Small Business Programs, Innovation, and Growth: Estimating Policy Effects Using Comprehensive Firm-Level Panel Data

小型企业计划、创新和增长：使用综合的企业级面板数据估计政策效果

基本信息

批准号：
1262269
负责人：
John Earle
金额：
$ 36.64万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-05-15 至 2017-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262269&HistoricalAwards=false
关键词：
Small Business Programs Innovation Growth

项目摘要

In an unusual partnership of university and government researchers working together to link and analyze several very large firm-level databases, this project evaluates the growth and innovation effects of small business support programs. Adapting and extending labor market program evaluation methods, the researchers link administrative program data to universal long-panel data and to a variety of survey data. The main estimation method is based on longitudinal matching combined with regression, and besides estimating the basic average treatment effect on the treated, the project investigates treatment intensity (loan/grant size), analyze potential general equilibrium effects, and examine the heterogeneity of effects by type of program, loan conditions, type of firm, and economic context. The results will enable the first rigorous estimates of government program effects on firm-level innovation and growth.Intellectual Merit:In addition to its direct policy relevance, this project contributes to theoretical and empirical questions about small firms, growth, innovation, finance, and fiscal policy. Do small business support programs foster growth and innovation? Innovation is proxied in several ways: sales and employment growth, productivity, skill-biased employment change, patents, R&D personnel, and survey-based measures; the analysis distinguishes market-expanding from business-stealing effects. Results for loan programs will contribute to the growth-finance debate by analyzing specific policy interventions varying at the firm level, which avoids many of the econometric problems plaguing estimates based on aggregate data. Other issues concern the degree to which firms are financially constrained and the size of the government spending multiplier, for which the project uses firm level microdata to estimate the impact of government programs on small business growth (the first-stage effect), and how it varies over the business cycle. Broader Impacts:The broader impacts of this research appear, first, in the contribution to urgent policy debates. The question is not only whether the support programs on average increase growth and innovation at recipient firms, but whether these benefits are offset by negative spillovers (displacement effects) or enhanced by positive spillovers (e.g., shared innovation), and also whether they are more effective in some environments (e.g., after negative shocks), for some types of firms (e.g., young start-ups), or for some types of program design (e.g., eligibility criteria and loan terms). Romania is included to provide an international replication, because of the interest in microcredit and financial development in transition economies, and because of the excellent data available for analysis. The project also makes methodological contributions of broader usefulness: exploiting the long panel data to extend matching methods, introducing new types of multiple control groups for more credible identification, and developing new approaches for analyzing the general equilibrium effects of economic policies. The project advances the broad research agenda of linking and analyzing diverse sets of microdata, one of the most promising recent developments in empirical economics. The ideas and methods could serve as models for researchers analyzing other policies, both in the US and in many other countries where similar programs and data exist. The specific data generated by the project through linking of many large databases will have considerable value, and another broad impact may result from making the data available to other researchers. PhD students can and will benefit from learning to work with the data, participating in professional conferences, and writing dissertations on the basis of the data and project.

在大学和政府研究人员之间的不寻常伙伴关系中，该项目共同链接和分析了几个非常大的公司级数据库，该项目评估了小型企业支持计划的增长和创新影响。研究人员适应和扩展劳动力市场计划评估方法，将行政计划数据与通用长面板数据联系起来，并将其与各种调查数据联系起来。主要估计方法基于纵向匹配与回归相结合，除了估计对治疗的基本平均治疗效应外，该项目还研究了治疗强度（贷款/赠款规模），分析了潜在的一般一般平衡效应，并检查了按计划，贷款，贷款条件，公司类型和经济环境的效果的异质性。结果将对政府计划对公司级创新和增长的影响进行第一个严格的估计。智能优点：除了其直接政策相关性外，该项目还有助于有关小公司，增长，创新，财务和财政政策的理论和经验问题。小型企业支持计划是否促进增长和创新？创新有多种代理：销售和就业增长，生产力，技能偏见的就业变化，专利，研发人员和基于调查的措施；该分析将市场扩张与偷窃业务效应区分开。贷款计划的结果将通过分析公司水平变化的特定政策干预措施来促进增长资金辩论，这避免了许多计量经济学问题基于汇总数据来困扰估计值。其他问题涉及公司在财务上受到限制的程度以及政府支出乘数的规模，该项目使用公司级别的微型数据来估计政府计划对小型企业增长（第一阶段效应）的影响，以及在商业周期中的影响。更广泛的影响：这项研究的更广泛影响首先是对紧急政策辩论的贡献。问题不仅是平均支持计划的支持计划是是否会增加接收者公司的增长和创新，而且这些收益是否被负面的溢出（位移效应）所抵消还是通过积极的溢出（例如共享创新）增强，以及它们在某些环境中是否更有效（例如，在某些类型的公司中（例如，E. ef）（例如，某些类型的启动），或者在某些环境中更有效标准和贷款条款）。由于对过渡经济体中的微货币和金融发展的兴趣以及可用于分析的出色数据，因此包括罗马尼亚提供国际复制。该项目还做出了更广泛有用性的方法论贡献：利用长面板数据扩展匹配方法，引入新型的多个对照组以获得更可靠的识别，并开发了分析经济政策一般均衡效应的新方法。该项目推进了联系和分析各种微型AT的广泛研究议程，这是经验经济学最有希望的最有希望的发展之一。这些想法和方法可以作为研究人员在美国和许多其他类似计划和数据的国家中分析其他政策的模型。项目通过链接许多大型数据库而产生的特定数据将具有相当大的价值，并且可能会导致其他研究人员提供数据。博士学位的学生可以并且将受益于学习与数据合作，参加专业会议以及根据数据和项目的写作论文。