Budgets and Data-Driven Government
By: What Works Cities
Tags: Cities, Data, Local Government, Budget, City Budget

Budgets and Data-Driven Government

By John Collier

City leaders work every day to provide essential services for their residents and are entrusted with the responsibility of using taxpayer dollars in an efficient and effective manner. A city’s ability to raise revenue can be limited by state restrictions, politics, and general macroeconomic trends. To further complicate things, cities also vary greatly in where they generate their revenue — with varying reliances on taxes, charges, and fees.

With increased pressure on cities to provide high-quality services with limited resources, dedicating resources to grow the data capacities of city staff can be quite challenging. Oftentimes, the natural assumption is that cities with larger budgets at their disposal are better positioned to demonstrate data-informed decision-making and excel on the practices outlined by the What Works Cities (WWC) Standard.

Our analysis shows, however, that this may not be the case. In order to gain a deeper understanding of why larger budgets may not be a prerequisite for excelling in data-driven practices, we analyzed the budget data of the cohort of cities that have completed a What Works Cities Assessment in this latest installment of The Data and the Details.

Familiarize yourself with the “basics” of the Certification data.

Data and Methods

With differing structures, responsibilities, and authorities, no two cities’ fiscal situations are alike — which makes the task of comparing their budgets particularly difficult. The data used in the following analysis relies on research and data from the Lincoln Institute of Land Policy’s Fiscally Standardized Cities dataset (FiSC) and the U.S. Census’ data on finances for individual local governments.

Specifically, we examined city-specific revenues and expenditures, as reported by the U.S. Census Bureau, as well as the “fiscally standardized city” that takes into account other local government spending such as overlying county governments, independent school districts, and special districts. The “fiscally standardized city” is valuable because it seeks to account for the fact that the responsibility of providing local public services is often divided among multiple governments and entities. These differences mean that comparisons across city-governments alone can be misleading. The FiSC dataset seeks to create a more accurate representation of the revenues and spending for a city resident.

The graphic above breaks down the average revenue characteristics of the cities in this FiSC dataset that overlap with cities in the WWC Network. These percentages are the average for the cohort of cities. City-specific breakdowns can vary significantly from city to city.

Of the 200 plus cities that have submitted a WWC Assessment, 78 cities had their financial information included in the FiSC dataset. The general fund revenues of these cities varied from over $13 billion down to $70 million, with an average revenue of about $1.6 billion. In this subset of cities, on average, about 20 percent of city revenues were intergovernmental (revenues generally from the state or federal government, either in the form of a grant or reimbursement), while 80 percent was own source revenue (collected from the local government’s own sources, such as taxes, fees, user charges, etc.).*

Comparing Revenues to Assessment Scores

Next, we looked at whether a city’s revenue has any relationship with performance on the WWC Assessment, which among other things, helps local governments evaluate if they have the right practices, policies and people in place to use data and evidence to inform decision-making. A simple linear regression model with WWC Assessment scores as the dependent variable produces statistically significant coefficients that seems to suggest that an additional $1,000 in revenue per capita is associated with anywhere from a 1.2 to 1.5 point increase in the city’s Assessment score. Results are similar when we use city-only budget data as opposed to the “fiscally standardized city” data.

There are two factors, however, that must be considered when interpreting these results to ensure we fully understand the results. First, while the results of the regression model were statistically significant, the R-squared value — which is a “goodness-of-fit” measure for linear regression models — was extremely low. Second, the regressions seem to be highly influenced by two outliers in the dataset with revenues per capita far higher than the rest. When these outliers are removed, the relationship is still positive but not statistically significant.

The graphs above display the results of two separate linear regression models with Validated Assessment Score as the dependent variable. The results on the left use only city revenue as a predictor of score while the results on the right utilize the Fiscally Standardized City’s revenue. Both regression lines (red) indicate a positive relationship between the two variables but the R-squared value for the model suggests these results to be unreliable.

The graphs above display the results of the same linear regression model with Validated Assessment Score as the dependent variable — but this time with outliers removed from the dataset. While the regression lines still are slightly positive in both cases, their coefficients are not statistically significant and do not indicate any relationship between revenues and Certification scores.

If the assumption that cities with larger budgets are better positioned to demonstrate data-informed decision-making and excel on the practices in the WWC Standard, we would expect to see a stronger statistical relationship in the regression models above. The lack of strong statistical evidence here indicates this assumption is not true given our available data.

These results are not necessarily surprising given that in our work we often see cities with limited resources be even more dedicated to data-driven practices in order to be good stewards of taxpayer dollars. Take, for example, 2020 Platinum Certified City Louisville, KY, whose budget is on the smaller end of cities in this cohort. Louisville’s smart use of data and technology allows them to perform on-demand traffic studies — a project that not only allows officials to make data-driven decisions that positively impact residents, but also saves thousands of taxpayer dollars by removing the need for traditional traffic studies.

Just as we have seen in previous Data and the Details installments, cities of all shapes and sizes have the means and ability to achieve WWC Certification when data-driven decision-making is identified as a priority.

Outstanding Questions and Future Research

This data and the subsequent analysis can only tell us so much — but we believe there are many more valuable questions to explore surrounding a city’s financial situation and its adoption of data-driven practices and policies. More in-depth analysis could be performed by using cities’ Comprehensive Annual Financial Reports (CAFRs) to get more accurate and up-to-date information on where and how cities raise and spend their resources year over year. More detailed data like this could unveil interesting insights, such as the relationship between a city’s spending on innovation teams, data analysts and other personnel, and their adoption of certain data-driven practices outlined in the WWC Standard.

Additionally, there are specific foundational practice areas within the WWC Standard that could potentially have causal relationships with a city’s fiscal well-being. For example, it would be interesting to look at cities’ performance specifically on our Repurposing criteria, which measure a city’s use of data to align its budget process with its strategic priorities whether they make budget decisions based on analyzed data.

Conclusion

Altogether, the Data and the Details series has just scratched the surface of questions surrounding the adoption and benefits of data-driven government. As more cities continue to complete the WWC Assessment and update us with their progress, we will be able to derive even more insights about what makes cities successful in data-driven decision-making. WWC is dedicated to utilizing the data and information we collect to inspire and inform the many city leaders working to make government work better for their residents.

If you have a question about cities that we could test through What Works Cities Assessment data, email our colleague Lisa at lisa@results4america.org.

John Collier is a Data Analyst on the What Works Cities Certification team. He also supported the recent launch of the What Works Cities City Budgeting for Equity and Recovery program.

Completing a What Works Cities Assessment is the first step to receiving exclusive, pro bono support from What Works Cities to continue building a more effective local government. The program is open to any U.S. city with a population of 30,000 or higher.

*Disclaimer/Endnotes: The data used from the Fiscally Standardized Cities dataset from the Lincoln Institute of Land Policy is from 2017, while the assessment data used in the analysis is from 2019–2021. City budgets are fluid and this analysis may not incorporate recent shifts in funding or major budget cuts. The FiSC dataset is also limited in its accuracy of the city’s budget as it derives its data from the US Census’ census of local governments. This analysis is meant to be exploratory in nature and may not fully reflect all cities in the WWC Network.