Assessing the effects of automation on women's employment in Ghana
| dc.contributor.author | Poku, Tracy Konadu | |
| dc.date.accessioned | 2022-12-20T17:22:35Z | |
| dc.date.available | 2022-12-20T17:22:35Z | |
| dc.date.issued | 2021-05 | |
| dc.description | Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2021 | |
| dc.description.abstract | As the age of robots and self-learning software is upon us, fears of the apocalyptic vision of a world where jobs will be destroyed with human labour to be replaced by robots causing severe unemployment keeps increasing. This paper sought to assess the effects of automation on the average Ghanaian woman by studying the labour force participation trends among women in Ghana and then using the Oxford study by Frey and Osbourne (2013) to estimate the probability jobs in Ghana being computerized. To do this, I employed the deductive research approach and comparative survey design. I used archival data of the 2015 Labour Force Report from the Ghana Statistical Service to get a better understanding of the types of jobs Ghanaian women are typically employed in. In conjunction, I put this information against the Frey and Osbourne (2013) probability index on automation risk to estimate the potential automation risk for each sector and what this means for the female labour force of Ghana. The participants of this study are women who are economically active (15-59 years old) and who are in the agriculture, fishing and forestry sector; wholesale and retail sector; manufacturing, mining and quarrying sector; hotels and restaurants sector or public administration sector. The research revealed that only about 15% of Ghanaian working women are at a high risk of automation or have above a 70% automation risk which is lesser than the 20% of women globally. Moreover, it was found that although the majority of both the male and female workforce in Ghana have a predominantly medium risk of automation, 42% of the total male workforce has a medium automation risk whilst 38% of the female workforce has a medium automation risk. Also, the male workforce has an 18% high automation risk whilst the female workforce has a 15% automation risk. This suggests that men in Ghana are affected slightly more than the women in Ghana by automation. | |
| dc.description.sponsorship | Ashesi University | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11988/791 | |
| dc.language.iso | en | |
| dc.subject | women's employment | en |
| dc.subject | Ghanaian labour force | en |
| dc.title | Assessing the effects of automation on women's employment in Ghana | |
| dc.type | Thesis |
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