Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Abbas, Syed Wasima; * | Rasul, Sajidb | Ahmad, Munira
Affiliations: [a] National College of Business Administration and Economics, 40/E-1, Gulberg-III, Lahore-54660, Pakistan | [b] Bureau of Statistics Punjab, Lahore, Pakistan
Correspondence: [*] Corresponding author: Syed Wasim Abbas, National College of Business Administration and Economics, 40/E-1, Gulberg-III, Lahore-54660, Pakistan. Tel.: +92 333 655 0630; Fax: +92 429 923 2903; E-mail: [email protected].
Abstract: Almost every public sector department produces some statistics and accumulates its share in the formulation of National Statistics. The accurate and timely statistics are vital for planning and development, budgeting and evaluation of the implemented programs. It may be reasonable to assume that datasets are being produced at almost all levels of the departments; nonetheless, a substantial number of valuable data-items are left unreported and hence they are unable to play their role in evidence-based planning and decision-making. There is a need to uncover these sources, to explore the reasons behind the non-reporting of data and to devise strategies to utilize these sources in the production of official statistics. In this paper, we present the results of a national-level survey conducted to collect information on data-processing and reporting mechanisms of public-sector organizations in Pakistan. Along with presenting the survey results, the paper discusses the potential sources of unreported data (including Big Data), the reasons for non-reporting at different sectoral levels and the confidentiality, privacy, and data-sharing constraints. Based on the above, the paper ends by proposing the compilation of a Directory of Administrative Data Sources (DADS) in order to establish an improved administrative infrastructure in the country.
Keywords: Official statistics, administrative data sources, unreported data, Big Data
DOI: 10.3233/SJI-180466
Journal: Statistical Journal of the IAOS, vol. 35, no. 3, pp. 359-370, 2019
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]