2 quality principles of the United Nations-National Quality Assurance Framework (UN- NQAF) manual of 2019 and other international quality frameworks such as the IMF’s Data Quality Assessment Framework (DQAF). Besides, this quality framework is also based on widely accepted definitions of quality as applied to official statistics as well as the UN Fundamental Principles of Official Statistics and Principles of African Charter for Statistics.
1.1 Rationale for a Data Quality Assurance Framework
Most users do not have enough information to determine whether or not the statistical
product fits for any particular purpose. The quality of statistics is defined in terms of its
fitness for use, that is the high-quality product offerings that build trust and loyalty with
customers in terms of how they were collected, concepts and classifications applied and
how the statistics were computed. Moreover, good quality statistics is essential for
informed public debate and decision-making. Therefore, objectivity and comparability in
evaluating the quality of statistics requires broad cooperation and mutually approved and
applied quality criteria. Official statistics that are produced and disseminated by any
national statistical agency are used for many different purposes so it is important to ensure
that statistics produced and disseminated by statistical authorities within the NSS meet
national and international quality standards.
Furthermore, at the national, regional and global levels, there is now a greater emphasis
on evidence-based policy and decision making to respond to the coordinated regional and
global development frameworks. These frameworks include: - the Tanzania Development
Vision (TDV) 2025, The Third National Five Year Development Plan 2021/22 - 2025/26
(FYDP III), Ruling Party Manifesto 2020 – 2025, Zanzibar Development Plan (ZADEP) 2021
- 2026, United Nations Agenda 2030 for Sustainable Development Goals (SDGs - 2030), the
East African Community (EAC) Vision 2050, Southern Africa Development Community
(SADC) Vision 2050; SADC Regional Indicative Strategic Development Plan (RISDP–2030)
and the Africa Union Agenda 2063 constitute potential demand for national statistics.
The frameworks have also increased the demand for high-quality data. Demand for data has also reshaped the landscape for statistics production whereas there is more use of data from non-traditional sources into the statistical process to complement data from traditional sources. The use of non-traditional data sources introduces newer data producers into the NSS. This creates a need for a Quality Assurance Framework to support
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the production of high-quality statistics and better guidelines for statistical operations
across the new frontiers of the statistical system. The main benefits of having TDQAF for
official statistics are:
a) It offers a mechanism for the systematic monitoring and ongoing identification of
risks and quality issues across the NSS to develop timely corrective measures. It
therefore supports quality improvements and their maintenance over time;
b) It supports NSS coordination by providing common guidance on quality assurance
and reference materials for training;
c) It gives greater transparency to the processes by which quality is assured and
reinforces the credibility of statistics producers within the NSS;
d) It serves as a common ground to promote dialogue on quality challenges and
opportunities at the national, regional and international levels; It also describes to
what extent statistical practices in Tanzania are in line with international
recommendations and good practice; and
e) It provides a basis for creating and maintaining a culture of quality within the NSS
by having more detailed information describing the processes and procedures used
to produce particular sets of statistics
1.2 Objectives of Quality Assurance Framework
The quality assurance framework will serve to formalize both operational standards and
criteria for evaluating the fitness of statistical data for their required purposes, as well as
the methodologies used for data collection, data processing, analysis and dissemination
strategy of official statistics which prevent or limit the occurrence of errors in all stages of
the statistical process during production of official statistics.
Therefore, Quality Assurance Framework will:
a) Create greater transparency and clarity necessary to manage and respond to data
users’ expectations, queries and demands while reinforcing the credibility of the
producer institute, accuracy, timeliness and accessibility;
b) Improve consistency, effectiveness and efficiency in the NSS by reducing
duplication of efforts; normalizing the use of international concepts, definitions,
classifications, standards, sampling frames, and methodologies, where appropriate;
and creating datasets that are responsive to data sharing demands as well as
interpretable that can be understood in its appropriate context;
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c) Provide a systematic mechanism for facilitating the ongoing identification of quality
problems and possible actions for their resolution;
d) Provide guidance for engagement with statistics producers and data providers
outside of the NSS in the production of official statistics;
e) Support quality improvements and their maintenance over time; and
f) Provides a basis for creating and maintaining a quality culture within the
organization and contains reference material that can be helpful for training.
1.3 Scope and Coverage
The framework describes the processes to be put in place in order to facilitate and ensure
effective management of quality in all statistical programs and organizational initiatives.
Therefore, it covers the various quality aspects of the entire statistical value chain (i.e.
need, design, plan, collection, processing, analysis, report writing and dissemination), for
all official statistics produced from both traditional, and non-traditional sources within the
NSS.
This framework is designed to address the quality of official statistics as well as non-
official statistics. Within this framework, there are set procedures to assess and attest any
kind of statistics to see whether they suffice to be termed as official or not. The framework
is prepared based on the policies and frameworks of Tanzania so its uses is purposively
intended to be within the context of Tanzania's statistical system. However, it can be
referenced anywhere since it adopts several best practices from regional and international
contexts. Besides, this framework is developed to address quality management in survey
data as well as administrative data. This encompasses also non-traditional data. Quality of
data from all thematic areas (economic and socio-demographic) and also cross-cutting
themes such as environment can be addressed using this data quality framework.
The TDQAF is designed to be used by producers of statistics whereby it sets criteria for
high-quality data and how data quality is assessed. In addition, the framework can be
used by users of statistics produced within the NSS to gauge their quality and fitness for
use.
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CHAPTER TWO
NATIONAL STATISTICAL SYSTEM IN TANZANIA
2.0
Coordination of National Statistical System
Achieving coordination of the work of different parties within the NSS is essential for
improving and maintaining the quality of official statistics produced by various actors.
The NSS in Tanzania has several actors, including data producers, providers, suppliers
and users as well as statistical training institutions. These actors include Ministries,
Departments and Agencies (MDAs); Local Government Authorities (LGAs); Public
Institutions and Statutory Corporations (PISCs); as well as Non-State Actors (NSAs). The
NSS therefore, must function efficiently and fulfill the requirements set on independence,
integrity and accountability to produce quality official statistics to serve different user
groups nationally and internationally.
The Statistics Act provides the legal basis for the NSS and establishes NBS and OCGS as
the principal agencies of government responsible for the collection, compilation, analysis,
publication and dissemination of statistical information. The Statistics Act also mandated
NBS and OCGS to coordinate the NSS by collaborating with other agencies or
organizations having duties related to the production of official statistics. Through this
coordination mandate, NBS and OCGS;
i)
Provide technical advice on statistics to other state entities;
ii) Promote coordination among producers and users of official statistics by forming
appropriate sector committees;
iii) Control and coordinate all statistics activities in a view of having a statistical
system and avoid duplication of effort in the production of statistics;
iv) Reduce the burden of respondents of providing data;
v) Ensure optimal utilization of available resources;
vi) Facilitate harmonization of concepts, definitions, classifications, sampling frames,
and statistical methods and dissemination policies to make statistical outputs
comparable across the NSS as well as in the wider data ecosystem;
vii) Conduct training for members of the system to update knowledge on the contents
and application of recommended standards and methodologies; and
6 viii) Develop guidelines on quality management of statistics produced by outsourced agencies and implementation of mechanisms to allow the assessment and guarantee the quality of statistics produced by NSS members.
The planning of statistical programs in Tanzania NSS is achieved through the
development and implementation of the National Strategy for Development of Statistics
(NSDS) which is known as Tanzania Statistical Master Plan (TSMP) and Sectoral Strategic
Plan. The Government of United Republic of Tanzania (URT) with the assistance of the
Word Bank (WB), has developed the second Tanzania Statistical Master Plan (TSMP II)
which intends to transform the NSS into three broader thematic areas, namely: -
a) Data harmonization, quality and dissemination;
b) Data production and development; and
c) Infrastructure and Institutional Development.
The NSOs as coordinators of the NSS must lead the process of ensuring that demands for
quality statistics are met in a timely manner. Availability of quality statistics will facilitate
evidence-based policy, planning, monitoring and evaluations that are key in tracking the
realization of national and international development frameworks.
2.1
NSS Current Situation
This section assesses statistics situation in the NSS, in terms of data production,
coordination, dissemination and use; and infrastructure. It highlights status and
performance, while pointing to the strengths, weaknesses, opportunities and challenges
(SWOC) that are addressed by Sector Strategic Plans for Statistics (SSPS) developed for
each MDA.
The SWOC analysis which provides an overview of the identified Strengths, Weaknesses, Opportunities and Challenges (SWOC) is a powerful diagnostic tool used to assess the statistical production processes within the NSS and its environment. This analysis was carried out for understanding the current status of statistics in the determination of strategies concerning statistical quality. The analysis results are summarized in Table 2.1.
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Table 2.1
SWOC Analysis
Strengths
Weaknesses
a) Availability
of
standard
guidelines
and
strategies for the production of statistics;
b) Enabling institutional and statistical systems for
the production of statistics (Statistical databases,
financial
management
system,
recruitment
system);
c) Availability of the statistics legislation and
associated regulations to govern the production
of statistics;
d) Existence of core competencies in most areas of
statistics production;
e) Good stakeholders’ relationships;
f)
Existence of Statistics Units in some sectors for
carrying out statistical work.
a) Inadequate staff in terms of number and skills,
especially in advanced data processing and
analysis,
sampling,
M&E,
communication,
marketing and dissemination;
b) Inadequate ICT infrastructure;
c) Inadequate financial resources for production
and dissemination of statistics;
d) Inadequate disaggregation of data to reflect the
situation at lower sub-national levels;
e) Limited coordination of the NSS, collaboration,
networking and information sharing resulting in
challenges of access to data;
f)
Inadequate Sector Management Information
Systems;
g) Lack of unified and comprehensive national
statistical training curricula in the NSS.
h) Inadequate capacity in some areas of statistics
production within NSS;
i)
Inadequate statistical advocacy; and
j)
Insufficient use of administrative data and non-
traditional data sources as a result of lack of
frameworks and capacity.
Opportunities
Challenges
a) Availability of infrastructure and tools for the
production and dissemination of statistics;
b) Increased Government commitment to the
development of statistics;
c) Existence of local and international training,
opportunities
and
expertise
in
statistics
production;
d) Increased demand for data to support national
and international development planning and
monitoring;
e) Data revolution (Big Data, routine data, data
science etc.) which has increased recognition and
demand for statistical information;
f)
Existence
of
national
and
international
standards,
guidelines,
classifications,
methodologies
and
partnerships
for
the
production of statistics;
g) Existing and potential funding and technical
assistance
for
statistics
from
development
partners; and
h) Recognition
of
sectorial
statistics
as
the
cornerstone for national statistical development.
a) Late disbursement of funds for implementation
of planned statistical activities;
b) Substandard
and
counterfeited
goods
and
services received from suppliers;
c) Conflicting and inconsistent statistics produced
by some data producers;
d) High cost for conducting censuses and surveys;
e) Insufficient budgetary allocation for statistical
production and development leading to high
dependency on external support;
f)
Diverse actors within NSS involve many
institutions, organizations, and stakeholders
which
creates
significant
coordination
challenges;
g) Use of external and unofficial statistics;
h) Externalities such as pandemic disease, for
example
COVID-19
that
disrupt
statistical
activities; and
i)
Advanced and fast-changing technology that
requires continuous training of users.