Income and Expenditure Accounts Technical Series
Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2025, technical methodology and quality report

1 Overview

The global economy has undergone significant structural shifts in recent years due in part to the 2008 global financial crisis, the rise of globalization, and more recently, the economic fallout from the COVID-19 pandemic. The pandemic triggered widespread disruptions in labour markets, supply chains, and consumer behavior, exacerbating pre-existing inequalities and accelerating digital transformation. In addition, cost-of-living pressures, fueled by inflation, housing affordability crises, and energy price spikes, have further strained households, especially in lower-income brackets. As these macroeconomic shifts continue to shape the lives of everyday citizens, inequality and financial stability remain central concerns for policymakers globally. Statistical agencies, such as Statistics Canada, can contribute significantly to scholarly research by providing more comprehensive, up-to-date data on national financial inequality, helping to inform policy decisions and address the economic challenges facing vulnerable populations.

Statistics Canada has undertaken the development of the wealth tables for the Distributions of Household Economic Accounts (DHEA), data sets that describe household net worth distributed according to various characteristics of households in order to more specifically define who holds wealth in Canada. This data provides policymakers and academics with additional tools to examine inequality and its impact on our society. The DHEA data brings together the detail available from micro-data sources with the System of National Accounts (SNA) concepts covered by macro-data, which have more complete coverage and are internationally comparable. The main micro-data source is the Survey of Financial Security (SFS), a household survey that collects information on assets, debts, and wealth (net worth). The SFS is not an annual survey, and so a different methodology is required in survey years and in non-survey years.

This paper presents in detail the methodology used to develop distributions of household net worth published in January 2026 for the fourth quarter of reference years 2010 to 2019 and each quarter starting in reference year 2020. These estimates are based on a methodology that has been developed and refined since releases prior to September 2021. It begins with a description of the international framework of the Organization for Economic Co-operation and Development (OECD) Expert Group on Disparities within the National Accounts (EG DNA), which provides recommendations on producing distributional information aligned with SNA concepts; followed by Statistics Canada’s implementation of each step. These include the adjustments to the National Accounts totals; a description of the micro-data sources; the methodology for the derivation of indicators in survey years and non-survey years; and a description of the potential sources of error. In the years for which survey data is not available, modelling is required to derive wealth distributions. The modelling approach will be described as well as the various adjustments required to ensure internal consistency of the tables and consistency with the macroeconomic totals.

This work is a step towards an integrated framework of distributions including income, consumption, saving and wealth. The estimates produced using this methodology are subject to revisions as the methodology is refined.

2 Introduction

Statistics Canada regularly publishes macroeconomic indicators on household assets, liabilities and net worth as part of the quarterly National Balance Sheet Accounts (NBSA). These accounts are aligned with the most recent international standards and are the source of estimates of national wealth for all sectors of the economy, including households, non-profit institutions, governments and corporations along with Canada’s wealth position vis-à-vis the rest of the world. While the NBSA provide high quality information on the overall position of households relative to other economic sectors, they lack the granularity required to understand vulnerabilities of specific groups and the resulting implications for economic well-being and financial stability.

There is a growing recognition, both in Canada and internationally, that building distributional dimensions into household macroeconomic indicators is becoming increasingly important. If information on disparities among households is consistent with macroeconomic indicators, it enriches the understanding of economic developments and allows for a more complete assessment of the risks associated with for example, rising inequality of income, consumption, saving and wealth.

Recent comprehensive revisions to the Canadian System of Macroeconomic Accounts published in 2012 and 2015 better positioned the macroeconomic statistical program to undertake this work. Changes were introduced to align measures with new international standards, including the creation of a separate sector for non-profit institutions serving households (NPISH) (previously included in the household sector), and the measurement of employer-sponsored pensions on an entitlement basis. More detail on the changes made during the revisions in 2012 and 2015 can be found in Statistics Canada (2012-10-15, 2015-12-01).

This documentation outlines a step towards a more complete program of distributional estimates for the household sector in the Canadian macroeconomic accounts. This documentation also presents the methodology used to develop distributions of wealth for the household sector of the NBSA in the September 2021 DHEA for the fourth quarter of reference years 2010 to 2019, and each quarter starting in reference year 2020. It describes the technical details of the methodology and includes a report on the quality of the estimated distributions. These estimates are based on a methodology that has been further developed and refined since releases previous to September 2021. Distributions of income, consumption and saving are also produced and are part of the DHEA ongoing statistical program. More detail can be found in Statistics Canada (2026-01-29).

3 International framework

In order to produce distributional information aligned with SNA concepts, Statistics Canada follows the basic steps recommended by the OECD EG DNA. Statistics Canada’s implementation of each step will be described in detail in the subsequent sections.

Description for Figure 1

A step-by-step approach for the estimation of distributional information, provided by the EG DNA

Step 1. Adjust national account totals.

Step 2. Determine relevant variables from micro sources in relation to the national accounts variables.

Step 3. Impute for missing elements and scale the micro data to the adjusted national accounts totals.

Step 4. Cluster households.

Step 5. Derive relevant indicators for the household groups.

4 Adjusting the national accounts totals

4.1 National Balance Sheet Accounts

The NBSA are statements of the non-financial assets owned/used in the economy and of the financial claims outstanding (financial assets and liabilities) among the economic units in the economy. They consist of the national balance sheet for the country as a whole, as well as the underlying sector balance sheets. At the core of the NBSA are assets and liabilities and the concepts of wealth and net worth.

The DHEA focusses specifically on the household sector of the national balance sheet. This covers the assets, liabilities, and net worth (including some sub-categories) of all households in Canada.

4.2 Adjustments

The OECD recommends isolating the household sector for distributional analysis; a process that may require adjusting the National Accounts sector total if it has been aggregated with the NPISH sector.

Prior to the comprehensive revision in 2012, there were three main resident institutional sectors in the Canadian System of National Accounts (CSNA): the persons and unincorporated business sector, the corporate sector and the government sector. The persons and unincorporated business sector included NPISH, credit unions, life insurance companies, fraternal organizations and collective investment schemes such as pension plans and mutual funds. Due to data limitations, this sector also encompassed activities of Indigenous governments.

With the 2012 comprehensive revision, the CSNA adopted the basic SNA institutional sectoring detail throughout the sequence of integrated accounts. The former persons and unincorporated business sector was split between households and non-profit institutions serving households.

Given this work was already done to isolate the household sector, adjustments to the current NBSA data were not needed.

5 Micro-data source and variables

5.1 Survey of Financial Security

The micro-data source identified for the majority of distributions of net worth and their components is the SFS. The purpose of the survey is to collect information from a sample of Canadian families on their assets, debts, employment, income and education. This helps in understanding how family finances change because of economic pressures. The SFS provides a comprehensive picture of the net worth of Canadians. Information is collected on the value of all major financial and non-financial assets and on the money owing on mortgages, vehicles, credit cards, student loans and other debts. A family’s net worth is defined as the value of a family’s assets minus their debt and can be thought of as the amount of money they would be left with if they sold all of their assets and paid off all of their debts.

The SFS is a sample survey with a cross-sectional design. It had been conducted on an occasional basis, in 1999, 2005, and 2012, 2016 and 2019. In 2023, the SFS was once again collected and incorporated into the DHEA with the January 2025 release. The SFS covers the population living in the ten provinces of Canada. Within the provinces, certain groups are excluded (for instance, persons living on reserves or other Indigenous settlements and chronic care patients living in hospitals or nursing homes), which represent about 2% of the population.

Over the years, the SFS sample size and design have varied. The initial sample size was approximately 20,000 dwellings for reference years 2012, 2016 and 2019, rising to almost 40,000 dwellings in 2023.

Data are generally collected directly from respondents, while in some cases additional information is extracted from administrative files and derived from other Statistics Canada surveys and other sources via record linkage. Examples include the use of personal tax data records and regulatory information on the terms and conditions of employer-sponsored pension plans. For the 2023 survey interviews were conducted via a self-completed questionnaire, while earlier survey interviews were conducted via Computer-Assisted Personal Interviewing (CAPI). For all survey years, the average interview length was approximately 45 minutes.
The survey is not mandatory and the response rates ranged from about 59% to 70% from 2012 to 2019, compared with 43.4% in 2023.

More information can be found on the Statistics Canada website under Definitions, data source and methods for SFS (survey number 2620) and tables 11-10-0016-01, 11-10-0049-01 and 11-10-0057-01.

5.2 Mapping and concordance

The full NBSA are comprised of 102 categories and sub-categories that contain all types of assets, liabilities and net worth in the economy. The DHEA data contain 11 of these categories. The NBSA categories were simplified for multiple reasons. One reason is that some types of assets and liabilities are not applicable in the household sector. Another reason is related to the quality of distributions that will be discussed in more detail in subsequent sections of this paper.

According to the United Nations Economic Commission for Europe (UNECE), “conceptually, macro and micro statistics on household income have much in common. However, there are significant differences in the objectives and purposes of the two datasets, in their coverage and the data sources used to compile them, and because of practical data reporting or estimation issues for individual households” (UNECE 2011). The concordance process allows for the identification of areas of conceptual difference between micro- and macro-data and provides an indicator of the suitability of specific micro-data variables as distributors of macro components.

The categories from the NBSA chosen for the DHEA are laid out in Table 1 below. The coverage ratios are shown for the SFS in 2012, 2016, 2019 and 2023, the years used to produce the DHEA wealth distributions based on the fourth quarter of each year from 2010 to 2019, and for each quarter starting in 2020. These categories contain sufficient detail for analysis of household financial well-being and are the categories for which a suitable variable (or combination of variables) from the SFS has been identified. The concordances found in Table 1 are built by mapping variables from the SFS to a condensed version of the NBSA; the detail of which variables from each source were used to create this table are found in Table 2. Some details relating to the mapping in Table 2 are in sections 5.2.1 to 5.2.2.

5.2.1 Conceptual differences – valuables and collectibles

Valuables and collectibles are not an observed category in the NBSA and are not a part of the macro accounts asset boundary. Therefore, in order to align the micro source with the macro source, the value of valuables and collectibles is not included in the SFS total for net worth and non-financial assets.

5.2.2 Conceptual differences – other liabilities

The category with the lowest coverage ratio is ‘other liabilities’. The main reason for the under coverage of this category is due to the conceptual definition of credit card debt, which is mapped to this category. The SFS asks respondents to report the amount of credit card debt still owing on the last bill excluding new purchases, while the NBSA reports the total balance outstanding at a specific point in time. The difference reflects the fact that many households use credit cards for consumption and pay off their balance at the end of each period.

6 Clustering households

6.1 Unit of analysis: the household

The unit of analysis chosen for the DHEA is the household, defined by the OECD as “either an individual person or a group of persons who live together under the same housing arrangement and who combine to provide themselves with food and possibly other essentials of living” (OECD 2013a). The SFS data is available at the family unit level, which is comprised of unattached individuals and economic families defined as “a group of two or more persons who live in the same dwelling and are related to each other by blood, marriage, common-law or adoption” (Statistics Canada 2024-10-29). For the DHEA project, the economic family units have been aggregated to the household level by combining economic families that reside at the same address, which creates a unit definition that includes groups of people who share resources but are not necessarily related by blood, marriage, common-law or adoption. This brings the SFS data as close as possible to the OECD definition of household.

6.2 Distribution categories

DHEA estimates for assets, liabilities and net worth include eight separate distribution variables. Households are grouped by province, age group, household type (multiple-person vs. one-person), housing tenure, equivalized household disposable income quintile, main source of household income, generation, and household wealth quintile. Province, age group, household type and housing tenure groupings are based on definitions used in the SFS. When possible, the distribution variables are crossed together to give more detailed distributions. In particular, the DHEA also includes cross-tabulated distributions of the totals for assets, liabilities and net worth that include region by age group, region by equivalized household disposable income quintile, and equivalized household disposable income quintile by age group.

6.2.1 Province and region

The province represents that of the principal residence of the household. Household members who are temporarily away from their principal residence, for instance for work or study, are included in the province of their principal residence.

As well, the region is defined by aggregating provinces into the following five groups: Atlantic (Newfoundland, Nova Scotia, Prince Edward Island, and New Brunswick), Quebec, Ontario, Prairies (Manitoba, Saskatchewan, and Alberta), and British Columbia.

6.2.2 Age group

Households are grouped by age group according to the age of the major income earner as identified by the SFS. This differs from the OECD definition of a reference person for a household, which requires applying a number of characteristic criteria to each member of each household.

The age group categories used are: under 35, 35 to 44, 45 to 54, 55 to 64, and 65 and over.

6.2.3 Household type

Grouping by household type is done according to a simplified definition of household composition, with only two categories: households composed of one person and households composed of more than one person. The OECD recommends that, in addition to household size, the household type category should be cross-referenced by age and family composition, including marital status and presence of dependent children. The feasibility of grouping households by size, age and family composition will be reviewed as the methodology is further developed.

6.2.4 Housing tenure

Housing tenure is identified according to whether a principal residence is owned, with or without a mortgage, or rented. While rental costs for a tenant may be fully or partially subsidized, such a distinction is not identified for this category.

6.2.5 Equivalized household disposable income quintile

The household disposable income concept is unique to the SNA and is not measured directly in the SFS. While the estimates of wealth are not equivalized, the breakdown by income quintile is based on an equivalized income concept to reflect differences in household size and composition. In order to assign SFS households to disposable income quintiles, equivalized household disposable income must first be estimated for each household as follows:

1. The SNA household disposable income aggregate is broken down into components (for example compensation of employees, transfers to and from other sectors, etc.) for which corresponding variables or proxies can be found on the SFS. The SFS income data is obtained from T1 income tax returns from Canada Revenue Agency (CRA) for the year prior to the survey year. When this data is not detailed enough to correspond to SNA household disposable income components, additional information is obtained from other tax files such as the Annual Income Estimates for Census Families and Individuals, commonly called the T1 Family File (T1FF) (see Statistics Canada (2025-07-18) and the T4A supplementary file.
2. For each income component, the SNA aggregate value is distributed over SFS households according to the value of the corresponding SFS variable or proxy. Survey weights are taken into account when calculating each household’s share of the component.
3. For each household, the distributed components are summed up to calculate the household’s estimated disposable income.
4. A final adjustment is done in order to equivalize the household disposable income. It consists of dividing the household disposable income by the number of consumption units for each household. This adjustment is based on the OECD-modified equivalence scale, which assigns a value of 1 to the first adult, 0.5 to each additional person aged 14 and over, and 0.3 for all children under 14.

The result is a new income variable for each SFS household, more closely aligned with the SNA concept of household disposable income than the available measure of after-tax income. Household equivalized disposable income is nevertheless highly correlated with equivalized after-tax income excluding capital gains, with a coefficient of correlation of 91.0% in 2012 and in 2016, 92.9% in 2019, and 70.5% in 2023.

Once every SFS household has been assigned an equivalized household disposable income, the households are grouped into equivalized household disposable income quintiles, which again are calculated taking into account the weights.

6.2.6 Main source of household income

The categories within the main source of household income are wages and salaries, self-employment income (i.e., farm and non-farm self-employment income, excluding rental income), net property income (i.e., interest and other investment income received less interest paid), pension benefits from corporations and governments, and other transfers (e.g., government social assistance, etc.).

6.2.7 Generation

Households are grouped into generation according to the birth year of the major income earner in the household as identified by the SFS. The generation categories used are: pre-1946 for those born before 1946, baby boom for those born between 1946 and 1964, generation X for those born between 1965 and 1980 and millennials for those born after 1980. Note that generation Z (for those generally born after 1996) has been combined with the millennials as their sample size is relatively small.

6.2.8 Household wealth quintile

Households are grouped by wealth quintile according to their total household net worth. Wealth quintiles group households into five equal parts, ranked from lowest to highest, each representing 20% of the total number of households in the economy. Contrary to the equivalization adjustment applied to disposable income quintiles, as described in section 6.2.5, wealth quintiles are not adjusted to account for differences in household size and composition. As noted in the literature, for inter-household comparison purposes, accounting for differences in the capacity of a household to consume wealth based on its size and composition at a given point in time may not be appropriate, since wealth tends to be consumed gradually over the life course. According to the OECD:

“Wealth is a stock of assets that is available to support consumption in the future, especially during retirement. When comparing households’ wealth as an indicator of economic well-being in terms of potential future consumption, consideration needs to be given to which household members are likely to benefit from that wealth. Of particular interest are households containing children. The children are likely to leave the household before the wealth of the household is used to support household consumption during retirement. Therefore, for this type of analysis, it does not seem relevant to equivalise wealth on the basis of the economies of scale in current consumption experienced with the current household structure. Rather, analysis of wealth should focus on examining data classified by life cycle group. Such a focus is consistent with the expectation that wealth is often built up during a person’s working life and then run down during retirement.” (OECD 2013b)

7 Deriving indicators in survey years

The SFS is the main source of distribution information for the DHEA for wealth. However, the SFS has been an occasional survey in the past and is triennial since 2016. This leaves gaps that need to be filled in order to produce a series of distributions in non-survey years. The methodology for deriving these distributions is two-fold, with a simpler, more direct approach being used in survey years and a calibration-based modelling approach in non-survey years. Throughout this section and the next two, descriptions will be given to show how much each step of the process modifies the estimates.

This section describes the first part of the methodology used to populate the tables in survey years. It consists of reweighting the SFS, scaling to NBSA totals, and then obtaining distribution estimates from the resulting data set. As of the September 2021 set of distributions, this methodology is used for survey years 2012, 2016, 2019 and, going forward, for every available survey year thereafter.

The next step, described in section 8, is to derive wealth measures for non-survey years using a modelling approach. After this is done, estimates for survey years and non-survey years are put together and adjustments are made to the estimates. First, estimates in survey years are adjusted in order to avoid introducing turning points when compared with the modelled estimates in adjacent non-survey years. Then adjustments for coherence and for the Northern territories of Canada are applied. This process is described in section 9.

7.1 Weight adjustments

The SFS weights currently used for the DHEA differ slightly from the version used to publish estimates obtained directly from the survey. The SFS sample is reweighted to reduce the impact of certain influential records and to take into account population control totals more closely related to the analytical categories of the DHEA.

The first step of the DHEA reweighting process is an adjustment to the weights of influential households that contribute significantly to net worth. Influential units can lead to unstable estimates and for this reason are often treated by adjusting their weights downward. Influential households, particularly in the lower income quintiles, are identified on the SFS samples and their weights are adjusted downward.

Following the weight adjustment for influential households, the SFS samples are recalibrated to population totals. Calibration consists of adjusting the weights of the sampled units so that estimates from the survey coincide with known totals at the population level. Potential benefits of calibration are consistency between the survey estimates and the known population totals, reduction of non-sampling errors, such as non-response errors and coverage errors, and improvement in the accuracy of estimators. The calibration totals used are estimates of counts for the sampled population based on projections from Statistics Canada’s Census of the Population, and are produced by Statistics Canada’s Demography Division. The totals used include counts of individuals by sex and age group categories, counts of households by household size, and counts of economic families by family size for select family sizes within provinces. The calibration methods used are as described in Deville and Sarndal (1992) and these methods are implemented in Statistics Canada’s G-EST software, which is described in Statistics Canada (2018-10).

These two reweighting steps, which together produce DHEA-specific weights, are part of the usual SFS weight calculation process that have been adjusted to align more closely with the DHEA analytical categories. This reweighting process is also used as a model for non-survey years, as described in section 8.1.

7.2 Adjustment for the elderly institutionalized population

The SFS excludes collective dwellings from its sample; in particular, nursing homes and residences for seniors are not sampled in the SFS. In order to account for the institutionalized elderly population, an adjustment is made to the DHEA wealth distributions. Prior to the June 2020 DHEA release, the institutionalized elderly population was accounted for in the DHEA income and consumption tables since this population is included in the Social Policy Simulation Database and Model (SPSD/M), but not the DHEA wealth tables. Globally, the institutionalized elderly population amounts to less than 1.3% of the Canadian population.

In order to ensure comparability between with DHEA income and consumption tables and the DHEA wealth tables, the adjustment made for the institutionalized elderly population follows a similar methodology to that used in the SPSD/M. Since no wealth data is collected for the institutionalized elderly population, the adjustment made is based on the assumption that those living in nursing homes or seniors residences are similar to individuals not in institutions who are aged 65 or older, live alone and are not in the labour force. The adjustment amounts to increasing the weights of these individuals on the SFS. The amount by which the weights are adjusted is based on Census counts of the institutionalized elderly population by province, age and sex. In order to account for the fact that this population is not living in their own home, adjustments are also made to certain asset and debt variables, such as those related to the principal residence.

7.3 Scaling to the National Balance Sheet Accounts

Following the methodology recommended by the OECD EG DNA, the survey data then is scaled to NBSA totals. Using these DHEA-specific weights, the SFS micro-data for every survey year from 2012 to 2023 are scaled to correspond with the NBSA totals for each wealth item. First, variables for asset and liability categories at the most granular level are scaled to the adjusted national accounts totals according to the concordance table in section 5.2. Subsequently, the values for the aggregate categories of assets and liabilities and for net worth are recalculated based on these scaled values for the granular-level categories of which they are composed.

7.4 Distribution estimates

For each of the DHEA tables, the total values of net worth and of each of the asset and liability sub-categories for each distribution category are estimated from SFS using the scaled values and the DHEA-specific weights.

The precision of the DHEA estimates in survey years is inherently linked to the precision of the SFS estimates. Measures of sampling error in the form of coefficients of variation (CVs) for SFS years are in the appendix in Tables 5 to 15. For the single dimensions, the CVs range from 1.4% to 12.4% for total net worth, from 1.3% to 11.7% for total assets, and from 1.8% to 14.7% for total liabilities among the provinces, age groups, household types, housing tenures, income quintiles, main sources of household income, generations and wealth quintiles.

8 Deriving indicators in non-survey years

Since the SFS is not undertaken annually, a different methodology is required to derive wealth measures for the DHEA in years for which survey information is not available. Without a direct measure of net worth and its components, the non-survey years must be modelled.

In previous versions of the DHEA, two modelling approaches were used: one based on calibration and another based on area-level models. More information on the methodology for these earlier releases can be found in Statistics Canada (2017-03-15, 2018-03-22). As of the March 2019 release (2019-03-27), a calibration-based approach has been exclusively used. Though the calibration and area-level model approaches are different mathematically, both often yielded similar distribution estimates for non-survey years.

8.1 Modelling using recalibration

The modelling approach used in this release is based on calibration. This approach is similar to the weight adjustments described in section 7.1. Starting with the influential value adjusted survey weights, the weights are recalibrated to demographic control totals for non-survey years. With the introduction of the January 2025 DHEA release, survey weights are also recalibrated on a quarterly basis for reference periods starting in 2020 and later using data on sub-annual demographic trends by household characteristic such as age, household size, and geographic location This recalibration adjusts the weights of the sampled units so that estimates from the survey coincide with these population totals for non-survey years, in essence adjusting the survey weights to reflect demographic shifts.

Once the weights of the SFS samples are adjusted to reflect demographic shifts, the SFS micro-data is scaled to the adjusted national accounts totals, as described above in section 7.3. Estimates for net worth and its components are then obtained for non-survey years by aggregating the micro-data and using the adjusted weights, as in section 7.4. This process is done once for every survey year giving four series of estimates for the time period between 2010 and 2024, one using the 2012 SFS, one using the 2016 SFS, one using the 2019 SFS and another using the 2023 SFS.

The series based on SFS 2012 is used for estimates for 2010 to 2012. For 2013 to 2015, the series are combined by linearly interpolating between the series. For example, for 2013, the combined estimate is calculated as ¾ × estimate from SFS 2012 recalibrated to 2013 + ¼ × estimate from the SFS 2016 recalibrated to 2013. Similarly, linear interpolation is applied to the 2016 SFS and 2019 SFS series to derive estimates from 2017 to 2018, while the SFS 2019 and 2023 are used to produce quarterly interpolated estimates from the fourth quarter of 2019 to the second quarter of 2023, with the 2023 SFS being used to estimate later quarters.

8.2 Performance of the recalibration approach

Tables 16 to 18 in the appendix show how the modelled distributions obtained by the recalibration approach compare to the SFS distributions in 2023, 2016 and 2012 for select components of net worth. The range of the absolute differences by category between the SFS distribution and modelled distribution is shown as a measure of distance between the SFS distributions and those obtained by modelling.

Before adopting a calibration-only approach, the results of the previously-used area-level models and the calibration models were compared and the strengths and weaknesses of each approach were considered (Wu and Boulet 2018). This comparison was done on the set of tables available as part of the March 2018 dissemination. Both methods are designed to ensure that the distributions in survey years concord with the SFS estimates. As a result, when the number of years between surveys is short, both approaches give similar results. In fact, once the final steps described in the next section were conducted, the difference in the share held by each group was less than 1% in nearly all cells of the tables.

The main advantage of the area-level model approach is that it can theoretically capture trends in wealth that are related to income. However, it was not clear that in practice area-level modelling was better able to capture the effect of large events, such as the 2008 recession. Given that the area models use income data, one might expect for example that the area models would show the wealth of the lowest income quintile being affected differently by the recession than the wealth of the highest income quintile. Unfortunately, the estimates are too noisy to distinguish any differences.

On the other hand, the calibration approach has important practical advantages. Principally, it is much less time and resource intensive than the area-level model approach in which models must be built individually for each distribution category. A by-product of this advantage is that it makes it more practical to build tables for additional distribution categories as has been done since adopting this modelling approach. Furthermore, the area-level approach depended on auxiliary information that is only available a year and a half after the end of the reference period whereas the control totals required for the calibration approach are available without such a time lag.

Given the significant practical advantages to the calibration approach, together with the similarity of the estimates coming from the two approaches, it was decided to adopt the calibration only approach for the March 2019 release and the following releases.

9 Adjustments to the final tables

9.1 Adjustments to survey years

After combining the two series of estimates obtained by recalibration, small adjustments are made to estimates in survey years in order to avoid introducing turning points at 2012, 2016, 2019 and 2023 when compared with the modelled estimates in adjacent non-survey years. A three-point centred weighted moving average is applied to the combined series in 2012, 2016, 2019 and 2023.

After applying these adjustments to the survey year data, the row and column sums of the resulting tables are not coherent in survey years. The sum of the distribution categories are not equal to the NBSA totals; in other words, the row sums are not coherent. As well, the relationships between assets, liabilities and net worth are not respected; in other words, the column sums are not coherent. Additionally, the sum of data in the cross-tabulation tables may not be exactly equal to the data in the one-dimensional tables.

An adjustment process is required to ensure consistency within and between the tables. This type of adjustment goes by many names: raking, balancing, and reconciliation. It re-establishes the relationships between net worth, assets, and liabilities categories while ensuring that the sum of the distribution categories are kept equal to the NBSA total and leaves the NBSA totals untouched. A key characteristic of raking is that it ensures that specified relationships are respected while minimizing the change to individual cells of the table.

The raking methods used are derived from the Dagum and Cholette (2006) regression-based approach and are further described in Quenneville and Fortier (2012), and the references therein. The procedures are implemented in PROC TSRAKING and the GSeriesTSBalancing macro in Statistics Canada’s G-Series software. They are described in Statistics Canada (2016-04) and the references therein, and can be obtained by contacting statcan.g-series-g-series.statcan@statcan.gc.ca.

The magnitude of the smoothing and raking adjustments to the internal cells of the wealth tables are in Table 3. These factors are calculated as values after raking divided by values after combining the estimates from recalibration. These adjustments are generally close to 1, indicating that smoothing and raking the tables does not result in major changes to the distributions.

9.2 Other adjustments

9.2.1 Quarterly wealth distributions

As the COVID-19 pandemic unfolded in Canada at the beginning of 2020, households experienced large changes in their economic well-being over a relatively brief period of time. To provide more timely information about the economic impacts of the pandemic on different households, as with previous releases over the last several years, this latest release includes wealth distributions on a quarterly basis starting with the 2020 reference year.

Based on distributions for liabilities as of the fourth quarter of reference year 2019 that are benchmarked to NBSA totals, quarterly projections of household liabilities are created using sub-aggregated data from Equifax Canada, a private consumer credit rating agency. Equifax credit registry include sub-annual data on the majority of household mortgage and non-mortgage credit transactions processed through more than 600 financial institutions across Canada (e.g., including banks, credit unions, mortgage financers, automobile dealers and financers, credit card companies, finance companies, and retailers), while coverage is relatively limited for alternative financial intermediaries (e.g., insurance providers, payday loan companies, etc.). Relative to NBSA benchmarks as of the second quarter of 2023, coincident with the timing of the latest survey data, Equifax data represent 81.1% of total liabilities, including 82.1% of mortgage liabilities and 78.3% of non-mortgage liabilities.

Quarterly trends in household liabilities are available from Equifax Canada by type of credit product (e.g., mortgage, credit card, car loan, etc.) and demographic characteristic, including province, age of household head, generation of household head, and household composition. In contrast with the method used to identify household head within the DHEA, which is based on the major income earner (see section 6.2), a different method is applied for projections based on Equifax data. Due to the lack of income data within the credit registry, household head is determined by Equifax Canada by applying the following criteria:

1. The household member who has an active mortgage or home equity line of credit (HELOC). If multiple or none of the members have an active mortgage or HELOC, then;
2. The member who has the highest total credit limits for all credit products. If multiple members have equal total credit limits or none of them have credit, then;
3. The member who is the oldest in the household. If age is equal for all members, then;
4. The member who is listed first in the credit registry database for that household.

Relative to DHEA estimates for household liabilities as of the second quarter of reference year 2023, as indicated in Table 4, coverage for Equifax totals by province range from 58.2% to 98.6%, by age group from 54.9% to 113.5%, by generation from 70.0% to 105.9%, and by household composition from 70.4% to 127.8%.

Statistics Canada (2019-03-27). "Distributions of Household Economic Accounts, Estimates of asset, liability and net worth distributions, 2010 to 2018, technical methodology and quality report". Income and Expenditure Accounts Technical Series (13-604-M).

To derive quarterly debt distributions related to income, including equivalized household disposable income quintile and main source of household income, a matching exercise is applied to link SFS and Equifax wealth distributions with households covered within the SPSD/M. Using the results from a special run of the SPSD/M (Statistics Canada, 2020-10-14), sub-annual trends in disposable income by distribution category are derived from Labour Force Survey wage trends and administrative data on government Covid-19 economic relief measures. Further adjustments are also applied to ensure alignment with modelled distributions for government benefits (e.g., Canadian Emergency Relief Benefits from Employment and Skills Development Canada, etc.). To ensure consistency of the quarterly trends in household liabilities observed from the credit registry data with the associated trends in non-financial assets to which those liabilities relate, corresponding adjustments are also applied to quarterly real estate asset values using distributional information from third parties on the housing market (Canada Mortgage and Housing Corporation 2023) and spending on consumer goods (Bank of Canada 2020).

As well, to more accurately reflect sub-annual demographic trends in the household population that are aligned with wealth and income, household counts benchmarked to distributions from Demography Division as of the 2019 reference year are grown on a quarterly basis starting in reference year 2020. Quarterly household counts are created using data available from the Equifax data and the SPSD/M, and are used to present wealth estimates by distribution category on an average dollars per household basis.

9.2.2 Territorial factor adjustments

An additional adjustment is applied to the DHEA wealth distributions by province and region to remove a portion of the national NBSA wealth control totals related to households in the territories. This adjustment is necessary as the SFS sample is drawn only from households in the ten provinces (see sections 5.1 and 10.2.2), while the published NBSA control totals are based on the total for all provinces and territories in Canada. Without this adjustment, and since the NBSA does not estimate wealth for the territories, DHEA estimates of wealth by province and region would be overestimated.

Since territorial wealth is not directly identifiable through existing data sources, a simplified adjustment is applied to the NBSA national control totals to adjust for territorial wealth. In particular, a fixed percentage is removed from each of the wealth categories based on SNA estimates for territorial net property income, scaled by average government bond yields and mortgage rates. The same percentage is removed for each province and region's wealth estimate. While the same percentage is used in any given year, this percentage can vary over time, depending on the growth in net property income relative to that for the national NBSA control totals. This simplified adjustment rests on the assumption that the territories have the same distribution as the total for the provinces included in the SFS sample.

9.2.3 Data quality adjustments

As well, as part of the DHEA data quality assessment process, adjustments are applied to some wealth distributions at the macro level to avoid introducing turning points in survey years or to avoid discrepancies with economic and demographic trends observed from other data sources, such as from surveys and government administrative data.

10 Sources of error

The DHEA are built by bringing together data from multiple sources. Each of these sources, as well as the way in which they are used and combined, are a potential source of gaps between the micro- and macro-level data. An overview of the sources of error for the DHEA wealth distributions is given below, categorized according to their source:

• national accounts totals;
• model;
• survey data.

A similar classification is found in Zwijnenburg (2016).

10.1 Quality of national accounts data

10.1.1 Quality of national accounts totals

The NBSA are estimated by using the most complete and high quality data sources available in order to establish benchmark estimates. This generally entails business surveys, administrative data files from the CRA, household survey files, information from pension funds, financial institutions and government public accounts. Data are analyzed for time series consistency, links to current economic events, issues arising from the source data, and finally with respect to coherence. It is not possible to produce an equivalent to national wealth or national net worth; nor is it possible to construct a balance sheet for the household sector, except periodically from household surveys. However, certain sub-sectors of the NBSA are largely comparable to estimates produced by source data divisions (e.g., pension funds, levels of government).

10.1.2 Quality of the adjustments to the national accounts totals

As previously mentioned, the adjustment to isolate the household sector from the NPISH sector was implemented in 2012. Work to build the NPISH sector began with the creation of a more broadly defined satellite account of non-profit institutions and volunteering, first released in 2004. The NPISH portion of this broader non-profit sector was implemented in the core SNA in 2012, with estimates built from a variety of sources including administrative files on registered charities and other non-profit institutions. A range of statistical improvements to better define the universe and account for measurement deficiencies were undertaken in addition to the sectoring changes. These included delineating the purchases of households from the NPISH sector. Revised industry and final demand estimates were correspondingly introduced in the supply-use framework.

10.2 Quality of survey data

10.2.1 Sampling error

Sampling error is inevitable in any sample survey and occurs because data is collected and inferences are made from a sample, rather than the entire population. The sampling error is measured by estimating the extent to which sample estimates would vary over all possible samples that could have been selected with the same design and sample size. The magnitude of the sampling error is affected by several factors: the inherent variability in the population of the characteristic being measured, the sample size, the sample design, and the response rate. With its smaller size, the 2005 SFS has a larger sampling error than do the other years of SFS.

The CV is a common measure of sampling error and can be used as one indicator of the accuracy of the estimates. It is defined as the ratio of the estimated standard error of the estimate to the value of estimate itself. The CVs for estimates of totals of net worth and its components from the SFS for survey years 2012 and later are in the appendix in Tables 5 to 15.

10.2.2 Coverage error

Coverage errors are omissions, erroneous additions, duplicates and errors of classification of units in the survey frame. They can create biased estimates and the impact can vary for different sub-groups of the population.

For the DHEA, the population targeted by the SFS and the NBSA totals differ. In particular, the territories are excluded from SFS, as are about 2% of persons in the provinces who are difficult to survey for a variety of reasons.

Due to a lack of information on the value of assets and liabilities for households in the territories and for the elderly institutionalized population, the DHEA applies simplistic adjustments to include these populations in its estimates. These adjustments are described in sections 7.2 and 9.2. More accurate estimates for the wealth of these populations may be derived in the future if data from alternative data sources covering these population becomes available.

10.2.3 Non-response error

There are two kinds of non-response: total non-response, not answering the whole survey, and item non-response, not answering some questions. In the SFS, this type of error is addressed by using follow-up procedures to minimize non-response, by weighting that takes into account non-response, and by imputation.

10.2.4 Measurement and processing error

Measurement error, also called response error, is the difference between the recorded response to a question and the “true” value. Measurement error can be caused by misunderstanding on the part of the respondent or the interviewer. Processing is required to transform survey responses into a form suitable to tabulation and analysis and may be a source of error.

10.3 Quality of the models used for non-survey years

In non-SFS years, the DHEA wealth distributions must be modelled. As such, their quality of the estimated distributions depends both on the quality of the auxiliary data on which the models are built and on the strength of the models themselves.

10.3.1 Quality of auxiliary data sources

The auxiliary data source used for the recalibration approach are demographic projections of person and household counts based on Statistics Canada’s Census of the Population. These projections cover the same sampled population as the SFS and exclude the same segments of the population (see section 10.2.2), and use the same household and family concepts as the SFS. They are of high quality and are used for the calibration of most social surveys at Statistics Canada.

10.3.2 Quality of the models

Models are a fundamental component of the DHEA wealth distributions methodology and, as with any model, they can only reflect the trends for net worth distributions that are related to trends in the auxiliary data. In this case, since the recalibration approach has been adopted as of the March 2019 release, this means trends related to demographics only. As discussed in section 8.2, when more than one survey year is available and when gaps between survey years are relatively short, this has a small impact on the final estimates. However, revisions to the years following the last available survey year should be expected to be greater than revisions to prior years. It should also be noted that the strength of the relationship between wealth distributions and demographics varies by the household category. This is reflected in the comparisons of the modelled and SFS distributions in the appendix in section 11.2.

10.4 Combining these sources

The DHEA brings together data from many difference sources and, so, it is not surprising that conceptual differences between micro- and macro-data sources are a major challenge. The methodology put forth in this paper and used to produce the DHEA wealth distributions is comprised of multiple steps (reconciliation of micro and macro concepts, modelling through recalibration, raking). Throughout these steps, the errors may accumulate or cancel out.

11 Appendix

11.1 Sample error coefficients of variation for the Survey of Financial Security, 2012, 2016, 2019 and 2023

The following tables contain the sampling error CVs, or ranges of CVs, from the SFS. These CVs are based on the SFS and do not include the steps of reweighting and scaling to the NBSA.

11.2 Comparisons of modelled distributions and the Survey of Financial Security distributions

The following tables show how the modelled distributions compare to the SFS distributions in 2023, 2016 and 2012 for net worth, total assets and total liabilities. The estimates from the calibration approach in these tables are based on the recalibrated SFS 2019 file. Distributions therefore match the SFS distributions in 2019 and for this reason are not included.

Table 16 shows the comparison for the distributions by the age group of the major income earner. It displays the range of the absolute differences by age group between the SFS and modelled distributions, which is used as a measure of distance between the SFS distributions and those obtained by modelling. It also includes the modelled and SFS distributions by age group of major income earner to illustrate how the ranges are derived. Table 17 shows the ranges of the absolute differences between the SFS and modelled distributions, for the one-dimensional tables. Similarly, Table 18 shows the ranges for the cross-tabulation tables; in this case the percentages held in each group are calculated out of the subtotal by region for the cross-tabulations by region, and out of the subtotal by income quintile for the cross-tabulation of income quintile by age group.

There are greater differences between the SFS and model in some of the distributions by housing tenure and major source of income than in the other one-dimensional distributions. The relationship between housing tenure and the demographic data used in modelling may not be as strong as the relationships between demographics and the other household categories.

11.3 Source of statistical revisions for Distributions of Household Economic Accounts estimates for wealth, January 2026 vs. October 2025 versions

Estimates for the Distributions of Household Economic Accounts (DHEA) for wealth are revised periodically to incorporate updated information from various data inputs and to enhance the methodology for producing such estimates. Most recently, the January 2026 DHEA wealth estimates were revised relative to the prior release in October 2025 to incorporate changes for data inputs that feed into the DHEA estimation process for household counts and for values of wealth items distributed by various household characteristic.

11.3.1 Household counts

Revisions to household counts between the January 2026 and October 2025 releases were due to the following changes:
Updates to data inputs:

• Demographic population estimates, which are updated:
• Annually to incorporate the latest estimates on births, deaths, as well as on interprovincial and international migration.
• Migration statistics are based on monthly Canada Revenue Agency data from the Canada child benefit program, and from annual tax filer data, the latter of which are available approximately one year after the reference period.
• Equifax quarterly counts, trends for which are re-based periodically to maintain consistency with the latest demographic control totals.

Examples of revisions:

Data table for Chart 1

Data table for chart 1
Table summary
This table displays the results of Data table for chart 1 January 2026 release and October 2025 release, calculated using number of households units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	number of households
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	2,968,570	2,968,570
2020
Q1	3,082,228	3,082,228
Q2	3,044,324	3,044,324
Q3	2,987,812	2,987,812
Q4	3,062,055	3,062,055
2021
Q1	3,035,539	3,035,539
Q2	3,015,354	3,015,354
Q3	2,966,106	2,966,214
Q4	3,092,772	3,093,732
2022
Q1	3,085,685	3,086,697
Q2	3,088,525	3,089,592
Q3	3,127,086	3,129,247
Q4	3,165,635	3,168,896
2023
Q1	3,204,112	3,208,462
Q2	3,242,577	3,248,025
Q3	3,290,285	3,282,787
Q4	3,337,980	3,317,541
2024
Q1	3,385,843	3,352,460
Q2	3,433,695	3,387,371
Q3	3,426,602	3,416,274
Q4	3,419,505	3,443,008
2025
Q1	3,412,408	3,466,851
Q2	3,405,303	3,486,846

Revision for households with a major income earner aged less than 35 years were due to new annual demographic benchmarks. On average, the January 2026 DHEA estimates for those less than 35 years of age were 0.1% lower than the October 2025 estimates over the period from the third quarter of 2021 to the second quarter of 2025.

Data table for Chart 2

Data table for chart 2
Table summary
This table displays the results of Data table for chart 2 January 2026 release and October 2025 release, calculated using number of households units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	number of households
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	5,083,259	5,083,259
2020
Q1	5,193,871	5,193,871
Q2	5,128,157	5,128,157
Q3	4,995,734	4,995,734
Q4	5,105,939	5,105,939
2021
Q1	5,117,375	5,117,375
Q2	5,142,197	5,142,197
Q3	5,033,176	5,033,188
Q4	5,237,462	5,237,551
2022
Q1	5,250,692	5,250,791
Q2	5,250,972	5,251,073
Q3	5,286,628	5,286,784
Q4	5,322,269	5,322,490
2023
Q1	5,366,641	5,366,918
Q2	5,411,011	5,411,348
Q3	5,463,123	5,454,042
Q4	5,515,226	5,496,731
2024
Q1	5,546,181	5,518,271
Q2	5,577,121	5,539,795
Q3	5,582,134	5,551,735
Q4	5,587,150	5,560,331
2025
Q1	5,592,607	5,564,934
Q2	5,598,500	5,564,069

Revision for one-person households was mainly due to undercount from external data from consumer credit registry relative to new demographic benchmarks and associated quarterly re-calibrated weights. On average, the January 2026 DHEA estimates for one-person households were 0.2% higher than the October 2025 estimates over the period from the third quarter of 2021 to the second quarter of 2025.

Data table for Chart 3

Data table for chart 3
Table summary
This table displays the results of Data table for chart 3 January 2026 release and October 2025 release, calculated using number of households units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	number of households
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	5,723,747	5,723,747
2020
Q1	5,796,755	5,796,755
Q2	5,862,916	5,862,916
Q3	5,926,070	5,926,070
Q4	6,091,991	6,091,991
2021
Q1	6,071,689	6,071,689
Q2	6,037,756	6,037,756
Q3	6,002,337	6,002,391
Q4	6,070,669	6,070,924
2022
Q1	6,070,749	6,083,112
Q2	6,085,719	6,106,181
Q3	6,120,638	6,148,347
Q4	6,189,884	6,190,541
2023
Q1	6,228,325	6,248,943
Q2	6,266,759	6,307,424
Q3	6,317,623	6,363,462
Q4	6,443,201	6,419,557
2024
Q1	6,486,002	6,467,948
Q2	6,528,791	6,516,686
Q3	6,533,775	6,553,682
Q4	6,544,952	6,586,766
2025
Q1	6,556,704	6,614,687
Q2	6,568,430	6,635,554

Revision for households that own their principle residence with a mortgage due to new demographic benchmarks and associated quarterly re-calibrated weights. On average, the January 2026 DHEA estimates for homeowners with a mortgage were 0.3% lower than the October 2025 estimates over the period from the third quarter of 2021 to the second quarter of 2025.

Data table for Chart 4

Data table for chart 4
Table summary
This table displays the results of Data table for chart 4 January 2026 release and October 2025 release, calculated using number of households units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	number of households
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	750,521	750,521
2020
Q1	702,443	702,443
Q2	602,176	602,176
Q3	761,283	761,283
Q4	695,071	695,071
2021
Q1	678,488	678,488
Q2	642,766	642,766
Q3	737,017	737,021
Q4	720,254	720,284
2022
Q1	674,240	672,109
Q2	696,253	624,421
Q3	820,385	840,385
Q4	710,277	724,164
2023
Q1	661,892	656,392
Q2	667,110	622,663
Q3	790,416	800,229
Q4	701,945	716,219
2024
Q1	654,442	650,359
Q2	622,050	608,355
Q3	763,039	761,232
Q4	698,076	694,698
2025
Q1	653,547	625,906
Q2	625,382	596,679

Revision for households with self-employment as a main source of income mainly due to quarterly re-calibrated weights, reflecting impact of seasonal employment patterns. On average, the January 2026 DHEA estimates for households with self-employment as a main source of income were 1.6% higher than the October 2025 estimates over the period from the third quarter of 2021 to the second quarter of 2025.

Data table for Chart 5

Data table for chart 5
Table summary
This table displays the results of Data table for chart 5 January 2026 release and October 2025 release, calculated using number of households units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	number of households
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	67,342	67,342
2020
Q1	68,642	68,642
Q2	68,058	68,058
Q3	66,030	66,030
Q4	69,135	69,135
2021
Q1	68,926	68,926
Q2	68,870	68,870
Q3	68,366	68,367
Q4	70,452	70,462
2022
Q1	70,583	70,593
Q2	70,954	70,964
Q3	71,522	71,538
Q4	72,089	72,111
2023
Q1	72,646	72,678
Q2	73,204	73,241
Q3	73,753	73,807
Q4	74,305	74,370
2024
Q1	74,845	74,923
Q2	75,383	75,477
Q3	75,696	75,893
Q4	76,008	76,263
2025
Q1	76,320	76,569
Q2	76,631	76,789

Revision for province of residence due to new annual demographic benchmarks, combined with associated quarterly re-calibrated weights. On average, the January 2026 DHEA estimates for households with a principle residence in Prince Edward Island were 0.1% lower than the October 2025 estimates over the period from the third quarter of 2021 to the second quarter of 2025.

11.3.2 Wealth values

Values for wealth distributed by household characteristic are affected by revisions to the following items:

• Benchmarks from the National Balance Sheet Accounts (NBSA), which are updated on a quarterly basis; and
• New quarterly weight re-calibration estimates.

Revisions for estimates of household debt tend to be lower than other wealth items due to use of consumer credit registry data for observable household characteristics such as by age group and province.

Examples of revisions for distributions of household debt:

Data table for Chart 6

Data table for chart 6
Table summary
This table displays the results of Data table for chart 6 January 2026 release and October 2025 release, calculated using dollars per household units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	dollars per household
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	148,754	147,693
2020
Q1	147,236	146,242
Q2	153,327	152,445
Q3	160,174	159,164
Q4	159,187	158,074
2021
Q1	159,648	158,450
Q2	164,775	163,400
Q3	167,724	166,364
Q4	166,241	164,862
2022
Q1	168,445	167,011
Q2	172,827	171,516
Q3	174,266	173,190
Q4	175,511	174,497
2023
Q1	176,430	175,498
Q2	180,680	179,505
Q3	182,716	181,557
Q4	184,766	183,492
2024
Q1	186,090	184,871
Q2	190,189	188,959
Q3	193,082	192,030
Q4	197,009	196,284
2025
Q1	198,572	198,604
Q2	202,078	202,927

The January 2026 DHEA estimates for average debt per household for those with a major income earner aged 55 to 64 years old were on average 0.6% higher than the October 2025 estimates over the period from the fourth quarter of 2019 to the second quarter of 2025.

Data table for Chart 7

Data table for chart 7
Table summary
This table displays the results of Data table for chart 7 January 2026 release and October 2025 release, calculated using dollars per household units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	dollars per household
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	66,796	66,757
2020
Q1	64,120	64,124
Q2	61,199	61,175
Q3	59,936	59,833
Q4	57,750	57,575
2021
Q1	56,665	56,450
Q2	58,007	57,736
Q3	59,368	59,084
Q4	59,025	58,711
2022
Q1	59,386	59,078
Q2	60,723	60,478
Q3	61,164	60,982
Q4	61,275	60,995
2023
Q1	60,599	60,407
Q2	61,222	60,905
Q3	61,403	61,163
Q4	61,467	61,212
2024
Q1	61,051	60,892
Q2	61,683	61,572
Q3	62,497	62,266
Q4	62,992	62,911
2025
Q1	62,740	62,801
Q2	63,538	63,765

The January 2026 DHEA estimates for average debt per household for homeowners without a mortgage were on average 0.3% higher than the October 2025 estimates over the period from the fourth quarter of 2019 to the second quarter of 2025.

Data table for Chart 8

Data table for chart 8
Table summary
This table displays the results of Data table for chart 8 January 2026 release and October 2025 release, calculated using dollars per household units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	dollars per household
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	81,093	80,305
2020
Q1	82,171	81,507
Q2	128,669	127,749
Q3	118,189	117,190
Q4	124,623	123,438
2021
Q1	120,094	118,849
Q2	117,514	116,172
Q3	122,366	120,969
Q4	100,759	99,626
2022
Q1	99,874	99,226
Q2	106,534	105,595
Q3	106,043	103,744
Q4	105,020	104,748
2023
Q1	105,109	103,471
Q2	108,866	109,488
Q3	109,238	104,858
Q4	106,606	105,088
2024
Q1	107,661	105,962
Q2	108,957	105,713
Q3	108,353	103,704
Q4	111,033	104,207
2025
Q1	112,706	110,216
Q2	114,566	109,376

The introduction of revised quarterly weight re-calibration estimates had a more significant impact on distributions related to income, which are not available from the consumer credit registry. Average debt per household for those with non-pension transfers as a main source of income was, on average, 1.8% higher for the January 2026 estimates relative to the October 2025 estimates.

Examples of revisions for distributions of household financial assets:

Data table for Chart 9

Data table for chart 9
Table summary
This table displays the results of Data table for chart 9 January 2026 release and October 2025 release, calculated using dollars per household units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	dollars per household
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	142,833	142,844
2020
Q1	130,638	130,584
Q2	151,423	151,347
Q3	170,710	170,609
Q4	188,167	188,062
2021
Q1	199,553	199,492
Q2	206,116	205,790
Q3	209,198	208,660
Q4	210,655	209,901
2022
Q1	207,654	207,190
Q2	197,372	197,990
Q3	197,792	196,039
Q4	201,424	199,590
2023
Q1	207,059	204,314
Q2	207,772	205,079
Q3	203,983	200,771
Q4	207,893	207,592
2024
Q1	213,637	214,542
Q2	214,603	215,258
Q3	221,831	223,293
Q4	233,577	226,835
2025
Q1	234,703	228,460
Q2	240,087	234,488

The January 2026 DHEA estimates for average financial assets per household for those with a major income earner less than 35 years old were on average 0.6% higher than the October 2025 estimates over the period from the fourth quarter of 2019 to the second quarter of 2025.

Data table for Chart 10

Data table for chart 10
Table summary
This table displays the results of Data table for chart 10 January 2026 release and October 2025 release, calculated using dollars per household units of measure (appearing as column headers).

	January 2026 release	October 2025 release
	dollars per household
Source: Statistics Canada, Distributions of Household Economic Accounts.
2019
Q4	142,213	142,217
2020
Q1	174,202	174,114
Q2	232,467	232,326
Q3	251,647	251,480
Q4	282,553	282,379
2021
Q1	317,457	317,322
Q2	335,632	335,092
Q3	308,776	307,994
Q4	313,623	312,602
2022
Q1	344,147	339,942
Q2	318,630	312,228
Q3	293,190	284,279
Q4	305,949	303,033
2023
Q1	327,423	326,067
Q2	315,578	313,440
Q3	287,421	279,634
Q4	291,828	292,194
2024
Q1	313,923	321,239
Q2	315,089	319,450
Q3	299,583	303,769
Q4	309,683	307,669
2025
Q1	329,123	337,815
Q2	328,179	334,796

Average financial asset values per household for those in the lowest 20% of the income distribution were, on average, 0.1% higher for the January 2026 estimates relative to the October 2025 estimates.

To improve the accuracy and timeliness of distributional estimates for financial assets, a method is currently being developed to more reliably estimate annual and sub-annual trends for financial assets distributed by household characteristic when SFS data is not available, specifically:

• The estimation process under development will apply a capitalization method, which is based on a modelling process that converts investment flows into financial asset values accumulated through time (see the process described in"Enhancing wealth distributions within the distributions of household economic accounts using a capitalization of income method," Statistics Canada, which is part of the Latest Developments in the Canadian Economic Accounts (Catalogue number 13-605-X)).

References

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Bérubé, J. and Fortier, S. (2009). PROC TSRAKING: An in-house SAS^® procedure for balancing time series. JSM Proceedings, Business and Economic Section. Alexandria, VA: American Statistical Association.

Canada Mortgage and Housing Corporation (2025). Housing Market Reports.

Dagum, E. B. and Cholette, P. A. (2006). Benchmarking, Temporal Distribution and Reconciliation Methods for Time Series Data. Springer-Verlag, New York. Lecture Notes in Statistics no. 186.

Deville, J.-C. and Sarndal, C.-E. (1992). "Calibration Estimators in Survey Sampling". Journal of the American Statistical Association, Vol. 87, No. 418, pp. 376-382.

OECD (2013a). OECD Guidelines for Micro Statistics on Household Wealth. OECD Publishing.

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Quenneville, B. and Fortier, S. (2012). "Restoring accounting constraints in time series - Methods and software for a statistical agency". Economic Time Series Modeling and Seasonality, edited by Bell, W.R., Holan, S.H., and McElroy, T.S., Chapman and Hall/CRC, New York, pp. 231-253.

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Statistics Canada (2016-04). The G-Series software, version 2.00.001. Internal document.

Statistics Canada (2017-03-15). "Annual Household Distribution Tables, Provisional estimates of asset, liability and net worth distributions, 2010 to 2016, Technical methodology and quality report". Income and Expenditure Accounts Technical Series (13-604-M).

Statistics Canada (2018-03-22). "Distributions of Household Economic Accounts, Estimates of asset, liability and net worth distributions, 2010 to 2017, technical methodology and quality report". Income and Expenditure Accounts Technical Series (13-604-M).

Statistics Canada (2018-10). Generalized Estimation System Version 2.02 – Calibration Module – Methodology Guide.

Statistics Canada (2019-03-27). "Distributions of Household Economic Accounts, Estimates of asset, liability and net worth distributions, 2010 to 2018, technical methodology and quality report". Income and Expenditure Accounts Technical Series (13-604-M).

Statistics Canada (2020-06-26). "Distributions of Household Economic Accounts, Estimates of asset, liability and net worth distributions, 2010 to 2019, technical methodology and quality report". Income and Expenditure Accounts Technical Series (13-604-M).

Statistics Canada (2020-10-14). “Social Policy Simulation Database and Model, COVID-19 glass box, version 3.0”. Daily release.

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Statistics Canada (2024-10-29). Survey of Financial Security (SFS). Tables 11-10-0016-01, 11-10-0049-01 and 11-10-0057-01.

Statistics Canada (2025-07-18). Annual Income Estimates for Census Families and Individuals (T1 Family File). Definitions, data source and methods. Integrated metadatabase (IMDB), record number 4105.

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Statistics Canada (2025-12-11b). National Balance Sheet Accounts (NBSA). Tables 36-10-0580-01 and 38-10-0235-01.

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Wu, M. and Boulet, C. (2018). Estimating annual wealth distributions within Canada’s System of National Accounts. Proceedings of Statistics Canada Symposium 2018. Ottawa, Canada.

Zwijnenburg, J. (2016). Further Enhancing the Work on Household Distributional Data - Techniques for Bridging Gaps between Micro and Macro Results and Nowcasting Methodologies for Compiling More Timely Results. OECD. Presentation to the 34th International Association for Research on Income and Wealth (IARIW) General Conference. Dresden, Germany.

Zwijnenburg, J., Bournot, S. and Giovannelli, F. (2017). Expert Group on Disparities within a National Accounts Framework: Results from a recent exercise, OECD Statistics Working Papers, 2016/10, OECD Publishing.