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Since its first application in 2008, more than 300 local governments have applied priority-based budgeting (PBB) to develop a data-driven decision-making framework to evaluate the alignment of programs and services toward the outcomes local government organizations are striving to achieve.

Today’s budget leaders have applied the PBB blueprint to reallocate millions to activate climate action plans (see Pittsburgh article) and transform the budget process to build more equitable communities. 

However, as staff shortages have hindered local government’s ability to pursue innovative ideas that require time and effort, a breakthrough has been needed to make PBB accessible and achievable, while minimizing and all but eliminating lift.

Over the past few months, Albuquerque, New Mexico, tested the application of ResourceX machine learning to their PBB implementation. Several departments applied the PBB methodology and developed their initial data sets. With the power of machine learning, the city was able to obtain fully scored programs and predicted program costs, reducing their implementation workload to advance their application of PBB data to achieve community results.

The Capacity Gap

As cities across the United States face capacity challenges and increased pressure to deliver more with less, many are turning to priority-based budgeting as a solution. This data-driven approach to budgeting allows cities to identify and prioritize their most pressing needs, strategically allocate resources based on data, and deliver results to residents. 

In Albuquerque, like many other cities, capacity remains a challenge and has been exacerbated by the lingering effects of COVID. Local governments continue their best efforts to serve residents and advance significant equity, infrastructure, and climate initiatives made possible through ARPA, IIJA and IRA funding opportunities. It has been a struggle to maintain staffing and retain legacy organizational knowledge lost due to retirement and position vacancies. As a result, Albuquerque is leveraging machine learning to address these challenges while developing and applying PBB data.

Albuquerque's Use of PBB 

In late 2022, the city of Albuquerque became the first city in the country to use ResourceX’s machine learning approach to its PBB data development process. Building on over 10 years of accumulated municipal program and service data, the city collaborated with ResourceX to develop a predicted data set of program inventory, costs, and scores across a pilot set of three city departments, and applied the predicted PBB data to advance city priorities. In this initial experiment, Albuquerque achieved approximately 85% accuracy in the development of their machine learning-generated PBB data in one large public safety department.

At last count with over 300 unique client priority-based budgets from implementing organizations, 125,014 individual local government programs, each with cost (and revenue), service level, workforce, and key program attributes, $34.7 billion in program costs tracked at the service level. 

By using the machine learning tool, Albuquerque was able to overcome its capacity gap to implement priority-based budgeting faster and generate invaluable decision-making data in front of city leaders. With local governments pressed for time and resources to implement a new budgeting approach, the machine learning PBB data development approach is proving to be a valuable addition to their process. 

Is this ChatGPT for PBB?

Interestingly enough, it’s not that far. With breakthroughs in natural language processing, ResourceX has been able to see trends in the PBB data mine shine through like never before. One can see the nuance in the difference between how Albuquerque defines a safe community, versus how Duluth, Minnesota, defines it; and, therefore, see a difference in how programs are scored. This allows for the uniqueness in how every community develops its own strategic plan and community vision for the future, facilitating meaningfully targeted predictions of the influence that their programs have on these results, and therefore predict how their budget is aligned.

Albuquerque's successful predictive development of PBB data thus accelerating the application of PBB data is an exciting step into the future of priority-based budgeting, proving that ResourceX's machine learning tool makes it easier than ever for cities to adopt and apply PBB and make smarter data-driven decisions driving strategic investment to outcomes through their budgets.

The Future of PBB

As cities continue to experience capacity challenges and increased pressure to deliver more with less, PBB offers a data-driven approach to budgeting that can help to allocate resources more strategically and efficiently. With the help Ai diving deep into ResourceX's comprehensive municipal program level dataset for predictive PBB data development and resources like the 21st Century Budgeting micro-certification from ICMA, the opportunity is now for cities to quickly adopt a data > insights > action approach to advancing community outcomes for the benefit of residents and the community.

Discover how local governments are using Priority-Based Budgeting to drive better budget decision making. Get a free copy of the ResourceX Annual Impact Report at ResourceX.net.