26
Table 3.6
Principle 6: Assuring Transparency
Requirement
Element to be assured
Description
6.1
The
terms
and
conditions
for
producing
and
disseminating
official statistics are
available
to
the
public.
There is a standard procedure for ensuring that respondents
understand the legal basis for a survey and the confidentiality
provisions for the data that are collected
NSOs should provide an official document to guide on
such terms and conditions. These may include the
Statistics Act and its Regulation, guidelines for the
production of official statistics, data dissemination and
access policy
Information on data sources, statistical concepts and methods used for
the development, production and dissemination of official statistics
are publicly available
The information on statistical standards are available to the public
Advance notice of major changes in methodology, source data, or
statistical techniques is given
The dissemination policy is shared with the public
It is disclosed if there is a privileged pre-release of statistical results
6.2
The
terms
and
conditions for the
governance
and
management
of
statistical
agencies
are available to the
public.
The procedures to be followed for the appointment and dismissal of
heads of the statistical agencies and the hiring and release of staff are
publicly available
NSOs should provide an official document to guide on
such terms and conditions. These may include the
Statistics Act and its Regulation, guidelines for the
production of official statistics
The
reporting
and
dialogue
of
statistical
agencies
with
administratively superordinate government bodies is well defined,
established and known to the public.
The work programs of the statistical agencies and periodic reports to
describe progress are made available to the public on a regular basis.
27 3.2.4 Principle 7: Assuring Statistical Confidentiality and Data Security NSOs and Statistical Units should guarantee that the privacy of data providers such as persons, households, enterprises, public institutions and other providers will be protected, and that the information they provide will be kept confidential, will not be accessed by unauthorized internal or external users and will be used for statistical purposes only. This principle is managed through six quality requirements elaborated in Table 3.7.
28
Table 3.7
Principle 7: Assuring Statistical Confidentiality and Data Security
Requirement
Element to be assured
Description
7.1
Statistical confidentiality is
guaranteed by law.
There is a law or some other clear formal provision in force
that mandates the proper management of information
received from respondents and data providers to ensure
statistical confidentiality and data security.
There should be a provision in the Statistics Act
and/or its Regulation and such provision should
be reflected in any data collection, processing and
dissemination
7.2
Appropriate
standards,
guidelines,
practices
and
procedures are in place to
ensure
statistical
confidentiality.
Guidelines and instructions on the protection of statistical
confidentiality throughout the statistical business process are
provided to all staff of the statistical agencies
Oath of Secrecy to data collectors, consent from
respondents
and
data
protection
and
confidentiality measures should be well known to
those who work for statistical assignments
There are regular and continuous training programs for all
staff on the concept of statistical confidentiality and best
practices to ensure the privacy of the information provided
The organizational structure and arrangements for the
development and implementation of practices for ensuring
statistical confidentiality is adequate to cope with the needs
The staff sign confidentiality agreements upon their
appointment, which is valid also after staff leaves the agency.
7.3
Strict protocols to safeguard
data confidentiality apply to
users with access to microdata
for
research
or
statistical
purposes.
Clear conditions for granting researcher access to confidential data for scientific purposes are set in the statistical law or other formal provision. National, regional and international protocols of ensuring anonymity of data should be adhered prior to the dissemination of microdata. This may include the use of standard microdata dissemination platforms (e.g TNADA) and standard data exchange protocols (e.g SDMX) Confidentiality rules, disclosure control and microdata access procedures apply throughout the statistical business process. The statistical agencies monitor the use of microdata sets to identify any circumstances in which data confidentiality may be breached, for example, through file matching, and take immediate corrective action to address such a situation. 7.4 Penalties are prescribed for any wilful breaches of statistical confidentiality. There are legal or other provisions in place that allow administrative, penal and disciplinary sanctions for the violation of statistical confidentiality. Provisions for such penalties should be spelled in the Statistics Act and/or its Regulations Information on the provisions that allow sanctions for the violation of statistical confidentiality is shared with all staff and is available to the public.
29
Requirement
Element to be assured
Description
7.5
Security and integrity of data
and
their
transmission
is
guaranteed by appropriate
policies and practices.
An IT security policy is in place and known to the staff.
National, regional and international protocols to be
observed need to be clearly spelled out in policies
such as ICT policy
Following the IT policy, appropriate physical security
measures and processes are in place to ensure data and
database security, in accordance with best practices and
international standards.
Regular security audits of the data security system are carried
out.
All access to data repositories and transmission channels are
monitored.
While data are being transferred, risk of a breach is assessed,
and appropriate procedures are applied to eliminate or
minimize this risk.
7.6
The
identification
risk
of
individual
respondents
is
assessed and managed.
There should be a balance between the acceptable level of risk
of identification of individual respondents and usability of
the data.
There should be a balance between such risk and
loss of potential information to users
Appropriate processes are in place to assess the risk of
disclosure of sensitive information and the risk that
individual respondents can be identified from the public
release of statistics or of microdata, and procedures are
applied in line with the data dissemination policy to
minimize this risk.
All procedures that are taken to adequately reduce the risk of
identification are properly documented and made available
as part of the metadata related to the statistical dataset.
Users are made aware that procedures to reduce the risk of
identification have been implemented and that this could
lead to a loss of information.
30 3.2.5 Principle 8: Assuring Commitment to Quality NSOs and Statistical Units should be dedicated to assuring quality in their work, and systematically and regularly identify strengths and weaknesses to continuously improve process and product quality. This Principle is explained by eight requirements elaborated in Table 3.8.
31
Table 3.8
Principle 8: Assuring Commitment to Quality
Requirement
Element to be assured
Description
8.1
There is a quality policy or a
statement
of
the
statistical
agency’s
commitment
to
quality,
which
is
publicly
available.
The statistical agency’s policy, declaration or message about
its commitment to quality of statistics is made publicly
available and clearly conveys and promotes the shared
concern for quality of all of its staff and includes information
about trade-offs affecting the statistical work program.
A quality statement needs to be prepared by
NSOs and made publicly available. This can be
achieved through a provision from the Statistics
Act and its Regulation or guidelines of quality
management
The statistical agency has quality guidelines that are made
available to external users, at least in a summary version.
8.2
The statistical agencies promote
a
culture
of
continuous
improvement.
Methodology and processes are regularly documented.
NSOs
should
conduct
evaluations,
quality
assessments and data audits for its statistical
programs. Reports of assessment and data audits
serve as evidence
Good statistical practices are exchanged among and between
statistical agencies.
Procedures are in place to ensure that the required
documentation on quality is regularly updated.
A quality assurance plan or similar mechanism is in place
that describes the work standards, the formal obligations
(such as laws and internal rules) and quality control actions
to prevent, monitor and evaluate errors and to control the
statistical production process.
Work plans, schedules and standard forms or templates are
used for facilitating the updating of the documentation of
quality assurance procedures and actions in a consistent
way
Statistical agencies use a national quality assurance
framework (NQAF) as a basis for regular quality
assessments (self-assessments and other assessments)
Statistical agencies use a NQAF which is based on one of the
accepted global or regional framework
32 Requirement Element to be assured Description General quality systems or frameworks such as Total Quality Management (TQM) and International Organization for Standardization (ISO) 9000 are utilized in conjunction with the NQAF
Quality initiatives of international and regional statistical
bodies such as the European Statistical System (ESS) are
followed up, as appropriate
8.3
There
is
a
specific
body
responsible
for
the
quality
management
or
the
coordination
of
quality
management
within
the
statistical agency, and it receives
necessary support to fulfil this
role.
A quality manager, quality committee, unit or group of
coaches or advisers is assigned responsibility for quality
management
A unit, permanent task force or committee need
to be established within NSOs or NSS for such
responsibility
An agency-wide data quality task force is established and
meets regularly.
Quality issues are discussed with and by management
regularly (for example at an annual quality review meeting)
8.4
The national statistical system
staff receives training on quality
management.
Staff training and development programs are in place to
ensure that staff are aware of the statistical agency’s quality
policy including the use of a NQAF, and that staff have an
understanding as to how quality is assured
NSOs should prepare and implement Training
Plan with a component of trainings on quality
management
A staff awareness “campaign” is undertaken to emphasize
the statistical agency’s commitment to quality
8.5
Guidelines
for
implementing
quality management are defined
and made available to the
public.
Guidelines for implementing quality management are
produced and issued which: -
•
describe the quality principles and framework
followed;
•
describe the entire statistical process and identify
relevant
documentation
for
each
stage
of
production;
•
describe the methods for monitoring the quality at
There should be a DQAF which is available to all
users and producers of statistics
33
Requirement
Element to be assured
Description
each stage of the statistical production process;
•
identify the indicators (quality measures) for
evaluating the quality of the main stages of
production, including indicators for source data
The guidelines, methodological manuals and handbooks on
recommended practices for quality assurance are made
available to the public.
Mechanisms are in place to assure the quality of data
collection (including the use of administrative data and
other sources) and data editing.
8.6
Indicators on statistical output
quality are regularly measured,
monitored,
published
and
followed
up
to
improve
statistical
products
and
processes.
Quality reports which are serving both producer and user
perspectives are prepared, published as appropriate, and
updated regularly.
Data quality assessments should be regularly
conducted and their findings being reported and
documented for follow-up
Quality indicators are defined, measured and monitored for
following up and improvements. Examples of quality
indicators:
•
References in media, hits on website, results from
user satisfaction surveys (relevance);
•
Standard
deviations
and
other
measures
of
accuracy, response rates (accuracy);
•
Number and size of revisions (reliability);
•
The length of time between the end of a reference
period
and
dissemination
of
the
statistics.
(timeliness);
•
Rate of statistics published when announced
(punctuality);
•
Respondent burden.
•
34
Requirement
Element to be assured
Description
8.7
Statistical
products
and
processes
undergo
periodic
reviews.
Periodic quality reviews of key products and processes to
assess adherence to internal guidelines and international
standards are performed.
This is especially for recurring exercises such as
monthly, quarterly and annual publications. It
also includes statistics produced from census and
surveys, for example population projection
Reviewing teams where both internal and external experts
can participate are set up.
The statistical agency’s internal reviewers are trained in
auditing methods and tools.
Improvement actions arising from the result of quality
reviews are defined and scheduled for implementation.
Top management is informed of the results of reviews to
follow up improvement actions
Benchmarking of key statistical processes with other
statistical agencies are carried out to identify good practices
Procedures are in place to monitor and manage the quality
of different stages of the statistical production according to
the Generic Statistical Business Process Model (GSBPM)
Trade-offs within quality are systematically examined (e.g.
trade-offs between accuracy, timeliness and costs).
External experts (also from international organizations)
conduct quality reviews, such as reviews of key statistical
domains (for example International Monetary Fund’s
Reports on the Observance of Standards and Codes
(ROSCs)) or other reviews such as peer reviews, external
audits, and rolling reviews.