Rice research explores how shopping data could reshape credit scores

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Everyday data like grocery store receipts can help expand access to credit and support upward mobility. Photo by Boxed Water Is Better on Unsplash

More than a billion people worldwide can’t access credit cards or loans because they lack a traditional credit score. Without a formal borrowing history, banks often view them as unreliable and risky. To reach these borrowers, lenders have begun experimenting with alternative signals of financial reliability, such as consistent utility or mobile phone payments.

New research from Rice Business builds on that approach. Previous work by assistant professor of marketing Jung Youn Lee showed that everyday data like grocery store receipts can help expand access to credit and support upward mobility. Her latest study extends this insight, using broader consumer spending patterns to explore how alternative credit scores could be created for people with no credit history.

Forthcoming in the Journal of Marketing Research, the study finds that when lenders use data from daily purchases — at grocery, pharmacy, and home improvement stores — credit card approval rates rise. The findings give lenders a powerful new tool to connect the unbanked to credit, laying the foundation for long-term financial security and stronger local economies.

Turning Shopping Habits into Credit Data

To test the impact of retail transaction data on credit card approval rates, the researchers partnered with a Peruvian company that owns both retail businesses and a credit card issuer. In Peru, only 22% of people report borrowing money from a formal financial institution or using a mobile money account.

The team combined three sets of data: credit card applications from the company, loyalty card transactions, and individuals’ credit histories from Peru’s financial regulatory authority. The company’s point-of-sale data included the types of items purchased, how customers paid, and whether they bought sale items.

“The key takeaway is that we can create a new kind of credit score for people who lack traditional credit histories, using their retail shopping behavior to expand access to credit,” Lee says.

The final sample included 46,039 credit card applicants who had received a single credit decision, had no delinquent loans, and made at least one purchase between January 2021 and May 2022. Of these, 62% had a credit history and 38% did not.

Using this data, the researchers built an algorithm that generated credit scores based on retail purchases and predicted repayment behavior in the six months following the application. They then simulated credit card approval decisions.

Retail Scores Boost Approvals, Reduce Defaults

The researchers found that using retail purchase data to build credit scores for people without traditional credit histories significantly increased their chances of approval. Certain shopping behaviors — such as seeking out sale items — were linked to greater reliability as borrowers.

For lenders using a fixed credit score threshold, approval rates rose from 15.5% to 47.8%. Lenders basing decisions on a target loan default rate also saw approvals rise, from 15.6% to 31.3%.

“The key takeaway is that we can create a new kind of credit score for people who lack traditional credit histories, using their retail shopping behavior to expand access to credit,” Lee says. “This approach benefits unbanked applicants regardless of a lender’s specific goals — though the size of the benefit may vary.”

Applicants without credit histories who were approved using the retail-based credit score were also more likely to repay their loans, indicating genuine creditworthiness. Among first-time borrowers, the default rate dropped from 4.74% to 3.31% when lenders incorporated retail data into their decisions and kept approval rates constant.

For applicants with existing credit histories, the opposite was true: approval rates fell slightly, from 87.5% to 84.5%, as the new model more effectively screened out high-risk applicants.

Expanding Access, Managing Risk

The study offers clear takeaways for banks and credit card companies. Lenders who want to approve more applications without taking on too much risk can use parts of the researchers’ model to design their own credit scoring tools based on customers’ shopping habits.

Still, Lee says, the process must be transparent. Consumers should know how their spending data might be used and decide for themselves whether the potential benefits outweigh privacy concerns. That means lenders must clearly communicate how data is collected, stored, and protected—and ensure customers can opt in with informed consent.

Banks should also keep a close eye on first-time borrowers to make sure they’re using credit responsibly. “Proactive customer management is crucial,” Lee says. That might mean starting people off with lower credit limits and raising them gradually as they demonstrate good repayment behavior.

This approach can also discourage people from trying to “game the system” by changing their spending patterns temporarily to boost their retail-based credit score. Lenders can design their models to detect that kind of behavior, too.

The Future of Credit

One risk of using retail data is that lenders might unintentionally reject applicants who would have qualified under traditional criteria — say, because of one unusual purchase. Lee says banks can fine-tune their models to minimize those errors.

She also notes that the same approach could eventually be used for other types of loans, such as mortgages or auto loans. Combined with her earlier research showing that grocery purchase data can predict defaults, the findings strengthen the case that shopping behavior can reliably signal creditworthiness.

“If you tend to buy sale items, you’re more likely to be a good borrower. Or if you often buy healthy food, you’re probably more creditworthy,” Lee explains. “This idea can be applied broadly, but models should still be customized for different situations.”

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This article originally appeared on Rice Business Wisdom. Written by Deborah Lynn Blumberg

Anderson, Lee, and Yang (2025). “Who Benefits from Alternative Data for Credit Scoring? Evidence from Peru,” Journal of Marketing Research.

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A map of U.S. data centers. Courtesy of Rice Businesses Wisdom

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

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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.”

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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.

Search trends predict future retail revenue up to three quarters in advance. Photo via Pexels

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

Nurturing what is known as “promotion focus” can help managers spot fresh ideas.
Photo by Diego PH on Unsplash

Houston researchers: Here's what it takes to spot a great new idea

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Having a “promotion focus” really does create a mental lens through which new ideas are more visible.

Key findings:

  • New ideas can be crucially important to businesses, driving innovation and preventing stagnation.
  • Recognizing those ideas, though, isn’t always easy.
  • Nurturing what is known as “promotion focus” can help managers spot fresh ideas.

Whenever the late surgeon Michael DeBakey opened a human chest, he drew on a lifetime of resources: the conviction that heart surgery could and should be vastly improved, the skill to venture beyond medicine’s known horizons and the vision to recognize new ideas in everyone around him, no matter how little formal training they had.

Appreciating new ideas is the heartbeat of business as well as medicine. But innovation is surprisingly hard to recognize. In a pioneering 2017 article, Rice Business Professor Jing Zhou and her colleagues published their findings on the first-ever study of the traits and environments that allow leaders to recognize new ideas.

Recent decades have produced a surge of research looking at how and when employees generate fresh ideas. But almost nothing has been written on another crucial part of workplace creativity: a leader’s ability to appreciate new thinking when she sees it.

Novelty, after all, is what drives company differentiation and competitiveness. Work that springs from new concepts sparks more investigation than work based on worn, already established thought. Companies invest millions to recruit and pay star creatives.

Yet not every leader can spot a fresh idea, and not every workplace brings out that kind of discernment. In four separate studies, Zhou and her coauthors examined exactly what it takes to see a glittering new idea wherever it appears. Their work sets the stage for an entirely new field of future research.

First, though, the team had to define their key terms. “Novelty recognition” is the ability to spot a new idea when someone else presents it. “Promotion focus,” previous research has shown, is a comfort level with new experiences that evokes feelings of adventure and excitement. “Prevention focus” is the opposite trait: the tendency to associate new ideas with danger, and respond to them with caution.

But does having “promotion focus” as opposed to “prevention focus” color the ability to see novelty? To find out, Zhou’s team came up with an ingenious test, artificially inducing these two perspectives through a series of exercises. First, they told 92 undergraduate participants that they would be asked to perform a set of unrelated tasks. Then the subjects guided a fictional mouse through two pencil and paper maze exercises.

While one exercise showed a piece of cheese awaiting the mouse at the end of the maze (the promise of a reward), the other maze depicted a menacing owl nearby (motivation to flee).

Once the participants had traced their way through the mazes with pencils, they were asked to rate the novelty of 33 pictures — nine drawings of space aliens and 24 unrelated images. The students who were prepped to feel an adventurous promotion focus by seeking a reward were much better at spotting the new or different details among these images than the students who’d been cued to have a prevention focus by fleeing a threat.

The conclusion: a promotion focus really does create a mental lens through which new ideas are more visible.

Zhou’s team followed this study with three additional studies, including one that surveyed 44 human resource managers from a variety of companies. For this study, independent coders rated the mission statements of each firm, assessing their cultures as “innovative” or “not innovative.” The HR managers then evaluated a set of written practices — three that had been in use for years, and three new ones that relied on recent technology. The managers from the innovative companies were much better at rating the new HR practices for novelty and creativity. To recognize novelty, in other words, both interior and external environments make a difference.

The implications of the research are groundbreaking. The first ever done on this subject, it opens up a completely new research field with profound questions. Can promotion focus be created? How much of this trait is genetic, and how much based on natural temperament, culture, environment and life experience? Should promotion focus be cultivated in education? If so, what would be the impact? After all, there are important uses for prevention focus, such as corporate security and compliance. Meanwhile, how can workplaces be organized to bring out the best in both kinds of focus?

Leaders eager to put Zhou’s findings to use right away, meanwhile, might look to the real-world model of Michael DeBakey. Practice viewing new ideas as adventures, seek workplaces that actively push innovation and, above all, cultivate the view that every coworker, high or low, is a potential source of glittering new ideas.

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This article originally appeared on Rice Business Wisdom.

Jing Zhou is the Mary Gibbs Jones Professor of Management and Psychology in Organizational Behavior at the Jones Graduate School of Business of Rice University. Zhou, J., Wang, X., Song, J., & Wu, J. (2017). "Is it new? Personal and contextual influences on perceptions of novelty and creativity." Journal of Applied Psychology, 102(2): 180-202.

ChatGPT enhances creativity and problem-solving in ways that traditional search tools can’t match. Photo courtesy of Rice Business Wisdom

Houston researchers find AI provides fresh perspectives to everyday problems

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We all know ChatGPT has forever changed how we do business. It’s modified how we access information, compose content and analyze data. It’s revolutionized the future of work and education. And it has transformed the way we interact with technology.

Now, thanks to a recent paper by Jaeyeon (Jae) Chung (Rice Business), we also know it’s making us better problem solvers.

Key findings:

  • A recent study finds ChatGPT-generated ideas are deemed an average of 15% more creative than traditional methods.
  • ChatGPT enhances “incremental,” but not “radical,” innovation.
  • ChatGPT boosts creativity in tasks normally associated with human traits, like empathy-based challenges.

According to the study published in Nature Human Behavior by Chung and Byung Cheol Lee (University of Houston), ChatGPT enhances our problem-solving abilities, especially with everyday challenges. Whether coming up with gifts for your teenage niece or pondering what to do with an old tennis racquet, ChatGPT has a unique ability to generate creative ideas.

“Creative problem-solving often requires connecting different concepts in a cohesive way,” Chung says. “ChatGPT excels at this because it pulls from a vast range of data, enabling it to generate new combinations of ideas.”

Can ChatGPT Really Make Us More Creative?

Chung and Lee sought to answer a central question: Can ChatGPT help people think more creatively than traditional search engines? To answer this, they conducted five experiments.

Each experiment asked participants to generate ideas for solving challenges, such as how to repurpose household items. Depending on the experiment, participants were divided into one of two or three groups: one that used ChatGPT; one that used conventional web search tools (e.g., Google); and one that used no external tool at all. The resulting ideas were evaluated by both laypeople and business experts based on two critical aspects of creativity: originality and appropriateness (i.e., practicality).

In one standout experiment, participants were asked to come up with an idea for a dining table that doesn’t exist on the market. The ChatGPT group came up with suggestions like a “rotating table,” a “floating table” and even “a table that adjusts its height based on the dining experience.” According to both judges and experts, the ChatGPT group consistently delivered the most creative solutions.

On average, across all experiments, ideas generated with ChatGPT were rated 15% more creative than those produced by traditional methods. This was true even when tasks were specifically designed to require empathy or involved multiple constraints — tasks we typically assume humans might be better at performing.

However, Chung and Lee also found a caveat: While ChatGPT excels at generating ideas that are “incrementally” new — i.e., building on existing concepts — it struggles to produce “radically” new ideas that break from established patterns. “ChatGPT is an incredible tool for tweaking and improving existing ideas, but when it comes to disruptive innovation, humans still hold the upper hand,” Chung notes.

Charting the Next Steps in AI and Creativity

Chung and Lee’s paper opens the door to many exciting avenues for future study. For example, researchers could explore whether ChatGPT’s creative abilities extend to more complex, high-stakes problem-solving environments. Could AI be harnessed to develop groundbreaking solutions in fields like medicine, engineering or social policy? Understanding the nuances of the collaboration between humans and AI could shape the future of education, work and even (as many people fear) art.

For professionals in creative fields like product design or marketing, the study holds especially significant implications. The ability to rapidly generate fresh ideas can be a game-changer in industries where staying ahead of trends is vital. For now, take a second before you throw out that old tennis racquet. Ask ChatGPT for inspiration — you’ll be surprised at how many ideas it comes up with, and how quickly.

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This article originally appeared on Rice Business Wisdom. Based on research by Jaeyeon (Jae) Chung and Byung Cheol Lee (University of Houston). Lee and Chung, “An empirical investigation of the impact of ChatGPT on creativity.” Nature Human Behavior (2024): https://doi.org/10.1038/s41562-024-01953-1.


Grocery purchase data can accurately predict credit risk for individuals without traditional credit scores, potentially broadening the pool of qualified loan applicants. Photo via Unsplash

Houston researchers find alternate data for loan qualification

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Millions of consumers who apply for a loan to buy a house or car or start a business can’t qualify — even if they’re likely to pay it back. That’s because many lack a key piece of financial information: a credit score.

The problem isn’t just isolated to emerging economies. Exclusion from the financial system is a major issue in the United States, too, where some 45 million adults may be denied access to loans because they don’t have a credit history and are “credit invisible.”

To improve access to loans and peoples’ economic mobility, lenders have started looking into alternative data sources to assess a loan applicant’s risk of defaulting. These include bank account transactions and on-time rental, utility and mobile phone payments.

A new article by Rice Business assistant professor of marketing Jung Youn Lee and colleagues from Notre Dame and Northwestern identifies an even more widespread data source that could broaden the pool of qualified applicants: grocery store receipts.

As metrics for predicting credit risk, the researchers found that the types of food, drinks and other products consumers buy, and how they buy them, are just as good as a traditional credit score.

“There could be privacy concerns when you think about it in practice,” Lee says, “so the consumer should really have the option and be empowered to do it.” One approach could be to let consumers opt in to a lender looking at their grocery data as a second chance at approval rather than automatically enrolling them and offering an opt-out.

To arrive at their findings, the researchers analyzed grocery transaction data from a multinational conglomerate headquartered in a Middle Eastern country that owns a credit card issuer and a large-scale supermarket chain. Many people in the country are unbanked. They merged the supermarket’s loyalty card data and issuer’s credit card spending and payment history numbers, resulting in data on 30,089 consumers from January 2017 to June 2019. About half had a credit score, 81% always paid their credit card bills on time, 12% missed payments periodically, and 7% defaulted.

The researchers first created a model to establish a connection between grocery purchasing behavior and credit risk. They found that people who bought healthy foods like fresh milk, yogurt and fruits and vegetables were more likely to pay their bills on time, while shoppers who purchased cigarettes, energy drinks and canned meat tended to miss payments. This held true for “observationally equivalent” individuals — those with similar income, occupation, employment status and number of dependents. In other words, when two people look demographically identical, the study still finds that they have different credit risks.

People’s grocery-buying behaviors play a factor in their likelihood to pay their bills on time, too. For example, cardholders who consistently paid their credit card bill on time were more likely to shop on the same day of the week, spend similar amounts across months and buy the same brands and product categories.

The researchers then built two credit-scoring predictive algorithms to simulate a lender’s decision of whether or not to approve a credit card applicant. One excludes grocery data inputs, and the other includes them (in addition to standard data). Incorporating grocery data into their decision-making process improved risk assessment of an applicant by a factor of 3.11% to 7.66%.

Furthermore, the lender in the simulation experienced a 1.46% profit increase when the researchers implemented a two-stage decision-making process — first, screening applicants using only standard data, then adding grocery data as an additional layer.

One caveat to these findings, Lee and her colleagues warn, is that the benefit of grocery data falls sharply as traditional credit scores or relationship-specific credit histories become available. This suggests the data could be most helpful for consumers new to credit.

Overall, however, this could be a win-win scenario for both consumers and lenders. “People excluded from the traditional credit system gain access to loans,” Lee says, “and lenders become more profitable by approving more creditworthy people.”

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This article originally ran on Rice Business Wisdom based on research by Rice University's Jung Youn Lee, Joonhyuk Yang (Notre Dame) and Eric Anderson (Northwestern). “Using Grocery Data for Credit Decisions.” Forthcoming in Management Science. 2024: https://doi.org/10.1287/mnsc.2022.02364.


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With boost from Houston, Texas is the No. 1 state for economic development

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Texas is on a 14-year winning streak as the top state for attracting job-creating business location and expansion projects.

Once again, Texas has claimed Site Selection magazine’s Governor’s Cup. This year’s honor recognizes the state with the highest number of economic development projects in 2025. Texas landed more than 1,400 projects last year.

Ron Starner, executive vice president of Site Selection, calls Texas “a dynasty in economic development.”

Among metro areas, Houston lands at No. 2 for the most economic development projects secured last year (590), behind No. 1 Chicago and ahead of No. 3 Dallas-Fort Worth.

In praising Houston as a project magnet, Gov. Greg Abbott cites the November announcement by pharmaceutical giant Lilly that it’s building a $6.5 billion manufacturing plant at Houston’s Generation Park.

“Growth in the Greater Houston region is a great benefit to our state’s economy, a major location for foreign direct investment and key industry sectors like energy, aerospace, advanced manufacturing, and life sciences,” Abbott tells Site Selection. “Houston is also home to one of the largest concentrations of U.S. headquarters for companies from around the world.”

In 2025, Fortune ranked Houston as the U.S. city with the third-highest number of Fortune 500 headquarters (26).

Texas retained the Governor’s Cup by gaining over 1,400 business location and expansion projects last year, representing more than $75 billion in capital investments and producing more than 42,000 new jobs.

Site Selection says Texas’ project count for 2025 handily beat second-place Illinois (680 projects) and third-place Ohio (467 projects). Texas’ number for 2025 represented 18% of all qualifying U.S. projects tracked by Site Selection.

“You can see that we are on a trajectory to ensure our economic diversification is going to inoculate us in good times, as well as bad times, to ensure our economy is still going to grow, still create new jobs, prosperity, and opportunities for Texans going forward,” Abbott says.

Houston e-commerce giant Cart.com raises $180M, surpasses $1B in funding

fresh funding

Editor's note: This article has been updated to clarify information about Cart.com's investors.

Houston-based commerce and logistics platform Cart.com has raised $180 million in growth capital from private equity firm Springcoast Partners, pushing the startup past the $1 billion funding mark since its founding in 2020.

Cart.com says it will use the capital to scale its logistics network, expand AI capabilities and develop workflow automation tools.

“This investment will strengthen our balance sheet and provide us with the flexibility to accelerate our strategic priorities,” Omair Tariq, CEO of Cart.com, said in a news release. “We’ve built a platform that combines commerce software with a scaled logistics network, and we’re just getting started.”

In conjunction with the funding, Springcoast executive-in-residence Russell Klein has been appointed to Cart.com’s board of directors. Before joining Springcoast, he was chief commercial officer at Austin-based Commerce.com (Nasdaq: CMRC). Klein co-led Commerce.com’s IPO, led the company’s mergers-and-acquisitions strategy and played a key role in several funding rounds.

“The team at Cart.com has demonstrated excellence in their ability to scale efficiently while continuing to innovate,” Klein said. “I’m excited to join the board and support the company as it expands its AI-driven capabilities, deepens enterprise relationships, and further strengthens its position as a category-defining commerce and fulfillment platform.”

Before this funding round, Cart.com had raised $872 million in venture capital and reached a valuation of about $1.6 billion, according to CB Insights. With the new funding, the startup has collected over $1 billion in just six years.

This is the income required to be a middle class earner in Houston in 2026

Cashing In

A new study tracking the upper and lower thresholds for middle class households across the nation's largest cities has revealed Houstonians need to make at least a grand more than last year to maintain their middle class status this year.

According to SmartAsset's just-released annual report, "What It Takes to Be Middle Class in America – 2026 Study," Houston households need to make anywhere from $42,907 to $128,722 to qualify as middle class earners this year.

Compared to 2025, Houstonians need to make $1,153 more per year to meet the minimum threshold for a middle class status, whereas the upper bound has stretched $3,448 higher. The median income for a Houston household in 2024 was $64,361, the study added.

SmartAsset's experts used 2024 Census Bureau median household income data for the 100 biggest U.S. cities and all 50 states and determined middle class income ranges by using a variation of Pew Research's definition of a middle class household, stating the salary range is "two-thirds to double the median U.S. salary."

In the report's ranking of the U.S. cities with the highest household incomes needed to maintain a middle class status, Houston ranked No. 80.

In the report's state-by-state comparison, Texas has the 24th highest middle class income range. Overall, Texas households need to make between $53,147 and $159,442 to be labeled "middle class" in 2026. For additional context, the median income for a Texas household in 2024 came out to $79,721.

"Often, the expectations that come with the term 'middle class' include reaching home ownership, raising kids, the comfort of modest emergency funds and retirement savings, and the occasional splurge or vacation," the report said. "And as the median household income varies widely across the U.S. depending on the local job market, housing market, infrastructure and other factors, so does swing the bounds on what constitutes a middle class income in America."

What it takes to be middle class elsewhere around Texas

Two Dallas-Fort Worth suburbs – Frisco and Plano – have some of the highest middle class income ranges in the country for 2026, SmartAsset found.

Frisco households need to make between $96,963 and $290,888 to qualify as middle class this year, which is the third-highest middle class income range nationwide.

Plano's middle class income range is the eighth highest nationally, with households needing to make between $77,267 and $231,802 for the designation.

Salary range needed to be a middle class earner in other Texas cities:

  • No. 28 – Austin: between $60,287 and $180,860
  • No. 40 – Irving: between $56,566 and $169,698
  • No. 44 – Fort Worth: between $55,002 and $165,006
  • No. 57 – Garland: between $50,531 and $151,594
  • No. 60 – Arlington: between $49,592 and $148,77
  • No. 61 – Dallas: between $49,549 and $148,646
  • No. 73 – Corpus Christi: between $44,645 and $133,934
  • No. 77 – San Antonio: between $44,117 and $132,352
  • No. 83 – Lubbock: between $41,573 and $124,720
  • No. 84 – Laredo: between $41,013 and $123,038
  • No. 89 – El Paso: between $39,955 and $119,864
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This article originally appeared on CultureMap.com.

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