 56.9 2.7 73.6
Whitehorse 20.7 45.4 2.1 68.2 13.6 60.7 2.9 77.1
Yellowknife 19.4 37.2 0.0 56.6 12.8 52.0 0.5 65.3
Iqaluit 22.8 49.1 5.3 77.2 16.7 56.1 6.1 78.9

Users of expenditure estimates related to communication, television, satellite radio or home security services should take the high level of imputation for the expenditure data into account when examining these individual services. A measure of the impact of imputation on each individual service has been produced in Table E1 of Appendix E for the provinces and in Table H4 of Appendix H for the territorial capitals. This measure represents the proportion of the total value of the estimate obtained from imputed data.

For expenditure data from the diaries, imputation is used in three ways. It is used to assign a value when the amount of a reported expenditure is missing. It is also used to assign a list of expenditure items (with individual costs) when only the total cost for a bundle of items has been provided. For example, imputation can assign grocery items and their individual costs in a case where the respondent has provided only the total amount of their grocery bill. Finally, imputation is used to assign an expenditure code that is more detailed than the one that could be assigned using the information provided by a respondent (e.g., the type of bakery product). The imputation rates for each of these three types of imputation are shown in Table F1 of Appendix F for Canada and in Table H5 of Appendix H for the three territorial capitals. Each rate represents the proportion of imputed items relative to all expenditure items from the diaries.

The risks of bias associated with the imputed data depend largely on the level of detail at which the SHS data are used. For example, food expenditure data in the SHS are produced at a high level of detail to meet the needs of Food Expenditure Survey (last conducted in 2001) users. Food expenditures are categorized using a hierarchical system of more than 200 expenditure codes. For some reported expenditure items, the food product may have been known (e.g., dairy products or even milk), but the level of detail required (e.g., skim milk, 1% milk or 2% milk) had to be imputed. This type of imputation creates a risk of bias only in expenditure estimates at a very detailed level. In other cases, however, almost no information on the type of expenditure was available before imputation (e.g., it was known only that the expenditure was for a good). When so little information is available, the risks of bias in the estimates of the expenditure categories are more significant.

Restaurant expenditures are reported using a slightly different format in the second section of the diary. Imputation is used primarily to assign a value when the total amount of the restaurant expenditure or the cost of alcoholic beverages is missing, or when the type of meal (breakfast, lunch, dinner or snack and beverage) has not been specified. The imputation rate for each of these three types of imputation is shown in Table F2 of Appendix F for Canada and in Tables H6 of Appendix H for the territorial capitals.

Lastly, households have the option of either providing receipts or recording their expenditure information in the diary. Table 4a shows the percentage of expenditures reported using each method for food expenditures, restaurant expenditures, and expenditures for other goods and services for Canada and the three territorial capitals respectively.


Table 4a
Methods for recording expenses in the diary, CanadaTable 4a Note 1, 2019
Table summary
This table displays the results of Methods for recording expenses in the diary. The information is grouped by Expenditure category (appearing as row headers), Transcriptions and Receipts, calculated using percentage units of measure (appearing as column headers).
Expenditure category Transcriptions Receipts
percentage
Food 18.4 81.6
Restaurant 81.1 18.9
Other goods and services 49.2 50.8

Table 4b
Methods for recording expenses in the diary, three territorial capitals, 2019
Table summary
This table displays the results of Methods for recording expenses in the diary. The information is grouped by Expenditure category (appearing as row headers), Transcriptions and Receipts, calculated using percentage units of measure (appearing as column headers).
Expenditure category Transcriptions Receipts
percentage
Food 13.0 87.0
Restaurant 69.2 30.8
Other goods and services 48.5 51.5

4.4 The effect of large values

For any sample, estimates of totals, averages and standard errors can be affected by the presence or absence of large values in the sample. Large values are more likely to arise from positively skewed populations. Such values are found in the SHS and are taken into account when the final estimates are generated. Section 3.9 provides a description of the method applied for SHS 2019 in order to adjust for these influential values.

5. Derivation of data tables

This section shows how the SHS data tables, previously known as CANSIM tables (see Section 6), have been derived. It then explains the calculations used most frequently to manipulate the data. Users are advised to refer to this section before undertaking data analysis.

As mentioned in Section 3.6, two different sets of weights are necessary for the SHS: one set for the interview and another set for the diary. These two weights are used to derive different estimates using the survey data.

5.1 Estimates of number of households

Adjustments made during weighting ensure that the estimated number of households at the provincial level is the same for both sets of weights (interview and diary) for the following domains:

  • Household sizes of one, two, and three or more persons.
  • Household income groups defined according to provincial quintiles.

By default, the estimate of the number of households for any aggregation of these domains is the same for both sets of weights.

For any other domain, the estimated number of households may differ to a certain extent between the two sets of weights, since the calibration strategy used to adjust weights differs between the interview and the diary outside of the domains listed above. The estimated number of households in the SHS tables has been produced using interview weights, as opposed to diary weights. The average household size is also estimated using the interview weights.

The estimated number of households and the average household size for the various domains for which expenditure estimates are published online are available in tables G1 and G2 of Appendix G for Canada and the provinces and in Table H7 of Appendix H for the territorial capitals.

5.2 Estimates of average expenditures per household

Estimates of average expenditures per household based on both interview and diary expenditure data are produced in two steps. Estimates are produced separately for the interview data and the diary data and are then added together in a second step.

For estimates of average expenditures per household, the interview average expenditures per household are first calculated using the weighted sum of expenditure data obtained from the interview divided by the sum of the interview weights. Similarly, the diary average expenditures per household are estimated using the weighted sum of expenditure data obtained from the diary divided by the sum of the diary weights. The two components are then added together to obtain the average expenditures per household. For domains in which the estimated number  of households differs between the two sets of weights, average expenditures per household derived using this method will not exactly match the combined interview and diary weighted sum of expenditures divided by the estimated number of households produced using the interview weights. Nevertheless, the approach ensures that the sum of the average expenditures per household for all categories equals the average total expenditure per household.

5.3 Examples of expenditure estimates

This section includes examples of expenditure estimates produced using a combination of interview and diary data. It also shows examples of the estimated number of households produced from the interview weights. These examples are provided to show how different expenditure estimates (presented in Section 5.4) can be calculated using published SHS data.

The data tables available online include estimates of average expenditures per household. The estimated number of households and the average household size are also available at the national, regional and provincial levels. The estimated number of households and the average household size for other domains are not included in these tables but are provided in tables G1 and G2 of Appendix G for Canada and the provinces and in Tables H7 of Appendix H for the three territorial capitals.

Table 5 shows the estimated number of households and average household size by household tenure as provided in the tables of Appendix G (not available in the data tables online), while Table 6 shows examples of average household expenditure estimates available to users through the online SHS data tables.


Table 5
Estimated number of households and average household size based on interview weights, by household tenure
Table summary
This table displays the results of Estimated number of households and average household size based on interview weights All households, Owner with mortgage, Owner without mortgage and Renter, calculated using number units of measure (appearing as column headers).
All households Owner with mortgage Owner without mortgage Renter
number
Estimated number of households 14,706,626 5,506,247 4,324,977 4,875,401
Average household size 2.48 3.13 2.18 2.02

Table 6
Average household expenditures obtained from interview and diary data, by household tenure
Table summary
This table displays the results of Average household expenditures obtained from interview and diary data All households, Owner with mortgage, Owner without mortgage and Renter, calculated using dollars units of measure (appearing as column headers).
All households Owner with mortgage Owner without mortgage Renter
dollars
Total expendituresTable 6 Note 1 49,078 67,798 42,203 33,907
Food expenditures 10,311 12,529 10,264 7,760
Food purchased from stores 7,536 9,148 7,462 5,718
Food purchased from restaurants 2,775 3,381 2,801 2,042
Shelter 20,200 30,734 13,325 14,401
Household furnishings and equipment 2,486 3,282 2,751 1,342
Clothing and accessories 3,344 4,047 3,204 2,675
Transportation 12,737 17,206 12,659 7,729

The following section provides examples demonstrating how to produce other estimates using tables such as Table 5 and Table 6 above.

5.4 Calculating various estimates

The following section explains some of the calculation methods most commonly used to manipulate SHS expenditure estimates.

5.4.1 Average expenditures per person

To calculate the average expenditures per person for a given category, divide the average expenditures per household for that category (Table 6) by the average household size (found on the second line of Table 5). For example, the average food expenditures per person for renter households are calculated as follows:

A v e r a g e   f o o d   e x p e n d i t u r e s   p e r   p e r s o n   f o r   r e n t e r   h o u s e h o l d s = A v e r a g e   f o o d   e x p e n d i t u r e s   p e r   r e n t e r   h o u s e h o l d A v e r a g e   s i z e   o f   r e n t e r   h o u s e h o l d MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaaqaaaaa aaaaWdbiaadgeacaWG2bGaamyzaiaadkhacaWGHbGaam4zaiaadwga caqGGaGaamOzaiaad+gacaWGVbGaamizaiaabccacaWGLbGaamiEai aadchacaWGLbGaamOBaiaadsgacaWGPbGaamiDaiaadwhacaWGYbGa amyzaiaadohacaqGGaGaamiCaiaadwgacaWGYbGaaeiiaiaadchaca WGLbGaamOCaiaadohacaWGVbGaamOBaiaabccacaWGMbGaam4Baiaa dkhacaqGGaGaamOCaiaadwgacaWGUbGaamiDaiaadwgacaWGYbGaae iiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiAaiaad+gacaWG SbGaamizaiaadohaaeaacqGH9aqpdaWcaaqaaiaadgeacaWG2bGaam yzaiaadkhacaWGHbGaam4zaiaadwgacaqGGaGaamOzaiaad+gacaWG VbGaamizaiaabccacaWGLbGaamiEaiaadchacaWGLbGaamOBaiaads gacaWGPbGaamiDaiaadwhacaWGYbGaamyzaiaadohacaqGGaGaamiC aiaadwgacaWGYbGaaeiiaiaadkhacaWGLbGaamOBaiaadshacaWGLb GaamOCaiaabccacaWGObGaam4BaiaadwhacaWGZbGaamyzaiaadIga caWGVbGaamiBaiaadsgaaeaacaWGbbGaamODaiaadwgacaWGYbGaam yyaiaadEgacaWGLbGaaeiiaiaadohacaWGPbGaamOEaiaadwgacaqG GaGaam4BaiaadAgacaqGGaGaamOCaiaadwgacaWGUbGaamiDaiaadw gacaWGYbGaaeiiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiA aiaad+gacaWGSbGaamizaaaaaaaa@B1FD@

Example:

$ 7 , 760 2.02 =   $ 3 , 842 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaWcaaqaaiaacscacaaI3aGaaiilaiaaiEdacaaI2aGaaGimaaqa aiaaikdacaGGUaGaaGimaiaaikdaaaGaeyypa0Jaaeiiaiaacscaca aIZaGaaiilaiaaiIdacaaI0aGaaGOmaaaa@4358@

When analyzing estimates of average expenditures per person, note that household composition (number of children and adults) is a significant factor in many expenditure patterns. As such, the method above provides only an approximation of the average per person. The SHS is not specifically designed to produce estimates of spending at the person level.

5.4.2 Percentage of average total household expenditures (budget share)

To calculate the budget share of an individual expenditure category as a percentage of average total household expenditures, divide the average expenditures per household for that category by the average total expenditures per household, and then multiply by 100. For example, using Table 6, the percentage of average total expenditures per household represented by the average expenditures on food per household, for renter households, is calculated by the following ratio:

A v e r a g e   e x p e n d i t u r e s   o n   f o o d a s   a   p e r c e n t a g e   o f   a v e r a g e   t o t a l   e x p e n d i t u r e s   f o r   r e n t e r   h o u s e h o l d s = A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   r e n t e r   h o u s e h o l d A v e r a g e   t o t a l   e x p e n d i t u r e s   p e r   r e n t e r   h o u s e h o l d ×   100 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaafaqabe Gabaaabaaeaaaaaaaaa8qacaWGbbGaamODaiaadwgacaWGYbGaamyy aiaadEgacaWGLbGaaeiiaiaadwgacaWG4bGaamiCaiaadwgacaWGUb GaamizaiaadMgacaWG0bGaamyDaiaadkhacaWGLbGaam4Caiaabcca caWGVbGaamOBaiaabccacaWGMbGaam4Baiaad+gacaWGKbaapaqaa8 qacaWGHbGaam4CaiaabccacaWGHbGaaeiiaiaadchacaWGLbGaamOC aiaadogacaWGLbGaamOBaiaadshacaWGHbGaam4zaiaadwgacaqGGa Gaam4BaiaadAgacaqGGaGaamyyaiaadAhacaWGLbGaamOCaiaadgga caWGNbGaamyzaiaabccacaWG0bGaam4BaiaadshacaWGHbGaamiBai aabccacaWGLbGaamiEaiaadchacaWGLbGaamOBaiaadsgacaWGPbGa amiDaiaadwhacaWGYbGaamyzaiaadohacaqGGaGaamOzaiaad+gaca WGYbGaaeiiaiaadkhacaWGLbGaamOBaiaadshacaWGLbGaamOCaiaa bccacaWGObGaam4BaiaadwhacaWGZbGaamyzaiaadIgacaWGVbGaam iBaiaadsgacaWGZbaaaaWdaeaacqGH9aqpaeaapeWaaSaaaeaacaWG bbGaamODaiaadwgacaWGYbGaamyyaiaadEgacaWGLbGaaeiiaiaadw gacaWG4bGaamiCaiaadwgacaWGUbGaamizaiaadMgacaWG0bGaamyD aiaadkhacaWGLbGaam4CaiaabccacaWGVbGaamOBaiaabccacaWGMb Gaam4Baiaad+gacaWGKbGaaeiiaiaadchacaWGLbGaamOCaiaabcca caWGYbGaamyzaiaad6gacaWG0bGaamyzaiaadkhacaqGGaGaamiAai aad+gacaWG1bGaam4CaiaadwgacaWGObGaam4BaiaadYgacaWGKbaa baGaamyqaiaadAhacaWGLbGaamOCaiaadggacaWGNbGaamyzaiaabc cacaWG0bGaam4BaiaadshacaWGHbGaamiBaiaabccacaWGLbGaamiE aiaadchacaWGLbGaamOBaiaadsgacaWGPbGaamiDaiaadwhacaWGYb GaamyzaiaadohacaqGGaGaamiCaiaadwgacaWGYbGaaeiiaiaadkha caWGLbGaamOBaiaadshacaWGLbGaamOCaiaabccacaWGObGaam4Bai aadwhacaWGZbGaamyzaiaadIgacaWGVbGaamiBaiaadsgaaaGaey41 aqRaaeiiaiaaigdacaaIWaGaaGimaaaaaa@E8B5@

Example:

$ 7 , 760 $ 33 , 907     ×   100   =   22.89 % MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaWcaaqaaiaacscacaaI3aGaaiilaiaaiEdacaaI2aGaaGimaaqa aiaacscacaaIZaGaaG4maiaacYcacaaI5aGaaGimaiaaiEdaaaGaai iOaiaacckacqGHxdaTcaqGGaGaaGymaiaaicdacaaIWaGaaeiiaiab g2da9iaabccacaaIYaGaaGOmaiaac6cacaaI4aGaaGyoaiaacwcaaa a@4D5F@

5.4.3 Combining expenditure categories

The average expenditures per household for different expenditure categories can be added together in one column to create new subtotals. For example, the average expenditures on shelter and transportation combined per renter household are calculated as follows:

A v e r a g e   e x p e n d i t u r e s   o n   s h e l t e r   p e r   r e n t e r   h o u s e h o l d + A v e r a g e   e x p e n d i t u r e s   o n   t r a n s p o r t a t i o n   p e r   r e n t e r   h o u s e h o l d MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaaqaaaaa aaaaWdbiaadgeacaWG2bGaamyzaiaadkhacaWGHbGaam4zaiaadwga caqGGaGaamyzaiaadIhacaWGWbGaamyzaiaad6gacaWGKbGaamyAai aadshacaWG1bGaamOCaiaadwgacaWGZbGaaeiiaiaad+gacaWGUbGa aeiiaiaadohacaWGObGaamyzaiaadYgacaWG0bGaamyzaiaadkhaca qGGaGaamiCaiaadwgacaWGYbGaaeiiaiaadkhacaWGLbGaamOBaiaa dshacaWGLbGaamOCaiaabccacaWGObGaam4BaiaadwhacaWGZbGaam yzaiaadIgacaWGVbGaamiBaiaadsgacqGHRaWkaeaacaWGbbGaamOD aiaadwgacaWGYbGaamyyaiaadEgacaWGLbGaaeiiaiaadwgacaWG4b GaamiCaiaadwgacaWGUbGaamizaiaadMgacaWG0bGaamyDaiaadkha caWGLbGaam4CaiaabccacaWGVbGaamOBaiaabccacaWG0bGaamOCai aadggacaWGUbGaam4CaiaadchacaWGVbGaamOCaiaadshacaWGHbGa amiDaiaadMgacaWGVbGaamOBaiaabccacaWGWbGaamyzaiaadkhaca qGGaGaamOCaiaadwgacaWGUbGaamiDaiaadwgacaWGYbGaaeiiaiaa dIgacaWGVbGaamyDaiaadohacaWGLbGaamiAaiaad+gacaWGSbGaam izaaaaaa@9BC6@

Example:

$ 14 , 401   +   $ 7 , 729   =   $ 22 , 130 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaGGKaGaaGymaiaaisdacaGGSaGaaGinaiaaicdacaaIXaGaaeii aiabgUcaRiaabccacaGGKaGaaG4naiaacYcacaaI3aGaaGOmaiaaiM dacaqGGaGaeyypa0JaaeiiaiaacscacaaIYaGaaGOmaiaacYcacaaI XaGaaG4maiaaicdaaaa@48E9@

5.4.4 Aggregate expenditures

To calculate aggregate expenditures, multiply the average expenditures per household from one column for an expenditure category (Table 6) by the estimated number of households from the same column in Table 5. For example, the aggregate expenditures on food for renter households are calculated as follows:

A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   r e n t e r   h o u s e h o l d   ×   E s t i m a t e d   n u m b e r   o f   r e n t e r   h o u s e h o l d s MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGbbGaamODaiaadwgacaWGYbGaamyyaiaadEgacaWGLbGaaeii aiaadwgacaWG4bGaamiCaiaadwgacaWGUbGaamizaiaadMgacaWG0b GaamyDaiaadkhacaWGLbGaam4CaiaabccacaWGVbGaamOBaiaabcca caWGMbGaam4Baiaad+gacaWGKbGaaeiiaiaadchacaWGLbGaamOCai aabccacaWGYbGaamyzaiaad6gacaWG0bGaamyzaiaadkhacaqGGaGa amiAaiaad+gacaWG1bGaam4CaiaadwgacaWGObGaam4BaiaadYgaca WGKbGaaeiiaiabgEna0kaabccacaWGfbGaam4CaiaadshacaWGPbGa amyBaiaadggacaWG0bGaamyzaiaadsgacaqGGaGaamOBaiaadwhaca WGTbGaamOyaiaadwgacaWGYbGaaeiiaiaad+gacaWGMbGaaeiiaiaa dkhacaWGLbGaamOBaiaadshacaWGLbGaamOCaiaabccacaWGObGaam 4BaiaadwhacaWGZbGaamyzaiaadIgacaWGVbGaamiBaiaadsgacaWG Zbaaaa@871B@

Example:

$ 7 , 760   ×   4 , 875 , 401   =   $ 37 , 833 , 111 , 760 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaGGKaGaaG4naiaacYcacaaI3aGaaGOnaiaaicdacaqGGaGaey41 aqRaaeiiaiaaisdacaGGSaGaaGioaiaaiEdacaaI1aGaaiilaiaais dacaaIWaGaaGymaiaabccacqGH9aqpcaqGGaGaaiijaiaaiodacaaI 3aGaaiilaiaaiIdacaaIZaGaaG4maiaacYcacaaIXaGaaGymaiaaig dacaGGSaGaaG4naiaaiAdacaaIWaaaaa@5184@

Note: Since the average food expenditure comes from diary data alone, and the estimated number of households in the domain used differs slightly depending on whether it is calculated using the interview weights or the diary weights, this estimate of aggregate expenditures only approximates the value that would have been obtained using the weighted sum of expenditures. Indeed, if the estimated number of households used in the calculation were based on the diary weights (not available online), the estimate of aggregate food expenditures for renter households would be slightly different at $37,508,696,191.

The estimates of aggregate expenditures are exact for all domains for which the sum of the interview weights and the sum of the diary weights are the same (see Section 5.1), as well as for all variables derived only from the interview.

5.4.5 Aggregate expenditures by combining data columns

To calculate aggregate expenditures for a given expenditure category for multiple columns, calculate the aggregate expenditures for this category for each column and then add them together.

For example, the aggregate expenditures on food by owner households (with or without a mortgage) are calculated as follows:

A g g r e g a t e   e x p e n d i t u r e s   o n   f o o d   f o r   o w n e r   h o u s e h o l d s   w i t h   o r   w i t h o u t   a   m o r t g a g e = ( A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   o w n e r   h o u s e h o l d   w i t h   a   m o r t g a g e ×   E s t i m a t e d   n u m b e r   o f   o w n e r   h o u s e h o l d s   w i t h   a   m o r t g a g e ) + ( A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   o w n e r   h o u s e h o l d   w i t h o u t   a   m o r t g a g e ×   E s t i m a t e d   n u m b e r   o f   o w n e r   h o u s e h o l d s   w i t h o u t   a   m o r t g a g e ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaaqaaaaa aaaaWdbiaadgeacaWGNbGaam4zaiaadkhacaWGLbGaam4zaiaadgga caWG0bGaamyzaiaabccacaWGLbGaamiEaiaadchacaWGLbGaamOBai aadsgacaWGPbGaamiDaiaadwhacaWGYbGaamyzaiaadohacaqGGaGa am4Baiaad6gacaqGGaGaamOzaiaad+gacaWGVbGaamizaiaabccaca WGMbGaam4BaiaadkhacaqGGaGaam4BaiaadEhacaWGUbGaamyzaiaa dkhacaqGGaGaamiAaiaad+gacaWG1bGaam4CaiaadwgacaWGObGaam 4BaiaadYgacaWGKbGaam4CaiaabccacaWG3bGaamyAaiaadshacaWG ObGaaeiiaiaad+gacaWGYbGaaeiiaiaadEhacaWGPbGaamiDaiaadI gacaWGVbGaamyDaiaadshacaqGGaGaamyyaiaabccacaWGTbGaam4B aiaadkhacaWG0bGaam4zaiaadggacaWGNbGaamyzaaqaaiabg2da9a qaa8aafaqabeGabaaabaWdbiaacIcacaWGbbGaamODaiaadwgacaWG YbGaamyyaiaadEgacaWGLbGaaeiiaiaadwgacaWG4bGaamiCaiaadw gacaWGUbGaamizaiaadMgacaWG0bGaamyDaiaadkhacaWGLbGaam4C aiaabccacaWGVbGaamOBaiaabccacaWGMbGaam4Baiaad+gacaWGKb GaaeiiaiaadchacaWGLbGaamOCaiaabccacaWGVbGaam4Daiaad6ga caWGLbGaamOCaiaabccacaWGObGaam4BaiaadwhacaWGZbGaamyzai aadIgacaWGVbGaamiBaiaadsgacaqGGaGaam4DaiaadMgacaWG0bGa amiAaiaabccacaWGHbGaaeiiaiaad2gacaWGVbGaamOCaiaadshaca WGNbGaamyyaiaadEgacaWGLbaapaqaa8qacqGHxdaTcaqGGaGaamyr aiaadohacaWG0bGaamyAaiaad2gacaWGHbGaamiDaiaadwgacaWGKb Gaaeiiaiaad6gacaWG1bGaamyBaiaadkgacaWGLbGaamOCaiaabcca caWGVbGaamOzaiaabccacaWGVbGaam4Daiaad6gacaWGLbGaamOCai aabccacaWGObGaam4BaiaadwhacaWGZbGaamyzaiaadIgacaWGVbGa amiBaiaadsgacaWGZbGaaeiiaiaadEhacaWGPbGaamiDaiaadIgaca qGGaGaamyyaiaabccacaWGTbGaam4BaiaadkhacaWG0bGaam4zaiaa dggacaWGNbGaamyzaiaacMcaaaaabaGaey4kaScabaWdauaabeqace aaaeaapeGaaiikaiaadgeacaWG2bGaamyzaiaadkhacaWGHbGaam4z aiaadwgacaqGGaGaamyzaiaadIhacaWGWbGaamyzaiaad6gacaWGKb GaamyAaiaadshacaWG1bGaamOCaiaadwgacaWGZbGaaeiiaiaad+ga caWGUbGaaeiiaiaadAgacaWGVbGaam4BaiaadsgacaqGGaGaamiCai aadwgacaWGYbGaaeiiaiaad+gacaWG3bGaamOBaiaadwgacaWGYbGa aeiiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiAaiaad+gaca WGSbGaamizaiaabccacaWG3bGaamyAaiaadshacaWGObGaam4Baiaa dwhacaWG0bGaaeiiaiaadggacaqGGaGaamyBaiaad+gacaWGYbGaam iDaiaadEgacaWGHbGaam4zaiaadwgaa8aabaWdbiabgEna0kaabcca caWGfbGaam4CaiaadshacaWGPbGaamyBaiaadggacaWG0bGaamyzai aadsgacaqGGaGaamOBaiaadwhacaWGTbGaamOyaiaadwgacaWGYbGa aeiiaiaad+gacaWGMbGaaeiiaiaad+gacaWG3bGaamOBaiaadwgaca WGYbGaaeiiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiAaiaa d+gacaWGSbGaamizaiaadohacaqGGaGaam4DaiaadMgacaWG0bGaam iAaiaad+gacaWG1bGaamiDaiaabccacaWGHbGaaeiiaiaad2gacaWG VbGaamOCaiaadshacaWGNbGaamyyaiaadEgacaWGLbGaaiykaaaaaa aa@5CC3@

Example:

( $ 12 , 529   ×   5 , 506 , 247 )   +   ( $ 10 , 264   ×   4 , 324 , 977 )   =   $ 113 , 379 , 332 , 591 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaeWaaeaaqa aaaaaaaaWdbiaacscacaaIXaGaaGOmaiaacYcacaaI1aGaaGOmaiaa iMdacaqGGaGaey41aqRaaeiiaiaaiwdacaGGSaGaaGynaiaaicdaca aI2aGaaiilaiaaikdacaaI0aGaaG4naaWdaiaawIcacaGLPaaapeGa aeiiaiabgUcaRiaabccapaWaaeWaaeaapeGaaiijaiaaigdacaaIWa GaaiilaiaaikdacaaI2aGaaGinaiaabccacqGHxdaTcaqGGaGaaGin aiaacYcacaaIZaGaaGOmaiaaisdacaGGSaGaaGyoaiaaiEdacaaI3a aapaGaayjkaiaawMcaa8qacaqGGaGaeyypa0JaaeiiaiaacscacaaI XaGaaGymaiaaiodacaGGSaGaaG4maiaaiEdacaaI5aGaaiilaiaaio dacaaIZaGaaGOmaiaacYcacaaI1aGaaGyoaiaaigdaaaa@6793@

5.4.6 Average expenditures per household by combining data columns

To calculate the average expenditures for a given expenditure category for multiple columns, calculate the aggregate expenditures for this category for each column, add them together, and then divide the total by the sum of the estimated number of households in those columns (Table 7). For example, the average expenditures on food per owner household (with or without a mortgage) are calculated as follows:

( A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   o w n e r   h o u s e h o l d   w i t h   a   m o r t g a g e ×   E s t i m a t e d   n u m b e r   o f   o w n e r   h o u s e h o l d s   w i t h   a   m o r t g a g e ) + ( A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   o w n e r   h o u s e h o l d   w i t h o u t   a   m o r t g a g e ×   E s t i m a t e d   n u m b e r   o f   o w n e r   h o u s e h o l d s   w i t h o u t   m o r t g a g e ) E s t i m a t e d   n u m b e r   o f   h o u s e h o l d s   ( w i t h   a n d   w i t h o u t   a   m o r t g a g e ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaafaqabe Gabaaabaaeaaaaaaaaa8qacaGGOaGaamyqaiaadAhacaWGLbGaamOC aiaadggacaWGNbGaamyzaiaabccacaWGLbGaamiEaiaadchacaWGLb GaamOBaiaadsgacaWGPbGaamiDaiaadwhacaWGYbGaamyzaiaadoha caqGGaGaam4Baiaad6gacaqGGaGaamOzaiaad+gacaWGVbGaamizai aabccacaWGWbGaamyzaiaadkhacaqGGaGaam4BaiaadEhacaWGUbGa amyzaiaadkhacaqGGaGaamiAaiaad+gacaWG1bGaam4Caiaadwgaca WGObGaam4BaiaadYgacaWGKbGaaeiiaiaadEhacaWGPbGaamiDaiaa dIgacaqGGaGaamyyaiaabccacaWGTbGaam4BaiaadkhacaWG0bGaam 4zaiaadggacaWGNbGaamyzaaWdaeaapeGaey41aqRaaeiiaiaadwea caWGZbGaamiDaiaadMgacaWGTbGaamyyaiaadshacaWGLbGaamizai aabccacaWGUbGaamyDaiaad2gacaWGIbGaamyzaiaadkhacaqGGaGa am4BaiaadAgacaqGGaGaam4BaiaadEhacaWGUbGaamyzaiaadkhaca qGGaGaamiAaiaad+gacaWG1bGaam4CaiaadwgacaWGObGaam4Baiaa dYgacaWGKbGaam4CaiaabccacaWG3bGaamyAaiaadshacaWGObGaae iiaiaadggacaqGGaGaamyBaiaad+gacaWGYbGaamiDaiaadEgacaWG HbGaam4zaiaadwgacaGGPaaaaaqaaiabgUcaRaqaamaalaaabaWdau aabeqaceaaaeaapeGaaiikaiaadgeacaWG2bGaamyzaiaadkhacaWG HbGaam4zaiaadwgacaqGGaGaamyzaiaadIhacaWGWbGaamyzaiaad6 gacaWGKbGaamyAaiaadshacaWG1bGaamOCaiaadwgacaWGZbGaaeii aiaad+gacaWGUbGaaeiiaiaadAgacaWGVbGaam4BaiaadsgacaqGGa GaamiCaiaadwgacaWGYbGaaeiiaiaad+gacaWG3bGaamOBaiaadwga caWGYbGaaeiiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiAai aad+gacaWGSbGaamizaiaabccacaWG3bGaamyAaiaadshacaWGObGa am4BaiaadwhacaWG0bGaaeiiaiaadggacaqGGaGaamyBaiaad+gaca WGYbGaamiDaiaadEgacaWGHbGaam4zaiaadwgaa8aabaWdbiabgEna 0kaabccacaWGfbGaam4CaiaadshacaWGPbGaamyBaiaadggacaWG0b GaamyzaiaadsgacaqGGaGaamOBaiaadwhacaWGTbGaamOyaiaadwga caWGYbGaaeiiaiaad+gacaWGMbGaaeiiaiaad+gacaWG3bGaamOBai aadwgacaWGYbGaaeiiaiaadIgacaWGVbGaamyDaiaadohacaWGLbGa amiAaiaad+gacaWGSbGaamizaiaadohacaqGGaGaam4DaiaadMgaca WG0bGaamiAaiaad+gacaWG1bGaamiDaiaabccacaWGTbGaam4Baiaa dkhacaWG0bGaam4zaiaadggacaWGNbGaamyzaiaacMcaaaaabaGaam yraiaadohacaWG0bGaamyAaiaad2gacaWGHbGaamiDaiaadwgacaWG KbGaaeiiaiaad6gacaWG1bGaamyBaiaadkgacaWGLbGaamOCaiaabc cacaWGVbGaamOzaiaabccacaWGObGaam4BaiaadwhacaWGZbGaamyz aiaadIgacaWGVbGaamiBaiaadsgacaWGZbGaaeiia8aacaGGOaWdbi aadEhacaWGPbGaamiDaiaadIgacaqGGaGaamyyaiaad6gacaWGKbGa aeiiaiaadEhacaWGPbGaamiDaiaadIgacaWGVbGaamyDaiaadshaca qGGaGaamyyaiaabccacaWGTbGaam4BaiaadkhacaWG0bGaam4zaiaa dggacaWGNbGaamyza8aacaGGPaaaaaaaaa@499E@

Example:

( $ 12 , 529   ×   5 , 506 , 247 )   +   ( $ 10 , 264   ×   4 , 324 , 977 ) 5 , 506 , 247   +   4 , 324 , 977 =   $ 11 , 533   MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaSaaaeaada qadaqaaabaaaaaaaaapeGaaiijaiaaigdacaaIYaGaaiilaiaaiwda caaIYaGaaGyoaiaabccacqGHxdaTcaqGGaGaaGynaiaacYcacaaI1a GaaGimaiaaiAdacaGGSaGaaGOmaiaaisdacaaI3aaapaGaayjkaiaa wMcaa8qacaqGGaGaey4kaSIaaeiia8aadaqadaqaa8qacaGGKaGaaG ymaiaaicdacaGGSaGaaGOmaiaaiAdacaaI0aGaaeiiaiabgEna0kaa bccacaaI0aGaaiilaiaaiodacaaIYaGaaGinaiaacYcacaaI5aGaaG 4naiaaiEdaa8aacaGLOaGaayzkaaaabaWdbiaaiwdacaGGSaGaaGyn aiaaicdacaaI2aGaaiilaiaaikdacaaI0aGaaG4naiaabccacqGHRa WkcaqGGaGaaGinaiaacYcacaaIZaGaaGOmaiaaisdacaGGSaGaaGyo aiaaiEdacaaI3aaaaiabg2da9iaabccacaGGKaGaaGymaiaaigdaca GGSaGaaGynaiaaiodacaaIZaGaaiiOaaaa@70E1@

5.4.7 Expenditure share of a subgroup among all households

Here the expenditure share is the percentage of the aggregate expenditures for a given expenditure category that belongs to a particular subgroup of households (e.g., the percentage of all food expenditures made by renter households). It is calculated by deriving the household subgroup’s aggregate expenditures for the expenditure category and dividing it by the aggregate expenditure for that expenditure category for all households. The result is then multiplied by 100. For example, the percentage of food expenditures made by renter households is calculated as follows:

P e r c e n t a g e   o f   f o o d   e x p e n d i t u r e s   m a d e   b y   r e n t e r   h o u s e h o l d s = A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   r e n t e r   h o u s e h o l d   ×   E s t i m a t e d   n u m b e r   o f   r e n t e r   h o u s e h o l d s A v e r a g e   e x p e n d i t u r e s   o n   f o o d   p e r   h o u s e h o l d   f o r   a l l   h o u s e h o l d s   ×   E s t i m a t e d   t o t a l   n u m b e r   o f   h o u s e h o l d s   ×   100 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaGabeaaqaaaaa aaaaWdbiaadcfacaWGLbGaamOCaiaadogacaWGLbGaamOBaiaadsha caWGHbGaam4zaiaadwgacaqGGaGaam4BaiaadAgacaqGGaGaamOzai aad+gacaWGVbGaamizaiaabccacaWGLbGaamiEaiaadchacaWGLbGa amOBaiaadsgacaWGPbGaamiDaiaadwhacaWGYbGaamyzaiaadohaca qGGaGaamyBaiaadggacaWGKbGaamyzaiaabccacaWGIbGaamyEaiaa bccacaWGYbGaamyzaiaad6gacaWG0bGaamyzaiaadkhacaqGGaGaam iAaiaad+gacaWG1bGaam4CaiaadwgacaWGObGaam4BaiaadYgacaWG KbGaam4Caaqaaiabg2da9aqaamaalaaabaGaamyqaiaadAhacaWGLb GaamOCaiaadggacaWGNbGaamyzaiaabccacaWGLbGaamiEaiaadcha caWGLbGaamOBaiaadsgacaWGPbGaamiDaiaadwhacaWGYbGaamyzai aadohacaqGGaGaam4Baiaad6gacaqGGaGaamOzaiaad+gacaWGVbGa amizaiaabccacaWGWbGaamyzaiaadkhacaqGGaGaamOCaiaadwgaca WGUbGaamiDaiaadwgacaWGYbGaaeiiaiaadIgacaWGVbGaamyDaiaa dohacaWGLbGaamiAaiaad+gacaWGSbGaamizaiaabccacqGHxdaTca qGGaGaamyraiaadohacaWG0bGaamyAaiaad2gacaWGHbGaamiDaiaa dwgacaWGKbGaaeiiaiaad6gacaWG1bGaamyBaiaadkgacaWGLbGaam OCaiaabccacaWGVbGaamOzaiaabccacaWGYbGaamyzaiaad6gacaWG 0bGaamyzaiaadkhacaqGGaGaamiAaiaad+gacaWG1bGaam4Caiaadw gacaWGObGaam4BaiaadYgacaWGKbGaam4CaaqaaiaadgeacaWG2bGa amyzaiaadkhacaWGHbGaam4zaiaadwgacaqGGaGaamyzaiaadIhaca WGWbGaamyzaiaad6gacaWGKbGaamyAaiaadshacaWG1bGaamOCaiaa dwgacaWGZbGaaeiiaiaad+gacaWGUbGaaeiiaiaadAgacaWGVbGaam 4BaiaadsgacaqGGaGaamiCaiaadwgacaWGYbGaaeiiaiaadIgacaWG VbGaamyDaiaadohacaWGLbGaamiAaiaad+gacaWGSbGaamizaiaabc cacaWGMbGaam4BaiaadkhacaqGGaGaamyyaiaadYgacaWGSbGaaeii aiaadIgacaWGVbGaamyDaiaadohacaWGLbGaamiAaiaad+gacaWGSb GaamizaiaadohacaqGGaGaey41aqRaaeiiaiaadweacaWGZbGaamiD aiaadMgacaWGTbGaamyyaiaadshacaWGLbGaamizaiaabccacaWG0b Gaam4BaiaadshacaWGHbGaamiBaiaabccacaWGUbGaamyDaiaad2ga caWGIbGaamyzaiaadkhacaqGGaGaam4BaiaadAgacaqGGaGaamiAai aad+gacaWG1bGaam4CaiaadwgacaWGObGaam4BaiaadYgacaWGKbGa am4CaaaacaqGGaGaey41aqRaaeiiaiaaigdacaaIWaGaaGimaaaaaa@1BCA@

Example:

$ 7 , 760 × 4 , 875 , 401   $ 10 , 311 × 14 , 706 , 626   ×   100   =   24.95 %   MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaWcaaqaaiaacscacaaI3aGaaiilaiaaiEdacaaI2aGaaGimaiab gEna0kaaisdacaGGSaGaaGioaiaaiEdacaaI1aGaaiilaiaaisdaca aIWaGaaGymaiaacckaaeaacaGGKaGaaGymaiaaicdacaGGSaGaaG4m aiaaigdacaaIXaGaey41aqRaaGymaiaaisdacaGGSaGaaG4naiaaic dacaaI2aGaaiilaiaaiAdacaaIYaGaaGOnaaaacaGGGcGaey41aqRa aeiiaiaaigdacaaIWaGaaGimaiaabccacqGH9aqpcaqGGaGaaGOmai aaisdacaGGUaGaaGyoaiaaiwdacaGGLaGaaiiOaaaa@6083@

6. Related products and services

6.1 Data tables (formerly CANSIM)

Previously, Statistics Canada data was available via CANSIM (the Canadian Socioeconomic Information Management System), a database consisting of multidimensional cross-sectional tables. CANSIM tables were replaced by data tables with the same or similar content. All of the content previously available has been integrated into the new data tables.

Eight tables presenting annual information from the Survey of Household Spending are available for Canada and the provinces. Table 11-10-0222-01 presents detailed household expenditure estimates, while tables 11-10-0223-01 to 11-10-0227-01 present data according to household income quintile, household type, household tenure, size of area of residence and age of the reference person, respectively. Table 11-10-0228-01 presents information on dwelling characteristics and household equipment. Finally, Table 11-10-0125-01 provides detailed food expenditure estimates.

Two tables are available with SHS estimates for the three territorial capitals. Table 11-10-0233-01 presents household expenditure estimates, while Table 11-10-0234-01 presents information on dwelling characteristics and household equipment.

6.2 Microdata products

Starting with SHS 2017, a Public-Use Microdata File (PUMF) will be produced on a regular basis. The SHS 2017 PUMF (62M0004X) is the first SHS PUMF based on data collected after the 2010 redesign. SHS microdata are also accessible through Statistics Canada’s Research Data Centre (RDC) and Real-Time Remote Access (RTRA) programs. Microdata products based on SHS 2019 will be made available in 2021.

6.3 Custom tabulations

For clients with more specialized data needs, custom tabulations can be produced to their specifications on a cost- recovery basis under the terms of a contract (subject to confidentiality restrictions). Detailed aggregate data on household expenditures are also available on a custom basis.

7. References

[1] Charlebois, J. and G. Dubreuil. 2011. Variance Estimation for the Redesigned Survey of Household Spending. Proceedings of the Survey Methods Section, Statistical Society of Canada Annual Meeting, June 2011.

Appendix A

Diary response rates among interview respondents, Canada and provinces


Table A1
Diary response rates among the respondents to the interview, CanadaTable A1 Note 1 and provinces, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable A1 Note 2
RefusalTable A1 Note 3 Unusable Usable Response rateTable A1 Note 4
number percentage
Canada 10,890 2,903 421 7,566 69.5
Atlantic provinces 3,827 908 180 2,739 71.6
Newfoundland and Labrador 1,066 211 71 784 73.5
Prince Edward Island 513 162 19 332 64.7
Nova Scotia 1,146 315 55 776 67.7
New Brunswick 1,102 220 35 847 76.9
Quebec 1,559 537 54 968 62.1
Ontario 1,317 362 37 918 69.7
Prairie provinces 2,960 727 116 2,117 71.5
Manitoba 975 190 32 753 77.2
Saskatchewan 933 257 38 638 68.4
Alberta 1,052 280 46 726 69.0
British Columbia 1,227 369 34 824 67.2

Appendix B

Response rates by collection month, Canada


Table B1
Interview response rates by collection month, CanadaTable B1 Note 1, 2019
Table summary
This table displays the results of Interview response rates by collection month Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable B1 Note 2
number percentage
All months 17,491 1,400 4,589 612 10,890 62.3
January 1,484 103 364 61 956 64.4
February 1,451 90 382 53 926 63.8
March 1,443 126 396 49 872 60.4
April 1,459 76 400 53 930 63.7
May 1,500 100 388 39 973 64.9
June 1,428 106 378 47 897 62.8
July 1,420 137 335 47 901 63.5
August 1,455 108 383 69 895 61.5
September 1,446 148 385 35 878 60.7
October 1,519 115 410 68 926 61.0
November 1,457 123 398 53 883 60.6
December 1,429 168 370 38 853 59.7

Table B2
Diary response rates by collection month, CanadaTable B2 Note 1, 2019
Table summary
This table displays the results of Diary response rates by collection month Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled householdsTable B2 Note 2 Interview non-respondentsTable B2 Note 3 DiariesTable B2 Note 4 Response rateTable B2 Note 6
RefusalTable B2 Note 5 Unusable Usable
number percentage
All months 17,491 6,601 2,903 421 7,566 43.3
January 1,484 528 191 42 723 48.7
February 1,451 525 184 37 705 48.6
March 1,443 571 205 41 626 43.4
April 1,459 529 230 32 668 45.8
May 1,500 527 266 50 657 43.8
June 1,428 531 215 34 648 45.4
July 1,420 519 251 28 622 43.8
August 1,455 560 257 30 608 41.8
September 1,446 568 248 34 596 41.2
October 1,519 593 290 27 609 40.1
November 1,457 574 258 35 590 40.5
December 1,429 576 308 31 514 36.0

Appendix C

Response rates by size of area of residence and by dwelling type, Canada


Table C1
Interview response rates by size of area of residence, CanadaTable C1 Note 1, 2019
Table summary
This table displays the results of Interview response rates by size of area of residence Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable C1 Note 2
number percentage
All population centres and rural areas 17,491 1,400 4,589 612 10,890 62.3
Population centre 1,000,000 and over 4,884 500 1,248 168 2,968 60.8
Population centre 500,000 to 999,999 1,567 129 441 74 923 58.9
Population centre 250,000 to 499,999 1,748 119 511 44 1,074 61.4
Population centre 100,000 to 249,999 2,888 209 813 106 1,760 60.9
Population centre 30,000 to 99,999 1,840 143 515 73 1,109 60.3
Population centre 1,000 to 29,999 1,964 125 468 59 1,312 66.8
Rural area 2,600 175 593 88 1,744 67.1

Table C2
Diary response rates by size of area of residence, CanadaTable C2 Note 1, 2019
Table summary
This table displays the results of Diary response rates by size of area of residence Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled householdsTable C2 Note 2 Interview non-respondentsTable C2 Note 3 DiariesTable C2 Note 4 Response rateTable C2 Note 6
RefusalTable C2 Note 5 Unusable Usable
number percentage
All population centres and rural areas 17,491 6,601 2,903 421 7,566 43.3
Population centre 1,000,000 and over 4,884 1,916 925 100 1,943 39.8
Population centre 500,000 to 999,999 1,567 644 212 27 684 43.7
Population centre 250,000 to 499,999 1,748 674 351 26 697 39.9
Population centre 100,000 to 249,999 2,888 1,128 391 70 1,299 45.0
Population centre 30,000 to 99,999 1,840 731 322 50 737 40.1
Population centre 1,000 to 29,999 1,964 652 289 57 966 49.2
Rural area 2,600 856 413 91 1,240 47.7

Table C3
Interview response rates by dwelling type, CanadaTable C3 Note 1, 2019
Table summary
This table displays the results of Interview response rates by dwelling type Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable C3 Note 2
number percentage
All dwelling types 17,491 1,400 4,589 612 10,890 62.3
Single detached 11,094 797 3,112 393 6,792 61.2
Double or row/terrace 1,744 128 461 55 1,100 63.1
Duplex, low-rise or high-rise apartment 4,259 437 921 148 2,753 64.6
Other 393 37 95 16 245 62.3
Not available 1 1 0 0 0 0.0

Table C4
Diary response rates by dwelling type, CanadaTable C4 Note 1, 2019
Table summary
This table displays the results of Diary response rates by dwelling type Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled householdsTable C4 Note 2 Interview non-respondentsTable C4 Note 3 DiariesTable C4 Note 4 Response rateTable C4 Note 6
RefusalTable C4 Note 5 Unusable Usable
number percentage
All dwelling types 17,491 6,601 2,903 421 7,566 43.3
Single detached 11,094 4,302 1,671 224 4,897 44.1
Double or row/terrace 1,744 644 299 48 753 43.2
Duplex, low-rise or high-rise apartment 4,259 1,506 882 135 1,736 40.8
Other 393 148 51 14 180 45.8
Not available 1 1 0 0 0 0.0

Appendix D

Diary response rates among interview respondents, by various household characteristics, Canada


Table D1
Diary response rates among the respondents to the interview, by household type, CanadaTable D1 Note 1, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable D1 Note 2
RefusalTable D1 Note 3 Unusable Usable Response rateTable D1 Note 4
number percentage
All household types 10,890 2,903 421 7,566 69.5
One person household 3,044 924 181 1,939 63.7
Couple without children 3,222 734 88 2,400 74.5
Couple with children 2,752 686 70 1,996 72.5
Couple with other related or unrelated persons 398 107 14 277 69.6
Lone-parent household with no additional persons 784 236 34 514 65.6
Other household with related or unrelated persons 690 216 34 440 63.8

Table D2
Diary response rates among the respondents to the interview, by household tenure, CanadaTable D2 Note 1, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable D2 Note 2
RefusalTable D2 Note 3 Unusable Usable Response rateTable D2 Note 4
number percentage
All household tenures 10,890 2,903 421 7,566 69.5
Owner without mortgage 3,718 881 118 2,719 73.1
Owner with mortgage 4,012 1,000 116 2,896 72.2
Renter (with or without rent paid) 3,160 1,022 187 1,951 61.7

Table D3
Diary response rates among the respondents to the interview, by age of the reference person, CanadaTable D3 Note 1, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable D3 Note 2
RefusalTable D3 Note 3 Unusable Usable Response rateTable D3 Note 4
number percentage
Reference person of all ages 10,890 2,903 421 7,566 69.5
Less than 30 years 939 285 42 612 65.2
30 to 39 years 1,736 497 62 1,177 67.8
40 to 54 years 2,755 742 113 1,900 69.0
55 to 64 years 2,329 597 85 1,647 70.7
65 years and over 3,131 782 119 2,230 71.2

Table D4
Diary response rates among the respondents to the interview, by before-tax income quintile, CanadaTable D4 Note 1, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable D4 Note 2
RefusalTable D4 Note 3 Unusable Usable Response rateTable D4 Note 4
number percentage
Total of all income quintiles 10,890 2,903 421 7,566 69.5
Lowest quintile 2,199 696 143 1,360 61.8
Second quintile 2,348 626 107 1,615 68.8
Third quintile 2,188 548 59 1,581 72.3
Fourth quintile 2,145 492 68 1,585 73.9
Highest quintile 2,010 541 44 1,425 70.9

Appendix E

Impact of expenditure imputation on communication, television, satellite radio and home security services, Canada


Table E1
Impact of expenditure imputation on communication, television, satellite radio and home security services, CanadaTable E1 Note 1, 2019
Table summary
This table displays the results of Impact of expenditure imputation on communication Impact of imputation, calculated using percentage units of measure (appearing as column headers).
Impact of imputationTable E1 Note 2
percentage
Landline telephone services 65.0
Cell phone and pager 12.2
Television and satellite radio services 64.2
Internet access services 55.2
Home security services 9.9

Appendix F

Imputation rates by type of imputation and recording method for diary expenses, Canada


Table F1
Imputation rates for goods and services including food from stores, by type of imputation and recording method, CanadaTable F1 Note 1, 2019
Table summary
This table displays the results of Imputation rates for goods and services including food from stores. The information is grouped by Type of imputation (appearing as row headers), Transcribed items, Items from a receipt and All items, calculated using percentage, TotalItems_Transcribed, TotalItems_Receipt, TotalItems_All, SHSCode_Transcribed, SHSCode_Receipt and SHSCode_All units of measure (appearing as column headers).
Type of imputation Transcribed items Items from a receipt All items
percentage
Imputation of a missing cost for a reported expense
Food from stores 2.6 0.1 1.3
Other goods and services 5.0 0.0 2.8
All expenditures 3.4 0.1 1.7
Imputation of expenditure items (and their individual cost) from a total expense
Food from stores 83.0 2.1 40.3
Other goods and services 23.7 1.5 13.8
All expenditures 63.2 2.0 32.4
Imputation of detailed expenditure code
Food from stores 3.1 3.3 3.2
Other goods and services 6.8 3.2 5.2
All expenditures 4.3 3.3 3.8

Table F2
Imputation rates for snacks, beverages and meals purchased from restaurants or fast-food outlets, by type of imputation and recording method, CanadaTable F2 Note 1, 2019
Table summary
This table displays the results of Imputation rates for snacks. The information is grouped by Type of imputation (appearing as row headers), Transcribed items, Items from a receipt and All items, calculated using percentage units of measure (appearing as column headers).
Type of imputation Transcribed items Items from a receipt All items
percentage
Imputation of total cost 0.56 0.00 0.45
Imputation of costs for alcoholic beverages 15.15 10.08 14.20
Imputation of meal type (breakfast, lunch, dinner or snack and beverages) 15.24 8.75 14.01

Appendix G

Estimated number of households and average household size by domain, Canada


Table G1
Estimated number of households and average household size by domain defined at the national level, CanadaTable G1 Note 1, 2019
Table summary
This table displays the results of Estimated number of households and average household size by domain defined at the national level. The information is grouped by Domain (appearing as row headers), Estimated number of households and Average household size (appearing as column headers).
Domain Estimated number of households Average household size
Canada
All classes 14,706,626 2.48
Region
Atlantic Region 1,025,256 2.30
Quebec 3,631,961 2.28
Ontario 5,531,719 2.59
Prairie Region 2,499,145 2.65
British Columbia 2,018,544 2.44
Province
Newfoundland and Labrador 223,031 2.31
Prince Edward Island 65,345 2.36
Nova Scotia 408,101 2.30
New Brunswick 328,779 2.28
Quebec 3,631,961 2.28
Ontario 5,531,719 2.59
Manitoba 493,858 2.58
Saskatchewan 434,150 2.52
Alberta 1,571,137 2.71
British Columbia 2,018,544 2.44
Before-tax household income quintile (national)
Lowest quintile 2,940,260 1.44
Second quintile 2,939,107 1.91
Third quintile 2,939,362 2.51
Fourth quintile 2,945,193 2.96
Highest quintile 2,942,705 3.60
Household type
One person households 4,338,648 1.00
Couples without children 3,846,270 2.00
Couples with children 3,918,625 3.99
Couples with other related or unrelated persons 830,236 4.77
Lone-parent households with no additional persons 784,638 2.56
Other households with related or unrelated persons 988,208 2.92
Household tenure
Owner 9,831,224 2.71
Owner with mortgage 5,506,247 3.13
Owner without mortgage 4,324,977 2.18
Renter 4,875,401 2.02
Size of area of residence
Population centre 1,000,000 and over 6,749,539 2.63
Population centre 500,000 to 999,999 1,295,961 2.53
Population centre 250,000 to 499,999 1,154,376 2.35
Population centre 100,000 to 249,999 1,671,319 2.33
Population centre 30,000 to 99,999 1,069,989 2.20
Population centre 1,000 to 29,999 1,475,740 2.34
Rural 1,289,702 2.36
Age of reference person
Less than 30 years 1,380,263 2.12
30 to 39 years 2,654,743 2.88
40 to 54 years 3,842,286 3.19
55 to 64 years 3,031,398 2.35
65 years and over 3,797,936 1.73

Table G2
Estimated number of households and average household size by domain, provincial level, 2019
Table summary
This table displays the results of Estimated number of households and average household size by domain. The information is grouped by Domain (appearing as row headers), Estimated number of households and Average household size (appearing as column headers).
Domain Estimated number of households Average household size
Newfoundland and Labrador
All classes 223,031 2.31
Lowest quintile 44,525 1.41
Second quintile 44,587 1.85
Third quintile 44,680 2.37
Fourth quintile 44,532 2.74
Highest quintile 44,707 3.16
Prince Edward Island
All classes 65,345 2.36
Lowest quintile 12,975 1.45
Second quintile 13,131 1.90
Third quintile 13,024 2.26
Fourth quintile 12,943 2.86
Highest quintile 13,273 3.32
Nova Scotia
All classes 408,101 2.30
Lowest quintile 81,534 1.43
Second quintile 81,583 1.85
Third quintile 81,680 2.34
Fourth quintile 81,650 2.84
Highest quintile 81,654 3.04
New Brunswick
All classes 328,779 2.28
Lowest quintile 65,557 1.39
Second quintile 65,748 1.82
Third quintile 65,872 2.20
Fourth quintile 65,664 2.65
Highest quintile 65,938 3.33
Quebec
All classes 3,631,961 2.28
Lowest quintile 723,195 1.24
Second quintile 728,499 1.72
Third quintile 726,921 2.25
Fourth quintile 725,444 2.84
Highest quintile 727,902 3.36
Ontario
All classes 5,531,719 2.59
Lowest quintile 1,102,005 1.47
Second quintile 1,106,124 2.07
Third quintile 1,110,318 2.61
Fourth quintile 1,104,307 3.02
Highest quintile 1,108,965 3.77
Manitoba
All classes 493,858 2.58
Lowest quintile 98,376 1.48
Second quintile 98,642 2.13
Third quintile 98,858 2.56
Fourth quintile 98,923 3.28
Highest quintile 99,059 3.47
Saskatchewan
All classes 434,150 2.52
Lowest quintile 86,719 1.48
Second quintile 86,746 1.97
Third quintile 86,904 2.66
Fourth quintile 86,863 3.00
Highest quintile 86,919 3.49
Alberta
All classes 1,571,137 2.71
Lowest quintile 313,624 1.72
Second quintile 313,582 2.27
Third quintile 314,263 2.86
Fourth quintile 314,599 3.03
Highest quintile 315,070 3.65
British Columbia
All classes 2,018,544 2.44
Lowest quintile 402,380 1.47
Second quintile 404,804 1.89
Third quintile 403,716 2.33
Fourth quintile 402,591 2.89
Highest quintile 405,053 3.60

Appendix H

Response rates, imputation rates, estimated number of households and average household size by domain, three territorial capitals


Table H1
Diary response rates among the respondents to the interview, three territorial capitals, 2019
Table summary
This table displays the results of Diary response rates among the respondents to the interview Interview respondents, Diaries, Refusal, Unusable, Usable and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Interview respondents DiariesTable H1 Note 1
RefusalTable H1 Note 2 Unusable Usable Response rateTable H1 Note 3
number percentage
Territorial capitals 590 222 4 364 61.7
Whitehorse 280 134 2 144 51.4
Yellowknife 196 38 1 157 80.1
Iqaluit 114 50 1 63 55.3

Table H2
Interview response rates by quarter, three territorial capitals, 2019
Table summary
This table displays the results of Interview response rates by quarter Eligible sampled households, No contacts, Refusals, Residual non-respondents, Respondents and Response rate, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households No contacts Refusals Residual non-respondents Respondents Response rateTable H2 Note 1
number percentage
Territorial capitals
All quarters 937 105 218 24 590 63.0
Quarter 1 234 20 70 4 140 59.8
Quarter 2 236 24 44 6 162 68.6
Quarter 3 222 23 48 3 148 66.7
Quarter 4 245 38 56 11 140 57.1
Whitehorse
All quarters 456 30 132 14 280 61.4
Quarter 1 112 8 44 2 58 51.8
Quarter 2 117 3 32 3 79 67.5
Quarter 3 110 9 27 2 72 65.5
Quarter 4 117 10 29 7 71 60.7
Yellowknife
All quarters 296 46 45 9 196 66.2
Quarter 1 70 7 14 2 47 67.1
Quarter 2 77 12 8 3 54 70.1
Quarter 3 74 12 11 1 50 67.6
Quarter 4 75 15 12 3 45 60.0
Iqaluit
All quarters 185 29 41 1 114 61.6
Quarter 1 52 5 12 0 35 67.3
Quarter 2 42 9 4 0 29 69.0
Quarter 3 38 2 10 0 26 68.4
Quarter 4 53 13 15 1 24 45.3

Table H3
Diary response rates by quarter, three territorial capitals, 2019
Table summary
This table displays the results of Diary response rates by quarter Eligible sampled households, Interview non-respondents, Diaries, Response rate, Refusal, Unusable and Usable, calculated using number and percentage units of measure (appearing as column headers).
Eligible sampled households Interview non-respondentsTable H3 Note 1 DiariesTable H3 Note 2 Response rateTable H3 Note 4
RefusalTable H3 Note 3 Unusable Usable
number percentage
Territorial capitals
All quarters 937 347 222 4 364 38.8
Quarter 1 234 94 53 1 86 36.8
Quarter 2 236 74 53 2 107 45.3
Quarter 3 222 74 60 1 87 39.2
Quarter 4 245 105 56 0 84 34.3
Whitehorse
All quarters 456 176 134 2 144 31.6
Quarter 1 112 54 29 0 29 25.9
Quarter 2 117 38 24 2 53 45.3
Quarter 3 110 38 40 0 32 29.1
Quarter 4 117 46 41 0 30 25.6
Yellowknife
All quarters 296 100 38 1 157 53.0
Quarter 1 70 23 10 0 37 52.9
Quarter 2 77 23 12 0 42 54.5
Quarter 3 74 24 7 1 42 56.8
Quarter 4 75 30 9 0 36 48.0
Iqaluit
All quarters 185 71 50 1 63 34.1
Quarter 1 52 17 14 1 20 38.5
Quarter 2 42 13 17 0 12 28.6
Quarter 3 38 12 13 0 13 34.2
Quarter 4 53 29 6 0 18 34.0

Table H4
Impact of expenditure imputation on communication, television, satellite radio and home security services, three territorial capitals, 2019
Table summary
This table displays the results of Impact of expenditure imputation on communication Impact of imputation, calculated using percentage units of measure (appearing as column headers).
Impact of imputationTable H4 Note 1
percentage
Landline telephone services 32.0
Cell phone and pager 4.3
Television and satellite radio services 41.9
Internet access services 31.2
Home security services 1.5

Table H5
Imputation rates for goods and services including food from stores, by type of imputation and recording method, three territorial capitals, 2019
Table summary
This table displays the results of Imputation rates for goods and services including food from stores. The information is grouped by Type of imputation (appearing as row headers), Transcribed items, Items from a receipt and All items, calculated using percentage, SHSCode_Transcribed, SHSCode_Receipt and SHSCode_All units of measure (appearing as column headers).
Type of imputation Transcribed items Items from a receipt All items
percentage
Imputation of a missing cost for a reported expense
Food from stores 1.3 0.1 0.6
Other goods and services 5.0 0.1 2.8
All expenditures 2.5 0.1 1.2
Imputation of expenditure items (and their individual cost) from a total expense
Food from stores 91.9 2.4 43.3
Other goods and services 27.0 2.2 15.9
All expenditures 71.2 2.4 35.6
Imputation of detailed expenditure code
Food from stores 1.7 3.0 2.4
Other goods and services 8.0 3.3 5.9
All expenditures 3.7 3.1 3.4

Table H6
Imputation rates for snacks, beverages and meals purchased from restaurants or fast-food outlets, by type of imputation and recording method, three territorial capitals, 2019
Table summary
This table displays the results of Imputation rates for snacks. The information is grouped by Type of imputation (appearing as row headers), Transcribed items, Items from a receipt and All items, calculated using percentage units of measure (appearing as column headers).
Type of imputation Transcribed items Items from a receipt All items
percentage
Imputation of total cost 0.58 0.00 0.40
Imputation of costs for alcoholic beverages 20.38 15.28 18.81
Imputation of meal type (breakfast, lunch, dinner or snack and beverages) 19.00 6.50 15.15

Table H7
Estimated number of households and average household size, three territorial capitals, 2019
Table summary
This table displays the results of Estimated number of households and average household size. The information is grouped by Domain (appearing as row headers), Estimated number of households and Average household size (appearing as column headers).
Domain Estimated number of households Average household size
Territorial capitals 22,874 2.53
Whitehorse 11,921 2.37
Yellowknife 7,788 2.76
Iqaluit 3,165 2.57

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