Abstract
This study explored how people perceive the causes of poverty. Literature revealed that there are three broad theoretical explanations of perceptions of the causes of poverty, namely individualistic explanations, where blame is placed squarely on the poor themselves; structural explanations, where poverty is blamed on external social and economic forces; and fatalistic explanations, which attribute poverty to factors such as bad luck or illness. To examine South Africans perceptions according to these dimensions secondary analysis was employed on one of the Human Sciences Research Council’s (HSRC) national representative client surveys. Approximately 3,498 respondents across South Africa were surveyed between 18 April and 30 May 2006. The bivariate analysis revealed that South Africans in general attribute poverty to structural over individualistic and fatalistic dimensions of poverty. Ordinary least square regressions revealed that these perceptions of poverty interacted with a host of socio-demographic and economic variables such as race and peoples’ lived experiences of poverty. In this regard, all three ordinary least square regressions showed that lived poverty had a significant impact in predicting respectively structural, individualistic and fatalistic perceptions of the causes of poverty. The second regression predicted individualistic perceptions and showed that being white was the most significant predictor. The third regression predicted fatalistic perceptions and established that being coloured was the most significant predictor.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Note that Julius Malema was suspended at the time this publication was finalized for publication.
In South Africa there are four broad settlement types: (1) formal urban areas, (2) informal urban areas, (3) commercial farms, and (4) tribal areas and rural informal settlements. According to Statistics South Africa a ‘rural area’ is ‘any area that is not classified urban. Rural areas are subdivided into tribal areas and commercial farms’ (i.e. relying on geographic type classification), while urban areas were classified ‘based on dominant settlement type and land use.’ Cities, towns, townships, suburbs, etc., are typical urban settlements. Within the urban areas Statistics South Africa distinguish between formal and informal settlement areas. The informal settlement areas are those areas defined as unplanned settlement on land which has not been surveyed or proclaimed as residential, consisting mainly of informal dwellings (shacks) (Statistics South Africa 2003).
The living standard measure is a unique means of segmenting the South African market. It cuts across race and other outmoded techniques of categorising people, and instead groups people according to their living standards using criteria such as degree of urbanisation and ownership of cars and major appliances. A total of 29 variables are used. Each variable carries a different weight, some positive, others negative, and the respondent’s position on the SAARF LSM scale is arrived at by adding together the weights of the variables that she/he possesses. A constant is also added to the total score to remove negative total scores (http://www.saarf.co.za).
Since the development of the perception of the causes of poverty scale (PCPS) by Feagin it has been applied in various countries under different circumstances. Sun (2001), for example, administered a revised version of the PCPS to determine how American social work students (SWS) and non-social work students (NSWS) perceived the causes of poverty. The study concluded that overall American SWS believe that poverty is more a cause of structural factors within the environment than individualistic factors. This result is contradictory to the general opinion of ordinary Americans. Another study described the Chinese perception of the causes of poverty scale (CPCPS). The CPCPS was developed to measure perceived causes of poverty in the Chinese culture (Shek 2002). The primary focus of the study was on the psychometric properties of the CPCPS. It was found that the scale was reliable and valid and measured the underlying poverty dimensions. CPCPS covered four categories of explanations: personal problems of the poor, lack of opportunities to escape from the poverty cycle, exploitation of poor people, and bad fate. A follow-up study by Shek (2004) investigated the beliefs about the causes of poverty in Chinese parents and adolescents experiencing economic disadvantages showed that the same four factors were stable across time and across different samples. A study by Weiss and Gal (2007) used measures similar to those found on the Feagin Scale and argued that the internal consistency of the revised questionnaire increased since some items were removed, some rephrased and some added. In addition, a panel of three social work researchers reviewed the questionnaire items for face validity and found that the items adequately measured the attitudes to the causes of poverty. In a much earlier study Marshall et al. (1999) investigated beliefs about inequality in thirteen established Western-democratic and newly post-communist industrial nations. The first 30 attitudinal question items used on their questionnaire, seek evidence about people’s perceptions of inequality. These items are also categorized into individualistic, structural and fatalistic dimensions. For example, the following reasons are given as to why people are poor: ‘lack of equal opportunity’ (structural), ‘lack of effort by poor themselves’ (individualistic), and ‘bad luck’ (fatalistic).
Please refer to the methodology for the actual question wording of the various question items measuring perceptions of the causes of poverty.
It should be note that this item measured structural perceptions of the causes of poverty.
The results of the possible statistical assumption violations, for missing values and outliers revealed that there were very few missing values reported for all the variables. The data was also normally distributed with all the P–P plots looking reasonably normal since the data points are all close to or on the diagonal lines. In addition, the skewness and kurtosis values were almost all within the acceptable range of −1 to +1 thus indicating a normal distribution. Next the collinearity among the independent variables is assessed. A review of the tolerance statistics of the three multiple regressions reveal that not a single tolerance value for any of the variables in all the multiple regressions were found to be less than or equal to 0.01. The results of the VIF also revealed that all the values for all three multiple regressions were less than 10 which imply no multicollinearity among the independent variables. In addition, the conditional index for each of the three multiple regressions showed that none of the independent variables is equal or greater than 30.
Dummy variables were entered for gender, race, geographical location, and employment status. More specifically, for race the dummy variables white, coloured and Indian were entered (with black being the implicit reference group). Dummy variables employed and not working were entered for employment status with unemployed being the implicit reference group. Geographic location had dummy variables for urban informal, traditional/tribal areas and rural formal except for urban formal (which acted as the implicit reference group), while gender had a dummy variable for male (with female being the implicit reference group).
It should be noted that the explanatory power of this linear regression model is quite weak (as indicated by Adjusted R²) and should therefore be interpreted with caution.
Dummy variables were entered for gender, race, geographical location, and employment status. These dummy variables are the same variables that were entered for the first regression with the structural index as the dependent variable.
Again it is important to note that the explanatory power of this linear regression model is quite weak (as indicated by Adjusted R²) and should therefore be interpreted with caution.
Dummy variables were entered for gender, race, geographical location and employment status. These dummy variables are the same variables that were entered for the first two regressions in the previous sections.
Also note that the explanatory power of this linear regression model is quite weak (as indicated by Adjusted R²) and should therefore be interpreted with caution.
References
Appelbaum, L. D., Lennon, M. C., & Aber, J. L. (2006). When effort is threatening: The influence of the belief in a just world on Americans attitudes toward antipoverty policy. Political Psychology, 27(3), 387–402.
Auletta, K. (1982). The underclass: Updated and revised. New York: Peter Mayer.
Bègue, L., & Bastounis, M. (2003). Two spheres of belief in justice: Extensive support for the bidimensional model of belief in a just world. Journal of Personality, 71(33), 435–463.
Bullock, H. E., & Waugh, I. M. (2005). Beliefs about poverty and opportunity among Mexican immigrant farm workers. Journal of Applied Social Psychology, 35(6), 1132–1149.
Campbell, D., Carr, S. C., & MacLachlan, M. (2001). Attributing third world poverty in Australia and Malawi: A case of donor bias? Journal of Applied Social Psychology, 31(2), 409–430.
Clarke, G., & Sison, M. (2003). Voices from the top of the pile: Elite perceptions of poverty and the poor in the Philippines. Development and Change, 34(2), 215–242.
Cozzarelli, C., Wilkinson, A. V., & Tagler, M. J. (2001). Attitudes toward the poor and attributions for poverty. Journal of Social Issues, 57(2), 207–227.
Davids, Y. D. (2006). Poverty in South Africa: Extent of access to food and income. HSRC Review, 4(4), 16–17.
Du Toit, A. (2005). Chronic and structural poverty in South Africa: Challenges for action and research. Programme for land and agrarian studies. University of the Western Cape, PLAAS Chronic Poverty and Development Policy Series No. 6.
Everatt, D. (2003). The politics of poverty. In D. Everatt & V. Maphai (Eds.), The real state of the nation: South Africa after 1990. Development Update, 4(3), 75–99.
Faull, J. (2005). Tracing the “Two Economies”: The politics, policy, and practice of pragmatism. ePoliticsSA. 1: 1–9. www.idasa.org/media/uploads/outputs/files/Epols%2005.01%20Two%20Econs.pdf. Accessed 30 July 2007.
Hajnal, Z. (2007). Black class exceptionalism: Insights from direct democracy on the race versus class debate. Public Opinion Quarterly, 71(4), 560–587.
Halman, L. & van Oorschot, W. (1999). Popular perceptions of poverty in Dutch society. Work and Organization Research Centre, Tilburg University, Work Paper 16. http://ideas.repec.org/s/dgr/kubwor.html. Accessed 10 July 2005.
Hanmer, L. C., Pyatt, G., & White, H. (1999). What do the World Banks’s Assessments teach us about poverty in Sub-Saharan Africa? Development and Change, 30, 795–823.
Hunt, M. O. (1996). The individual society, or both? A comparison of Black, Latino and White beliefs about the causes of poverty. Social Forces, 75(1), 293–323.
Hunt, M. O. (2004). Race/ethnicity and beliefs about wealth and poverty. Social Science Quarterly, 85(3), 827–853.
Lund, F. (2008). Changing social policy—the child support grant in South Africa. Cape Town: HSRC Press.
Marshall, G., Swift, A., Routh, D., & Burgoyne, C. (1999). What is and what ought to be: Popular beliefs about distributive justice in thirteen countries. European Sociological Review, 15(4), 349–367.
Mattes, R., Bratton, M., & Davids, Y. D. (2002). Poverty, survival and democracy in Southern Africa. CSSR Working Paper No.27, Centre for Social Science Research, University of Cape Town.
Mbanjwa, B. (2011). Whites trapped in sense of denial: Boesak. Daily News, 20 September 2011, http://www.amren.com/mtnews/archives/2011/09/whites_trapped.php. Accessed 21 Sept 2011.
Moore, M., Choudhary, M. & Singh, N. (1998). How can we know what they want? Understanding local perceptions of poverty and Ill-being in Asia. Working Paper 80, Institute of Development Studies, University of Sussex.
Mukherjee, S., & Benson, T. (2003). Determinants of poverty in Malawi, 1998. World Development, 31(2), 339–358.
Nasser, R., Abouchedid, K., & Khashan, H. (2002). Perceptions of the causes of poverty comparing three national groups: Lebanon, Portugal and South Africa. Journal of CRISP, 8(7). http://www.uiowa.edu/~grpproc/crisp.8.7.html. Accessed 30 July 2004.
National Planning Commission (2011). Nation building diagnostic. The Presidency, Pretoria. www.npconline.co.za/MediaLib/Downloads/Home/Tabs/Diagnostic/Nation%20Building%20diagnostic.pdf. Accessed 21 Sept 2011.
Ngwane, A. K., Yadavalli, V. S. S., & Steffens, F. E. (2003). Poverty in South Africa: statistical inference for decomposal poverty measures. Development Southern Africa, 20(2), 283–291.
NUM. (2010). ‘NUM calls on ANC to broaden nationalisation debate’. 25 August, http://www.num.org.za. Accessed 21 Sept 2011.
Padayachee, V. (2006). Development discourses in post-apartheid South Africa. In U. Padayachee (Ed.), The development decade? Economic and social change in South Africa, 1994–2004 (pp. 55–85). Cape Town: HSRC.
REUTERS (2011). ANC nationalisation report due in 2012. Independent Online, 31 August, http://www.iol.co.za/business/international/anc-nationalisation-report-due-in-2012-1.1128308. Accessed 21 Sept 2011.
Reutter, L. I., Veenstra, G., Stewart, M. J., Raphael, D., Love, R., Makwarimba, E., et al. (2005). Lay understandings of the effects of poverty: A Canadian perspective. Health and Social Care in the Community, 13(6), 514–530.
Roberts, B. (2006). The happy transition? Attitudes to poverty and inequality after a decade of democracy. In U. Pillay, B. Roberts, & S. Rule (Eds.), South African social attitudes changing times, diverse voices (pp. 101–130). Cape Town: HSRC Press.
Salmond, C., Crampton, P., King, P., & Waldegrave, C. (2006). NZiDEP: A New Zealand index of socio-economic deprivation for individuals. Social Science and Medicine, 62, 1474–1485.
SAPA (2011). Criticism on nationalisation talk valid—Phosa. Mining Weekly, 28 July. http://www.miningweekly.com/article/criticism-on-nationalisation-talk-valid-phosa-2011-07-28-1.
Seekings, J. (2000). Introduction: Urban studies in South Africa after apartheid. International Journal of Urban and Regional Research, 24, 832–840.
Shek, D. T. L. (2002). Chinese adolescents’ explanations of poverty: The perceived causes of poverty scale. Adolescence, 37(148), 789–803.
Shek, D. T. L. (2004). Beliefs about the causes of poverty in parents and adolescents experiencing economic disadvantage in Hong Kong. Journal of Genetic Psychology, 165(3), 272–292.
Smith, K. B., & Stone, L. H. (1989). Rags, riches and bootstraps: Beliefs about causes of wealth and poverty. The Sociological Quarterly, 30(1), 93–107.
Statistics South Africa. (2003). Concepts and definitions. Report no. 03-02-26 (2001) Version 2. http://www.statssa.gov.za/census01/html/concepts%20&%20definitions.pdf. Accessed 10 Nov 2011.
Studies in Poverty and Inequality Institute (2007). The measurement of poverty in South Africa project: Key Issues. Project Report. Richmond, Johannesburg. http://www.treasury.gov.za/publications/other/povertyline/SPII%20document.pdf. Accessed 9 July 2008.
Sun, A. P. (2001). Perceptions among social work and non-social work students concerning causes of poverty. Journal of Social Work Education, 37(1), 161–174.
The Presidency. (2008). Towards a fifteen year review: Synthesis report. The Presidency, Pretoria. http://www.thepresidency.gov.za. Accessed 21 Sept 2011.
UNDP (2010). Human development report 2010, New York. Oxford University. http://hdr.undp.org/en/report/global. Accessed 3 Dec 2010.
UNECA (2010). Economic Report on Africa: Promoting high-level sustainable growth to reduce unemployment in Africa, Addis Ababa. http://www.uneca.org/eca_resources/Publications/books/era2010/index.htm. Accessed 17 August 2010.
Weiss, I., & Gal, J. (2007). Poverty in the eyes of the beholder: Social workers compared to other middle-class professionals. British Journal of Social Work, 37, 893–908.
Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.
Wilson, G. (1996). Toward a revised framework for examining beliefs about the causes of poverty. The Sociological Quarterly, 37(3), 413–428.
Acknowledgments
This article is mostly based on a Doctoral dissertation of Yul Derek Davids completed in 2010 in the Political Science Department at Stellenbosch University. The authors are therefore grateful to the HSRC and Stellenbosch University for financial support which enabled Yul Derek Davids to complete his doctoral studies. The authors also acknowledge the use of the HRSC 2006 Client Survey data for this article. Finally, the views expressed are those of the authors and do not necessarily reflect those of any other party.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1
See Table 7.
Appendix 2
Rights and permissions
About this article
Cite this article
Davids, Y.D., Gouws, A. Monitoring Perceptions of the Causes of Poverty in South Africa. Soc Indic Res 110, 1201–1220 (2013). https://doi.org/10.1007/s11205-011-9980-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-011-9980-9