Houston research: It matters how we talk about social, economic disparities

houston voices

How we describe inequality is significant because it impacts our view of who causes it and how society should address it. Photo via Getty Images

Look closely at any news article about inequality and you will quickly notice that there is more than one way to describe what is happening.

For example:

“In 2022, men earned $1.18 for every dollar women earned.”

“In 2022, women earned 82 cents for every dollar men earned.”

“In 2022, the gender wage gap was 18 cents per dollar.”

When pointing out differences in access to resources and opportunities among groups of people, we tend to use three types of language:

  1. Advantaged — Describes an issue in terms of advantages the more dominant group enjoys.
  2. Disadvantaged — Describes an issue in terms of disadvantages the less dominant group experiences.
  3. Neutrality — Stays general enough to avoid direct comparisons between groups of people.

The difference between these three lenses, referred to as “frames” in academic literature, may be subtle. We may miss it completely when skimming a news article or listening to a friend share an opinion. But frames are more significant than we may realize.

“Frames of inequality matter because they shape our view of what is wrong and what should be fixed,” says Rice Business Professor Sora Jun.

Jun led a research team that conducted multiple studies to understand which of the three frames people typically use to describe social and economic inequality. In total, they analyzed more than 19,000 mainstream media articles and surveyed more than 600 U.S.-based participants.

In Chronic frames of social inequality: How mainstream media frame race, gender, and wealth inequality, the team published two major findings.

First, people tend to describe gender and racial inequality using the language of disadvantage. For example, “The data showed that officers pulled over Black drivers at a rate far out of proportion to their share of the driving-age population.”

Jun’s team encountered the same rhetorical tendency with gender inequality. In most cases, people describe instances of gender inequality (e.g., the gender pay gap) in terms of a disadvantage for women. We are far more likely to use the statement “Women earned 82 cents for every dollar men earned” than “Men earned $1.18 cents for every dollar women earned.”

"We expected that people would use the disadvantage framework to describe racial and gender inequalities, and it turned out to be true,” says Jun. “We think that the reason for this stems from how legitimate we perceive different hierarchies to be.” Because demographic categories like gender and race are unrelated to talent or effort, most people find it unfair that resources are distributed unevenly along these lines.

On the other hand, Jun expected people to describe wealth inequality in terms of advantage rather than disadvantage. The public typically considers this form of inequality to be more fair than racial or gender inequality. “In the U.S., there is still a widespread belief in economic mobility — that if you work hard enough, you can change the socioeconomic group you are in,” she says.

But in their second major finding, she and fellow researchers discovered that the most common frame used to describe wealth inequality was no frame at all. We find this neutrality in statements like “Disparities in education, health care and social services remain stark.”

Jun is not sure why people take a neutral approach more frequently when describing wealth inequality (speaking specifically of economic classes outside of gender and race). She suspects it has something to do with the fact that we view wealth as a fluid and continuous spectrum.

The merits of the three frames are up for debate. Using the frame of disadvantage might seem to portray issues more sympathetically, but some scholars point to potential downsides. The language of disadvantage installs the dominant group as the measuring stick for everyone else. It may also put the onus of change on the disadvantaged group while making the problem seem less relevant to the dominant group.

“When we speak about the gender gap in terms of disadvantage, and helping women earn more compared to men, we automatically assume that men are making the correct amount,” says Jun. “But maybe we should be looking at both sides of the equation.”

On the other hand, Jun cautions against using a one-size-fits-all approach to describing inequality. “We have to be careful not to jump to an easy conclusion, because the causes of inequality are so vast,” she says.

For example, men tend to interrupt conversations in team meetings at higher rates than women. “Should we frame this behavior in terms of advantage or disadvantage, which naturally leads us to prompt men to interrupt less and women to interrupt more?” asks Jun. “We really don’t know until we understand the ideal number of interruptions and why this deviation is happening. Ultimately, how we talk about inequality depends on what we want to accomplish. I hope that through this research, people will think more carefully about how they describe inequality so that they capture the full story before they act.”

------

This article originally ran on Rice Business Wisdom and was based on research from Sora Jun, Rosalind M. Chow, A. Maurits van der Veen and Erik Bleich.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Property Showcase

New Rice Brain Institute partners with TMC to award inaugural grants

brain trust

The recently founded Rice Brain Institute has named the first four projects to receive research awards through the Rice and TMC Neuro Collaboration Seed Grant Program.

The new grant program brings together Rice faculty with clinicians and scientists at The University of Texas Medical Branch, Baylor College of Medicine, UTHealth Houston and The University of Texas MD Anderson Cancer Center. The program will support pilot projects that address neurological disease, mental health and brain injury.

The first round of awards was selected from a competitive pool of 40 proposals, and will support projects that reflect Rice Brain Institute’s research agenda.

“These awards are meant to help teams test bold ideas and build the collaborations needed to sustain long-term research programs in brain health,” Behnaam Aazhang, Rice Brain Institute director and co-director of the Rice Neuroengineering Initiative, said in a news release.

The seed funding has been awarded to the following principal investigators:

  • Kevin McHugh, associate professor of bioengineering and chemistry at Rice, and Peter Kan, professor and chair of neurosurgery at the UTMB. McHugh and Kan are developing an injectable material designed to seal off fragile, abnormal blood vessels that can cause life-threatening bleeding in the brain.
  • Jerzy Szablowski, assistant professor of bioengineering at Rice, and Jochen Meyer, assistant professor of neurology at Baylor. Szablowski and Meyer are leading a nonsurgical, ultrasound approach to deliver gene-based therapies to deep brain regions involved in seizures to control epilepsy without implanted electrodes or invasive procedures.
  • Juliane Sempionatto, assistant professor of electrical and computer engineering at Rice, and Aaron Gusdon, associate professor of neurosurgery at UTHealth Houston. Sempionatto and Gusdon are leading efforts to create a blood test that can identify patients at high risk for delayed brain injury following aneurysm-related hemorrhage, which could lead to earlier intervention and improved outcomes.
  • Christina Tringides, assistant professor of materials science and nanoengineering at Rice, and Sujit Prabhu, professor of neurosurgery at MD Anderson, who are working to reduce the risk of long-term speech and language impairment during brain tumor removal by combining advanced brain recordings, imaging and noninvasive stimulation.

The grants were facilitated by Rice’s Educational and Research Initiatives for Collaborative Health (ENRICH) Office. Rice says that the unique split-funding model of these grants could help structure future collaborations between the university and the TMC.

The Rice Brain Institute launched this fall and aims to use engineering, natural sciences and social sciences to research the brain and reduce the burden of neurodegenerative, neurodevelopmental and mental health disorders. Last month, the university's Shepherd School of Music also launched the Music, Mind and Body Lab, an interdisciplinary hub that brings artists and scientists together to study the "intersection of the arts, neuroscience and the medical humanities." Read more here.

Your data center is either closer than you think or much farther away

houston voices

A new study shows why some facilities cluster in cities for speed and access, while others move to rural regions in search of scale and lower costs. Based on research by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard).

Key findings:

  • Third-party colocation centers are physical facilities in close proximity to firms that use them, while cloud providers operate large data centers from a distance and sell access to virtualized computing resources as on‑demand services over the internet.
  • Hospitals and financial firms often require urban third-party centers for low latency and regulatory compliance, while batch processing and many AI workloads can operate more efficiently from lower-cost cloud hubs.
  • For policymakers trying to attract data centers, access to reliable power, water and high-capacity internet matter more than tax incentives.

Recent outages and the surge in AI-driven computing have made data center siting decisions more consequential than ever, especially as energy and water constraints tighten. Communities invest public dollars on the promise of jobs and growth, while firms weigh long-term commitments to land, power and connectivity.

Against that backdrop, a critical question comes into focus: Where do data centers get built — and what actually drives those decisions?

A new study by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard Business School) provides the first large-scale statistical analysis of data center location strategies across the United States. It offers policymakers and firms a clearer starting point for understanding how different types of data centers respond to economic and strategic incentives.

Forthcoming in the journal Strategy Science, the study examines two major types of infrastructure: third-party colocation centers that lease server space to multiple firms, and hyperscale cloud centers owned by providers like Amazon, Google and Microsoft.

Two Models, Two Location Strategies

The study draws on pre-pandemic data from 2018 and 2019, a period of relative geographic stability in supply and demand. This window gives researchers a clean baseline before remote work, AI demand and new infrastructure pressures began reshaping internet traffic patterns.

The findings show that data centers follow a bifurcated geography. Third-party centers cluster in dense urban markets, where buyers prioritize proximity to customers despite higher land and operating costs. Cloud providers, by contrast, concentrate massive sites in a small number of lower-density regions, where electricity, land and construction are cheaper and economies of scale are easier to achieve.

Third-party data centers, in other words, follow demand. They locate in urban markets where firms in finance, healthcare and IT value low latency, secure storage, and compliance with regulatory standards.

Using county-level data, the researchers modeled how population density, industry mix and operating costs predict where new centers enter. Every U.S. metro with more than 700,000 residents had at least one third-party provider, while many mid-sized cities had none.

ImageThis pattern challenges common assumptions. Third-party facilities are more distributed across urban America than prevailing narratives suggest.

Customer proximity matters because some sectors cannot absorb delay. In critical operations, even slight pauses can have real consequences. For hospital systems, lag can affect performance and risk exposure. And in high-frequency trading, milliseconds can determine whether value is captured or lost in a transaction.

“For industries where speed is everything, being too far from the physical infrastructure can meaningfully affect performance and risk,” Pan Fang says. “Proximity isn’t optional for sectors that can’t absorb delay.”

The Economics of Distance

For cloud providers, the picture looks very different. Their decisions follow a logic shaped primarily by cost and scale. Because cloud services can be delivered from afar, firms tend to build enormous sites in low-density regions where power is cheap and land is abundant.

These facilities can draw hundreds of megawatts of electricity and operate with far fewer employees than urban centers. “The cloud can serve almost anywhere,” Pan Fang says, “so location is a question of cost before geography.”

The study finds that cloud infrastructure clusters around network backbones and energy economics, not talent pools. Well-known hubs like Ashburn, Virginia — often called “Data Center Alley” — reflect this logic, having benefited from early network infrastructure that made them natural convergence points for digital traffic.

Local governments often try to lure data centers with tax incentives, betting they will create high-tech jobs. But the study suggests other factors matter more to cloud providers, including construction costs, network connectivity and access to reliable, affordable electricity.

When cloud centers need a local presence, distance can sometimes become a constraint. Providers often address this by working alongside third-party operators. “Third-party centers can complement cloud firms when they need a foothold closer to customers,” Pan Fang says.

That hybrid pattern — massive regional hubs complementing strategic colocation — may define the next phase of data center growth.

Looking ahead, shifts in remote work, climate resilience, energy prices and AI-driven computing may reshape where new facilities go. Some workloads may move closer to users, while others may consolidate into large rural hubs. Emerging data-sovereignty rules could also redirect investment beyond the United States.

“The cloud feels weightless,” Pan Fang says, “but it rests on real choices about land, power and proximity.”

---

This article originally appeared on Rice Business Wisdom. Written by Scott Pett.

Pan Fang and Greenstein (2025). “Where the Cloud Rests: The Economic Geography of Data Centers,” forthcoming in Strategy Science.

Houston climbs to top 10 spot on North American tech hubs index

tech report

Houston already is the Energy Capital of the World, and now it’s gaining ground as a tech hub.

On Site Selection magazine’s 2026 North American Tech Hub Index, Houston jumped to No. 10 from No. 16 last year. The index relies on data from Site Selection as well as data from CBRE, CompTIA and TeleGeography to rank the continent’s tech hotspots. The index incorporates factors such as internet connectivity, tech talent and facility projects for tech companies.

In 2023, the Greater Houston Partnership noted the region had “begun to receive its due as a prominent emerging tech hub, joining the likes of San Francisco and Austin as a major player in the sector, and as a center of activity for the next generation of innovators and entrepreneurs.”

The Houston-area tech sector employs more than 230,000 people, according to the partnership, and generates an economic impact of $21.2 billion.

Elsewhere in Texas, two other metros fared well on the Site Selection index:

  • Dallas-Fort Worth nabbed the No. 1 spot, up from No. 2 last year.
  • Austin rose from No. 8 last year to No. 7 this year.

San Antonio slid from No. 18 in 2025 to No. 22 in 2026, however.

Two economic development officials in DFW chimed in about the region’s No. 1 ranking on the index:

  • “This ranking affirms what we’ve long seen on the ground — Dallas-Fort Worth is a top-tier technology and innovation center,” said Duane Dankesreiter, senior vice president of research and innovation at the Dallas Regional Chamber. “Our region’s scale, talent base, and diverse strengths … continue to set DFW apart as a national leader.”
  • “Being recognized as the top North American tech hub underscores the strength of the entire Dallas-Fort Worth region as a center of innovation and next-generation technology,” said Robert Allen, president and CEO of the Fort Worth Economic Development Partnership.

While not directly addressing Austin’s Site Selection ranking, Thom Singer, CEO of the Austin Technology Council, recently pondered whether Silicon Hills will grow “into the kind of community that other cities study for the right reasons.”

“Austin tech is not a club. It is not a scene. It is not a hashtag, a happy hour, or any one place or person,” Singer wrote on the council’s blog. “Austin tech is an economic engine and a global brand, built by thousands of people who decided to take a risk, build something, hire others, and be part of a community that is still young enough to reinvent itself.”

South of Austin, Port San Antonio is driving much of that region’s tech activity. Occupied by more than 80 employers, the 1,900-acre tech and innovation campus was home to 18,400 workers in 2024 and created a local economic impact of $7.9 billion, according to a study by Zenith Economics.

“Port San Antonio is a prime example of how innovation and infrastructure come together to strengthen [Texas’] economy, support thousands of good jobs, and keep Texas competitive on the global stage,” said Kelly Hancock, the acting state comptroller.

View All Listings