1 / 8

Lessons from Southern Africa Census and Survey Data

Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town. Lessons from Southern Africa Census and Survey Data. Outline. Lessons learned … … from CSOs … from censuses … from surveys Suggestions and questions for the future. Lessons learned … from CSOs.

sorena
Download Presentation

Lessons from Southern Africa Census and Survey Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town Lessons from Southern Africa Census and Survey Data

  2. Outline • Lessons learned … • … from CSOs • … from censuses • … from surveys • Suggestions and questions for the future

  3. Lessons learned … from CSOs • Dangers associated with the loss of institutional memory • Paradoxically, strengthens desire to perpetuate analytical approaches and processes of the past • The need to properly archive data, and maintain strict version control • Or, what happens when your server crashes? • The need to preserve documentation • Or, what happens when you have handed out the last existing copy of your report?

  4. Lessons learned … from CSOs • Need to ensure reasonable access to data • Not handing it over to outsiders who promise to act as a gatekeeper, but then deny access later • Not abusing the confidentiality of unit-record data to deny access to bona fide researchers • Political, social or developmental goals can lead to pressure on CSOs to ensure that demographic indicators are ‘moving in the right direction’ • Estimation methods chosen to give the ‘right’ results or which conform to other expectations • E.g. Millennium Development Goals

  5. Lessons learned … from censuses • Need to distrust data put there by editing rules; and particularly those dreamed up by foreign consultants • Enumerators matter • Not using enumeration as a (temporary) job-creation exercise • Not using unskilled enumerators • Proper training of enumerators • Not using untried technology for data capture or processing

  6. Lessons learned … from censuses • Need for greater checks on the data, and a willingness to admit to errors • Need to validate results against those from other sources (e.g. school enrolments by age give a minimum population at that age) • Need for long(er)-term planning cycles • Rushed censuses invariably produce bad data • Longer-term planning horizon will lead to less of a boom-to-bust cycle in terms of staffing and management

  7. Lessons learned … from surveys • Not all DHSs are of good quality • Depends on the partner organisation used to do the fieldwork • Not all DHSs are in the public domain • The same points as the two above apply, but to an even greater extent, to other national surveys

  8. Things that might be challenged • How do we view Post-Enumeration Surveys? • Do we adjust the data for estimated undercount? • What are the implications of not doing so? • Subnational estimates • What are the implications of doing so? • Would a short-form census be better? • A poor census enumeration has knock on effects onto surveys using that census to determine a sampling frame • Transparency in editing of censuses data

More Related