Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type
| dc.contributor.advisor | Branson, Nicola | |
| dc.contributor.advisor | Leibbrandt, Murray | |
| dc.contributor.author | Culligan, Samantha | |
| dc.date.accessioned | 2023-03-02T07:53:05Z | |
| dc.date.available | 2023-03-02T07:53:05Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-02-20T12:30:14Z | |
| dc.description.abstract | The socio-economic profile of students who are participating in post-school education; and the distribution of their socio-economic characteristics between universities and colleges, between institutions of a similar type, and within particular institutions is not well understood. Part of the reason for this is because potential data sets that could be used to answer this fall short on dimensions needed to fully explore the extent of socio-economic differences amongst student bodies by institutional type. I, therefore, generate a data set that draws on institutional, census, and geospatial information to estimate the socio-economic background of students' home postal code. Using this data set, I compare the mean statistic and generalised entropy index of a range of individual and household socio-economic postal code indicators for student bodies by institutional type to descriptively analyse their socio-economic profile. I show student bodies at traditional universities and Unisa appear socio-economically similar and display higher socio-economic circumstances than that of student bodies at comprehensive universities, universities of technology and TVET colleges who appear socio-economically similar. Between 2008 and 2019, the mean socio-economic profile declined for all student bodies, whereas there was no uniform trend for whether socio-economic heterogeneity was increasing or decreasing over time by university type. Lastly, my findings suggest there is more evidence for horizontal stratification between particular universities (regardless of their institutional type) rather than between university types, or between universities and TVET colleges. | |
| dc.identifier.apacitation | Culligan, S. (2022). <i>Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type</i>. (). ,Faculty of Commerce ,School of Economics. Retrieved from http://hdl.handle.net/11427/37101 | en_ZA |
| dc.identifier.chicagocitation | Culligan, Samantha. <i>"Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type."</i> ., ,Faculty of Commerce ,School of Economics, 2022. http://hdl.handle.net/11427/37101 | en_ZA |
| dc.identifier.citation | Culligan, S. 2022. Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type. . ,Faculty of Commerce ,School of Economics. http://hdl.handle.net/11427/37101 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Culligan, Samantha AB - The socio-economic profile of students who are participating in post-school education; and the distribution of their socio-economic characteristics between universities and colleges, between institutions of a similar type, and within particular institutions is not well understood. Part of the reason for this is because potential data sets that could be used to answer this fall short on dimensions needed to fully explore the extent of socio-economic differences amongst student bodies by institutional type. I, therefore, generate a data set that draws on institutional, census, and geospatial information to estimate the socio-economic background of students' home postal code. Using this data set, I compare the mean statistic and generalised entropy index of a range of individual and household socio-economic postal code indicators for student bodies by institutional type to descriptively analyse their socio-economic profile. I show student bodies at traditional universities and Unisa appear socio-economically similar and display higher socio-economic circumstances than that of student bodies at comprehensive universities, universities of technology and TVET colleges who appear socio-economically similar. Between 2008 and 2019, the mean socio-economic profile declined for all student bodies, whereas there was no uniform trend for whether socio-economic heterogeneity was increasing or decreasing over time by university type. Lastly, my findings suggest there is more evidence for horizontal stratification between particular universities (regardless of their institutional type) rather than between university types, or between universities and TVET colleges. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Economics LK - https://open.uct.ac.za PY - 2022 T1 - Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type TI - Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type UR - http://hdl.handle.net/11427/37101 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37101 | |
| dc.identifier.vancouvercitation | Culligan S. Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type. []. ,Faculty of Commerce ,School of Economics, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37101 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | School of Economics | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Economics | |
| dc.title | Using Census, Institutional and Geospatial Data to Estimate the Socio-Economic Profile of Post-School Students by Institutional Type | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MCom |