42022 40(4): 4-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Student poverty in South African universities: Promoting the wellbeing and success of students Abstract The fact that poverty has largely been conceptualised from a narrow financial perspective in South African higher education may have contributed to its perpetuation among students. There is limited research on the multidimensionality of poverty, particularly its wide-ranging effect on students in universities. Using the capabilities approach, this study explores the extent of poverty, as well as the way various dimensions of deprivation interplay to affect the wellbeing and success of students in universities. The study employs a sequential-mixed research design and gathers data at a university, initially using three focus group discussions followed by a survey questionnaire administered to 2306 students and 470 student voices and unfiltered stories. Both focus group discussion data and the short stories were coded and analysed using NVivo 12. The themes generated included basic needs, resources, psychological wellbeing, living conditions and participation. The findings of survey questionnaires, which were analysed using Stata, R, SPSS and Microsoft Excel, indicate a complex relationship between the dimensions of deprivations that affect students and the corrosive effect lack of finances has. While providing funding only does not sufficiently address student poverty, the study recommends that universities should consider devising robust measures to identify those financially deprived and provide them with adequate funding. Concurrently, universities should address other forms of deprivations, using mentorship programmes, for instance, to prevent and reduce psychological stress, shame, stigma and loss of dignity among poor students. Keywords: Student poverty, success in higher education, multidimensional deprivation, Capabilities Approach, South Africa 1. Background of the study South Africa has high inequalities, with a Gini Co-efficient of 0,63, where 0 represents an equal society and 1 an unequal one (World Population Review, 2021). The Poverty Trends in South Africa Report indicates that approximately 47,4% of the country’s population is impoverished (United Nations Development Programme, 2020). The impact of poverty negatively affects the wellbeing of students, which reduces their chances of succeeding when others have flourishing lives (Wilson-Strydom, 2015; Gore, 2021). Though some AUTHOR: Dr Anesu Ruswa1 Dr Oliver Gore2 AFFILIATION: 1University of the Free State, South Africa 2North-West University, South Africa DOI: http://dx.doi.org/10.38140/ pie.v40i4.6379 e-ISSN 2519-593X Perspectives in Education 2022 40(4): 4-18 PUBLISHED: 23 December 2022 RECEIVED: 27 May 2022 ACCEPTED: 1 August 2022 Research Article http://dx.doi.org/10.38140/pie.v40i4.6379 https://orcid.org/0000-0002-1475-4175 https://orcid.org/0000-0002-1475-4175 http://dx.doi.org/10.38140/pie.v40i4.6379 52022 40(4): 5-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities progress has been made in addressing inequality and poverty in the South African general population (David et al., 2018), not much success has been achieved in addressing student poverty in higher education. In 2015 and 2016 students protested financial exclusion through #FeesMustFall and many other broader societal issues such as decolonising universities through #RhodesMustFall (Burchardt, 2022). The protests were thus essentially beyond financial as they were aimed to remove the deprivations rooted in the legacy of the past apartheid and colonialism that impoverished most black students (Konik & Konik, 2017). South African higher education has mainly addressed student poverty by funding students who are financially disadvantaged through the National Student Financial Aid Scheme, which takes a financial approach to student poverty (Gore & Ruswa, 2021). Despite the financial interventions, student poverty persists as evidenced by student protests at the beginning of 2020 and the high attrition rate at universities (Khuluvhe et al., 2021). Approximately 28,1% of the students dropped out of the education system before reaching the second year of study in 2017, while half of the students drop out by year five of their study (Department of Higher Education and Training, 2021). Exploring and addressing multidimensional student poverty is crucial for promoting individuals’ wellbeing. Walker (2020) avers that when resources are limited, students do not have the opportunity to function fully during their studies which is an indication that achieving wellbeing is foundational for their success. It is vital for the South African higher education system to grapple with the real challenges poor students are facing. As Gore and Walker (2020) feel that the use of vaguely defined and limited terminology such as “disadvantage” in South African higher education, the policy lacks clarity and focus. Studies such as Walker et al. (2009); Spaull (2013); and Van der Berg (2018) may provide a deeper understanding of the multiple dimensions in which students are deprived in South African universities. Besides a lack of financial resources, poverty is made up of dimensions such as loss of dignity and the absence of psychological wellbeing hence its multidimensionality (Walker 2020). There is a need for further research on the extent and multidimensionality of student poverty to bridge this information gap. 2. Literature review and problem statement Different approaches have been employed to conceptualise poverty and a commonly used one is the unidirectional approach, which is also highlighting the financial status. The unidirectional approach regards students as poor if they are positioned below a certain financial threshold, yet students might not be poor when we assess the other dimensions of their wellbeing (Alkire et al., 2015). Thus, student poverty encompasses various aspects that range from economic, educational and contextual factors. Alkire et al. (2015) brought forward a mathematical index for measuring poverty with special attention given to the determination of dimensions and indicators of poverty, and their associated weights. Central to Alkire et al.’s (2015) methodology is that individuals who are deprived in some dimensions but not multidimensionally poor overall are left out of account when determining the incidence and intensity of multidimensional poverty (Alkire et al., 2015). This is a unique and relevant advantage of Alkire et al.’s (2015) methodology, which was used in this research to identify students who are deprived in certain dimensions but are not poor. Simultaneously, individuals might be financially well off but lacking in other dimensions, such as psychological wellness and dignity, during their studies (Ruswa & Gore, 2021). http://dx.doi.org/10.38140/pie.v40i4.6379 62022 40(4): 6-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) The multidimensional approach to student poverty, which recognises the multifaceted nature of poverty and its underpinnings, is based on the notion that students are deprived in multiple aspects of their wellbeing. As yet this multidimensional approach, despite being widely used to conceptualise poverty in the general population (Statistics South Africa, 2014; Fransman & Yu, 2018), has not been adopted in research and interventions in South African universities (Breier, 2010; Firfirey & Carolissen, 2010; Van der Bank & Nkadimeng, 2014; Council on Higher Education, 2016; van Breda, 2018). Literature suggests that student poverty is influenced by the intersectionality of race and class, gender and other factors (Gore, 2021). Besides experiencing psychological stress and restricted access to learning resources, the students have limited social networks and are unlikely to participate in campus social activities (Stahl, McDonald & Stokes, 2020; Bye, Muller & Oprescu, 2020). These studies affirm the multidimensionality of poverty in universities worldwide. However, the intensity and interrelationship of dimensions constituting poverty in South African higher education remain under-researched. Universities need to understand the magnitude of poverty and the interplay of dimensions constituting poverty if they are to promote students’ wellbeing and success. Failure to understand and adequately address students’ needs has contributed to the perpetuation of poverty and lower outcomes among the black low-income groups (Ruswa & Gore, 2021). The study explored the severity of student poverty at one university in South Africa. It addressed the following questions: What is the extent of multidimensional poverty among university students? How do the dimensions of deprivation relate to each other in affecting the wellbeing of students? 3. A Capabilities Approach to exploring poverty The study adopted the capabilities approach developed by Sen (1999). Sen (1999: 80) argues that evaluative judgements about poverty should be made using ‘wellbeing’ of individuals as wellbeing reflects their ‘quality of life’. The use of income in assessing poverty is inadequate because income is only a means to achieve a person’s wellbeing (Robeyns, 2017). The capabilities approach was adopted for this study because it focuses on the wellbeing of people at an individual level as well as assessing whether the social arrangements allow people to have opportunities to achieve what they value (Robeyns, 2017). The concepts that constitute the capabilities approach are ‘capabilities’, ‘functionings’, ‘agency’ and ‘conversion factors’. Capabilities are reflective of the effective ‘opportunities’ individuals ‘will [have] when choosing from the options open to them’ while the outcomes from the capabilities are the functionings (Robeyns, 2017:8). Agency relates to the different ways one can have the power to make decisions, work towards their goals and challenge constraining situations (Sen, 1999), for example, to succeed in their educational journeys. Both capabilities and agency are enabled or constrained by the conversion factors which are reflective of the personal, environmental and social arrangements to which an individual has access. Poverty is thus a deprivation in one or more of the dimensions of students’ wellbeing. As a result of a lack of effective opportunities, students do not function sufficiently to optimally succeed in their studies. In other words, poverty to a larger extent reflects ill-being or the absence of wellbeing (Wolff, Lamb & Zur-Szpiro, 2015), which contributes to lower academic outputs. By focusing on the wellbeing of individuals, the capabilities approach suggests that poverty is multidimensional as it is through the different dimensions of wellbeing that judgements about deprivations are made (Robeyns, 2017). Although poverty cannot be explained using income only, lack of finances still plays a central role in identifying poverty (Wolff et al., 2015; http://dx.doi.org/10.38140/pie.v40i4.6379 72022 40(4): 7-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities Walker 2020). In conceptualising the significant dimensions of student poverty, this paper draws on Wolff and De Shalit’s (2007:121) notions of ‘corrosive’ to determine how certain dimensions of deprivation contribute to the student being deprived in other dimensions. Put differently, the dimensions of deprivation that are corrosive result in student poverty being more severe because of the compounded effects these dimensions have on students’ lives. 4. Methodology The study employed an exploratory sequential mixed method design, with qualitative insights informing quantitative inquiry, to investigate student poverty as this methodology offers a rigorous analysis of poverty and its potential to achieve richer results through the integration of qualitative and quantitative findings (Creswell & Creswell, 2018). Although quantitative techniques are helpful in developing and testing a multidimensional poverty index, they are limiting as they do not explain the nuances of poverty in detail, and they also exclude direct student voices which carry invaluable insight. These shortcomings are addressed by the qualitative research component of the design. Figure 1 summarises the design: Figure 1: Exploratory sequential mixed research design The initial qualitative phase comprised three focus groups discussion (FGDs) constituting eight members each drawn from a higher education institution in South Africa. Participants for the FGDs were recruited using convenience sampling from undergraduate degree programmes through an open invitation posted on university social media platforms. Racially and gender-diverse participants came from fields like Medicine, Quantity Surveying and Geography, Humanities, Business Studies, Education, Agriculture Economics and Law. The FGDs were audio recorded and fully transcribed and were, together with unfiltered stories (obtained in the next phase explained below) imported into Nvivo 12, a qualitative software for data analysis. The data were descriptively coded and analysed using thematic analysis. The analysis identified five primary deprivation dimensions which are: basic needs, resources, psychological wellbeing, living conditions and participation. The second phase, the quantitative one, involved data collection using a survey questionnaire, which has proven an effective tool for gathering quantitative data (Healy et al., 2018). The survey questionnaire was developed from the dimensions and indicators of poverty drawn from the FGDs. The questionnaire was designed to establish the severity of student poverty and verify the individual indicators that emerged from the FGDs. To enhance its validity and Phase 1 Qualitative Data Collection and Analyisis ( 3 FGDS) Phase 2 Quantitative Data Collection Questionnares (2306). From the total 470 unfiltered short stories received Phase 3 Triangulation of findings Figure 1: Exploratory sequential mixed research design The initial qualitative phase comprised three focus groups discussion (FGDs) constituting eight members each drawn from a higher education institution in South Africa. Participants for the FGDs were recruited using convenience sampling from undergraduate degree http://dx.doi.org/10.38140/pie.v40i4.6379 82022 40(4): 8-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) programmes through an open invitation posted on university social media platforms. Racially and gender-diverse participants came from fields like Medicine, Quantity Surveying and Geography, Humanities, Business Studies, Education, Agriculture Economics and Law. The FGDs were audio recorded and fully transcribed and were, together with unfiltered stories (obtained in the next phase explained below) imported into Nvivo 12, a qualitative software for data analysis. The data were descriptively coded and analysed using thematic analysis. The analysis identified five primary deprivation dimensions which are: basic needs, resources, psychological wellbeing, living conditions and participation. The second phase, the quantitative one, involved data collection using a survey questionnaire, which has proven an effective tool for gathering quantitative data (Healy et al., 2018). The survey questionnaire was developed from the dimensions and indicators of poverty drawn from the FGDs. The questionnaire was designed to establish the severity of student poverty and verify the individual indicators that emerged from the FGDs. To enhance its validity and precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines (minimal sample size) for a population of N with a confidence level of % and a margin of error of the following relationship holds: precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines 𝑛𝑛 (minimal sample size) for a population of N with a confidence level of 𝑥𝑥% and a margin of error of ±𝑒𝑒 the following relationship holds: 𝑛𝑛 = � � ∙�∙(���) � � or 𝑛𝑛 = � � ���� � � � � � Where: Z = Z value from the normal table (e. g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as a decimal e = confidence interval or margin of error, expressed as a decimal (e. g. , .04 = ±4) When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct Where: precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines 𝑛𝑛 (minimal sample size) for a population of N with a confidence level of 𝑥𝑥% and a margin of error of ±𝑒𝑒 the following relationship holds: 𝑛𝑛 = � � ∙�∙(���) � � or 𝑛𝑛 = � � ���� � � � � � Where: Z = Z value from the normal table (e. g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as a decimal e = confidence interval or margin of error, expressed as a decimal (e. g. , .04 = ±4) When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines 𝑛𝑛 (minimal sample size) for a population of N with a confidence level of 𝑥𝑥% and a margin of error of ±𝑒𝑒 the following relationship holds: 𝑛𝑛 = � � ∙�∙(���) � � or 𝑛𝑛 = � � ���� � � � � � Where: Z = Z value from the normal table (e. g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as a decimal e = confidence interval or margin of error, expressed as a decimal (e. g. , .04 = ±4) When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines 𝑛𝑛 (minimal sample size) for a population of N with a confidence level of 𝑥𝑥% and a margin of error of ±𝑒𝑒 the following relationship holds: 𝑛𝑛 = � � ∙�∙(���) � � or 𝑛𝑛 = � � ���� � � � � � Where: Z = Z value from the normal table (e. g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as a decimal e = confidence interval or margin of error, expressed as a decimal (e. g. , .04 = ±4) When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct precision, the questionnaire was piloted and refined based on the gaps identified. The administration of the questionnaire was as follows: through an online link sent through the university’s online survey, Evasys, to all undergraduate registered students at one university with the help of Student Affairs, Housing and Residence Affairs. The data were hosted and stored in Evasys. The total number of responses from the online questionnaire was 1791 students. To boost the sample size, a simple random sampling technique was used to distribute hard copies of the questionnaire to the general students across the campus, and this increased the total responses to 2306. As Aono and Nguyen (2017) state, random sampling yields representative samples. The hardcopy administration phase was administered by 15 research assistants strategically deployed in university residences, departments and social spaces to distribute the survey and then scan the responses back into Evasys so that they could be prepared for analysis. The data from the scanned hard copies were integrated with the online data in Evasys. This was done automatically and a consolidated dataset compatible with all statistical analysis software was generated. After consolidation, the researchers received 2306 completed questionnaires (515 hardcopies and 1791 electronic) which are well above the 1800 sample size initially expected. The researchers employed Tanaka’s (1987) model, which determines 𝑛𝑛 (minimal sample size) for a population of N with a confidence level of 𝑥𝑥% and a margin of error of ±𝑒𝑒 the following relationship holds: 𝑛𝑛 = � � ∙�∙(���) � � or 𝑛𝑛 = � � ���� � � � � � Where: Z = Z value from the normal table (e. g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as a decimal e = confidence interval or margin of error, expressed as a decimal (e. g. , .04 = ±4) When the above formula was applied to the total student population of 27 000 students, the minimum required sample was 345, with a confidence interval of 95% and a margin of error of 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct http://dx.doi.org/10.38140/pie.v40i4.6379 92022 40(4): 9-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities 4. The responses obtained were above the minimum sample required which means inferential statistical analysis could be conducted. The final phase involved the triangulation of the findings from the FGDs and the survey questionnaires. These survey questionnaires were also used to gather additional direct student voices and unfiltered (unedited) stories from students who gave personal accounts of their experience of multidimensional poverty. Out of the 2306 students who completed the questionnaire, 470 students provided stories of their lived experience which were captured in NVivo and analyzed using thematic analysis. A passworded computer was used to store the data. SPSS was used to clean the data. and then Microsoft Excel was used to create visuals and R was instrumental in running statistical tests. Stata was used primarily for the implementation of the Alkire-Foster Methodology (2009) which enabled us to make normative decisions regarding the weights, dimensions, and indicators to develop the Multidimensional Student Poverty Index (MSPI). We proactively upheld ethical values throughout all the phases of the research, including obtaining permissions from the university, ensuring that the participation of the students was voluntary, protecting the anonymity of respondents, upholding the principle of no harm, respecting the rights of vulnerable students and not disclosing sensitive information. The study was ethically approved by the university and participants signed the consent form before taking part in the study. 5. Findings The following themes, which formed the dimensions of deprivations, emerged from the analysis of the focus group data: basic needs, resources, psychological wellbeing, living conditions and participation; and these were developed from the subthemes listed in Table 1. Table 1: Dimensions of deprivations, themes and subthemes Dimension (theme) Basic Needs Learning resources Living conditions Participation Psychological wellbeing Subtheme/ capability -Food security -Access to basic healthcare -Physical health -Amenities -Tuition Fees -Resources for learning -Management of resources (budgeting) -Conducive study environment - Safety and security -Access to transport -Participation in learning -Participation in leisure activities -Emotional wellbeing -Dignity -Social support The data show complex interaction of the dimensions of deprivations. We discuss these findings to illuminate the relationship, corrosive effects and intensity of these dimensions that deprive the students and on how they affect the wellbeing of students. http://dx.doi.org/10.38140/pie.v40i4.6379 102022 40(4): 10-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) a) The interplay of the dimensions of deprivations The data indicates that the financial deprivation of some of the students resulted in other deprivations, primarily failing to access adequate learning resources and lacking basic needs i.e., food, accommodation, basic amenities, access to health care and physical health. Astoundingly, about 8% of poor students from the censored head counts did not have a place to live and the same proportion did not have access to electricity and water. Figure 2 below, shows the proportion of students who lacked financially and also had other deprivations. (a) The interplay of the dimensions of deprivations The data indicates that the financial deprivation of some of the students resulted in other deprivations, primarily failing to access adequate learning resources and lacking basic needs i.e., food, accommodation, basic amenities, access to health care and physical health. Astoundingly, about 8% of poor students from the censored head counts did not have a place to live and the same proportion did not have access to electricity and water. Figure 2 below, shows the proportion of students who lacked financially and also had other deprivations. Figure 2: Deprivations affected by financial lack (i) Basic needs The survey data further illustrates that close to 32.8% of the students were deprived in the health indicator. Students positioned health care above all the resource-based indicators, suggesting its significance in their wellbeing and success in their studies. The results corroborate with other research that reveals the centrality of the health dimension (United Nations, 2016) It emerged that lack of physical health constrained students’ opportunities to achieve their wellbeing more than those who were not deprived as far as health is concerned. There is a reciprocal association between physical health and multidimensional poverty with students who are multidimensionally poor being deprived in their physical health, concurrently, those deprived in their physical health experienced multidimensional poverty (Wolff, Lamb & Zur- Szpiro, 2015). Thus, multidimensional poverty constrains individuals from having opportunities 8,2% 8,0% 13,5% 17,7% 20,9% Water & Electricity Place to live Access to basic health care Food security Physical health Financially deprived students also lack Figure 2: Deprivations affected by financial lack i. Basic needs The survey data further illustrates that close to 32.8% of the students were deprived in the health indicator. Students positioned health care above all the resource-based indicators, suggesting its significance in their wellbeing and success in their studies. The results corroborate with other research that reveals the centrality of the health dimension (United Nations, 2016). It emerged that lack of physical health constrained students’ opportunities to achieve their wellbeing more than those who were not deprived as far as health is concerned. There is a reciprocal association between physical health and multidimensional poverty with students who are multidimensionally poor being deprived in their physical health, concurrently, those deprived in their physical health experienced multidimensional poverty (Wolff, Lamb & Zur- Szpiro, 2015). Thus, multidimensional poverty constrains individuals from having opportunities and accomplishments in other dimensions. A proportion of 23.7% of students were reported to have experienced food insecurity, that is lacking access to one meal a day. Up to three-quarters of these food-insecure students were multidimensionally poor. Unlike other deprivations, food insecurity immediately threatened the optimal functioning of individuals. Most of the food-insecure students were from low-income backgrounds who also had attended low-income schools in township and rural areas, as shown in Figure 3. http://dx.doi.org/10.38140/pie.v40i4.6379 112022 40(4): 11-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities Figure 3: Food-deprived or non-deprived students and their type of high school (Raw count refers to the actual number of students) Students were likely not to have access to resources for their educational needs as revealed in their stories from the survey data. ii. Learning Resources The learning resources dimension constitutes aspects such as living expenses, university fees and the ability of students to budget as summarised in Figure 4. Figure 4: Students deprived in indicators for learning resources The censored headcounts indicate that a proportion of 22% of all multidimensionally poor students was deprived in all the indicators, which together increase the learning resources dimension. This means that a fifth of all multidimensionally poor students struggles the most with resources and skills necessary for learning. As mentioned in the preceding section, the South African government introduced free higher education in 2018 but this excluded students from families earning above R350 000 per year and those who were already enrolled in the previous year. Hence students who took part in this study had not yet benefitted from the new funding policy and were still having to find alternative funding. Only 38.4% used NSFAS, compared to the 61.6% who were funded by family (26.4%), other bursaries (19.2%) and other means including part-time work (16.0%). Even when students received their NSFAS funds, some of them could not budget their money properly to buy textbooks and to cater for their living expenses. (iii) Living conditions Living conditions emerged as another indicator of deprivation. Figure 5 shows that most of the students who have poor living conditions are also multidimensionally poor. 22% 28% 32% 34% 24% 31% 39% 49% 50% 38% I have means of paying my study fees this year I have means of paying for other university expenses this year I have means of paying my study fees in coming years. I have means of paying for other university expenses in coming years I am able to manage my financial resources i.e. to budget well Pe rc en ta ge d ep ri ve d Indicators of deprivation in resources for learning Censored head-count Deprived head-counts Figure 4: Percentage of students whose learning resources are an indication of deprivation The censored headcounts indicate that a proportion of 22% of all multidimensionally poor students was deprived in all the indicators, which together increase the learning resources http://dx.doi.org/10.38140/pie.v40i4.6379 122022 40(4): 12-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) dimension. This means that a fifth of all multidimensionally poor students struggles the most with resources and skills necessary for learning. As mentioned in the preceding section, the South African government introduced free higher education in 2018 but this excluded students from families earning above R350 000 per year and those who were already enrolled in the previous year. Hence students who took part in this study had not yet benefitted from the new funding policy and were still having to find alternative funding. Only 38.4% used NSFAS, compared to the 61.6% who were funded by family (26.4%), other bursaries (19.2%) and other means including part-time work (16.0%). Even when students received their NSFAS funds, some of them could not budget their money properly to buy textbooks and to cater for their living expenses. iii. Living conditions Living conditions emerged as another indicator of deprivation. Figure 5 shows that most of the students who have poor living conditions are also multidimensionally poor. Figure 5: Percentage of students whose living conditions are an indication of deprivation A Two-Way ANOVA to test if there is a statistically significant difference between the poverty scores of approximately 58% of the students who lived off-line versus their on-campus counterparts showed that there is a statistically significant difference between the places of residence and the poverty score of the students (F (2, 2116); F=6.844; p= 0.03 ∴ < 5%). Looking at these deprivations, living conditions play a central role in students’ ability to exercise their agency and choices. As indicated above, students were constrained from participating in the university activities that they valued due to a lack of safety and transport. Living conditions thus play a pivotal role in determining the degree to which students can participate actively in academic and co-curricular activities (Gore 2021). In this instance living conditions constrained off-campus students. The data shows that some students within the university system do not have any accommodation at all. A total of 184 students (10,7%) were accommodation insecure as they did not have a place to live since they could not afford to pay rent. I am concerned about our safety as bursary students who are not living on-campus. We have not received our private accommodation allowance which means we are constantly worried because we face the reality of getting evicted. This is such a distraction when I must study…I really do not have anywhere to go if I get evicted. I may end up staying at the library like some of my classmates who do not have accommodation (Anonymous student online). While highlighting the connectedness between inadequate of finances and the failure of the 22,0% 18,3% 18,0% 8,4% 12,4% 31,5% 29,6% 24,9% 13,0% 20,1% I live in accommodation which is conducive for my studies I experience challenges with transport that negatively affect my studies I feel safe and secure where I live I have not been a victim of assault on campus I have not been a victim of assault off campus Pe rc en ta ge D ep ri ve d Indicators for deprivation in Living conditions Censored head-counts Raw head-count Figure 5: Percentage of students whose living conditions are an indication of deprivation A Two-Way ANOVA to test if there is a statistically significant difference between the poverty scores of approximately 58% of the students who lived off-line versus their on-campus counterparts showed that there is a statistically significant difference between the places of residence and the poverty score of the students (F (2, 2116); F=6.844; p= 0.03 ). Looking at these deprivations, living conditions play a central role in students’ ability to exercise their agency and choices. As indicated above, students were constrained from participating in the university activities that they valued due to a lack of safety and transport. Living conditions thus play a pivotal role in determining the degree to which students can participate actively in academic and co-curricular activities (Gore 2021). In this instance living conditions constrained off-campus students. The data shows that some students within the university system do not have any accommodation at all. A total of 184 students (10,7%) were accommodation insecure as they did not have a place to live since they could not afford to pay rent. I am concerned about our safety as bursary students who are not living on-campus. We have not received our private accommodation allowance which means we are constantly Figure 5: Percentage of students whose living conditions are an indication of deprivation A Two-Way ANOVA to test if there is a statistically significant difference between the poverty scores of approximately 58% of the students who lived off-line versus their on-campus counterparts showed that there is a statistically significant difference between the places of residence and the poverty score of the students (F (2, 2116); F=6.844; p= 0.03 ∴ < 5%). Looking at these deprivations, living conditions play a central role in students’ ability to exercise their agency and choices. As indicated above, students were constrained from participating in the university activities that they valued due to a lack of safety and transport. Living conditions thus play a pivotal role in determining the degree to which students can participate actively in academic and co-curricular activities (Gore 2021). In this instance living conditions constrained off-campus students. The data shows that some students within the university system do not have any accommodation at all. A total of 184 students (10,7%) were accommodation insecure as they did not have a place to live since they could not afford to pay rent. I am concerned about our safety as bursary students who are not living on-campus. We have not received our private accommodation allowance which means we are constantly worried because we face the reality of getting evicted. This is such a distraction when I must study…I really do not have anywhere to go if I get evicted. I may end up staying at the library like some of my classmates who do not have accommodation (Anonymous student online). While highlighting the connectedness between inadequate of finances and the failure of the 22,0% 18,3% 18,0% 8,4% 12,4% 31,5% 29,6% 24,9% 13,0% 20,1% I live in accommodation which is conducive for my studies I experience challenges with transport that negatively affect my studies I feel safe and secure where I live I have not been a victim of assault on campus I have not been a victim of assault off campus Pe rc en ta ge D ep ri ve d Indicators for deprivation in Living conditions Censored head-counts Raw head-count http://dx.doi.org/10.38140/pie.v40i4.6379 132022 40(4): 13-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities worried because we face the reality of getting evicted. This is such a distraction when I must study…I really do not have anywhere to go if I get evicted. I may end up staying at the library like some of my classmates who do not have accommodation (Anonymous student online). While highlighting the connectedness between inadequate of finances and the failure of the students to secure decent accommodation, the unfiltered story demonstrates that poverty stripped some students of their security contributing to further deprivations in the physical and mental health dimensions. Regarding participation, off-campus students were more deprived than those living on campus. iv. Psychological stress The data from the unfiltered stories in the survey, also show how poverty places students in a desperate position where they experience severe distress. Eighty-two per cent of all students mentioned that they were worried, while 35,9% of them were multidimensionally poor, as illustrated by Figure 6. Worry is a huge concern among students of all economic classes as reflected by the big variation between the raw and censored headcounts. students to secure decent accommodation, the unfiltered story demonstrates that poverty stripped some students of their security contributing to further deprivations in the physical and mental health dimensions. Regarding participation, off-campus students were more deprived than those living on campus. (iv) Psychological stress The data from the unfiltered stories in the survey, also show how poverty places students in a desperate position where they experience severe distress. Eighty-two per cent of all students mentioned that they were worried, while 35,9% of them were multidimensionally poor, as illustrated by Figure 6. Worry is a huge concern among students of all economic classes as reflected by the big variation between the raw and censored headcounts. Figure 6: Percentage deprived in indicators for psychological wellbeing Figure 6 shows that very few multidimensionally poor students had adequate social support and self-confidence. Students were also not only worried about their current situations, but also their future security and future responsibilities since they were expected to take care of their siblings and family members who experience poverty because of intergenerational poverty. This interdependency perpetuates multidimensional poverty. (iv) Participation The data points out that some students faced deprivation in their ability to participate in academic and social activities. However, students were more likely to be deprived more in leisure-related activities than academic-related activities as shown in Figure 7. This is in part 12,9% 25,4% 14,2% 20,7% 35,9%16,4% 43,4% 21,7% 29,4% 82,1%  I have adequate social support to succeed in my studies  I am sometimes ashamed of who I am because of what I have or do not have I am a self- confident person I have someone to talk to when I feel worried or anxious I am often worried or anxious about the challenges I face as a student Pe rc en ta ge d ep ri ve d Indicators of Psychological wellbeing Censored head-counts Raw head-counts Figure 6: Percentage of students whose psychological wellbeing is an indication of deprivation Figure 6 shows that very few multidimensionally poor students had adequate social support and self-confidence. Students were also not only worried about their current situations, but also their future security and future responsibilities since they were expected to take care of their siblings and family members who experience poverty because of intergenerational poverty. This interdependency perpetuates multidimensional poverty. v. Participation The data points out that some students faced deprivation in their ability to participate in academic and social activities. However, students were more likely to be deprived more in leisure-related activities than academic-related activities as shown in Figure 7. This is in part attributed to the fact that interventions to support poor students focus on just granting academic access with little consideration for social access. http://dx.doi.org/10.38140/pie.v40i4.6379 142022 40(4): 14-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) attributed to the fact that interventions to support poor students focus on just granting academic access with little consideration for social access. Figure 7: Percentage deprived in indicators of Participation Campus climate, diction and articulation or accents, safety and security, the status of residence where one lived, and access to information about social events emerged as key barriers to students’ participation. Bye et al. (2020) maintain that universities should create friendly environments for students to establish social networks and for their participation. (v) Shame, stigma and loss of dignity Some of the students experienced shame because of the extent and nature of deprivation, which was partly because of their socio-economic background and the inadequacy of their bursaries. Student poverty to me is a rather critical and sensitive matter, that I feel no one cares about. There are a lot of students here at the university that do not have money for basic needs ... most of them feel ashamed of asking because then it’s like why they decided to come to such an environment where they have no funds to sustain themselves. This poverty then leads to depression, poor academic performance and general spiritual unwellness (Anonymous student online). Most students 1212 (68,8%) reported having felt ashamed in their lives. Fear of being judged, ridiculed, and their peers feeling pity for them were the main reasons for being ashamed (Walker, 2014). 10,5% 15,5% 22,3% 18,4% 25,0% 36,5% The university environment is socially inclusive of diverse students. I participate freely in study related activities I participate freely in leisure activities Pe rc en ta ge d ep ri ve d Participation Censored head-counts Raw head-counts Figure 7: Percentage of students whose participation is an indication of deprivation Campus climate, diction and articulation or accents, safety and security, the status of residence where one lived, and access to information about social events emerged as key barriers to students’ participation. Bye et al. (2020) maintain that universities should create friendly environments for students to establish social networks and for their participation. vi. Shame, stigma and loss of dignity Some of the students experienced shame because of the extent and nature of deprivation, which was partly because of their socio-economic background and the inadequacy of their bursaries. Student poverty to me is a rather critical and sensitive matter, that I feel no one cares about. There are a lot of students here at the university that do not have money for basic needs ... most of them feel ashamed of asking because then it’s like why they decided to come to such an environment where they have no funds to sustain themselves. This poverty then leads to depression, poor academic performance and general spiritual unwellness (Anonymous student online). Most students 1212 (68,8%) reported having felt ashamed in their lives. Fear of being judged, ridiculed, and their peers feeling pity for them were the main reasons for being ashamed (Walker, 2014). b) Who are the most deprived groups? The data indicated that multidimensional student poverty is distributed differently across the races, with black students being worse off. The study shows that black students were three times more likely to be multi-dimensionally poor than white students. A small number of white and coloured students experienced multi-dimensional poverty at the institution. It is also true that some black families have experienced social mobility and do not face these deprivations (Spaull & Jansen, 2019). Gender appears to be another factor in student deprivation. For example, female students seem to have a greater struggle to find accommodation. Sixty- five percent of the 184 students who did not have a place to live were females and 35% were males. Nevertheless, the MSPI score was higher (21%) for male students compared http://dx.doi.org/10.38140/pie.v40i4.6379 152022 40(4): 15-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Ruswa & Gore Student poverty in South African universities to females (18%). This implies that male students were overall more deprived than female ones despite female students being highly deprived in the accommodation dimension. Consequently, interventions should take these factors into account. 7. Discussion of findings The findings elucidated the potency of the Alkire-Foster (2015) methodology in illuminating both the incidence and intensity of multidimensional poverty among students. The results suggest that a lack of finances increases the severity and prevalence of student poverty as it affects other dimensions in a corrosive manner. The survey data shows that students from poor households, where a family cannot afford to pay for their fees, were more than two times more likely to experience multidimensional poverty (MSPI of 29%) than their counterparts whose families could afford to fund their studies (MSPI of 13%) The capabilities approach enabled the researchers to establish the multiple dimensions constituting poverty. The funding policy and the university’s tuition facilities and arrangements were some of the conversion factors that disabled students from improving their wellbeing. In the absence of finances, students’ effective opportunities to access learning resources, attain psychological wellness and exercise their agency to participate meaningfully in the university’s academic and social activities were limited, which gave them lower chances of graduating (Walker, 2020). Although finances are an important dimension, the findings demonstrate that the multidimensional nature of poverty means that providing finances alone is insufficient to address poverty (Robeyns, 2017). The significant effect of basic needs, living conditions, psychological and participation on students’ wellbeing means that the university has to tackle the multidimensional nature of poverty. As was shown in this study, it needs to allow the capabilities approach not only to provide a platform for identifying the institutional arrangements and their effectiveness in addressing student poverty but also to point out sites where interventions should be (Robeyns, 2017). Finances directly limit individuals’ opportunities to access resources for them, resulting in their being deprived in other spheres of their lives (Walker, 2020). In other words, a lack of finances is corrosive because it prevents students from accessing effective opportunities. However, access to finances is helpful only to the extent that it enables particular functionings in other dimensions in which students are deprived, “…rather it is a facilitating precondition of many functionings” (Wolff et al., 2015). Nonetheless, receiving high amounts of funding might not necessarily address the other dimensions of deprivations such as psychological stress, worries for the future and participation. This means that understanding student poverty only from a financial lens is limiting as this excludes other non-commensurable dimensions that affect the wellbeing of students (Burchi, Muro & Kollar, 2018). Despite finances not reflecting student poverty on their own, they are a key aspect of multidimensional student poverty. Therefore, what this study emphasises that other studies do not is the interrelatedness of multidimensional poverty, in particular the corrosive effect a lack of finances has on other dimensions of students’ wellbeing, and the intensity and significant role played by the other dimensions of poverty have, in their own right, on student success. http://dx.doi.org/10.38140/pie.v40i4.6379 162022 40(4): 16-18 http://dx.doi.org/10.38140/pie.v40i4.6379 Perspectives in Education 2022: 40(4) 8. Conclusion and recommendations It is shown in this paper that lack of finances has a corrosive effect on the capacity of students to deal with physical needs, to have access to food, to have decent living standards, to enjoy mental health and to participate in the university’s social events. These deprivations contribute to students feeling ashamed, being stigmatised and losing dignity, performing poorly and being forced out of the education system. The clear implication is that higher education should provide adequate funding for low-income students to address financial deprivation as well as to allow students to reduce deprivation in other dimensions. This could be through allocating adequate funds and disbursing these funds timeously to financially deprived students. More so, the findings in this study have highlighted the limitations of providing finances only to students as there is a need for interventions to address other dimensions of deprivations to promote the wellbeing of students and ensure their subsequent success. Most students face extreme psychological challenges which are caused or worsened by their experiences in higher education. Psychological deprivations have a corrosive effect, leading to other deprivations. Providing accessible mental health support and embedding empathy in the running of universities will contribute to alleviating students’ plight. Universities should consider assisting students to develop their confidence and self-efficacy as a way of curbing shame to help them perform well and graduate. Mentorship programmes are also helpful in reducing the effects as they widen students’ opportunities to graduate and to have flourishing lives (Stahl et al., 2020). This could be conducted by peers or senior students from university residences. Specific groups of students who are worse off such as black students living in off-campus accommodation, those with disabilities and women could be targeted. Implementing interventions on raising awareness of and respect for diversity will encourage student participation in university social events such as sports activities. These strategies have the potential to enhance the wellbeing and subsequent success of students’ educational journeys. References Alkire, S., Foster, J., Seth, S., Santos, M.E. Roche, J.M. & Ballon, P. 2015. Multidimensional Poverty Measurement and Analysis. 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