EDUCATIONAL STATISTICS CAPACITY BUILDING PROGRAMME TO SUPPORT THE

REPUBLIC OF GHANA

Diagnostic Report

September 2004

Jointly prepared by the UNESCO Institute for Statistics and

the National Technical Committee on Educational Statistics Capacity Building

Funded by the European Commission

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TABLE OF CONTENTS

1. INTRODUCTION ....................................................................................................... 5

2. UIS APPROACH AND METHODOLOGY ON STATISTICAL CAPACITY BUILDING ............................................................................................... 6

3. ORGANIZATION OF THE SYSTEM OF EDUCATION IN THE REPUBLIC OF GHANA ............................................................................................. 8

3.1 Overview of the Ministry of Education, Youth and Sports ................................. 8 3.2 Education strategic plan and EMIS development prospects ............................. 9 3.3 Departments concerned with the production of education statistics................ 10 3.4 Other institutions involved in the production of data pertinent to education .... 11

4. DIAGNOSIS OF INSTITUTIONAL AND INFRASTRUCTURE SUPPORT FOR PRODUCING EDUCATION STATISTICS ...................................................... 12 4.1 Review of the organisational structure and responsibilities of the educational statistical services........................................................................ 12 4.2 Resource assessment .................................................................................... 14

5. DIAGNOSIS OF COLLECTION, PROCESSING AND USE OF EDUCATIONAL STATISTICS.................................................................................. 20 5.1 Diagnostic of the statistical production chain .................................................. 20 5.2 Analysis on data collection instruments (annual school census forms)........... 24 5.3 Review of data processing software ............................................................... 27 5.4 Tools for exploiting data.................................................................................. 33

6. PRODUCTION AND USE OF DATA AND INDICATORS........................................ 35 6.1 Information needs ........................................................................................... 35 6.2 Review of key outputs, dissemination and accessibility or use ....................... 38 6.3 Analytical aspects ........................................................................................... 39

7. ANALYSIS OF DATA QUALITY............................................................................... 41 7.1 Analysis using DQAF (Data Quality Assessment Framework)........................ 41 7.2 Analysis of the methods and systems for the collection, production and use of data ...................................................................................................... 45

8. GENERAL SUMMARY OF DIAGNOSTIC STUDY, PROPOSALS AND RECOMMENDATIONS............................................................................................ 49 8.1 General summary ........................................................................................... 49 8.2 Recommendations .......................................................................................... 51 8.3 Next steps: Preparatin of action plan .............................................................. 53

REFERENCES.............................................................................................................. 54

ANNEX I: Proposed structure of the education system in Ghana ...................... 55 ANNEX II: Ministry structure flowchart................................................................. 56 ANNEX III: Education strategy.............................................................................. 57 ANNEX IV: Statistical information targets ............................................................. 59 ANNEX V: List of persons met ............................................................................. 64

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ACRONYMS BECE Basic Education Certificate Examination BOG Board of Governors EC European Commission EFA Education for All EMIS Education Management Information System ESR Education Sector Review FCUBE Free Compulsory Universal Basic Education FTI Fast Track Initiatives GES Ghana Education Service GOG Government of Ghana GPRS Ghana Poverty Reduction Strategy GSS Ghana Statistical Service ICT Information and Communication Technology IMF International Monetary Fund IPPD Integrated Personnel/Payroll Database JSS Junior Secondary School MDG Millennium Development Goals MOEYS Ministry of Education, Youth and Sports NCTE National Council for Tertiary Education NDPC National Development Planning Commission NFED Non-Formal Education Division NGO Non-Governmental Organization SRIMPR Statistics, Research, Information Management and Public Relations SSS Senior Secondary School TTC Teacher Training College TVET Technical and Vocational Education and Training UBE Universal Basic Education UIS UNESCO Institute for Statistics UNESCO United Nations Educational, Scientific and Cultural Organization UNICEF United Nations Children’s Fund WB The World Bank

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1. INTRODUCTION 1. Sound policy and good planning require reliable and timely data. The demand for quality and timely data on education is ever increasing. At the international level, this demand has been driven by the need to monitor progress towards Education for All (EFA) goals and the education targets of the Millennium Development Goals (MDGs). 2. In this respect, statistical units in the Education Ministry, as well as other ministries and institutions which provide educational-related data, play an important role. To respond and cater to the demands of today’s dynamic society, the need to strengthen national capacities of these statistical units has become essential. 3. To realise these needs, the UNESCO Institute for Statistics (UIS) received funding from the European Commission (EC) for the project of “Developing National Statistical Capabilities for the Monitoring of Education for All”. Within the scope of this project, the UIS works with countries to build sustainable national capacities for the monitoring of progress towards EFA and national education sector goals. Ghana is one of 11 countries participating in the project. 4. The initial phase of the project includes an in-depth diagnostic of the state of the production and use of education statistics in managing the educational system and monitoring progress towards national and international education goals. It will assess the strengths and weaknesses of the system and identify priority areas in need of strengthening. 5. The current report was jointly prepared by the UNESCO Institute for Statistics (UIS) and the National Technical Committee (NTC) of Ghana.

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2. UIS APPROACH AND METHODOLOGY ON STATISTICAL CAPACITY BUILDING

6. It is recognised that measures to improve the capacities of existing education statistical services need to be country-specific. Even within the country, levels of development, as well as available resources of the various units involved in education statistics, may differ among levels of education. Therefore, a system-wide diagnostic is an essential part of the national statistical capacity building process to identify the gaps and develop appropriate activities and actions to address weaknesses. The project approach includes:

• setting up a National Technical Committee (NTC) which manages the project. The NTC’s main responsibility is to facilitate and ensure the smooth and successful implementation of the sector-wide capacity building activities;

• carrying out a full diagnostic study of the production and use of education statistics in order to: (i) identify the areas that need strengthening; and (ii) develop a national action plan building on and around the existing capacities and initiatives; and

• implementing the action plan in collaboration and partnership with in-country developmental agencies and other stakeholders who play a critical role in building national capacities.

7. It is crucial for the project to obtain the necessary political support and commitment from the country’s administration. The Ministry of Education, Youth and Sports (MOEYS) accepted the invitation from the Director of the UIS to participate in the project. The process of forming the NTC ─ composed of members from various ministerial departments and institutions which are the producers and users of education statistics ─ has already been started in Ghana. 8. The diagnostic study, which was carried out from 17 to 28 May 2004, analysed the state of the production and use of education statistics. This included an examination of EMIS and the questionnaires, as well as the accuracy of data collected from educational institutions, the identification of information needs of education sub-sectors at different administrative levels, and how well these were being met by the data produced at the time. 9. Since interaction among all sub-sectors within the entire education sector is critical for building successful and sustainable national capacities, an appropriate action plan needs to address the issues of all sub-sectors in an integrated way. However, project intervention would be emphasised in the sub-sectors of Education for All (EFA) and Millennium Development Goals (MDGs).

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10. One of the key elements of the UIS approach is to initiate and maintain close collaboration with local development agencies and partners that are active in the education sector, especially in education statistics, so as to harmonise with existing country programmes, as well as to utilise and yield synergies with local resources and expertise. The main advantage of the UIS involvement in the country is that its interventions and approaches to address the issues are guided by best practices, lessons learned and cost-efficient and sustainable solutions gathered from similar experiences in other countries.

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3. ORGANIZATION OF THE SYSTEM OF EDUCATION IN THE REPUBLIC OF GHANA

11. Similar to other English-speaking countries, Ghana’s formal education system has three levels: primary, secondary and tertiary. Compulsory education starts at age six up to fourteen, which is the official age at which the junior secondary level must be completed. The structure of the education system consists of two years of pre-primary education, six years of primary education, six years of secondary education (three years of junior secondary and another three years of senior secondary), and six years and more of tertiary education. There are also three-year vocational and technical education programmes after the completion of junior secondary level. Moreover, various non-formal education programmes related to literacy are organised throughout the country. See Annex 1 for the proposed structure of Ghana’s education system as shown in the Education Strategic Plan (ESP). 12. Both the public and private sectors provide education services at different levels. However, the Ministry of Education, Youth and Sports (MOEYS) is the main body responsible for overseeing all educational services. The private sector is mainly involved in the areas of vocational and technical education as well as tertiary education. 3.1 Overview of the Ministry of Education, Youth and Sports 13. MOEYS has the overall responsibility for the education sector's policy, planning and monitoring and consists of four major divisions: (i) Planning, Budgeting, Monitoring and Evaluation (PBME); (ii) Statistics, Research, Information Management and Public Relations (SRIMPR); (iii) General Administration and Finance; and (iv) Human Resource Management. Delivery and implementation of education falls under the responsibility of the institutions, districts and regions through the various agencies of MOEYS. Under the Ministry, there are several departments responsible for the implementation of different education services. The major agencies include:

• Ghana Education Service (GES), which is responsible for pre-primary, primary and secondary education. It is the largest department in the Ministry;

• the National Council for Tertiary Education (NCTE), which is responsible for tertiary education; and

• the Non-Formal Education Division (NFED), which is responsible for non-formal education programmes especially functional literacy programmes.

14. In addition, there are number of organisations which contribute to policy implementation, including the Ghana Institute of Languages (GIL), the Ghana Academy of Arts and Sciences (GAAS), the National Accreditation Board (NAB), the National Coordinating Committee for Technical and Vocational Education and Training (NACTVET), and the West African Examinations Council. Please see Annex II for a flowchart of the Ministry's structure.

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15. Article 38 of the Constitution requires the government to provide access to “Free Compulsory Universal Basic Education (FCUBE)” and, depending on resource availability, to senior secondary, technical and tertiary education and life-long learning. The MOEYS launched FCUBE in September 1995 with the goal of improving access to quality basic education over the ten years to 2005. Its four broad strategic objectives were:

• to improve the quality of teaching and learning; • to improve management for increased efficiency within the education sectors; • to improve access and participation; and • to decentralise the education management system.

3.2 Education strategic plan and EMIS development prospects 16. In May 2003, Ghana developed a sector-wide programme known as “the Education Strategic Plan (2003 to 2015)” (ESP) through a participatory and transparent process between relevant government ministries and institutions, their bilateral and multilateral development partners, the CSOs/NGOs and other education stakeholders. The ESP, which is currently the principle strategic agenda related to education, aims to be tightly anchored to the country’s international commitments (the Dakar Framework of Action on Education for All, the Millennium Development Goals, the Children’s Rights Convention and the International Human Rights Convention), as well as national strategic policy frameworks. 17. In this strategic plan, within the education policy framework, prominence has been given to the development or improvement of the EMIS capable of producing reliable data and indicators that are relevant, accurate and timely to support policy formulation and planning, as well as monitoring and management of activities. The ESP document, clearly states:

“The Ministry and the management bodies at all levels of the education system will make the most efficient use of scarce resources within clearly defined roles that will develop a culture of service, information sharing and dissemination, support and mutual trust. There will be emphasis on the principles and practices of decentralisation with greater involvement of civil society and the private sector in general and, in particular, school management through a review of current partnership arrangements. The Ministry recognises that there needs to be an inclusive framework for financing education, one that embraces equity principles, medium-term financial development planning within a set of agreed and costed minimum national standards and which should involve cost sharing and cost recovery where feasible. This should meet the need to address societal and international imperatives and to enhance co-operation between those engaged in the delivery of education and the wider stakeholder society. To strengthen planning and management, there needs to be significant improvements in administrative capacity, knowledge and expertise and development of skills in prioritisation and targeting (as identified in the GPRS) at all levels of the system.

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Accordingly there will be considerable emphasis upon capacity building and the development and use of management tools during the plan period.”

18. In this context, the government has set up nine policy objectives with indicative targets and strategies. See Annex III for more details. 3.3 Departments concerned with the production of education statistics 19. At the central level, the Statistics and Information Management Units of SRIMPR in MOEYS are responsible for the collection, processing and production of statistics for the basic education levels (pre-primary, primary and secondary). Every year, the Statistics Unit carries out the annual school census. The Ghana Education Service (GES) also collects data when these are not covered by EMIS. 20. Regarding teaching and non-teaching staff, the unit of Integrated Personnel/Payroll Database (IPPD) under the administrative division collects and updates their information and reports to the Chief Controller Office, which administers the payroll of government staff. 21. The statistics unit of NCTE is mainly in charge of compiling, processing and disseminating data on public and private tertiary institutions – universities, technical colleges and polytechnic institutes. The data for technical and vocational institutions are compiled by NACTVET. The NFED also has its own statistics unit which mainly collects data from its own literacy programmes.

Table 1. Type of education statistics covered by different statistics units

Statistics unit of: Education level

SRIMPR NCTE NFED

Pre-primary education X

Primary education (or first stage of basic education) X

Lower secondary education (or second stage of basic education) X

Upper secondary education X

Post-secondary non-tertiary education X

First stage of tertiary education (leading into advanced research programmes) X

First stage of tertiary education (not leading into advanced research programmes) X

Second stage of tertiary education (leading into advanced research qualification) X

Non-formal education X

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3.4 Other institutions involved in the production of data pertinent to education 22. The Ghana Statistical Service (GSS) is one of the main sources of data related to education. It is a lead agency for not only the collection and compilation of the national statistics but also for the conducting of surveys and censuses. It is responsible for the national population census and national household surveys. The GSS carries out a rich set of household surveys on socio-economic issues. These surveys contain education variables and are useful sources of education data, complementing the administrative data produced by the MOEYS. The GSS has conducted these two surveys:

i) The Populating and Housing Census (2000) ii) The fourth round of the Ghana Living Standards Survey (GLSS) in 1998/1999.

23. Household surveys compile information that cannot be obtained via administrative data, for example data on: socio-economic status, characteristics of the out-of-school population, outcomes of education as measured by literacy. Additionally, the household survey data on school attendance can be triangulated against the results of the administrative data as a means of quality assurance. 24. Data from household surveys should be used to complement administrative data produced by MOEYS in order to formulate stronger and more realistic education policy. So far, there is little evidence that the data are in fact being used to formulate policy and plan programmes. 25. Population data are among the most important data produced by the GSS for the education sector. The data are derived from population censuses and household surveys. The population data, particularly of the school-age population, are main components for the calculation of a number of indicators for the educational sector, such as gross and net enrolment ratios, intake rates, etc. To produce the educational indicators which require population data, the MOEYS uses projections produced by the GSS based on the last census. These demographic data and their projections from the GSS comprise the country’s official data on population. Furthermore, its publication “Quarterly Digest of Statistics” includes statistics on educational institutions, enrolment and teachers by various classifications.

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4. DIAGNOSIS OF INSTITUTIONAL AND INFRASTRUCTURE SUPPORT FOR PRODUCING EDUCATION STATISTICS

4.1. Review of the organisational structure and responsibilities of the

educational statistical services 26. The institutional context and the policy framework in which a statistical unit operates have a strong impact on its performance. Experience has shown that the higher the immediate supervision of a statistical unit, the more powerful the unit in terms of professional, financial or technical support. In the case of Ghana, the statistics units form part of larger units, such as the planning department, and in some cases are headed by middle-level management officials. 27. On an administrative level, the Ghana education system is divided into four levels – central, regional, district and institutional (school). Data and information flow through these levels, especially at the basic education sub-sector. Central level 28. Currently, most of the statistical activities are carried out at the central level. Although some initiatives to decentralise the process have already started, they have not spread to the entire country yet. The SRIMPR has the mandate to compile, produce and disseminate education data from pre-primary to secondary education at a national level. Its tasks include:

• Preparation of questionnaires for the school census; • Training of regional and district staff on school census forms; • Distribution of school census forms to the regions; • Data entry and processing of school census forms; • Consolidating sub-national data to produce the national school census database; • Maintaining the annual school census databases; • Producing and publishing the annual yearbook; and • Responding to requests from ad hoc users on education statistics.

29. Although there is a mandate to perform these tasks, the department has not been given the full responsibility for ownership and maintenance of the EMIS system. The computerised EMIS system currently in use was developed by a consultant through donor project support. Although the project was completed, the department still has to rely on the previous consultant to fix problems or to make changes to the questionnaires and reports. The EMIS source codes which were provided were incomplete or unusable. 30. For the other education sub-sectors (tertiary, vocational and technical, and non-formal), statistical functions and activities (processing and producing of reports) are mainly carried out at the central level. The statistics units at the central level directly contact the individual intuitions to collect the data.

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Regional level 31. For the annual school census, the role of regional level education authorities is mainly to follow up the requests made by the central level. This administrative level is involved in the management of the statistical chain by:

• Assisting the central level professional staff in training for completing school census forms at the district and school levels; and

• Collecting the school census forms from districts to be sent to the central level. 32. Some regional and district offices used to enter the school census forms for their respective areas; however, it was on a trial basis and was not part of their responsibilities at that time. 33. The regional education offices also gather data from both district and school levels for their own use. Although some of the data might have been already included in the annual school census forms (due to the lack of effective processing and dissemination mechanisms), local authorities at regional and district offices are unable to easily use the information from these forms. District level 34. The district level plays a major role in the school census activities. This level is the contact point for the schools, which are the primary sources of data. Each district education office is responsible for managing 20 to 50 schools ─ primary, secondary or both. District education offices report to district councils which allocate the funding to the schools in their respective areas. 35. The involvement of the district education officers in the annual school census includes three main activities: training school headmasters on how to complete the questionnaires; distribution of the questionnaires; and validation and follow-up of the returned questionnaires. 36. UNICEF is currently piloting a project in four districts to produce a district education profile to assist the local decisionmaking process. It is planning to expand the project gradually to other districts. The district education officers, especially those in charge of education statistics, would be heavily involved in the project. Institutional level 37. It is mandatory for the schools to submit their census forms annually. These forms collect not only information on students, staff, supplies and infrastructure, but also on finances. On request, the schools also provide the data to various authorities such as the district council and GES. (See Table 2 for the roles and responsibilities of the different administrative levels involved in the school census activities.)

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Table 2. Responsibilities of the EMIS services by administrative level

CENTRAL REGIONAL DISTRICT

Education level Dat

a co

llect

ion

Dat

a va

lidat

ion

Dat

a en

try

Stat

istic

al p

ublic

atio

n an

d re

port

s

Dat

a an

alys

is

Dat

a co

llect

ion

Dat

a va

lidat

ion

Dat

a en

try

Stat

istic

al p

ublic

atio

n an

d re

port

s

Dat

a an

alys

is

Dat

a co

llect

ion

Dat

a va

lidat

ion

Dat

a en

try

Stat

istic

al p

ublic

atio

n an

d re

port

s

Dat

a an

alys

is

Pre-school X X X X X X* X X*

Primary X X X X X X X*

General secondary X X X X X X X*

Technical and vocational X X X X X X

Tertiary X X X X X

* Data collection indicates where the data requests have been initiated.

4.2 Resource assessment 38. The quality of outputs and delivery capacity of the statistics units are highly dependent on the number of staff and their experience. The following section provides a brief assessment on the resource availability for the EMIS services at the different administrative levels in the areas of human and financial resources, infrastructure and equipment. 4.2.1. Human resources, skills and knowledge For pre-primary, primary and secondary education statistics 39. At the central level, there are three statisticians in the statistics section. They all have at least a Bachelor's degree as their minimum academic qualification. In terms of experience in the area of education statistics, these statisticians have more than three years of experience. They also have received in-country and overseas training. The head of the section has completed the IIEP training course on educational planning, participated in various UIS workshops and completed training at the World Bank Development Institute. 40. In the information management section, which is responsible for processing the school census questionnaires, two computer programmers oversee and maintain the EMIS databases which contain data from pre-primary to secondary levels. Though they are knowledgeable in database management, they are unable to maintain and

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adapt the existing EMIS database application since no proper handover had been done by the consultants who developed the system. Data entry has been carried out by part-time employees. Depending on the workload, more than 20 data entry employees are hired in the peak time. 41. One of the emerging areas in EMIS – which most of the education ministries in the region are paying attention to these days – is the development of the Geographical Information System (GIS). It is a useful application for school mapping and spatial analysis for education data which are essential for educational planning and management. The Ministry set up a GIS unit under the SRIMPR, which is currently run by two people. It was created during the previous EMIS project aimed to enhance the ministry’s planning tasks. However, the unit’s functions are not clear at the moment and the GIS seems underutilised. 42. Similar to other countries in the region, a shortage of staff and weak analytical skills are the main challenges in terms of human resources. The central level, with few professional staff, is too under-resourced to fulfil the broader institutional mandate described above. 43. Normally, regional and district education offices have at least one to two staff to deal with statistics. However, their function is mostly limited to support the process of data collection. As part of the previous World Bank project, 26 districts were provided with computer facilities and training in order to capture data at their levels. In these pilot districts, the data capture software was installed and staff were trained to capture the data. While this data capture was on a trial basis, it has never evolved into the operational stage, and the actual data processing continues to be done at the central level. 44. In the decentralisation of EMIS, it is assumed that local level offices will take on more roles and responsibilities to undertake functions such as processing data and producing local education reports. However, it has been noted that there are discrepancies among regions in terms of adequate staffing and high turnover at the local education office level. In earlier pilot projects, the relevant skill development programmes associated with the decentralisation process that initiated the transfer of part of the EMIS functions to the regional and district levels was not sufficient to achieve a sustained increase in skill levels. In this regard, significant staff capacity and capability gaps exist in all districts. 45. Staff at the district level who are assigned EMIS responsibilities have basic computer skills, i.e. operating a computer and rudimentary knowledge of spreadsheets and word processing. They lack the knowledge of and skills in database management, such as querying and producing reports, that are essential for planning purposes. Moreover, the statistical and analytical skills for educational planning, such as calculating education indicators and enrolment projection, need to be introduced.

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46. Lack of training in Microsoft Access makes it impossible for staff at the regional and district levels to fully exploit the existing database. Data are derived manually from the paper questionnaires, and the summary tables are prepared using Microsoft Excel. In one case, the mission team discovered that even these Excel tables are prepared without using its build-in functionalities (for example, instead of computing total enrolment by adding male and female enrolment, the total figures were manually calculated and then manually keyed in). 47. The staff who are assigned to operate the EMIS software have computer knowledge. They were exploited by senior officials to assume other administrative tasks which required using a computer. In order to establish a functional and operational EMIS at district level, a dedicated assignment along with clear terms of reference and authority is required for those persons. Tertiary and non-formal education 48. At the tertiary level, the statistics unit has one statistician who handles compilation of the data. In contrast, the EMIS unit in the NFE department has relatively more personnel than other statistics units in the ministry. There are approximately 10 members, although most are involved in data entry and funded by a World Bank-sponsored project. 49. Though analysis and research are included in the mandate of the statistics units, there is no evidence that such activities are being done. The main activities of the statistics units have been to produce the traditional statistical summary (the yearbook). However, in order to be productive in a statistics unit under current trends it is essential to have skills in analysis and statistical software. To date, staff in all of the statistics units have skills in the use of spreadsheet applications but not statistical application packages, such as SPSS. At the regional and district levels, skill sets are even lower. Some staff members possess basic computer skills, with rudimentary skills in the use of spreadsheets and other Microsoft Office software, skills which ought to be upgraded. The development needs are much greater for those who lack basic computer skills. 4.2.2 Technical infrastructure and equipment 50. It is essential to have an adequate computing environment to perform EMIS functions to produce quality education statistics in a timely manner. This includes a proper network and computing infrastructure, sufficient server capacity, back-up and security facilities, along with appropriate and regular maintenance. The equipment requires highly technical skills and knowledge to maintain and operate, in addition to significant financial resources. In the case of Ghana, basic computing facilities were provided by the Ministry but greater improvements need to be made to meet present and future demands.

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51. At the central level, staff members are adequately equipped with a ratio of one computer to one staff member. However, the Ministry does not have an operative computer network to share the resources. The equipment is poorly maintained and some do not functioning properly. There are neither standard procedures nor guidelines for maintaining and supporting the I.T. facilities. Different versions of Microsoft Windows operating systems (Windows 95, 98, ME, XP) are installed, and there is no dedicated I.T. team assigned to support the Ministry’s I.T. facilities. It seems that this I.T. configuration was deployed without an electronic data processing master plan or even a functional needs assessment. 52. In the EMIS computer room, which is where the education census data are processed, there are 18 computers along with two to three laser printers. There appears to be a computer network but presently it is not working well. During the previous project period, data were fed into a server from data entry computers via the Local Area Network. Later, the network had technical problems and since then the data have been entered in different stand-alone computers. Files were then copied onto diskettes and merged into the database. 53. In terms of detailed specifications, this unit has two servers. These servers are used as database servers. The data entry computers are mostly Pentium II (with Some Pentium IV) with a memory of 128 MB and hard disk capacity of 20 GB. The GIS section has a plotter and colour printer. However, due to insufficient maintenance and lack of budget for operational costs, this equipment does not seem to be fully operational. 54. The Ministry has two LCD projectors which can be used for training, but only one seems to be operational. 55. There are at least one or two computers at the regional and district offices. However, these computers are used mainly for office operations, such as preparation of letters, reports, etc. Neither a separate computer room nor a computer network exist at the district and regional levels. It seems that the I.T. facilities provided for EMIS activities at these local levels are underutilised. There were some data entries of school census forms at district levels on a trial basis. Besides this, producing summary statistics tables using Microsoft Excel would be the most frequent statistical task done at the local levels. There is no evidence of validation and analysis of data using computers at these levels. 56. Out of more than 110 districts, 26 districts and regional offices have a computerised EMIS. However, the system is still in the experimental stage and hasn’t been functional yet. There is a plan to equip all district and regional offices with computers and to decentralise the EMIS activities in the next phase of project financed by MOEYS via World Bank loan credits, which will start in 2005.

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57. Similar to the situation at the central level, the I.T. equipment at the local levels is poorly maintained and underutilised due to a lack of regular support and maintenance services. These offices are not normally allocated adequate funds for the maintenance and purchasing of required peripherals like toners, ink cartridges, disks, etc. Furthermore, a lack of knowledge and awareness on how to maintain the equipment, as well as frequent power outages, shorten the machine life and capacity. Also, computer networks and Internet facilities are not widely available. 58. It should be noted that some of the schools, especially the large senior secondary schools, have I.T. facilities and even school management information systems with individual student level data. Some even have access to the Internet. Most of the tertiary and technical institutions are equipped with I.T. facilities and automated data systems for students, staff and finances. 4.2.3 Financial resources for statistical services 59. In order to analyse the availability of financial resources and the Ministry’s support to its statistical services, some information such as the percentage of the Ministry’s budget on its statistical units, cost of staff for statistical operations and administration of these statistical units is necessary. However, similar to other countries in the region, this information is not readily and easily available in Ghana, possibly due to:

• the possibility that the heads of statistics units may not have direct access to and decisionmaking power on the use of the budget devoted to their units. This is the most frequent factor contributing to weakening the professional and authoritative status of any statistics unit;

• bureaucratic procedures – it also might be that the funds devoted to statistical operations are not disbursed in substantial instalments or are rarely or never disbursed within the fiscal year to allow the statistics units to promptly and efficiently accomplish their work; and

• the fact that the required skills to draw up realistic and convincing budgets do not exist within these statistics units. In this situation there is more often a tendency to just replicate previous budgets which does not take into proper account the key parameters, such as possible inelasticity of the capabilities of existing manpower or existing facilities for data capture and processing within a given time constraint, while the number of schools for which data are to be captured is increasing.

60. Regarding the external assistance to EMIS development activities, there has been strong involvement from the World Bank. In recent years, the EMIS activities for basic education were carried out under a project funded with World Bank loan credits. The project started in 1998 and was completed in 2002. During the project, the EMIS system was developed and the school census was carried out annually. However, after the completion of the project, the Ministry was not able to fully support the ongoing costs of the EMIS activities. As a result, the data collection activities were delayed and

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the yearbook stopped. Much of the equipment at the central level and at the local level (provided by the project) is not functioning properly due to the lack of proper and adequate operation and maintenance budgets. 61. In the absence of adequate financing provided for in the national budget, the collection of statistical information is still dependent on external developmental partners. Starting next year, a project called EMIS Enhancement and Extension will be funded with World Bank loan credits. The duration of project will be from 2004 to 2009. 62. In the non-formal education sector, another project funded by World Bank credits is supporting the development of the Non-Formal Education Management Information System (NFEMIS) – a database containing the programmes in this field. 63. Other education sub-sectors – tertiary, vocational and technical education – do not receive any external support and have suffered from a lack of financial resources necessary to develop the systematic EMIS at the national level. As a result, there are no systematic and comprehensive EMIS databases for those sub-sectors.

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5. DIAGNOSIS OF COLLECTION, PROCESSING AND USE OF EDUCATIONAL STATISTICS

64. With assistance from the World Bank, MOEYS has established a regular annual school census collecting data from pre-school to the secondary level. An annual school census report is produced from these data. For other sub-sectors, such as the tertiary level and non-formal education, there is a process of data collection; however the coverage is varied from one sub-sector to another. Primary and secondary general education tend to be better covered than other sub-sectors. A brief analysis on the collection and production of education statistics at all education levels is provided below. 5.1 Diagnostic of the statistical production chain 65. Although the statistics unit at SRIMPR collects and produces the statistics from schools (pre-school to secondary), from time to time other departments also collect data from local levels – regional, districts and schools – for various purposes. This causes duplication in data collections among the departments. The data collected by departments are used mainly for internal planning purposes, and the reason the departments collect them is either because the information is not being collected in EMIS or it is being collected in EMIS but is not available in a timely fashion. Most of the annual reports from concerned departments and regional offices include some statistics on their respective areas. However, these reports contain only basic statistics and very little analysis and other education indicators. 66. Besides tertiary education, vocational and technical education, for which standardised questionnaires are collected directly from institutions, other education sub-sectors involve sub-national levels in the data collection, while data processing is done at the central level. 5.1.1 Pre-primary, primary, lower secondary and upper secondary education

67. The data collection forms are initiated and distributed from the central level. Data are collected from both public and private schools and institutions. Every year around November, a month after the school year starts, the statistics unit at the central level sends out the questionnaires to schools via the regional- and district-level education offices. Once the forms are received, the district education officers organise a one-day meeting with school principals and teachers (responsible for the annual questionnaires at the district education office) to distribute the questionnaires and explain any changes. 68. Four copies of the questionnaire are distributed to each school. One set is returned to the statistics unit at the central level. The district and regional offices each keep one copy for reference and validation purposes. The last copy is kept at the school for its reference and use.

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69. Schools are expected to return the questionnaires within two weeks. School principals return the completed questionnaires to the district education officers, at which point the district statistician meets with the principal to verify the completeness and accuracy of the information. The set of verified questionnaires is then forwarded to the regional offices. Sometimes the district education officers bring the questionnaires to the regional education offices where meetings of the district education officers, regional education officers and EMIS staff members at central level are organised to review and validate a second time. These questionnaires are then sent to the central level, which captures and consolidates the data into a national database. 70. On average, it takes three to four months to receive the completed questionnaires from the schools to the EMIS unit at the central level (i.e. they are expected by February). However, due to the shortage of funds and delay in releasing the budgets for data collection, the round of data collection for 2003/04 was carried out more than six months after its normal schedule. There are also delays and cases of not reporting the data at local and school levels. Although, most of the public schools (more than approximately 90%) return the questionnaires, a significant portion of the private institutions do not return them or provide the correct data. 71. The data processing is carried out by the information management unit at the central level. Normally it takes between two to three months to process the data, during which time the unit hires additional data entry people. Thereafter, the annual yearbook, containing information on every district, is produced. This process requires extensive manipulation and running of customised programmes. The annual yearbook is scheduled to be produced in August as a means to help plan for the next school year. However, this has never been the case, and in 2004, the situation further deteriorated with a significant delay in the data collection. 72. Currently, data are processed using the computerised EMIS developed in Microsoft Access and Visual Basic. This data capture interface was written in Visual Basic. This interface conforms to the questionnaire but the absence of source code prevents any evolution of the questionnaires and data structure. As previously mentioned, the computers used for data capture are not networked because of operating system incompatibility between the EMIS server and the computers being used for data capture. As a result, the data capture is done in single-unit mode and consolidated manually into the database. 73. There was a pilot project to decentralise capture operations in the regions and districts. However, the decentralisation of the data capture was not very successful due to a lack of technical training on EMIS software, and there did not seem to be any link between the data capture and other operations and activities. There was a copy of the database at the regional offices and pilot districts; however, the database is not up to date. From the regions and districts visited, it does not seem that adequate evaluation has been done to assess the data capture activities at regional and district levels.

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74. Apart from the annual school census, other departments and local administrative and education offices gather data from schools periodically as the need arises for different planning purposes. One of the activities is a UNICEF-supported initiative on school mapping and production of district education profiles which aims to be used for local-level planning and administration. In addition to the data collected by the annual school census, this exercise also collects other useful information relevant to the local level covering out-of-school students, efficiency, teaching and learning environment. 75. Furthermore, the GES also collects the data from schools and districts. The purpose is to use the data to prepare the budget, estimate teaching and material needs, and propose the opening of new schools for the next school year. Data are requested through the district education offices. The data collection forms are sent out in April and schools are requested to return the forms in June. Once data are returned, they are entered into the computer and stored as Microsoft Excel files. The department has assigned two to three people to process the data; however, only one statistician is in the department. Most of the requested data are similar to the data sought from the statistics unit of SRIMPR. 76. Like the GES, other departments – such as the secondary and the vocational and technical education department – also collect data from their schools and various local education authorities as needed. 5.1.2 Tertiary education 77. The statistics unit at the Tertiary Education Council is responsible for collecting and processing statistical data for the tertiary education sub-sector. Every year, the unit directly distributes the questionnaires to universities and higher education institutions. The collected data includes new entrants, enrolment, staff by gender and by discipline. At the institute level, most of these requested data are extracted from the school registry and record systems. Since most of the tertiary institutions have Internet access, the unit is planning to gather the data electronically using email. 78. Although private universities and institutions are a major part of the tertiary level (there are more than 25 private tertiary institutions compared with 18 public institutions), currently data are only gathered from public institutions. With the liberalisation of this sector, there have been a growing proportion of private tertiary institutions. It is important to develop the system to capture the information from all these institutions. 79. There are also two questionnaires (the Technical Institute Form and the University Form) for higher education. The data derived from these questionnaires are stored in the computer using Microsoft Excel and are included in various Ministry reports. However, the data from tertiary education are neither integrated in the EMIS database of the SRIMPR nor are they part of its publications.

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5.1.3 Non-formal education 80. Although the NFED used to collect data from its programmes, it was not systematic or comprehensive. Moreover, as a result of the lack of rigorous follow-up mechanisms, the responses were low. With financial support from the World Bank, the non-formal education department is currently developing a management information system aimed at gathering various quantitative and performance statistics to monitor the progress of its non-formal education programmes. The data will be collected in a five-month cycle and processed at the data processing section of NFED in Accra. The questionnaires are already designed and printed, and the collection will start soon. 5.1.4 Special education 81. There are 22 schools and institutions which offer education services to children with special needs. The Special Education Department at the Ministry collects basic statistical information, such as enrolment by gender by type and teaching staff by gender by subject. The collected data are processed manually and are kept as paper files. From time to time, the department summarises some information to be used for its internal planning. 5.1.5 Staff information 82. In terms of the staff information, IPPD at the central level is feeding the data into the centralised mainframe database at the Chief Controller's Office, which has more than 10 full-time data entry personnel. The data are used for the staff payroll system and, at present, not for statistical purposes. 83. Figure 1 shows the distribution and collection of education statistics at various sub-sectors.

Figure 1. Data collection cycle of education statistics and responsible units

ADMINISTRATIVE LEVEL

NATIONAL

REGIONAL

DISTRICT

ESTABLISHMENT

EDUCATIONAL LEVEL

Pre-school, Primary, Secondary Tertiary Non-formal

EMIS Unit, SRIMPR Stat unit, Tertiary Council Stat unitDNFE

Regional Education Offices

Dist Education Office

CentresInstitutionsSchools

Nov Jan

Every 5 months cycle

Nov March

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5.2 Analysis on data collection instruments (annual school census forms)

84. The school census questionnaires on basic education collect important information for policy formulation and planning. However, this has never been fully utilised due to the lack of timely dissemination. In other sub-sectors like tertiary education, only basic information is collected.

85. There are three main questionnaires used in the annual school census to collect data on basic education:

• The questionnaire to collect data from pre-primary to the first three years of secondary level (junior secondary school);

• The questionnaire to collect data from the general and technical senior secondary level; and

• The questionnaire to collect data from teacher training colleges. 5.2.1 Questionnaire for Pre-School, Primary and Junior Secondary

86. The questionnaire consists of 10 pages and is accompanied by an instruction manual. An annex about the impact of HIV/AIDS on the school has been added. The questionnaire makes it possible to collect information relating to three cycles of education:

• Pre-primary; • Primary (P1 to P6); and • Junior secondary school (JS1 to JS3).

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87. The gathering of data from these three cycles in only one questionnaire is inefficient and confusing, particularly for the pre-school level. Data are collected by age, gender and year of study and aggregated by year of study, and not by pedagogical group. The pedagogical group refers to the group of students in one classroom at a given level of education. Specific information on new entrants, in particular for grades P1 and JSS1, are not collected, making it impossible to directly calculate net admission rates since these data are essential for this calculation. (Although using the number of repeaters, enrolment by age and year of study, it is possible to estimate the number of entrants in the education system.) However, in this case, information relating to the age of repeaters is not collected. In addition, the complexity of the data model makes it more difficult to calculate these kinds of indicators. 88. Information about the state of the school is not detailed and does not, for example, support the development of a precise programme of building rehabilitation. No sociological information on pupils, such as the social and economic background of the parents, is collected. 89. There is a combined questionnaire for pre-primary, primary and junior secondary levels. Because the number of secondary schools is small compared to the number of primary schools, and the number of schools that offer both primary and secondary education is miniscule, there is no real benefit for a combined questionnaire. It adds unnecessary length and complication for the primary schools since parts of the questionnaire are not applicable to them. Additionally, when one questionnaire is used for both levels, some questions on supplies and school facilities cannot be targeted to both primary and secondary education. 90. The questionnaires collect information on students, staff, school infrastructure and supplies; but in effect these variables are all collected independently within each school. To illustrate this, one can take a non-independent data modelling approach that has been developed by the UIS. This approach is structured around the concept of a pedagogical group – which is the group of students at one grade level taught by the same teacher, at the same time, in the same classroom. Under the UIS data modelling approach, the questionnaire collects the number of students by age and gender by pedagogical group. The link between pedagogical group, teachers, classroom and supplies is collected. This modelling feature of the UIS approach permits qualitative analyses, for example of the relationship between teacher gender and gender parity of enrolment. Such analysis cannot be undertaken if there is no linkage in the data between students and their teachers. 91. In conclusion, it seems worthwhile to separate the questionnaires relating to the three cycles of education. The current style of grouping all these categories into only one questionnaire is a badly structured presentation and does not allow for the useful collection of information on a specific cycle. There is no link among the questions asked in the questionnaire, such as described above in the UIS modelling approach. The development of a systemic approach, which can produce both quantitative and qualitative education indicators from the school census data, should allow for the

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restructuring of data items by centring the questions on the pupils and their environment. This could be supported in particular by the adoption of the concept of a pedagogical group as a focal point of the questionnaire. A pedagogical group approach fulfils the requirements of a quantitative analysis of the system, while offering potential to analyse the variables related to the quality of education. 5.2.2 Senior Secondary School Form 92. The structure of this questionnaire is similar to the questionnaire for basic education (pre-school, primary and junior secondary) and includes 11 pages of specific items related to this level of education. Most of the remarks made on the previous questionnaire also are equally valid for this questionnaire. 93. Six pages are dedicated to staff information, representing more than one-half of the questionnaire. Information is collected separately for teaching and non-teaching staff. Much of the information collected may remain the same from one year to another..The disproportionate importance given to staff information suggests that the questionnaire may have been intended for administrative purposes and to provide statistical information for management and planning. 94. Although staff data gathered by the school census could provide much useful information in many aspects, another system – the IPPD – exists for the collection and maintenance of detailed administrative data on teachers. It is not certain that any data exchange procedures between these two data collections exist. If no such procedure exists, it should be set up to enable the administrative and management services to utilise the information from the EMIS and IPPD databases. At the same time, in order to avoid duplication of data collection, the annual school census should focus on the collection of staff information if only to have a snapshot of the staff situation at the school level. 95. No specificities on technical and vocational education are available. For example, no specific information is collected on equipment whereas they are clearly detailed in the Technical Institute Form. 96. This questionnaire should be revised in order to obtain more information related to pupils and the effectiveness of the system, in order to provide more relevant information for management and planning. The collection of information on examination results would certainly be a significant benefit. Another means of obtaining such information would be through linking the school census database with the examinations database.

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5.2.3 Technical Institute Form 97. This questionnaire includes 15 pages of which three are devoted to staff. The questionnaire mainly collects data on the number of students per type of course. However, age-specific enrolment data and sociological information are not gathered. Information on the cost of schooling is included.

5.2.4 Teacher Training College Form 98. This questionnaire consists of 12 pages and is addressed to the 42 teacher training centres. It provides little information on students except for the number of students by gender. Like the forms for the technical institute, this questionnaire focuses mainly on teaching staff and does not address the effectiveness of the training given. Additionally, no data are requested for labour market outcomes of graduates, such as whether they obtain teaching jobs. 5.3 Review of data processing software 99. A good modelling of the data is necessary for the adaptability of the information system. The following table describes the evaluation of the database structure for the years 2000, 2001 and 2002. Data from these three databases are from the last three school censuses for which databases and reports were available, namely 2000/01, 2001/02 and 2002/03. The database structures have evolved year by year; since 2000 in particular many structural changes have occurred. 100. Table 3 shows all the tables which are present in the EMIS database. As their names indicate, many of these are temporary tables, e.g. “temp” appears in the filename. Other tables, such as “First Page03”, are likely used to produce outputs. This mixture of raw data tables and the functional data used by the application makes it more difficult to comprehend the basic structure, contributing to the difficulty in accessing the data by anyone other than the computer specialist staff.

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Table 3. Database tables in the existing computerised EMIS

TABLE 2000 2001 2002 TABLE 2000 2001 2002

2001 population projections x x Master Region Temp x x

2002 population projections x NON_TEACHING_STAFF x

Accra schools x PARAMETER x

ASSISTANCE x x POLITICALCON x x

ATT_REP_TRAN x x RANGETYPE x

ATT_REP_TRAN1 x REGION x x

Bece x REPORT_LIST x

CENSUS x x REPORT_RANGE x

CIRCUIT x x REPORT_TOPIC x

CLASSROOM x x SCHOOL x x

DISTRICT x x SUBTYPEFACILITY x x

ENROLLMENT x x Table1 x

Enrollment_District_YoB x Tables_Det x

Enrollment_National_YoB x Tables_Mag x

Enrollment_Region_YoB x Tdenrage x

FACILITIES x x Tdenrtran x

First Page x Tdfacility x

First Page02 x Tdschool x

First Page03 x tdteacher x

First Page04 x tdtextbook x

First Page05 x TEACHER x x

First Page06 x TEACHER_PRO x x

First Page07 x Temp Master x

First Page08 x TEXTBOOK x x

First Page09 x THistory_delsch x

First Page10 x TMage x

GENDER_PUPIL x TMcircuit x

GENDER_TEACHER x TMdistrict x

MailMerge x TMenritems x

MANAGEUNIT x x TMlang x

Master District Temp x TMmanage x

Master District Temp x TMP_RPT_DATAENTRYDISTERR x

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TABLE 2000 2001 2002 TABLE 2000 2001 2002

Master Enrol by District and YOB x TMP_RPT_DATAENTRYSUMMARY x

Master Enrol by National and YOB x TMP_RPT_DATAENTRYUSRERR x

Master Enrol by Region and YOB x TMplpop x

Master National Temp x x TMpolcon x

Master Region Temp x x TMrank x

NON_TEACHING_STAFF x TMregion x

PARAMETRE x TMsptype x

POLITICALCON x x TMSubject x

RANGETYPE x TMtype x

REGION x x TOILETS x x

REPORT_LIST x TOTAL_ATT_REP_TRAN x

REPORT_RANGE x TOTAL_ENROLMENT x

REPORT_TOPIC x TOTAL_TEACHER x

SCHOOL x x TYPE_SCHOOL x x

SUBTYPEFACILITY x x TYPEASSISTANCE x x

Table1 x TYPECLASSROOM x x

Tables_Det x TYPEFACILITY x x

Tables_Mag x TYPELANGUAGE x x

Tdenrage x TYPELEVEL x x

Tdenrtran x TYPERANK x x

Tdfacility x TYPESANITATION x x

Tdschool x TYPESTATUSTRAINING x x

tdteacher x TYPESTUDENT x x

tdtextbook x TYPESUBJECT x x

TEACHER x x TYPETEACHER x x

TEACHER_PRO x x TYPEYOB x x

101. In the database, each table containing annual data has a specific field to identify the corresponding school census year. However, this is not useful as the data for each school year have been stored in separate databases. The fact that the data are not structured and compiled in a single database induces redundancy and inconsistency of data. This model of storing data in separate databases does not facilitate the processing of missing data. For example, in the case of questionnaires not completed by the schools, data from the previous year would be useful for estimations. It is also common practice to transfer basic information, such as the school address, from the

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previous year to the current year. These types of tasks cannot be automated if the data are not stored in a single database. 102. The table on ENROLMENT does not use the concept of pedagogical group which is useful in connecting a group of pupils to their teacher and classroom. Information concerning different teaching cycles, from nursery school (kindergarten) to the first years of secondary school, are grouped together in this table. 103. Most importantly, the existing data structure is inefficient and generalised query tools are not accessible. Enrolment at each level is defined as an independent variable. Currently two-thirds of the fields of data contain 0 as a value – an indication of inefficiency in the design of the database. Another approach to database structuring would be to define primary variables,(i.e. enrolment) and then classification variables which correspond to the different dimensions for which the primary variable can be disaggregated. 104. In the case of enrolment, classification variables include: grade level, gender, geography, school level, etc. Such a data modelling approach and structure of the database would facilitate future possibilities for user-friendly tools to query the database and data analyses, while notably decreasing the size of the database and improving its performance significantly. Improved performance is an issue given the age and vintage of much of the hardware. In addition, some fields are redundant or useless. This is true in particular for fields YOB (year of birth) and Age. Two fields with the same nature can only cause inconsistency. This also applies to the field EnrolTotal which is the sum of elementary numbers.

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105. A similar structure, which is functionally identical but more effective, is presented below.

106. The ATT_REP_TRAN table gathers different information but aims at measuring the internal efficiency of the education system. This information is characterised by StudentType coding.

107. It is clear, even more than for the ENROLMENT table, that the lack of coding for the year of study hinders the analytical use of this information. For example, this structure does not allow the direct calculation of a simple indicator, such as rate of repeaters. 108. To complete this short analysis of the model, it is advisable to demonstrate its rigidity with respect to the spatialising of the data. Currently, the administrative structure of Ghana operates on three levels: school, district and region. This administrative structure is taken into account by the data model, but its implementation makes any change – such as the addition or elimination of a level – almost impossible. Such change is very likely to happen with increased decentralisation. The screen below

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shows that each administrative entity is referenced in a table. To add a level, for example between school and district, all codes must be rewritten.

109. To conclude these various comments about the data model, it should be added that, although Microsoft Access is not a database engine likely to manage effectively a significant volume of information, it nevertheless features relational functionalities allowing for an update of the data which guarantees its referential integrity. These functionalities are not used in the model suggested by EMIS as indicated by the screen below.

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5.4 Tools for exploiting the data 110. There are three types of tools:

i) Data capture application; ii) Queries using the database; and iii) Reports produced from the database.

111. The data capture application is written in Visual Basic but the various services that use it do not have the source codes or the technical documentation. The evolution of the application is thus problematic. An example of this was the decision not to integrate the data resulting from the Annex questionnaire on HIV/AIDS into EMIS. 112. The data capture application returns the exact contents of the paper questionnaires used for data gathering. We can further note that the database structure is itself an image of the paper questionnaires. Hence, as discussed above there is little or no modelling of the database structure into a form that would be more efficient and facilitate data query and analysis. 113. There are 113 data queries built into the database for 2002. This high number is the direct consequence of the lack of data modelling and coding for the year of study. The number of queries is multiplied by the number of grades, increasing in the same proportion the software maintenance of these components.

114. The majority of the requests are labelled by year, whereas there are separate databases for each year of data. Hence in the current non multi-year database, this labelling serves no purpose and actually complicates the use of these queries from one year to the next.

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115. The reports are not numerous, their execution is slow and some results are inconsistent. For example, the screenshot above shows that teenagers from 10 to 17 years old are in pre-primary (kindergarten). This illustrates the deficiencies of the data modelling, which has limited tests for systematic coherences. The produced reports are not configurable for different levels of geography level, rather they are duplicated for each of the three levels. It should also be noted that coverage in terms of content is particularly weak with regard to financial and age-disaggregated data. No procedure is put in place to impute data where these are missing. 116. After the consolidation phase, data processing at the central level takes place with the compilation of summaries using the software developed by consultants. Although the menu contains many report titles, most of them cannot be produced. Moreover, the software does not seem to have enough flexibility to produce customer-defined tables, so these must be produced by EMIS staff using Microsoft Excel and Word. 117. One can conclude that the database structure used in EMIS, with its ’hard-coded’ approach, is not robust. The system has a number of queries pre-programmed, which produce a pre-defined set of tables. However, tables other than the pre-specified ones need to be programmed by someone with this expertise. 118. There is another consequence of this ‘hard-coded’ structure of the database that relates to the maintenance of the system and the ability to handle changes, i.e. in the education or administrative structures. In the current system, if a new district is introduced by subdividing a number of existing districts, source codes need to be rewritten to reflect this.

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6. PRODUCTION AND USE OF DATA AND INDICATORS 6.1 Information needs 6.1.1 At the central level 119. Under the national plan, the government sets out performance indicators to measure progress, for which baseline data and annual targets have been established. Annex IV shows the baseline data and targets to be achieved by the completion of the national plan from 2003 to 2015. These are they key indicators for monitoring education and progress towards national goals at the central level. Data are collected for all these indicators, either through the school census or other means – and results are reported in the annual statistical abstract produced by the Ministry. 6.1.2 At the regional level 120. Under the national education plan, each region prepares a regional plan and indicator set for monitoring and evaluation purposes which corresponds to the indicators established at the central level. 121. In addition to data needs for monitoring and evaluation of regional plans, the regions have information needs for planning purposes. The regional education offices prepare plans for their respective region, including the construction of schools and the provision of textbooks. Regional offices are also partly responsible for the management of personnel information. Planning takes place between July and September. In order to be used in the planning, data are required in July. If they are not available, then data from the previous year are used to project for future planning purposes. 122. Since 2001/02 and with the transfer of responsibility for education services to the district level, the flow of financial information to the regional level (and hence to the central level) has been interrupted. Regions are not informed of the education share of overall expenditures at a district level. 6.1.3 At the district level 123. As indicated earlier, the districts are in the process of acquiring real administrative autonomy. They are responsible for the delivery of local government services across all sectors. In this regard, the budgetary aspects connected with the financial management of schools are fundamental to the district education offices. 124. In the budget planning process, the districts need data on the number of teachers, number of newly-recruited teachers, enrolment and number of classrooms constructed. Using these data, the districts project them to the upcoming school year and submit plans to regional education offices and the local administrative authority called the Local Council.

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6.1.4 International information needs: MDGs, FTI Indicative Framework, EFA indicators

125. Developing countries and the international community alike are committed to important development goals. These include the Millennium Development Goals (MDGs), Education for All (EFA) goals, the EFA FTI Indicative Framework and the UN Literacy Decade. 126. An important objective of the capacity building of education statistics project funded by the EU in 11 FTI eligible countries is to develop the capacity for production and use of indicators for monitoring and evaluating progress towards these important development goals within countries. It is important that these indicators are produced in a timely fashion and according to internationally accepted definitions and methodologies to enable cross-national comparisons. Such comparisons are important both to countries and the international community to measure the progress of countries. 127. This section briefly analyses the EU-funded project in terms of how it is contributing and will contribute towards the monitoring and evaluation of a number of the specific MDG, EFA and FTI goals. 128. The UIS is mandated within the United Nations system to collect data from countries in UNESCO's areas of specialisation – education, science and technology, culture and communications. In the area of education, data are collected via an annual Education Survey that includes three questionnaires on basic education, education finances and higher education. These forms are completed by ministries of education and returned to the UIS, where the data are checked for quality assurance and added to an international database maintained by the UIS. In a separate data collection, the UIS also gathers international data on literacy from each country. The education and literacy data are published yearly by the UIS in the Global Education Digest. These data also serve as the basis for international statistics on education used in UNESCO’s EFA Global Monitoring Report, the World Bank’s World Development Indicators, the UNDP’s Human Development Index and UNDP's global reports on MDGs. There is interest by the FTI partnership in the international monitoring of the indicators set out in the indicative framework for FTI-eligible countries. Increasingly, organizations are identifying indicator sets that will be used for establishing eligibility and priorities for Official Development Assistance (ODA). The EC has established such criteria (EC, 2002), hence production of the indicators in the framework is crucial to be considered for ODA. 129. There are a number of data quality issues for the international data – many of these are the same or similar to national data quality issues. These include the following. 130. Timeliness: Currently the UIS publishes its international education database 18 months after the end of the reference school year. Particularly for FTI-eligible countries, there is a strong demand for more timely data. One of the focuses of the EU-

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financed project will be to accelerate the provision of data to the UIS by countries participating in the project. This can be achieved in two ways: first, by reducing the time delay from when data are produced nationally to when they are submitted to the UIS; and second, further gains can be made by speeding up the collection, production and release of data nationally – which is one of the national priorities. These gains can carry over into improved timeliness of international data. 131. Coverage and completeness: There is a need to improve the coverage and completeness of the data reported to the UIS. This is particularly the case for financial data. There are a lot of gaps in the financial data currently reported internationally for Ethiopia. This financial data is needed for the international monitoring of the FTI indicative framework indicators. 132. For EFA, data are needed on gender parity and equity; improved data on non-formal education, literacy and life-skills; and data on primary completions and quality of education. Each of these is discussed briefly below. 133. In many cases these international needs coincide with demands for improved information on a national level. Action plans need to address how to meet these demands. 134. Gender: Gender equity in education is both an EFA and MDG indicator. Gender and age are two basic demographic variables collected in school censuses. Hence the school census data permit analysis of gender parity at national and sub-national levels. The analysis includes rural versus urban, and region versus district. Such analysis is needed to target efforts to achieve gender equity where it is most needed. If the UIS data modelling approach were to be adopted in the annual school census, the richness of data collected would permit the analysis of factors impacting gender parity, i.e. the impact of teacher gender and the presence of gender-specific latrines on female enrolment rates. 135. Improved data on non-formal education, literacy, life skills: Non-formal education is a priority in many developing countries. It aims at developing literacy and other life skills in the adult population that has not completed formal schooling. Such programmes respond to two EFA goals – reduction of the levels of illiteracy in the population by one-half and the improvement of life skills. The UIS is active in developmental work through the Literacy Assessment and Monitoring Programme (LAMP) which measures literacy levels; formulating concept definitions of life skills; and development of an EMIS module for non-formal education. Once the developmental cycle is complete, these approaches will be part of the methodologies and systems that the UIS can offer countries to adapt to their specificities. 136. Universal primary school completion: The UIS has been working actively with the World Bank to define alternate measures of primary completion and to begin to collect and publish these data. For the time being, a proxy measure of entry into the last grade of primary education as a percentage of the typical age population cohort has

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been adopted. There is also an effort to find more measures. One such measure could be those completing end-of-primary exams successfully as a percentage of the typical age population cohort. Also being considered is the development of an EMIS module that will link school examinations and school census databases, and permit calculation of such completion rates in a timely fashion, prior to the beginning of the next school year and disaggregated by gender, school characteristics, etc. Ghana is a possible candidate to be a pilot country in the development of this module 137. Quality of education: Linkage of school census and examinations data will yield several additional indicators on the quality of education from a national perspective, based on the proxy of examination results. This will include derivation of indicators on exam results by rural versus urban areas; socio-economic status characteristics of the parents; school infrastructure and supplies; and teacher qualifications. Such indicators and analyses will be helpful in identifying factors impacting data quality to help influence policy formulation. In addition to national measures of data quality based on examinations results, the UIS will help countries participate in regional or international assessments of student achievements, where this coincides with national priorities. 6.2 Review of key outputs, dissemination and accessibility or use 138. Publications by the departments managing EMIS are generally produced annually and presented in the form of a statistical yearbook (Educational Statistics Yearbook). The annual school census yearbook produced by the statistics unit of SRIMPR is a core statistical publication describing the situation of the basic education level (pre-school to secondary) in Ghana. The report includes data from national to district levels. It is voluminous yet contains only basic summary information. Except for the publication of a bulky statistical yearbook that is not widely diffused, education data in Ghana are not very accessible. SRIMPR used to produce 500 copies of the yearbook during the previous project. The yearbooks were distributed to the educational authorities at the local, regional and central levels, as well as to other concerned ministries, institutions and agencies. However, in the last two years, the Division has been unable to mass produce the report due to a lack of funds. There were less than five copies of the yearbook produced for the last school year. This indicated that the government did not have a position on or interest in disseminating education data on a large scale. Even in previous years, the number of yearbooks available was insufficient at the decentralised levels. Although the UIS study did not cover all of the regions and districts, it seems the publications are not used much at the decentralised level. Most people at the district level did not seem to use or were not aware of its existence. 139. Apart from the yearbook, all departments and regional education offices produce reports which contain some basic statistics. However, due to the unavailability of data and lack of expertise, these reports contain little or no analysis. The utilisation of the data is also low.

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140. A small amount of information and indicators are available in comparison with the data requested for the annual school questionnaires. A number of indicators important for EFA and MDG goals have not been produced. The quality and consistency of the data are also a concern among users. 141. Under the school mapping project, which received support from UNICEF, some of the pilot districts produced a district school mapping report which contains varied information and analysis. However, producing such a report for all districts would not be possible for the near future. 142. At the tertiary level, the Tertiary Education Council publishes limited numbers of the annual statistical report, which contains information on individual institutions and universities. Some of the universities also produce their own statistical reports but most are prepared in a brief and ad hoc manner. There is no summary information to present the status at the national level. 143. The distribution mechanisms of these publications are not systematic. They clearly lack defined procedures and strategies. Intended or actual users do not always receive the publications, and in most cases, barely enough publications are produced for even internal users. There is no indication of electronic distribution of information. 144. Throughout the various data collections – specifically in the basic education sector (primary and general secondary) – large amounts of data are collected. However, little information has been produced. In addition, there is little data use by either the Ministry or other institutions and agencies. This low utilisation of statistics is partly due to the fact that: i) products produced from the collected data have deficiencies which are barriers to further data use, for example the database is accessible only by persons in the EMIS unit possessing programming skills, while the yearbook has only pre-specified tables in paper format; ii) weak analysis capability and lack of human resources in the statistics and planning services; and iii) little or no culture or acquired skills in the use of educational statistics among planners. Furthermore, the delay in the production and diffusion of statistics makes the situation more severe. This is one of the main reasons why other departments gather the data themselves. The data and information from various household surveys and censuses are not used effectively, especially socio-demographic data that could be helpful in the projection of education demand and resources.

6.3 Analytical aspects

145. One of the goals of the production of statistical data is to provide sound analysis and interpretation in order to facilitate and support the decision-making process. This requires adequate training of staff, and such training is currently not being undertaken in a systematic fashion. At the same time, technical difficulties accessing data for analysis also discourage staff and researchers from carrying out analysis. This situation stems from the lack of staff training, the weak approach to data modelling and the lack of simple user-friendly interfaces for access.

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146. At the institutional level, although the statistics units are normally under the Policy and Planning Department in the Ministry, collaboration between the statisticians and policy analysts is weak. Under SRIMPR, there is a separate research unit, even though no analytical work has been done thus far. Although the first part of the statistical yearbook contained some analysis of annual school data with some key education indicators, a more in-depth use of the data for policy-related measures is needed. Also institutional links between statisticians in the Ministry of Education and other ministries and agencies, such as GSS, are also weak. 147. The EMIS staff at the Ministry are mostly confined to the collection, production and dissemination of school census data. The MOEYS does not make use of the rich array of data from household surveys that provide complementary information on education. The only external data regularly used are population data, which are needed for calculating indicators such as net and gross enrolment rates. Institutionally stronger links are needed to ensure better use of household survey data to inform education policy and debate. Stronger institutional links can also help in finding ways to maintain and develop the professional capacities of MOEYS statistical staff. Furthermore, the utilisation of GIS for policy analytical purposes is not visible.

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7. ANALYSIS OF DATA QUALITY 7.1 Analysis using DQAF (Data Quality Assessment Framework) 148. An important element of the diagnostic study is the completion of the DQAF in order to provide a benchmark for data quality at the outset of the project and to help pinpoint weak areas in need of improvement. 149. As part of an initiative being lead by the IMF to improve the overall standards and comparability of national and international data, the UIS – together with World Bank specialists – have developed an education-specific DQAF instrument to assess the overall settings required from education statistics units around the world in order to certify the quality of their statistical outputs. 150. In this section, the quality of education data is assessed by using DQAF. This instrument is based on six main dimensions considered pertinent for an evaluation of data quality. They are:

i) Prerequisite conditions to quality; ii) Integrity; iii) Methodological soundness; iv) Accuracy and reliability; v) Functionality; and vi) Accessibility.

151. Taking into account all of these dimensions in the evaluation makes it possible to provide an overall vision of the data quality in the education sector. Although it may not reflect the details and situation of the data quality issues in the education data, this assessment could be a useful guidance tool to strengthen the country’s statistical system by identifying the strengths and weaknesses of the system. 152. The assessment has been carried out for the pre-primary, primary, secondary and tertiary sectors. However, a deeper review, including vocational and technical education and non-formal education, will be possible after the implementation of the diagnostic recommendations. Table 4 describes the outcomes of the assessment by using DQAF.

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Table 4. Outcomes of the assessment by using DQAF

Detailed Evaluation of the Quality of the Data Pre-

school Primary General secondary Tertiary

0 Prerequisites to quality 33% 61% 61% 33%

0.1 Legal and institutional environment 40% 80% 80% 40%

0.2 Resources 25% 50% 50% 25%

0.3 Quality awareness (the quality of Data are crucial for statistical work) 40% 60% 60% 40%

1 Integrity 88% 88% 88% 88%

1.1 Professionalism 100% 100% 100% 100%

1.2 Transparency 60% 60% 60% 60%

1.3 Ethics 100% 100% 100% 100%

2 Validity of the methods 59% 88% 88% 65%

2.1 Concepts and definitions correspond to the standard statistical framework. 67% 83% 83% 67%

2.2 Scope (the scope corresponds to the norms, guidelines and practices recognised on an international scale.)

50% 83% 83% 50%

2.3 Classification/sectorisation 50% 100% 100% 50%

2.4 Bases for recording (the data are recorded in a way that corresponds to the norms, guidelines and practices recognised on an international scale.)

67%

100%

100%

100%

3 Accuracy and reliability 47% 71% 71% 53%

3.1 Sources of data (sources of data available for the compilation of statistics are adequate.) 40% 80% 80% 40%

3.2 Statistical techniques 50% 75% 75% 50%

3.3 Evaluation and validation of raw data 33% 67% 67% 67%

3.4 Evaluation and validation of intermediate data and statistical products 67% 67% 67% 67%

3.5 Examination of revisions 50% 50% 50% 50%

4 Functionality 39% 50% 50% 39%

4.1 Relevance (the statistics cover relevant areas of the domain.) 25% 50% 50% 25%

4.2 Currency and periodicity (the currency and periodicity of the data meet international standards for distribution.)

33% 33% 33% 33%

4.3 Coherence 43% 57% 57% 43%

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Detailed Evaluation of the Quality of the Data Pre-

school Primary General secondary Tertiary

4.4 Policies and practices of revision (the revision of data are periodic and follows a regular and transparent procedure.)

50% 50% 50% 50%

5 Accessibility 36% 43% 43% 43%

5.1 The statistics are presented in a clear and comprehensible manner; the methods of distribution are appropriate; and the statistics are distributed in an impartial manner.

50%

50%

50%

50%

5.2 Accessibility of metadata 20% 40% 40% 40%

5.3 Assistance to users 33% 33% 33% 33%

SCORE 50% 67% 67% 53% 153. As seen in Table 4, the primary and secondary education sub-sectors perform better than their counterparts at pre-primary and tertiary. However, in some areas the results of the tertiary education are the same as for the primary and secondary levels. The overall performance is more or less the same in comparison with other project countries, although some areas score differently in some detailed areas. 154. The analysis showed that Ghana has already prepared and established the required foundation for a statistical information system on education which can provide timely and policy-oriented information to its users. There are established legal and administrative mechanisms, and relevant staff members have minimum competencies to carry out their functions and responsibilities. Methodologies and techniques to produce the necessary educational indicators and information mostly comply with international standards. Methods and procedures to validate and improve the quality of data are in place. The factors concerning quality assurance are mainly:

• Currency and periodicity; • Relevance; and • Availability of resources.

155. Although the major criteria (pre-requisites to quality, integrity, validity, accuracy and reliability) for primary and secondary education have been above average, the remaining two criteria – functionality and accessibility – are about and below average. This is due to the above-mentioned factors. However, much work is needed for other sub-sectors as they are about or below average in all the criteria except integrity – as indicated in Figure 1.

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Figure 1. Radar graph screening of data quality framework assessment results by domain in relation to a 100% target score

0%

20%

40%

60%

80%

100%Prerequisites to quality

Integrity

Validity

Accuracy and reliability

Functionality

Accessibility

PreschoolPrimary & SecondaryTertiary

156. This clearly depicts the classic cycle between statistics units missing the mark of servicing policymakers with data in a timely manner, and in turn, not receiving recognition nor enough financial and political support from the policymakers. This situation progressively worsens the overall condition of work for statisticians, no matter what their professional and technical capabilities. 157. Because the minimum professional competencies are already in place, the necessary action would be for policymakers to take stock of and clearly formulate their demands for data or indicators and further put in place the adequate institutional framework and resources (human, technical and financial) required to meet their identified needs. In turn clear goals should be set for the statistics units. The goals need to be regularly monitored and achievements constantly assessed. 158. These findings indicate some commonalities and discrepancies among the key statistics units in the Ministry. Synthesis of the findings among the statistical units at the Ministry are summarised as follows.

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Table 5. Assessment of the capacity and performances of the statistical units

Aspects being measured SRIMPR Stat unit NTC

Stat unit, DNFE

Pre-school Primary Secondary Higher education Non-formal

Clear and non conflicting mandate ++ ++ ++ ++ ++

Adequacy of policy level support -- + + + +

Financial support - - - - ++

Data collection strategy + + + + +

Comprehensiveness of data collection instruments - + + - +

Organisational process & impact on timing - - - - -

Level of questionnaire recovery -- ++ ++ _ +

Data processing

Adequate number of experienced/skilled staff + + + - +

Adequate computing facilities + + + - ++

Software capabilities and timeliness of data processing - - - * *

Sound methodologies of missing data treatment -- -- -- * *

Sound errors tracking methodologies - - - * *

Outputs, dissemination & use

Timeliness of output production -- -- -- - *

Dissemination media and strategy -- -- -- -- *

Analytical content -- -- -- -- *

+++ Very Good, ++ Good, + Needs improvement, - Weak, -- Very weak, * Insufficient information or inexistent

7.2 Analysis of the methods and systems for the collection, production and use

of data 159. Furthermore, a brief analysis of the existing system to produce education statistics has been made to address the following issues: • Changes in the administrative hierarchy governing education or changes in the

education system such as the number of years of primary education 160. As noted in the discussion on database structure, EMIS is not well-suited to handle these changes. The number of districts is growing and EMIS staff are unable to modify the system on their own to reflect this growth.

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• Changes to the questionnaire to respond to changing information needs

161. There is difficulty to adapt the primary-secondary education questionnaire to respond to changing information needs. The questionnaires were developed many years ago. If there is a need for changes to the questionnaire, the Ministry needs to contract the consultant who developed the system to carry these out. With the implementation of the new questionnaire, there was a need to fix a number of technical problems in the existing system.

• The possession of source code in order for the Ministry staff to take ownership

of the operation and maintenance of the system

162. The Ministry does not possess the source code and is dependent on the consultant who originally built the system to make any changes or to fix problems as they arise. In addition, there is no proper documentation for the reference to make any changes and to maintain the system. • The mechanism for the production of required outputs, such as tables for the

yearbook, and the extent to which these production processes are automated

163. Once data have been entered and verified, it takes three to four months to produce the yearbook. The production of tables is not highly automated. It involves extractions from the database, and then manipulation and sometimes formatting in Microsoft Excel. 164. Since last year, the statistics unit has been unable to produce the yearbook due to lack of funds from the government. Since the yearbooks are always produced late, when they are released they are no longer as useful for planning purposes.

• Whether the database is multi-year or if the system generates a separate

database for each year

165. The EMIS system generates a separate database for each year. This limits the ability to carry out the longitudinal, multi-year analysis. The longitudinal analysis of data can also be an important tool for quality assurance, by examining the logical consistency of changes in the data. 166. The UIS mission team has linked databases for 2001/02 and 2002/03, and found various inconsistencies in the data. With a longitudinal database, these inconsistencies could be removed during the processing and cleaning stage.

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• The ease of access to the database: Does a user-friendly interface exist or is access to the database limited to those with I.T. skills?

167. Access to the database by querying other than pre-identified tables could only be done by those with I.T. skills. The absence of user-friendly access to the database is seen as one of the major limitations to the current system, both at the central and local (regional and district) levels. This hinders the use of data by education planners and policy analysts, who are restricted to working with pre-specified tables and have to rely on the specialists to produce the customised tables or analyses.

• The existence of advanced features, such as GIS

168. Although the Ministry has a GIS section, it only has two staff. The unit does not seem to be very involved in the operational activities and utilisation of GIS as a tool for planning. Both the central and local levels have identified the need for GIS for school mapping as a micro-planning tool, particularly to facilitate planning the construction of new schools.

• The potential for the EMIS system to be decentralised to the district level

169. Although there is a plan to decentralise EMIS tasks to the local level, from a systems perspective, success would depend to a large extent on the ease of use of the system both in data production and report generation. In this regard, the EMIS software should have a user-friendly interface and facilities to meet local needs, such as the production of standard reports at the regional and district levels and the ability to query the database to produce customised reports or analyses. The current system does not have these capabilities – it requires persons with advanced computer skills to operate the system and hence is not particularly well-suited to decentralisation to the local level.

• The potential for the enrichment of EMIS through linkage with other data

7.2.1 Data on education finances

170. Since decentralisation took place, financial data are no longer available centrally or regionally. Although schools report diverse financial information, such as quarterly budgets and audited expenses, to the district level, there is no consolidation at the higher levels. In most cases, such information is available on paper but is not integrated into EMIS. There is no information on education finance in the yearbook.

171. In addition to budget information, there is a need to examine in more detail the requirements for timely information on education expenditures and how to address these. This includes the need for timely financial indicators required under the FTI indicative framework, such as:

• Public domestically-generated revenues as a % of GDP; • Education share of budget (%); • Primary education share of education budget (%);

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• Average annual salary of primary school teachers, separate indicators for civil service and contract teachers; and

• Recurrent spending on items other than teacher remuneration as a percentage of spending on education.

7.2.2 Personnel management data

172. Data on personnel, both teachers and non-teaching staff, are aggregated during the educational census. At the same time, staff information is collected by IPPD for the payroll system. In this aspect, the collected data may not meet certain educational management objectives. However, part of the information collected by this system could be substituted for the information obtained from the annual education census. There is now a risk of two data collection processes that are not coherent with each other, and that would result in increased response burden.

173. The descriptive variables concerning teaching staff, such as pre-service and in-service training, the age at which they start teaching and years of experience are some of the major dominant factors in pedagogic behaviour and are ultimately used to measure the quality of the education system. The eventual adoption of the concept of pedagogic group as an elementary entity within the school structure, and the element of data collection for educational census in Ghana would establish a particular relationship between the student and the teacher. This data structuring process will be especially effective in the primary education sector where there has been a direct relationship between the pedagogic group and the teacher.

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8. GENERAL SUMMARY OF DIAGNOSTIC STUDY, PROPOSALS AND RECOMMENDATIONS

8.1 General summary 174. The diagnostic study revealed very uneven performances of statistical information systems in the education sector. Regarding the general status of the information system, the primary and general secondary levels are equipped with a relatively well-organized system in terms of data collection and processing. There are still weaknesses that partly explain why school statistics, particularly the statistical yearbook, are produced late. However, the availability of data at the primary and general secondary levels is better than at other education sub-sectors. There are weaknesses in terms of the dissemination of data and an absence of more analytical productions. 175. Despite the fact that the Ministry has already set up a system on the production of the primary and general secondary education statistics, the existing system is not performing adequately and needs to be replaced. 176. As mentioned before, while the questionnaires request a tremendous amount of input, little in the way of useful outputs are produced. This is compounded by the delay in production, forcing other agencies to collect data themselves. Since most of the activities are conducted in an uncoordinated manner, there is duplication and inconsistency among the data, which leads to EMIS data as not being viewed and recognised as the authoritative source of information. 177. There is a data validation mechanism at all levels (district, regional and central); however, due to the lack of skills and human resources, rigorous measures cannot be taken. Again, this affects the reliability and consistency of data. Furthermore, centralised data processing makes local participation in data validation and utilisation more difficult and hence less likely to occur. 178. There is little collaboration among the different statistical units in the Ministry, as well as those statistics units and GSS. Stronger institutional links are needed to ensure better use of data to inform education policy and debate. This will also lead to the sharing of resources and expertise among the statistical units. For example, in the area of GIS systems, education mapping could fit into a larger integrated multi-sectoral GIS, combining population data with data from a number of line ministries, such as education, health, etc. 179. Since population data, especially for the school-age population, are used to calculate the various educational indicators, it is necessary to have reliable population projections with relevant classifications, such as urban/rural, school-age population, region, etc. This is an area where MOEYS and GSS could work together.

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180. As is often the case in Africa, statistical products are affected by protracted production delays. The data and the publications are never available before the new school year. The delays give a negative impression to the statistical units. Furthermore, the “statistical products” are not adequately disseminated to all levels. This situation discourages the use of statistical information by staff. At the same time, plans are formulated based on unreliable data. 181. The following is a summary of findings on the existing EMIS system during the diagnostic study. Summary of limitations of the current EMIS system 182. Currently, only SRIMPR uses a computerised EMIS system to gather and produce the statistics for basic education (pre-primary, primary and secondary). However, this system has the following limitations:

i) Closed nature of the system. Developed by a consultant, the source code is not available to MOEYS. Hence there is reliance on the original consultant to fix all problems, which brings with it some serious risks.

ii) Inflexibility in accommodating changes to questionnaire. Similarly, the consultant is needed for any changes to the questionnaire. The last changes were made a year ago and there were problems in implementing them.

iii) Inflexibility in accommodating changes to the structure of the education system and to the administrative structure. Due to the database structure, such changes are difficult to accommodate within the system.

iv) Database structure limits the possibilities for automation of functions, such as yearbook production and user-friendly queries.

v) Current database is not multi-annual, making it difficult to carry out longitudinal analyses. Also a separate database for each year limits the possibilities for verification of data coherence from one year to the next, which is an important means of quality control.

vi) Complexity of the system for users limits its potential for use at the district and regional levels as a tool for facilitating local information and planning needs.

vii) No underlying data modelling permitting the collection of information at the pedagogical group level (which refers to the students at a particular level taught by a teacher in a classroom). The introduction of the pedagogical group makes it possible to contribute to the qualitative analysis of the education system. With such data modelling, one can carry out multivariate analyses of the relationship between qualitative factors at a classroom and school level, and equity of girls versus boys’ enrolment, retention, etc.

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Summary of EMIS needs i) A system that is owned and operated by national authorities, where these

authorities are empowered and trained to have the expertise required for maintaining and adapting the system according to their needs.

ii) An open system that is capable of growing and evolving, as information needs change.

iii) A system with advanced features so that important functions such as yearbook production at central, regional and district level can be done within the EMIS system, and with only a small amount of customised programming.

iv) A system offering access to users through a user-friendly interface, permitting non-technical users to query the database to obtain data they need, to build tables, and to export data to other software applications such as Microsoft Excel.

v) A system that is sufficiently easy to use so as to be suitable for deployment at the district level, where those using the system are non-technicians. The need is for simple user interfaces for data entry, production of summary data both at a district and school level and for the ability for a user-friendly query of the database, and the production of customised reports as needed.

8.2 Recommendations Recommendations for improving organizational and/or institutional capacities 183. The plan for capacity building of education statistics needs to be developed in coordination and harmony with the national statistics development plan prepared by GSS. Included in this plan will be the assurance that the statistics units are given the mandate and the human resources and training needed to incorporate use of data from household surveys in the analysis of education issues, to complement the information available from the school censuses and to strengthen the link with the Central Statistics Authority in achieving this. 184. The current diagnostic study has helped to identify a number of issues to be addressed from the perspective of the education sector. These need to be examined to see their relationship to a system-wide statistical development plan and to ensure that efforts aimed at capacity building of education statistics through the EU-funded project are coordinated with and complementary to national system-wide efforts. 185. The strategy for capacity building of education statistics needs to be developed at different levels – central, regional and district – and it needs to address institutional, human and technical capacities. It is only through addressing the issues together that sustained capacity can be built. This would include re-orienting the role and mandate of the local level to be more involved in EMIS activities and allocating an adequate operational budget, at both central and local levels, to effectively carry out the necessary tasks.

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186. Within MOEYS, there is a need for a more harmonised and coordinated approach to the collection, production and use of education statistics. The EMIS unit has a mandate to collect and produce information, but other areas are often collecting the same information on their own. The duplication occurs because the data is not made available in a timely manner. In other instances, it is due to the inflexibility of the EMIS system to add new content. At a ministerial level, an EMIS development plan is needed, where the EMIS unit can be the source of comprehensive data, empowering it to fulfil its mandated role to collect, produce and disseminate data, while satisfying the needs of all agencies and departments within the Ministry. 187. Policymakers and planners within the agencies need to have their skills strengthened in the areas of research and policy-oriented analysis. Recommendations for revitalising computerised EMIS and use of ICT 188. Due to limitations of the current EMIS system, the adoption of the UIS EMIS system is recommended initially for primary and secondary education. A multi-year plan is recommended, under which the UIS approach would be adopted at the central level in 2004/05, with extension to the regional and district levels from 2005/06 to 2006/07. An initial step would be to migrate historical data to the UIS data modelling approach. This would result in a multi-year database that could be accessed via a simple interface. The activities would include:

• Redesign the questionnaires to have a maximum amount of useful, reliable data while making it easier for the data providers;

• Transform outdated, inefficient data processing systems into cost-effective scalable systems;

• Migrate historical data from the old system to the new system; • Customise the generalised report-generating and data analysis module to

provide pre-defined and user-defined reports and analysis specific to Ghanaian needs; and

• Develop an enrolment projection and resource simulation tool to assist policy planning.

189. The Ministry should also develop a strategy to upgrade all equipment at the end of their respective life cycle and to maximum utilisation of I.T. and Internet technology. This includes:

• Installing the Internet at the Ministry; • Identifying the nearest Internet facilities and making them available to the local

education authorities; • Refurbishing and repairing existing equipment as much as possible; • Identifying equipment needs at the central and local levels and prioritising them; • Developing guidelines for the use and maintenance of I.T. facilities; and

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• Developing an awareness strategy for the use of I.T. and statistics among various user levels.

Recommendations for strengthening technical and managerial capacities among the staff 190. Building the capacity of staff at all levels (central to school) through training activities is one of the essential components for capacity building. The activities would include:

• Developing training materials for EMIS activities; • Training the central and local education officers:

on methods and use of educational indicators and statistics; in analysis of educational data for plan formulation, implementation and

monitoring; and on other EMIS-related activities (data validation, record maintenance,

surveys and sampling); • Training the central and local computer operators on how to maintain and

operate I.T. equipment effectively and efficiently; and • Training the central level I.T. staff in database management programming and

network maintenance. 8.3 Next steps: Preparation of action plan 191. The action plan will identify and prioritise a set of actions needed to address the weaknesses identified by the diagnostic study. The action plan will be integrated with initiatives already underway or planned as part of existing programmes and other capacity building initiatives, such as the EMIS Enhance and Extension Project. 192. The action plan will identify a coherent plan for capacity building of education statistics and will identify the costs associated with implementing the plan. It will serve as the basis for discussion with the local donor community and mobilise additional resources as required to cover the in-country costs associated with implementing the plan.

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REFERENCES

Education Development Centre Inc.. – Monitoring and Evaluation Resource Handbook (working draft), USAID Quality Improvement in Primary Schools (QUIPS) Community School Alliances Project, July 2002.

Ministry of Education – Copy of EMIS database 2000, 2001, 2003. EMIS Project, 2004.

Ministry of Education – EMIS Enhancement and Expansion Plan (2004-2009) Draft, June 2004.

Ministry of Education – Education Strategic Plan 2003-2015, Vol. 1 Policies, Targets and Strategies, May 2003.

Ministry of Education – Education Strategic Plan 2003-2015, Vol. 2 Work Programme, May 2003.

Ministry of Education – Report on Basic Statistics and Planning Parameters for Basic Education in Ghana 2002-2003. Statistics, Research, Information Management and Public Relations (SRIMPR) Division, EMIS Project, November 2003.

Ministry of Education – Basic Schools Census Manual, 2003, 2003-2004.

Ministry of Education – Annual School Census, Basic School Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – Annual School Census, Basic School Supplementary Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – Annual School Census, Education Management and Other Staff Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – Annual School Census, Senior Secondary Schools Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – Annual School Census, Teacher Training College Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – Annual School Census, Technical Institute Questionnaire 2003-2004 Academic Year, 2003.

Ministry of Education – FCUBE Mid-Term Stocking Report, 1996-2000. Implementation Coordinating Unit (ICU), GES Headquarters, 2002.

Ministry of Education – Presidential Review Committee, Meeting the Challenges of Education in the Twenty First Century, 2001.

National Council for Tertiary Education, Strategic Plan 2004-2008.

Annex I

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ANNEX I – PROPOSED STRUCTURE OF THE EDUCATION SYSTEM IN GHANA

Annex 2

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ANNEX II – MINISTRY STRUCTURE FLOWCHART

7

Annex III

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ANNEX III – EDUCATION STRATEGY

Annex 3

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Annex IV

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ANNEX IV – STATISTICAL INFORMATION TARGETS

Annex IV

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Annex IV

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Annex IV

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Annex IV

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Annex V

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ANNEX V – LIST OF PERSONS MET

Designation Name Telephone E mail address Ministry of Education, Youth and Sports Minister Hon.Kwadwo Baah-Wiredu Mob. 233 (020) 2013170 yes@edughana.org

Chief Director Ato Essuman 233 21 666070 aessuman@edughana.org Statistics, Research, Information Management and Public Relations (SRIMPR) Division Director Mathew Kambonaba Mob. 233 (024) 26 59 89 mkambonaba@yahoo.co.uk Assistant Director Statistics Unit

Thomas Hutton Coleman Mob. 233 27 75 93 767 thomashuttoncoleman@yahoo.com

Asst. Dir., Statistics Daniel M. Zogblah 233-21-683678 danzogg@softhome.net Programmer/Analyst Samuel Adjetey Okang EMIS AIS Specialist Joseph Dotto Chonia Joedecho@yahoo.com Procurement Executive Officer

Peter AhliJah 233-021-683675 Ahli_lipet@yahoo.com

Office Manager EMIS Project

Nancy Yelpoe

Planning Budgeting Monitoring and Evaluation Division Director James Oppong Afrani 233-21-685058 Ghana Education Service Director General Rev Ama Afo Blay Mob. 233 020 814 0350 afoblay@yahoo.com Manager,Technical Vocational Division P. K. Jampeky-Djan 233 021 683636 jimpekeydjan@yahoo.com

Asst. Dir. (PGME) Felix Asiamah 233 021 683720 Strategic Planner Kaikarri-Ababio 233 024 488236 Regional Director Regional Education Office

Akua Akobuor Debrah

National Council for Tertiary Education Executive Secretary Paul Effah Mob. 233 020 81 38 710 ncte@ug.edu.gh Deputy Executive Secretary Aladji Salifu Abdel Seidu 233 21-770197 aseidu@ncteghana.org

Planning Officer Emmanuel Newman

Annex 5

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Designation Name Telephone E mail address National Youth Council National Youth Coordinator Joe Mantey

Educational Financial Advisors Group Programme Officer European Union Benedetta Musillo 233 21 774094 benedetta.musillo@delcomgh.

org GTZ Programme Coordinator Dr.Elke Nicolai Mob. 233 24 324484 elke.nicolai@gtz.de

UNICEF Project Officer Augustine Agu 233 21 772524 aagu@unicef.org

Director, USAID Lisa Franchett 233 21 228440 Ifranchett@usaid.gov Education Specialist World Bank Eunice Y.B. Dapaah 233 21 229681 edapaah@worldbank.org

Ghana Bureau of Statistics Director Twum Baah UNESCO National Commission Secretary General, Ghana Representative, Executive Board of UNESCO

John Kusi-Achampong

Mob. 233 028 210 354 natcom.unescogh@edughana.

org

Education Specialist Dr.Boubakar Camara 233 21 765497 b.camara@unesco.org Communication and Information Advisor Hezekiel Dlamini 233 21 765497 h.dlamini@unesco.org

Other Organisations Policy Advisor; Global Health, Education, Foreign Affairs

Joe Appiah-Kusi Tel. (206) 634-2121 Kwasimpo@yahoo.com