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APCAS/12/7
Asia and Pacific Commission on
Agricultural Statistics
Twenty-fourth Session
Da Lat, Viet Nam, 8-12 October 2012
Agenda Item 5
Report on Initial Country Assessments
Introduction
The Global Strategy for Improving Agricultural and Rural Statistics involves the development of a
Regional Implementation Plan that provides guidelines for strengthening the capacity of national
statistical systems to produce information for informed decision-making related to food security,
sustainable agriculture and rural development. This global effort involves interaction between data
producers, data users and partners in development. The Asia and Pacific Region has initiated efforts
to evaluate the capacity of countries in the region and to identify areas where inputs would be helpful.
This paper makes preliminary assessments of the capacity of national statistical systems and
identification of countries where in-depth assessments could be used to develop technical assistance,
training and research strategies.
Background
The agriculture sector has long been forced to adjust to low priorities and limited National Statistics
Office (NSO) resources to obtain current statistics about the sector. The Global Strategy for
Improving Statistics for Food Security, Sustainable Agriculture and Rural Development1 was
developed by the World Bank (WB) and the Food and Agriculture Organization of the United Nations
(FAO) in close consultation with a large number of national and international stakeholders to
emphasize its importance.
At early stages of development of the Global Strategy to Improve Agriculture and Rural Statistics a
need was felt to develop a standard tool to monitor the capacity of National Statistical Systems to
produce agriculture statistics. An international group was formed at Fifth International Conference on
Agricultural Statistics, Kampala in October 2010 to develop the tool. The core group comprising FAO,
USDA and Australia was formed. AfDB, Russia and Brazil were invited to participate to evolve a
globally acceptable approach. The initial thinking of the group was to take advantage of the work
already done by international organizations like the World Bank (WB), International Monetary Fund
(IMF) and the United Nations Educational, Scientific and Cultural Organization (UNESCO). The
group observed that these Data Quality Assessment Frameworks (DQAFs) have a great deal of
October 2012
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convergence but do not address the specificity of agriculture sector in general and agriculture statistics
in particular.
Given that the main objective of the Global Strategy is to enable countries to produce a globally
agreed core set of agriculture statistics, as well as integration of agriculture statistics into the
mainstream of statistical activities at the country level, the emphasis of the work to develop the tool
was shifted towards assessing the “Capacity of the countries to produce core data” and monitoring
this. As a tradition of collecting data required for this exercise was already there in Asia and Africa
through the FAO regional commissions on agriculture statistics (APCAS and AFCAS, respectively), a
Standard Country Assessment Questionnaire (CAQ) was developed through a consultative process
involving the African Development Bank, United Nations Economic Commission for Africa
(UNECA) and other partners. The Global Strategy identifies a list of seventy-six core data items (see
Table 1, Annex 2) that should be available for making decisions about agricultural and rural
development. Flexibility was given to the regions to modify and adapt the standard questionnaire to
more accurately assess the data requirements. Supplementary information about the agriculture sector
for Asia and Pacific Countries is found in Table 2, Annex 2; this information can be used to compare
the role of agriculture in the countries.
Objectives of the questionnaire
By integrating inputs from various international agencies, statistical offices and donors, the basic
country assessment questionnaire (CAQ) used in the Asia and Pacific Region was prepared prior to the
undertaking of Global Strategy initiatives in Africa, the first region to be reviewed and the location of
the first regional implementation plan.
The immediate objective of the country assessment questionnaire is to categorize countries by their
capacity to produce a minimum set of core data and to serve as a basis for preparation of
comprehensive technical assistance, training and research programmes. Detailed requirements for
technical assistance, training and research for implementation of individual country strategies and
action plans will be finalized as a result of country visits and following consultations with political,
business and farm entities.
The CAQ has been designed to identify the types, frequency of reporting and quality of key
agricultural sector variables and to solicit information about the roles of different government
institutions and of private sector agencies/ individuals in the statistical processes.
The questionnaire responses should be the result of cooperation and interaction among the various
agricultural sector data producers. It was emphasized that the heads of the data producing agencies
support and contribute to the completion of the questionnaire.
Preparation for the preliminary assessments
An early assessment of countries had been made based on various types of feedback from
representatives of some countries during participation in regional meetings and workshops and from
available metadata. It was followed up by sending a 10-sheet EXCEL workbook to heads of statistics
in 59 countries and territories in the Asia and Pacific Region for completion following consultation
with the major agricultural sector statistics producers.
These countries / territories were determined based on the scope of work for FAO, UN ESCAP and
Asian Development Bank (ADB), the partners for implementing the Global Strategy in Asia and the
Pacific. It is possible to classify them into East and North-East Asia, South-East Asia, South and
South-West Asia, North and Central Asia and Pacific sub-regions. Most of the countries are
considered to be developing countries, although several have well-established statistics systems that
provide the majority of agricultural sector statistics that have been included in the core data for the
Global Strategy. In some cases the evaluations are carried out by more than one “region”;
consequently it is possible that some questionnaires were not returned to all regions.
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The country assessment questionnaire (an EXCEL workbook containing one worksheet with
instructions plus ten worksheets for responses) was emailed to countries in November 2011; they were
prefilled by ADB based on metadata available from ADB, ESCAP and FAO. Only 45 of the 59
countries responded even after reminders (see Table 1, Annex 1); also, many returned questionnaires
were incomplete necessitating further action.
A digital form was made available on www.surveymonkey.com for those countries that wanted to use
that alternative; only four countries did and one also completed the EXCEL questionnaire. The
responses for the other three countries were sparse, but were transferred to EXCEL format later for
ease of comparison.
Target Indicators for Monitoring of Country Capacity
It is widely recognized that “Quality” is a wider concept that just the “Quality of the Output”. The
CAQ was thus designed to capture the information on the entire production chain, including:
Institutional environment, Inputs (human and financial resources), Throughput (activities) and Outputs
(available data and their quality as seen by their timeliness, reliability and accessibility), besides
gathering information just the data availability. Thus indicators are needed which allow monitoring
progress of the countries on each of these dimensions and their elements separately and as a whole.
An FAO team in Rome developed a draft set of indicators which could be compiled using the key data
available from the questionnaires, while the data on questionnaires was being collected from countries
in Asia. A set of draft Guidelines on Processing the Country Assessment Questionnaires was
circulated as a work in progress, and is open to comments towards perfection as a globally accepted
tool for monitoring the “country capacity to produce agriculture statistics” over the life span of the
Global Strategy and perhaps beyond. The indicators proposed in the guidelines shall be useful for
comparing the level of development of agriculture statistics system of countries not only within a
region but across continents.
In developing the guidelines the team has taken into account the following:
Recent work of UNSD on a national data quality assessment frame work (NQAF) which
builds on earlier international work in this domain. A correspondence table on dimensions and
elements being measured through the proposed capacity indicators and in those included in
other important international works is available for reference.
The WB Statistical Capacity Indicators which are designed for overall capacity of the National
Statistics System but lack the focus on the agriculture statistics. Further, a review of the
literature suggests that these indicators give emphasis to activities and do not measure the
inherent national capacity of the system. It is well known that many of the large-scale
statistical activities in developing countries are largely governed by availability of donor
funding. Though these activities leave behind some built-in capacity, it is often not sustained
for a variety of reasons.
An attempt thus was made to define and compile a set of 20 indicators on Country Capacity to produce
agriculture statistics in a cost-effective and integrated manner. The draft guidelines for compiling these
indicators based on responses to the CAQ is available for reference and comments.
Basically, the country capacity indicators represent the four dimensions of statistical capacity, viz.,
Capacity Indicator I: Institutional Infrastructure (INFRASTRUCTURE DIMENSION)
Capacity Indicator II: Human and Financial Resources (INPUT DIMENSION)
Capacity Indicator III: Statistical Methods and Practices (THROUGHPUT DIMENSION)
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Capacity Indicator IV: Statistical Information and Availability (OUTPUT DIMENSION)
(based on responses to 59 data items broadly in agreement with core list of data items
identified by the Global Strategy)
A breakdown of these aggregate indicators into indicators for each element defining this dimension
may be seen in the Table 7, Annex 1. These indicators show some convergence with WB indicators to
the extent they are measuring the same element of dimension of the country capacity. Some limitations
in the responses were noted to compile these indicators. It is to be noted that based upon the data
available through the CAQ (Initial Assessment), it was not possible to compile the two indicators for
Capacity Indicator II which related to human and financial capacity or the input dimension. This
indicator should be compiled at the in-depth assessment stage as it needs information from various
sources including both users and producers.
It is foreseen that, ultimately, these indicators would be used as the measure of country capacity and
would be updated periodically to monitor the changes in the country capacity. At this stage it is
apparent that the proposed indicators are in line with the international thinking on the subject. Prima
facie it is concluded that the proposed indicators are more robust than those compiled by WB on
regular basis as these are based on a larger set of questions, and they are specifically designed to
measure the aspects relating to improvement in agriculture statistics.
The FAO Global Strategy team plans to validate the proposed indicators in Africa and elsewhere, and
finalize them towards the end of the year 2012 through a consultative process of partners and
international experts. In the mean time, the indicators compiled on the basis of present level of work
and the available data could be used for:
1. Selection of pilot countries in Asia. Even after more accurate data becomes available
the picture is not likely to change substantially as the convergence of proposed set of
indicators and the other available indicators is already visible. Further, it is expected
that the indicators could provide only a short list of potential countries for selection.
Many other selection criteria, including subjective evaluations of government
commitment, identified in the Global Implantation Plan, need to be considered for
taking a decision.
2. Getting an overall assessment of the main issues and weaknesses in the agriculture
statistics systems in the region. The identification of major common deficiencies in
the country capacities could be useful for deciding the research and training agenda of
the implementation plan for Asia.
In-depth assessments at country level
While these initial country assessments are useful for making a selection of countries and for defining
the priorities of the regional work plan, an in-depth assessment will be carried out in each selected
pilot country. These in-depth assessments will focus on identifying the specific weaknesses in the
countries and will result in recommendations on critical areas of interventions for improvements in the
agriculture statistics system. Guidelines for standard in-depth country assessments are being developed
based on pilot work being done in Laos, Tanzania and Peru in partnership with PARIS 21 and USDA.
At this stage, it is expected that these assessments will be carried out through the expert missions and
involvement of all stake holders at the country level. The assessments will lay the foundation for
preparation of Sector Strategic Plan for Improvement of Agriculture Statistics. It is also expected that
the in-depth assessments will fill any of the data-gaps in compilation of indicators through a
consultative process with countries.
Results of Initial Country Assessments in Asia and Pacific
The pilot exercise of initial Country Assessment in Asia and Pacific showed some limitations in
responses received from the countries. Responses to some sections of the EXCEL worksheets for the
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CAQ are blank, indicating the lack of information or “no”, “none” or “not applicable”. For example, it
is known that some countries do collect and publish core data items, but did not report it on the CAQ.
Similarly, the capacity of some statistical systems did not correspond to the perceptions of
organizations working in these countries. These countries were contacted to verify the responses on
the questionnaire and some replied that they misunderstood the meaning of the responses. It is noted
that these responses are preliminary and must be validated when combined with information from
other sources and from an in-depth country assessment.
Perceptions about the content of the questionnaire and the means to obtain the necessary feedback are
dependent on the reader as are the interpretations of the actual questions on the CAQ. There are
several items on the questionnaire where non-native English speakers might have difficulty in
answering the questions as they were intended. Even for English speakers some of the questions in the
worksheets may be misleading and/or incorrect. Also in many cases, the responses related to only the
National Statistics Office or Ministry of Agriculture and do not reflect the totality of the country
situation.
Preliminary assessments of country capacity
Despite the limitations in the responses, the available data could be utilized to draw some meaningful
conclusions about the situation in Asia and the Pacific, and indications on the level of statistical
development in the countries.
Fifteen constraints were listed in the CAQ questionnaire with a request for the NSO and MOA to
evaluate separately each constraint’s impact on agriculture statistics. A difficult constraint would be
classified as “5”; a response of “1” would indicate that this constraint was not a constraint and any
changes would have little or no impact. The average of country reported levels of constraints is found
in Table 1. A summary of significant constraints by country is found in Annex 1, Table 2.
Table 1: Constraints on Agricultural Sector
Statistics and sub-region averages for countries
that responded Southea
st Asia
South and
Southwest
Asia
North/
Central
Asia East
Asia Pacif
ic Develope
d Number of professional staff at headquarters for
statistical activities 3.00 3.33 2.67 3.00 3.43 1.00
Technical skills of the available statistical staff 2.00 2.50 2.17 3.00 3.14 1.00 Turnover of professional staff. 2.17 3.33 2.50 3.00 3.17 1.00 Transport equipment for field activities 2.50 3.50 2.33 3.00 3.00 1.00 Funds for field-oriented statistical activities vis-à-vis
plans. 3.50 2.83 3.00 3.33 3.00 1.00
Up-to-date information technology hardware 3.50 2.50 2.00 2.67 2.57 1.00 Up-to-date information technology software 3.17 2.33 2.17 2.67 2.86 1.00 Number of field workers for statistical activities 2.67 2.83 3.50 3.33 2.86 1.00 Number of professional staff in the field for
statistical activities 2.83 2.67 3.50 3.00 2.86 1.00
Sound methodology implemented for agricultural
surveys 2.50 3.67 3.17 2.33 3.17 1.00
Building space for office 2.67 3.50 3.00 2.67 3.00 1.00 Appreciation at the policy-making level for
importance of statistical activities 2.83 2.00 2.50 2.00 3.67 1.00
Support at political level in the Government for
statistical activities 3.17 2.50 2.33 2.67 3.43 1.00
Number of support staff at headquarters for
statistical activities 2.83 1.67 2.17 3.00 3.00 1.00
Level of demand for statistics 2.83 2.83 2.67 2.67 4.00 1.50 Note :1=No constraint; 2=Little constraint; 3=Relative constraint; 4=Significant constraint;
5=Dominant constraint ; Averages refer to an arithmetic average of 31 reporting countries across
FAO/UNESCAP defined sub-regions
Based on the rating of critical constraints as reported by the countries, the field programme for
agriculture statistics had significant shortcomings; the lack of capable professional staff (especially in
the Pacific Region) and updated information technology were also concerns.
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A summary table with frequency distribution by sub-Region for some criteria is found in the table
below:
TABLE 2: Summary of Critical Characteristics for Countries in Asia and Pacific Region
Category1 Number
of
Countries
Number2
Reportin
g
Stat
Law3
Stat
Law /w
Agric3
NSDS3
Coord.
“Board”
3
Agric
Census
after
2001
Ave
PCT
Core
items
Ave
PCT
Priority
items
Southeast
Asia 10 10 100 80 70 70 60 42 77 South and
Southwest
Asia 10 9 100 78 72 44 67 51 78 North/
Central
Asia 9 7 100 86 100 43 57 48 85 East Asia 7 5 100 60 80 40 60 47 82 Pacific 19 8 100 75 50 13 63 21 38 Developed 3 3 100 100 100 100 100 53 89 Region 58 42 100 79 75 48 64 44 75
1: Sub regions within Asia-Pacific are defined as those observed by FAORAP. Countries which did
not fall under the mandate of FAORAP were categorized according to the sub region definitions by
UNESCAP. The Russian Federation (which did not report) has not been included as part of the
definition for North/ Central Asia sub-region.
2: Some countries reported minimal or partial information, but did respond to the request for a
Country Assessment Questionnaire.
3: Presented as a percent of countries reporting.
The developed countries satisfy the infrastructural characteristics, but did not indicate that they
provide all of the core or priority data items. Western and Central Asia responses did not reflect the
role of agriculture in its economies, but emphasized the structure and strength of the national statistical
offices. Other Asia countries depend heavily on agriculture, but have maintained a national statistics
office focus that did not allow development of line ministries capacities and support their data
requirements. Many Pacific Island Countries and Territories (PICTs) do not survey agricultural
production and obtain many data items in an agricultural census that occurs less frequently than every
ten years. Few of the PICTs reported to have statistics law and NSDS in place. It will be critical to
review data requirements for the PICTs when a sub-regional set of core data is determined.
Classification of country statistical system
Two methods for evaluation of the country questionnaires have been used to classify the country
statistical systems and are presented in this document. As outlined earlier, the main concerns are
institutional structure, staff capacity and data access. It should be noted that not all countries had
responded to the requests for information on the CAQs and some of the information was incomplete.
It was also clear that language was an obstacle to understanding the content of the questions and that
the responses may not be accurate. Note that the objective methods can only evaluate the information
officially provided and may reflect limitations in the statistical systems that do not exist.
Although efforts have been made to quantify the key characteristics and to develop an indicator that
can be used for comparison of the country agricultural statistical systems, many of the critical
responses were missing and/or contradictory. Each of the methods uses a slightly different approach
to classify the countries into five categories – developed, progressive or above average, developing or
average, developing with constraints and least developed. In-depth country assessments will be used
to fill in gaps in information and to identify specific technical assistance, training and research
requirements.
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A third method, proposed in the Global Action Plan1 (Chapter 8), to classify countries according to
five Levels of Statistical Development was also attempted1. The emphasis of this methodology is on
availability of core data items. However, preliminary results showed inconsistencies. It was
determined that in the Asia and Pacific Region, country requirements were much more diverse and
that use of this methodology was meaningless. The corresponding information is included in Table 3,
Annex 1, but it will not be further discussed here.
Method 1 --- Food Security, Legal Infrastructure and User Participation
For making the second preliminary assessment, several key responses have been highlighted in Table
3, below. The most significant are the presence of a statistical law for collection of data in the
country and then inclusion of the agriculture sector as part of the mandate. In addition there should
be a National Strategy for the Development of Statistics (NSDS) that includes the various sectors
of agriculture – crops, livestock, fisheries, forestry, water resources, and rural development. And
interaction between data producers and data users should exist. Conduct of a recent agricultural
census or of a population and housing census that obtained key agricultural sector responses was
considered.
The Global Strategy identifies a list of seventy-six core data items (see Annex 2) that should be
available for making decisions about agricultural and rural development. Realizing that many
countries are not able to collect the entire set of core variables, an FAO team in Rome identified
fifteen priority items among the core data that every country should collect (listed in Annex 1, Tables
4-6). It does not mean that the remaining items are not essential, but that these fifteen items are
considered more crucial for monitoring food security. Although information about all core items was
requested, the tables below highlight the responses received from the countries for the priority items.
It should be noted that the Institutional Environment has not been completely defined – the reason is
that any responsibility for agricultural and rural statistics should carry with it the financial resources
necessary to complete the task. Several possibilities can happen: One -- the legal authority exists to
carry out agricultural sector surveys; it may be in the hands of one or more institutions. Second -- the
authority may be delegated by the agency with the legal authority without further legal action. Third –
further legal action is required to carry out an agricultural census or agricultural sector surveys.
In some statistical laws it is possible for the NSO to "direct" a line ministry to collect appropriate
information about, say, the health, education, or agricultural sector. In other countries it is possible/
necessary to prepare a statistical decree that allows the Ministry to collect information; in both cases
the mechanism exists, but in one case a simple communication is required while in the other, formal
legal steps must be taken. And directing a line ministry to collect information does not always carry
with it the resources to do it or to do it properly.
In some cases the NSO or the MOA completed the CAQ without feedback from the other; in these
cases responses are missing. In other cases both agencies provided responses to the CAQ, but the
responses were not consistent. For many countries it was necessary to request additional information,
especially an evaluation of the quality of the core statistics produced and of the constraints faced by
the agricultural statistics system.
Some sections of the table are blank, indicating the lack of information on the CAQ; it is known that
some countries do collect and publish core data items, but did not report it on the CAQ. Similarly, the
capacity of some statistical systems did not correspond to FAO perceptions. These countries were
contacted to verify the responses on the questionnaire and some replied that they misunderstood the
meaning of the responses.
1 The five levels consider the percent of core data produced on a regular basis, the recent conduct of an
agricultural census or of a population census that has questions about the agricultural sector, the existence of a
National Statistical Development Strategy (NSDS) that has an agricultural component, a functioning
coordination system and elements of a master sample frame or area frame.
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In the CAQ table for availability (see Table 4, Annex 1), many of the cells were prefilled with “1”s
(available). The remaining cells were left blank. Based on the responses from countries, it is apparent
that countries used the blank as a substitute for “2” (not available) or “3” (not applicable), although it
is not always clear which response is meant. Thus, the quantitative assessment is the number of “1”s.
Some countries report the statistics for some commodities more often than for other commodities.
Since the categories /responses do not specify the crop/livestock, then the least frequent timing is used
for the evaluation – an asterisk “*” will indicate this situation (see Table 5, Annex 1). In some cases
the country did not make an evaluation of the quality of its data; consequently there will be blank
columns for these countries (see Table 6, Annex 1).
As part of the CAQ, countries were asked to identify constraints that would need to be overcome in
order to establish a strong agricultural statistics system. Some involve technical assistance --- which
can assume many forms. As implied by the wording, the assistance can involve consultancies with
experts in the technical skills related to survey design, questionnaire design, data collection and
processing and analysis. The starting point for determining the gaps in capacity will be technical
assistance for carrying out the country assessments and providing guidance with the development of
the Sector Strategic Plans to produce the minimum set of core data. Further technical assistance will
support efforts to establish the governance structure to integrate agriculture into the national statistical
system, provide advocacy promoting the national statistical system, determine the methodology to be
used, and provide guidance for the overall implementation.
TABLE 3: Summary of country statistical characteristics and relative capacity
Country Legal Agric NSDS Users AgCens Stat Percent
priority Constr Rating
Afghanistan 1 1 1 4 10-- x 67 1
American Samoa
Armenia 1 1 4 6 12-2.0 80 2.47 3
Australia 1 1 2 3 2011 13-1.2 87 1.00 5
Azerbaijan 1 1 3 4 2005 14-1.9 93 1.60 4
Bangladesh 1 1 0.5 2 2008 13-1.6 87 2.53 3
Bhutan 1 1 3 3 2009 11-3.0 73 2.93 2
Brunei
Cambodia 1 1 1 0 12 –x 80 3.53 2
China
Cook Islands 1 1 0 0 2011 2-2.0 13 3.13 1
DPR Korea* *
Fiji 1 1 1 6 2009 13-2.1 87 3.00 3
French Polynesia
Guam * 2007
Georgia 1 1 1 3 2004 14-2.0 93 2.92 4
Hong Kong, China 1 0 1 0 8-2.0 53 3
India 1 0.5 4 0 2010 14-2.1 93 2.53 3
Indonesia 1 1 2 1 2003 11-2.4 73 3
Iran 1 1 4 0 2003 15-1.4 100 2.00 4
Japan 1 1 4 1 2010 15--x 100 5
Kazakhstan 1 1 1 0 2006 14-1.9 93 4.33 3
Kiribati 1 0 1 0 2-x 13 2.57 2
Republic of Korea 1 1 1 3 2010 13-1.5 87 2.27 4
Kyrgyzstan
Lao PDR 1 1 0.5 5 2011 10-3.0 67 2.47 2
Macao, China 1 0 1 0 2-1.5 13 3
Maldives 1 1 4 1 8-2.3 53 4.07 2
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Marshall Islands
Malaysia 1 1 4 6 2005 15-1.8 100 3.53 4
Micronesia * 0
Mongolia 1 1 4 3 2012 15-1.7 100 2.93 5
Myanmar 1 1 0.5 5 11-x 3
Nauru * 0 0 1
New Caledonia
Northern Marianas
Nepal 1 1 3 3 2001 13-1.9 87 2.73 3
New Zealand 1 1 3 6 2007 12-1.0 80 1.08 5
Niue 1 1 1 4 2009 0 0 4.27 1
Pakistan * 1 1 1 2 2000 0 2
Palau 1 0 0 0 2011 4-2.0 27 3.87 1 Papua New
Guinea * 1 1 1 0 5-x 33 1
Philippines 1 1 4 5 2002 13-1.0 87 2.87 4 Russian
Federation
Samoa 1 1 0.5 4 9-1.1 60 2.00 3
Solomon Islands
Singapore* 1 0 0 0 0 0 1.00*
Sri Lanka 1 0 0.5 0 2002 10--x 67 2
Tajikistan 1 0 2 1 10--x 60 3.07 3
Taiwan, China 1 1 0.5 0 2010 14-1.8 93 3.13 3
Thailand 1 1 2 0 2003 13-1.2 87 2.13 4
Timor Leste 1 0 8 –x 53 1
Tonga
Turkey
Turkmenistan 1 1 4 0 11-1.0 73 1.47 4
Tuvalu
Uzbekistan
Vanuatu 1 1 0 5 2006 2- x 13 3.00 2
Viet Nam 1 1 4 5 2011 12 --x 80 2.33 3
* partial submission and/or providing no information
KEY:
Legal -- 1, if legal or statutory basis for statistical activities; 0, if not.
Agric -- 1, if agriculture is included in Statistical Law; 0, if not.
NSDS – 1 up to 4 agriculture sub-sectors, if exists; 0.5, if, in process; 0 if not.
Users – number of user categories involved in meetings (max =6)
Statistics— number of priority data items available (0-15); average reliability (1) high to (5)
unacceptable; x, if not evaluated);
Percent priority – Percent of 15 priority variables for food security
Constraints– Average of fifteen responses ranging from 1 to 5 (see Table 1). Not all countries
responded to these questions; for most countries the NSO and MOA responses were repeated.
DPRK gave only one response.
Rating – Infrastructure and Food Security Capacity Indicator
Considering the existence of a legal basis for agricultural statistics, the conduct of a recent agricultural
census, the availability of the 15 priority core items and the number of constraints, the country
statistical systems have been classified as:
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Excellent (4 countries):
a legal basis for agricultural statistics is
present, an agricultural census recently
conducted, at least 12 priority core items
available, average constraint less than 3
Australia, Japan, Mongolia, New Zealand,
Above Average (8 countries) Azerbaijan, Georgia, Iran, Republic of Korea, Malaysia,
Philippines, Thailand, Turkmenistan
Average (14 countries) Armenia, Bangladesh, Fiji, Hong Kong (China), India,
Indonesia, Kazakhstan, Macao (China), Myanmar, Nepal,
Samoa, Taiwan (China), Tajikistan, Viet Nam
Below Average (6 countries) Bhutan, Cambodia, Lao PDR, Maldives, Pakistan, Sri Lanka
Limited (11 countries) Afghanistan, Cook Islands, DPRK, Kiribati, Micronesia,
Nauru, Niue, Palau, Papua New Guinea, Timor Leste,
Vanuatu
Unknown (16 countries) Brunei, China, Kyrgyzstan, Russian Federation, Turkey,
Uzbekistan, American Samoa, French Polynesia, Guam,
Marshall Islands, New Caledonia, Northern Marianas,
Singapore, Solomon Islands, Tonga, Tuvalu
It should be noted that because of the absence of information on the CAQ, some of the countries could
have a stronger national statistical system than was observed. This preliminary classification reflects
the coordination, or lack of, between the NSO and MOA. It also assumes that availability of priority
items is timely. In one country the presence of an NSDS was noted, but also mentioned that it was not
current.
Method 2 –Capacity Indicators
The application of the target objective methodology -- using only the responses provided by the
countries --- is found in Annex 1, Table 7. It is based on the capacity indicators an FAO Team has
developed using draft guidelines4 for evaluation of country assessment questionnaires. The evaluation
is based on four types of capacity indicators –INFRASTRUCTURE, INPUT (Human and Financial
Resources), THROUGHPUT (Statistical Methods and Practices) and OUTPUT (Statistical
Information and Availability).
This indicator provides a summary of measurements on the five main elements of the dimension
relating to intuitional infrastructure as defined below. The overall score on this indicator is taken as the
geometric mean of the scores on the five indicators which measure the five elements of this dimension
of the quality of the agriculture statistical system. The geometric mean (GM) has been chosen
specifically to highlight the weaknesses in the system. In calculation of the GM equal weight is
assigned to the score on each of the elements. For responses that are missing or zero, a value of 20 has
been used in the calculation of the geometric mean.
Some of the indicators have not been calculated at this time because of the lack of information about
budgets and staff capacity and uncertainty about the completeness of responses on data core items.
Five categories of countries are presented. They correspond to the criteria outlined in the draft
guidelines4. It is noted that by choosing a classification based on quartiles for each indicator, country
group levels can be forced downward although the quality of the statistical system is similar to those
in higher groups.
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GROUP 5 (Developed) Mongolia, New Zealand, Philippines
GROUP 4 (Progressive) Armenia, Australia, Azerbaijan, Georgia, India, Iran,
Malaysia, Maldives, Republic of Korea
GROUP 3 (Developing) Bangladesh, Bhutan, Fiji, Indonesia, Japan,
Kazakhstan, Lao PDR, Taiwan (China), Thailand,
Turkmenistan, Viet Nam
GROUP 2 (Developing with constraints) Cambodia, Cook Islands, Hong Kong (China),
Kiribati, Macao (China), Niue, Nepal, Samoa,
Vanuatu
GROUP 1 (Least developed) Afghanistan, Myanmar, Nauru, Pakistan, Papua New
Guinea, Sri Lanka, Tajikistan, Timor Leste,
A summary table that shows the results of the two evaluation methods is found in Table 8, Annex 1.
It should be noted that many of the lower groupings are the result of missing or incomplete
information. Other countries did not provide information.
It is anticipated that countries can update assessments with corresponding changes in these indicators.
Recent updates are not included in this table.
CONCLUSIONS
The criteria used for evaluation of countries using the global action plan methodology are biased
toward producing, on a regular basis, a large percentage of core items, many of which have little
significance for countries in the region. An approach that emphasizes monitoring food security has
more impact on assessing the contribution of agricultural as measured by the national statistical
system. However, it is also necessary to determine the integration of agricultural statistics into the
national statistical system and the requirements to achieve collection, processing and dissemination of
the vital core items on a regular basis.
Constraints in funds for data collection and processing and the overall availability of staff, both
professional staff and support staff, and the level of commitment of national resources to sustain an
integrated national statistics system are serious issues to be addressed during the in-country
assessments.
References
1 The Global Strategy for Improving Statistics for Food Security, Sustainable Agriculture and Rural
Development Statistics
2 Framework for Assessing the Quality of Agriculture and Rural Development Statistics
3 Global Action Plan
4 Guidelines on Compiling Country Capacity Indicators to Produce Agricultural Statistics
Annex 1
12
TABLE 1: Summary of country responses to CAQ
Country Agency Date Country Agency Date
Afghanistan FAO Jan-12 Micronesia* FAO Jan-12
American Samoa ESCAP Mongolia FAO Feb-12
Armenia ADB Dec-
11
Myanmar FAO Mar-12
Australia FAO Dec-
11
Nauru* FAO Jan-12
Azerbaijan ADB Jan-12 New Caledonia ESCAP
Bangladesh FAO Dec-
11
Northern Marianas ESCAP
Bhutan FAO Feb-12 Nepal FAO Feb-12
Brunei ADB New Zealand FAO Dec-11
Cambodia FAO Mar-
12
Niue ESCAP Jan-12
China FAO Pakistan FAO Dec-11
Cook Islands FAO Feb-12 Palau FAO Jul-12
DPR Korea* FAO Papua New Guinea
* FAO Apr-12
Fiji FAO Mar-
12
Philippines FAO Jan-12
French Polynesia ESCAP Russian Federation FAO
Guam* ESCAP Apr-
12
Samoa FAO Jan-12
Georgia ADB Dec-
11
Solomon Islands FAO
Hong Kong, China ESCAP Feb-12 Singapore* ADB Jan-12
India FAO Jan-12 Sri Lanka FAO Dec-11
Indonesia FAO Dec-
11
Tajikistan ADB Jan-12
Iran FAO Jan-12 Taiwan, China ADB Mar-12
Japan FAO Dec-
11
Thailand FAO Nov-11
Kazakhstan ADB Jan-12 Timor Leste FAO Mar-12
Kiribati FAO Jun-12 Tonga FAO
Republic of Korea FAO Mar-
12
Turkey ESCAP
Kyrgyzstan ADB Turkmenistan ADB Feb-12
Lao PDR FAO Mar-
12
Tuvalu FAO
Macao, China ESCAP Dec-
11
Uzbekistan ADB
Maldives FAO Dec-
11
Vanuatu FAO Jul-12
Marshall Islands FAO Viet Nam FAO Jan-12
Malaysia FAO Jan-12
* partial submission and/or providing little information
KEY:
Agency – focal point agency for statistics
Date – month of response to questionnaire
Annex 1
13
TABLE 2: Main constraints reported by countries
Con
stra
int
A A A B B C C D F G I I K K K L M M M N N N P P S T T T T V V
r u z g h m o P i e n r a i o a d a n e e i a h a a a h k a n
m s r d u b o R j o d a z r r o v l g p w u l l m i j a m n m
t k K i i n e a a Z e a o
n a a y l u a 1 * ** ** * * ** ** 2 * ** ** ** 3 * ** ** ** ** 4 * * * * ** ** ** ** ** 5 * * * ** ** * * ** 6 * * * * * * * * * 7 * ** ** ** * ** 8 * * * ** ** ** * * * * 9 * * * ** * ** * * *
10 ** * * * ** ** ** ** 11 * * ** ** * ** * * * 12 * * ** ** * * 13 * ** ** * * ** ** * 14 * * ** * * ** ** * * * * 15 ** ** ** ** ** * *
Blank or “ “no constraint; * a significant constraint; ** a dominant constraint;
Afghanistan, Hong Kong (China), Indonesia, Japan, Macao (China), Myanmar, Micronesia, Nauru, Pakistan, Sri Lanka and Timor Leste did not provide responses to these
questions;
DPR Korea only responded to the first constraint.
1 Number of professional staff at headquarters for statistical activities 9 Up-to-date information technology software
2 Number of support staff at headquarters for statistical activities 10 Funds for field-oriented statistical activities vis-à-vis
plans.
3 Number of professional staff in the field for statistical activities 11 Transport equipment for field activities
4 Number of field workers for statistical activities 12 Building space for office
5 Technical skills of the available statistical staff 13 Sound methodology implemented for agricultural
surveys
6 Appreciation at the policy-making level for importance of statistical
activities 14 Level of demand for statistics
Annex 1
14
7 Support at political level in the Government for statistical activities 15 Turnover of professional staff.
8 Up-to-date information technology hardware
Annex 1
16
Table 3: Criterion for Statistical Development based on country responses to CAQ
Country NSDS Agric Censu
s
Pop
w/agric Coord
w/users Percent core
Afghanistan 1 4 31
American Samoa
Armenia 4 2011 6 53
Australia 2 2011 L 3 38
Azerbaijan 3 2005
M 4 61
Bangladesh 0.5 2008 L 2 64
Bhutan 3 2009 L 3 35
Brunei
Cambodia 1 0 49
China
Cook Islands 0 2011 L 0 7
DP Republic
Korea
Fiji 1 2009
M 6 62
French Polynesia
Guam 2007 L
Georgia 1 2004 L 3 47
Hong Kong, China 1 0 19
India 4 2010 L 2011 0 69
Indonesia 2 2003
M 1 34
Iran 4 2003 L 2011 0 81
Japan 4 2010
M 1 77
Kazakhstan 1 2006 4 58
Kiribati 1 0 7
Republic of Korea 0 2010
M 2010 3 47
Kyrgyzstan
Lao PDR 2011 L 5 28
Macao, China 1 0 14
Maldives 4 1 53
Marshall Islands
Malaysia 4 2005 L 2010 6 70
Micronesia
Mongolia 4 2012
M 2010 3 74
Myanmar 0.5 5 32
Nauru
New Caledonia
Northern Marianas
Nepal 3 2001 3 53
New Zealand 3 2007 L 2006 6 45
Niue 1 2009 4 31
Annex 1
17
M
Pakistan 2000 2
Palau 2011 L 0 18
Papua New
Guinea 3
Philippines 4 2002 L 2010 5 58
Russia
Samoa 0.5 2009 M 4 19
Singapore 0 0
Solomon Islands
Sri Lanka 0.5 2002 0 26
Country NSDS Agric Censu
s
Pop
w/agric Coord
w/users Percent core
Tajikistan 2 1 38
Taiwan, China 0.5 2010 L 83
Thailand 2 2003
A 0 47
Timor Leste 1 2010 0 22
Tonga
Turkey
Turkmenistan 4 0 37
Tuvalu
Uzbekistan
Vanuatu 1 2006 L 2009 6 7
Viet Nam 4 2011 L 5 35
KEY: NSDS – 1 up to 4 agriculture sub-sectors, if exists; 0.5, if, in process; 0 if not.
Agric census/frame – date of last agricultural census and frame used (L-List; M-Multiple; A-
Area)
Pop/w agric – date of last population census that included questions on agriculture
Coord w/users - number of user categories involved in meetings (max =6)
Percent core –percent of core items regularly available
TABLE 4: Availability of priority data Availability: 1. Yes; 2. No; 3. Not applicable
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
A A A A B B C C F G H I I I J K K K L M M
f r U z g h m o i e k n n r a a i o a a d
g m S r d u b o j o n d d a p z r r o c v
t k i i o n a k e a
n a n n a o
I. PRODUCTION
Crop
Crop production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
Crop yield per area 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
Area planted 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
Area harvested 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2
Livestock
Livestock production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
Fishery
Fishery and aquaculture production: quantity 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 2 1
Forestry
Forest production of wood: quantity 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 2 3
II. EXTERNAL TRADE
Import: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 1 1 1 1 2 1 1 1 1 1 1 2 2 2
Stocks of main crops: quantity 1 1 1 1 1 1 1 2 2 2
Land and use 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1
Water-related:· Irrigated areas 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
IV. INPUTS
Fertilizer quantity 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 1
VI. PRICES
Producer prices 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1
Consumer prices 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Annex 1
19
TABLE 4 (cont): Availability of priority
data Availability: 1. Yes; 2. No; 3. Not applicable
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
M M M M N N N P P P P T S S T T t T V V
a i n m a e i a a n h a a r a h l k a n
l c g r u p u k l g l i m i j a s m n m
a r r a e a o L
y o u l u a k
I. PRODUCTION
Crop
Crop production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Crop yield per area 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Area planted 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Area harvested 1 1 1 1 1 1 1 1 1 1 1 1 1
Livestock
Livestock production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Fishery
Fishery and aquaculture production: quantity 1 1 1 1 1 1 1 1 1 1
Forestry
Forest production of wood: quantity 1 1 2 1 1 1 1
II. EXTERNAL TRADE
Import: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 1 1 1 1 1 1 1 1 1
Stocks of main crops: quantity 1 1 1 2
Land and use 1 1 1 1 1 1 1 1 1
Water-related:· Irrigated areas 1 1 1 1 1 1 1 1 1
IV. INPUTS
Fertilizer quantity 1 1 1 1 1 1 1 1 3 1
VI. PRICES
Producer prices 1 1 1 1 1 1 1 2 1 1 1 1
Consumer prices 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Annex 1
20
TABLE 5: Frequency of priority data Frequency: 1. Annual; 2. Seasonal (six monthly); 3. Quarterly; 4. Monthly; 5.Weekly; 6. Daily;
7. Ad-hoc; 8. Every 5-10 years ‘*’ indicates that some reports are more frequent
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
A A A A B B C C F G H I I I J K K K L M M f r u z g h m o i e k n n r a a i o a a d g m s r d u b o j o n d d a p z r r o c v
t k i i o n a k e a
n a n n a o
I. PRODUCTION
Crop
Crop production: quantity 7 4 1 1* 4 1 3 1 1 3 3 1 1 1 2 4*
Crop yield per area 7 1 1 1* 4 1 3 1 1 3 3 1 1 1 2
Area planted 7 1 1 1* 4 1 3 1 1 3 4 1 1 1 2
Area harvested 7 1 1* 4 1 3 1 3 4 1 1 1 2
Livestock
Livestock production: quantity 7 4 1 1* 7 1 3 3 1 1 1 4 3 1 7
Fishery
Fishery and aquaculture production: quantity 4 1 1 1 1 1 1 3 1 4 4 1 4*
Forestry
Forest production of wood: quantity 3 1 1 1 1 1 7 1 4 1
II. EXTERNAL TRADE
Import: quantity 4 4 4 1 4 4 4 4 4 2 4 1 4 1 4 4
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 1 1* 1 7:8 6 3 1 1 7
Stocks of main crops: quantity 7:8 1 4 1 1 1
Land and use 7 1 1 1 1 7:8 1 1 1 1 7
Water-related:· Irrigated areas 7 1 1 1 1 7:8 3 1 1 1 7 1
IV. INPUTS
Fertilizer quantity 1 1* 1 1 3 3 6 1 1 1 7
VI. PRICES
Producer prices 4 4 4 1 3 3 3 4 4 1 4 1 3 1
Consumer prices 4 4 4 4 4 3 4 3 3 4 4 4 4 1 4 4 4 4 3 4 4
Annex 1
21
TABLE 5(cont): Frequency of priority
data Frequency: 1. Annual; 2. Seasonal (six monthly); 3. Quarterly; 4. Monthly; 5.Weekly; 6. Daily;
7. Ad-hoc; 8. Every 5-10 years ‘*’ indicates that some reports are more frequent
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
M M M M N N N P P P P T S S T T T T V V
a i n m a e i a a n h a a r a h l k a n
l c g r u p u k l g l i m i j a s m n m
a r r a e a o L
y o u l u a k
I. PRODUCTION
Crops
Crop production: quantity 1 1* 1 8 3 2 7 1* 1 1 1* 3
Crop yield per area 1 1 1 3 2 7 1* 1 1 1 3
Area planted 1 1* 1 8 7 3 2 8 1* 1 1 2 3
Area harvested 1 1* 1 8 3 2 8 1 1 6 3
Livestock
Livestock production: quantity 1 1 1 8 7 3 3 8 1* 1 1 1 3
Fishery
Fishery and aquaculture production: quantity 1 1 1 3 1 4 1* 1 4 1
Forestry
Forest production of wood: quantity 1 1 3 1* 1
II. EXTERNAL TRADE
Import: quantity 4 1* 4 1 1 4 4 4 1 4 4 1 4
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 1 1 1 4 3 1 7 1 1
Stocks of main crops: quantity 1 1 4
Land and use 1 1 8 1 7 1 7
Water-related:· Irrigated areas 1 1 1 3 1 1 1
IV. INPUTS
Fertilizer quantity 1 1 1 4 1 4
VI. PRICES
Producer prices 4 1* 1 3 4 4 7 4 1
Consumer prices 4 1* 4 1 5 4 4 4 4 3 4
Annex 1
22
TABLE 6: Quality of priority data Quality/Reliability of data: 1. High reliable; 2. Reliable; 3. Acceptable; 4. Workable; 5. Unacceptable.
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
A A A A B B C C F G H I I I J K K K L M M f r u z g h m o i e k n n r a a i o a a d g m s r d u b o j o n d d a p z r r o c v
t k i i o n a k e a
n a n n a o
I. PRODUCTION
Crops
Crop production: quantity 2 1 2 1 3 3 2 2 2 2 1 1 3 4
Crop yield per area 2 1 2 1 3 3 2 3 2 2 1 1 3
Area planted 2 1 2 1 3 3 2 3 2 2 1 1 3
Area harvested 2 1 2 1 3 3 2 2 2 1 1 3
Livestock
Livestock production: quantity 2 1 2 1 3 3 2 1 2 1 1 3 4
Fishery
Fishery and aquaculture production: quantity 2 3 2 2 3 3 2 3 2 1 3 3
Forestry
Forest production of wood: quantity 2 2 2 2 3 1 2 2 3
II. EXTERNAL TRADE
Import: quantity 2 1 2 2 2 2 1 2 1 1 3 1 1
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 1 2 2 1 1 2 2 3
Stocks of main crops: quantity 2 1 2 2 2 3
Land and use 2 1 2 2 3 1 2 2 2 3 2
Water-related:· Irrigated areas 2 1 2 2 1 3 3 4 1
IV. INPUTS
Fertilizer quantity 1 2 2 3 2 2 1 2 3 1
VI. PRICES
Producer prices 2 1 1 2 2 3 1 2 2
Consumer prices 2 1 1 1 4 1 2 1 3 1 2 2 1
Annex 1
23
TABLE 6 (cont): Quality of priority data Quality/Reliability of data: 1. High reliable; 2. Reliable; 3. Acceptable; 4. Workable; 5. Unacceptable.
PRIORITY CORE DATA ITEMS / 41
COUNTRIES THAT RESPONDED to
SECTION 2
M M M M N N N P P P P T S S T T T T V V
a i n m a e i a a n h a a r a h l k a n
l c g r u p u k l g l i m i j a s m n m
a r r a e a o L
y o u l u a k
I. PRODUCTION
Crops
Crop production: quantity 2 1 4 2 1 1 2 1 2 1 1
Crop yield per area 2 1 4 2 1 1 2 1 2 1 1
Area planted 2 1 4 2 1 2 1 2 1 2 1 1
Area harvested 2 1 4 2 1 1 2 1 2 1 1
Livestock
Livestock production: quantity 2 1 2 1 2 1 2 1 2 1 1
Fishery
Fishery and aquaculture production: quantity 2 3 2 1 2 2 1 1 1
Forestry
Forest production of wood: quantity 2 2 1 2
II. EXTERNAL TRADE
Import: quantity 1 1 1 3 2 1 1 1 1
III. STOCK OF CAPITAL /RESOURCES
Livestock Inventories 2 2 2 2 1 2 1 1
Stocks of main crops: quantity 2 2 1
Land and use 2 2 4 2 2 1 2
Water-related:· Irrigated areas 2 2 4 2 1 1 2 1
IV. INPUTS
Fertilizer quantity 2 3 4 2 3 1 2 1
VI. PRICES
Producer prices 1 2 2 1 2 1
Consumer prices 1 1 2 1 2 1 1 1
24
Table 7. Capacity Indicators Dimensions measured
Elements MAX afg arm aus azr bgd btn cmb cok fij geo hkg idn ind irn jpn kir kor kzk lao mdv mng
Capacity Indicator I & II Institutional Infrastructure, Human and Financial Resources INFRA-STRUCTURE (INPUT)
1.1 Legal framework 5 40 100 100 100 40 40 80 100 80 100 20 80 100 100 80 20 100 100 100 60 100 1.1 1.2 Coordination in
Statistical System 5 20 80 40 60 80 60 60 0 0 80 0 0 100 100 100 0 100 0 80 100 0
1.2 1.3 Strategic Vision and Planning 6
83 100 100 100 33 50 0 0 17 50 100 0 50 100 100 83 0 17 33 33 100 1.3 1.4 Integration of
Agriculture in National Statistical System
10 0 10 30 50 10 10 0 40 20 20 0 40 90 80 60 0 50 0 60 0 70
1.5 1.5 Relevance (user interface)
11 64 91 73 91 64 64 0 73 73 73 0 55 18 9 27 0 27 64 45 45 73 1.6 2.1 Financial Resources
1.7 2.2 Human Resources
Capacity Indicator III Statistical Methods and Practices (THROUGHPUT
DIMENSION)
3.1 Statistical software capability 3 0 100 33 67 100 100 0 33 33 67 67 67 67 100 33 67 67 33 67 67 67 3.2 Data capture technology 3 67 100 67 67 67 67 0 67 100 67 67 67 67 100 67 0 100 0 67 100 67 3.3 IT infrastructure 3 0 100 0 33 67 0 0 33 67 67 33 0 0 67 0 100 33 67 33 33 67 3.4 International Classifications
4 75 100 100 50 75 25 50 75 75 50 75 100 75 100 25 75 100 25 75 75 50
3.5 General Statistical Activities 7
43 71 86 71 86 57 57 57 43 71 43 86 100 43 57 71 71 71 57 100 71 3.6 Agricultural Sector Statistical
Activities 24
Prices 10 20 40 10 10 50 10 20 10 0 20 40 20 80 10 50 10 50 60 10 50 40
Agriculture 14 14 50 50 64 64 36 7 29 14 43 43 43 93 64 64 0 50 79 21 36 57
3.7 Analysis and use of data 7 13 75 38 50 50 38 25 25 50 50 13 63 63 13 13 13 13 25 13 38 50
Capacity Indicator IV Statistical Information and Availability (OUTPUT DIMENSION)
4.1 Core data availability 5 34 61 37 64 71 42 57 8 62 54 24 36 71 83 81 33 48 58 27 56 78
4.2 Timeliness 3 33 100 100 10 33 33 67 67 33 67 67 100 100 33 67 100 33 67 67 100 100
4.3 Quality, reliability and consistency of data 3 20 80 100 80 80 60 0 100 80 80 80 80 80 100 100 0 60 0 60 100 80
4.4 Data Accessibility 3 33 100 100 100
100 100 100 33 33 100 100 100 100 100 100 33 100 33 33 100 100
4.5 Quality Consciousness 3 0 100 100 33 100 100 0 0 0 33 0 33 67 33 67 33 33 33 33 0 100
COMPOSITE SUM OF PERCENT OF MAX FOR
EACH ELEMENT 1800 559 1458 1163 1191 1170 891 523 750 780 1091 771 968 1320 1235 1091 639 1036 732 882 1093 1269
Capacity Indicator I 38.5 59.2 61.4 77.1 36.8 37.7 32.9 41.0 32.9 56.6 27.6 37.1 60.6 59.2 66.6 26.6 48.7 33.5 59.2 44.9 63.3
Capacity Indicator III
27.3 75.6 40.4 45.1 68.0 35.5 23.1 35.0 42.3 50.9 42.3 50.3 64.8 46.8 35.5 34.0 51.5 41.8 33.8 57.0 57.6
Capacity Indicator IV
27.3 86.6 82.0 70.2 71.7 60.9 43.3 32.4 40.5 62.6 48.0 62.6 82.3 62.1 81.5 37.4 50.1 38.6 41.3 64.5 91.0
25
Table 7. Capacity Indicators (cont.)
Dimensions measured
Elements MAX
mo mmr mys nru npl nzl niu pak pal png phl sam sri tai taj tha tkm tls van vnm
Capacity Indicator I & II Institutional Infrastructure, Human and Financial Resources INFRA-STRUCTURE
(INPUT)
1.1 Legal framework 5 20 80 100 0 60 100 100 0 40 40 100 100 20 100 40 60 100 20 80 100 1.2 Coordination in Statistical System
5 0 0 100 0 100 100 80 0 0 0 100 0 0 100 80 20 100 0 0 0
1.3 Strategic Vision and Planning
6 0 0 100 0 50 100 0 0 0 100 100 50 0 33 0 100 100 0 33 100
1.4 Integration of Agriculture in National Statistical System
10 0 10 100 0 60 80 40 0 10 0 90 20 0 10 20 20 50 0 30 90
1.5 Relevance (user interface) 11 0 64 100 0 55 82 73 0 0 0 100 82 0 18 27 18 27 0 55 73 1.6 Financial Resources
1.7 Human Resources
Capacity Indicator III Statistical Methods and Practices (THROUGHPUT
DIMENSION)
2.1 Statistical software capability
3 67 100 67 0 0 100 33 33 33 0 100 33 0 100 0 0 67 100 67 67
2.2 Data capture technology 3 67 67 67 0 0 67 67 67 33 0 67 67 0 67 0 0 100 33 67 67 2.3 IT infrastructure 4 0 0 33 0 0 67 0 0 100 0 67 67 0 0 0 0 33 0 0 33 2.4 International Classifications
4 75 75 100 0 50 75 50 0 75 0 100 75 25 75 50 75 25 50 75 100
2.5 General Statistical Activities
7 57 43 86 0 57 100 43 0 57 0 100 71 71 100 57 86 71 43 57 71
2.6 Agricultural Sector Statistical Activities
24
Prices 10 0 10 10 0 10 30 0 0 10 0 60 10 10 100 10 30 10 10 10 20
Agriculture 14 0 14 50 0 7 71 14 0 36 0 79 14 14 71 29 57 50 21 14 79 2.7 Analysis and use of data 7 13 25 25 0 38 38 13 13 13 0 63 25 13 50 38 38 50 13 38 38
Capacity
Indicator IV Statistical Information and Availability (OUTPUT DIMENSION)
3.1 Core data availability 5 17 32 71 0 56 45 31 0 15 33 63 19 26 82 42 46 18 22 7 35 3.2 Timeliness 3 100 0 67 0 100 67 33 0 67 0 100 33 0 67 67 100 100 0 100 67 3.3 Quality, reliability and consistency of data 3
100 40 80 0 80 100 60 0 80 0 100 80 0 80 100 100 80 0 0 0 3.4 Data Accessibility 3 33 67 100 0 100 100 33 100 33 0 100 33 100 67 33 33 33 33 33 100 3.5 Quality Consciousness 3 0 0 33 0 0 100 67 33 33 0 33 33 0 33 0 67 33 0 0 33
COMPOSITE SUM OF PERCENT OF MAX
FOR EACH ELEMENT 1800 548 626 1288 0 822 1421 736 246 636 173 1521 813 279 1154 592 850 1048 345 665 1072
Capacity Indicator I 20.0 29.0 100.0 20.0 62.9 91.9 54.1 20.0 20.0 31.7 97.9 43.9 20.0 36.0 32.3 33.7 67.1 20.0 38.7 66.6
Capacity Indicator III
34.3 33.4 44.7 20.0 22.3 63.6 27.6 23.4 34.7 20.0 77.6 36.0 20.0 65.9 26.5 36.7 42.1 28.0 34.6 53.0
Capacity Indicator IV
40.8 32.1 66.1 20.0 61.7 78.7 42.4 30.6 20.0 22.1 73.2 35.4 29.1 62.8 45.1 63.4 43.7 22.6 24.8 43.5
26
Table 8: Preliminary Country Groupings for Statistical Capacity
Country Method
1 Method
2
Country Method
1 Method
2 Composit
e WB CI
Afghanistan 1 1 Mongolia 5 5 5 79
Armenia 3 4 New Zealand 5 5 5
Australia 5 4 Australia 5 4 4.5
Azerbaijan 4 4 Philippines 4 5 4.5 87
Bangladesh 3 3 Azerbaijan 4 4 4 78
Bhutan 2 3 Georgia 4 4 4 94
Cambodia 2 2 Iran 4 4 4
Cook Islands 1 2 Japan 5 3 4
Fiji 3 3 Malaysia 4 4 4 76
Georgia 4 4 Republic of Korea 4 4 4
Hong Kong,
China 3 2
Armenia 3 4 3.5 92
India 3 4 India 3 4 3.5 81
Indonesia 3 3 Thailand 4 3 3.5 83
Iran 4 4 Turkmenistan 4 3 3.5 34
Japan 5 3 Bangladesh 3 3 3 73
Kazakhstan 3 3 Fiji 3 3 3 61
Kiribati 2 2 Indonesia 3 3 3 83
Lao PDR 2 3 Kazakhstan 3 3 3 92
Macao, China 3 2 Maldives 2 4 3 66
Malaysia 4 4 Taiwan, China 3 3 3
Maldives 2 4 Viet Nam 3 3 3 70
Micronesia 1 Bhutan 2 3 2.5 78
Mongolia 5 5 Hong Kong, China 3 2 2.5
Myanmar 3 1 Lao PDR 2 3 2.5 61
Nauru 1 1 Macao, China 3 2 2.5
Nepal 3 2 Nepal 3 2 2.5 61
New Zealand 5 5 Samoa 3 2 2.5 40
Niue 1 2 Cambodia 2 2 2 72
Pakistan 2 1 Kiribati 2 2 2 33
Palau 1 1 Myanmar 3 1 2 48
Papua New
Guinea 1 1
Tajikistan 3 1 2 72
Philippines 4 5 Vanuatu 2 2 2 54
Republic of
Korea 4 4
Cook Islands 1 2 1.5
Samoa 3 2 Niue 1 2 1.5
Sri Lanka 2 1 Pakistan 2 1 1.5 74
Taiwan, China 3 3 Sri Lanka 2 1 1.5 78
Tajikistan 3 1 Afghanistan 1 1 1 47
Thailand 4 3 Micronesia 1 1 24
Timor Leste 1 1 Nauru 1 1 1
Turkmenistan 4 3 Palau 1 1 1 28
27
Vanuatu 2 2
Papua New
Guinea 1 1 1 36
Viet Nam 3 3 Timor Leste 1 1 1 52
Annex 2
28
Table 1: Statistical Domains for Core Variables
ECONOMIC V. AGRO-PROCESSING
I. PRODUCTION Main crops
Crop Post harvest losses
Crop production: quantity Main livestock
Crop production: value Fish: quantity
Crop yield per area Fish: value
Area planted VI. PRICES Area harvested Producer prices
Livestock Wholesale prices
Livestock production: quantity Consumer prices
Livestock production: value Agric. Input prices
Fishery Agric. Export prices
Fishery and aquaculture production: quantity Agric. Import prices
Fishery and aquaculture production: value VII .INVESTMENT SUBSIDIES OR TAXES Forestry Public investment in agriculture
Forest production of wood: quantity Agricultural subsidies
Forest production of wood: value Fishery access fees
Forest production of non wood: quantity Public expenditure for fishery management
Forest production of non wood: value Fishery subsidies
II. EXTERNAL TRADE Water pricing
Export: quantity VIII. RURAL INFRASTRUCTURE /SERVICES
Export: value Area equipped for irrigation
Import: quantity Crop markets
Import: value Livestock markets
III. STOCK OF CAPITAL AND RESOURCES Rural roads (Km)
Livestock Inventories Railways (Km)
Agricultural machinery Communication
Stocks of main crops: quantity Banking and insurance
Land and use SOCIAL
Water-related: Population dependent on agriculture
· Irrigated areas Agricultural workforce (by gender)
· Types of irrigation Fishery workforce (by gender)
· Irrigated crops Aquaculture workforce (by gender)
· Quantity of water used Household income
· Water quality ENVIRONMENTAL
IV. INPUTS Soil degradation
Fertilizer quantity Water pollution due to agriculture
Fertilizer value Emissions due to agriculture
Pesticide quantity Water pollution due to aquaculture
Pesticide value Emissions due to aquaculture
Seeds quantity GEOGRAPHIC LOCATION
Seeds value Geo-coordinate of the statistical unit (parcel,
province, region, country) Animal Feed quantity Animal Feed value Forage quantity
Forage value
Animal vaccines and drugs quantity
Animal vaccines and drugs value Aquatic seeds quantity Aquatic seeds value
Annex 2
29
TABLE 2: Summary Table for Additional criteria (2010) used to Select Pilot Countries
Country Value
Added Agriculture
Percent of
World Cereal
Production
Percent of
population in
rural
Afghanistan 35.0 0.2 77.4
American Samoa 7.0
Armenia 19.2 0.0 35.8
Australia 2.5 1.4 10.9
Azerbaijan 5.7 0.1 48.1
Bangladesh 18.6 2.1 71.9
Bhutan 19.0 0.0 65.3
Brunei 0.7 0.0 24.3
Cambodia 36.1 0.4 79.9
China 10.1 20.4 53.0
Cook Islands 5.1 0.0 24.7
DP Republic Korea 21.2 0.2 39.8
Fiji 12.6 0.0 48.1
French Polynesia 2.4 0.0 48.6
Guam 0.0 6.8
Georgia 8.3 0.0 47.3
Hong Kong, China 0.1 0.0 0.0
India 19.0 9.7 70.0
Indonesia 15.3 3.5 55.7
Iran 9.2 0.9 29.2
Japan 1.4 0.5 33.2
Kazakhstan 4.5 0.5 41.5
Kiribati 26.3 0.0 56.1
Republic of Korea 2.6 0.2 17.0
Kyrgyzstan 20.0 0.1 65.5
Lao PDR 32.0 0.2 66.8
Macao, China 0.0 0.0
Maldives 5.0 0.0 59.9
Marshall Islands 10.0 0.0 28.2
Malaysia 10.4 0.1 27.8
Micronesia 26.0 0.0 77.3
Mongolia 16.2 0.0 38.0
Myanmar 36.4 1.4 66.4
Nauru 6.2 0.0 0.0
New Caledonia 1.6 0.0 42.6
Northern Marianas 0.0 8.7
Nepal 35.0 0.3 81.4
New Zealand 5.5 0.0 13.8
Niue 0.0 62.5
Pakistan 21.2 1.4 64.1
Palau 3.2 0.0 16.6
Papua New Guinea 31.9 0.0 87.5
Philippines 12.3 0.9 51.1
Russia 4.0 2.5 26.8
Samoa 9.6 0.0 79.8
Solomon Islands 28.3 0.0 81.4
Singapore 0.0 0.0 0.0
Sri Lanka 14.1 0.2 85.7
Tajikistan 21.7 0.0 73.7
Annex 2
30
Country Value
Added Agriculture
Percent of
World Cereal
Production
Percent of
population in
rural
Taiwan, China 2.6 0.1 5.8
Thailand 12.4 1.5 66.0
Timor Leste 29.6 0.0 71.9
Tonga 19.9 0.0 76.6
Turkey 9.4 1.3 30.4
Turkmenistan 14.5 0.1 50.5
Tuvalu 21.6 0.0 49.6
Uzbekistan 23.0 0.3 63.8
Vanuatu 19.5 0.0 74.4
Viet Nam 20.6 1.8 69.6 NOTE: Value added and percent of population from ESCAP Data Centre (except Taiwan, China);
Percent of World Cereal Production from FAOSTAT