Led by experts from Esri, this 3-hour workshop focuses on strengthening analytical workflows through the use of spatial tools. The training is designed for researchers and practitioners interested in improving the efficiency and impact of their data analysis and reporting.

The workshop will highlight the use of Business Analyst Online (BAO) to support more efficient data access, integration, and visualization. Participants will be introduced to practical approaches for incorporating spatial context into analysis, enabling a more comprehensive understanding of economic and demographic patterns.

An overview of the ArcGIS environment will also be provided, with particular attention to ArcGIS StoryMaps as a platform for presenting findings. The session will demonstrate how StoryMaps can be used to communicate complex information in a structured and accessible format.

Given that many institutions already maintain access to these tools, the session emphasizes practical application and integration into existing workflows. Spatial analysis represents an opportunity to extend traditional approaches and provide additional depth to applied research and reporting.

What you’ll take away:

• Faster workflows for data access, analysis, and reporting in BAO.
• Practical techniques for enhancing data visualization outputs.
• A working understanding of StoryMaps for communicating research findings.
• Immediately applicable methods that complement your existing analytical approach.

Workshop Registration

• Select “Workshop” when registering for the conference.
• Workshop fee includes a boxed lunch, available Noon – 1:00 p.m.
• Discount for undergraduate and graduate students (must show student ID at check-in).

Information forthcoming.

Regional economic data, such as metro-area-level inflation, arrives late and without seasonal adjustment, limiting its usefulness for real-time decision-making. This project addresses both problems for the Detroit Consumer Price Index, a metro-area measure published every two months with a one-month lag. The first improvement is nowcasting. Rather than waiting for the official release, the project uses monthly data that arrives on time, including components of the national CPI, energy prices, shelter costs, to predict Detroit’s inflation figure one month before it’s published. The second improvement is seasonal adjustment. Because Detroit’s seasonal spending patterns have diverged from national trends since 2022, standard national seasonal factors distort the local signal. The project interpolates missing monthly observations and applies city-specific seasonal adjustment to produce a cleaner, higher-frequency series. Both methods are replicable across the roughly twenty other metro areas that share Detroit’s data limitations.

The Kenan Institute of Private Enterprise at the University of North Carolina-Chapel Hill, is building a statewide resource for county-level economic nowcasts and forecasts to support more informed local and regional decision-making. The initiative addresses a gap in North Carolina’s data infrastructure by combining econometric forecasting models for all 100 counties in the state, along with public-facing dashboards, place-based indicators, and translational research products. In parallel, we are conducting stakeholder outreach and building partnerships with county leaders, community colleges, chambers of commerce, and state agencies to ensure the tools respond to real decision-making needs.

This session will share our model-building approach, stakeholder-driven design process, and lessons from translating economic forecasts into actionable tools for practitioners. We will highlight our county‑level forecasts for GDP, employment, and housing affordability.

Regional economic structures are undergoing dramatic change. In the last several decades, projections of economic growth in the Sunbelt led to forward-thinking investments in transportation and infrastructure; economic forecasting and analysis enabled many states and cities transform from manufacturing-based to service-based economies.

These models also allow policymakers to evaluate alternative scenarios: proposed initiatives—such as infrastructure investments, tax policy changes, or resilience strategies—can be simulated to assess their potential economic effects compared to the baseline or business-as-usual forecast.

In an era of accelerating change and uncertainty, regions that invest in rigorous forecasting and policy analysis will be best positioned to anticipate challenges, evaluate trade-offs, and actively shape their economic future.

In 1976, the US Congress created the federal historic tax credit in an effort to preserve historic structures in the face of increasing population and economic development pressures. Since then, many states have implemented similar, state-level historic tax credits. As of June 2023, 38 states offered a version of a state historic tax credit, although the credit’s characteristics vary by state.

Minnesota enacted a state historic rehabilitation tax credit in 2010. In the bill’s language, the legislature directed the State Historic Preservation Office (SHPO) to annually measure the economic impact of the tax credit. By 2025, nearly 200 historic rehabilitation projects had been completed in the state using the credit.

University of Minnesota Extension has partnered each year with SHPO to produce an economic impact study.

My paper will argue that a transfer tax is an effective revenue stream in seasonal communities, and can be implemented without intense distortion of the existing housing market. This will give municipal officials and planners in seasonal communities’ evidence to begin implementing similar taxes in their communities. The study will also be useful for communities that are less dependent on seasonal activity but still experience intense housing pressure. By unlocking new revenue streams, these communities will be able to support sustainable housing for year-round residents and workers.

The growth of the short-term rental (STR) market has generated concerns about increased housing prices and negative externalities in residential neighborhoods. I study Denver’s 2017 STR regulations, which introduced a primary residence requirement aimed at preventing landlords from developing STR investment portfolios. I use a Poisson differences-in-differences model to quantify the effect of Denver’s policy on Airbnb supply. Relative to a set of control cities, regulations decreased active listings by 24.3%, or 12 per zip code. Pre-existing listings were significantly more likely to exit the market, especially properties hosted by commercial hosts. Active enforcement of the policy through citations did not have an additional effect on supply. I suggest that relatively low fines did not deter non-compliance when hosts expect high revenue. Finally, the regulations caused, on average, a $30 reduction in long-term monthly rental prices, concentrated in Downtown Denver.

Childcare is commonly viewed as a household constraint influencing labor supply, yet its broader role in shaping local economic outcomes remains underexplored. Focusing on counties across Texas, the analysis examines whether childcare deserts—defined as areas where licensed childcare capacity falls short of the population of young children—generate uneven growth patterns across industries.

Using a county-by-sector panel dataset, this paper implements a difference-in-differences framework that leverages temporal changes in childcare access alongside variation in industries’ reliance on female labor.

Overall, the study highlights childcare availability as a critical determinant of regional economic structure, with implications for workforce participation, sectoral composition, and long-term growth across Texas regions.

This study, conducted by the Center for Business and Economic Research at The University of Alabama, examines recent trends in incidence and prevention funding. Using data from NCANDS and state administrative sources, the analysis focuses on the 2023 cohort of first-time victims.

Findings indicate that child maltreatment imposed over $8.0 billion in tangible economic costs in 2023, representing approximately 3.2% of Alabama’s GDP. With 9,194 first-time victims, the estimated lifetime cost per victim is $871,979, reflecting substantial long-term impacts across health care, education, criminal justice, and labor market outcomes.

The findings highlight that strengthening investments in child maltreatment prevention is both a social priority and a strategic economic imperative, with strong potential to enhance long-term workforce productivity and reduce public expenditures.

This research project focuses on the availability of childcare in an eight-county rural and urban area in coastal Georgia. The Hyundai Motor Group Meta plant America is directly adding 8,500 jobs to this region, increasing the demand for labor in this area. Community leaders identified Childcare as a factor keeping people out of the labor market. This study establishes, for both rural and urban counties, the current and future supply of and demand for childcare in the region. It also identifies where gaps in care exist and strategies to address the challenge.

This study examines how generative artificial intelligence (AI) is reshaping entry-level labor market opportunities for young workers in East Texas, with a focus on Smith County. Using American Community Survey (ACS PUMS) data matched with the AI Occupational Exposure Index (AIOE), the analysis measures the extent to which jobs held by workers aged 20–24 overlap with AI capabilities. Results indicate that nearly half of young workers are employed in moderately to highly AI-exposed occupations, while a significant share of entry-level job demand is concentrated in these same roles. Drawing on the distinction between automation and augmentation, the findings suggest that AI is compressing traditional entry-level pathways by reducing routine roles and transforming others.

The United States is undergoing a major demographic transition known as the “Gray Tsunami,” as the Baby Boomer generation steadily exits the workforce. This shift is reshaping labor markets by tightening labor supply and increasing old-age dependency ratios, particularly in regions with older populations such as East Texas. This study examines how aging demographics place structural pressure on regional economies through reduced workforce participation and a growing share of retirees. The analysis also explores how strategies such as flexible work arrangements and knowledge transfer can mitigate labor shortages and sustain economic growth. Understanding these dynamics is essential for helping policymakers and communities transform demographic change into economic opportunity rather than constraint.

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