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Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance
Corruption is a phenomenon in which many South Africans are well versed. While it continues to headline the news, the true extent of corruption is difficult to determine. Perception based indices have been proven to be inaccurate and experience-based data is also likely to incorrectly estimate the l...
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| Format: | Thesis |
| Language: | English |
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School of Economics
2020
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| _version_ | 1867613170969870336 |
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| access_status_str | Open Access |
| author | Unite, Emma |
| author2 | Bhorat, Haroon |
| author_browse | Bhorat, Haroon Unite, Emma |
| author_facet | Bhorat, Haroon Unite, Emma |
| author_sort | Unite, Emma |
| collection | Thesis |
| description | Corruption is a phenomenon in which many South Africans are well versed. While it continues to headline the news, the true extent of corruption is difficult to determine. Perception based indices have been proven to be inaccurate and experience-based data is also likely to incorrectly estimate the level of corruption. Forensic economics have come forward to fill this gap. These methods, however, are not always feasible as they rely on special datasets which are often difficult to come by. Using the National Income Dynamics Survey (NIDS) Waves 3, 4 and 5, this paper measures the difference in income underreporting between the public and private sectors. This difference is argued to represent the relative level of petty corruption in the public sector. Estimation results show an increasing trend in petty corruption over the period 2012-2017 with the public sector underreporting their income by, on average, 31.71%. Petty corruption is highest in law enforcement and the general government sectors. Evidence shows spatial variation in petty corruption with rural areas having the highest levels of underreporting. Petty corruption is also found to vary across the income distribution as levels of underreporting increase with income. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31307 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:53.390Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | School of Economics |
| publisherStr | School of Economics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31307 Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance Unite, Emma Bhorat, Haroon Economics Corruption is a phenomenon in which many South Africans are well versed. While it continues to headline the news, the true extent of corruption is difficult to determine. Perception based indices have been proven to be inaccurate and experience-based data is also likely to incorrectly estimate the level of corruption. Forensic economics have come forward to fill this gap. These methods, however, are not always feasible as they rely on special datasets which are often difficult to come by. Using the National Income Dynamics Survey (NIDS) Waves 3, 4 and 5, this paper measures the difference in income underreporting between the public and private sectors. This difference is argued to represent the relative level of petty corruption in the public sector. Estimation results show an increasing trend in petty corruption over the period 2012-2017 with the public sector underreporting their income by, on average, 31.71%. Petty corruption is highest in law enforcement and the general government sectors. Evidence shows spatial variation in petty corruption with rural areas having the highest levels of underreporting. Petty corruption is also found to vary across the income distribution as levels of underreporting increase with income. 2020-02-25T10:48:17Z 2020-02-25T10:48:17Z 2019 2020-02-25T08:03:58Z Master Thesis Masters MCom http://hdl.handle.net/11427/31307 eng application/pdf School of Economics Faculty of Commerce |
| spellingShingle | Economics Unite, Emma Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| thesis_degree_str | Master's |
| title | Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| title_full | Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| title_fullStr | Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| title_full_unstemmed | Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| title_short | Predicting Petty Corruption in the Public Sector through Household Survey Non-Compliance |
| title_sort | predicting petty corruption in the public sector through household survey non compliance |
| topic | Economics |
| url | http://hdl.handle.net/11427/31307 |
| work_keys_str_mv | AT uniteemma predictingpettycorruptioninthepublicsectorthroughhouseholdsurveynoncompliance |