The behavioural economics of savings groups as model commitment saving devices

Although much research has been conducted on saving behaviour, inadequate savings rates continue to be reported for individuals and households across the world. Little is known about the drivers of positive saving behaviour to design saving-promotion interventions and effective commitment saving devices. On the other hand, research evidence suggests that savings groups are effective in mobilising savings from low income populations with limited resources. Thus, in this study, savings groups were explored to gain insights in their saving behaviour; attributes that influence behavioural change; and valued features as commitment saving devices. Through this multilevel study, semi-structured interviews were conducted with ten savings groups and ten individual members of savings groups, and their perspectives were analysed through a behavioural economic lens. The study found seven characteristics of savings groups that potentially serve as interventions to change its non-standard (irrational) saving behaviour, and seven valued features of this model commitment saving device. This study contributes towards literature by combining the fields of saving behaviour, savings groups, saving-promotion interventions and commitment saving devices in a single behavioural economic study. As a result, a framework is proposed to product developers for the design of commitment saving devices that are based on behavioural design levers.Mini Dissertation (MBA)--University of Pretoria, 2018.ms2019Gordon Institute of Business Science (GIBS)MB

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions 1, with behavioural science increasingly part of this process 2. In April 2020, an influential paper 3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

DATA AVAILABILITY : All data and study material are provided either in the Supplementary information or through the two online repositories (OSF and Tableau Public, both accessible via https://psyarxiv.com/58udn). No code was used for analyses in this work.Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.The National Science Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); the Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development); National Science Foundation grants; the European Research Council; the Canadian Institutes of Health Research.http://www.nature.com/naturehj2024Gordon Institute of Business Science (GIBS)Non

Determining the potential of informal savings groups as a model for formal commitment saving devices

BACKGROUND : Saving behaviour has attracted research attention over the past 20 years. Typically, individual and household saving rates among low-income groups are inadequate. Research suggests that informal savings groups are effective vehicles for encouraging saving among low-income individuals. Yet little is known about the drivers of positive saving behaviour among informal savings groups, which makes it difficult for formal providers to design interventions that promote higher levels of saving. AIM : This study aimed to explore both the rational and non-rational drivers of saving behaviour among low-income members of informal savings groups, the attributes of informal savings groups that positively influence their collective saving behaviour, and to identify the valued features of savings groups that encourage the adoption of informal commitment saving devices (CSDs). METHODS : The study was informed by a literature review followed by field research in which semi-structured interviews were conducted with 10 savings groups and 10 individual members of savings groups. The participants’ perspectives were analysed and compared within the context of behavioural economic theory. RESULTS : The study revealed seven characteristics of informal savings groups that potentially serve as interventions to explain non-rational saving behaviour. It also identified seven features valued by users of informal CSDs (including flexibility, restricted access to savings and no transaction fees) which could be salient to providers of formal CSDs. CONCLUSION : On the basis of the findings, a behavioural design framework was proposed to inform the design features of formal CSDs that may ensure customer retention and improved saving outcomes.http://www.sajems.orghj2022Gordon Institute of Business Science (GIBS

A synthesis of evidence for policy from behavioural science during COVID-19.

Acknowledgements: We thank Z. Ji, M. Nair Dedhia, A. Lazara, G. Wilson, J. Usseglio, A. Asfa Durrani and M. Kobotis, as well as Corpus Christi College and the Centre for Business Research, University of Cambridge; and S. Kousta. K.R. reports financial support from the National Science Foundation (2218595). P.B. reports funding from (1) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education) — 88887.310255/2018; (2) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education) — 1133/2019; and (3) the Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development) — 309905/2019-2. E.P. reports financial support from National Science Foundation grants (SES-2017651 and SES-2022478). K.M.D. reports financial support from the European Research Council (101018262). R.S.R. reports financial support from the Canadian Institutes of Health Research (172681). Elements of Fig. 1 come from Apple Keys software.Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization