How Michigan’s universities are helping secure Detroit’s economic future
The potential and value of data lie in the ability to connect the dots across different datasets and generate actionable insights.
With the right datasets and expertise, data analytics have the power to inform and guide industries not only to solve existing problems but also to avoid making subpar decisions in the future.
That is exactly what the City of Detroit University Economic Analysis Partnership is setting out to achieve in the coming five years.
The partnership between the University of Michigan (UM), Michigan State University (MSU), Wayne State University (WSU), and the city’s Chief Financial Officer’s budget office will use data analytics to generate key insights on the city’s economic conditions, and use it to improve city administration and lift the local economy.
Detroit Chief Financial Officer David Massaron said the collaboration would support stringent efforts by city administrators to exercise fiscal prudence in handling the city’s finances.
He noted that it was only five years ago that Detroit’s financials were back in the black–the municipality had to file for bankruptcy in 2013, becoming the largest US city to do so.
“… we have completed a remarkable turnaround in the way we manage the city’s finances and we are now moving forward in a fiscally responsible way, but we are always looking for ways to improve,” Massaron said in a release.
“Thanks to this partnership with the universities, we will gain access to even better data, allowing us to make strategic decisions that will ultimately improve the quality of life for Detroiters.”
UM Research Seminar in Quantitative Economics (RSQE) Director Gabriel Ehrlich said that “[the] partnership with the city of Detroit represents another important step in the city’s post-bankruptcy recovery and can be an important positive step in the eyes of residents, investors, business leaders, and the credit market.”
The partnership is a US$230,000 to US$250,000 annual commitment that Detroit is pledging in alignment with its post-bankruptcy objective, while UM will provide a US$30,000 in-kind contribution annually.
UM’s RSQE will lead the collective effort, using the same econometric modelling it uses for its annual state- and national-level forecasts.
MSU’s Centre for Local Government Finance and Policy will contribute revenue modelling and forecasting while WSU’s Department of Economics will provide locally relevant data and its housing and property tax modelling. MSU and WSU researchers have extensive experience in using local governments’ internal data in analyses.
The universities will work with the city’s forecasting and economic analysis units within Massaron’s office.
They will use publicly-available data collected by a number of national agencies, including the Bureau of Labor Statistics, American Community Survey, Quarterly Census of Employment and Wages, County Business Patterns, Internal Revenue Service, Bureau of Economic Analysis and Federal Housing Finance Agency.
They will also pull data from Detroit city and rely on local sources such as Data Driven Detroit, the Detroit Economic Growth Corp. and Detroit Future City.
According to WSU’s Master’s Programme in Economics Director Allen Goodman, the researchers will combine the datasets to construct “Detroit-specific estimates of building activity, measures of activity in residential and commercial real estate, total commerce and tourism activity.”
At the moment, most public economic data are only relevant to the county or regional level. Detroit-specific data would help city administrators would better plan, design, finance and evaluate local programmes to improve economic conditions for Detroiters.
MSU Extension Centre for State and Local Government Policy Director Eric Scorsone said Detroit-based establishments would be able to achieve more accurate forecasting results by combining the use of both the city’s forecast as well as their own.
“The research team will make the core forecasting model available to city staff for the city’s use,” he added. “They will be able to change forecast assumptions to produce their own forecasts or to perform scenario analysis.”