(Item 2 of the Agenda)
9. The objectives for the workshop and the reasons for holding it were presented with the Provisional Agenda and Provisional Timetable (see annexes 2 and 3 respectively).
10. The paper Improving the relevance and contribution of food and agricultural Statistics in poverty reduction strategies and food security programmes by Prof Ben Kiregyera, Statistical Consultant and Chairman, Board of Directors, Uganda Bureau of Statistics, was used to introduce the first topic. Prof Kiregyera noted that in many countries food and agricultural statistical systems have been developed in a piecemeal and ad hoc or quick fix project fashion involving carrying out ad hoc surveys and censuses which were mainly donor and producer driven, with limited Government commitment and ownership and carried out to meet specific data needs.
11. To improve the situation he suggested that what was needed, was to redirect the development of food and agriculture statistics using a new approach, the integrated approach. He said that integration would result in enhancements in consistency: by (i) improving inter-institutional coordination and linkages among institutions that produce data, (ii) system-wide adoption and application of standardized concepts, definitions and classifications, (iii) collecting data during the same period of the year. Incompleteness would be reduced by implementing comprehensive survey programmes with surveys designed so that data could be collected to fill data gaps. The likelihood of accurate data would be more assured if best practices and appropriate methods were used, data collection instruments were properly designed and administered by the right personnel, and if the collected data were properly handled during the post-enumeration period.
12. He also mentioned that by using a Geographic Information System (GIS), new analytical products such as vulnerability and poverty maps could be produced. The maps helped to analyse and simplify the presentation of often complex sets of information and relationships related to poverty and vulnerability. The poverty maps, for instance, showed the distribution of poverty across the different parts of a country and helped to capture heterogeneity within regions, to identify geographical factors affecting poverty, to improve targeting of resource and interventions, to improve communication about poverty conditions and to facilitate GIS functionality, i.e. using location to integrate information from heterogeneous sources.
13. Mr Baker suggested that the countries in the Pacific would benefit by integration of agricultural statistics into a national system, but that support for such a system would still depend on demonstration of the value of current statistics in decision-making. He encouraged the participants to take advantage of the new analysis techniques to identify those persons vulnerable to food insecurity and to poverty.
14. The topic Millennium Development Goals, Targets and Indicators (measuring the progress) was presented by Mr Garth PARRY, Statistician of the Secretariat of the Pacific Community (SPC). He explained the history of their development, an overview of the associated targets and indicators (MDGIs), and the reporting implications for National Statistical Systems (NSSs) in the region. He listed each indicator briefly, highlighting those of relevance to agricultural statistical systems, and noted that Pacific Island NSSs were largely unprepared for the work that would be necessary to report on the MDGIs in 2004 as per country commitments.
15. He summarised a case study undertaken in Vanuatu by SPC in collaboration with UN agencies, the main outcomes of that work being: recognition of the need for greater awareness of the MDGs; the problems of conflicting and incorrect data circulating internationally for national MDGIs; and a relatively optimistic outlook for NSSs that more of the underlying data needed for the MDGIs already exists than is generally believed, and as a result many MDGIs will be measurable with some expert assistance. In this context he mentioned SPCs strong commitment to assisting PICTs with MDG-related work, including through the recently developed PRISM and popGIS projects, with a very strong emphasis on highlighting the potential benefits to PICTs from using MDGIs to improve national planning and decision-making.
16. In the course of the presentation he highlighted two instances where national estimates of MDGIs for one of the participating countries were markedly different from data circulating internationally. The meeting agreed strongly that there should be clear national ownership of the MDGIs, including the use of national statistical websites as the definitive source for national data.
17. The workshop was advised of the initiative of the consortium, Partnerships in Statistics for Development in the 21st Century (PARIS21) to encourage Better statistics for decision-making. Papers prepared by Ms Makiko Harrison (PARIS21 Coordinator, Development Economics Data Group, The World Bank) for a PARIS21 Forum were summarized.
18. It was noted that PARIS21 had developed Statistical Capacity Building (SCB) Indicators to provide a comparative overview of the countries statistical capacity with a view to facilitate capacity building and that these indicators reflected a snapshot of a countrys statistical conditions, a focus on opportunities by highlighting strengths and weaknesses, and a means to track over time results of capacity building efforts.
19. The SCB indicators would be used to support the development agenda with its new demand for better statistics by building national statistical capacity and strengthening the worldwide statistical system.
20. Ms Harrison had pointed out that among the challenges facing developing countries was that the statistical systems were under pressure. She said that these systems commonly were part of an unsupportive political environment with limited feedback from users and with limited institutional coordination. The agencies often had insufficient analysis and dissemination capacity and problems of staff motivation and incentives. In addition they were faced with new demands for data, but there were often budget constraints.
21. She noted that PARIS21 had proposed several strategies to achieve better statistics including 1) building of demand for statistics within the country with country ownership of the statistics; 2) investing in national capacity; 3) improving efficiency and making better use of existing resources; 4) making use of new statistical tools and technologies; and 5) enhancing international cooperation.
22. The workshop learned about the Poverty Analysis and Data Initiative (PADI) of the World Bank Institute as part of its Capacity Building Program to Support the Poverty Reduction Strategy. The objectives of PADI were to enhance in-country analytic capacity in poverty analysis, monitoring, and evaluation; strengthen in-country statistical capacity in improved poverty data collection, management and dissemination; improve quality of and timely access to poverty data; enhance in-country policy research on poverty reduction and program evaluation; and link policy-making to poverty analysis and data initiatives.
23. It was mentioned that the four-step strategy of PADI included 1) poverty measurement and diagnostics; 2) monitoring poverty and social indicators; 3) poverty and social impact evaluation; and 4) better data collection and dissemination.
24. It was pointed out that PADI activities were carried out through regional workshops/seminars to promote analytical and statistical capacities; policymakers seminars for advocacy and awareness; in-country workshops to broaden local capacity; regional seminars/workshops/fora to disseminate research findings; and two websites (POVERTY DATA BANK and community of practitioners (CoP)).
25. The session on Food and Agricultural Statistics within the National Statistics System was introduced by Mr David MARSHALL, Senior Statistician, FAO Statistics Division, with an overview of the integrated approach to the development of a food and agricultural statistics system. It was noted that within the Pacific Island Countries, agriculture remains a key sector of the economy and many of the statistics compiled in the countries are relevant to agriculture. As well as the traditional statistics on agricultural production, external trade data and data from household consumption and expenditure surveys are also key sources of information on the agricultural sector.
26. It was mentioned that National Statistics Offices (NSOs) were faced with ever increasing demands for data which have to be balanced with the ever present resource constraints. In particular the Millennium Development Goals (MDGs) required the compilation of some 41 indicators and the development of a National Food Insecurity and Vulnerability Information and Mapping Systems (FIVIMS) also required the development of a subnational information system.
27. Typical data sources (such as Census and Survey (traditional) data, household survey data, nutrition data and FIVIMS/Mapping of data/GIS/database) were briefly presented as well the issues and constraints faced by the countries of the region.
28. Mr Allan NICHOLLS (Australia) presented the case study on Australian Food and Agriculture Statistics in the context of Australias National Statistical System. He pointed out that while the Australian Bureau of Statistics (ABS) was Australias official statistical agency, there were a wide range of other organizations which collect, analyze and/or disseminate statistical information. These organizations included other commonwealth government agencies, state government agencies, industry associations and private businesses. Through these activities, these organizations made a substantial contribution to the overall suite of information available about Australia.
29. Mr Nicholls then described the functions and mission statement of the ABS, as well as selected aspects of its operation. Some of the functions identified were responsibility for coordination of various Australian government statistical activities and services, and the promotion of standard concepts and definitions across all agencies. Similar descriptions were then given for the other major organizations involved in food and agriculture statistics. The level of cooperation and coordination between ABS and these organizations was also discussed.
30. Aspects of ABS business surveys were then reviewed, including the standard population frame, standard statistical unit and steps taken to maintain the cooperation of providers of data. Finally, integration of ABS statistical data was discussed in terms of the level of integration:
- across financial surveys;
- between financial surveys and taxation data;
- between agricultural finance and commodity collections;
- between business surveys and household surveys, and;
- between the agricultural census and population census.
31. The paper went on to describe each of the main statistical collections related to food and agriculture (of both ABS and other organizations), but there was insufficient time to discuss them in detail during the presentation.
32. In the discussions, the importance of a sound institutional framework was highlighted. The role of the NSO as a coordinator of all governmental statistical activities was also discussed.
33. The case study on Fiji Food and Agriculture Statistics described the integration of agricultural statistics into the national statistics system and the development of tikina profiles for each of the districts in Fiji.
34. It was explained that The Fiji Islands Bureau of Statistics (FIBOS) is Fijis official statistical agency which is authorized by law to collect statistical information. However, there are a wide range of other organizations which collect, analyse and disseminate statistical information. As well as the Fiji Island Bureau of Statistics, the main organizations involved in food and agriculture statistics were the Ministry for Agriculture, Sugar & Land Resettlement (MASLR), Ministry for Health (Food and Nutrition Centre), The Fiji Sugar Corporation Limited, Fiji Meat Industry Board, Coconut Development Authority, and the Quarantine Department, MASLR. Other line ministries collecting data related to food and agriculture included The Ministry for Housing and Rural Development and The Ministry for Fijian Affairs.
35. At this time the main contribution of FIBOS to food and agriculture statistics was the provision of statistical information to draw up the FBS (Food Balance Sheet) and to determine the category of the urban population that would not have the purchasing power to source food. If data collection for economic indicators for FIVIMS were added, it would require personnel training in economic statistics and strengthening of the collection and analysis skills.
36. The programmes of the Ministry for Agriculture, Sugar and Land Resettlement were aimed at revitalizing the agricultural sector in ensuring food and income security for all the people of Fiji. It had a wide range of information sources with database on crops and livestock. The Agriculture Statistics Unit (ASU) had a major role to play in the achievement of the overall objective of the Ministry as it produced accurate, timely and informative data to assist the management and policy makers of the Ministry in the decision-making process. The annual reporting system from the Crops and Livestock Divisions provided the database and source of information that monitors production trends of crops and livestock.
37. The ASU, which has ten enumerators working closely with the Extension staff of the Ministry, was currently undertaking a survey at the district level, known as the Tikina Profile. The survey aimed to establish an agricultural profile for every district in the country. It was fully funded by the Government and will be conducted for all 187 districts in Fiji. The establishment of Tikina Profiles will provide an indication of food availability at household levels.
38. The ASU staff also compiled statistical reports on both a quarterly and annual basis using the quarterly reports that are submitted by the district staff. The two major operational divisions (Crops and Livestock), which were managed at the regional level, remained the main source of data for the Unit. These two divisions produced quarterly reports and annual reports with the basic agriculture data for total area planted, area harvested per commodity and livestock data and production.
39. Data were also obtained from the Quarantine Department (MASLR) and Fiji Island Bureau of Statistics on Trade. Data for copra and sugar, the commodities updated most often, were reported by the respective authorities, the Coconut Development Authority and Fiji Sugar Corporation. Slaughter figures for livestock were normally compiled by the Livestock Division.
40. Several constraints were pointed out. These included inconsistency in reporting format from the various divisions, estimates that tend to reflect underreporting, lack of trained personnel on the different levels of data processing and collection, and lack of computer expertise to write programs and develop and maintain databases.