Van instroom tot herstel (P102) - Een kwantitatieve systeemanalyse van dynamiek en beleid in online kansspelen
report
Since the entry into force of the Remote Gambling Act (Wet kansspelen op afstand), the legal market for online gambling in the Netherlands has grown substantially. This growth has been accompanied by concerns about gambling related harms. The new Vision on online gambling calls for a fundamental shift in the policy approach, with greater emphasis on consumer protection and harm reduction. To further elaborate this Vision, insight is needed into how the gambling population (participants in online gambling) develops over time and how policy measures can intervene in that development.
This study builds on an earlier TNO study2 in which the main factors, policy measures and dynamics in online gambling were mapped qualitatively. In this follow up phase, we examined whether a quantitative systems analysis can support policy decision making.
Problem Statement
The policy objective to prevent and reduce gambling related harms requires a well substantiated choice of measures and a coherent policy package. In practice, however, it is difficult to assess the effects and unintended effects of measures ex ante, because relevant data are limited, causal relationships are not always unambiguous and developments unfold dynamically over time. The central problem statement is therefore: how can useful quantitative insight be generated, from a systems perspective and with limited data, into the development of the online gambling population and the expected (unintended) effects of policy measures that are intended to further operationalise the Vision?
Research Approach
In this second research phase, TNO quantified the dynamics of the online gambling population within the legal market. To do so, we reviewed the scientific literature, collected and combined available Dutch data sources and held two expert sessions with the Ministry of Justice and Security (JenV) and external experts to analyse assumptions, mechanisms and measures.
On this basis, we developed a system dynamics simulation model describing changes over time in inflow, escalation, de-escalation and recovery within the gambling population. Where possible, the model was calibrated against historical trends in the available data and used to conduct exploratory simulation experiments.
Findings and Conclusions
Using a quantitative systems analysis, we brought the interplay between gambling behaviour, market incentives and policy into a single coherent picture. We developed a system dynamics simulation model and combined insights from the scientific literature, expert sessions and the limited Dutch data available. This enabled us to clarify key mechanisms and plausible directions of policy effects and to make the most important gaps in the available data explicit.
At the same time, limitations in data, literature and the availability of quantitative expert estimates meant that it was not possible to validate the model to a level that would support reliable statements about the exact magnitude of the effects of specific measures.
The collected data and knowledge provide insight into how the gambling population develops over time and where the system can be influenced. This includes measures that reduce exposure and inflow, curb early escalation, strengthen de escalation and support recovery.
The model development process helps integrate these insights, and the resulting model and simulations provide additional insight into the effects of intervening at different points in the system. The insights from model development and the final model therefore offer entry points for further elaboration of policy on online gambling.
Interventions aimed at limiting inflow and preventing early escalation among gamblers are likely to be most effective at reducing overall gambling harms within the legal market. Secondarily, further escalation can be curtailed and de‑escalation and recovery can be strengthened. It cannot be established with certainty that this approach also minimises total gambling‑related harm when effects within both the legal and illegal markets are taken into account. This therefore requires not only additional insight, but also a considered policy judgement on how harm is weighted across different groups of gamblers. Building a stronger data position will support addressing these issues and enable more effective monitoring and adjustment of policy implementation.
Applications
The knowledge generated is directly applicable to the further elaboration of the Vision on online gambling. The systems analysis helps to structure policy options along the main mechanisms in the system and to test assumptions against the collected evidence. This supports the development of coherent policy packages and improvements to the data position for future ex ante evaluation.
The method is applicable beyond online gambling. Quantitative systems analysis can be used for policy issues where insight is needed into how a problem develops over time and how policy affects that development. At the same time, this study also shows that when data and knowledge are limited, a simulation model can only provide high level, indicative insight for policy making, while the model development process remains valuable for structuring and integrating the available data and evidence.
This study builds on an earlier TNO study2 in which the main factors, policy measures and dynamics in online gambling were mapped qualitatively. In this follow up phase, we examined whether a quantitative systems analysis can support policy decision making.
Problem Statement
The policy objective to prevent and reduce gambling related harms requires a well substantiated choice of measures and a coherent policy package. In practice, however, it is difficult to assess the effects and unintended effects of measures ex ante, because relevant data are limited, causal relationships are not always unambiguous and developments unfold dynamically over time. The central problem statement is therefore: how can useful quantitative insight be generated, from a systems perspective and with limited data, into the development of the online gambling population and the expected (unintended) effects of policy measures that are intended to further operationalise the Vision?
Research Approach
In this second research phase, TNO quantified the dynamics of the online gambling population within the legal market. To do so, we reviewed the scientific literature, collected and combined available Dutch data sources and held two expert sessions with the Ministry of Justice and Security (JenV) and external experts to analyse assumptions, mechanisms and measures.
On this basis, we developed a system dynamics simulation model describing changes over time in inflow, escalation, de-escalation and recovery within the gambling population. Where possible, the model was calibrated against historical trends in the available data and used to conduct exploratory simulation experiments.
Findings and Conclusions
Using a quantitative systems analysis, we brought the interplay between gambling behaviour, market incentives and policy into a single coherent picture. We developed a system dynamics simulation model and combined insights from the scientific literature, expert sessions and the limited Dutch data available. This enabled us to clarify key mechanisms and plausible directions of policy effects and to make the most important gaps in the available data explicit.
At the same time, limitations in data, literature and the availability of quantitative expert estimates meant that it was not possible to validate the model to a level that would support reliable statements about the exact magnitude of the effects of specific measures.
The collected data and knowledge provide insight into how the gambling population develops over time and where the system can be influenced. This includes measures that reduce exposure and inflow, curb early escalation, strengthen de escalation and support recovery.
The model development process helps integrate these insights, and the resulting model and simulations provide additional insight into the effects of intervening at different points in the system. The insights from model development and the final model therefore offer entry points for further elaboration of policy on online gambling.
Interventions aimed at limiting inflow and preventing early escalation among gamblers are likely to be most effective at reducing overall gambling harms within the legal market. Secondarily, further escalation can be curtailed and de‑escalation and recovery can be strengthened. It cannot be established with certainty that this approach also minimises total gambling‑related harm when effects within both the legal and illegal markets are taken into account. This therefore requires not only additional insight, but also a considered policy judgement on how harm is weighted across different groups of gamblers. Building a stronger data position will support addressing these issues and enable more effective monitoring and adjustment of policy implementation.
Applications
The knowledge generated is directly applicable to the further elaboration of the Vision on online gambling. The systems analysis helps to structure policy options along the main mechanisms in the system and to test assumptions against the collected evidence. This supports the development of coherent policy packages and improvements to the data position for future ex ante evaluation.
The method is applicable beyond online gambling. Quantitative systems analysis can be used for policy issues where insight is needed into how a problem develops over time and how policy affects that development. At the same time, this study also shows that when data and knowledge are limited, a simulation model can only provide high level, indicative insight for policy making, while the model development process remains valuable for structuring and integrating the available data and evidence.
TNO Identifier
1028585
Publisher
TNO
Collation
74 p.
Place of publication
Den Haag