09 SES 07 B, Investigating Carrier Pathways and Dropout
The T.E.A.C.H. project (Territory, Empirical research, Advocacy, Community, Hosting) aimed at preventing early school leaving by means of educational and training support for lower secondary students and by carrying out activities aimed at supporting parental skills in Como, Italy. The project involves Cometa Il Manto, a cooperative providing support for students, namely those affected by risk of dropout; in partnership with 3 lower secondary schools. The project includes the development of a social impact measurement system, to assess the effects of the actions on direct and indirect beneficiaries. For this reason, the Tiresia research center of the Politecnico di Milano, in collaboration with the partners, has defined and applied a social impact measurement methodology during the two years of the project.
Measuring social impact is a complex task. Organizations with a social purpose are usually multi-objective, multi-stakeholder and it is therefore hard to identify a single effective measurement methodology (Amati et al., 2017). Moreover, the qualitative and sometimes subjective nature of social impact often does not allow to quantify all the results achieved. There is also a problem of attribution: it is not always easy to define who is really responsible for positive change in the community (Millar and Hall, 2013). The changes in society take place in the long term and it is therefore difficult to keep track of the evolution of the benefits produced. For this reason, in the last decade, universities, international organizations, financial institutions, trade associations and individual companies (Mulgan, 2010; Grieco, et al., 2015) have developed several methods for measuring social impact with different characteristics in line with the aims of the different stakeholders (Zamagni, et al., 2015).
The international literature (Nicholls et al., 2015) underlines the importance of identifying methodologies that are able to enhance the social impact of organizations by integrating them with the existing, and non-existing, economic indicators. Furthermore, several authors are converging on the definition of methodologies that use both synthetic and process-based models, together with indicators provided by standard frameworks (Hornsby, 2012, Global Social Venture Competition, 2012; Bengo et al, 2016). The synthetic indicators allow in fact rapid comparisons between organizations and are therefore preferred by investors. However, they have an inherent risk of loss of information (Beckerman & Pasek, 2001): the final value is closely linked to the indicators chosen, but no longer visible (Ebrahim, et al., 2014). The methodologies based on the processes, on the contrary, allow to determine impact indicators having a view of the social value generated in the different phases and are therefore favored by policy makers and social entrepreneurs. Finally, the impacts can be defined as the portion of outcome that occurred as a direct result of the intervention, net of that part that would have also taken place even without intervention (Grieco et al., 2015). To this extent, one of the suggested methodology is the counterfactual analysis.
The methodology developed for the T.E.A.C.H. project is a hybrid between: - the Social Impact Assessment - SIA (Global Social Venture Competition, 2012), which allows the identification of results and impacts and the definition of results and impact indicators; - a synthetic indicator - in this case corresponding to the monetization of the impact related to early school leaving -; - and the counterfactual analysis to determine the effect of an intervention on an individual as the difference between the result observed in the presence of the treatment and the result that would be observed, for the same individual, in his absence (European Commission, 2017). After the definition of a control sample formed by a group of untreated individuals who must present characteristics comparable to the persons participating in the project, two groups of twenty-five students each were identified, coming from three different secondary schools of the Como area: the "TEACH Group", made up of students who use the services of Il Manto and participate in the TEACH project and the "Control Group", made up of students with characteristics similar to the "TEACH Group" but who do not use the services of Il Manto and are not part of the TEACH project.The characteristics that have been taken into consideration for the selection of the two groups are: class of belonging; nationality; school results; presence of certified disabilities. The phases of the research include: - Stakeholder analysis, to identify the main actors, their needs and interests (even of the type information) in order to involve them immediately in the process of measuring social impact. - Mapping the change process, to identify the project's value and related relationships through the social value chain. - Definition of the methodology for measuring impact and indicators, to create a methodology and ad hoc KPIs consistent with the peculiarities of the educational sector and with the characteristics of the project. - Data collection, to obtain all the information necessary to calculate the output, outcome and impact indicators, by surveys and data mining. - Evaluation of the social impact, to understand and interpret the results obtained from the social impact measurement process.
The results emerging from the measurement of the social impact of the T.E.A.C.H. project show the effectiveness of the collaboration between schools and community in facing and preventing early school leaving. In fact, the activities carried out jointly by Il Manto and the three schools contributed to reduce the risk of drop-out for 60% of the students participating in the project, generating more than € 200,000 in terms of savings for the community. The results obtained from the social impact measurement also show improvements in the short term that contribute not only to reduce school drop-out but also to increase well-being and student personal growth. Among the factors contributing to the reduction and containment of drop-out risk, for example: behavioral performance improved (with 72% of students and families who think that minors have improved family behavior during the year subject to analysis), the level of self-esteem (with an increase in personal qualities and the level of utility perceived by 40% of minors, 8% more than what would have happened in the absence of the intervention), the participation in voluntary activities by young people (36% of the participants in the project carry out voluntary activities, 24% more than those who do not participate) and the awareness of their emotions (the teachers believe that the 48% of the students participating in the project in the 2016/2017 school year have improved their awareness of their emotions, 12% more than the guys who did not take part in the project).
Amati, T., Arena, M., Bengo, I., & Caloni, D. (2017). Social Impact Measurement and Management: Between Theory and Practice. In Handbook of Research on Emerging Business Models and Managerial Strategies in the Nonprofit Sector (pp. 371-388). IGI Global. Bengo, I., Arena, M., Azzone, G., Calderini, M. (2016). Indicators and metrics for social business: a review of current approaches. Journal of Social Entrepreneurship, 1 (2), pp. 1-24. Beckerman, W., & Pasek, J. (2001). Justice, Posterity and the Environment. Oxford: Oxford University Press. Ebrahim, A., Battilana, J., & Mair, J. (2014). The governance of social enterprises: Mission drift and accountability challenges in hybrid organizations. Research in Organizational Behavior, 34, pp. 81–100. European Commission (2017). Counterfactual impact evaluation https://ec.europa.eu/jrc/en/research-topic/counterfactual-impact-evaluation Global Social Venture Competition (2012). Social Impact Assessment Guidelines. http://www.iedu.org.cn/gsvc/download/2011_GSVC_SIA_Guidelines.pdf. Grieco, C., Michelini, L., & Iasevoli, G. (2015). Measuring value creation in social enterprises: A cluster analysis of social impact assessment models. Nonprofit and voluntary sector quarterly, 44(6), 1173-1193. Hornsby, A. (2012). The Good Analyst, Impact Measurement and Analysis in the Social-Purpose Universe. Investing for Good. Millar, R., & Hall, K. (2013). Social return on investment (SROI) and performance measurement. Public Management Review, 15(6), 923–941. Mulgan, G. (2010). Measuring social value. Stanford Soc Innov Rev, 8(3), 38-43. Nicholls, A., Emerson, J., Paton, R. (Eds.). (2015). Social Finance. Oxford University Press, Oxford. Zamagni, S., Venturi, P., & Rago, S. (2015). Valutare l’impatto sociale. La questione della misurazione nelle imprese sociali. Impresa Soc, 6.
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