Approval Regulation

在美国和其他发达工业社会，许多经济活动都有两个共同特点。首先，私人团体（如个人或公司）执行项目的研究和开发（R&amp；D），通常花费相当大的费用。其次，政治机构有能力否决这些项目的进入或使用。这种研发和监管审议的结合，被称为审批监管，是现代经济大部分的重要组成部分。例如，它是制药行业上市前活动的特征，2003年美国的销售额超过1600亿美元，约占GDP的1.5%。这个项目有两个科学目标。首先，建立了一套理论模型。这是至关重要的，因为目前还没有描述审批监管过程的理论。在过去的几十年里，政治学家和经济学家已经开发出了一些模型，这些模型描述了上述研发和监管决策的每一个组成部分。然而，研发模型没有考虑战略监管机构的影响，监管审批模型也没有考虑产品提交的战略开发。因此，为了对企业和监管机构的策略进行预测，有必要建立一个将这两种方法结合起来的审批监管理论。第二个目标是测试理论模型的预测。这些研究的重点是美国制药市场。主要的挑战之一将是建立适当的数据集。正如研发和监管决策的整合所建议的那样，这需要收集食品和药物管理局（FDA）监管决策的数据，以及公司的制药研发项目（包括那些从未提交审查的项目）。此外，一些关键变量难以测量。需要解决的具体问题包括监管错误和延迟的发生率，公司特征（如规模）对研发和监管策略的影响，以及最近FDA改革的影响。该项目建立在过去两年在哥伦比亚大学和哈佛大学进行的初步工作的基础上。这项工作已经产生了一些有希望的初步结果，包括理论和实证。已经建立了一个基本的理论模型，将企业和监管者之间的相互作用视为不完全信息的博弈。该模型预测了企业规模与监管错误之间的一些反直觉关系。这些预测在很大程度上得到了现有数据的证实。然而，需要做更多的工作来解决第二类错误（本质上更难以衡量），以及纳入多公司竞争和更现实的监管审查过程。更广泛的价值：这个项目的结果将引起学术界和政策专家的兴趣。除了填补关于监管和官僚政治的学术文献的重要空白之外，该项目还承诺提供关于一个高度争议的问题领域的及时和与政策相关的知识。例如，最近关于FDA处理COX-2抑制剂和其他产品的争议突出了I型错误（即批准“坏”产品）和II型错误（即拒绝“好”产品）之间的紧张关系。了解提交公司和FDA面临的动机对于在两种错误之间取得平衡至关重要。这些结果也将有助于评价可能的政策变化的影响，例如征收提交费，或设立一个独立的核准后监测机构。最后，这些研究中使用的数据集将成为对该主题感兴趣的其他分析师的资源。

In the United States and other advanced industrial societies, many economic activities share two features. First, private parties (such as individuals or firms) perform research and development (R&D) of projects, often at considerable expense. Second, political agencies have the ability to veto the entry or use of these projects. This combination of R&D and regulatory deliberation, termed approval regulation, is a significant component of large segments of modern economies. For example, it characterizes the pre-market activity of the pharmaceutical industry, which saw U.S. sales of over $160 billion in 2003, or approximately 1.5% of GDP.This project has two scientific objectives. First, it develops a set of theoretical models. This is essential because there are presently no theories that describe the approval regulation process. Over the past few decades, political scientists and economists have developed models that describe each of the above components-R&D and regulatory decision-making-piecemeal. However, models of R&D do not consider the influence of a strategic regulator, and models of regulatory approval do not consider the strategic development of products for submission. To generate predictions about firm and regulator strategies, it is therefore essential that a theory of approval regulation integratethe two approaches. The second objective is to test the predictions of the theoretical models. These studies focus on the U.S. pharmaceutical market. One of the main challenges will be that of building the appropriate data sets. As the integration of R&D and regulatory decision-making would suggest, this requires collecting data on Food and Drug Administration's (FDA) regulatory decisions, as well as firms' pharmaceutical R&D projects (including those that were never submitted for review). Moreover, some of the critical variables are difficult to measure. Specific issues to be addressed include the incidence of regulatory error and delay, the effects of firm characteristics (such as size)on R&D and regulatory strategy, and the effects of recent FDA reforms.The project builds on preliminary work conducted at Columbia and Harvard universities over the past two years. This work has resulted in a number of promising preliminary results, both theoretical and empirical. A basic theoretical model has been developed that treats the interaction between a firm and a regulator as a game of incomplete information. The model predicts some counter-intuitive relationships between firm size and regulatory error. These predictions are largely confirmed by the available data. However, more work is needed to address Type II errors (which are inherently more difficult to measure), as well as to incorporate multi-firm competition and amore realistic regulatory review process.Broader Value: The results of this project will be of interest to both academics and policy specialists. In addition to filling an important gap in the scholarly literature on regulation and bureaucratic politics, the project promises to provide timely and policy-relevant knowledge about a highly controversial issue area. For example, recent controversies over the FDA's handling of COX-2 inhibitors and other products have highlighted the tensions between Type I errors (i.e., approving "bad" products), and Type II errors (i.e., rejecting "good" products). An understanding of the incentives faced by submitting firms and the FDA is crucial for striking a balance between both types of error. The results will also be useful for evaluating the effects of possible policy changes, such as the imposition of submission fees, or the implementation of an independent post-approval monitoring agency. Finally, the data set used in these studies will be a resource for other analysts interested in the topic.