The diffusion of development: Extending RCTs with agent-based modelling to understand spillovers of development interventions

随机对照试验（RCTs）被广泛认为是评估发展干预措施影响的金标准。然而，随机对照试验只能帮助发展组织回答他们感兴趣的关键问题：他们的干预措施是否改善了人们的生活？毕竟，实验影响评估只能告诉我们，与对照组相比，接受治疗的个体的表现如何。与此同时，发展组织实施干预措施的目的是希望受益范围超出它们的直接受益者。因为不可能向每个需要帮助的人提供援助、培训或其他援助，这些组织通常希望他们的干预措施产生溢出效应，尽管只针对少数人，但对整个社区产生持久和自我可持续的影响。此外，一些干预措施可能会产生溢出效应，因为个人需要社区的支持才能做出改变生活的决定。我们建议通过将随机对照试验与基于主体的计算模型（ABM）结合起来，改变研究人员和发展组织评估发展干预措施影响的方式。我们希望以一种新颖的方式应用基于主体的模型，通过使用它们来增强随机对照试验，并提供一种具有成本效益的措施，以衡量干预措施在直接受益者之外的广泛影响。这种方法大大改进了适用于研究溢出效应的现有实验设计——不仅因为它更灵活、更具成本效益，而且因为它可以揭示更多关于溢出效应的信息。首先，现有的实验设计只允许我们在严格定义的单位上观察溢出效应。我们的方法将使我们能够观察到人口范围内的溢出效应。此外，由于我们可以观察到整个人口的溢出效应，我们可以看到干预措施的变化如何在宏观层面上影响结果。最后，虽然我们知道，当足够多的个体采取了期望的行为时，干预措施通常会起作用，或者效果最好，但使用现有的实验设计很难观察到这些阈值在哪里。我们的方法将允许我们观察到临界点，在这个临界点上，治疗已经影响了足够多的人，随后的改变很容易被接受。我们将把我们提出的方法应用于一项发展干预措施——由英国国际发展部资助，由国际移民组织（IOM）实施——旨在改善冈比亚和塞内加尔自雇的前景。我们的最终目标是为适当的实验设计提供透明和彻底的指导方针，以捕捉社区效应；在NetLogo（一个免费且易于使用的ABM平台）中实现该方法的编程包；还有一个用户友好的应用程序，可以进行初步测试和功率计算。

Randomised controlled trials (RCTs) are widely considered the gold-standard to assess the impact of development interventions. However, RCTs can only go so far in helping development organisations answer the key question that interests them: Do their interventions improve people's livelihoods? After all, experimental impact evaluations can only tell us how treated individuals fare in comparison to control individuals. Meanwhile, development organisations implement interventions in the hope that benefits extend beyond their direct beneficiaries. Because it is impossible to provide aid, training, or other assistance to everyone who needs it, such organisations often hope for spillover effects of their interventions that have long-lasting and self-sustainable effects on the whole community despite only targeting a few individuals. Moreover, it is plausible that some interventions require spillovers as individuals need support from their communities to make life-changing decisions. We propose to transform the ways that researchers and development organisations assess the impact of development interventions by combining RCTs with agent-based computational modelling (ABM). We hope to apply agent-based modelling in a novel way, by using them to enhance RCTs and providing a cost-effective measure of the widespread effects of interventions beyond the direct beneficiaries. This methodology greatly improves upon existing experimental designs that are adapted to study spillovers - not only because it is more flexible and more cost-effective, but because it can reveal much more about spillover effects. First, existing experimental designs only allow us to observe spillovers on strictly defined units. Our method will allow us to observe population-wide spillovers. Moreover, because we can observe population-wide spillovers, we can see how changes to interventions can shape outcomes at the macro-level. And finally, while we know that the interventions will often work or work best when a sufficient number of individuals have adopted the desired behaviour, it is very difficult to observe where these thresholds lie using existing experimental designs. Our method will allow us to observe tipping points at which the treatment has affected sufficient numbers of people that subsequent change is readily accepted.We will apply our proposed methodology to a development intervention - funded by DfID and implemented by the International Organisation for Migration (IOM) -- aimed at improving prospects for self-employment in the Gambia and Senegal. Our ultimate aim is to provide transparent and thorough guidelines on appropriate experimental designs that can capture community effects; programming packages to implement the methodology in NetLogo, a free and easy-to-use ABM platform; and a user-friendly app to conduct preliminary tests and power calculations.