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United Nations Regional Seminar on Census Data Archiving for Africa Addis Ababa, Ethiopia, 20 United Nations Regional Seminar on Census Data Archiving for Africa Addis Ababa, Ethiopia, 20 -23 Sep 2011 Country Paper on: Access Policy and Dissemination of Micro-Data: Egypt’s Experience Prepared by Khaled Hamed El-Deib Director of decision support systems

 Introduction. Explain the components of electronic Archive table. International Household Survey Network (IHSN) Introduction. Explain the components of electronic Archive table. International Household Survey Network (IHSN) Micro-data Management Toolkit in CAPMAS. Micro-data Access. Data Dissemination. Future of Micro-data Access by the Public. Future of Dissemination. Key Challenges.

Statistical main objective of the implementation of the census is the process of access Statistical main objective of the implementation of the census is the process of access the product meets the needs of data users at the suitable time and high accuracy. From this point the cooperation Each of the statistics sector and the IT sector to achieve this goal, according to prior planning. Was conducted the last census the population in Egypt in 2006, a thirteenth. Begin preparations for the census before the date of its inception three years.

 The use of electronic archive during the census: it consists of three tables The use of electronic archive during the census: it consists of three tables : ØIn first table each row represents a specific record data. ØIn the second table each row represents data for a particular image and the content of the barcode. ØIn the third table each row represents the data and form number and record number and the number of the smallest administrative unit.

A- Data storage of the image such as the type and number form the A- Data storage of the image such as the type and number form the smallest administrative unit and the record number and number of the form and inquire about any of them B-Know the status of each record (Is the work of quality control of the record or not) C - Know the status of each form (it was the work of ICR or not) D - Does each form a face and back, No. scanner was scanning through E - Type barcode on each form

Ø We collect information for statistical purposes and it is never used to identify Ø We collect information for statistical purposes and it is never used to identify individuals. -Information released from the site should not be sold to others . Violating this rule is a crime with severe penalties -We recognize the value of your trust and we believe that when you answer our survey we must serve as caretakers of your information -On information dissemination users should indicate that the source of information is CAPMAS

International Household Survey Network (IHSN) Micro-data Management Toolkit in CAPMAS Application of international standards International Household Survey Network (IHSN) Micro-data Management Toolkit in CAPMAS Application of international standards in the use of DDI , DCMI in Egypt The beginning of December 2009 started CAPMAS in cooperation with Paris on the 21 training criteria mentioned by 6 surveys. Training was done "On Job Training" by the staff of CAPMAS five other projects. Been the work of the session included the other nine projects, and thus the total projects to 20 projects. Is a monthly follow-up action by the President of the CAPMAS on the progress made in the documentation process, and this work offers a monthly position

The next step Ø The use of SDMX in support of the program to The next step Ø The use of SDMX in support of the program to facilitate the exchange of data. Ø Documentation of surveys carried out by CAPMAS to third-party. Ø Training of other government data producers on the documentation See everything that is new to the development of

Micro Data Access ØResearcher access Availability has improved over the past 20 years. Household Micro Data Access ØResearcher access Availability has improved over the past 20 years. Household micro-data are more accessible than establishment data.

Data Dissemination Ø Methods of data dissemination are evolving. Ø Newspapers are becoming less Data Dissemination Ø Methods of data dissemination are evolving. Ø Newspapers are becoming less important for data dissemination. ØNewspaper employment down in case of data dissemination. Ø Web has revolutionized data dissemination and analysis.

Data Dissemination Ø Statistical agencies increasingly interact with customers directly through their websites. Ø Data Dissemination Ø Statistical agencies increasingly interact with customers directly through their websites. Ø Users have access to more data than ever before. Ø However, data are dispersed across agencies , Can be hard to locate proper data set Explanations of methodologies underlying the data are hard to find.

Future of Micro-data Access by the Public Ø Match confidential and public use data Future of Micro-data Access by the Public Ø Match confidential and public use data files within and across agencies. Ø Will advances in firewall and security technologies allow increased researcher access to confidential data?

Future of Micro-data Access by the Public Ø Access to the sampling frames of Future of Micro-data Access by the Public Ø Access to the sampling frames of other agencies may improve sampling efficiency. Ø Access to administrative records from other agencies may reduce duplication.

Future of Dissemination Ø Need to recognize broad range of data users (not just Future of Dissemination Ø Need to recognize broad range of data users (not just policy-makers). ØEach group has distinct needs. ØVarying technical abilities. ØRealign focus from outputs to “outcomes”.

Future of Dissemination Ø Data Delivery. ØInternet – websites are now the “faces” of Future of Dissemination Ø Data Delivery. ØInternet – websites are now the “faces” of statistical agencies. Web-based data releases ØSocial media and other mobile electronic devices. ØPrivate sector helping to fill in the gap – Google Public Data Explorer

Future of Dissemination Ø Harmonize measurement concepts across programs ØExample: industry classifications are standardized, Future of Dissemination Ø Harmonize measurement concepts across programs ØExample: industry classifications are standardized, but products and services classifications are not ØExample: concept of earnings and how it is measured across different surveys can be very confusing

Future of Dissemination Ø Centralize data warehousing? ØData by concept: One place to find Future of Dissemination Ø Centralize data warehousing? ØData by concept: One place to find all government data on housing, education, retirement, health, etc. Ø Customizing data ØAllow the customer to customized the data they need with options for delivery of update notices

Future of Dissemination Ø Transparency ØClear, easy to understand, explanations of concepts underlying data Future of Dissemination Ø Transparency ØClear, easy to understand, explanations of concepts underlying data ØUp-to-date technical documentation

The Accelerated Data Program ( ADP ) International Household Survey Network (IHSN) Micro-data Management The Accelerated Data Program ( ADP ) International Household Survey Network (IHSN) Micro-data Management Toolkit IHSN Activities ØCoordinating survey programs Web based information on planned surveys ØHarmonizing concepts & methods Planning, sampling, questionnaire design, processing, data disclosure & confidentiality, etc. ØMaintaining a survey catalog Web based central catalog ØDeveloping data dissemination tools Micro-data Management Toolkit

Improving survey quality and data harmonization Ø Documenting helps harmonizing. Ø Documenting helps identifying Improving survey quality and data harmonization Ø Documenting helps harmonizing. Ø Documenting helps identifying data gaps. Ø ADP : assessing and improving quality of survey data. Ø Mid term proposed solution: international / national question banks (XML repository of re-usable survey metadata).

ADP implementation: pilot Countries Ø Africa Burkina Faso, Cameroon, Ethiopia, The Gambia, Ghana, Guinea ADP implementation: pilot Countries Ø Africa Burkina Faso, Cameroon, Ethiopia, The Gambia, Ghana, Guinea *, Kenya, Liberia, Mali, Mauritania *, Mozambique, Niger, Nigeria, Senegal, Tanzania, Tunisia, Uganda (+ Botswana, Lesotho, Namibia and Swaziland [TFSCB]) Ø Asia Bangladesh, Fiji, Indonesia, Lao PDR, Mongolia, Philippines, Sri Lanka, Thailand, Vietnam Ø Central and South America Argentina, Bolivia, Costa Rica, Dominica, Guatemala, Haiti*, Honduras, Mexico, Peru, Uruguay Ø Middle East Yemen

Looking Forward: Tools and guidelines Ø Micro-data Management Toolkit: Building on success Ø Dissemination Looking Forward: Tools and guidelines Ø Micro-data Management Toolkit: Building on success Ø Dissemination Policy Guidelines: Ø Anonymization tools: Preserving Confidentiality Ø Comprehensive protocol in metadata preservation Ø Guidelines on improving: Data Processing Data Analysis Data Quality Survey Methodology Ø Release of the Survey Quality Assessment Framework

Micro-data Management Toolkit Ø Software + guidelines + training materials Ø For data documentation: Micro-data Management Toolkit Ø Software + guidelines + training materials Ø For data documentation: DDI Metadata Editor

Dissemination Policy Guidelines Dissemination of micro-data files Ø Access policies and procedures are crucial Dissemination Policy Guidelines Dissemination of micro-data files Ø Access policies and procedures are crucial to protect data. Ø IHSN “Policy Guidelines & Recommendations”. Ø Why / how / when / to whom should micro-data be disseminated? . Ø Practical guidelines with suggested access agreement forms, etc. Ø To be adapted to country specificities.

Micro-data Management Toolkit Ø For data dissemination: open-source tools CDROM Web cataloguing system and Micro-data Management Toolkit Ø For data dissemination: open-source tools CDROM Web cataloguing system and Web-Builder

Anonymization Tools Ø By a Task Force; building on existing work Ø Not an Anonymization Tools Ø By a Task Force; building on existing work Ø Not an integrated software. Ø Using power of statistical packages for processing complex algorithms Ø A collection of specialized tools for: Measuring the risk Reducing the risk Assessing the information loss Intelligent system To generate anonymized subsets on-demand

Key Challenges Ø Overlap between the individuals responsible for various stages of census data Key Challenges Ø Overlap between the individuals responsible for various stages of census data and the individuals responsible for the archiving of census data. Ø Do not specify a time for archiving. Ø The presence of more than one source has the census data, which impedes the archiving of census data. Ø Metadata collection and delivery Ø Ensuring solution integrated with data dissemination Ø Access to Egypt-wide outputs Ø Micro-data provision Ø Product definition and delivery Mechanisms

E-mail: pres_capmas@capmas. gov. eg E-mail: khaled_man@live. com E-mail: [email protected] gov. eg E-mail: [email protected] com