2020-2024 Strategic Plan
Statistical Institute for Asia and the Pacific

20 February 2020

1|PageContents
ForewordI.

Introduction
A. Background
B. Rationale for the 2020-2024 Strategic PlanII.

Lessons from Implementation of the 2015-2019 Strategic Plan
A. Review of the Actions taken in 2015-2019
B. Independent Evaluation

III. Procedure Adopted for Formulation of the Plan
IV. SIAP’s Commitments
A. Mission
B. Core Values
C. SIAP’s Aspirations
V. The Evolving Landscape for Statistical Development
VI. Broad Strategic Directions
VII. Affecting Change
VIII. Goals, Outcomes, Outputs and Activities for 2020-2024
A. Goals, Outcomes and Key Activities
B. Results Based Framework
C. Budget Framework
D. Monitoring and Evaluation Framework
Annexes

2|PageForeword
The Statistical Institute for Asia and the Pacific, setup in 1970, has gone through a long journeyof 50 years providing its services to personnel engaged in statistical activities in the region. The
Institute has been evolving itself to meet the challenges faced by the statistics community during theyears and has been supporting them to meet these challenges in production of high-quality statistics
for its effective use in policy and planning and hence improving the lives of citizens.
The 2020-2024 Strategic Plan of the Institute has been developed in a transparent manner
through a consultative process where all the countries in the region, statistical training institutions,sub-regional organizations and international organizations have contributed to its formulation.
Statistical landscape is fast changing with the emerging technologies and availability of datafrom various sources including administrative data, big data, geospatial data and citizen generated
data. There is a deluge of data demand on National Statistical Systems (NSS) specially so in the context
of large number of indicators required to assess progress on Sustainable Development Goals (SDGs).
These challenges require substantial change in the manner NSS are organized. There is also changing
demand on the personnel in NSS with increasing requirement of having understanding and skills of adata scientist.
NSS are also to reorient their data dissemination systems so that the data produced isincreasingly and fully used in policy-and decision making to meet the expectation of evidence-based
policy making. Under the principle of “Leaving No one Behind” under the Agenda 2030, there isincreasing pressure on NSS to produce disaggregated indicators by different population groups andgeographic areas using appropriate technology and robust statistical methodologies. One of the mostchallenging area is to bring about organizational change in the context of fast changing technology. To
handle this challenge, NSS are not only required to update domain knowledge of its personnel but also
to develop managerial skills of its senior level managers.
The Institute has to assist the NSS to deal with the challenge of technological change and its
impact on the statistical systems. It has already taken a lead in developing and reorienting its training
programmes so that the personnel of NSS not only learn to compile new set of indicators but also
develop capabilities to deal with various sources of data and develop skills to process such a data. The
Institute has been imparting leadership training to heads of national statistical systems, but it has tofurther expand its scope to impart managerial skills including communication skills to senior managers
in NSS.
The 2020-2024 Strategic Plan of the Institute has addressed most of the new requirements of
NSS. I am sure the Institute would achieve objectives set out in the plan and would continue to provide
best possible and innovative services to the members and associate members of the Economic andSocial Commission for Asia and the Pacific (ESCAP).
I would like to take this opportunity to thank the Government of Japan, the host country of theInstitute, for its support to the activities of the Institute. I am also thankful to all the member countries
3|Pageand associate members of the ESCAP for their support. I highly value and acknowledge the support
provided by corporations, universities, research institutes, and international agencies at all the level of
sub-region, region and the globe, in contributing to and actively supporting the activities of theInstitute.

Ashish Kumar
Director
Statistical Institute for Asia and the Pacific4|PageI.

Introduction

A. Background1.

The first year of the period covered by the 2020-2024 Strategic Plan 1 coincides with the 50th

anniversary of the establishment of the Statistical Institute for Asia and the Pacific (SIAP), a regional
institution of the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP). The
Institute was established in 1970 by 20 countries in the Asia-Pacific region, the United Nations and theUnited Nations Development Programme on the initiative of the Government of Japan and accorded
the status of subsidiary body of ESCAP in 1995. Over the last half century, SIAP has become the premiertraining institution for official statistics in the region in pursuit of its mandate “… to strengthen,through practically oriented training of official statisticians, the capability of the developing members
and associate members and economies in transition of the region to collect, analyze and disseminate
statistics as well as to produce timely and high-quality statistics that can be utilized for economic andsocial development planning, and to assist those developing members and associate members andeconomies in transition in establishing or strengthening their statistical training capability and otherrelated activities.” 2
2.

ESCAP member states have relied on SIAP to be effective, efficient and dynamic in providing,leading and coordinating statistical training in the region. The Commission, at its 72nd session in May
2016, “highlighted the important role of reliable, timely, accurate and high-quality data and statistics
for successful implementation of the 2030 Agenda, in particular for monitoring progress, and as
evidence in support of the formulation of effective policies and the appropriate use of publicresources.” 3 In this context, the Commission “underlined the need for training on computation for
Sustainable Development Goal (SDG) indicators”. The Commission appreciated the important role andcontributions of SIAP in strengthening national statistical systems through its training programmes. 4

B. Rationale for the 2020-2024 Strategic Plan
3.

In the last five years, the fundamental role that official statistics play in development has been

increasingly recognized due to the data demands for monitoring progress towards the achievement of
the SDGs set out under the Agenda 2030 of the United Nations. SIAP responded by increasing the focus
of its training courses on the statistical requirements of the globally agreed SDG indicators.
4.

There is no doubt to the point that the main challenge facing statistical systems in developing

countries of Asia and the Pacific over the next decade will be the demands for better and new nontraditional sources of data and quicker and more efficient ways to produce and disseminate statistics, inlight of the 2030 Agenda. At the same time, there continues to be lack of capacity to produce evensome of the most basic data for a wide range of core economic, agricultural, social and environment
statistics in many developing statistical systems. Considering that major focus of the members states of
1 SIAP has been preparing the five-year plan from its inception for effective strategic and operational management andcoordination of its activities. The plan is revised based on training needs every fifth year since 1970. This Strategic Plan
started getting its approval from the Governing Council from the plan 2005-2009.
2 http://www.un.org/en/ecosoc/docs/2005/resolution%202005-36.pdf.
3 E/ESCAP/72/34, para. 142.
4 E/ESCAP/72/34, para. 146.

5|Pagethe region during the next five years would be to regularly produce SDG related indicators, thisstrategic plan for the period 2020-2024 has been developed as SDG focused plan.
5.

As the premier regional statistical training institution, SIAP faces the challenge of transforming

the nature, content, focus and effectiveness of statistical training in the region to better support
capacity building of statistical systems to provide data for informed policy- and decision making.
6.

In view of increasing demand on statistical systems in the region and lack of capacity of many

countries to meet this demand due to lack of technical capacity of the personnel in the system, SIAP
has to provide strong, effective and dynamic leadership in statistical training in the region. This 2020–2024 Strategic Plan has been formulated to serve as a forward-looking document to address thetraining needs of the member countries. The Plan will be the basis for its annual programming startingin 2020.
7.

The following outcomes will be achieved by 2024.
 Government officials introduce measures to improve the availability and quality of official
statistics for SDG monitoring National statistical training institutes have expanded coverage and quality of their training
programmes Statistical training institutions conduct statistical training/ events collaboratively with otherpartners
 ESCAP member and associate members and partners increased their contribution to SIAP and
SIAP delivered training using improved IT infrastructure and service

6|PageII. Lessons from Implementation of the 2015-2019 Strategic Plan
A.
8.

Review of the Actions taken in 2015-2019

The 2015-2019 Strategic Plan of SIAP was approved at the 10th session of the Governing

Council in 2015. Following this plan, the Institute held 109 training courses, workshops and seminars
during 1 January 2015 to 31 December 2019. These were attended by 6,224 participants from 97
countries and territories. Except for e-learning courses, all courses were delivered in collaboration withnational or international organizations, research institutes or universities. After the adoption of the2030 Agenda for Sustainable Development by the United Nations General Assembly in September 2015
and the subsequent entrusting of responsibility for the monitoring of SDGs and targets to national
statistical systems, the focus of statistical training changed towards compilation of the globally-agreed
SDG indicators.
9.

Training programmes organized by SIAP covered basic and advanced technical subjects insocial, economic, agricultural and environment statistics as well as on statistical methods and datacollection. Courses also focused on tools and methods for building statistical registers for improving
use of administrative data to produce official statistics. New courses, for example, on Computer
Assisted Personal Interviewing (CAPI), on big data, and on climate-change statistics were introduced.
10.

The training programmes were delivered with the help of stakeholders and partners (Annexes

Table 1 and Table 2). Stakeholder analysis carried out during 2015-2019 Strategic Plan still remains
useful and hence should be useful for development of training programmes. There has been increasein number of courses and number of participants of the training programmes during 2015-2019. The
Institute maintained over all positive gender ratio at 1.41. This had been increasing from 1.03 in 2015
to 1.55 in 2018 but decreased to 1.51 in 2019. (Table 3a) Trends in number of courses and participants,
and gender ratio by broad subject area and by training modality from 2015 to 2019 are presented in
Annexed Tables 3a and 3b.
Sub-regional and country courses
11.

During five years from 2015 to 2019, the Institute conducted a few sub-regional courses andwithin-country courses but conducted large number of regional courses. The sub-regional and withincountry courses were well received, and demand was high, especially in Central Asia. However, the2015-2019 Strategic Plan had prioritized regional training courses and therefore, this demand could not
be met.
Funding support received during 2015-201912.

During the period 2015-2019, SIAP received cash contributions from 32 of 58 ESCAP

member states and associate members in Asia and the Pacific. Annual contributions during this period
were $2.3 million per year including the contribution from the host country, Japan ($1.8 million peryear).
Technical Cooperation

7|Page13.

During the course of the 2015-2019 Plan, SIAP received funding from Food and Agriculture

Organization (FAO) to implement training component of the Regional Action Plan for the Global
Strategy to Improve Agricultural and Rural Statistics (RAP). This activity delivered an additional
investment of $1.7 million to SIAP and resulted in conduct of 30 training programmes to 715
participants from 43 countries. The RAP project was completed in 2018.
Network for the Coordination of Statistical Training
14.

National statistical training institutions which are members of the Network for the

Coordination of Statistical Training in Asia and the Pacific serve as collaborators for the training
delivered by SIAP through those institutions. Over the course of the 2015-2019 Plan, SIAP organized
three meetings of the Network. A fourth meeting is scheduled in December 2019. The Network has
established three subgroups to support the training activities. These are - the subgroup on training for
agricultural and rural statistics, subgroup on gender statistics training and the subgroup on training
activities on economic statistics. The activities of the Network and its subgroups have also guided theannual workplan of the statistical training institutions in the region and support required from SIAP.
Database on training programmes, materials and resource persons
15.

Database on training programmes, materials and resource persons have been created incollaboration with the Government of Japan and developed during the period of the 2015-2019
Strategic Plan. Nevertheless, their contents and usability leave room for further development.
Results of course evaluation
16.

As a whole, most performance indicators in the 2015-2019 Strategic Plan exceeded thebaselines and reached the targets. Number of face-to-face courses exceeded expected number every
year. (Table 4a) Development of new e-learning courses exceeded the target in 2018 while it was lower
than the target in 2019. (Table 4b) Number of participants exceeded the target in 2019. (Table 4c)
Percentage of participants who evaluate the course as very useful for their work and much improved
confidence in applying training received in their job exceeded the target in 2019. (Table 4d)
17.

Nevertheless, the number of indicators of achievement for which data were available formonitoring was limited, and largely confined to just one of the institutional goals, namely Excellence inProviding Training. It has been felt that methodology for evaluation of the courses need to be madesufficiently robust and should be more comprehensive.

B. Independent Evaluation
18.

An independent evaluation was commissioned by ESCAP in 2019 to review performance of

SIAP during the 2015-2019 Strategic Plan period, including the substantive relevance and financial
viability of the Institute. The evaluation has made recommendations so that critical points emerging
from the evaluation may be taken into account in formulation of 2020-2024 Strategic Plan of the SIAP.
The recommendations made in the evaluation report are reproduced below.
Strategic Issues

8|Page1)

For SIAP to work on a variety of approaches to bring statistical capacity development to focus
more at the organizational level and to bring results to scale through adopting innovative roles
in relation to statistical capacity development in the region

2)

For SIAP to further develop the approach to e-learning, making the e-learning courses more
attractive to users and providing enhanced opportunities for interaction across participants
and sharing of information

3)

For SIAP to stay informed on the developments of the SDG indicators of Tiers 2 and 3 andfollow up on methodologies agreed, providing practical training to support their assessment
for countries in the region, in close collaboration with UN agencies that are the custodians ofspecific SDG indicators

Management Issues
4)

For SIAP Governing Council, in close consultation with ESCAP, to decide on the human resource
setup of SIAP in the coming strategic plan period in order to align human resources with therequirements of the programmatic contents of the strategic plan

5)

For SIAP to enhance the use of results-based management in the strategic plan as well as in allof its initiatives, identifying clear results and providing monitoring and evaluation at the levelof reaction, knowledge, behavioural change and at organization level, identifying when to use
which level of assessment, with limited use of the latter level

6)

For SIAP, with support from ESCAP Strategy and Programme Management Division (SPMD), todevelop a resource mobilization strategy that includes, but also goes beyond requesting
member states to enhance their contributions to SIAP

19.

These recommendations have been taken into account for development of the 2020-2024

Strategic Plan. Actions relating to these recommendations have been specifically incorporated in broad
strategic directions; affecting changes; outcomes, outputs and activities in the strategic plan period of
2020-2024.

9|PageIII. Procedure Adopted for Formulation of the Plan
20.

The Governing Council of SIAP in its 13th session held in December 2017 endorsed the

proposal of the Institute to review the current Strategic Plan and initiate the development of its fiveyear plan. The Council created a “Friends of the Chair” group (FOC) composed of eight experts from
China, India, Pakistan, Russian Federation, Japan, Philippines and Vanuatu to assist SIAP for formulationof the Plan.
21.

Accordingly, a report on the stocktaking of the progress made on implementation of the 2015-2019 Strategic Plan for the period 2015 to 2017 was prepared by the Institute and circulated to all themembers of FOC on 2 February 2018 5 for comments/suggestions.
22.

The report of the progress made in implementation of Strategic Plan 2015-2019 was presented

before the Governing Council in its 14th session held in November 2018. In addition to considering thereport of the implementation of the 2015-2019 Plan, the Council provided advice on the developmentof the 2020-2024 Plan. In particular, the Council recommended that the drafting of the 2020–2024
Strategic Plan take the following into consideration: improving course effectiveness; impact of elearning; supply and demand analysis; use of country expertise; mobilizing necessary resources tomeet short-term demands; flexibility in delivery modalities and mobilization of expertise in highly
specialized areas; better targeting and multiplier effect; and delivery through partnerships withnational training institutions and other development partners. Based on this recommendation, thereport of the implementation of the 2015-2019 Plan was updated, adding data from 1 October to 31
December 2018 and carried out further analysis of participants’ evaluation. The Council also agreed inits 14th session to establish a small working group of Council members to craft a first draft of the 2020–2024 Strategic Plan, with a view to further consultation with the Council and possibly the membershipof the Network for the Coordination of Statistical Training in Asia and the Pacific, sub-regionalorganizations and other important stakeholders.
23.

In January 2019, considering that 2020-2024 Strategic Plan of SIAP was to be prepared in 2019,

the Council agreed that the evaluation of the Institute may be carried out in 2019 which would provide
important input in formulation of the strategic plan. The evaluation of the Institute was carried out by
an independent evaluator: Mr Frank Noij, an expert in evaluation. Mr Jonathan Palmer, a statistics
expert, provided technical support to the evaluator in an advisory role. Evaluation report was
submitted by the evaluators in October 2019.
24.

In the meantime, draft 2020-2024 strategic plan was prepared by SIAP based on thesuggestions made by the small working group on the earlier draft developed by SIAP in accordancewith the guidelines given by the Council during its 14th session. Later, this draft was revised by SIAP toaccount for the recommendations contained in the evaluation report. The draft was considered andendorsed in the 15th session of the Council on 9 December and the meeting of the Network for the
Coordination of Statistical Training on 10 December 2019. As advised by the Council, the draft wascirculated to three subregional organizations―Association of Southeast Asian Nations (ASEAN), South
5 E/ESCAP/SIAP/FOC/1: “Report of the Director of the Statistical Institute for Asia and the Pacific for 2015-2017”10 | P a g e

Asian Association for Regional Cooperation (SAARC) and Secretariat of the Pacific Community (SPC).
The comments received from these organizations have been incorporated in preparation of final
version. The revised version with minor changes based on suggestions and update of data wassubmitted for the approval of the Council members. This process was done through electronic
consultation. This version is finally approved version of the 2020-2024 Strategic Plan.

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IV. SIAP’s Commitments
A.

Mission6

25. SIAP’s institutional mission reflects the fundamental basis for its existence.

•

to strengthen, through practically oriented training of official statisticians, the capability of thedeveloping members and associate members and economies in transition of the region tocollect, analyze and disseminate statistics as well as to produce timely and high-quality statistics
that can be utilized for economic and social development planning, and•to assist those developing members and associate members and economies in transition inestablishing or strengthening their statistical training capability and other related activities

26.

Since its establishment, the Institute’s continued existence is firmly dependent on theenduring trust and belief of ESCAP member states and associate members, that SIAP addresses thecapacity building needs of national statistical systems to carry out their core mandates of making
available quality official statistics. The continued relevance of SIAP to governments, capacity
development partners and other stakeholders in the region is founded on the recognition that thetraining provided and coordinated by SIAP strongly supports and adds value to their own efforts inbuilding statistical capacity.

B. Core Values
27.

As a United Nations institution, SIAP embraces the core values of professionalism, integrity andrespect for diversity. SIAP integrates the principles and qualities embodied in these values in its
programmes, activities and practices.

• Professionalism. Instill professional competence and mastery of both statistics and training.
Meet commitments and achieve planned results.

• Integrity. Promote the Fundamental Principles of Official Statistics 7 . Be guided by the
Principles Governing International Statistical Activities 8.

• Respect for Diversity. Work effectively with people from all backgrounds. Promote genderequality. Respond to the diverse training needs and priorities of member states and associate
members.

6 http://www.unsiap.or.jp/about/index.html
7 https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx
8 https://unstats.un.org/unsd/accsub-public/principles_stat_activities.htm

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C. SIAP’s Aspiration
28.

SIAP has long history of being a premier regional training institution. It aspires to be globally

recognized as a top-class centre of excellence in training on official statistics and a model institution
for relevant, practical and results-oriented statistical training, especially in the present context whenthe 2030 Agenda has put immense responsibility on official statistical systems.
29.

The Institute has following four institutional aspirations:• Excellence in Providing Training• Excellence in Training Resources• Strengthened Engagement with Stakeholders• Excellence in organizational management and development

These have been the principles that have guided the work of SIAP.
30.

Excellence in providing training means to provide training that is relevant, innovative,

distinctive and is of the highest quality. Relevance means that training content and modality matches
the needs of target groups. Innovative training means addressing gaps in training, developing costeffective ways of delivering training and developing models for instructional design for face-to-face,
blended and e-learning modalities. Distinctive means training products that are recognized as SIAP
products. Quality means training is delivered by expert trainers based on quality-certified training
materials and that the training is objectively evaluated as meeting the learning objectives.
31.

Excellence in training resources means training is designed and delivered by a pool of

trainers with the right combination of expertise in the subject matter and effectiveness as a trainer for
face-to-face and e-learning courses. Training materials apply appropriate learning models, core skills
frameworks, and results of training needs assessments and evaluations in designing and preparing
training materials for different modalities. Excellence in training resources also means quality certified
resources. Excellence also refers to having standardized materials that facilitates well-organized andcoordinated training by the Network members.
32.

Strengthened Engagement with Stakeholders means leveraging stakeholder interests instatistical training to advocate for support for statistics development such as through the Network andother development partners. It means building on existing relationships and building on new initiatives
that institutionalize partnerships and create specific mechanisms for collaboration and coordination.
33.

Excellence in organizational management and development means the responsible planningand management of resources. This requires processes and structures for managing, allocating andmonitoring resources—staff, financial resources and infrastructure. Excellence in this regard means
enough resources are generated, expenditure decisions reflect cost-effectiveness, decision-making istransparent and collaborative, and accountability is monitored.

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V. The Evolving Landscape for Statistical Development
34.

Since the adoption of the 2015-2019 Strategic Plan by the Governing Council at its 10th session

in 2014, several decisions by members of the United Nations at the global and regional levels havepositioned official statistics at the centre of policy formulation and evaluation for and monitoring SDGs
adopted by the United Nations General Assembly.
At the global level
35.

The 2030 Agenda for Sustainable Development, together with a set of 17 Goals and 169

targets was adopted by the United Nations General Assembly in September 2015. This was followed by
the formulation of a set of 232 global SDG indicators by the Inter-agency and Expert Group on
Sustainable Development Goal Indicators (IAEG-SDGs), agreed to and subsequently refined throughdecisions by the United Nations Statistical Commission at its 2016-2019 sessions. 9
36.

The pledge that no one will be left behind is a key element of the 2030 Agenda. In terms of

demand for official statistics this translates into the need to track the progress of identifiable,
vulnerable subpopulations towards the targets. Target 17.18 addresses this need: “By 2020, enhancecapacity-building support to developing countries ... to increase significantly the availability of highquality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory
status, disability, geographic location and other characteristics relevant in national contexts”.
Moreover, the formulation of many of the individual SDG indicators in the global set identifies
additional subpopulations as dimensions and categories for disaggregation. 10
37.

In response to the increased demand for more detailed, including disaggregated, and timelier

data emanating from the 2030 Agenda and the SDGs, the global official statistics community, throughthe Statistical Commission, has developed a transformative agenda for official statistics with a view tohaving modernized national statistical systems in place that produce official statistics more efficiently,
more cost-effectively and in a sustainable, integrated and internationally coordinated way, based on an
inclusive partnership.
38.

Cape Town Global Action Plan for Sustainable Development Data 11 was prepared by the High-level Group for Partnership, Coordination and Capacity-building for Statistics for the 2030 Agenda for
Sustainable Development in UN World Data Forum on 15th January 2017. The Global Action Plan for
Sustainable Development Data proposed six strategic area, each associated with several objectives andrelated implementation actions. This plan identified innovation and modernization of national
statistical systems as a strategic area and set an objective to achieve “Modernized governance andinstitutional frameworks to allow national statistical systems to meet the demands and opportunities
of constantly evolving data ecosystems”. The action plan also recognized the need to mobilize
resources and coordinate efforts for statistical capacity building.

9 https://unstats.un.org/sdgs/indicators/indicators-list
10

For example, indicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex,
distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, workinjury victims and the poor and the vulnerable.
11 https://unstats.un.org/sdgs/hlg/cape-town-global-action-plan/

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At the regional level39.

The 2030 Agenda has been translated into a regional plan for action, endorsed by 4th Asia-Pacific

Forum on Sustainable Development and via ESCAP resolution 73/9, of a “Regional Road Map for
Implementing the 2030 Agenda for Sustainable Development in Asia and the Pacific”. 12 The Regional
Road Map identifies investing in responsive national statistical systems; formulating and implementing
strategies for the development of statistics; providing and promoting capacity-building support;
modernizing business processes and tools; and building skills of national statistical systems staff andmanagement as opportunities for regional cooperation in data and statistics.
40.

The collective vision and framework for action by the Asia-Pacific statistical community, 13

endorsed by the ESCAP Committee on Statistics at its 5th session in 2016, sets out a vision that “… by2030, national statistical systems are enabled and empowered to lead development of and to deliver
innovative, trusted and timely products and services for urgently needed and evolving statisticalrequirements of Agenda 2030”, to be achieved through taking collective action in the areas of engaging
users and investing in statistics; assuring quality and instilling trust in statistics; integrated statistics for
integrated analysis; modernizing statistical business processes; and having requisite skills sets.
41.

The declaration on navigating policy with data to leave no one behind endorsed by the ESCAP

Committee on Statistics at its 6th session in 2018 reiterated its commitment to implementation ofcollective vision and framework for action with collaborative and integrated efforts between policy
makers and statistics producers through the integration of statistics development with national
development policies and plans; develop national monitoring framework integrating it with planningand budgeting processes; establish high level advisory body comprising of various stakeholders both ingovernment policy making and private users including media; improve coordination and cooperation
among statistical units in government at all levels; empower heads of national statistical offices to takelead role in national statistical system; strengthen statistical systems to take full advantage of new,innovative and frontier technologies; improve communication with regard to statistics for evidencebased policy making including organizing awareness programmes; and revisiting national policies andstrategies to enable implementation. This was later endorsed by the ESCAP commission in its 75th
session in 2019. 14
42.

UN Statistical Commission in its 50th session held in March 2019, while reviewing the work of theregional statistical development, commended the significant work undertaken by Asia-Pacificcountries, regional institutions and other development partners supporting statistical coordination anddevelopment in the region. The Statistical Commission noted the work of Statistical Institute for Asiaand the Pacific in providing statistical training to strengthen regional statistical capacity development.
The Statistical Commission supported the call by the Asia-Pacific countries for development partners todevelop, strengthen and modernize international statistical standards and related skills, and continue
to provide technical assistance and training for their implementation.
12

See https://www.unescap.org/publications/regional-road-map-implementing-2030-agenda-sustainable-developmentasia-and-pacific.
13 E/ESCAP/CST(5)/1.
14 https://www.unescap.org/sites/default/files/ESCAP_CST_2018_7_Declaration.pdf

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Need for capacity development relating to SDG monitoring Indicators at regional level43.

The adoption of 2030 Agenda for Sustainable Development has resulted in quantum jump in

the need for statistics since the number of indicators required for tracking the progress increased fromjust 60 in MDG era to 232. This fourfold increase in demand for indicators and hence related statistics,
though not achieved even by developed countries, has led to increased demand on national statisticalsystems. With increase of use of administrative data in addition to official statistics generated fromcensuses and surveys has resulted in increasing demand for capacity development of statistics both innational statistics offices and line ministries. Further, field of statistics has considerably changed withthe emergence of new kinds of data, including “big data”, geospatial data mapped on traditional data
and data collected through use of mobiles and scanners. This increased demand for statistics could not
be met by the countries due to severe budgetary cuts on statistical activities. While there is increased
demand for statistics for evidence-based decision-making, there is usually not enough trained
statisticians and there is lack of financial resources for the purpose. There is increasing need forcapacity development in diverse areas of statistics. 15

VI.
44.

Broad Strategic Directions

To respond to the challenges in the Plan period, SIAP will follow two broad directions: bettertargeting and greater multiplier effect.

Better targeting45.

In terms of rationalizing priority areas, SIAP will be guided by:• Data requirements for monitoring the implementation of the 2030 Agenda and tracking
progress towards the SDGs
• The associated requirements for the modernization of business processes of national
statistical systems• Priorities of work expressed by ESCAP member states through the Committee on Statistics
that have a well-defined training component

46.

A focus on the data requirements for the 2030 Agenda and the SDGs and the modernization of

statistical business processes implies the need to design and deliver training at an intermediate toadvanced level.
47.

SIAP will continue to play a key role in designing and delivering short term training in priority

areas of work identified by the ESCAP Committee on Statistics. Such priorities might include theregional-, sub-regional- and country-level implementation of global strategies and programmes and be

15 This has been based on needs assessment surveys on agricultural and rural statistics carried out by SIAP and on

gender statistics carried out by UN Women; generic training needs assessment survey carried out by PARIS21(Response to the joint survey on New approaches to Capacity Development and Future priorities, Draft Report for theUnited Nations Statistical Commission 2018) and by GIST (Key messages from training needs assessments, summary ofneeds assessment exercise, https://unstats.un.org/gist/resources/Outputs-from-Task-Teams/2018/).

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accompanied with dedicated resources for implementation.
48.

Asia and the Pacific is a vast and diverse region, with national statistical systems facing distinct

challenges and are at different levels of development. The consequent statistical training needs arethus most effectively addressed at the country and sub-regional levels. SIAP should increase its focus
on training of personnel through sub-regional and in-country programme. These training courses
should be taken up based on proposals from countries and sub-regional training institutes every year
when annual work plan will be finalized. A sub-regional approach could be particularly useful in Central
Asia, for reasons of common history and language, and the Pacific, due to geography and the lack ofnational training capacity. To increase efficiency and sustainability, new collaboration should becultivated and developed such as ASEAN Community Statistical System (ACSS), SAARC and SPC.
Improving course effectiveness and impact of e-learning programmes
49.

SIAP has been delivering three types of training programmes with different objectives. The

training courses delivered by the Institute can be classified into long term training programmes, shortterm training programmes and e-learning programmes. Brief objective and other details are given
below:(i)Long term training programmes:
These programmes are of duration from one and half month to three and a half months. Basicobjective of the programmes is to build capacity of national statistical systems on long termbasis. Training focused on statistics and indicators related to SDGs yet focuses on building
capacity of the system which would be of use in overall development. These programmes areorganized in collaboration with the Japan International Cooperation Agency (JICA).

(ii)

Short term training programmes (duration of one week or less):
These programmes have specific subject/ SDG indicator(s) focus with participation from theofficials presently dealing with the subject matter. Some of these programmes are in thedomain of use of big data for official statistics. Purpose of these programmes is to impartknowledge and skills to the participants on their current work.

(iii) E-learning programmes:
These programmes relating to statistical business processes or SDG Indicator compilations aredeveloped with an effort to reach larger audience to provide information and skill
development for all persons dealing with issues relating to official statistics. Different
participants of these programmes have different objectives in pursuing these programmes.
50.

SIAP will develop content of each of these programmes keeping in view the objectives of thetargeted participants and hence will address the requirement of the member states in the present
context.
51.

SIAP will further develop the approach to e-learning, making the e-learning courses more

attractive to users and will provide enhanced opportunities for interaction across participants and
17 | P a g e

sharing of information.

Greater multiplier effect
52.

The designing of the courses, development of training material and delivery of courses will

shift to a further enhanced collaborative approach with relevant partners. Main approaches are thefollowings.
(1) In the context of orienting the curriculum towards the 2030 Agenda and the SDGs,
considerable progress has been made over the last four years or so to deliver training on specific
SDG indicators, integrated into long term courses and through dedicated short term courses, incollaboration with international organizations such as the Food and Agricultural Organization of theUnited Nations (FAO), International Labour Organization (ILO), United Nations Entity for Gender
Equality and the Empowerment of Women (UN Women), United Nations Educational, Scientific andCultural Organization (UNESCO) and its Institute for Statistics (UIS) and World Health Organization(WHO). This approach would be further expanded to collaborate with other custodian agencies of
SDG indicators.
(2) SIAP will develop collaboration with Statistics Division of ESCAP for undertaking training oftrainers. SIAP can take lead in organizing such training programmes which fits into the mandate of
SIAP.
(3) SIAP has been collaborating in the area of modernizing statistical business processes withorganizations such as the United States Census Bureau, on National Strategies for the Developmentof Statistics (NSDS) with Partnership in Statistics for Development in the 21st Century (PARIS21), andon electronic data capture and big data for statistical purposes with the World Bank. For
improvement of quality of official statistics, there has been a collaboration with UNSD of
Department of Economic and Social Affairs and International Monetary Fund (IMF). Suchcollaboration will be further expanded in new and emerging areas of statistics.
(4)

SIAP will develop collaboration with other divisions of ESCAP in delivering training to address

their data and statistics requirement. This can be on various social statistics, especially, on statistics
relating to disability with Social Development Division, on Geographic Information System (GIS) with
Disaster Risk Reduction Division, etc. SIAP will also consider collaborating with the regional
institutions for delivery of training in the areas of their interest of work. This would include data–
driven governance with Asian and Pacific Training Centre for Information and Communication
Technology for Development (APCICT), statistics relating to agriculture machinery usage with Centre
for Sustainable Agriculture Mechanization (CSAM) and on disaster statistics and related indicatorswith Asian and Pacific Centre for the Development of Disaster Information Management (APDIM).
Mobilization of expertise in highly specialized areas
53.

The Institute has been hiring experts from various academic institutions for imparting training

on topics where such expertise is not available within the Institute especially in the areas relating todeveloping estimates using small area estimation technique to pursue the objective of estimation of
18 | P a g e

SDG indicators for subpopulations and small geographic areas. Such approach will be further pursued
for delivery of training in other advanced statistical techniques. The Institute will also use the expertiseavailable in countries’ statistical system for delivery of training at regional, sub-regional and country
level.
Partnership with national statistical training institutions and other agencies
54.

The Network, with SIAP as secretariat, will continue to facilitate information sharing andpromoting coordination among national statistical training institutions, regional and international
statistical training providers, and donor agencies providing funding for statistical training in the AsiaPacific region.
55.

SIAP has also contributed to the global network on statistical training. Global Network of

Institutions for Statistical Training (GIST) is a network of international and regional training institutions,working together to build sustainable statistical capacities through efficient, effective, and harmonized
delivery of training. SIAP as a member of GIST has participated in its meetings and task teams andshared information. SIAP has also participated in UN Statistical Commission’s Global Working Group on
Big Data for Official Statistics. The Institute shall continue to contribute to the GIST, the Global Working
Group on Big Data and other such forums created for development of statistical capacities throughstatistical training.
Meeting short term demands
56.

SIAP will have flexibility to meet short term training demands of the member states even if

such a programme could not be included at the time of preparation of work plan for the year, if such ademand can be met without compromising already approved work programme. The flexibility will be in
mobilizing necessary resources through collaboration with other international agencies and national
training institutions for delivery of such programmes.

19 | P a g e

VII. Affecting Change
57.

The broad strategic directions delineated above suggest that SIAP needs to take the following

steps:
Change in mix of activities
58.

SIAP has been presently focused on delivery of training programmes in its campus and with its

collaborating national statistical training institutions at their campuses. In addition to delivery of
training, it has also been performing role of coordination as secretariat of the Network for the
Coordination of Statistical Training. This will be further strengthened over the years to deliver training
on SDG related indicators and will also take other roles in capacity building in the following manner:a. At the regional level expand the work with UN organizations in the further development oftraining materials on data gathering and analysis of SDG related to Tier I indicators and expand
cooperation with custodian agencies including UN agencies for development of training
materials and delivery of training on data collection on the SDG indicators;
b. Network amongst national statistical offices in the region and identify and facilitate
opportunities for South-South and triangular cooperation on specific statistical training issues,
facilitating support in particular for countries with special needs in this respect;c. Expand the focus of individual level capacity development to mid- and higher-level staff
positions in NSO and other relevant statistical Government agencies as these staff havesubstantial opportunities to inform and enhance institutional statistical systems and to adapt
statistical business processes;
d. Enhance SIAP’s own capabilities in terms of engaging with the development of capacities oftraining institutes, including the development of an approach to such engagement in the Asia
and Pacific region and partnerships concerned. This would require preparation andimplementation of capacity development plan for staff members of the institute; ande. Continue to play a role in regional level statistical capacity development in terms of theleadership and management seminars conducted, and link the contents of the seminars withthe capacity development initiatives that occur at the sub-regional level, providing a platform
to enhance the debate on statistical capacity development in the Asia and Pacific region.
Developing a sub-regional footprint
59.

The ESCAP region is home to several national statistical training institutes that are qualified –also because of prior experience – to deliver training at the sub-regional level; SIAP has over recent
years worked with most of these institutes.
60.

SIAP, in partnership with the ESCAP Statistics Division, is in the process of initiating tailored

training in official statistics for the countries of Central Asia with financial support of the Governmentof the Russian Federation and in cooperation with its Federal State Statistics Service (Rosstat) andHigher School of Economics. ESCAP has secured a second tranche of funding which will be available till
20 | P a g e

2021-22. This work will be pursued in collaboration with ESCAP Statistics Division.
61.

SIAP, ESCAP Statistics Division and Subregional Office for the Pacific have over recent years

jointly undertaken several activities including to meet the training demand of Pacific countries, inparticular, in the area of environment statistics (System of Environmental-Economic Accounting (SEEA)
and climate change-related statistics). To institutionalize this work, as well as to develop a sustainableapproach for meeting the demand on principles and practices of official statistics in the subregion,
ESCAP, including SIAP, is in the process of working through options with the University of the South
Pacific and the Pacific Financial Technical Assistance Centre (PFTAC).
62.

SIAP will provide support to statistical training institutions in various subregions to enhance thecapacities on technical as well as leadership, managerial and coordination aspects, including throughpeer learning and with special attention to adaptation of training to the specific needs of Least
Developed Countries (LDCs), Land Locked Developing Countries (LLDCs) and Small Island Developing
States (SIDS) .
Making e-learning effective
63.

Overall, SIAP’s distance e-learning courses have been successful in reaching large numbers ofparticipants. There are, however, two major issues.

i.

SIAP has been struggling to establish a modern, effective and visually attractive e-learning
platform.

ii.

It has also been noticed that participants from countries with large number of participants ofthese courses enrolled in the same course to circumvent individual testing by sharing andsubmitting the same answers irrespective of questions asked.

64.

Although delivering training remotely is perhaps inherently more challenging than face-to-face,

SIAP will rethink its approach. This will involve:(i)

Development of a range of e-learning modalities geared towards different ways oflearning, including moderated and non-moderated forms of learning, making use of adiverse set of ways to transfer information;(ii)

Expanding e-learning beyond technical statistical topics, to include governance andmanagement issues of national statistical offices as well as communication, coordinationand negotiation skills needed for statisticians to successfully work with other parts ofgovernment and with non-governmental stakeholders in data gathering, dissemination
and use;(iii)

Further development of blended courses, including use of e-learning to ensure thattrainees will be sharing similar knowledge and skill levels and can be expected from thestart to have the required motivation to follow-up on training, using their enhanced
knowledge and skills in the practice of their work;(iv)

Balancing the use of e-learning with face-to-face interaction within training courses as
21 | P a g e

well as across all SIAP supported training;(v)

Increasing use of on-line forum for trainees as well as alumni to interact, shareexperiences and learnings and become part of an online community of statistics
practitioners;(vi)

Making stand-alone e-learning continuously available online so that statisticians andothers interested in the topic concerned can take the course at their own speed andtiming; make support available through online communication, e-mail or other meansand provide opportunities for peer support;(vii)

The adoption and continuously updating of a modern learning management system andrelated infrastructure for e-learning;(viii) Reviewing and further developing the current distance learning content, together where
feasible with partners that embody relevant expertise;(ix)

Examine how at the beneficiaries end the apparent current culture of compliance can bechanged into one of active learning;(x)

Introduction of system of conducting webinars during the course of delivery of an elearning programme.

Tracking developments on Tier 2 and 3 SDG indicators to meet training needs
65.

SIAP has been providing training on SDG indicators for which international methodologies havebeen developed. These are both for Tier 1 and Tier 2 indicators. These training programmes are mostly
delivered with the assistance and collaboration with the relevant custodian agencies. Tier 3 of SDG
indicators are gradually being classified to Tier 2 after their methodologies are developed and pilot
testing for the suitability is done by the countries. For SIAP to stay informed on the developments ofmethodology of compilation of Tier 2 and 3 indicators, it will have to actively participate in themeetings of Inter Agency Expert Group on SDG Indicators (IAEG-SDGs). The Institute will also have todevelop close interaction and better coordination with custodian agencies of these indicators fordelivery of training on compilation of these indicators and also for development of training material.

22 | P a g e

VIII. Goals, Outcomes, Outputs and Activities for 2020-2024
66.

Overall institutional mission, core values, institutional aspirations, broad strategic directions,affecting change, objectives of this plan, focus of the plan and goals of the plan are summarized in the
Table 1 below.
Table 1. Summary of Focus, Goals, and Outcomes for 2020-2024
Institutional Mission
16

SIAP’s institutional mission reflects the fundamental basis for its existence:• to strengthen, through practically oriented training of official statisticians, the capability of the developing members andassociate members and economies in transition of the region to collect, analyze and disseminate statistics as well as toproduce timely and high-quality statistics that can be utilized for economic and social development planning, and• to assist those developing members and associate members and economies in transition in establishing or strengthening theirstatistical training capability and other related activities
Core Values
Professionalism. Instill professional competence and mastery of both statistics and training. Meet commitments and achieve
planned results.
Integrity. Promote the Fundamental Principles of Official Statistics 17. Be guided by the Principles Governing International
Statistical Activities.
Respect for Diversity. Work effectively with people from all backgrounds. Promote gender equality. Respond to the diverse
training needs and priorities of member states and associate members.
Institutional Aspirations
Excellence in Providing
Excellence in Training
Strengthened
Excellence in organizational
Training
Resources
Engagement withmanagement andStakeholder
development
Broad Strategic Directions
Better targeting
Improving course effectiveness and impact of e-learning programmes
Greater multiplier effectMobilization of expertise in highly specialized areas
Partnership with national statistical training institutions and other agencies
Meeting short term demands
Affecting Change
Change in mix of activities
Developing a sub-regional footprint
Making e-learning effective
Tracking developments on Tier 2 and 3 SDG indicators to meet training needs
Focus for 2020-2024
Monitoring of SDGs through global and national indicators for supporting decision- and policy-making to achieve the goals
Goals for 2020-2024
Goal 1: Improved
Goal 2: Improved coverage
Goal 3: Improved coverage
Goal 4: Sustained increase inavailability and quality ofand quality of nationaland quality of statisticalfinancial and humanofficial statistics for SDG
statistical systems to deliver
training through enhanced
resources, and improved
monitoring in ESCAP
training
partnership and innovation
statistical training and othermembers and associateservices provided andmembers
coordinated by SIAP
Outcomes 2020-2024
Government officials
National statistical training
Statistical training
ESCAP member and associate
introduce measures toinstitutes have expanded
institutions conduct
members and partners
improve the availability andcoverage and quality of their statistical training/ events
increased their contribution toquality of official statistics
training programmes
collaboratively with other
SIAP and SIAP delivered
for SDG monitoring
partners
training using improved IT
infrastructure and service

A.
67.

Goals, Outcomes, Outputs and Key Activities

The four goals of the Institute in 2020-2024 will have the following outcomes, outputs and16 http://www.unsiap.or.jp/about/index.html
17 https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx

23 | P a g e

key activities.
Goal 1: Improved availability and quality of official statistics for SDG monitoring in ESCAP members
and associate members
Outcome: Government officials introduce measures to improve the availability and quality of official
statistics for SDG monitoring
Output: Enhanced knowledge and skills of government officials to produce, process, analyse andmanage quality official statistics for SDG monitoring
Key activities:
Develop and deliver training programmes and workshops that focus on production of data forcompilation of Tier 1 and Tier 2 global indicators for monitoring of SDGs. This activity will be
undertaken through conduct of the following training programmes every year:
a. Statistics related training programmes(1) Long term programmes (2 or 3 programmes)(2) Short term programmes:
a. regional (8)b. sub-regional (10)c.

in-country (6)(3) Delivery of e-learning programmes including development of new programmes
a. moderated and non-moderated (8)b. blended programmes (6)c.

Development of new programmes (3)(4) Training programmes in Russian language for Central Asian countries (3 courses included in(3a) above)(5) Training programmes for countries in Pacific region (3 courses included in (2b) above)(6) Training of trainers (1)(7) Research-based training (1)b. Management related training programmes(1) Statistical quality workshops for senior-level managers (1)(2) Leadership training (1)(3) Conduct of training programme on management and governance for middle level personnel innational statistical systems (1)(4) Management seminar for heads of national statistical offices (1)24 | P a g e

Priority topics for statistical and management training
Priority subjects/ topics are given below:•

Principles and practices of official statistics: Compilation of indicators for monitoring ofSDGs, United Nations Fundamental Principles of Official Statistics, legal framework,
statistical methodologies, data collection, processing, statistical analysis, dissemination ofdata and result of analysis.

•

Specific domains for delivery of training are:o Populationandsocialstatistics:

principles,international

standards

andrecommendations for population censuses; analysis and use of census data; time use
surveys; regional core set of social and population statistics; labour statistics including
labour mobility, measurement of decent work and informality in employment as wellas labour accounts; conduct of victimization surveys and compilation of crime
statistics and statistics related to criminal justice; and measuring sustainable tourism
and tourism accounts
o Gender statistics: regional core set of gender statistics and indicatorso National Accounts and economic statistics: National Accounts in the framework of
System of National Accounts 2008 (2008 SNA); advanced topics in National Accounts;
regional core set of economic statistics; statistical business registers; International
Comparison Programme/ Purchasing Power Parity (PPP); government financestatistics; and industrial statistics including banking, airline and wholesale; Supply and
Use Tables serve as the basis for compiling a range of accounts such as regional
accounts, environmental accounts, labour accounts, tourism accounts as well as
National Accounts including computation of effect of globalization, digitization andsustainable development including wellbeing.
o Agricultural and rural statistics: SDG indicators related to agriculture sector including
fishery statistics
o Environment statistics: Framework for development of environment statistics (FDES),SEEA central framework and compilation of physical flow accounts; disaster-related
statistics; compilation of ocean accounts; SDG indicators related to environment anddisaster; and indicators related to climate changeo Advanced statistical methodologies and IT applications: standards and frameworks
for statistical data production; Goal specific monitoring of SDGs under the 2030
Agenda, generation of official statistics from administrative registers and otherreporting systems; official statistics from non-conventional sources of data such as big
data including satellite data, citizen generated data and scanner data; computerassisted data collection such as CAPI and computer assisted coding; small-area
estimation techniques; analysis of complex survey data; data revolution; and25 | P a g e

geospatial statisticso Training-of-Trainers for Statistics Training Programme: training in designing,developing and delivering of statistical training programmes, training to certify trainers
in specific statistical areas (Indicative areas: basic statistical methods; administrative
statistics; sampling methodologies; gender statistics; National Accounts; SEEA;
agricultural statistics; and IT applications in statistics)o Research-based training programme: thematic topics on developing methodologies
and analyzing data relevant to immediate concerns of countries
o Management and Governance related training programmes: Governance andmanagement of national statistical organizations; communication, coordination andnegotiation skills for statisticians; and communication skills for advancing statistical
literacy of stakeholders
o Leading, Managing and Modernizing for Quality Statistics: high-level management
seminars/ workshops for discussing and debating leadership and management issues
related to national statistical systems; senior-level workshops on quality assurance
frameworks and their implementation; and modernizing statistical information
systems
Goal 2: Improved coverage and quality of national statistical systems to deliver training
Outcome: National statistical training institutes have expanded coverage and quality of their training
programmes
Output:Strengthened capacity of national statistical systems to provide statistical training formonitoring SDGs
Key Activities:
1. Support conduct of sub-regional training programmes by the national training institutes incollaboration with FAO on agricultural statistics, with ESCAP Statistics Division on economic
statistics, with UN Women on gender statistics and with other custodian agencies on related
SDG Indicators
2. Coordinate development of training materials for delivery of training programmes with FAO,
ESCAP Statistics Division, UN Women and other international organizations
3. Develop database of experts classified by statistical domains and other relevant areas
4. Conduct of training needs assessment survey
Goal 3: Improved coverage and quality of statistical training through enhanced partnership andinnovation
Outcome: Statistical training institutions conduct statistical training/ events collaboratively with otherpartners
26 | P a g e

Output: Enhanced partnership and coordination among statistical training institutions in the region,international organizations and civil society organizations
Key Activities:
1. Provide secretariat support to the Network for the Coordination of Statistical Training in Asiaand the Pacific2. Organize the meetings/ discussions on the subgroups of the Network
3. Develop capacity development plan for national statistical training institutions
4. Establish partnership with academic institutions to collaborate on research-based training
programmes
5. Participate in and contribute to international, regional and national events on official statistics
6. Develop partnership with private sector including civil society organizations
Goal 4: Sustained increase in financial and human resources, and improved statistical training andother services provided and coordinated by SIAP
Outcome: ESCAP member and associate members and partners increased their contribution to SIAP
and SIAP delivered training using improved IT infrastructure and service
Output 1: Enhanced awareness of ESCAP members and associate members on the achievements andvalue of SIAP
Key Activities:
1.

Develop an effective resource mobilization strategy with the ESCAP SPMD in 2020

2.

Develop communication and advocacy strategy

3.

Organize consultation with potential donors for implementation of resource mobilization
strategy

Output 2: Improved training facilities and infrastructure and ICT equipment and facilities in SIAP
Key Activities:
1.

Review the present system of delivery of e-learning courses in the Institute and suggest
improved IT setup by hiring an expert

2.

Implement these requirements in collaboration with host government and if necessary, fromresources available with the InstituteOutput 3: Aligned human resources to the expected outputs
Key Activities:
1. Align human resources to the broad strategy and affecting changes in operations of SIAP for
achieving objectives in the plan
2. All the vacancies of the professionals are filled
27 | P a g e

3. Hire consultants according to requirement of training programmes
The following considerations will be kept for implementing activities under this goal:(a) Training programmes in the Institute are delivered with the help of its faculty and with thesupport of consultants hired for the areas in which the faculty does not have adequate
expertise. Human resources should be aligned with the requirements of the contents of theStrategic Plan. This is to be done to ensure that long term courses are delivered effectively
and there is scope for SIAP to innovate on statistical capacity development and play multiple
roles at regional and sub-regional level.

(b) Capability improvement of SIAP: Enhance SIAP’s own capabilities in terms of engaging withthe development of capacities of training institutes, including the development of an
approach to such engagement in the Asia and Pacific region and partnerships concerned.
This is to be done based on development of an internal capacity development plan and its
implementation.
(c) Human capacities in the Institute are to be improved so that appropriate training facilities andequipments including web-based technologies are available and effectively used. Other
requirement would be to maintain SIAP website, development and delivery of e-learning
programmes and setting up and maintaining on-line sharing platforms and databases.

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B.
68.

Result Based Framework

SIAP has developed a results framework for the Strategic Plan with its goals, outcomes,

outputs and a set of indicators to monitor achievement of results. This has been developed in close
collaboration and guidance of the ESCAP SPMD. Outputs, Short-medium term and long term outcome
and impact related to the activities undertaken in SIAP are presented in the Annex Chart whichindicates the theory of change adopted by the Institute.
69.

SIAP shall enhance the use of results-based management in all of its initiatives. This willcommence with the use of results-based management for its long term training programmes in thefirst two years of the implementation of this Strategic Plan. Based on the experience gained, thissystem will be introduced in other training programmes and other initiatives.
70.

The target number of training programmes to be undertaken in each category is based on the

average number of training programmes delivered in each category during the preceding five-year
period 2015-2019 and the changing focus of the plan to deliver more sub-regional and e-learning
programmes.
71.

Achievement of the outcome and outputs of the Plan is contingent on the following major

assumptions:•

Relevant stakeholders at the national, regional and international levels continue toaccord the necessary priority to statistical capacity building through training;•

National statistical institutions and statistical offices in line ministries who are theintended beneficiaries of SIAP training:oConsider SIAP training as an integral part of their own human resource
development programmes;oIntegrate e-learning as a modality for training in their own human resource
development programmes;oNominate officials with appropriate qualifications for the training programmes;oProvide trainees with appropriate opportunities to develop and implement actionplans for applying training received;•

National and international training providers are willing to pursue regional cooperation
on statistical training, including:oActively contributing to the work of the NetworkoParticipate in certification and related processes for establishing satellite training
institutions affiliated with the Network and certifying trainers and training
materials•

Sufficient human and finance resources are provided to SIAP to implement the Plan. Also,
the monitoring and evaluation aspects are included in annual work and financial plans.
29 | P a g e•Adequate extra-budgetary resources are mobilized to ensure that the supporting
technological requirements are in place and utilized and key activities are undertaken•

Member states and the host government continue to provide sufficient financial and inkind support to efficiently carry out activities of the Institute under the condition that
SIAP/ESCAP conducts trainings in line with needs of member states and appropriateorganizational management.

72.

The logical framework matrix for the 2020-2024 Plan is presented below. Indicators ofachievement have been identified for measuring and monitoring the outcome for each Goal, outputs andactivities. Indicators of achievement would be revisited at the end of the planning period to decide next setof indicators and targets.

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Logical Framework Matrix
Goal 1
OutcomeIndicator of achievement
OutputEnhanced knowledge andskills of government
officials to produce,
process, analyse andmanage quality official
statistics for SDG
monitoring

Improved availability and quality of official statistics for SDG monitoring in ESCAP members and associate members
Government officials introduce measures to improve the availability and quality of official statistics for SDG monitoring
Percentage of SIAP trainees who report that SIAP training contributed to improvement in their work
Key Activities
Indicators
1. Conduct of long term training programme• Number of government officials trained in long
• At least one programme on training on official statistics
term programmes
for monitoring of SDGs
• Percentage of participants who evaluate the• At least one programme from the two:
programme as very relevant for their work(i) Disaggregation of data to meet the objective of• Percentage of participants who report much improved
agenda 2030 “Leave no one behind”
confidence in applying training received in their job(ii) Application of ICT to improve statistical businessprocesses and use of big data, geospatial data andadministrative data
2. Conduct short term training programmes: regional, sub• Number of government officials trained in shortregional and in-country training courses
term programmes
Expected number by type:• Percentage of participants who evaluate the(i) Regional: 8 per year
progaremme as very relevant for their work(ii) Sub-regional: 10 per year
• Percentage of participants who report much improved(iii) In-country: 6 per year
confidence in applying training received in their job
3. Develop and deliver e-learning programmes• Number of government officials trained in e-learning(i) Deliver e-learning programmes
programmes
Expected number: 8 per year
• Percentage of participants working in specific area who(ii) Conduct blended-learning programmes
report much improved confidence in applying eExpected number: 6 per year
learning training received in their job(iii) Develop new e-learning programmes
Expected Number: 3 per year
4. Conduct training programmes in Russian language for• Number of government officials from Central AsianCentral Asian Countries (included in e-learning
countries trained
programmes (3(i) above)
Expected number: 3 per year
5. Conduct training programmes for countries in Pacific• Number of government officials trained from Pacificregion (included in Sub-regional courses in 2(ii) above)
countries
Expected number: 3 per year
6. Conduct Training-of-Trainers programmes on selected• Number of statistical trainers trained and certified forareas of official statistics
delivery of further training
31 | P a g e

Expected number: 1 per year
7. Conduct research-based training programmes based onpriority needs of countries
Expected number: 1 every other year
8. Conduct management related training programmes:(i) Conduct statistical quality workshops for seniorlevel managers
Expected number: 1 per year(ii) Conduct leadership training programme
Expected number: 1 per year(iii) Conduct of a training programme on management andgovernance for middle level personnel in national
statistical systems
Expected number: 1 per year
9. Conduct management seminar for heads of national
statistical offices (material shared on website)
Expected number: 1 per year

• Number of government officers completed researchbased training• Number of government officials trained• Percentage of participants who evaluate theprogramme as very relevant for their work
• Percentage of participants who report much improved
confidence in applying training received in their job• Number of participants• Percentage of participants who find the programme
usefulTarget of indicators• Target number of government officials is 20 for each face-to-face programme except for the leadership training programme.
• Target number of government officials is 1,000 for all e-learning courses delivered in a year.
• Target percentage of participants who evaluate the programme as very relevant for their work and target percentage of participants who report much
improved confidence in applying training received in their job are 80% for each programme.
• Target number of participants in the management seminar is 50 for each seminar.
• Target percentage of participants who find the management seminar useful is 80%.

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Goal 2
OutcomeIndicator of achievement
OutputStrengthened capacity ofnational statistical systems
to provide statisticaltraining for monitoring
SDGs

Improved coverage and quality of national statistical systems to deliver training
National statistical training institutes have expanded coverage and quality of their training programmes
Number of national statistical training institutes that expanded coverage and quality of training programmes
Key Activities
Indicators
1. Support conduct of sub-regional training programmes by• Number of sub-regional training programmes
the national training institutes in collaboration with FAO,
conducted by national training institutions
ESCAP Statistics Division and UN Women on• Number of topics on which materials developed by
I. Agriculture statistics
SIAP used for sub-regional training conducted by
II. Economic statistics
national training institutes
III. Gender statistics(Topics to be covered shall be diversified depending on
availability of partner organization.)
2. Coordinate development of training materials for deliveryof training programmes with FAO, ESCAP Statistics Division
and UN Women
3. Develop database of experts classified by statisticaldomains and other relevant areas for delivery of training
in areas where expertise not available in countries
4. Conduct of training needs assessment survey with theassistance of members of the Network for the
Coordination of Statistical Training at sub-regional level

33 | P a g e

Goal 3
OutcomeIndicator of achievement
Outputs
Partnership with Statisticaltraining institutions in theregion, internationalorganizations and civilsociety organizations forjoint training activities

Improved coverage and quality of statistical training through enhanced partnership and innovation
Statistical training institutions conduct statistical training/ events collaboratively with other partners
Number of training/ events collaboratively developed, organized and delivered
Key Activities
Outputs
1. Provide secretariat support to the Network for the• Number of training programmes organized jointly
Coordination of Statistical Training in Asia and the Pacificwith national statistical training institutions and/ or
2. Organize the meetings/ discussions on the subgroups ofinternational organizations
the Network• Number of events in which SIAP is represented
3. Develop capacity development plan for national statisticaltraining institutions through the Network for the
Coordination of Statistical Training in Asia and the Pacificand support its implementation
4. Establish partnership with academic institutions tocollaborate on various training programmes, speciallyresearch-based training programmes
5. Participate in and contribute to international, regionaland national events on official statistics including IAEGSDGs meetings/ working groups
5. Develop partnership with private sector including civilsociety organizations to promote statistical training

Target of indicators• Target number of training programmes organized jointly with national statistical training institutions and/ or international organizations is 26 per year.
• Target number of events in which SIAP is represented is 24 per year.

34 | P a g e

Goal 4
OutcomeIndicator of achievement
Outputs
1. Enhanced awareness of
ESCAP members andassociate members on
the achievements andvalue of SIAP
2. Improved training
facilities andinfrastructure and ICT
equipment and facilities
in SIAP
3. Aligned humanresources to the
expected outputs

Sustained increase in financial and human resources, and improved statistical training and other services provided andcoordinated by SIAP
ESCAP member and associate members and partners increased their contribution to SIAP and SIAP delivered training using
improved IT infrastructure and service(i) Amount of contribution (cash and in-kind) received by SIAP(ii) Number of member countries and donors provided resources for SIAP activities
Key Activities
Indicators
1. Develop an effective resource mobilization strategy with • Resource mobilization strategy finalized and implemented
the support of ESCAP SPMD in 2020
• Number of resource mobilization activities/ events
2. Develop communication and advocacy strategy for SIAP
conducted
3. Organize consultation with potential donors for
implementation of resource mobilization
1. Review the present system of delivery of e-learning • Number of e-learning programmes with voice-overprogrammes in the Institute and suggest improved IT
deliveredsetup by hiring an expert• Number of webinars delivered
2. Implement these requirements in collaboration with • Number of non-moderated e-Learning programmes
host government and if necessary, from resourcesdeliveredavailable with the Institute
1. Align human resources to the broad strategy and • Number of consultants and experts engaged in delivery ofaffecting changes in operations of SIAP for achieving
training in highly specialized areas
objectives in the plan
2. All the vacancies of the professionals are filled
3. Hire consultants according to requirement of training
programmes

35 | P a g e

C.
73.

Budget Framework

With reference to the United Nations budgetary framework, SIAP is funded fully through extra-budgetary

resources. The financial resources of the Institute are generated mainly from (a) voluntary cash contributions provided
on an annual basis by ESCAP member states and associate members, (b) technical cooperation project funds and (c) inkind contributions provided by ESCAP member states and associate members including cost-sharing and co-funding
arrangements with national statistical training institutions and regional and international statistics development
partners.
74.

These resources are utilized to cover expenditures for personnel, travel, training, miscellaneous and programme

support cost.
Voluntary Cash Contributions
75.

The annual cash contributions from member states and associate members of ESCAP will continue to be themajor source of funding for the implementation of this strategic plan. The host government of SIAP, Government ofJapan, has provided around 80% of these contributions. For the Strategic Plan period, it is anticipated that Japan willcontinue to contribute at least at the same level, contingent on its relevant and applicable laws and regulations and inaccordance with its budgetary appropriations.
76.

During the period 2015-2019, SIAP received cash contributions from 32 of 58 ESCAP member states andassociate members in Asia and the Pacific 18. Annual contributions during this period was $2.3 million per year. With theincreased relevance of the training and other activities delivered, stronger support to institutional strengthening andwith continuing advocacy for implementing recommendations of the Commission for increasing contributions to SIAP, it
is anticipated that an increased number of countries will start to contribute, and current contributors will increase theirlevel of contribution.
Technical Cooperation Project Funds
77.

During the period of the 2020-2024 Strategic Plan, SIAP will continue to collaborate in the ESCAP project ofStrengthening Requisite Skill Sets for Improved Quality of SDG statistics in Central Asia which has created fund of $0.4
million from the Russian Federation during 2021-2022.
78.

In addition, it is imperative that SIAP raises additional resources to support training development activities andoperational costs of the Network for the Coordination of Statistical Training in Asia and Pacific, the Training-of-Trainers
for statistics programme, Management Seminar 19 and special training programmes in support of capacity-building for
population and housing censuses and SDG monitoring indicators.
In-kind Contribution
79.

Furthermore, the host government will continue to provide substantial in-kind contributions covering (i) officespace, training facilities, LAN-network, equipment and costs of maintenance and office supplies in Chiba, Japan andsupport services of officials and temporary staff assigned by the Ministry of Internal Affairs and Communication to
18 Member states and associate members in Asia and the Pacific who could not contribute in 2015-2019 are 26 member states and associate
members: Afghanistan, American Samoa, Armenia, Azerbaijan, Cook Islands, Democratic People's Republic of Korea, French Polynesia,
Georgia, Guam, Kyrgyz Republic, Lao People's Democratic Republic, Marshall Islands, Micronesia (Federated States of), Nauru, Nepal,
New Caledonia, New Zealand, Niue, Northern Mariana Islands, Palau, Papua New Guinea, Solomon Islands, Tajikistan, Turkey,
Turkmenistan and Uzbekistan.
19 Management Seminars were held once in two years but are scheduled to be held every year in the 2020-2024 Strategic Plan.

36 | P a g e

SIAP and (ii) fellowships for the long residential courses through JICA. These items are estimated as $9.1 million during
2020-2024.
Cost-sharing and Co-funding80.

Support for strengthening national statistical training institutions as focal centres for training in specific areas

will be provided through cost-sharing arrangements between SIAP and the national training institutes. This may besupplemented by support from development partners.
81.

Co-funding/cost-sharing with member states and international organizations are estimated as $3.3 million

dollars during the plan period.
Medium-Term Budget Framework
82.

Projected expenditures and financial resources for the five-year period from 1 January 2020 to 31 December

2024 are summarized in Table 2.
Table 2. Medium Term Budget Framework: 2020–2024(In United States Dollars (thousand dollars))
Projected Expenditures
Personnel (Professional staff,
general support staff)
TravelTraining
Miscellaneous
Programme Support Cost
Total

9,422
250
1,419
135
1,459
12,685

Projected Financial Resources
Cash contributions from ESCAP
member states and associate
members

12,685• Personnel (Professional staff, general support staff) is the salary costs for staff, estimated based on the structure ofstaff: 1 D1, 1 P5, 4 P3 and 4 G staff, with which was agreed by the 14th session of the Governing Council.
• Travel is trip costs for business travel by SIAP staff.
• Training consists of participants travel costs including daily subsistence allowances and consultancy fees related
training.
• Miscellaneous consists of annual contractual services, operating costs, consumable office supplies and purchase ofsmall equipment.
• Cash contribution from the host government was estimated considering contributions in 2015-2019 and theirchange from previous five years: 2010-2014.
• Cash contributions from other ESCAP member states and associate members are estimated that they will increase
with 5% every year from the average contributions for 2015-2019. While calculating average contribution from acountry, the years when no contribution received, have been excluded. (See Annex Table 5)• Program Support Cost is expenditure for management in ESCAP: 13% of total of expenditures.
• Table 2 excludes technical cooperation project funds, co-funding/ cost-sharing and in-kind contributions frommember states and international organizations.

83.

In terms of cash contribution, the Governing Council expressed appreciation in its 8th session held in 2012,

for the positive response from some member States to its letter issued in April 2012 recommending that
contributions to SIAP be increased and that the Institute be provided with a minimum annual contribution of$5,000, with increase of at least 5 per cent annually 20 . The Council reiterated its recommendations for increasing
contributions to SIAP 21 in the 9th session in 2013. To reach the proposed level of contribution from member states,
it is necessary that such a recommendation is reiterated every year.
84.

Anticipated cash contributions from ESCAP member states and associate members, excluding host country

20 E/ESCAP/69/14
21 E/ESCAP/70/18

37 | P a g eduring the plan period has been estimated with the assumption that these countries shall increase theircontributions at the rate of 5% every year from the average contribution made by them between 2015-2019,
Summary table showing country wise contribution from 2015 to 2019, average annual contribution and projectedvoluntary contributions for each year is in Annex Table 5.

38 | P a g e

D.
85.

Monitoring and Evaluation Framework

Monitoring and Evaluation System adopted in SIAP is in line with the ESCAP policy and guidelines presented in

ESCAP Monitoring and Evaluation: Policy and Guidelines 2017. SIAP will regularly monitor progress in theimplementation of the Strategic Plan and will evaluate achievement of its institutional goals and outcomes. The basis
for monitoring and evaluation is the results framework presented in the previous section.
Monitoring progress
86.

Progress in plan implementation will be based on the indicators of achievements for the outputs and activities

for each goal. These will be compiled on an annual basis and reported by the Director to the SIAP Governing Council at
its regular sessions for its review and recommendation.
87.

The main sources of verification for the indicators of achievement consist of the following:• For ratings on effectiveness of training (reaction and learning levels): Evaluation forms for training courses• For ratings on effectiveness of training (behavior level): Evaluation forms for training courses and reports on
implementation of action plans on application of training (from participants and supervisors)• Number of participants, courses/ workshops, frequency: Administrative reports• Number of topics for which training materials developed collaboratively: Administrative reports• Number of external trainers/ resource persons providing training in specialized areas: Administrative reports

88.

Evaluation of effectiveness of specific course outcomes will be based on a course monitoring and evaluation

framework and guidelines for the purpose have been developed. In addition to measuring outcomes at the individuallevel, the framework also includes measurement of impact at the institutional level.
Annual Planning and Review of Expenditures and Financial Resources
89.

The Director will report progress to the Governing Council at its annual sessions including a presentation of theannual expenditures and income statement prepared by ESCAP. An annual programme of work and a gap analysis ofprojected expenditures vs income will also be presented and reviewed by the Council.
Evaluation of the Institute90.

Evaluation of achievements will be based on the outcome and output indicators, in addition to indicators

relating to each activity. Outcome indicators will be the main basis for assessing achievement. The source of data forverification based on these indicators will be through an evaluation survey which can be conducted by SIAP as part of an
external evaluation exercise.
91.

According to the decision by ESCAP, an evaluation of all the regional institutions under ESCAP will be carried out

once in five years by an external evaluator. Since the evaluation of the Institute is carried out in 2019, the next
evaluation will be carried out in 2024.
Evaluation of training programmes
92.

The Institute has adopted a system of evaluation of training programmes by participants. Evaluation of training

programmes will follow the evaluation criteria of effectiveness, relevance, efficiency and sustainability.
39 | P a g e

93.

The methodology of training programme evaluation captures only the information that percentage of participants

who found the course as very useful for their work and percentage of participants who report much improved confidence inapplying training received in their jobs. These measures do not adequately reflect the concerns of participants who did not
find the course useful or did not find it helpful in improving their confidence. The evaluation system adopted now seeks
specific suggestions related to relevance, effectiveness and efficiency of the training programmes from all participants which
would be used for designing of future programmes including selection of participants. Thus, the concern raised would be
addressed.
94.

It is also necessary to follow up with the participants after a gap of six months or one year to find out whether they

have used the knowledge gained in the training programme in their work. Feedback from the heads of national statistical
offices in terms of utility of the training programmes, and extent and nature of improvement in performance of participants
would be useful for improving the content and delivery in future training programmes. Since short term training programmes
are implemented with specific purposes, in order to evaluate the effect of trainings, what has been achieved and what new
action has been planned or undertaken in national statistical offices as the result of participation will be monitored one year
later after training programmes.
95.

System of evaluation of e-learning training programmes has to be different from the system of face-to-facetraining programmes. Effectiveness of these courses will be measured from the point of view of the present work
assignment of the participant who is taking the course and also his/her motivation. Therefore, appropriate questions
have been included in the evaluation for such courses.

40 | P a g e

Annexes
Chart.

SIAP Theory of Change

Table 1.

Stakeholders’ Analysis Matrix

Table 2.

List of Partners

Table 3a.

Trends in number of courses and participants by training category in 2015-2019

Table 3b.

Trends in number of courses and participants by training modality in 2015-2019

Table 4.

Performance Indicators in 2015–2019

Table 5.

Contributions in 2015-2019 and projected voluntary contributions for 2020-2024

41 | P a g eChart. SIAP Theory of Change
SIAP programmes and activities
Long term courses(Principles and practices ofofficial statistics for SDGs)
Short term courses/workshop(new data, management, datavisualization, analysis, etc. for
SDGs)

E-learning

Training of trainers

Community of statisticians

Outputs
Increased capacity of
government statisticians
to produce quality data
for SDG monitoring andother domain
Increased access bygovernment statisticians
to regional knowledge
and expertise throughnetworking
Increased number ofqualified trainers
Increased capacity ofnational statisticaltraining institutes
Increased opportunities
for cooperation andcoordination in statisticaltraining at regional andnational level

Short-medium term
outcomeNSOs
implement measures
to improve quality ofstatistics for SDG
monitoring andother domain

NSOs increase
coverage andquality of training
programmes

NSOs, regional andinternationalorganizations
undertakecooperation training
activities/eventsLong term
outcome

ImpactCountries increaseavailability andquality of statistics
for SDG monitoringand other domain

Improved
monitoringof SDGs

Countries increaseuse of statistics forpolicymaking

Improved
evidencebased
policymaking

42 | P a g e

Table 1. Stakeholders’ Analysis Matrix
Stakeholder

Producers of official statistics• National statistical offices

Related Mandate••

Production, processing, tabulationand dissemination of data andstatistics
Coordination

Interest in Statistical Training
•
•
•
•
•

•

•

Other producers of official
statistics who are also data
users
(e.g., ministries;
centralbanks;
civil
registration offices)
Statistical training andresearch institutions

•
•
•

•

Data production usually fromadministrative reporting systems
Data analysis
Policy monitoring
Providing statistical training andeducation; conducting statisticalresearch

•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

Improving statistical skills and increasing
knowledge of staff in producing core statistics andindicators and applications of statistical methods
Increasing knowledge and skills in specialized
fields of statistics
Capability to respond to statistical data needs forthe 2030 Agenda
Updating
knowledgeonstandards,
recommendations and methodologies
Capacity to apply IT to increase efficiency instatistical data processing
Sharing knowledge, practices and experiences
Improving quality of statistics
Capability to provide basic training to staff
Communication and advocacy skills for statistics
Improving statistical skills and increasing statisticalknowledge of staff applicable to subject area
Improving quality of data from administrative
reporting systems
Data analysis
Providing relevant training to meet local demand
Recognition as provider of quality training
Providing training for other countries
Training-of-Trainers
Research-based training
Sharing training materials
Access to and availability of external experts
Collaborative offering of training courses,
including e-learning

Resources
•
•
•
•
•

•
•
•
•
•
•
•
•

Statistical experts
Good practices and experiences invarious aspects of data collection,
processing and dissemination
Leadership and management
experience
Training resources
Attract financial resources fortraining

Knowledge of subject area
pertaining to their mandates
Proximity to policy-makers
Advocacy for resources forstatistics
Experts
Training expertise
Training infrastructure
Training materials
Financial resources

43 | P a g e

Stakeholder

Related Mandate•

Policy-makers••

Governments••

Academic institutions••

Development partners•

Formulating development policies
and plans
Ensure the availability of quality
data
Provide formal education andconduct methodological research

Support to statistics development
andcapacitybuilding
inprogramme areas•

Donors•Development assistance•Private sector•

Private-public partnership

•
•
•
•

•

Interest in Statistical Training

Collaborative development of training materials
Data availability
Use of statistics for policy and planning
Reliable data for monitoring progress andachievement of development goals

Collaboration on statistical training, including elearning• Development and sharing of teaching materials• Training and learning models• Potential employment of statistics graduates• Timely and reliable data and statistics in theirprogramme areas including:• Formulating national statistical development
strategies

Resources
•
•
•

•
•
•
•
•

•
•

• Capacity development as part of development •assistance programme• Corporate social responsibility•• Training facilities and products•• Statistical products

Advocacy for adequate resources
for statistics
Allocation of resources
Enabling environment
Expertise on adult learning
Statistical experts
Curricula, syllabuses and training
materials
Infrastructure
Technical experts and facilitationof sharing of training resourcesand expertise
Financial resources
Advocacy for statistics

Financial resources

Financial resources (under UN
Guidelines)
Technologies and expertise

44 | P a g e

Table 2. List of Partners
A. National Statistical Offices• Afghanistan: National Statistics and Information Authority• American Samoa: Research & Statistics Division, Department of Commerce, American Samoa Government• Armenia: National Statistical Service of the Republic of Armenia• Australia: Australian Bureau of Statistics• Azerbaijan: The State Statistical Committee of the Republic of Azerbaijan• Bangladesh: Bangladesh Bureau of Statistics• Bhutan: National Statistics Bureau• Brunei Darussalam: Department of Economic Planning and Development, Prime Minister Office• Cambodia: National Institute of Statistics• China: National Bureau of Statistics• Cook Islands: Cook Islands Statistics Office• Democratic People's Republic of Korea: Central Bureau of Statistics• Fiji: Fiji Bureau of Statistics• French Polynesia: Institut de la Statistique de la Polynésie française
• Georgia: National Statistics Office of Georgia• Guam: Bureau of Statistics and Plans• Hong Kong, China: Census and Statistics Department• India: Ministry of Statistics and Programme Implementation• Indonesia: BPS-Statistics Indonesia• Iran (Islamic Republic of): Statistical Centre of Iran
• Japan: Director-General for Policy Planning on Statistical Standards, Ministry of Internal Affairs and Communications• Kazakhstan: Committee on Statistics, Ministry of National Economy of the Republic of Kazakhstan• Kiribati: National Statistics Office• Kyrgyz Republic: National Statistical Committee of the Kyrgyz Republic• Lao People's Democratic Republic: Lao Statistics Bureau, Ministry of Planning• Macao: Statistics and Census Service• Malaysia: Department of Statistics Malaysia• Maldives: National Bureau of Statistics• Marshall Islands: Economic Policy, Planning and Statistics Office• Micronesia (Federated States of): Office of Statistics, Budget and Economic Management, Overseas Development
Assistance and Compact Management• Mongolia: National Statistics Office• Myanmar: Central Statistical Organization• Nauru: Bureau of Statistics• Nepal: Central Bureau of Statistics• New Caledonia: Institut de la Statistique et des Études Économiques• New Zealand: Stats NZ• Niue: Statistics Niue
• Northern Mariana Islands: Central Statistics Division• Pakistan: Pakistan Bureau of Statistics• Palau: Bureau of Budget and Planning• Papua New Guinea: National Statistical Office• Philippines: Philippine Statistics Authority• Republic of Korea: Statistics Korea• Russian Federation: Federal State Statistics Service• Samoa: Samoa Bureau of Statistics• Singapore: Department of Statistics• Solomon Islands: National Statistics Office• Sri Lanka: Department of Census and Statistics• Tajikistan: Agency on Statistics under the President of the Republic of Tajikistan• Thailand: National Statistical Office
45 | P a g e

•
•
•
•
•
•
•
•

Timor-Leste: General Directorate Statistics
Tonga: Tonga Statistics Department
Turkey: Turkish Statistical InstituteTurkmenistan: State Statistical Committee of Turkmenistan
Tuvalu: Central Statistics DivisionUzbekistan: State Committee of the Republic of Uzbekistan on Statistics
Vanuatu: Vanuatu National Statistics Office
Viet Nam: General Statistics Office of Viet Nam

B. Training Institutes• China: Statistical Education and Training Centre, National Bureau of Statistics• India: National Statistical Systems Training Academy, Ministry of Statistics and Programme Implementation• Indonesia: Education and Training Centre, BPS-Statistics Indonesia• Iran (Islamic Republic of): Statistical Research and Training Centre• Malaysia: Statistical Training Institute, Department of Statistics Malaysia• Philippines: Philippine Statistical Research and Training Institute• Republic of Korea: Statistical Training Institute, Statistics Korea
C. International and Regional Organizations• ADB: Asian Development Bank
• ASEAN: Association of Southeast Asian Nations• CELADE: Economic Commission for Latin America and the Caribbean• ESCAP: Economic and Social Commission for Asia & the Pacific• FAO: Food and Agriculture Organization• GSARS: Global Office of the Global Strategy to Improve Agricultural and Rural Statistics• ILO: International Labour Organization• IMF: International Monetary Fund
• ISI: International Statistical Institute• PARIS21: The Partnership in Statistics for Development in the 21st Century• SAARC: South Asian Association for Regional Cooperation• SPC: Secretariat of the Pacific Community• UNESCO: United Nations Educational, Scientific and Cultural Organization• UNFPA: United Nations Population Fund
• UNICEF: United Nations Children's Fund
• UNODC: United Nations Office of Drugs and Crime• UNSD: United Nations Statistics Division• UN Women: United Nations Entity for Gender Equality and the Empowerment of Women• USP: The University of the South Pacific• WHO: World Health Organization• World Bank

46 | P a g e

Table 3a. Trends in number of courses and participants by training category in 2015-2019
2015
2016
2017
2018
2019
A. Number of courses
Total
Principles and practices of official statistics
Population and social statistics
National Accounts and economic statistics
Agricultural and rural statistics
Environment statistics
Modernizing national statistical systems
Enhancing multiplier effect and assuring quality
Country-specific courses
B. Number of participants

Total

23
2
1
7
3
1
3
6

21
2
6
4
2
4
1
2

22
3
1
6
4
2
3
2
1

24
5
4
3
6
1
3
1
1

19
4
3
1
2
1
5
1
2

109
16
9
23
19
7
18
5
12

Total
727
Principles and practices of official statistics
49
Population and social statistics
22
National Accounts and economic statistics
325
Agricultural and rural statistics
69
Environment statistics
92
Modernizing national statistical systems
79
Enhancing multiplier effect and assuring quality
Country-specific courses
91
C. Gender ratio (female participants per male participants)

646
48
314
89
54
81
24
36

807
47
25
323
91
218
68
24
11

2,786
389
663
1,441
196
19
58
7
13

1,258
237
142
537
113
17
152
8
52

6,224
770
852
2,940
558
400
438
63
203

Total
Principles and practices of official statistics
Population and social statistics
National Accounts and economic statistics
Agricultural and rural statistics
Environment statistics
Modernizing national statistical systems
Enhancing multiplier effect and assuring quality
Country-specific courses

1.13
0.85
1.83
0.93
0.86
0.59
0.41
0.50

1.43
0.74
1.50
1.86
1.17
1.99
0.74
0.50
0.57

1.55
2.16
1.60
1.52
1.13
1.11
1.15
0.17
0.44

1.51
1.49
1.41
2.00
1.35
1.43
0.57
1.00
3.00

1.41
1.58
1.55
1.64
1.05
1.37
0.63
0.47
1.33

1.03
0.75
1.20
1.36
0.57
0.80
0.46
1.68

47 | P a g e

Table 3b. Trends in number of courses and participants by training modality in 2015- 2019
Table 3b-1. Number of courses
Year

Total
e-learning course
Face-to-face course
Blended course
Face-to-face/blended courseLong term course
PAMID/SADIDICPOS/TAPOSAICTI/INDAM
Short term course
Regional short course
Sub-regional course
Country course

Year

Total
e-learning course
Face-to-face/blended courseLong term course
PAMID/SADIDICPOS/TAPOSAICTI/INDAM
Short term course
Regional
Sub-regional
Country

2015

23
5
14
4
18
2
1
1
16
10
0
6

2016

21
4
15
2
17
2
1
1
15
11
2
2

Table 3b-2. Number of participants
2015
727
343
384
49
23
26
335
244
0
91

2016
646
280
366
48
28
20
318
225
57
36

2017

22
3
17
2
19
3
1
1
1
16
13
2
1

2017
807
457
350
47
10
26
11
303
261
31
11

2018

24
7
13
4
17
2
1
1
15
12
2
1

2018
2,786
2,453
333
40
17
23
293
245
35
13

2019

19
4
13
2
15
3
1
1
1
12
10
0
2

2019
1,258
920
338
39
21
9
9
299
247
52

Abbreviations:
PAMID Production and Statistical Analysis of Monitoring Indicators in Support of Inclusive Development Policies
SADID Statistical Analysis of Disaggregated SDG Indicators for Inclusive Development Policies
ICPOS
Improving Capability in Producing Official Statistics Relating to MDGs Indicators
TAPOS Theory and Practices in Official Statistics for Monitoring SDGs
AICTI
Applying ICT Innovations for Modernizing Official Statistical System
INDAM Innovations in Use of New Data Sources and Methodologies for SDG Statistics

48 | P a g e

Table 4. Performance Indicators in 2015–2019
Table 4a. Number of courses
Year
Total
e-learning course
Face-to-face course
Blended courseLong term course
Short term course
Regional short term course
Sub-regional course
Country course

Year

Expected number per
year

2015

2016
23
5
14
4
2
16
10
0
6

6
8
8
at least 2
22
6
10
6

2017
21
4
15
2
2
15
11
2
2

22
3
17
2
3
16
13
2
1

Table 4b. Increase in number of e-learning courses and blended courses
Baseline 22

Target

2015

2016

2019

2018

2017

Year
Total
e-learning course

Baseline
515
100

Target
1,000
500

2015
727
343

2016
646
280

2017
807
457

4
13
2

3
12
10
0
2

2018

2019

Increase in number of e25300learning courses
Note: The number in each year is the number of newly created e-learning courses and blended courses.
Table 4c. Number of participants

19

24
7
13
4
2
15
12
2
1

7

2

2018
2,786
2,453

2019

1,258
920

Table 4d. Percentage of participants who evaluate the course as very useful for their work and much improved
confidence in applying training received in their job (%)

Yearvery useful for their work

Baseline
40
60much improved confidence

Target
60
70

2015

54
72

2016

Table 4e. Number of staff of SIAP

Year
Professional staff
Supporting staff

Target

6
3

2015
3.6
3.8

2016
2.5
3.1

58
58

2017

49
44

2017
6.2
3.3

Note: Derived by dividing man months each year by total number of months

2018

60
54

2018
5.5
4.0

Table 4f. Attendance rate in the meetings of target participants (%)

Year
Attendance
rateoftargetparticipants (e.g., heads; seniorlevel managers) invited to events

Target

2015

80

2016

100

2017

81

2018

81

Baseline

Target

4

2015

4

2016

9

14

4.0

2019

81

2018

13

84

3.8

76

2017

91

2019

Table 4g. Number of events where SIAP representative participate in as speaker

Year
Number of events where SIAP
participated in as speaker (chair,presenter, discussant) (per year)

2019

2019
10

22 Baseline values are generally derived from the corresponding 2010-2013 performance indicator values.

49 | P a g e

24

Table 5. Contributions in 2015-2019 and projected voluntary contributions for 2020-2024(In United States Dollars (thousand dollars))
Targets
PerformancesCountries╲Year
2015 2016 2017 2018 2019 mean total 2020 2021 2022 2023 2024 mean total
Australia
100 100
76
76
35
77 386
81
85
89
94
99
90 448
5
5
5
5
0
5
20
5
6
6
6
6
6
29
Bangladesh
1
0
1
0
0
1
2
1
1
1
1
1
1
6
Bhutan
15
15
15
15
15
15
75
16
17
17
18
19
17
87
Brunei Darussalam
2
2
2
2
0
2
8
2
2
2
2
3
2
12
Cambodia
70
70
70
70
70
70 350
74
77
81
85
89
81 406
China
9
10
9
10
9
9
47
10
10
11
11
12
11
54
Fiji
30
30
30
30
30
30 150
32
33
35
36
38
35 174
Hong Kong, China
0
0
25
25
25
25
75
26
28
29
30
32
29 145
India
30
30
30
30
30
30 150
32
33
35
36
38
35 174
Indonesia
Iran
15
0
15
0
0
15
30
16
17
17
18
19
17
87
Kazakhstan
5
5
5
5
5
5
25
5
6
6
6
6
6
29
Kiribati
5
5
0
0
0
5
10
5
6
6
6
6
6
29
20
20
20
20
20
20 100
21
22
23
24
26
23 116
Macao, China
Malaysia
20
20
20
0
20
20
80
21
22
23
24
26
23 116
Maldives
0
0
2
2
2
2
6
2
2
2
2
3
2
12
7
0
8
7
10
8
32
8
9
9
10
10
9
47
Mongolia
Myanmar
1
0
1
1
0
1
3
1
1
1
1
1
1
6
Pakistan
15
15
15
13
15
15
73
15
16
17
18
19
17
85
18
18
19
18
18
18
92
19
20
21
22
24
21 107
Philippines
Republic of Korea
53
55
56
58
54
55 274
58
61
64
67
70
64 318Russian Federation
30
0
15
30
15
23
90
24
25
26
27
29
26 131
Samoa
2
0
2
2
0
2
6
2
2
2
2
3
2
12
Singapore
15
15
15
15
0
15
60
16
17
17
18
19
17
87
Sri Lanka
10
10
10
10
10
10
50
11
11
12
12
13
12
58
Thailand
20
20
23
23
23
22 109
23
24
25
26
28
25 126
Timor-Leste
2
2
2
0
0
2
6
2
2
2
2
3
2
12
Tonga
5
5
5
0
0
5
15
5
6
6
6
6
6
29
Tuvalu
0
0
0
1
0
1
1
1
1
1
1
1
1
7
Vanuatu
2
3
3
0
0
2
7
3
3
3
3
3
3
14
Viet Nam
15
15
17
17
20
17
84
18
18
19
20
21
19
97
Total
522 469 515 485 425 527 2,417 554 581 610 641 673 612 3,058

50 | P a g e