Survey of Financial Security (SFS)

Detailed information for 2005

Status:

Active

Frequency:

Occasional

Record number:

2620

This survey provides information of the net worth (wealth) of Canadian families, that is, the value of their assets less their debts.

Data release - December 7, 2006

Description

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 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 survey data are used by government departments to help formulate policy, the private sector and by individuals and families to compare their wealth with those of similar types of families.

Subjects

  • Household assets, debts and wealth
  • Household spending and savings
  • Income, pensions, spending and wealth

Data sources and methodology

Target population

The target population for the SFS is families across the ten provinces of Canada.
Excluded from the survey are:
- the territories,
- those living on reserves and other Indigenous settlements,
- official representatives of foreign countries living in Canada and their families,
- members of religious and other communal colonies,
- members of the Canadian Forces living in military bases,
- people living in residences for senior citizens, and
- people living full time in institutions, for example, inmates of penal institutions and chronic care patients living in hospitals and nursing homes.
These exclusions represent approximately 2% of the population.

Instrument design

The final questionnaire content was determined in consultation with Human Resources and Social Development Canada, the Bank of Canada, Canada Mortgage and Housing Corporation, Finance Canada and Industry Canada. A pilot test was also conducted in 2004.

Sampling

This is a sample survey with a cross-sectional design.

The total sample for the 2005 Survey of Financial Security was drawn from two sources totaling approximately 9,000 dwellings.

The main sample, drawn from an area frame, consisted of approximately 7,500 dwellings. This area sample was a stratified, multi-stage sample selected from the Labour Force Survey (LFS) sampling frame. Dwellings selected for this survey had not previously participated in a labour force or financial survey conducted by Statistics Canada. Sample selection comprised three steps: the selection of clusters (small geographic areas) from the LFS frame, field listing of all addresses within each selected cluster, and the selection of dwellings within these selected clusters. At the time that the SFS sample was selected the LFS frame was using 2001 Census geography.

The second portion of the sample, approximately 1,500 households, was drawn from geographic areas in which a large proportion of households had what was defined as "high-income". This sample was included to improve the representation in the sample of high income families, as a disproportionate share of net worth is held by such higher-income family units. For purposes of this sample the income cutoff was total family income of at least $200,000 or investment income of at least $50,000. The latter was used to take into account those family units that may not have high income from employment but have substantial assets that generate investment income.

Data sources

Data collection for this reference period: 2005-05-01 to 2005-07-14

Responding to this survey is voluntary.

Data are collected directly from survey respondents and extracted from administrative files.

Personal interviews were conducted using a paper questionnaire. Respondents could, if they wish to do so, complete the questionnaire by themselves, but very few chose this option. Respondents were encouraged to use bills and other material to provide a more accurate response. The average interview lasted approximately 50 minutes.

Information was not gathered from persons temporarily living away from their families (for example, students at university), because it would be gathered from their families if selected. In this way, double counting of such individuals was avoided.

For families, the interview was held with the family member with most knowledge of the family's financial situation. If necessary, follow-up was done with other family members. Proxy response was accepted. This allowed one family member to answer questions on behalf of any or all other members of the family, provided he or she was willing and able to do so.

Use of administrative data

To reduce response burden, for the questions on income, respondents could give Statistics Canada permission to use the income information from their T1 tax return. Close to 80% of survey respondents gave their consent to use these administrative records.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Treatment of Large Values

For any sample, estimates can be affected disproportionately by the presence or absence of extreme values from the population. In an asset and debt survey, a few extreme values are expected in the sample, as valid extreme values do exist in the population. Values outside defined bounds were identified and reviewed in relation to other information reported for that respondent. If the value was judged to be the result of a reporting or processing error, it was adjusted. Otherwise, it was retained.

Imputation

Missing responses were imputed for all key fields in the questionnaire. Where possible, information was imputed deterministically, using other information reported by the respondent. For example, when the respondent could not estimate the value of their vehicle, the reported make, model and year was used to impute a value. This value was determined by consulting a reference book. When deterministic imputation was not possible, hotdeck imputation methods were used in most cases, and for all components of income and net worth, nearest neighbour techniques were employed. These methods involve identifying another individual or family with similar characteristics to become the "donor" and provide the imputed value. Income data obtained from tax returns are considered complete and thus do not require imputation.

Estimation

Weighting

The population totals used for the SFS were based on Statistics Canada's Demography Division population counts for different province - age - sex groups. The weights were also adjusted to ensure that the number of 1-person and 2-person households, and the number of 1-person and 2-person family units agreed with known totals by province.

Additionally in 2005, two new sources of weight adjustments were introduced. The first adjustment was based on administrative data from the T4 file. Weight adjustments were made to ensure that the survey distribution of earnings reflected approximately the same distribution as the T4 population. The second new adjustment made use of Survey of Labour and Income Dynamics (SLID, record number 3889) data to improve estimation. SFS as the smaller sample survey borrowed strength from SLID, the larger sample survey to not only improve SFS estimates but also to bring estimates for the two surveys more in line with each other.

Quality evaluation

Data are compared to the results of other data sources: Census, administrative and other Statistics Canada surveys.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

1. Response rates

The overall response rate for the 2005 Survey of Financial Security was 68%.

2. Data Suppression

Data reliability of the survey estimates has been assessed based on the calculated coefficients of variation. Estimates with a coefficient of variation less than 33% are considered reliable for general use. Estimates with coefficients of variation greater than 33% are deemed to be unreliable.

3. Impact of sampling and non-sampling errors on SFS estimates

Due to the combined effect of these errors, the quality of net worth data is judged to be lower than the quality of income data. This is largely because records of the current value of assets and the outstanding amount of debt are not as readily available as records of income. For example, respondents with numerous bank accounts and investments may receive several different statements, with different reference periods. Compiling this information can be difficult; most income information, on the other hand, would be available in one document, if the respondent had completed an income tax return for the year in question.

Direct comparisons with outside sources, such as the Financial and Wealth Accounts of the System of National Accounts, are difficult to make due to definitional, coverage and treatment differences. However, based on rough comparisons the following general conclusions can be drawn:

(a) SFS appears to underestimate some net worth components, particularly financial assets and consumer debt.

(b) The quality of estimates of real assets (e.g., owner-occupied homes, vehicles) is much better than that of financial assets.

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