Miller's Monthly Report

Status:
Active
Frequency:
Monthly
Record number:
3403

The data collected by this survey are part of supply-disposition statistics of major grains and allow the calculation of the domestic disappearance of grains for human and industrial uses.

Detailed information for November 2014

Data release - December 22, 2014

Description

This is a census of the large Canadian millers. The data collected by this survey are part of supply-disposition statistics of major grains and allow the calculation of the domestic disappearance of grains for human and industrial uses. They are also required to verify grain production and farm stocks data. The information is used by the federal and provincial governments, as well as by grain millers, farmers and other private businesses for the purpose of market research and consultation.

Reference period:
Month
Collection period:
Collected during the ten days following the reference month.

Subjects

  • Agriculture
  • Crops and horticulture

Data sources and methodology

Target population

The target population for this survey is mills processing over 500 metric tonnes of cereal grains per month.

Instrument design

The survey was last redesigned in 2000. The questionnaire was developed by subject matter specialists through consultation with industry experts. Questions will be changed, added or removed as the need arises. Required changes are usually identified through such means as subject matter specialist research and changes in market trends.

Sampling

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

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The data are collected through a mail-out/mail-back process, while providing respondents with the option of telephone or other electronic filing methods. Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time: respondents are phoned or sent a facsimile to remind them to send their questionnaire in order to reach the survey target response rate.

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

Error detection

Plant output is compared to plant input and to standard extraction rates. Data are also compared on a month-to-month basis to estimates of other plants producing similar products. Since data normally are fairly stable, large variations are reviewed and verified.

Imputation

Current month data are imputed when necessary based on the data from the same plant in the previous month. Data are revised on or after the annual survey or when actual data are received. Occasionally, data from industry sources are also used for imputation where they are available.

Quality evaluation

This is a census and the data quality is maintained by standard editing techniques which are rigorous. Apparent data discrepancies are either scrutinized by professional staff or the company involved is contacted. On a monthly basis, the most likely source of error is related to the imputation for a small number of non-respondents. Since the data are fairly stable on a month-to month basis, the impact of the imputation would be minimal.

Summary data are compared with information from the Canadian Grain Commission and the Canadian Wheat Board. Average extraction rates and supply-disposition analyses also aid in data validation.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which 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.

Revisions and seasonal adjustment

Data are revised for non-response or for incorrect reporting when revisions are received. Revisions are also made on or after the annual survey (record number 3443).

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of non-sampling error. Examples of non-sampling error are coverage error, data response error, non-response error and processing error.

Coverage error can result from incomplete listing and inadequate coverage of the population of mills. However, given the infrastructure and the supplies of grain needed for a mill, it is unlikely that a new plant could start-up undetected and that any coverage error would be temporary and would have only a minimal effect on the resulting estimates. The estimates also include data from the small mills reporting to the Millers' Annual Report.

Data response error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. These errors are controlled through careful questionnaire design and the use of simple concepts and consistency checks. This survey has been in place for many years and most respondents are well versed in the survey concepts.

Non-response error is related to respondents that may refuse to answer, are unable to respond or are too late in reporting. In these cases, data are imputed. The extent on any imputation error decreases with increases in the response rate and attempts are therefore made to obtain as high a response rate as possible. Final response for this survey is about 90% on a monthly basis and 100% annually. Analysts keep in contact with the mills and the related industry associations to maintain the high response rate.

Processing error may occur at various stages of processing such as data entry, editing and tabulation. Measures have been taken to minimize these errors. A few trained staff work on this survey. Data entry and edit are performed simultaneously due to the spreadsheet design which allows errors to be quickly seen. Historical ratios also aid in eliminating outliers created by data entry. Tabulation is automated to eliminate human error.