Activating Social Capital among young people in South Africa

Kim Ingle
Image: Joseph Redfield Nino on Pixabay.

Introduction

Growing up in a poor neighbourhood shapes individual opportunities and outcomes. The most compelling evidence of these so-called “neighbourhood effects” comes from mobility experiments in some of the more well-known inner city neighbourhoods of places like New York, Boston and Chicago. These experiments, dubbed “Moving to Opportunity” (MTO), subsidised the housing of low-income families if they moved to low-poverty neighbourhoods. The strongest impacts are recorded for very young children [1–3]. Recent quasi-experimental evidence corroborates this finding by showing that children with longer exposure to better neighbourhoods fare better in adulthood [4].

A key pathway mediating these effects is through role models and peer networks. Children from poorer families that have access to peers from richer ones have higher predicted rates of economic mobility than do poor children without these forms of social capital [5]. These findings imply that greater connectedness between low socioeconomic status (SES) children and higher SES children has the potential to create better opportunities for the children of the poor.

The policy experiment undergirding these findings involves promoting higher levels of socio-economic integration in communities through key sites of contact within communities (e.g., schools and religious organisations). However, when communities are relatively homogeneous in terms of class, there will be little scope for social capital to affect greater economic mobility. This is the reality of contemporary South Africa. Therefore, the lessons from programmes like MTO (and its antecedents like the famous Gautreaux programme in Chicago) do not map very well into contemporary South Africa, where the legacy of racial spatial planning means that there is little scope for socio-economic integration. For young South Africans aspiring to better economic opportunities, social contacts from communities other than their own are more valuable.

In this article, we report on a novel youth empowerment experiment in South Africa aimed at demonstrating this potential. The Activate! Change Drivers programme aims to promote prosocial behaviour among young adults living in distressed communities in South Africa. A key assumption of this programme is the idea that many of the outcomes that correlate with community distress (poor social cohesion, low trust etc.) are poverty traps. For example, low trust behaviour is both a cause and a consequence: the fewer trusting people in a given community, the less the incentive for any single individual to trust. In this type of setting, the incentives for pro-sociality have to be generated externally. Activate! does this by building a network of young people across communities that leverage their ties with one another.

 The Experiment

Activate! is an empowerment programme targeted at young people between the ages of 20 and 30. The selected participants are drawn from communities across South Africa. Most live in low SES neighbourhoods with low levels of community efficacy, social cohesion and trust. The goal is to identify “change drivers” who could serve as ambassadors for prosocial behaviours. Candidates are enrolled in 30 days of resident training covering a range of perspective-taking tasks (self-belief, goal-orientation, creative thinking, resilience), communication skills (social networking), trust building, project management, problem solving, and civic engagement. These interventions are theorised to trigger prosocial preferences, moderate risky behaviour, increase tolerance for delayed gratification, and increase civic engagement. The study was designed as a randomised intervention where half of the participants were randomly chosen to undergo the training in year 1 (treatment group), while the other half underwent the training in year 2 (control group). Outcomes for both groups were measured at the end of year 1.

Measures

In this article, we report on a central outcome: did Activate! succeed in building trust among the participants? Trust is a key correlate of community efficacy and is central to the idea of neighbourhoods as poverty traps. We measure trust through an experimental game: the trust game. In the trust game, the proposer (Player A) is given a monetary endowment and asked what portion (if any) of this endowment they would like to pass on to an anonymous partner, the trustee (Player B). The offer by the first mover is tripled before passing it on to the second mover, who must then decide how much, if anything, to send back to the first mover. The amount sent by the first mover is a measure of trust, while the amount returned by the second mover is a measure of reciprocity or trustworthiness [6]. While the efficient outcome in this strategic interaction implies that the proposer should send their entire endowment to the trustee, economic theory predicts that no transfer of resources will occur at all.

There has been much debate about the usefulness of survey-based measures of trust [7–10]. Figure 1 shows the current state of knowledge on the most widely used survey-based measure (the World Values Survey (WVS) trust question), measures of trust from the trust game, and the behavioural content associated with each of these measures.

A fairly close to consensus view now holds that the behaviour of Player A in the trust game can reflect risk preferences, social preferences (including altruism), a person’s beliefs about the trustworthiness of others and their own trustworthiness. The behaviour of Player B is a measure of trustworthiness and can reflect either altruism or reciprocity. Trustworthiness measures from the trust game correlates well with the WVS trust question. By contrast, trust measures from the trust game do not correlate well with the WVS trust question. Thus survey-based measures are good at eliciting measures of trustworthiness but are bad are eliciting measures of trust [10–12].

 Figure 1: The behavioural content of trust measures

Main findings

Figure 1 shows that risk aversion, altruism and beliefs are potential confounds to experimental measures of trust from the trust game: what might look like trusting behaviour might have more to do with risk attitudes because trustworthiness, by definition, is an uncertain prospect. Similarly, acts of pure altruism could explain apparent trusting behaviour. Finally, a person’s beliefs about trustworthiness (i.e., their mental models of how trustworthy others are) could also explain their trusting behaviour. To isolate the causal effect of Activate! on trust, it is important to check for whether the programme also operates through these channels. We find treatment effects for all three channels. However, only beliefs are effect modifiers. Specifically, conditional on risk, beliefs, and altruism, the Activate! programme causes an increase in trust for participants whose perceptions of the trustworthiness of others (beliefs) are altered by the programme.

Conclusion

In earlier work, we have shown that social learning (i.e., learning from others) is a strong mediator of trust behaviour among programme candidates. This finding, coupled with the latest evidence of programme impacts reviewed in this article, is consistent with a peer network interpretation of how Activate! works to build social capital. Given the daunting challenges South Africa faces with young people, these findings offer hope that change can be affected through social capital interventions that focus not on incentivising young people to leave their communities in search of a better life, but rather to invest in their communities by serving as change ambassadors capable of activating higher levels of trust and other prosocial norms that are key drivers of social cohesion and community efficacy. The results of the Activate! experiment demonstrates that social capital is malleable and offers an alternative vision to the dystopian reality that Place represents for the vast majority of young people in South Africa.

References

  1. Chetty, , Hendren, N. & Katz, L. F. The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment. American Economic Review 106, 855–902. https://www.aeaweb.org/articles?id=10.1257/aer.20150572 (2016).
  2. Ludwig, et al. Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults. Science 337, 1505–1510. issn:  0036-8075. eprint:  http://science.sciencemag.org/content/337/6101/1505 (2012).
  3. Ludwig, J. et al. Neighborhoods, Obesity, and Diabetes – A Randomized Social Experiment. New England Journal of Medicine PMID: 22010917, 1509–1519. eprint: http://dx.doi.org/10.1056/NEJMsa1103216 (2011).
  4. Chetty, & Hendren, N. The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects*. The Quarterly Journal of Economics 133, 1107–1162. eprint: http://dx.doi.org/10.1093/qje/qjy007 (2018).
  5. Chetty, et al. Social capital I: measurement and associations with economic mobility. Nature 608, 108–121. https://doi.org/10.1038/s41586-022-04996-4 (2022).
  6. Berg, J., Dickhaut, J. & McCabe, K. Trust, Reciprocity, and Social History. Games and Economic Behavior 10, 122–142. issn: 0899-8256. https://www.sciencedirect.com/science/article/pii/S0899825685710275 (1995).
  7. Miller, S. & Mitamura, T. Are Surveys on Trust Trustworthy? Social Psychology Quarterly 66, 62–70 (2003).
  8. Glaeser, L., Laibson, D. I., Scheinkman, J. A. & Soutter, C. L. Measuring Trust. Quarterly Journal of Economics 115, 811–846. issn: 0033-5533 (2000).
  9. Lazzarini, G. S. G., Madalozzo, R., Artes, R. & de Oliveira Siqueira, J. Measuring trust: An experiment in Brazil. IBMEC Working Paper 049, 1–27 (2003).
  10. Fehr, On the Economics and Biology of Trust. Journal of the European Economic Association 7, 235–266. issn: 1542-4766 (2009).
  11. Ashraf, , Bohnet, I. & Piankov, N. Decomposing trust and trustworthiness. Experimental Economics 9, 193–208. issn: 13864157 (2006).
  12. Cox, C., Kerschbamer, R. & Neururer, D. What is trustworthiniess and what drives it? Games and Economic Behavior 98, 197–218 (2016).
  13. Ferrari, S. & Cribari-Neto, F. Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics 31, 799–815. eprint: http://dx.doi.org/10.1080/0266476042000214501 (2004).
  14. Ospina, & Ferrari, S. L. A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis 56, 1609 –1623. issn: 0167-9473. http://www.sciencedirect.com/science/article/pii/S0167947311003628 (2012)

We gratefully acknowledge funding from the DG Murray Trust and KfW (the German Development Bank). We especially thank Janet Jobson for initiating and shepherding this project and David Harrison for his critical engagement with our work throughout the process. We are also grateful to the team at Activate! Change Drivers, especially Chris Meintjies, Landy Wright, Carmen Louw-Shang, Althea Louw and Tarryn Abrahams as well the teams of trainers that ran the training workshops: Lauren Daniels, Koko Zaka, Phumla Willie, Ashley Roman, Silindile Mncube, Casca Johnson, Malusi Mazibuko, Melissa Nefdt, Sibongile Segobola, Ise-Lu Mӧller, Caroline Mmabatho Seremane, Tebogo Motlana, Mduduzi Manci, Denese Reddy, Darian Smith, Sanele Hadebe, Sbonokuhle Nyembe, Thandeka Teledi as well as Injairu Kulundu and Renee-Hector-Kannemeyer. For their excellent research assistance, we are grateful to Lauren Antrobus, Asheen Bhagwandin, Larrisa Bhagwandin, Bianca Bohmer, Ionna Branga-Peicu, Patricia Chirwa, Samantha Filby, Ashleigh Fynn, Aimee Hare, Scott Hunt, Kim Ingle, Safia Khan, George Kinyanjui, Hayley Kornblum, Thembi Losi (late), Sibahle Magadla, Nangamso Manjezi, Moeketsi Masemene, Lovemore Mawere, Tatenda Mbofana, Jamie McGraw, Caitlin Miles, Hilary Moguto, Sofia Monteiro, Ditsheho Motetsi, Aurea Mouzinho, Sihle Nontshokweni, Mwape Nthala, Joan Obakwa, Nick Owsley, Sarah Pennington, Chloe Perling, Daniel Perling, Lorraine Rupande, Jeanine Rwishema, Reinhard Schiel, Jessica Standish-White, Alexandra Swanepoel, Ross Tonkin, Charles Tsikirayi, Anzisca Van Rooyen, Gary White, Sonja Winkler, and Ece Yagman. We also thank colleagues at the Universities of KwaZulu-Natal, Pretoria and Wits for generously making available venues at the various institutions where we conducted many of our experimental sessions. We especially thank Volker Schor, Imraan Valodia and Alexander Zimper.