STATISTICAL ORGANIZATION FOR POVERTY MONITORING



STATISTICAL ORGANIZATION FOR POVERTY MONITORING

By

Prof. Ben Kiregyera

PARIS21 Consultant and Chairman,

Uganda Bureau of Statistics

SUMMARY

Poverty reduction is the overarching development goal of most developing countries. A broad range of information is required to inform the poverty reduction processes. The main sources of this information are outlined. A case is made for enhancing the relevance and effectiveness of the statistical organization for poverty monitoring through: (i) promotion of greater coherence and mutual reinforcement in the National Statistical System, (ii) keeping stakeholders in the loop, (iii) a paradigm shift from ad hoc to integrated approach in the development of national statistics, leading design and implementation of National Statistical Master Plans, (iv) improving inter-institutional coordination and linkages, (vi) enhancement of data quality, (vii) improving data analysis and producing new analytical products such as poverty maps, and (viii) improving data dissemination, networking, information sharing and access.

1. The Scourge of Poverty

It has been reported that across the globe, 1 person in 5 lives on less than $1 a day and 1 in 7 suffers chronic hunger (OECD et al) [1]. In addition, the report states that while “the numbers of poor people are greatest in South Asia, the proportion of poor people is highest in Sub-Saharan Africa”. The report further observes that to-day, there are 150 million underweight children in the developing world and that the proportion of malnourished children is falling every where except Africa.

It is estimated that 44% of Africa’s population live below the region-wide poverty line of $39 per capita per month. However, the depth and incidence of poverty varies between and within sub-regions. It is reported by ECA (2000) that in North Africa, only 22% of the population are under poverty line of $54 per capita per month, while in Sub-Saharan Africa, 51% of the population is below the poverty line of $ 34 per capita per month[2]. The ECA report observes that “significantly, more poor people live in the rural areas”. These and other reports show that poverty and hunger are a serious problem especially in Sub-Saharan Africa.

Eradication of extreme poverty and hunger is one of the internationally agreed development goals drawn from various United Nations conferences and summits held in the last decade. These goals, which are known as Millennium Development Goals (MDGs), provide a focus for international development efforts. The other goals are:

• Achieving Universal Primary Education,

• Promoting gender equality and empowering women,

• Reducing child mortality

• Improving maternal health,

• Combating HIV/AIDS, malaria and other diseases,

• Ensuring environmental sustainability, and

• Developing a global partnership for development.

Of the MDGs, eradication of poverty and hunger is the greatest challenge to the international community and indeed, poverty reduction is now the overarching development goal of African countries.

2. The Need for Information to Inform Poverty Reduction Processes

Poverty being a complex phenomenon, a broad range of information is required to illuminate all its manifestations and the and to inform the poverty reduction processes viz. developing appropriate poverty reduction strategies, programmes and actions, their implementation and monitoring their outcomes and effects.

Poverty being a complex phenomenon, policy and decision-makers, planners, managers and development partners want to know, inter alia:

❑ the profile of the poor i.e. who are the poor, where are they, how many are they, how severe are poverty levels, what are the categories of the poor. This information is essential for targeting intervention programmes and resources.

❑ causes of poverty at different levels i.e. all the main factors that cause change in poverty including causal relations between these factors at different levels. This information is essential for designing appropriate and effective programmes for poverty reduction.

❑ the spatial and temporal changes in the level and depth of poverty as well as their causes. They want evidence that policies and actions (public and other actions) intended to reduce poverty are actually having the desired effect. This involves linking inputs to outcomes. There is also the general concern about whether the target of reducing absolute poverty by half by 2015 will be met.

It is clear from the above that a stream of information is required, on a continuing basis, on all dimensions of poverty and the whole poverty reduction process. For the information to be usable, it needs to be comprehensive, consistent between sources and in time, accurate and timely. The national statistical system producing the information should be coordinated, dynamic, demand-driven and sustainable.

3. Statistical Organization for Poverty Monitoring

3.1 A taxonomy of required information

There are basically two types of information required for poverty monitoring. These are:

• statistical or quantitative information

• non-statistical or qualitative information.

Statistical or quantitative information are collected through statistical surveys, censuses and structured interviews, and the data are analysed using statistical techniques. Non-statistical or qualitative information are collected through participatory poverty assessments/approaches and interactive interviews. Qualitative data which mainly relate to peoples’ judgements, attitudes, preferences, priorities, and/or perceptions are analysed using sociological or anthropological research techniques. An international workshop held in Kampala during 2001 examined the qualitative/quantitative debate. The workshop took note of the strengths of both types of information.

The strengths of quantitative information were summarised as: making data aggregation and generalization possible, allowing systematic disaggregation of data (can measure trends within sub-groups), allowing comparison over time (particular strength with panel survey data), allowing simulation of different policy options and providing results whose reliability is measurable. And those of the qualitative information were summarized as providing a richer definition of poverty and insight into casual processes, flexibility to collect information on certain questions and the possibility of being holistic (looking at a set of relationships as a whole), making it possible to go immediately back to data and interrogate initial findings/puzzles (further interviews and observation), the possibility to take advantage of a wide range of resources for “triangulation” (systematic cross checking) and the possibility to identify a range of both intermediate and final processes and issues that are important to poverty reduction.

The workshop concluded that in order to have realistic conclusions it would be important to combine both types of information and exploit their complementarities while maintaining the essential differences between them. Doing so will deepen our understanding of the phenomenon pf poverty in all its manifestations and how ist may be reduced.

2. Statistical information for poverty monitoring

There are many sources of statistical information for informing the process of poverty reduction. In addition to providing data, these sources provide opportunities for combining and comparing information from different sources to check consistency, improve the design of data collection instruments and introduce new products e.g. disaggregated poverty maps. The main sources of information, however, include:

(a) Management Information Systems

Management Information Systems (MISs) of line Ministries especially those of Health, Education and Agriculture are a rich source of statistical information for poverty monitoring. A lot of data are collected as a matter of routine by Government ministries mainly for internal use which includes planning, decision-making and reporting. Generally data from villages and service providing facilities e.g. health centres, schools, etc. are collected, summarised and then passed on to higher levels of administration for further compilation, use and dispatch to the ministry headquarters. At district, province and Ministry Headquarters, the information are collated, stored and made available for use by planners, administrators and decision-makers at different levels.

MISs have the advantage of providing data relatively cheaply and at regular intervals of time e.g. monthly, quarterly, annually, etc. Also the data are disaggregatable. However, there are problems with data from MISs. One problem is the difficulty of ensuring data quality and timeliness. Another problem is that the formats used to report data are not always appropriate.

(b) Household Surveys

Household surveys are another source of socio-economic data required to monitor poverty. Survey data tends to be more timely, less costly and more accurate. However, surveys are unable to provide highly disaggregated data (small area statistics). This is a serious limitation especially in those African countries where decentralisation policies have been implemented. Also surveys are subject to sampling errors. However, the magnitude of these errors can be controlled and measured when the surveys are based on samples that will have been scientifically (randomly) selected.

Household surveys include, inter alia,

Household Budget Survey (HBS)

This survey provides detailed household income, expenditure and consumption data. Such data are necessary in determining the money-metric poverty line which specifies the minimum expenditure required to satisfy basic requirements of food, clothing and shelter. This survey is complex and relatively expensive to conduct. It is usually conducted at intervals of at least 5 years.

Demographic and Health Survey (DHS)

This survey provides data for poverty monitoring including data on fertility levels and preferences, use of family planning, maternal and child health, nutritional status of young children, childhood mortality levels, knowledge and behaviour regarding HIV/AIDS, and availability of health services to communities, etc.

Agricultural survey

This survey is the main source of current agricultural data which are required for monitoring the performance of the agricultural sector. It collects data on performance indicators like cropped area, input supplies, crop yield and production, etc.

Core Welfare Indicators Questionnaire (CWIQ) Survey

This is a rapid household survey which provides essential and timely data for measuring changes in key social indicators for different population groups – especially indicators of access, utilization and satisfaction with core social and economic services. Additional modules can be added to collect data on such items as HIV/AIDS, gender, etc.

(c) Censuses

Censuses are the main source of benchmark data in various areas. Censuses are generally expensive and difficult to plan and execute. That is why the main censuses are carried out in Africa at intervals of at least 10 years.

Some of the main censuses carried out in Africa include:

Census of Population and Housing

This is the most important census to be undertaken in any African country. It plays a pivotal role in the development of national statistical systems. In particular, the census provides benchmark data needed to plan for socio-economic development and population numbers from the census form the denominator for indicators in almost all sectors. The census, therefore, is necessary to monitor progress on poverty eradication, improving efficiency in the delivery of social services and in collecting reliable data through household surveys. It also provides lists and supplementary information needed for efficient organization of statistical surveys and contributes to further development of national data collection capabilities. Generally, the census programme provides for training and development of technical skills of personnel in various areas, acquisition of various equipment and means of transport, sensitisation and statistical advocacy, etc.

Agricultural census

An agricultural census is the best source of data and information on the organization and structure of the agricultural sector. The census provides data and information on the sector for the country as a whole and for the lower administrative sub-divisions as a well using the agricultural holding[3] as the recommended statistical unit i.e. sampling, reporting and tabulation unit.

School Census

In many African countries, a School Census is conducted to collect information on total enrolment of pupils by age, gender and class in all primary and secondary schools as well as several other parameters. Unlike other censuses, this census is conducted annually.

3.3 Enhancing the Effectiveness of Statistical Organizations for Poverty Monitoring

Most African countries including South Africa have a decentralized statistical system with many data producers, data users and data suppliers. Data users include: Government (policy, decision-makers and administrators); researchers; public & private sector operators; NGOs; donors, international organizations; the press; and the public. The main data producers include National Statistical Office (NSO), line Ministries, public sector, NGOs, etc. while data suppliers included households, farmers, establishments, institutions, etc.

The following chart depicts the main players in the national statistical system:

In their present form, National Statistical Systems (NSSs) which are supposed to inform the poverty reduction processes in Africa are by and large weak, vulnerable, largely donor driven and unsustainable in the long-term.

(a) Weaknesses of the NSSs in Africa

There are many weaknesses of the NSSs in Africa including:

(i) Insufficient data user/producer dialogue

In many countries, NSSs are by and large supply and donor-driven with national data users remaining on the periphery of the system and playing down-stream reactive roles. Generally in the past, there have been consultations between data users and producers and moreover, these have tended to be infrequent and far in between. What is more, these consultations have tended to be informal and non-institutionalized. Also the crucial role data users can play in the data production process has not been internalized either by the users themselves or the data producers.

The supply-driven systems have led to the paradox of data gaps on some important indicators on the one hand and on the other hand, a plethora of data on other indicators, some of which are not used. This paradox is best illustrated by Cisse (1990) who quotes a senior official in the Government of Mali lamenting thus:

“on the one hand, we somehow or other, with limited resources, produce statistical data which are scarcely usable and on the other hand we suffer badly from lack of relevant data to satisfy the basic requirements of most users”.

This situation is not tenable given that some data users are policy and decision-makers who dispense resources. To the extent that this situation is not halted and reversed, to that extent: the relevance of the statistical systems will not be assured, demand and resources for and use of data will remain low and the sustainability of the NSSs will be impaired.

Related to the above is the fact that statistical development has not been high on the list of priorities of many African Governments. Indeed the decline in statistical production in Africa in the 1970s, 1980s and 1990s can be explained by limited Government commitment.

(ii) Limited coordination

In many countries, the NSSs have been developed in a piecemeal and ad hoc fashion with many institutions collecting/compiling data using their own definitions, methodologies and classifications. There has also been limited coordination between sources of data e.g. between the Census of Population and Housing and the Census of Agriculture and Livestock. Limited coordination among data producers and data sources has resulted into:

• inability to establish priorities for data production,

• duplication of effort which invariably leads to inconsistent statistical products,

• wasteful utilisation of scarce resources,

• working at cross-purposes, and

• production of poor statistical products - less relevant, accurate, consistent, timely, comprehensive, etc..

It should also be mentioned that in many countries, there has been limited coordination and collaboration between data producers and research and statistical training institutions. This has resulted into limited or improper analysis of data. Generally, there are oceans of data and few drops of information from them. Also generally subject-matter knowledge has not been sufficiently exploited to add value to data analysis. In addition, many training institutions are not producing practical statisticians and/or contributing to the development of appropriate methodologies for data collection. All this has implications for data quality and sustainability of the data production processes.

May National Statistical Offices have by and large failed to play an effective coordinating and supervisory role in the development of NSSs. This has been attributed to lack of empowering legislation, shortage of staff and other resources, and lack of sufficient commitment to the virtues of coordination.

(iii) Data quality

Lack of data quality has been identified as one of the reasons limiting data use in many African countries. The following are some of the main data quality problems:

Consistency: Data inconsistency arises basically due to poor technical and inter-institutional co-ordination and linkages among institutions that produce data. This is because the institutions use different definitions, data collection methodologies and classifications, and generally work at cross-purpose. In Uganda, for instance, participatory poverty assessments and the National Household Survey have shown different trends in poverty levels. A reconciliation exercise found, among other things, that the two were not covering the same reference period, etc.

Incompleteness: Available data series in many countries are incomplete in the sense that some sectors or parts of the country or other domains are omitted, and generally there are yawning gaps in available data e.g. on gender, employment and under-employment, environment, HIV/AIDS, governance, etc. It has been observed that where there are data gaps, users tend to collect their own data to fill these gaps.

Inaccuracy: In many African countries, a lot of existing data are inaccurate. The experience in many countries is that MISs which are a major source of data for poverty monitoring generally give poor quality data due to the way data are collected, lack of training and supervision for data collectors, lack of morale among data collectors, etc. It should be mentioned also that efficient planning of household surveys in Africa very much depends on information from previous Population and Housing Census e.g. Enumeration Areas (EAs) and population count. Invariably this information out of date at the time it is used and this affects the accuracy of survey data.

Lack of timeliness: A lot of essential data in Africa lack timeliness and this compromises their relevance and usefulness. In fact delays in data release has oftentimes constrained planning and reporting processes. It ought to be mentioned also that there are a number of countries without data release calendars or which do not stick to the calendars (where they exist).

Insufficiently disaggregated data: There is a lot of demand for highly disaggregated data for different purposes, including administrative and planning functions at lower administrative levels in view of decentralization of governments in many countries as well as targeting of intervention programmes and resources. Unfortunately, one of the main sources of data, sample surveys, is unable to provide highly disaggregated data and survey data almost always need to be combined with census and/or participatory poverty assessment results in order for it to be used at highly disaggregated levels. Production of poverty maps is a case in point.

b) Enhancement of the relevance and effectiveness of statistical organization for poverty monitoring

A number of actions need to be taken to enhance the relevance and effectiveness of the statistical organization for poverty monitoring. These include, inter alia:

(i) Consolidation of the National Statistical System (NSS)

There is a need to promote greater coherence and mutual reinforcement in the NSS, and to create general awareness about the role and importance of statistics in society. Efforts should be made to create "statistical thinking" and "numeracy" among members of the public.

(ii) Keeping policy, decision-makers and other key stakeholders in the loop

Policy, decision-makers and other key stakeholders should be kept in the loop and encouraged to play proactive up-stream roles in the development of NSSs. This is crucial for advancing "common understanding of policy issues and related data requirements, setting data priorities, clarifying the objectives for data collection and agreeing on the best methods for collecting data" products (UN Statistical Office, 1991). Data users need to routinely specify their data needs, the form in which data are required (e.g. summary data in form of indexes, trends, rates, etc.), the detail the data should take (level of disaggregation) and the time frame for data presentation (e.g. monthly, quarterly, annually) and to be informed on potential application for existing data. On the other hand, data producers need to indicate what data are available and their quality, how available data can be accessed, what data are expected to be collected, what problems are experienced data production, etc. Above all, they need to create demand for statistics, promote use of the statistical products and enlist further Government commitment to the development of national statistics.

(iii) Designing and implementing National Statistical Master Plans (NSMS)

There is a need for a paradigm shift in the approach to development of national statistics. A holistic approach rather than the current ad hoc, piece-meal and project approach should be encouraged. This augurs well for national integrated governance. In this context, it is necessary for countries to design and implement National Statistical Master Plans (NSMPs) to provide a “road map” for coordinated development of national statistics and a mechanism for harnessing a “critical mass” of resources with emphasis on national resources and their improved allocation across sectors. Such Plans should be “best practices” compliant i.e. should be based on a critical assessment of existing data gaps, identification and prioritization of current and future user needs, full identification of required resources, activities to be carried out, outputs to be produced and outcomes and effects that are intended to be achieved in order to meet user needs especially for policy, decision-making and planning. The Plans should take into account such important factors as user focus, synergy, efficiency and effectiveness. They should also be SMART i.e. Specific, Measurable, Achievable, Relevant and Time bound. National Statistical Offices should take the initiative for the development of NSMPs.

(iv) Improving co-ordination and linkages among institutions that produce data

There should be improvements, in the context of the NSMS, in inter-institutional co-ordination and linkages among institutions that produce data. In particular, there should be system-wide adoption and application of standardized concepts, definitions and classifications in order to establish priorities for data production, prevention of duplication of effort and data inconsistencies, eliminate wasteful utilisation of scarce resources and achieve synergy and cost-effectiveness, facilitate pooling of meagre resources for greater impact, and produce statistical products.

As the focal point for statistical development in countries, National Statistical Offices should take on the responsibility of coordinating and supervising the entire NSS. If the enabling legislation is not in place, it should be put in place as a matter of priority.

(v) Enhancement of data quality

For effective monitoring of poverty, there should be data quality enhancement to avoid statistical illusions and making wrong conclusions about indicators of poverty. The likelihood of higher quality data is more assured if “best practices” and appropriate methods are used, data collection instruments are properly designed and administered by the right personnel, and if the collected data are properly handled during the post-enumeration period.

In particular, there is a need to improve the MISs which are a main source of data for poverty monitoring. This can be done through human resources development, improved methods of data collection and handling, etc.

There is also a need to improve client-response time.

(vi) Improving data analysis

Data production processes are incomplete until data have been processed, analysed and results reported. This last stage requires skills and expertise that, in the context of many African countries, are lacking in National Statistical Offices and other institutions that produce data. This in part explains why there are mountains of unprocessed or incompletely processed data in many National Statistical Offices in Africa.

A lot of the analysis that is usually done is basic analysis, covering a wide range of subjects. Such analysis is good for general use but may not have much policy value. Oftentimes, however, there is need for theme-specific analysis to meet specific needs, interests and perspectives of well-targeted users e.g. policy-makers to create impact[4]. This is usually done by carrying out detailed and theme-specific analysis.

Detailed data analysis aimed at advocacy and contributing to policy design and programme development is not a common occurrence in Africa.

There are various ways in which this problem can be addressed, including:

• designing tailor-made training programmes in data analysis for staff involved in data production;

• collaborating with national institutions which have capacity to do policy-related analysis. This is happening in countries like Zambia (Institute of Economic and Social Research at the University of Zambia) and Uganda (Economic Policy Research Centre at Makerere University and Poverty Monitoring and Analysis Unit, Ministry of Finance, Planning and Economic Development);

• hiring private consulting firms. In countries like Botswana which have a shortage of statistical personnel, the problem may be handles by contracting out analysis of datasets.

There is evidence that data users and subject-matter specialists from different government ministries and institutions are increasingly playing a role, as indeed they should, in analysis of national data and reporting. Where this has been done, it has very much enriched the analysis and added value to the original data.

Also new and more informative analytical products such as poverty maps should be produced.

(vii) Improving data dissemination, networking, information sharing and access

It must be emphasized that statistical information is of no value unless it reaches those who need it, can be easily understood and is actually used. It is, therefore, of crucial importance that statistical information is widely disseminated as part of the development infrastructure or a public good. Dissemination is the last activity in the data production process and it has to be planned and budgeted for well in advance. The dissemination programme should aim to provide information in the form required by key users. As much as possible, this should be done in a user-friendly manner, making it easy for users to understand what story is being told.

Countries which have not done so are encouraged to establish and stick to comprehensive dissemination programmes including data release calendars. As much as possible, different media should be used including a combination of the following:

□ publication of statistical reports

□ press releases

□ circulation of statistical tables (in advance of reports)

□ dissemination seminars

□ electronic media, including internet.

Dissemination and communication with users can be improved by data producers hiring the services of professional communicators. For instance, realizing that statisticians are poor communicators, the Uganda Bureau of Statistics has recruited a Communications Officer, a professional with post-graduate training in mass communication, to handle the public relations, communication and dissemination functions of the Bureau.

Efforts should be made to network and share information. This can be done through better data management including data warehousing and data mining e.g. the cutting-edge World Bank Live Database which, (i) allows the consolidation of all data in one location, (ii) provides powerful, yet easy to use, analytical tools, (iii) helps “tell a story” and thus improve decision-making, and (iv) facilitate dynamic publishing and web dissemination to various constituencies[5].

4. Conclusions

The complexity of the phenomenon of poverty requires a wide range of information to illuminate all its aspects and inform the poverty reduction processes. Both quantitative and qualitative information are required. There are a wide variety of data sources for poverty monitoring, which provide opportunities for combining and comparing information from different sources. These include Management Information Systems, Sample Surveys and Censuses.

There are many things that need to be done to enhance the relevance and effectiveness of the statistical organization for poverty monitoring including:

a) promoting greater coherence and mutual reinforcement in the NSS, and to create general awareness about the role and importance of statistics in society. Efforts should be made to create "statistical thinking" and "numeracy" among members of the public.

b) getting policy and decision makers as well as other key stakeholders fully involved in the development of the national statistical systems to ensure that the systems are demand-driven, and that the national statistics are policy-relevant and their production is sufficiently funded.

c) a paradigm shift in the development of national statistics away from the “quick fix” approach to the integrated approach, leading to the formulation of the National Statistical Master Plans which provide a “road map” for coordinated development of national statistics and a mechanism for harnessing a “critical mass” of resources with emphasis on national resources and their improved allocation across sectors.

d) enhancing data quality among other things by promoting use of standards, “best practices” and appropriate methods for data collection and handling, and data collection instruments are properly designed and administered by the right personnel.

e) improving data analysis by training data producers in data analysis, using institutions that do policy-related analysis and subject-matter specialists, and by producing new analytical products like poverty maps.

f) improved dissemination of statistical information using different media, engaging professional communicators, networking and sharing of information. Improving data access and information sharing by building live databases and connectivity.

References

Charumbira, G. Indicators of Development Progress, INTER-STAT, Special Issue, October 1998.

Institute of Statistics and Applied Economics et al, Report of PARIS21 Sub-regional Workshop for East Africa and the Great Horn, Kampala and Paris, 2001.

International Monetary Fund Web Site, Introduction to the Data Quality Reference Site

International Monetary Fund Web Site, General Data Dissemination System (GDDS), Project for Anglophone Africa, PARIS21 Annual Consortium Meeting, Paris, 4 and 5 October 2001

Kiregyera, B. Uganda at the Cutting-edge of Statistical Reform in Africa, a Presentation made on Africa Statistics Day, Kampala, Uganda, 2000.

Kiregyera, B. Improving the Relevance and Contribution of Food and Agricultural Statistics to the Poverty Reduction Strategies and Food Security Programmes, a Paper presented at a Workshop on Strengthening Food and Agricultural Statistics in Africa Support of Food Security and Poverty Reduction Policies and Programmes, 22-26 November 2001, Pretoria, South Africa

OECD et al, Progress Towards the International Development Goals, A Better World for All,

Singh, P., Statistics and Indicators for Measuring Development paper presented at the Joint IAOS/AFSA Conference, Addis Ababa, May 1995.

Uganda Bureau of Statistics, Report on Uganda National Household Survey 1995-1996, Main Results of the Crop Survey Module, Entebbe, 1997.

Uganda Bureau of Statistics, Framework for the Development of Agricultural Statistics in Uganda, , Entebbe, September 2000.

United Nations Economic Commission for Africa, Economic Report on Africa 1999: The Challenge of Poverty Reduction and Sustainability, Addis Ababa, Ethiopia

United Nations, Web Site on Good Practices in Official Statistics.

United Nations Food and Agriculture Organization, Guidelines for National Food Insecurity and Vulnerability Information and Mapping Systems (FIVIMS): Background and Principles, Committee on World Food Security, CFS:98/5, FAO, Rome, April, 1998

World Bank Web Site, Strengthening Capacity to Improve the Monitoring and Analysis of Poverty and Development, the 2nd Generation LIVE DATABASE

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This paper was presented at a Workshop on Monitoring Development and Indicators, Cape Town, South Africa 3-6 April 2002

[1] Progress Towards the International Development Goals, A Better World for All, OECD et al, 2000

[2] Economic Report on Africa 1999: The Challenge of Poverty Reduction and Sustainability,

Economic Commission for Africa, Addis Ababa, Ethiopia

[3] An agricultural holding is defined as an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form, or size.

[4] Guidelines for National Food Insecurity and Vulnerability Information and Mapping Systems (FIVIMS): Background and Principles, Committee on World Food Security, CFS:98/5, FAO, Rome, April, 1998

[5] Strengthening Capacity to Improve the Monitoring and Analysis of Poverty and

Development, the 2nd Generation LIVE DATABASE, The World Bank

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Data suppliers

Households, farmers, establishments, institutions, etc.

Data Collectors

NSO, line Ministries, public sector, NGOs, etc.

Data Users

Govt., researchers, public & private sector, NGOs, donors, international organizations, press, public

Main Stakeholders

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