An Evaluation of Funding Decisions and Outcomes from BTOP-Funded Programs

This research uses a dataset of all funded and non-funded programs that were proposed under the Broadband Technology Opportunities Program (BTOP) to gain understanding of objectives of various types of applicants and the applicants’ ability to meet their objectives; to analyze the degree to which the larger policy goals were emphasized among applicants and funders; to clarify the similarities in funding across the four specified funding areas (infrastructure, public computer centers, sustainable adoption, and state data and development); and to ascertain whether political bias occurred in funding decisions. The data include all applicants classified by type of project and type of applicant (for example public or governmental applicants versus private companies or universities). We also include location of the applicant, the project’s target group, the stated goals of the project, the associated socio-demographic data (population, per capita income, education level, employment level, and ethnicity, among other descriptive statistics) and political data (for example the governor’s political party, congressmen on key funding committees, and registered voting population). In total, we have approximately 1,700 projects with 72 descriptive variables for each. To date, our statistical analysis (using STATA) has focused on analyzing the degree to which the stated goals of the project as written in the proposals is correlated firstly with positive funding (rather than the proposal not being funded) and secondly with the amount of funding that is awarded for those projects that were funded. Along with this question we address the degree to which there may have been bias either in funding, or in the amount awarded. For this secondary question we use the political data associated with each project’s applicant. We then designed models to attempt to determine whether projects’ goals are appropriately specified. For example, a public computer center project in a low-income, minority community is in agreement with federal policy goals for the BTOP; however, if the public computer center is proposed to be located in a high-income rural area, it is possible another factor played a significant role in the project’s funding. Other analyses consider whether certain groups are more adept at applying for funds, for example by determining whether cities or counties are funded more frequently or in larger amount than private applicants, all else equal. Finally, some research has suggested that local programs may be more effective than national programs in meeting adoption goals, while the converse might be true for infrastructure projects. Our data allows us to address this question as well. Models considering the stated purpose of each applicant’s project and the extent to which those goals are compatible with the needs of the recipient area have yielded interesting results to date, but our analyses are preliminary. We anticipate that our ongoing statistical analyses will produce additional useful results. Our preliminary paper is available now upon request if it would prove useful to assess the scope of the paper and its completeness.