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

随机对照试验(RCT)被广泛认为是评估发展干预措施影响的黄金标准。然而，RCT在帮助发展组织回答他们感兴趣的关键问题方面只能做到这一点：他们的干预措施是否改善了人们的生计？毕竟，实验性影响评估只能告诉我们，与对照组相比，接受治疗的个体表现如何。与此同时，发展组织实施干预措施，希望惠及的不仅仅是直接受益者。由于不可能向每个有需要的人提供援助、培训或其他援助，这些组织往往希望他们的干预措施产生溢出效应，尽管只针对少数人，但这些干预措施对整个社区具有长期和自我可持续的影响。此外，一些干预措施可能需要溢出效应，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代理的计算建模(ABM)相结合来改变研究人员和开发组织评估开发干预措施影响的方式。我们希望以一种新的方式应用基于代理人的建模，利用它们来加强区域协调，并提供一种成本效益高的措施，以衡量干预措施对直接受益者以外的广泛影响。这种方法大大改进了现有的用于研究溢出效应的实验设计--不仅因为它更灵活、更具成本效益，而且还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观察严格定义的单位的溢出效应。我们的方法将使我们能够观察到整个人口的溢出效应。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影响宏观层面的结果。最后，虽然我们知道，当足够多的人采取了所需的行为时，干预措施往往会奏效或效果最好，但使用现有的实验设计很难观察到这些阈值在哪里。我们的方法将使我们能够观察到临界点，在这些临界点上，治疗已经影响了足够多的人，随后的变化很容易被接受。我们将把我们提出的方法应用于由DFID资助、由国际移民组织(IOM)实施的发展干预措施，旨在改善冈比亚和塞内加尔的自营职业前景。我们的最终目标是为能够捕捉社区效应的适当实验设计提供透明和彻底的指导方针；提供在NetLogo(一个免费且易于使用的ABM平台)中实施该方法的编程包；以及一个用户友好的应用程序，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.