DEVELOPING A PROTOTYPE OF GEOINFORMATION SYSTEM FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Case study of Ha Tinh province
Nguyen Chan Huyen February, 2003
DEVELOPING A PROTOTYPE OF GEOINFORMATION SYSTEM FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM
Case study of Ha Tinh province
by Nguyen Chan Huyen
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geoinformatics.
Degree Assessment Board
Dr. Ir. R.A. de By (Chairman) Prof. Ir. P. van der Molen (External Examiner) Mr. C.M.J. Paresi (First Supervisor) Dr. Ing. W.H. de Man (Second Supervisor)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS Disclaimer
This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.
ABSTRACT
Poverty-Alleviation Program is the most important agenda in development strategies of Vietnam Government. The present study presents an approach toward the development of a geoinformation system (GIS) for poverty assessment to support decision making in the implementation of this program.
Presently, poverty assessment is formulated at three administrative levels: district, province and national levels to define the list of poor communes for the whole country. It follows a bottom-up approach, which makes assessment in higher levels dependent on information from lower levels. Another main shortcoming is that it uses only socioeconomic data and manual method for poverty analysis. In this context, GIS is considered as a technique, which could strengthen the effectiveness of poverty assessment by changing bottom-up approach to a more integrated approach, providing stronger tools and adding spatial data for poverty analysis.
For developing a GIS for poverty assessment this study employs the Structured System Development Methodology which includes three phases: problem definition, system design and implementation.
For problem definition, the study takes into consideration the problems of current poverty assessment process, the needs and the requirements of the users of poverty information to ensure that this process will be improved and all users will be provided with the information they need according to their interests. The results of this phase prove that apart from socioeconomic data, poverty assessment needs to include spatial data as well to apply more powerful tools for poverty analysis that could be supported by GIS. System design has been performed on the base of process modeling and data modeling. The process model illustrates the improvements of poverty assessment that could be achieved by using GIS. This model integrated operations with the objective to overcome the problems of current process. Data modeling explains the principle for integrating geodatabase from socioeconomic database and spatial database in order to support poverty assessment in GIS environment. A prototype was developed to examine the effectiveness of proposed system for poverty assessment by using MS Access and ArcView software packages.
At the end, this study gives some discussions about technical and institutional conditions for adopting and implementing GIS for poverty assessment in the framework of Poverty Alleviation Program.
i AKNOWLEDGEMENT
At the moment of completing my thesis, I dearly extend my best thanks to every one who helped me, supported me and worked with me during the time of my study at ITC - the remember able period of my life.
I would like to express my warmest appreciation to the Dutch Government for the fellowship that made my study at ITC possible through the Netherlands Fellowship Program (NFP).
My special thanks are expressed to leaders of my organization - Vietnam GDLA, Prof. Dang Hung Vo and Dr. Le Phuoc Zung who gave me an opportunity to undertake my study in the Netherlands.
My sincere and deepest gratitude is to my first supervisor, Mr. Chris Paresi and my second supervisor, Dr. Erik de Man for their guidance, feedback and recommends. Whenever needed you always gave me constructive comments and suggestions, and left the necessary free-space for me to realize my own ideas.
My best thanks to Dr. Dick Van der Zee, Dr. Martin Ellis for your “by-pass” conversations about the life and the study. The talks with you helped me very much to refresh my brain and to recover my enthusiasm to study.
Also my faithful thanks to Mr. C.G. Huurneman MSc - program director, Dr. T. Bouloucos - student advisor, and all teachers at ITC for your nice guides, lectures, lessons that helped me to absorb knowledge in the best ways.
Many thanks to my classmates (GFM2-2001) from around the world who gave me very nice company and I have leaned a lot about different cultures. I was really fortunate to be with you. I also want to thank my country mates and all friends from different countries I met at ITC for their friendship and spending time together.
Finally, I would like to express my heart-felt thanks to my parents, my brothers, sisters, my husband and children, and my friends-colleagues from my department - Cartographic Publishing House. I am so thankful for your support and encouragement to me. You are all the reasons that I could overcome any trouble I had here, far away from home.
ii
TABLE OF CONTENTS
Abstract ...... i Acknowledgement ...... ii Table of contents ...... iii List of figures ...... v List of tables ...... vi List of abbreviations ...... vii
Chapter 1: Introduction ...... 1 1.1. Background ...... 1 1.2. GIS use for poverty alleviation purpose in other countries ...... 2 1.2.1. Monitoring poverty in Bangladesh in the spatial domain ...... 2 1.2.2. GIS use in support of social services, South Africa ...... 2 1.2.3. GIS use in urban poverty management in Nigeria ...... 3 1.3. Case study area ...... 3 1.4. Problem definition ...... 5 1.5. Research Objectives ...... 6 1.6. Research Questions ...... 6 1.7. System development methodology ...... 7 1.8. Research plan ...... 8 1.9. Research structure ...... 9
Chapter 2: Literature review on concepts and approaches for poverty assessment .... 10 2.1. Introduction ...... 10 2.2. Concept for poverty analysis and assessment ...... 10 2.2.1. Definition of poverty ...... 10 2.2.2. Multidimensionality of poverty ...... 11 2.3. Poverty measurement and assessment ...... 11 2.3.1. Why poverty measurement and assessment? ...... 11 2.3.2. Poverty measurement ...... 12 2.3.3. Poverty line ...... 13 2.3.4. Generating a summary statistics ...... 14 2.4. Geography of poverty and Models of poverty assessment ...... 16 2.5. Poverty mapping ...... 17 2.6. Concluding remarks ...... 18
Chapter 3: Situation and requirement analysis ...... 19 3.1. Introduction ...... 19 3.2. Current situation review and context analysis ...... 19 3.2.1. Process of PA ...... 19 3.2.2. National poverty Criteria for PA ...... 23 3.2.3. Data used for PA ...... 24 3.2.4. GIS application in PA ...... 26 3.3. User requirement analysis ...... 26 3.4. Problem analysis ...... 28 3.5. Appropriate solution for PA ...... 30 3.5.1. Implications for GIS application in PA ...... 30 3.5.2. Criteria and verifiable indicators ...... 31
iii Chapter 4: System design ...... 34 4.1. Introduction ...... 34 4.2. Process modeling ...... 34 4.2.1. Proposed process for PA ...... 34 4.2.2. Main sources of information and proposed institutional context of PA at province level ...... 35 4.2.3. Main sources of information and proposed institutional context of PA at national level ...... 37 4.3. Data modeling ...... 37 4.4. Proposed GI system for PA ...... 41 4.5. Concluding remarks ...... 43
Chapter 5: The prototype ...... 44 5.1. Introduction ...... 44 5.2. Description of collected data ...... 44 5.2.1. SE data ...... 44 5.2.2. Spatial data ...... 45 5.3. Data input ...... 46 5.4. Poverty analysis within GIS ...... 47 5.4.1. Classification of poor communes ...... 47 5.4.2. Derivation of indicators from geospatial database ...... 49 5.4.3. Correlation between poverty and SE and geographic factors ...... 51 5.4.4. Linking PA outputs to decision making ...... 52 5.5. Evaluation of the prototype ...... 53 5.5.1. Advantages ...... 53 5.5.2. Limitations ...... 54 5.5.3. Potential problems ...... 54
Chapter 6: Discussion of conditions for implementation of a GIS for PA ...... 55 6.1. Introduction ...... 55 6.2. Technical arrangement ...... 55 6.3. Institutional arrangement ...... 57 6.4. Concluding remarks ...... 58
Chapter 7: Conclusion and Recommendations ...... 59 7.1. Conclusion ...... 59 7.2. Recommendations ...... 60 7.3. Recommendations for further researches ...... 60
References ...... 61 Appendices Appendix 1: Vietnam Poverty line ...... 64 Appendix 2: Example of output of PA in Ha Tinh, 1999 ...... 65 Appendix 3: Example of data duplication, inconsistency and redundancy...... 66 Appendix 4: Table of translation from problems and objectives ...... 68 Appendix 5: Description of attributes in geodatabase for PA ...... 69 Appendix 6: Schema for calculating general poor index ...... 70
iv
LIST OF FIGURES
Figure 1.1. The conceptual scheme of process for urban poverty inventory mapping and Alleviation simulation in Ibadan metropolis ...... 3 Figure 1.2. Location of Ha Tinh - Case study ...... 4 Figure 1.3. Research plan ...... 8
Figure 2.1. Lorenz Curve ...... 14
Figure 3.1. PA on the continuous and alternative processes of attacking poverty ...... 19 Figure 3.2. Overview of current PA process ...... 20 Figure 3.3. Context diagram of PA at district level ...... 21 Figure 3.4. Context diagram of PA at provincial level ...... 22 Figure 3.5. Context diagram of PA at national level ...... 23 Figure 3.6. Problem Tree ...... 29 Figure 3.7. Objective Tree ...... 30
Figure 4.1. Top-level data flow diagram of improved process of PA ...... 35 Figure 4.2. Institutional/information context of proposed process of PA at provincial level...... 36 Figure 4.3. Institutional/information context of proposed process of PA at national level ... 37 Figure 4.4. Levels for data modeling ...... 37 Figure 4.5. Conceptual E-R diagram for PA database model ...... 39 Figure 4.6. Proposed GIS for PA with its components and levels of implementation ...... 42
Figure 5.1. Data relationships in database for SE indicators in Microsoft Access ...... 46 Figure 5.2. Classification of poor commune by individual indicators ...... 48 Figure 5.3. Classification of poor communes by general poor index ...... 49 Figure 5.4. Extraction of indicator landless by using GIS functions ...... 50 Figure 5.5. Analysis of correlation between polluted areas and adult malnutrition ...... 51 Figure 5.6. Relationship between poor communes, road network and service centers ...... 52 Figure 5.7. Selection the suitable communes for a project ...... 53
v LIST OF TABLES
Table 3.1. Criteria/Indicators for definition of poor commune ...... 24 Table 3.2. Used poverty criteria/indicators and data sources ...... 25 Table 3.3. Logical framework of GIS for PA ...... 32
Table 4.1. Main information sources for PA at provincial level ...... 36 Table 4.2. Description of defined entities ...... 38
Table 5.1. Derivation of criteria/indicators from collected data ...... 45 Table 5.2. Description of collected spatial data ...... 46
vi
LIST OF ABBREVIATIONS
AAV Action Aid Vietnam CEMMAD Committee for Ethnic Minorities and Mountain Area Development DOSTE Department of Science, Technology and Environment GDLA General Department of Land Administration GSO General Statistic Office MARD Ministry of Agriculture and Rural Development MOLISA Ministry of Labor, Invalids and Social Affairs MPI Ministry MRDP Vietnam-Sweden Mountain Rural Development Program LSS Living standard survey NGO Non-Government Organization PA Poverty Assessment PAP Poverty Alleviation Program PC People Committee PDOLISA Provincial Department of Labor, Invalids and Social Affairs PPA Participatory Poverty Assessment SE Socioeconomic UNDP United Nations Development Program VLSS93 Vietnam Living Standard Survey undertaken in 1992-1993 period VLSS98 Vietnam Living Standard Survey undertaken in 1997-1998 period
vii
CHAPTER 1: INTRODUCTION
Chapter 1 Introduction
1.1 Background
Like many other developing countries, on the way to economic development, Vietnam Government is faced with a vital problem of poverty. Although accurate statistics of poverty before the early 1990s are not available, The World Bank conjectured that in that time approximately 70 percent of the population in Vietnam was living in poverty (World bank, 2002 [3]). To escape from this situation, “the Government of Vietnam takes poverty reduction as a cutting-through objective in the process of country socio-economic development” (Vietnam Government, 2002). In the last dedicate from 1991 to 2000, Vietnamese people have considerable progressed in poverty reduction. By 1998, socio- economic statistics showed that the poor population reduced to 37 percent. Simulations based on data from 1993 to 1998 estimated that by 2001, 32 percent of the population lives under the World Bank International Poverty line (World Bank, 2002 [3]). However, using the Vietnam National Poverty line developed by Ministry of Labor, Invalids and Social Affairs (MOLISA) of Vietnam, it was announced that the total poverty incidence in whole country has reduced to 17 percent at the beginning of 2001 (Vietnam Government, 2002).
The poverty reduction has been realized through the Poverty Alleviation Program (PAP) under the government anti-poverty policy with relevant and concrete targets. In the development plan for 2001-2010 periods, the Government of Vietnam sets the following targets (World Bank, 2002 [3]): - By 2005: to reduce poverty from 17 percent to 10 percent. - By 2010: to reduce the incidence of poverty to below 5 percent.
Implementation of PAP in Vietnam does not belong to any specific ministry or organization. All activities in poverty agenda are supported by government policies and carried out through various encourage-development projects, which involve many organizations such as local authorities, banks, professional associations, etc. There are two poverty alleviation sub-programs, which are called Program 133 (Hunger Eradication and Poverty Reduction - HEPR) and Program 135 (Support for the Most Difficult and Remote Communes). Program 133 is executed under the supervision of MOLISA and focuses on investment in infrastructure, assistance in extremely poor areas, extension for agriculture, forestry and fisheries, credit provision in delta parts of the country. Whereas, Program 135 works under Committee for Ethnic Minorities and Mountain Area Development (CEMMAD) supervision and takes care about assistance in residential planning, promoting agriculture production and processing in high land areas (Yukio, 2001).
In the framework of Poverty Alleviation Program in Vietnam, poverty assessment (PA) is an important step. Its aim is to provide information for the process of identifying appropriate objectives, areas of focus, delivery mechanisms and performance indicators. To this end, poverty assessment draws on existing data and research to identify the poor, where they are located, how and why they are poor.
1 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM
The formulation and evaluation of poverty reduction strategies in many countries has increased the need for better poverty assessments (World Bank, 2002 [1]). Therefore, development of methodology and process for PA has became central in a numbers of studies that have been supported by many international, national government and non-government organizations. An example as the Poverty Analysis Initiative project of World Bank aims at enhancing the capacity in poverty analysis, poverty monitoring and impact evaluation (World Bank, 2002 [1]). Other examples are the Norway-funded project “Improving Methods for poverty and food Insecurity Mapping and Its Use at Country Level” (Davis B. & Siano R., 2001) and the project of “The Methodology of Poverty Assessment” undertaken by Development Research Group and East Asia and Pacific Region, Poverty Reduction and Economic Management Sector Unit (Martin Ravallion, 2002).
Despite the fair recognition of benefit of GIS for linking the results of information analysis to decision making in development planning, its application in PAP, particularly in PA has being adopted irregularly. As a decision-support tool, GIS can help to integrate poverty data sets from different sources into accessible database, and to analyze complex relationships between socioeconomic indicators and geographic factors within an ordered spatial framework. This capability is the most important since data sources for PA are multivarious as reflected in the varied poverty indicators in use. GIS has strong analytical potential functions for spatial analysis, that help socioeconomic analysts to get deep understanding of the ‘where’, ‘how’ and ‘why’ of poverty as it varies from place to place. All these capabilities coupled with the ability of GIS to quickly update information, makes it much suited as a timely and reliable tool for improving effectiveness of poverty assessment to support decision making in the framework of PAP in Vietnam.
1.2. GIS use for poverty alleviation purpose in other countries
The potential of the use of GIS in support of poverty alleviation goals has attracted attention from various international and national organizations that are involved in PAP implementation in many countries. Some examples of GIS use in this matter could be listed as below:
1.2.1. Monitoring poverty in Bangladesh in the spatial domain (Quadir D.A., Zahedul Islam A.Z.MD and Mustafa K. Mujeri, 1999)
A method for integration of socioeconomic (SE) database with spatial database has been developed by a group of researchers from Bangladesh Space research and Remote Sensing Organization and Center of Integrated Rural Development for Asia and the Pacific. Within developed system, SE data have been integrated with the GIS attribute data, then, been subjected to spatial domain analysis to monitor and visualize the geographic distribution of the poverty status in Bangladesh. The outputs of this system show a number of important aspects of spatial characteristics of poverty in this country, which are not visible in conventional methods of poverty analysis. This developed system has been suggested to be used for designing geographic priority based PAP in Bangladesh.
1.2.2. GIS use in support of social services, South Africa (Millar J. and Mansell R., 1999)
The Human Sciences Research Council (HSRC) of South Africa initiated to develop an integrated welfare information GIS application for use in KwaZulu-Natal area. The system is intended to map the availability of existing welfare facilities and services within the region and to provide information on the welfare of the society in particular areas,
2 CHAPTER 1: INTRODUCTION including information on the population with HIV/AIDS. This initiative was intended to assist in the prediction, planning and management of welfare provision for communities in the region. However, according to the Director of the GIS Centre, lack of funding is hampering the application’s development.
1.2.3. GIS use in urban poverty management in Nigeria (Akinyemi, 2001)
In Nigeria, urban poverty receives more attention than rural because of its rapid spreading from year to year. The heterogeneity of the urban setting requires a holistic and more generic approach to urban poverty management. In alleviating poverty within areas of jurisdiction, the local government plays the most important roles. Urban poverty management necessitates assessing poverty differences occurring in different neighborhoods making up the city. GIS use was primarily applied for poverty inventory mapping in Ibadan metropolis. Poverty inventory mapping involves assessing and mapping the various levels of poverty as it occurs in households and aggregating household poverty to derive poverty levels in neighborhoods.
Figure 1.1. The conceptual scheme of process for urban poverty inventory mapping and alleviation simulation in Ibadan metropolis
1.3. Case study area
Ha Tinh province is located in north of central part of Vietnam about 350 km south of Hanoi and stretches from the Lao border to the South China Sea. The province is bounded by Nghe An province to the north and by Quang Binh province to the south. This is a relatively new administrative unit, which has been established in 1991 from the southern districts of former Nghe Tinh province. Ha Tinh has a strong tradition of revolutionary activity. The province was also a focal point for American bombing attacks during the 1960s. Ha Tinh province is divided into eight rural districts (Duc Tho, Cam Xuyen, Huong Khe, Huong Son, Thach Ha, Nghi Xuan, Ky Anh, and Can Loc), and two towns (Hong Linh and Ha Tinh). These districts and towns are also divided into 262 communes and wards.
The total population of Ha Tinh Province is 1,275,346 with an average population density of 210 person/km2. It has a population growth rate of 1.58% (1996), which is below the national population growth rate of 2.1%. Following to the census statistic in 1997, 50.8% of the province population was female and 91.8% of them lived in rural areas.
3 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM
Main economic activities in this province are agricultural. In 1996, 56.6% of GDP was generated by agricultural, forestry and fishery activities while only 10.7% came from industry and construction. Ha Tinh Province is among the poorest in Vietnam and the north- central region is the second poorest region in the country (after the central highlands). Per capita GDP in 1996 was 1,774,800 VND per year which was about two-thirds that of the national average of 2,720,400 VND (AAV, 1999).
Figure 1.2. Location of Ha Tinh - Case study area
Ha Tinh Province covers some 6,054 km2 making up approximately 1.8% of the total area of Vietnam. The province can be divided into four main agro-ecological zones. These zones moving from the coast to the Lao border including: coastal strip, delta/plain, hills, and mountains. Most of the land in the province is unusable land (42.4%) followed by forest land (39.3%) and only 16% is used for agricultural purposes. Per capita agricultural land is only 0.47 hectares per capita (Ha Tinh DOSTE, 1998).
The climate in Ha Tinh Province is particularly difficult and features a long dry season marked by frequent droughts and hot dry winds from Lao mountains and a lengthy wet season punctuated by flooding. In addition, typhoons often strike the province, sometimes causing heavy damage.
In 1999, Action Aid Vietnam (AAV) helped Provincial Peoples’ Committee (PPC) of Ha Tinh to undertake the PPA exercise with the purpose to understand poor peoples’ perceptions, analyses, and thoughts about issues and aspects related to poverty and about ways and means of overcoming poverty. This PPA exercise covered seven communes as following (Action Aid Vietnam, 1999):
4 CHAPTER 1: INTRODUCTION
Thinh Loc and Thuong Loc Communes (Can Loc district): rural, delta plain/coastal Ky Lam Commune (Ky Anh district): rural, mountain Son Ham Commune (Huong Son district): rural, mountain Cam Duong Commune (Cam Xuyen district): rural, coastal Thach Dinh Commune (Thach Ha district): rural, delta plain/coastal Dai Nai Commune (Ha Tinh Town): suburban, delta plain
Under the leadership of AAV, The Hanoi Economic Institute, with technical advice from the World Bank, designed and managed a sample household survey using quantitative methods, whereas The Hanoi Research and Training Centre for Community Development (RTCCD) carried out a participatory poverty assessment (PPA) using qualitative methods.
The main findings show that the causes of poverty are:
• The most commonly cited cause of poverty in Ha Tinh Province is lack of capital. This stems from three primary factors: poor natural resources and conditions within the province, a low-level of market development, and heavy taxes and contributions.
• Disparity between households exists due to poor households’ lack of inheritance, investment capital, reserves, social relationships, and the heavy burden of social costs (weddings, funerals, etc.), although the gap between the wealthiest and the poorest households is not extremely wide. The causes of this disparity are: a lack of skills or knowledge, personal characteristics, and lack of chances.
1.4. Problem definition
There are two main sources of information on poverty in Vietnam: the General Statistics Office (GSO) and the Ministry of Labor, Invalids and Social Affairs (MOLISA). GSO has a mandate of making census and living standard surveys and receives technical assistance from the World Bank. GSO with its branches in every province also works as the provider on socio-economic information at national and provincial levels. Meanwhile, MOLISA focuses on labor statistics and poverty assessment on the base of a relative (national) poverty line.
Except these organizations, there are also a number of other organizations such as local authorities, banks, professional associations carrying out poverty assessment for implementing certain PAP projects. For example, in 1999, four participatory poverty assessments (PPA) were implemented: (1) The Vietnam-Sweden Mountain Rural Development Program carried out a PPA in two districts of Lao Cai province, an upland area with a high proportion of ethnic minorities living in remote villages, (2) Vietnam Action Aid coordinated a PPA in six districts of Ha Tinh province in the north central coastal region, typhoon-prone area with very poor natural endowments, (3) Oxfam (Great Britain) carried out a PPA in two districts of Tra Vinh province, a coastal region with a large ethnic minority population and growing problems of landlessness, (4) In Ho Chi Minh City, Save the Children Fund (UK) implemented a study of three poor, urban districts of Vietnam’s most prosperous city (Turk, 2002). These four PPA agencies worked in four very different parts of Vietnam and so have been able to provide insights into nature and dynamics of poverty in a very diverse ranges of social, economic and geographical situations. Each of these agencies
5 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM used a range of techniques included mapping, socio-economic mapping, well-being ranking, problem scoring, pair-wise ranking, trend analysis, seasonal calendar, daily timetables, household timelines, cause and effect trees, institutional ranking/mapping and institutional strengths and weaknesses analysis.
Although a considerable amount of qualitative information on poverty has been produced in Vietnam, it has rarely grabbed the attention of policymakers who have tended to view such information as “unscientific” and lacking in credibility. The problematic situation can be summarized as inadequacy of poverty assessment and will be considered as a core problem for this research. Analyzing problems of current PA process will be given in detail in chapter 3.
With regard to provide more valuable information to support PAP implementation, poverty assessment needs an improved process and a cost and time effective systems for the integration of poverty data, its processing and analysis, and the presentation of poverty data. Within this context, GIS can act as an information management solution, which improves PA process by a number of advanced techniques for integrating poverty information from different sources, poverty analysis, poverty mapping, etc.
1.5. Research Objectives
This research attempts to show the improvements that GIS can provide to PA in the implementation of PAP in Vietnam.
The main study’s objective is to develop a prototype of geo-information system to support PA in the framework of PAP. This objective could be achieved through several specific objectives as below: Objective 1- Analysis of PA process, its current problems and requirements for geographic information Objective 2- Development of a model for geoinformation system including process model and data model to support PA. Objective 3- Development of a corresponding prototype and testing. Objective 4- Definition of the conditions for implementation of GIS in PA. The data used for testing effectiveness of prototype information system will be based on the SE data and spatial data of Ha Tinh province.
1.6. Research Questions Each objective of this research requires the answers to a number of questions such as: Objective 1: - What is the current process of PA and what are the problems? - What kind of spatial and non-spatial data are needed for poverty assessment? Objective 2: - What is an appropriate process model for PA? - What is an appropriate data model for poverty information? - What is an appropriate model of GIS for PA?
6 CHAPTER 1: INTRODUCTION
Objective 3: - How should the processes and the data be structured to build a geoinformation system for PA? - What kind of analyses on poverty information should be implemented by the prototype? - How could the poverty information be made available and presented to users? Objective 4: - What are the conditions for adopting and implementing geoinformation system in PA process?
1.7. System development methodology
System development methodology defines a set of steps and productivity tools at each step to ensure that a system is built in the most effective way. According to Hawryszkiewycz, (1997) five system development methodologies have been developed to build an information system. These are formal, structured, soft, socio-technical, and object oriented methods. Each of them includes different sets of steps and processes.
For the development of the geoinformation system to support PA, the approach of structured methodology will be mainly used, sometimes supported by other methodologies like soft methodology used during the system analysis process. This approach structures information system and thus enables defining the functional specifications of the system to be developed in the manageable way (Paresi, 2000). During developing the prototype of PA GIS, structured methodology combines different techniques like Data Flow Diagram (DFD), Data dictionaries, problem tree, logical framework, Entity Relationship (ER) diagram and relational data model.
Structured methodology separates the system development into three main phases:
1. Problem definition. This phase aims at understanding an existing system or process, its strengths, weaknesses and requirements for setting up a new system. This step goes through identifying the information requirements and design of needed system architecture and infrastructure (Paresi, 2000). The data flow diagram (DFD) is one of the most important modeling tools that are used in this step to describe and analyze current process of PA. For problem analysis, the key problems and core problem of the current process will be defined and then graphically displayed in a Problem Tree, with the causes forming the roots and the effects forming the branches of the problem tree. Objective tree will be translated from problem tree and then converted into the Logical Framework (Pitt, 1998), which is applied to evaluate the success of GIS for PA.
2. System design. The objective of this phase is to design a new system, which shows the different transactions (input data and outputs expected) in a new operational context. This step includes process modeling and data modeling. A process model for PA will be proposed on the base of results of problems and requirement analyses form previous phase. Data modeling is performed through several levels such as: conceptual, logical and physical levels. Entity-Relationship (E-R) method is chosen for the conceptual data modeling. Then, data structures will be described by the mean of skeleton tables. Physical data modeling is excluded in this study.
7 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM
3. Implementation. This step realizes the model of system that has been designed on previous step. At this step a prototype is developed in order to test system design. Some of the verifiable indicators formulated from the first step are applied to evaluating the prototype.
1.8. Research plan
The plan of this research includes four tasks: 1- conceptualizing the topic, 2- designing system, 3- implementation and 4- writing report. The steps of each task, their sequences and relationships between them are illustrates in figure 1.3.
Task 1 CONCEPTUALIZING THE TOPIC
Situation and requirement analysis Literature review
Situation Problem Requirement The approach re view analysis analysis
Criteria & verifiable
Task 2 SYSTEM DESIGN
Process model Conceptual model
Logical model
Model of GIS for PA
Task 3 IMPLEMENT ATION
Task 4 WRITING REPORT
Figure 1.3. Research plan
8 CHAPTER 1: INTRODUCTION
1.9. Research structure
The structure of this thesis will follow the stream of information system development. It includes seven chapters as below:
Chapter 1: Introduction - This chapter gives an introduction to PAP in Vietnam and to the case study area then it focuses on the definition of problems, the identification of objectives and the formulation of questions that have to be answered in this research. Research methodology and research plan are also included in this part.
Chapter 2: Literature review on concepts and approaches for poverty assessment. The aim of this chapter is to explain basic concepts for poverty assessment. The concept of poverty and its characteristics will be presented. Other important concepts in this field like measurement and assessment, geography of poverty and models of poverty assessment, poverty line, indices, etc. will also be reviewed here.
Chapter 3: Situation and requirement analysis - The current situation, existing process of PA, user requirements will be analyzed in this chapter. These will be supported by analyzing data captured from fieldwork and will be used to define requirements for adoption of GIS in PA.
Chapter 4: System design - The process model and the data model will be developed in this chapter. In the base of these models, an appropriate model of GIS for PA will be proposed as well.
Chapter 5: The prototype - A prototype is developed in order to test possible implementation and to evaluate the PA GIS.
Chapter 6: Discussion of conditions for implementation of a GIS for PA - This chapter discuses some aspects of technical and institutional conditions that are considered as the most important factors for adopting/implementing GIS for PA within PAP framework.
Chapter 7: Conclusion and Recommendations - This chapter will present the conclusion and recommendations for realization of the present research and for further researches in this field.
9 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM
Chapter 2 Literature review on concepts and approaches for poverty assessment
2.1. Introduction
In order to assess poverty it is important to elaborate on the core concepts and approaches that are applied in this work. Therefore, this chapter is aimed at providing basic understanding of the objectives of poverty assessment, its aspects and methods. Section 2.2 gives the definition of poverty and highlights its multidimensionality. A general introduction about criteria, indicators for poverty measurement and the concepts related to methods of poverty assessment such as poverty line, statistic indices are given in section 2.3. Instruments of poverty assessment including assessment models and poverty mapping are described in sections 2.4 and 2.5.
2.2. Concepts for poverty analysis and assessment
2.2.1. Definition of poverty
Poverty and well-being are opposite concepts of one category. For example, we can think of one’s well-being as the command over commodities in general; people are better off if they have a greater command over resources. Or we can think of the ability to obtain a specific type of consumption good (e.g. food, housing). People who lack the “capability” to function in society might have lower well-being or be more vulnerable to income and weather shocks. Thus, poverty means either lack of command over commodities in general (i.e., a severe constriction of the choice set) or a specific type of consumption (e.g., too little food energy intake) deemed essential to constitute a reasonable standard of living in a society, or lack of "ability" to function in a society.
Poverty intersects and overlaps with other concepts, notably development and equity. (Henniger, 1998). Poverty and development are both multidimensional. Development looks at a community as a whole and measures change and advancement along different dimensions of well-being, whereas poverty focuses on a segment of a community. It compares different dimensions of human well-being to a standard (poverty line), and then classifies a person or household as poor or non-poor. This standard can be defined in absolute or relative terms. For example, an absolute standard could be all households that do not have the means for human survival. A relative standard simply compares different households according to their degree of deprivation. Poverty is closely connected to equity. While poverty captures deprivation, equity looks at the distribution of an indicator. Poverty itself is generally the result of larger inequity. More sophisticated poverty measures usually incorporate the distributional aspects of poverty.
Poverty is a dynamic phenomenon. Households can move in and out of poverty or shift in their relative status of well-being, depending on changes in household characteristics, such as sudden unemployment of a household member, and external circumstances, such as failure of crops or increase in food prices. Similarly, poor areas, for instance, a commune or hamlet, could become poorer after natural hazard or become better-off under the impact of poverty alleviation program or socioeconomic reform policy.
10 CHAPTER 2: LITERATURE REVIEW ON CONCEPTS AND APPROACHES FOR POVERTY ASSESSMENT
2.2.2. Multidimensionality of poverty
Poverty is a truly multidimensional phenomenon. Poverty is a deprivation of essential assets and opportunities to which every human being is entitled. Thus, clearly, one can think of poverty from a non-monetary perspective. Although widely used, monetary poverty is not the exclusive paradigm for poverty measurement and non-monetary dimensions of poverty are useful as well in assessing poverty components. Poverty is also associated with insufficient outcomes with respect to health, nutrition and literacy, to deficient social relations, to insecurity, and to low self-confidence and powerlessness.
In their global assessment of rural poverty, the International Fund for Agricultural Development (IFAD) identified eight broad components of poverty (Henninger, 1998) as below: 1. Material deprivation. This includes inadequate food supplies, poor nutritional status, poor health, poor education, and lack of clothing and housing, fuel insecurity, and absence of provisions for emergencies. 2. Lack of assets. It covers both material assets (land, agricultural input, etc.) and human capital (education, training, etc.). 3. Isolation. This component tries to capture social, political and geographic marginalization. The latter can be found in remote areas, far from development and service institutions with very limited access to transport, roads, markets and communication links. 4. Alienation. Alienation results from isolation and exploitative social relations and includes people that lack identify and control, are unemployed or underemployed, lack of marketable skills, and have limited access to training and education. 5. Dependence. Poor people are often exposed to skewed dependency relationships that can be found for example between landlord and tenant, employer and employee, creditor and debtor, buyer and seller, or patron and bonded laborer. 6. Lack of decision making power. This is a result of limited participation and freedom of choice. 7. Vulnerability to external shocks. External shocks can be caused by factors found in nature (droughts, floods, cyclones, locusts, etc.), markets (collapse in commodity price, labor supply and demand, etc.), demography (loss of a household’s earning member, death, divorce, etc.), health (illness of earning member), and war. 8. Insecurity. This is defined as the risk of being exposed to physical violence.
In practice, a large number of indicators that are applied to poverty measurement reveal this multidimensionality.
2.3. Poverty measurement and assessment
2.3.1. Why poverty measurement and assessment
“Our dream is a World free of poverty”, World Bank statement (World Bank, 2002 [1]). The institution’s success in attacking poverty can only be judged if there are adequate measures of poverty. These measurements provide information to help policy makers understand the characteristics of poverty and evaluate the impact of growth strategies. For example, with measures of poverty over time, we can assess if poverty has increased or
11 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM decreased, or whether general economic growth helped the poor. We can use poverty data to inform economy-wide policy reforms and how the poor are affected by such reforms. Information on poverty is also an useful source to simulate the impact of different policies. Poverty assessments can help governments identify potential targeting by region, employment, education and gender. Probably the most important use of the poverty assessment results is to support efforts to target development resources towards poorer areas, aiming to reduce aggregate poverty through regional targeting.
For policy purposes, the most important reason for measuring poverty is not the need for a descriptive number, but to make poverty comparisons in order to develop antipoverty programs and monitor development progress and growth strategies.
A credible results of PA can help policy/decision making to: - identify the poor areas and its characteristic, - describe the situation and problems, - identify and understand causes of poverty, - develop/adjust/justify development strategy, plan of socio-economic development project, - assess the effects of projects, or crises, or government policies, on poverty, - monitor and compare poverty over time, and - compare the rate of poverty reduction with other areas or other countries.
Poverty assessments are typically developed from results of poverty measurement. Three steps need to be taken in measuring poverty. These are (MOLISA, 2002[2]): • poverty measurement including indicator definition and measurement, • establishing a poverty line, and • generating a summary statistic to aggregate the information for poverty assessment.
2.3.2. Poverty measurement
Despite of the multidimensional nature of poverty, poverty measurement must focuses on some indicators. The choice of indicators may respond to philosophical preconceptions, to data limitations or as the results of analysis. Poverty measures range from statistical techniques to participatory studies where poverty indicators are revealed with the population being studied. These measures can be grouped into four major categories (Henniger, 1998):
Economic. These include monetary indicators of household well-being, particularly food and non-food consumption or expenditure and income. These also include non- monetary proxies of household well-being, such ownership of productive assets or durables.
Social. These include other non-monetary indicators of household well-being, such as quality and access to education, health, other basic services, nutrition, and social capital.
Demographic. These indicators focus on the gender and age structure of households, as well as household size.
12 CHAPTER 2: LITERATURE REVIEW ON CONCEPTS AND APPROACHES FOR POVERTY ASSESSMENT
Vulnerability. These indicators present the level of exposure of households to shocks, which can affect poverty status, such as environmental endowment and hazard, physical insecurity, and the diversification and riskiness of alternative livelihood strategies.
Poverty measurements are performed for generating data to support poverty assessment. Methods of poverty measurement could be divided into bottom-up and top-down approaches.
Bottom-up approaches to measure poverty solicit active participation of the poor, incorporate their perspectives into assessment, and generally are more qualitative in nature. These approaches have advantage of allowing participants to apply their own criteria to define poverty, less costly and produce outputs faster. Thus the most stakeholders of PA like to use them. The disadvantage of bottom-up approaches is that they use relatively small samples that make it difficult to extrapolate results and compare different surveys. The quality of participatory approaches varies greatly with the skill of the facilitators and the established level of trust between facilitators and participants. Examples of bottom-up approaches are Knowledge-Attitude-Practice Surveys, Intensive Anthropological and Social Methods, Participatory Poverty Assessment (Henninger, 1998).
Top-down approaches rely on questionnaires and collect information via a survey or census, and tend to be more quantitative in nature. These approaches include Population and Housing censuses, Demographic and Health surveys, Living Standard Surveys, etc. (Henninger, 1998). Population censuses are designed to provide information on the structure and distribution of the population, not on poverty. However, poverty analysts may collect information on education attainment and some other social indicators such as occupation, size of dwelling, water supply and cooking facilities. Many household surveys, on the other hand, contain detailed questions on economic indicators of well-being (e.g. consumption, income) or non-economic measures related to health, education and services.
Other survey methods used by World Bank to assess and monitor poverty include Integrated Surveys and Priority Surveys under the Social Dimension of Adjustment Program (Henninger, 1998). An Integrated Surveys is an in depth survey, provide information to assess impacts of structural adjustments on households. A Priority Surveys is conducted more frequently (ideally annually) and uses a large sample to insure that all population groups are represented.
2.3.2. Poverty line
The poverty line is conceptualized as a minimum standard required by an individual to fulfill his or her basic food and non-food needs. Poverty line is used as a threshold to define the poor or non-poor households (or people). Poverty line could be relative or absolute.
A relative poverty line is applied for defining poorest segment (e.g. a fifth, or two fifths) of the population; these are the relatively poor. Therefore, rich countries have higher poverty lines than poor countries do. This explains why, for instance, the official poverty rate in the early 1990s was close to 15% in the United States and also close to 15% in (much poorer) Indonesia. Many of those counted as poor in the U.S. would be considered to be comfortably well-off by Indonesian standards.
An absolute poverty line is fixed in terms of the standard of living it commands over the domain of poverty comparisons. An absolute poverty line is essential if one is trying to
13 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM judge the effect of anti-poverty policies over time, or to estimate the impact of a project (e.g. micro credit) on poverty. Legitimate comparisons of poverty rates between one country and another can only be made if the same absolute poverty line is used in both countries. The World Bank needs absolute poverty lines in order to be able to compare poverty rates across countries – useful in determining where to channel resources, and also in assessing progress in the war on poverty. It commonly uses two measures: a)critically poor: an estimated 1.200 billion people worldwide live on less than US $1 per day, and b)poor: approximately 2.8 billion people worldwide live on less than $2 per day (World Bank, 2002 [2]). These are absolute poverty lines.
2.3.3. Generating a summary statistics
Given information on poverty from measurements, and a poverty line, then the only remaining problem is deciding on an appropriate summary measure of aggregate poverty. There are a number of aggregate measures of poverty that can be computed. Some of these are Lorenz curve, Gini index, headcount index, poverty gap index, Squared poverty gap index, Sen Index, The Sen-Shorrocks-Thon index.
Lorenz curve. This is a cumulative frequency curve that compares the distribution of a specific variable (e.g. income) with the uniform distribution that represents equality. To construct Lorenz curve, households (or people) should be arranged in sequence from poor to rich in horizontal axis and the cumulative percentage of expenditure (or income) should be presented on the vertical axis. This gives the Lorenz curve as shown in figure 2.1.
Figure 2.1. Lorenz Curve (MOLISA, 2002 [2])
14 CHAPTER 2: LITERATURE REVIEW ON CONCEPTS AND APPROACHES FOR POVERTY ASSESSMENT
Gini index. In the graph of Lorenz curve (figure 2.1), the diagonal line represents perfect equality. The Gini coefficient is defined as A/(A+B), where A and B are as shown on the graph. If A=0 the Gini coefficient becomes 0 which means perfect equality, whereas if B=0 the Gini coefficient becomes 1 which means complete inequality.
Headcount index. By far the most widely used measure is the headcount index, which simply measures the proportion of the population that is counted as poor, often denoted by P0 with forma: 1 N Np P0 = --- ƒ I(yi= The great virtue of the headcount index is that it is simple to construct and easy to understand. These are important qualities. The disadvantages of this measure is that it doesn’t take the intensity of poverty into account (e.g. expenditure in province A lower thanprovince B) and neither indicate how poor the poor are, and, hence, does not change if people below the poverty line become poorer. Poverty gap index. This is the average over all people, of the gaps between poor people’s standard of living and the poverty line, expressed as a ratio to the poverty line. It adds up the extent to which individuals fall below the poverty line (if they do), and expresses it as a percentage of the poverty line. More specifically, define the poverty gap (Gn) as the poverty line (z) less actual income (yi) for poor individuals; the gap is considered to be zero for everyone else. Using the index function, we have Gn = (z-yi).I(yi<=z) Then the poverty gap index (P1) may be written as 1 N Gn P1 = --- ƒ --- Ν ι=1 Ζ This measure is the mean proportionate poverty gap in the population (where the non- poor have zero poverty gap). Some people think of this measure as the cost of eliminating poverty (relative to the poverty line), because it shows how much would have to be transferred to the poor to bring their incomes (or expenditure) up to the poverty line. A shortcoming of this measure is that it may not convincingly capture differences in the severity of poverty amongst the poor. Squared poverty gap index. This index is used to solve the problem of inequality among the poor. This is simply a weighted sum of poverty gaps (as a proportion of the poverty line), where the weights are the proportionate poverty gaps themselves; a poverty 15 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM gap of (say) 10% of the poverty line is given a weight of 10% while one of 50% is given a weight of 50%; this is in contrast with the poverty gap index, where they are weighted equally. Hence, by squaring the poverty gap index, the measure implicitly puts more weight on observations that fall well below the poverty line. Formally: 1 N Gn P1 = --- ƒ (---)2 Ν ι=1 Ζ The measure lacks intuitive appeal, because it is not easy to interpret and so it is not used very widely. Sen Index (PS). In 1976, Sen proposed an index that sought to combine the effects of the number of poor, the depth of their poverty, and the distribution of poverty within the group. The index is given by: PS=P0(1-(1-Gp)µp/z) where P0 is the headcount index, µp is the mean income (or expenditure) of the poor, and Gp is the Gini coefficient of inequality among the poor. The Gini coefficient ranges from 0 (perfect equality) to 1 (perfect inequality), and is discussed further below in the context of measuring inequality. The Sen index has been widely discussed, and has the virtue of taking the income distribution among the poor into account. However the index is almost never used outside of the academic literature, perhaps because it lacks the intuitive appeal of some of the simpler measures of poverty The Sen-Shorrocks-Thon index (PSST). This index is the product of the headcount index, the poverty gap index (applied to the poor only), and a term with the Gini coefficient of the poverty gap ratios (i.e. of the Gn.s). This Gini coefficient typically is close to 1, indicating great inequality in the incidence of poverty gaps. The formula is performed as: P ^P PSST=P0P1 (1+G ) This index may be decomposed into: P ^P ∆Ln(PSST) = ∆Ln(P0) + ∆Ln(P1 ) +∆Ln(1+G ) which may be interpreted as, % change in SST index = % change in headcount index + % change in poverty gap index ( among poor) + % change in (1+Gini coefficient of poverty gaps). The strength of the SST index is that it can help give a good sense of the sources of change in poverty over time. This index allows us to analyze poverty into three aspects: are there more poor? are the poor poorer? and is there higher inequality among the poor? 2.4. Geography of poverty and Models of poverty assessment Poverty comparisons often show spatial clustering in a few geographic areas. Analysts examining the causes and spatial clustering of poverty, generally point to individual or 16 CHAPTER 2: LITERATURE REVIEW ON CONCEPTS AND APPROACHES FOR POVERTY ASSESSMENT structural explanations. Individual explanations concentrate on human capital (e.g. education, skills, etc.) and endowments of productive resources. Structural explanations focus on structural factors that constrain opportunities. They include constrains imposed by the economy, social system and geography, for example limited job supply, discrimination, and poor natural resource endowments. Explanations for poor areas are summarized into two theoretical models for PA, one named individualistic model and the other is geographic model (Henniger, 1998). Poverty analysts using an individualistic model try to identify poverty at the individual level. They do not attribute any causal significance to spatial inequalities in resource endowments (geographical capital), although they see differences in geographic endowment as the sorting mechanism that leads to spatial poverty concentrations. Consequently, they would target their anti-poverty measures toward improving the endowment of individuals, for example by providing training opportunities. In geographic model, the mobility of individuals is restricted and poverty has a causal link to geography. Local factors such as climate, soil type, infrastructure, and access to social services change the marginal returns of investments, for example to a given level of education. Barriers to migration ensure that these differences persist. The degree to which individual or geographic factors are causing poverty has implications for developing strategy aimed at improving the situation of the poor. In practice, PA usually needs combination of both models to identify the causes of poverty and its spatial concentration (Henninger, 1998). 2.5. Poverty mapping Poverty analysis is often based on national level indicators that are compared over time or across countries. The broad trends that can be identified using aggregate information are useful for evaluating and monitoring the overall performance of a country. For many policy and research applications, however, the information that can be extracted from aggregate indicators may not be sufficient, since they hide significant local variation in living conditions within countries. For example, poverty within a region can vary across districts. Researchers and policy makers therefore increasingly demand for poverty maps that provide information about the spatial distribution of poverty. Poverty map are usually conducted by merging information from two types of data sources: detailed household surveys (e.g. the LSS) and census data. The detailed poverty maps capture the heterogeneity of poverty within a country. That is, areas that are better-off and those that are worse-off will be more clearly defined. Sometimes regions that have less poverty overall may have substantial pockets of poverty that are lost in the aggregate poverty statistics. To create a poverty map, first use the household survey data to estimate a model of per capita consumption expenditure (or any other household or individual-level indicator of wellbeing) as a function of variables that are common to both the household survey and the census. Then use the resulting estimated equation to predict per capita expenditures for each household in the census. The estimated household-level measures of poverty and inequality may then aggregate for small areas, such as districts, villages, or even neighborhoods (Henninger, 1998). 17 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Poverty maps can improve the targeting of interventions. In designing poverty alleviation projects and allocating subsidies, resources will be used more effectively if the most needy groups can be better targeted. This reduces the leakage of transfer payments to non-poor persons, and it reduces the risk that poor persons will be missed by a program. Poverty maps can also help governments to articulate their policy objectives. Basing allocation decisions on observed geographic poverty data rather than subjective rankings of regions increases the transparency of government decision making. Such data can thus help limit the influence of special interests in allocation decisions. There is a related role for well-defined poverty maps to lend credibility to government and donor decision making. 2.6. Concluding remarks PA is an indispensable tool for policy work related to poverty reduction process involving poverty identification, measurement, monitoring and evaluation. PA is a complex work because poverty is a naturally multidimensional phenomenon. To enhance capacity of PA as expected results of this research, it is important to understand the basic concepts concerning to poverty analysis. In the limitation of these papers, current chapter provides only some general definitions and descriptions that are necessary to follow the research stream. However, the last two sections (2.4 and 2.5) drove attention to the geographic dimension of PA and poverty mapping. The necessity of geographic model and the usefulness of poverty maps lead to the implication for adding spatial data for poverty analysis to support PA. 18 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS Chapter 3 The Situation and Requirement Analysis 3. 1. Introduction The implementation of PAP is a continuous process that usually encompasses steps of poverty data collection, poverty assessment (PA), decision making (including strategy formulation and project design), implementation and evaluation of PAP implementation (see figure 3.1). Inside this cycle, all these steps are alternative and PA plays very important role. Most of decision makers or anti-poverty project designers consider it as a powerful instrument for understanding of specific issues in poverty in local area and informing intended policies on poverty. Poverty data collection Poverty Assessment Evaluation of PAP Strategy formulation, Implementation Project design of PAP Figure 3.1. PA on the continuous and alternative processes of attacking poverty In order to improve the results of PA to support the implementation of PAP in Vietnam, it is necessary to study all expects related to the PA process and its existing situation. Regarding to this purpose, this chapter gives detail analyses on the current situation and problems concerning to the process and used data of PA. The detail review of situation and used data inside PA process are given in section 3.2. In section 3.3 the user requirements are analyzed to motivate the reasons for improving the PA process. The next section 3.4 analyzes PA problems by the mean of problem tree. Criteria and verifiable indicators for evaluating the successes of GIS for PA will be presented in logical framework matrix in the last section. 3.2. Current situation review and context analysis 3.2.1. Process of PA The process of PA includes three steps performed every two years at three administrative levels: district’s, provincial and national levels. In district’s level, PA is undertaken by district’s People Committee (PC) and in province and national (higher) levels 19 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM it is usually formulated by a group of specialists which is called Poverty Working Group (PWG). The process of PA follows the bottom-up approach with individualistic model for poverty analysis, since it mainly bases on human capital (e.g. education, skills, etc.) and endowments of productive resources (see section 2.4.3.). Overview of current process is described graphically in figure 3.2. Formulation of national plan for PAP and anti- poverty projects Description of poor communes in whole country GSO - Censuses - Living standard surveys Government PWG - Annual SE atatistics Anayze and verify the MOLISA Decision making, lists of poor commune Labor statistics province anti- poverty plan Related Ministries Government List of poor communes Provide statistics on: in province and their - Socioeconomic dev. Issue criteria for characteristics - Education poverty identification - Health care - Transportation - etc. Province's PWG NGOs Anayze and verify the lists of poor Provide results of commune Participatory poverty assessment District's PC Related Departments Anayze and subbmit the list of poor Provide statistics on: communes - Socioeconomic dev. - Education - Health care - Transportation - etc. Local knowledge Legend: Organization and its Target of PA functions in PA process Outputs of PA Data Data flows Figure 3.2. Overview of current PA process 20 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS At district level, PA is formulated by district’s PC. The committee’s members analyze the economic situation in their district and identify poor communes relying upon their knowledge about the local region, their SE statistics and national criteria/indicators for poverty definition. The result of this process is the list of poor communes in a district. Then this list should be submitted to Province’s PWG for further assessment. The inputs and outputs of the PA process at this level are presented in figure 3.3. Province PWG List of poor communes in District Criteria for Poverty Government poverty Office definition assessment Legend Annual report on Process Local knowledge S-E development Data flow District's PC Organization Figure 3.3. Context diagram of PA at district level The task of PA at provincial level belongs to provincial PWG. The members of provincial PWG includes representatives from province PC and other related departments of this province such as Department of Planning and Investment, Department of Labor, Invalids and Social affairs (PDOLISA), Department of Agriculture and Rural Development and Department of Land Administration (MOLISA, 1998). Input data for PA in this step is quite diversified as below: - National poverty definition criteria/indicators, - List of poor communes from district levels, - SE data from related departments like average per capita income of every commune, illiterate rate statistics, rate of poor householders per commune, information about infrastructure, for example, the transport status, number and location of clinics or health care centers, etc. and - Results of poverty measurement from NGO. Province’s PWG undertakes poverty analysis on the base of these data and verify the list of poor communes of whole province. This list plays very important role in decision making for planning anti-poverty agenda or SE development program within a province. Providers of input information in province’s level are professional departments under province authority such as Provincial Statistic Bureau, PDOLISA, NGOs and many other related departments. The context of PA process in this level is show in the figure 3.4 below. 21 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Provincial Statistic Bureau PDOLISA GPWG LSS data S-E Labor statistics Census statistics List of poor communes data in province Government Office Criteria for poverty NGOs definition Results of PPA Related departments - Health care Annual report or Poverty Information for - Education and reports on request assessment project planning training - Agricultural and Rural Poverty information development in specific Annual report on - Transportation indicators S-E development - etc. Information for List of poor prov. anti-poverty communes in Social program District Associations Province PC District's PC Figure 3.4. Context diagram of PA at provincial level PA at national level is completed by Government PWG. Government PWG is a group of socioeconomic experts from different related ministries. Because the poverty in high mountain regions in Vietnam is widespread and very critical, therefore the chairman of Government PWG is the representative of the Committee for Ethnic Monitories and Mountainous Area Development (CEMAD). Other members of Government PWG are representatives from Ministry of Planning and Investment (MPI), MOLISA, Ministry of Agriculture and Rural Development (MARD), General Department of Land Administration (GDLA) and Women’s Union (Turk, 2002). The PA process in national level is going similarly to the PA process in province’s level. In this step, Government PWG should integrate lists of poor communes from different provinces then makes poverty analysis and approves the list of poor communes for whole country ( MOLISA, 1998). The list of poor communes for whole country is issued every two year and is one of the most important documents for formulation of action plan for realizing economic growth and poverty reduction objectives. External entities of PA process at this top level are many different organizations and each of them has its own role. Coordinator of PA is Government Office. MOLISA composes the criteria for definition of national poverty lines relying on the data from GSO and other related organization such as Ministry of Agriculture and Rural development, Ministry of Education and training, etc. These criteria must be approved by Government Office. In this process, GSO with mandate of socio-economic statistic becomes a main data provider. The figure 3.5 presents the context of PA process in national level. 22 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS GSO MOLISA Government LSS data Census Office S-E data Info. for statistics Labor policy making Info. for Criteria for statistics formulation of poverty poverty reduction strategy definition Annual report Related Ministries Poverty Results of - Health care NGOs Information for assessment PPA - Education and project planning traning Information for - Agricultural and Rural project planning development Information for - Transportation project planning Llist of poor - etc. Information for prov. anti-poverty Annual report on communes in program S-E development province Social Associations Province Province PC PWG Figure 3.5. Context diagram of PA at national level In addition, NGOs and other social associations (e.g. Peasant Association, Women’s Association) also make PA themselves. Since there are no guidelines or standard process for this work, organizations undertake PA in arbitrary way depending on the purpose of a concrete project. Nevertheless, their PA must be based on the national poverty criteria. 3.2.2. National poverty criteria for PA Poverty assessment is quite complicated because of poverty multidimensionality and ambiguity of criteria for poverty definition. However, it is important for policymakers and researches to qualify suspected regional disparities in living standards and identify which areas are falling behind in the progress of economic development. For this purpose, poverty assessment must rely on common and concrete criteria that reflect essential characteristics of socioeconomic situation of certain country or region. In Vietnam, poverty is identified in commune level. To define poor commune, poverty analysts usually use a large number of criteria/indicators, but these should be compiled and translated into the criteria/indicators for poverty definition approved by Vietnam Government. According to the decision No. 1998/QD-TTg issued on 31 July 1998 by Vietnam Government (Government Office, 1998), PA must be based on following five criteria: 1. Geographic location of habitat: implementation of PAP pays special attention to the communes in remote, coastal, frontier areas and islands. 2. Infrastructure criterion concerns to roads, electricity use, clean water resources, availability of middle school and heath service within commune, etc. 3. Social conditions: this criterion refers to the rate of literacy, health conditions, ownership of consumer durables (such as television, radio, bicycle...) 4. Agricultural conditions: indicators of this criterion are landless, implements for husbandry. 5. Economic conditions include the rate of the unemployed within labor age people and the rate of households having income under poverty line. 23 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Table 3.1. Criteria/Indicators for definition of poor commune (source: Government Office, 1998). Poor commune Extremely poor Criteria/Indicators commune I. Geographic Location: Commune in remote, coastal, >20 km frontier areas or island far away from developmental center II. Infrastructure: - Asphalt road No Asphalt road to commune center - Health care center within commune Under normal condition - Rate of households having access to clean water <40 % <20% - Rate of household using electricity as a main source of <40% <20% lighting - Middle school within commune Under normal condition III. Social conditions - Rate of illiterate >20% >30% - Rate of households having TV, radio and consumer <40% <20% durables - Rate of stunting among children 0-59 months >20% >30% - Rate of malnutrition in adults >20% >30% - Rate of drop-outs among children 10-14 years >20% >40% IV. Agricultural conditions - Landless (for cultivation commune: per capita sown area) <720m2 <360m2 - Backward implements for husbandry (using buffaloes and >50% oxen for plough) V. Economic conditions - Rate of households having income under poverty line (*) >40% >60% - Rate of unemployed within labor age people >15% >30% (*) Vietnam national poverty lines are showed in Appendix 1. 3.2.3. Data used for PA The existing body of information on poverty in Vietnam is relatively rich in comparing with many other developing countries (Vietnam Government, 2002). Current PA uses socioeconomic or population related data form different sources because of large number of indicators for poverty definition. Within the data sources, the most detailed and large, nationally representative, data were gathered from the 1992-93 and the 1997-98 Vietnam Living Standard Survey (VLSS93 and VLSS98, respectively) conducted by the GSO with assistance from UNDP, SIDA and the World Bank. Living standard surveys (LSS) focus on households and base on a sample of the population (typically less than 1%), contained questions on economic indicators of well-being (e.g. consumption, income) or non-economic measures related to health, education and services. Except VLSS93 and VLSS98 as mentioned earlier, since 2001, in order to strengthen support to PAP, GSO starts adopting two year LSS but only for poorest provinces but not for whole country. 24 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS An other important source for PA is Vietnam census data. The newest census in Vietnam has been made in 1999 by GSO. These data were collected in the entirely country during one day, the 1st April 1999. This data set has more than two hundreds indicators that are grouped into 66 criteria organized in 66 tables. These criteria presented seven aspects of the SE situation in Vietnam at the surveyed time, as General demography, Migration, Marriage, Education and literacy, Labor and jobs, Professional level and Accommodation status. Although the number of indicators is big, it provides only some information that can be used on PA such as information on education, type and size of dwelling, clean water supply, etc. Census data are compiled for administrative areas: for communes, districts and provinces. Further more, participatory poverty assessments (PPA) and other kinds of poverty measurements carried out by NGOs are also very important data source for PA. Nevertheless, these data are relatively small samples that make them difficult to extrapolate results. Other data sources for PA could be annual socio-economic report from local government (province’s and district’s PC) and reports on request from related organizations. Social associations like Women’s Association, Peasant Association and other research institutes also partly create information on poverty in specific region focusing on some indicators according their concrete purpose. It is important to mention that in the most of cases, SE data are created for a wide variety of purposes, and consequently much of the data is specialized in nature therefore they are not ideally suited to the requirements of PA. In order to get data for poverty analysis, analysts must extract data that are necessary for this purpose from different sources and then translate them into criteria/indicators for poverty definition. In addition, indicators applied for PA in Vietnam are mainly head count indexes (see section 2.3.3). The table 3.2 shows the used poverty indicators and their sources. The problems concerning to the data used for PA will be discussed in section 3.4. - Problem analysis. Table 3.2. Used poverty criteria/indicators and data sources Criteria/Indicators Data source Data creator I. Geographic Location: Commune in remote, coastal, Annual report Local government frontier areas or island far away from developmental center II. Infrastructure: - Asphalt road to commune center Report on Transportation request department - Health care center within commune Annual report Local government - Rate of households having access to clean water Census or GSO or NGOs results of PPA - Rate of household using electricity as a main source of Census or GSO or NGOs lighting results of PPA - Middle school within commune Annual report Local government III. Social conditions - Rate of illiterate LSS or report on GSO or Education request department - Rate of households having TV, radio and consumer LSS or results of GSO or NGOs durables PPA - Rate of stunting among children 0-59 months Report on Health care dept. 25 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM request or or NGOs results of PPA - Rate of malnutrition in adults Report on Health care dept. request or or NGOs results of PPA - Rate of drop-outs among children 10-14 years Report on Education request department IV. Agricultural conditions - Rate of landless households Report on Agricultural request department - Backward implements for husbandry (using buffaloes and Annual report Local government oxen for plough) V. Economic conditions - Rate of households having income under poverty line Report on PDOLISA or request or NGOs results of PPA - Rate of unemployed within labor age people Report on PDOLISA request 3.2.4. GIS application in PA Currently, the idea of using geoinformation for PA is quite new for PAP workers, especially at province and lower levels. The term and concept of geoinformation system is hardly known even by the administrative decision-makers. Reasons for this are the lack of infrastructure (e.g. computers, computer network and so on), lack of trained staff, limited of suitable data and absence of leadership in information management in poverty domain. However, many socioeconomic analysts and policy/decision makers are aware of potential use of GIS in PA. In the Congress about Assessment of Implementation of Vietnam PAP in Hai Phong, February 2001, the geoinformation system for PA has been dealt with great attention. It was emphasized that “present poverty data are still weak on the environment side” (MOLISA, 2002[1]) and outputs of PA are often not comparable with the environmental surveys, or are not georeferenced. Regarding to initiating GIS into PA, World Bank Vietnam and NGOs organized some training courses on Poverty Mapping in the University of Ho Chi Minh City and in MOLISA (MOLISA, 2002 [1]) in 2001. 3.3. User requirement analysis The synthesis of user requirements made during the fieldwork covered the following categories of potential users: governmental organizations with anti-poverty mandate (e.g. MOLISA, Provincial People Committee), UNDP, International NGOs and some other related organizations. Users of poverty information usually are policy/decision makers, researchers, socioeconomic analysts, development project designers and other people who are interested in progress of implementation of PAP. These people could be divided into two groups as: First group: Poverty analysts, who are doing PA. Second group: End-users, who are using outputs of PA. 1- The major concerns of poverty analysts are followings: 26 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS - Access to information on poverty. Most of these people loss their time to look for data source. For example, even the formal documentation of criteria for definition of national poverty line is not easy to find out. They need one center or organization, which will be response for integrating all poverty information into unique and accessible database. - Adding geospatial data into poverty analysis. Many analysts think that poverty analysis should include relationship between geospatial and socioeconomic data. Upon their opinion, poverty is spatially concentrated and geospatial data provides information on the spatial distribution of poverty. This data also may shed light on the geographic factors associated with poverty, such as mountain terrain or distance from major cities. - More powerful tools for data analysis. Analysts need more powerful tools to explore causal relationship between various poverty related indicators, to visualize the outputs of analyses or to simulate the trend of poverty change. - Improvement of communication between users and producers of information and sharing data between related organizations. This is very urgent requirement, because people want to avoid their duplicate efforts and reduce the time for data collection. Further more, better communication will strengthen effectiveness of PA results to policy/decision making in implementation of PAP. 2- From the end-user’s aspect, main requirements are concentrated on: - Easier handling outputs of PA. Like users of first group, researchers, policy makers and project designers often have to waste a lot of time to look for data sources. They need better means of communication with poverty analysts. - Data quality and transparency. This refers to the credibility of results of PA, because the lack of descriptive evidences (Carrie Turk, 2002). Currently, outputs of PA are mainly in the form of textual reports with long explanation text lacking of quantity figures do not convince users. An example of PA output is illustrated in appendix 2. To understand how the generated information should need to be link to decision making, these people also need to understand the way of generating results of PA. - Adequacy of PA analysis and outputs. Many users mentioned that outputs of PA are mainly focusing on identification of poor commune (area), but they weakly prove “why” and “how” these areas are poor. Therefore, PA outputs are often not sufficient for users to link them to decision making. For example, The Participatory Poverty Assessment conducted in Ha Thinh (Action Aid Vietnam, 1999) provides some indication of the vulnerability of fishing households but with far less discussion of the causes of those involved. - Link between socioeconomic data with spatial data. Researchers and policy/decision makers increasingly require geographically disaggregated indicators that provide information about the spatial distribution and geographic characteristics of poverty within a country. This information can be used to quantify suspected regional disparities in living standards and identify which areas falling behind the process of economic development. In addition, it facilitates the targeting of programs whose purpose is to alleviate poverty such as education, health, credit, and food aid. - Poverty maps. Poverty maps are useful tools for users of this group to explore causal relationship between various related indicators. They also hope that poverty maps help them to understand the conceptual framework, which lays behind the PA to understand how the generation of information would need to be linked to decision making. 27 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM 3.4. Problem analysis The problems will be analyzed in the basic aspects that play important roles in the improvement of the PA process. Further more, problem analysis should be based on the overview of current PA process and user requirement analysis. From this point of view, PA process, data and technique for data analysis could be the most concerned aspects. Problems from the PA process Through situation review, it can be seen that the current PA process is unsuitable. Its main problem is bottom-up approach. Because this approach with analog inputs and outputs makes assessment in higher levels to be dependent on the assessment (or report) from lower level. By this approach, PA starts from district level, where district’s PC plays as representatives of poor people. In most districts with active and consultative leadership, this may work well. But, in some other districts, because of limited knowledge and skill of PC members, PA could be done by improperly approach, e.g. misunderstanding criteria/indicators or lack of local knowledge for poverty definition. Problems from data aspect From data aspect, the main problem is insufficiency of data for PA. The causes of this problem are mainly related to infrastructure for data handling and data management. Problems concerning the infrastructure for data handling include the lack of infrastructure and its causal manual method of data handling. In data management in the poverty domain, the absence of leadership leads to the messy situation in data handling. The overlaps among the data creators make SE data duplicate, inconsistent and redundant (see appendix 3). In this situation, SE criteria/indicators for PA are heterogeneous both semantic and schematic, scattering in different organizations, timeless (not up-to-date) and very difficult to be handled. Insufficiency of data also refers to the no-use of geographic information in poverty analysis. Because of this, there is no link of SE data to space. Problems in data analysis The key problem in this matter is defined as unsuitable technique for poverty analysis. This problem appears in term of limitation of tools for data analysis and visualizing results of PA because of manual analysis method. In the analyzed above context of the PA process, unsuitable process, insufficient data and unsuitable technique for poverty analysis could be defined as key problems that directly cause the core problem of whole PA process: inadequate poverty assessment. This core problem is in term of insufficient assessment of poverty causes, neglecting geographic factors relating to poverty, analog outputs, etc. Key problems of whole PA process, their relationships with causes and effects are described in the figure 3.6 - The Problem Tree. 28 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS Weak support to policy/decision making for PAP in relation to space s Difficulty in the link of PA results to Limited dissemination t c decision making in PAP of PA results for wide use e f f E Ambiguous Non-standard description of poverty outputs Inadequate poverty assessment Insufficient data unsuitable Unsuitable for PA technique process No link between Obstacle of Manual data SE data and Bottom-up analysis spatial data approach s e Improper s Difficulty in SE No use of Analog input SE u Timeless data approach of PA a data handling geospatial data data in district level C SE Data SE Data Manual method scattering in diff. heterogeneity, for data handling organizations duplicate,etc. No leadership in Lack of information infrastructure management Figure 3.6. Problem Tree Subsequently, problem tree is translated into a set of future solutions to the problems. Each negative problem is converted into an objective (see appendix 4). All objectives will follow the cause and effect logic of the underlying problem tree to formulate the objective tree (Groenendijk, 2002). Objective tree is presented in the figure 3.7. 29 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Strong support to policy/decision making for PAP in relation to space Easiness in the link of PA results to Better dissemination decision making in PAP of PA results for wide use Poverty maps for Digital data set of description of poverty outputs Improvement of poverty assessment Integrated Applying GIS Improved geodatabase for for poverty process PA analysis Model for GIS prototype for Model of new integrating SE & PA process spatial DBs Integrating PA at Keeping data up- Accessibility of Geographic DB SE database for district & prov. levels to-date poverty DBs for PA PA into one step Center for Standard for SE Improved method poverty data for data handling information Leadership in Investment into information infrastructure management Figure 3.7. Objective Tree 3.5. Appropriate solution for PA 3.5.1. Implications for GIS application in PA GIS are essentially database management systems that use geographic location as a reference for each database record. It supports the integration of different types of data from heterogeneous sources and visualization of PA results. It also adds the spatial data as a new analytical dimension to the analysis of poverty. In a GIS environment, detailed information about the distribution of the poor enables researchers to investigate whether the spatial disparities in living standards have been caused by geographically defined factors. For instance, agro-ecological resource endowment, access to input and output markets, and availability of educational and health facilities all influence to the well being of households. The potential functions of GIS such as Retrieval, Classification and Measurement functions, Overlay functions and Neighborhood functions and Network functions (Sun et al, 2001) are powerful tools for poverty analysis. For examples, overlay operations can give explanatory information including survey and census data as well as GIS derived indicators on soil suitability, the number of livestock per capita, the distance to nearest health facilities and the rate of households having access to clean water. Another example of the use of GIS 30 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS to generate auxiliary information is in measuring accessibility to markets and service centers to determine the quality of public infrastructure. Geodatabase of PA GIS also could have an important role in communicating and sharing information on poverty within country. From the poverty database, many poverty maps can be produced for PAP purpose such as maps of locations of poorest communes in certain district, maps of distributions of householders, who can return and can not return the loans, etc. These maps present information in a way that it is easily comprehensible by non- specialist audience and help users to look for spatial trends, clusters or other patterns. Therefore, poverty maps are useful not only to governments and decision makers, but also to local communities. Availability of poverty geodatabase encourages data sharing and exchange within related organizations since it supports possibility of integrating data from different sources created by different actors and computer network facilities. 3.5.3. Criteria and verifiable indicators The Logical Framework method (Pitt, 1998) is applied to evaluate success of GIS for PA. This is the “logical framework” of objectives, based on causes and effects. It keeps under review as the research is implemented to facilitate monitoring. The logical framework summarizes the research of developing a GIS for PA by clarifying the hierarchy of objectives and their indicators, and recording assumptions at each level. Assumptions at a particular level are the conditions that the research can not control, but are necessary for successful achievement of objectives at higher levels. The common way of setting the logical framework is to derive objectives from objective tree beginning from core objective, then, to translate these into its first column (Groenendijk, 2002). From object tree, it could be found that the goal for developed GIS for PA is improvement of PA to strengthen its effectiveness to support decision making in PAP. This improvement refers to improved process, better technique to be applied and better results to be produced. With GIS, the goal of PA GIS could be achieved by: - Generation of needed data for PAP: higher quality, more diversified and easier understood by means of data visualization tools. - Easiness of data extraction, dissemination and exchange for further use, research and decision making. For this goal, a GIS for PA should gain its purposes. The most essential verifiable indicators for achieving its purposes are: - Change bottom-up approach to integrated approach in PA. - Use of integrated geodatabase for PA instead of list of poor commune from local governments. - Applying combination of individualistic and geographic models in PA by adding spatial data into poverty analysis. - Use of stronger (GIS) tools for poverty analysis. - Reduction of time in data analysis. - Increasing a number of output types. 31 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Detail verifiable indicators, their hierarchy, the information and method for verification are described in the table 3.3 - Logical framework of GIS for PA. Table 3.3. Logical framework of GIS for PA NARRATIVE OBJECTIVELY MEANS OF IMPORTANT SUMMARY VERIFIABLE VERIFICATION ASSUMPTION INDICATOR Goal: 1. Improvement of PA to 1. Generation of needed Documents of Policy/decision makers in strengthen its data for PAP: higher policy/decision making in implementation of PAP effectiveness to support quality, more diversified the implementation of have desire on using policy/decision making in and easier understood by PAP. results of PA to formulate PAP. means of data anti-poverty visualization tools. Questionnaire or programs/projects. interview from end-users 2. Easiness of data (decision/policy makers). extraction, dissemination and exchange for further use, research and decision making Purpose: 1. Improved process of 1.1. Change bottom-up 1. The proposed process 1. All users of poverty PA. approach to integrated model is accredited by information (poverty approach in PA. the appropriate analysts and decision authorities and operated makers) are familiar with 2.1. Use of integrated for PA by PWG. using GIS. 2. Integrated geospatial geodatabase for PA database for PA instead of list of poor 2. Input data sets for PA. commune from local governments. 2,3.1. Infrastructure for IT (including hardware, 3. 1. Applying combination software and data 3. Application of GIS to of individualistic and 3.1. Questionnaire or sharing facilities) is improve technique for geographic models in PA interview from poverty sufficient within poverty analysis. by adding spatial data into analysts framework of PAP poverty analysis. 3. 2. Results of PA 2,3.2. Staffs of PWG are 3.2. Use of GIS tools for well trained in computer poverty analysis. skill as well as in GIS 3.2. Reduction of time in data analysis. 3.3. Increased number of output types. Outputs: 1. Model of new designed 1.1. Reduction of steps in 1. Description/top-level - Availability of process for PA. the new process. DFD of the new designed information center who is 1.2. PA outputs in the process model. responsible for form of geospatial data information management set. in poverty domain. 2. Model of Integrated - All needed spatial data (e.g. Administrative, topo, geographic database for PA: soil, landuse... maps) are 2.1.1. Time for introducing 2.1. Calendars and available in digital format. 2.1 Simple and users to GIS for PA teaching/learning normalized data model materials of the training - All necessary SE 2. 1.2. No. of duplicate indicators for PA had and redundant records course in GIS for staffs of PWG. been collected. 2.2. A query efficient 2.2.1. Type of - Problem of workable/unworkable 2.2,3. Feedback from poverty geo-database poverty analysts. heterogeneity in SE data queries had been solved. 2.2.2. Response time to queries and the variety of queries 32 CHAPTER 3: THE SITUATION AND REQUIREMENT ANALYSIS queries 2.3. An accurate poverty geo-database 2.3.1. No. of missing entities 2.3.2. No. of missing attributes 2.3.3. No. of updated records 3. Prototype of GIS 3.1. Understandability of prototype 3.2. Functions of 3. Prototype testing and prototype evaluating 3.3. No. of output types Activities Inputs Possibility of using collected data 1. System/requirement 1. Socio-economic and analysis spatial data collected from fieldwork 2. Designing system. 2.1. Current situation 2.1. Creating database of review SE indicators 2.2. Requirement analysis 2.2. Integrated geographic data into 2. 3. Problem analysis database for PA purpose 2.3. Integrating SE and geographic databases into geodatabse for PA 3. System implementation 3,4. Hardware, software, 4. System operation in training courses, readers reality and communication facilities (tel., email, internet, etc.) 33 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Chapter 4 System design 4.1. Introduction System design is a step of process modeling and data modeling. A design of GI system for PA must be based on the information gathered through literature and current situation analysis (chapter 2 and chapter 3). As mentioned in previous chapters, GIS was considered as the most promising technique for poverty analysts to achieve a dramatic improvement in PA process. However, GIS application requires appropriate data model i.e. inside GIS environment poverty data should structured in such way that they can be handled by GIS functions to provide the required information. In this chapter, the process modeling in order to improve PA process will be presented in section 4.2. Then, in section 4.3, the conceptual data model will be constructed accordingly. On the base of process model and data model, the model of proposed GIS for PA will be conducted and described in the section 4.4. During all steps of system design, the inputs, major processing functions and outputs of the system are defined relying on the user requirements. 4.2. Process modeling 4.2.1. Proposed process of PA The need to urgently re-engineer the current PA process, replacing the bottom-up approach by a more integrated and more generic approach to poverty analysis has been discussed in previous chapter. This approach is revealing through following aspects: - Adding spatial information into poverty analysis. Since objects of PA in Vietnam are communes therefore spatial data for its implementation should include commune administrative polygons. Other spatial data needed to examine poverty causes will be topo maps, land use map, soil map, environment maps, and cadastral map. These data should be added into data for PA. - Creation of SE indicators database. Because this kind of database in poverty does not exists, it is necessary to create a database for these data in a first step toward data integration. Although poverty is characterized by a large number of multidimensional indicators, in the database these indicators must be translated to quantitative indicators and must be grouped into criteria in the way that they can match the national criteria of poverty definition. - Integrated geospatial database. Integrating non-spatial and spatial data from different sources into geospatial database for poverty analyses. - Applying GIS to provide stronger tools for poverty analysis and disseminating PA outputs in order to improve support from PA to decision making in the framework of PAP. - Reduction of steps. In the new proposed process, PA at provincial and district’s levels will be integrated into one step performed at provincial level (see figure 4.1). 34 CHAPTER 4: SYSTEM DESIGN Complete integrated Formulation of national geospatial data set of PA support plan for PAP and anti- in whole country poverty projects Government PWG - Integrate geodatasets from different provinces - Use GIS to analyze and verify the classification of poor commune and geographic causes for whole Decision making, country province anti- poverty plan s u p p o rt Geospatial data set of NGOs PA in province Provide results of Participatory poverty assessment Government Province's PWG Issue criteria for Province PC - Integrate geospatial DB poverty identification from spatial and SE data for Annual reports on SE PA development - Use GIS to analyze, identify and classify poor communes and geographic causes District's PC Local knowlege Related departments Provide spatial data Provide statistics on: including: - Socioeconomic dev. - topographic map - Education - Administrative units - Health care - Soil map - Transportation - Land use map - etc. - Environment map - etc. Figure 4.1. Top-level data flow diagram of improved process of PA 4.2.2. Main sources of information and proposed institutional context of PA at province level In the new designed process, PA at provincial level would be the starting point and become the most important step. Except current external entities that provide SE data, like Provincial Statistic Bureau, PDOLISA, Provincial Department of Health Care, etc., there is also a number of other departments under province authority that need to be involved in this process in order to provide spatial data. These are provincial Department of Land Administration, provincial Department of Science and Environment and Provincial Department of Agriculture and Rural development. District’s People Committee will play the role of an information provider who provides local knowledge about poor communes. 35 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Main information sources are listed in table 4.1. An improved context for PA process in this level is, therefore, proposed in figure 4.2. Table 4.1. Main information sources for PA at provincial level Information source Provided Information Government Criteria for poverty identification Province People committee Annual reports on SE development District People committee Local knowledge Province Statistic Bureau Census data, LSS data, Statistic in SE development PDOLISA Labor statistics, average income of household Provincial department of Health care Information about health of community Provincial department of Education and Information of education of community Training NGOs Socioeconomic and infrastructure information Social association Specific information and petitions from poor people Provincial Department of Land Administrative maps, Topomaps, Landuse maps, Administration Cadastral maps Provincial DOSTE Environment maps, climate maps Provincial Department of Agriculture and Soil map, indicators of agricultural condition Rural development Provincial Statistic Bureau PDOLISA GPWG LSS data S-E Labor statistics Census statistics Geodata set for poverty data analysis in province Info. for Government policy making Criteria for poverty NGOs definition Results of PPA Related departments Annual reports or of report on request Poverty Information for - Health care assessment project planning - Education and Geospatial traning data Poverty information - Agricultural and Rural in specific development indicators Annual reports on - Land Administration Information for - Science and SE development prov. anti-poverty Local knowledge Environment program Social - etc. Associations Province PC District PC Figure 4.2. Institutional/information context of proposed process of PA at provincial level 36 CHAPTER 4: SYSTEM DESIGN 4.2.3. Main sources of information and proposed institutional context of PA at national level GIS with accessible, query-able geodatabase and strong tools for geospatial data analyses and visualization will make results of PA at provincial level become transparent to end users (i.e. decision/policy makers). Therefore, PA at national level could rely only on the outputs of PA in province level. The main tasks at national level is to integrate PA outputs in the form of geospatial data sets from different provinces into one poverty database which can support to PA for the whole country. In this case, main sources of information for PA at national level will be PWG from different provinces. The improved context for PA at national level is described in figure 4.3. GSO MOLISA Government Info. for Criteria for formulation of poverty Info. for poverty reduction strategy statistics on policy making definition poverty NGOs Information for project planning Related Ministries Poverty - Health care Information for - Education and project planning assessment traning - Agricultural and Rural Information for development project planning Social - Transportation Associations - etc. Information for Geodata set for poverty prov. anti-poverty analysis in province program Province Province PC PWG Figure 4.3. Institutional/information context of proposed process of PA at national level 4.3. Data modeling Data modeling is a process of representing and manipulating information within the framework of a database system. A data model is an abstract representation of reality. It defines the way data items are organized and related (Davis G., et al, 1985) and also the structure of stored data that will be accessed by a process. Several levels of data modeling have been adopted in order to structure data in such way that a computer can handle. These levels are conceptual, logical and physical data modeling (Figure 4.4). Context mapping discipline Application discipline Spatial modeling Geo-information theory Conceptual modeling Logical data modeling Computer science Physical data modeling Fugure 4.4. Levels for data modeling (Molenaar, 1998) 37 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Since most of the users will not be concerned with internal storage structure or organized files at the physical level of data modeling, this section only represents approaches for the conceptual and the logical data modeling. Conceptual data model of database for PA Conceptual data model is the first level of abstraction from reality. It consists of describing how the spatial and non-spatial data can be linked in database, how the data about reality can be abstracted and represented in database. Occasionally this level is called as semantic analysis, because it should capture the semantics of the data (Hawryszkiewycz, 1997). The conceptual data modeling is concerned with creating a conceptual schema for database on the base of analyzing the information requirements. It is a concise description of the data requirements of the users and includes detailed description of the data type, relationships and constraints but does not include any implementation details. The primary concern of data modeling in current study is to create an integrated database of SE and geospatial data in a manner that any user can access the database easily and safely. There fore, data model must present the method of the link between SE data and geospatial data. For associating SE (non-spatial) data with spatial data, relational data model is the most suitable (Aronoff, 1989). This model is successfully presented by entity- relationship diagram. Entity-Relationship (E-R) diagram Entity-Relationship (E-R) method was first introduced by Chen in 1976 and now is widely used (Hawryszkiewycz, 1997). The basic components of this model are entities (entity sets), relationships (relationship sets) and attributes. In the case of this study, three entities have been defined as: Commune unit, SE indicators and geographic feature. These entities are described in table 4.2. Table 4.2. Description of defined entities Entity Description Commune unit An administrative unit with clear boundaries; spatial reference of each area is shared by different geographic features. SE indicator Relevant indicator describing SE status of each commune and using for poverty assessment. Geographic feature A geographic feature that can be found on commune administrative unit. The relationships between these entities are: - “Link” associates commune unit and SE indicator by one-to-one relationship, - “Overlay” associates commune unit and geographic features by one-to-many relationship. Figure 4.5 shows conceptual E-R diagram of the data model of PA database. 38 CHAPTER 4: SYSTEM DESIGN SE indicator Link Attribute Have 1 1 N N 1 Commune Asign unit 1 1 Commune 1 Have polygon 1 Overlay N Spatial Have 1 element 1 Geographic 1 Legend feature 1 Asign Entity N Non-spatial relationship N Have AttributeN Spatial relationship Figure 4.5. Conceptual E-R diagram for PA database model Enterprise rules These are the rules that try to capture the meaning of the data in a database. Enterprise rules define the relation and specify the number of relationships in which an entity can appear or participate. In data modeling for PA, the rules governing relationships between defined entities could be elaborated as below: 1. One commune is a distinct administrative unit. 2. A commune may have many geographic features. 3. One geographic feature can intersect with many communes. 4. An SE indicator is a value used to evaluate poverty. 5. A commune may have many different indicators. 6. An indicator can be found in many communes. 7. One commune can have only one value of each SE indicator. The logical data model is intermediate level of data representation. It follows conceptual data model and is completed when a particular database structure is chosen based on design of the conceptual model. The logical data modeling involves the normalization of the E-R diagram in order to eliminate repeating groups, to check the dependency of all attributes on their identifiers, and to check that there is no hidden dependency between 39 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM attributes. However, in this particular case, because of its simplicity of data model for PA, the relationships are normalized. Therefore, logical data model is the same as conceptual data model. Skeleton tables The structure of database is presented by the mean of skeleton tables. A skeleton table is a table that shows entities with their corresponding attributes in normal form. Skeleton tables show primary and foreign keys. The attribute whose value is uniquely identifying an entity in a table is called a primary key for that entity. The primary key could be single if it is constructed by only one attribute and composite if it is constructed by two attributes. An attribute, which refers to the primary key of another table, is called foreign key. According to conceptual E-R diagram (figure 5.4), it could be seen that SE indicator is only non-spatial entity whereas two other entities (commune and geographic feature) have spatial and non-spatial components. Therefore, all these data could be organized in two related databases: one of them is non-spatial database including only SE indicators and another one is spatial database, which contains spatial layers and relevant attribute tables of commune units and other geographic features. These will be called SE database and geographic database. In SE database, all commune indicators for poverty definition could be arranged in one table. Nevertheless, for better support to poverty analysis they will be organized in different tables that are separated by criteria. These tables are: C_Admin (commune administration), C_Agri (agricultural condition in commune), C_econo (economic condition), C_Infras (infrastructure condition), C_Loca (geographic location) and C_socio (social condition). Basic skeleton tables for entities and their attributes in normal form of the proposed database are showed below with primary keys are underlined and foreign key italic: C_Amin (Dname, DID, CID, Cname, Landscape, Area, Pop, Household, Po_Den) C_Agri (CID, Cname, Landless, ProdMean, Occupation) C_econo (CID, Cname, Unemployra, Poor_HHrate) C_Infras (CID, Cname, Transport, HealthC, Cwater, Electr, School) C_geo (CID, Cname, Location) C_socio (CID, Cname, illrate, Inf_HH, Childstunt, Adultmanut, Dropout) In geospatial database, geographic features are separated by themes and organized in different spatial layers. Each layer is assigned with relevant attribute table. The major constraint for integrating spatial database is that all geographic features must be projected in the same georeferenced system. Themes, spatial layers and their attribute tables are described as follows with primary key underlined and foreign key italic: Theme Layer Attribute table Description Communes Commune (CID, CName) Commune’s administrative units Com_centers (Object_ID, CID, Name) Administrative center of commune 40 CHAPTER 4: SYSTEM DESIGN Topo Topo map of province SCenter (Object_ID, Admin_type, Name) Service centers Cont (Object_ID, height) Contours of elevation Roads (Object_ID, Type) Road network Rivers (Object_ID) Rivers and coastal lines Water (Object_ID) Water areas Villages (Object_ID, Hib_type) Villages Cadastre Cadastre (Parcel_ID, CID, Type_ID, Name) Cadastral map Landuse Landuse (Object_ID, CID, Type_ID, Name) Land use map Soil Soil (Object_ID, CID Soil_ID, SoilName), Soil map Environment Environment map Flood (Object_ID, type_ID, Haztype) Flood prone areas Storm (Object_ID, type_ID, Haztype) Storm prone areas Pollt (Object_ID, type_ID, Haztype) Polluted areas Draught (Object_ID, type_ID, Haztype) Draught prone areas CID is the identifier of each commune. This attribute is used as common key to link all tables of SE database together and to link SE database to geospatial database. Therefore, it should have well defined and recognized structure. CID is formulated on the base of the administrative structure of Vietnam using 6 digits as XXYYZZ, where XX represents province code, YY – district code inside each province, XX – commune code inside each district. The description of other attributes will be given in appendix 5. 4.4. Proposed GI system for PA From the results of process and data modeling, the appropriate model of GI system for PA will be constructed. This is typical GIS, which contains four major functional components: 1-data collection, input and correction; 2-storage and retrieval; 3-data manipulation and analysis; and 4-data output and reporting (David, 1991). It works at both province and national levels as combination of different steps of data creation, integration and analysis. These steps are described as follows: At province level Step of Creation of SE database is performed in any general DBMS. This task includes gathering socio-economic data from related provincial departments, NGOs and social associations; compiling and translating those collected into national criteria and indicators for poverty definition (this task is necessary because of multidimensional character of poverty and heterogeneity of information related to poverty). Step 1 of data integration. In this step all necessary spatial data for PA (that were described above) of the province will be integrated on the base of unique georeferenced system into one temporary spatial database for PA. Step 2 of data integration integrates SE database and spatial database. The link between these databases is formulated through CID, which acts as common field between attribute table of commune theme and the tables of SE database. This key also provides access to the integrated database. Step of data analysis within GIS software. Within GIS environment, data from integrated database will be analyzed by queries and GIS functions such as overlay 41 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM intersection, clip, etc. in order to classify poor communes, verify correlation between factors related to poverty and produce other needed information for PA. At national level Step 3 of integration will be formulated at national level. Geospatial data sets of provincial PA from different provinces should be integrated into national database of PA. Geodata set of PA in whole country Data output and Intergration step 3 National level reporting Provincial level Geodata set of PA in Geodata set of PA in province N province A Poverty analysis using GIS functions: - Classification of poor communes Data - Examining correlations between SE, manipulation verifying geographic factors related to & analysis poverty and producing needed infor. - Visualyzing the results of PA Data storage & retrival Temporary geodatabse for PA Data collection, input & SE databse Intergration step 2 correction Ctreation of SE databse Statistic data: - SE development Temporary geo-database - Education Compilation and translation of SE - Health care for administrative - Transport data into criteria/indicators for PA boundaries - Infrastructure - etc. Pov. LSS Census survey data data data Intergration step 1 Cadas. Envir. Landuse Topo Admin. Soil map map map map map map Legend: Databse Process Analog data Data flow Figure 4.6. Proposed GIS for PA with its components and levels of implementation 42 CHAPTER 4: SYSTEM DESIGN 4.5. Concluding remarks In this chapter, new process and data models for PA were proposed. In comparing with current process of PA, the new designed process integrates steps of PA at district’s and provincial levels into one step and adds spatial data into poverty analysis. Following new process, PA will be formulated at provincial and national levels, but mainly at provincial level. This was achieved by mean of GIS application and integrated geodatabase. On the base of definition of entities and relationships between them, the conceptual data model has been constructed in order to integrate SE data with geospatial data in systematic manner. On the base of process modeling and data modeling the proposed GIS for PA was formulated. 43 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Chapter 5 The prototype 5.1. Introduction The purpose of this chapter is to illustrate the possible implementation of the designed process and data models. Objectives of developing the prototype are: - To test and evaluate the process and data models, - To examine the possible application of the spatial analysis functions of GIS to PA, - To address the problems that could occur in the application of GIS in PA process. The development of the prototype of GIS for PA is based on the process and data models that have been designed in previous chapter and the data that have been collected during the fieldwork. SE database would be created in any general DBMS (e.g. Microsoft Access, Dbase) rather than in GIS, because it will save cost, since DBMS software and operating manpower are less costly than that of GIS. Meanwhile, geographic database will be performed in GIS software then links to SE database through the common key CID (commune identifier). Therefore, software used to develop this prototype are Microsoft Access and ArcView 3.2 (ESRI). The concrete contents of collected data will be described in details in section 5.2. Implementation of data model will be explained in section 5.3 as a procedure of data input. Section 5.4 illustrates the application of GIS for poverty analysis in PA at province level. Section 5.5 discuses the improvements and limitations as well as the possible problems that may occur in PA by applying GIS. 5.2. Description of collected data Data that have been collected for developing a prototype of GIS for PA include SE data and geospatial data. 5.2.1. SE data For developing prototype of GIS for PA in the case study of Ha Tinh province, the following SE data sets have been collected: 1. Vietnam Census data 1999 has been created by GSO on the base of census survey for whole country which was undertaken on the 1st April 1999 (the description of this data set was given in section 3.2.4). This is a large digital data set stored in CD-ROM, which provides information presenting demography per administrative units of different levels from whole country, to province, district and commune. 2. Ha Tinh LSS 2001 was created by Ha Tinh Provincial Statistic Bureau in 2001 under the guide from GSO. This data set includes indicators in education, health, average per capita income of communes, primary occupation in of commune population etc. 3. Report of poor communes in Ha Tinh, 2000 was provided by province PC describes the SE status of poor communes in the reported time (i.e. in 2000 year). It contains 44 CHAPTER 5: THE PROTOTYPE information about the status of school, school children, health care centers and transport status of every poor commune. 4. Ha Tinh statistic manual 1998-2001 provides statistics on SE situation of communes through years such as area, population, land use structure, sown area of crops, production of food, education, health care, etc. From these data sets criteria/indicators for poverty definition are derived as showed in the table 5.1 below. Table 5.1. Derivation of criteria/indicators from collected data Criteria/Indicators Data source * I. Geographic Location: Commune in remote, coastal, [3] frontier areas or island far away from developmental center II. Infrastructure: - Asphalt road to commune center [3] - Health care center within commune [4] - Rate of households having access to clean water [1] - Rate of household using electricity as a main source of [1] lighting - Middle school within commune [4] III. Social conditions - Rate of illiterate [2] - Rate of households having TV, radio and consumer [2] durables - Rate of stunting among children 0-59 months [3] - Rate of malnutrition in adults [3] - Scholl unenrolment rate among children 6-14 years [2] IV. Agricultural conditions - Rate of landless households [4] - Backward implements for husbandry (using buffaloes and [3] oxen for plough) - Primary occupation [2] V. Economic conditions - Rate of households having income under poverty line [2] - Rate of unemployed within labor age people [1] (* Data source: [1]- Vietnam Census data 1999, created by GSO. [2]- Ha Tinh LSS 2001, created by Ha Tinh Statistic Bureau. [3]- Report of poor communes in Ha Tinh, 2000, created by province PC. [4]- Ha Tinh statistic manual 1998-2001, published by GSO) 5.2.2. Spatial data Relying on spatial data that are available for Ha Tinh, collected spatial data include several data sets such as Communes, HTtopo, Landuse, Soil and Environment. All these data are in vector format and referenced in UTM projection, but created in different software. Geographic features in data sets are clipped by provincial boundary and some of them are also clipped by commune boundaries. 45 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM The descriptions of these data sets are given in table 5.2. Table 5.2. Description of collected spatial data Data sets Contents Format/software Origin scale Projection Creator Communes Commune Vector/MapInfo 1:100000 UTM - Zone GDLA polygons, centers 48N and their attributes Httopo Contour lines, Vector/ 1:1000000 UTM - Zone GDLA roads, rivers, Microstation 48N service centers and their attributes Landuse Land use units, their Vector/ MapInfo 1:100000 UTM - Zone GDLA attributes 48N Soil Soil type units, their Vector/ ArcInfo 1:100000 UTM - Zone MARD attributs 48N Environment Flood prone areas, Vector/MapInfo 1:100000 UTM - Zone Ha Tinh storm prone areas, 48N DOSTE polluted areas 5.3. Data input In order to verify the performance of the designed data model, two databases must be created from collected data. These databases are SE database and spatial database. SE database As mentioned earlier, in this study, SE database is created in Microsoft Access. This database includes six tables as C_Admin, C_Agri, C_infras, C_socio, C_loca, and C_infras. Relationship between these tables is presented in figure 5.1. Figure 5.1. Data relationships in database for SE indicators in Microsoft Access 46 CHAPTER 5: THE PROTOTYPE Geospatial database All collected spatial data sets are integrated in ArcView. Since source data were created by different software, data conversion should take in place. This task could be implemented by some tools insides GIS packages like Universal Translation in Mapinfo, Import/Export tool in Microstation and ArcCatalog, etc. Link between SE and geospatial databases The link between SE and geospatial databases is performed by common key. In the study case, this key is CID. Each record of each table in the SE database created in Access must be correctly referenced to the respective commune area in geospatial database through the common key. In any GIS package the tool for connecting table and queries from non- spatial database (created in general DBMS) to geospatial database is usually available. In the case of ArcView, this tool is SQL connect. 5.4. Poverty analysis within GIS In the framework of PAP, the most important questions arising for poverty analysts and policy/decision makers are: - Which communes are poor? - Where are they located? - How (in which level) are they poor? - Why are they poor? Examples given below demonstrate how to derive information from poverty database to get answers to these questions. 5.4.1. Classification of poor communes Results of classification of poor commune are utilized in a number of policy and research applications because they indicate which communes are poor and in which level they are poor. Their visualization within a GIS environment can show the poverty situation of different areas on a comparative basis. Classification of poor communes can be formulated under different categories so that it can describe poverty in different perspectives. Depending on the purpose of the use, categories for classification could be relied on individual indicator or on composite indicator. Classification by individual indicator is the most useful for setting up a concrete anti- poverty project. For example, a project in clean water supply will focus attention on the area where concentrated by communes that have low rate of household having access to clean water. This kind of classification classifies poor communes by any one of the national indicators for poverty definition. The figure 5.2 presents results of commune classifications by some individual indicators. 47 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM a) b) c) d) Figure 5.2. Classification of poor commune by individual indicators (a- rate of households having low income, b-rate of adult malnutrition, c- per occupation and d- rate of households having access to clean water) In PA, each value of combination of indicators forms a composite category, and the nomenclature of the categories are given in relative terms. As an example, combination of “adult malnutrition rate”, “children stunting rate” and “health care center conditions” indicators will be a category of “health care condition”. Calculation of composite category requires a well-designed model which concerns all related variables (Groten, 1990). Since relative importance of the basic components of the composite indicator is not well known, the weight for all the components are usually taken as unity. In the case of PA in Vietnam, the most often used composite category is general poor index, which is combined from all indicators using to define poor communes that are listed in table 3.1 (MOLISA, 2000). Calculation of this composite category follows the model presented in appendix 5. Classification by general poor index has a great importance in policy making in PAP implementation. For instance, to define coefficients of salary-subsidy for staffs of government organizations located in different areas, to arrange areas in priority of investments, to evaluate progress of PAP implementation, etc. Classification of poor communes in Ha Tinh following this approach is illustrated in figure 5.3. 48 CHAPTER 5: THE PROTOTYPE Figure 5.3. Classification of poor communes by general poor index 5.4.2. Derivation of indicators from geospatial database In conventional methods of PA, all indicators are extracted from SE data, such as censuses, LSS, annual socioeconomic reports, etc (see section 3.2.4). Now, inside GIS, there are some indicators that could be derived from geospatial database. Example below shows how to extract indicator landless by using GIS functions. In ArcView, Intersection Overlay is specified to use for producing layer Com_luse (Land use of communes) from layer Luse (land use) and Communes. In the layer Com_luse, areas of each land use type are clipped by commune boundaries. Then, from attribute table of Com_luse, land use per capita will be calculated by queries illustrated below. Total area of each land use type for each commune is calculated by query (Luse_areaS_C): SELECT [Luse_type], [COM_ID], SUM([SHAPE_Area]) AS Area_s FROM Com_Luse GROUP BY [Luse_type], [COM_ID]; Land use per capita in communes is calculated by dividing commune’s total area of each land use type to its population by query: SELECT t1.CID, t1.CNAME, t1.POP, t2.Area_s, t2.Luse_type, t2.Area_S/t1.POP AS Luse_pp FROM C_admin AS t1, Luse_areaS_C AS t2 WHERE ((t1.CID)=[t2].[COM_ID]) ORDER BY t1.CID; Then, according to national indicator “landless” (see table 3.1), the “Map of classification of cultivating communes per landless” will be constructed. 49 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Landuse layer Communes layer Map of land use per communes Map of classification of cultivating communes per landless Figure 5.4. Extraction of indicator landless by using GIS functions 50 CHAPTER 5: THE PROTOTYPE 5.4.3. Correlation between poverty and SE and geographic factors With poverty data mapped in commune level, the correlation between poverty and its causes, will appear clearly. Essentially, analyzing correlation should start with sensitive hypothesis and with maps containing a certain numbers of categories. Two following examples will illustrate the approaches to examine and explain correlations between some factors. Correlation between the rate of adult malnutrition and polluted areas This example examines how pollution in the case study area effects to the health of local people. The first map (see figure 5.5) shows the locations of polluted areas in Ha Tinh. In the next map, three layers: polluted areas, rate of adult malnutrition in communes and villages are overlaid. Different patterns in these areas could be explained by degree of pollution and location of villages, e.g. in the west-north area most villages in these communes are located within polluted area and in the south area, the pollution exists but its degree is not so high. Use of function Select by theme then query Select From Set gives the list of communes where population health is affected by pollution. a) Map of polluted areas b) Map of polluted areas, rate of adult malnutrition and villages c) Selection of affected communes Figure 5.5. Analysis of correlation between polluted areas and adult malnutrition 51 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Relationship between communes having high rate of poor households, roads and service centers The second example is applied to explain the hypothesis that “Poor communes are usually located in remote areas far from service centers”. In this case, poor communes are defined by the rate of poor households (who has income below poverty line). The map showed in figure 23 highlights the adequate picture of given hypothesis in Ha Tinh: most of highly better-off communes are clustered near the service center, where transport net work is quite developed whereas poorest communes are found in frontier areas far from developmental centers. Figure 5.6. Relationship between poor communes, road network and service centers 5.4.4. Linking PA outputs to decision making GIS can provide policy/decision makers a numbers of effective and easy tools for designing the geographically prioritized poverty alleviation programs. Based on the query- able database and visual form of PA outputs the planners define anti-poverty projects (plans) in a more effective way. Let suppose there is a project in Agricultural Encouragement, which purposes to help peasants to improve their working condition by investment on heavy farming implements such as tractors. The first priority of this project is given to communes, whose primary occupation is cultivation, per capita sown land is more than 1000 m2, and rate of poor households is more than 40%. Thereby, selection of these communes is performed by Join related tables together then running the query as: SELECT CID, Cname, Occupation, Poor_HHrate, Sownland_pp FROM Communes WHERE Occupation ='Cultivation' And Poor_HHrate>=40 And Sownland_pp>=1000; Objective communes of this project are showed in the map in figure 5.7. 52 CHAPTER 5: THE PROTOTYPE Figure 5.7. Selection the suitable communes for a project 5.5. Evaluation of the prototype The implementation of PA by using data of Ha Tinh province here has been done following the proposed GIS, which was designed in the previous chapter. From the findings and relying on the criteria and verifiable indicators that are mentioned in section 3.5.3, the sections below will discuss about the advantages and limitations of this system. It also describes the potential problems that PA could face with by applying GIS. 5.5.1. Advantages The prototype of PA GIS allows adding spatial data into poverty analysis. The examples of classification of poor communes and poverty analyses (illustrated in whole section 5.4) have been performed by using data form integrated geodatabase for PA instead of the list of poor commune from local governments. By this, it was showed that PA could be performed in one step at provincial level. Use of GIS tools for PA. According to data model, SE data and spatial data were integrated in systematic structure that enables applying GIS functions for poverty analyses. The Database of SE criteria/indicators also ensures the reduction of data redundancy and duplication. In GIS environment, results of PA become easier understood and easier to link to decision making. They are displayed in visual form, thereby they improve decision making capacity of planner because from the maps, poor areas on direct individual indicator, like rate of poor households, adult malnutrition, etc. or on composite poor indexes can be identified very easily. Correlation between factors effecting to poverty is obtained by overlay function. Different patterns showed on the map can help analysts to have deeper understanding of the causes or potential affected sectors of poverty. Generation of needed data for PAP. Classifications of poor communes by different indicators are very useful for formulating plans, policies addressing to anti-poverty purposes 53 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM at local level as well at national level. Structured database and GIS functions enable PA in all provinces to be performed relying only on criteria/indicators for poverty definition approved by government. This is very important for assessing poverty situation in whole country, because it allows comparing poverty of different areas on the same basis. 5.5.2. Limitations The main limitation is logical data model. Except national indicators for poverty definition, PA also needs to use a lot of other indicators that are called “auxiliary data”. Therefore, the logical data model proposed in this research seems quite small. In practice, it has to be extended by adding more tables of indicators. This will need more time for information requirement analysis. 5.5.3. Potential problems Application of GIS in PA may face with following problems: Validation of SE data. The sources for SE data are numerous and diverse. However, in some case they are not useable for PA. For example, indicators of one criterion can be derived from two or three data sets. But the difference between the times of data generation is too big, even more than 3, 4 years. Another issue is that SE data, sometimes, are not presented for commune units, but for district units. Lack of data. Necessary data for PA are not always available for every province including SE data and spatial data. Different georeference of spatial data. Spatial data of a province could be differently georeferenced. Because according to national regulations, topomaps and land use maps are projected on national georeference system, but other maps such as cadastral maps, soil maps, environmental maps could represented based on local reference system. This will lead to the spatial mismatching data between spatial data or need for data conversion (geo-referencing). Data loss. During integrating spatial data from different sources, the conversion from one format to another could cause data loss, especially attribute data. Ambiguity and inconsistency of SE data. Definition and description of SE data are usually ambiguous. For example, the indicator of “using clean water” is described in one source as “the rate of households having well-water,” but in other as households having “tap- water or cistern”. This leads to difficulties in translating SE data into indicators for creating database. Before initiating GIS into PA for any province, it is recommended that all mentioned problems should be addressed and studied carefully to get possible solutions. Or, at least, PWG should be aware of these problems and their possible limitation. 54 CHAPTER 6: DISCUSSION OF POSSIBLE IMPLEMENTATION OF A GIS FOR PA Chapter 6 Discussion of conditions for implementation of a GIS for PA 6.1. Introduction GIS not only opens the door to the integration of different types of data and the visualization of the heterogeneity of poverty, it also adds a spatial dimension to the poverty analysis. The implementation of the designed system will certainly promote support from PA to decision making within the framework of PAP. However, it is clear that adoption of GIS application into PA is much more than a combination of hardware and software for poverty analysis. This work could be implemented only under the certain conditions of data sharing and system interoperability. The current chapter will discuss some aspects of technical and institutional arrangements because technical conditions and institutional structure are among the most crucial factors that can enhance or retard implementation and optimal use of GIS. 6.2. Technical arrangement As explained in chapter 4, the designed GIS for PA is a computer based system, which consists of four functional components (see section 4.4). Within this system, each component needs different means of technologies to work properly. Some processes require general DBMS for data input and data store whereas others necessitate GIS applications for spatial data handling and processing. The effectiveness of all activities aimed to apply GIS into PA depends very much on hardware, software and human-ware. Hardware and software selection Several considerations have to be taken into account for the selection of hardware and software. Hardware and software selection belongs to the category of low-level standards that are vitally important for the interoperability of computer system. The value of chosen standards for GIS is reflected in Portability, Inter-operability and Information access and Maintainability (Croswell, 2001). In practice, this selection is defined by the criteria that depend on various factors such as: - The tasks to be fulfilled, data complexity and data input, output format; - Cost-benefit analysis relying on the cost with regard to the duty/function to be performed; - Previous experience within organization dealing with; - Availability of domestic IT market; - Trend of IT development in host country and in the World. Actually, the application of GIS in PA does not need costly investment for hardware as well as for software, because the whole system of PWG of the whole country including both provincial and national levels is quite small. Software for PA includes a general DBMS and a GIS package, e.g. Microsoft Access and ArcView. 55 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Staff training The success of GIS application in PA also needs qualified personnel. Therefore, training staffs is indispensable. Since GIS is still a quite new concept for PAP workers (see section 3.2.5), computer skill and GIS knowledge are necessary to be acquired by the staffs of PWG at both provincial and national levels as well as for decision makers who are involved to PAP. The component functions of GIS are closely related to the particular professional disciplines. In order to avoid inefficiency in training courses, it is recommended to separate trainees into groups according to their professions or their duties. For those who are responsible for data input and data maintenance, special courses of database management are required. Poverty analysts have to be trained in data query and spatial analysis. Further more, for these people the training course addressing mapping and cartographic techniques for presentation of PA outputs is essential as well. It should be understood that the leaning curve of GIS is longer than the average of other software training and therefore it is expensive. Given the cost involved, training should be assigned carefully aiming at those who likely will stay and benefit from it. Data availability GIS could be applied for PA if, of course, data for it are available and useable. Unfortunately, as mentioned in the last section of previous chapter, required data for PA are not available for every province, including both SE data and spatial data. Among available data, good quality data should have high priority in improving PA. Having sufficient data for PA necessitates cooperative efforts from related organizations including GSO, MOLISA, GDLA and authorities at national and local levels. Data standards It is important to mention that SE data are generated for a wide variety of purpose. Consequently much of the data is specialized in nature, and may not be ideally and directly suited to the requirements of PA. “Data standards”, in this context, refers to agreed way of presenting poverty data in a system in term of content, type and format. These could be achieved under the common regulations and guidelines for data collection and compilation with the purpose of PA. Poverty data generated under common regulations and guidelines can help poverty analysts to save their time in data compilation in order to translate SE data from different sources into criteria/indicators for poverty definition. Data standards constrain the common way for data gathering, therefore it can solves the problems of data heterogeneity and redundancy. They allow PA in different provinces become identical because these PA are based on the same criteria. Data standards also facilitate the data exchange between related organizations within PAP. 56 CHAPTER 6: DISCUSSION OF POSSIBLE IMPLEMENTATION OF A GIS FOR PA 6.3. Institutional arrangement As mentioned before (in section 3.4.2) that the weakness in institutional arrangement leads to messy situation in PA as well as in monitoring PAP implementation in general. The proposed GIS could be applied in PA only if existing institutional arrangement would be improved. It is suggested to gain the expected improvements through establishing a Center of poverty information and through data sharing facilities. Establishment of a Center of poverty information Leadership in information management plays very important role in the adoption of GIS into PA as well as in any application domain. In the context of PAP implementation, this expected center should be concerned with organizing activities that can strengthen the effectiveness of PA to support decision making in PAP implementation. These activities may be: - To establish regulations and guidelines for data creation and collection in PA purpose to solve the problems of data heterogeneity by data standard. - To act as an effective repository of poverty information and PA outputs for wider dissemination and use; - To establish a mechanism to ensure institutional collaboration and coordination to integrate different efforts in order to avoid duplication and to identify and address existing gaps, overlaps between information creators; - To establish and strengthen the links between data creators and data users to create conditions under which the poverty data and PA outputs may be shared and used by various actors to support decision making in monitoring implementation of PAP. It is recommended that the proposed center should work under a ministry or organization which has a mandate of poverty reduction and is fully equipped by computer facilities, e.g. MOLISA or Government Office. Data sharing arrangement PA needs to use a large amount of SE data as well spatial data that are created by many different organizations. It also needs to disseminate outputs to users from different organizations. Therefore, facilities in data sharing are urgently required. Unfortunately, sharing data for poverty alleviation purpose is rather weak in Vietnam. Even PAP is being supported by government policies, but there is limited data exchange among related organizations and no conditions under which the poverty data and analyses may be shared and used by various actors. Data generated by different organizations or individual researchers are often stored in different places and in different formats. Data sharing facilities firstly require the join efforts from government, non-government and civil society organizations related to PAP. It also needs to be supported by government policies, e.g. copyright laws, obligations of both data producer and data users etc. Commitments between organizations, national and organizational attitudes to data security and copyrighting can be of overwhelming importance. In fact, organizational and behavioral impediments to data sharing appear to far outstrip the technical impediments. Reluctance in 57 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM this matter within and between organizations requires deeper researches for finding solutions. The effectiveness of data sharing may also depend on technical ability constructed by different implements such as Intranet, Internet and data standards. Improvements in data sharing strengthen the support of PA to decision making in PAP implementation because of overcoming the big gaps that exist between poverty analysts and decision makers. Support from Government Adoption of GIS application into PA needs support from Government at both national and provincial levels. First of all, the important role of PA in the framework of PAP as well as in SE development of the country should be recognized by the Government. Policy makers from government organizations (ministries) with their appreciation of PA outputs and requirements to understand geographic characteristics of poverty will promote the use of new technique, like GIS, in PA. Quality of PA outputs requires government efforts to define the official poverty line and standard indicators to be used for the purpose of official poverty monitoring. Using GIS in PA as well as in other application domain necessitates the changes in government policy to provide access to data generated by government organizations such as GSO, GDLA, MARD, etc. 6.4. Concluding remarks This chapter emphasized some matters of technical and institutional aspects that are considered as the basic conditions for possible implementation of GIS for PA. The successes of GIS application in PA depend very much on these conditions. In fact, even though GIS is still not used in PA yet, but it has been adopted broadly in many other fields in Vietnam. In addition, Government promulgates prior policy for IT development. In this atmosphere, required conditions for applying GIS into PA to support implementation of PAP are feasible. 58 CHAPTER 7: CONCLUSION AND RECOMMENDATIONS Chapter 7 Conclusion and Recommendations 7.1. Conclusion Information on the spatial distribution of poor people can significantly improve policy/decision making or design of strategies/projects aimed at poverty alleviation. Thus, this study attempts to prove the potential capacity of GIS technology for PA in producing information to support decision making in the framework of PAP in Vietnam. The study has been conducted following research questions and analysis, modeling and prototyping to find the answers to those questions. The basic concepts of poverty and approaches for PA have been introduced in order to provide fundamental knowledge about studies of poverty assessment domain. For this purpose, the most important descriptions of the definition of poverty, its characteristics and the tools for PA such as poverty measurement, indexes, poverty maps, etc. have been presented in chapter 2. In the study, much attention has been given to review and analysis of current situation of PA in the framework of PAP and user requirements. The current process of PA and its relationship with related organizations (external entities) within PAP have been described. The study divided users of poverty information into two groups: poverty analysts and end- users (policy/decision makers), then analyzed their requirements from their view respectively. The key problems, their relationships as causes and effects from the core problem inadequate poverty assessment have been analyzed. Analysis details were graphically presented by the mean of problem tree. The Logical Framework was used to review criteria and verifiable indicators to facilitate the evaluating success of GIS for PA. System design part dealt with process modeling and data modeling. Based on the results of current situation analysis and user requirements, a new process model and data model have been proposed. New proposed process tried to overcome the problems of current one by changing bottom-up approach of PA to integrated approach and adding spatial data into PA. Data model has been designed for integrating SE data with spatial data for PA purpose. The common key used to link SE data together and to commune units is CID (commune identifier) and the constraint to overlay spatial data is their georeference. The proposed GIS of PA has been described as typical GIS, which includes four components: data input; data storage and retrieval; data manipulation and analysis; and output and reporting. By developing a prototype, the study succeeded to show the potentials of implementing GIS for PA. The samples of PA for the case study (Ha Tinh province) based on data collected during the fieldwork were demonstrated. These samples proved that if PA follows designed process and if data is structured according to the designed model, and GIS functions are applied for poverty analyses, the improvements in PA will be achieved. According prototype evaluation relying on criteria/verifiable indicators, the main gained improvements in PA include integrated process, adding spatial data into PA, using GIS tools for poverty analysis and visual form of PA outputs. These strengthen support from PA to policy/decision making in the framework of PAP. The potential problems of applying GIS in PA also have been indicated. 59 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM Last but not least, the study looked at required technical and institutional conditions for the use of GIS in PA to support PAP implementation in Vietnam. Because proposed system has created a capability to combine various forms of information from different sources, therefore, the establishment of favorable condition for GIS application in PA requires join efforts from all of actors especially in the perspective of data sharing. It also needs support from government. 7.2. Recommendations The study of developing a prototype of GIS for PA is an initial step forward to adopting GIS into implementation of PAP. Although the samples showed by developed prototype proved a number of improvements that GIS can bring for this domain, it is recommended that the proposed system must be tested in some different provinces to define the most effective way to apply GIS for PA in current conditions of technical/institutional arrangements and available data. The recommendations from users, in this case, should be considered seriously to improve the proposed system. In order to promote GIS application in PA, the conditions for its implementation, particular technical and institutional (as mentioned in chapter 6), should be considered carefully and arranged. Due to limitations of logical data model (as mentioned in chapter 5), it is recommended to extend this model in order to add more SE indicator into PA. Relying on the data gathered during the fieldwork, this study tried to design the GI system including process model and data model for assessing only poor communes. However, assessing poor situation of administrative units in higher levels (i.e. districts and provinces) is still very important for SE planning. Therefore, this system should be extended to enable to aggregate data for administrative units at higher level, from lower level. In this case, more researches on approaches, process and data requirements and models for PA of district and province are required. 7.3. Recommendations for further researches Because of time and data limitation, this study focused only on GIS application in PA. In fact, GIS can be applied for PAP in broader scenario. For example, PAP needs a GIS for poverty monitoring, which enable PWG not only to assess poor situation but also to monitor implementation of PAP in different regions over times. This system will be, hopefully, benefit to policy makers in SE development planning. Following this purpose, some related issues should be addressed further as: - How to improve dissemination of PA outputs? - What are the costs and benefits associated with GIS application in poverty monitoring in terms of data, technical expertise, and the precision and contents of outcomes? - How can GIS be used for other matters of PAP such as management of anti-poverty projects? - How can the development of GDI in Vietnam improve data sharing and system interoperability in the framework of PAP? It is hope that through the answers to above questions, the role of GIS in the SE development will be recognized clearly in Vietnam. The application of GIS for poverty alleviation purpose is a good example of answers to the questions: “Does the advanced technology like GIS, can help poor people?”. 60 REFERENCES IX. REFERENCES 1. Action Aid Vietnam (1999), Ha Tinh province: A participatory poverty assessment. Hanoi. 2. Akinyemi F.O. (2001). A GIS database design for urban poverty management. Proceeding papers of International Conference on Spatial Information for Sustainable Development, October 2001, Nairobi, Kenya. 3. Aronoff S., 1989. Geographic information system: A management perspective. WDL Publications, Ottawa, Canada. 4. Benyon D., 1990. Information and data modeling. Blackwell Sci, Victoria. 5. Centre for International Economics, Canberra and Sydney (2002), Vietnam poverty analysis. WWW site http://www.vdic.org.vn/eng/pdf/Ausaid_vnpan.pdf (accessed 08.9.2002) 6. Croswell P.L. (2000), The role of standards in support of GDI. Geospatial Data Infrastructure. Concepts, cases and good practice. Groot R., McLaughlin J., Oxford University Press, New York, USA: 77-81 pp. 7. Davis G. B. and Olson M.H., 1985. Management Information System: Conceptual Foundations, Structure and development. Series in Management information system, McGraw-Hill, New York. 8. Davis B. and Siano R. (2001). Issues and concepts for the Norway-funded project “Improving Method for Poverty and Food Insecurity Mapping and Its Use at Country Level”. FAO. http://www.povertymap.net/pub.htm (accessed 10.08.2002). 9. Development Research Group and East Asia and Pacific Region, Poverty Reduction and Economic Management Sector Unit (2002). The Methodology of Poverty Assessments. 10. Government Office, Hanoi, 1998. List of 1715 most difficult communes. 11. Groenendijk L. (2002). Problem and Objective Tree Analysis. ITC Lecture note. 12. Groten S.M.E. (1990). Design of spatial models: GIS-applications to monitoring for food security. Reprinted form FAO -ITC ARTEMIS and GIS training course for food security early warning. 13. Ha Tinh DOSTE (1998). Assessment of current situation of environment in Ha Tinh. 14. Hawryszkiewycz I.T. (1997). Introduction to System Analysis and Design (4th Edition). Prentice Hall, Australia. 15. Henninger N. (1998). Mapping and Geographic Analysis of Human Welfare and Poverty - Review and Assessment. World Resources Institute, Washington, D.C., USA. 16. Millar J. and Mansell R. (1999). Software Applications and Poverty Reduction A review of experience. WWW site http://www.digitalpartners.org/sapr.pdf (accessed 21.08.20012) 61 DEVELOPING A PROTOTYPE FOR POVERTY ASSESSMENT TO SUPPORT THE IMPLEMENTATION OF POVERTY ALLEVIATION PROGRAM IN VIETNAM 17. Martin D. (1991). Geographic Information system and their socioeconomic applications. Routledge, London, Great Britain. 18. Martin Ravallion (2002). The methodology of Poverty Assessment. WWW site http://www.worldbank.org/poverty/wdrpoverty/ (accessed 12.08.2002) 19. MOLISA (2000). Atlas of Regional Subsidy. 20. MOLISA (2002) [1]. Material of training course on “Poverty Monitoring Evaluation System”, which was undertaken in Hanoi, 22-26 April 2002, by MOLISA and German Development Cooperation on Self Help-oriented Poverty Alleviation. 21. MOLISA (2002) [2]. Materials of training course on “Poverty measurement and analysis”, which was undertaken in Hanoi, 10-21 June 2002, by MOLISA and World Bank. 22. MRDP (1999), Lao Cai province: A participatory poverty assessment. Hanoi. Vietnam- Sweden Mountain Rural Development Program and the Vietnam-Sweden Health Cooperation Program. 23. Oxfam GB (1999), Tra Vinh province: A participatory poverty assessment. Hanoi. 24. Paresi C. (2000). Information System Analysis and Design. ITC Lecture notes. 25. Pitt C. (1998). Putting the Logical Framework in its place: The potential contribution of the Logical Framework to the Sustainability of Donor Funded Urban Management Projects, The Developing Planning Unit – The Bartlett – University College London. 26. Quadir D.A., Zahedul Islam A.Z.MD and Mustafa K. Mujeri (1999). Monitoring Poverty in Bangladesh in the Spatial Domain. The proceeding papers of Geoinformatics’99 Conference Ann Arbor, Michigan, on “Geoinformatics and Socioinformation”. 27. Save the Children UK (1999), Ho Chi Minh City: A participatory poverty assessment. Hanoi. 28. Sun Y., van Westen C.J. & Sides E.J. (2001). Spatial data analysis. Principles of Geographic Information System. R.A.. de By. ITC, Enschede, the Netherlands. 29. Turk C. (2002), Linking Participatory Poverty Assessment to policy and policymaking: experiences from Vietnam. WWW site http://encon.worldbank.org.vn (accessed 15.8.2002). 30. UNDP (2000), Vietnam Development report 2000: Attacking Poverty. 31. Vietnam Government (2002), The comprehensive poverty reduction and growth strategy (approved by the Prime Minister Phan Van Khai). WWW site http://vdic.org.vn/eng/pdf (accessed 15.08.2002). 32. Yukio I. (2001), Poverty Alleviation Policies and Ethnic Minority People in Vietnam. WWW site. http://www.worldbank.org.vn/partnerships (accessed 20.08.2002). 62 REFERENCES 33. World Bank (2001), A synthesis of Participatory Poverty Assessment from four sites in Vietnam: Lao Cai, Ha Tinh, Tra Vinh and Ho Chi Minh City. WWW site http://www.worldbank.org/wbi/povertyanalysis (accessed 15.08.2002). 34. World Bank (2002) [1], Poverty Analysis Initiative. http://www.worldbankgroup.org/wbi/povertyanalysis/ (accessed 20.8.2002) 35. World Bank (2002) [2], Measuring Poverty. http://www.worldbank.org/poverty/mission/up2.htm (accessed 20.1.2003). 36. World Bank (2002) [3], Vietnam’s development goals and targets. Vietnam Development Report 2002. WWW site http://www.worldbank.org.vn (accessed 14.08.2002). 63 Appendix 1 VIETNAM POVERTY LINE In 1997, Vietnam set a poverty line under the national PAP to apply to poverty measurement in the 1996-2000 and 2001-2005 periods. Poverty line base on the average per capita income, which is defined different for different areas/regions, as follows: Areas/regions Poverty line for period Poverty line for period 1996-2000 2001-2005 Rural mountain and island areas 55,000 VND per month 80,000 VND per month Rural plain and midland areas 70,000 VND per month 100,000 VND per month Urban areas 90,000 VND per month 150,000 VND per month Source: Vietnam Government, 2002. 64 Appendix 2 EXAMPLE OF OUTPUTS OF PA IN HA TINH, 1999 Without linking with a map and tolls for visualization, the description about poverty causes in textual and graphical forms like example below becomes ambiguous to end-users (decision makers). Since it did not give the explanation about the location of assessed commune, thus users can not understand why there was “not safe water”, “poor soil”, etc. Source: Action Aid Vietnam 1999. 65 Appendix 3 EXAMPLE OF DATA DUPLICATION, INCONSISTENCY AND REDUNDANCY The tables bellow show the indicators used for poverty assessment that were provided by two different sources. The first table presents the indicators for defining poor communes of Ha Tinh in PPA undertaken in 1999 by AAV. The second table shows the same indicators derived from VLSS93-98. From these tables, it could be seen that the indicators for defining the same poverty criterion are not the same (see criterion “infrastructure”). Another problem is that some indicators have the same meaning but are inconsistently described in these tables. For example, for infrastructure, indicator referring to the use of electricity in table 1 is presented by percent of households whereas in table 2 by percent of population. The reason of this situation, maybe, is the absence of guidelines for generating data for PA. Table 1. Indicators provided by PPA in Ha Tinh (Action Aid Vietnam, 1999) 66 Table 2. Indicators provided by VLSS1993-1998 (UNDP, 2000) 67 Appendix 4 TABLE OF TRANSLATION FROM PROBLEMS TO OBJECTIVES Problem Objective Inadequate poverty assessment Improvements of PA Insufficient data for PA Integrated geodatabase for PA Unsuitable technique for poverty analysis Applying GIS for PA Unsuitable process Improved process Insufficient data for PA Geographic DB for PA Obstacle of Bottom-up approach Model of new process Manual data analysis GIS prototype for PA No link between SE data and spatial data Model for integrating SE and spatial databases No use of geospatial data Model for integrated geodatabase for PA Analog input SE data Model for DB of SE criteria/indicators Improper approach of PA in district level Integrating PA at district and provincial levels into one step Difficulty in SE data handling Accessibility of poverty DBs Timeless data Keeping data up-to-date Manual method for data handling Improved method for data handling SE Data heterogeneity Standard for SE data SE Data scattering in diff. organizations Center of poverty information No leadership in information management Leadership in information management Lack of infrastructure Investment into infrastructure Ambiguous description of poverty in space Poverty map for description of poverty in space Arbitrary format of outputs Digital geodata sets of outputs Limited dissemination of PA results Better dissemination of PA results for wide use Difficulty in the link of PA results to Easiness in the link of PA results to decision decision making in PAP making in PAP Weak support to policy/decision making for Strong support to policy/decision making for PAP in relation to space PAP in relation to space 68 Appendix 5 DESCRIPTION OF ATTRIBUTES IN GEODATABASE FOR PA Attribute name Description DNAME District name DID District identify number CID Commune identify number CNAME Commune name LANDSCAPE Main characteristics of landscape of commune AREA Commune area POP Commune popolation HOUSEHOLD Total number of households in commune Pop_Den Ppulation density of commune LANDLESS Percents of households having landless (per capita sown land less than 360 m2) ProdMean Value =1 if most HH use backward implements for husbandry (using buffaloes and oxen for plough) Occupation Main occupation of commune population UNEMPLOYRA Percents of unemployed within people of working age POOR_HHrate Percents of households having income lower than national poverty line LOCATION Commune location: Value =1 if communes are in coastal zones or mountain areas – they are the first priority of PAP TRANSPORT Transport condition: 1- commune has asphalt roads accessing to the administrative center; 0- commune has no asphalt roads to admin. center HEALTHC Health service center: 1-(or 0-) commune service center under (or upper) normal condition (defined by Health Care Ministry) CWATER Percents of households with assess to clean water ELECTR Percents of households using electricity a main source of light SCHOOL School within commune: 1- (0-) commune school under (or upper) normal condition (defined by Education & Training Ministry) ILLRATE Percents of illiterate among commune population INF_HH Percents of households owning durable means of communication such as television or radio CHILSTUNT Percents of stunting among children 0-59 months ADULMALNUT Percents of malnutrition among adults (body mass less than 18.5) Dropout Percents of drop-out among children 10-14 age Object_ID Identifier of any geographic feacture Admin_type Administrative level, to which service center belongs Name Name of any geographic feature height Height of a contour Type Type of road Hib_type Habitant type of a village Parcel_ID Identifier of a parcel Type_ID Identifier of land use type Soil_ID Identifier of soil type SoilName Name of soil type Haztype Hazard type (flood, storm, polluted and Draught prone areas) 69 Appendix 6 SCHEME FOR CALCULATING GENERAL POOR INDEX (MOLISA, 2000) CRK11 CRC1 Geographic K11 C1 Location CRK21 K21 Legend CRK22 Criteria for poverty definition K22 CRK23 CRC2 Calculation K Coefficient for indicators Infrastructure K23 C2 K11 Distance from comm center to dev. center K21 Asphalt road to commune center K22 Health care center within commune CRK24 K24 K23 Rate of HH having access to clean water K24 Rate of household using electricity K25 Middle school within commune e CRK25 K25 s K31 Rate of illiterate a b K32 Rate of HH having consumer durables a CRK31 t a K33 Rate of stunting among children K31 d CRC K34 Rate of malnutrition in adults c i CRK32 C K35 Rate of drop=out among children 10-14 ys m o CRC3 K41 Rate of landless households n K32 o Rate of HH depen on forest and c CRK33 K42 e shifting cultivation - o i K51 Rate of poor households c Social conditions K33 C3 o K52 Rate of unemployed people S CRK Calculation rule for calculating coefficients relying on indicators. For CRK34 K34 example, if Rate of illiterate>20%, then K31=1, else K31=0. C1 Coefficient for crit. Geo. Location C2 Coefficient for crit. Infrastructure CRK35 K35 CRC4 C3 Coefficient for crit. Social conditions CRK41 C4 Coefficient for crit. Agri. conditions C5 Coefficient for crit. Econo. conditions K41 C General poor index Agricultural C4 conditions Calculation rule for calculating coefficients of each criterion: CRK42 K42 CRC1 C1=K11 CRC2 C2=(K21+K22+K23+K24+K25)/5 CRK51 CRC5 CRC3 C3=(K31+K32+K33+K34+K35)/5 K51 Economic CRC4 C4=(K41+K42)/2 conditions C5 CRC5 C5=(K51+K52)/2 CRC C=C1+C2+C3+C4+C5 CRK52 K52 70