In recent years, the Internal Revenue Service (IRS) has experienced declines in most of its enforcement programs, including declines in audits and in efforts to collect delinquent taxes. Increasing enforcement productivity is one strategy that can help reverse these declines. To this end, IRS is currently planning and has begun implementing enforcement process improvement projects. GAO was asked to assess the extent to which the planning for the projects followed steps consistent with both published GAO guidance and the experiences of private sector and government organizations. Specifically, GAO assessed the extent to which four judgmentally selected projects followed the 20 planning steps.
Planning for the four enforcement process improvement projects GAO reviewed included most of the 20-step framework developed to assess the projects. This increases the likelihood that projects target the right processes for improvement, choose the best target process from among alternatives, effectively implement the project, accurately assess project outcomes, and properly manage the change to the new process. However, none of the projects completed all of the steps. For example, some projects did not fully identify the causes of productivity shortfalls, leaving a risk that the project did not fix the right problem. In the course of this work, GAO found that IRS managers do not have guidance about the steps to follow in planning process improvement projects, increasing the possibility of omitting steps. A recurring issue in the four projects was that IRS's enforcement data only partially adjust for the complexity and quality of cases worked. This issue is also a problem for IRS enforcement productivity data generally. Failing to adjust for both complexity and quality increases the risk that trends in productivity will be misunderstood. For example, a decline in the number of cases closed per employee at the same time that case complexity is increasing may not be a real decline in productivity. GAO recognizes that some options for improving productivity data could be costly. However, costs could be mitigated by using existing statistical methods and IRS complexity and quality data.
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