Student Financial Health at Western Washington University

Key Findings

This report presents primary survey data collected from 458 Western Washington University students in early 2026, examining financial stress, budgeting behavior, income composition, debt exposure, and demand for financial wellness tools. Findings are contextualized against Bellingham and Whatcom County community benchmarks to situate student financial health within the broader local economic environment.

Methodology

Survey Findings

The following findings are drawn from the full valid response set (n=458 for core questions; n=201 for year in school and rent, which were added mid-collection). Percentages are calculated against the applicable valid response base for each question.

Respondent Demographics

Year in school was collected from a subset of respondents (n=201, ±6.87%) following a mid-distribution addition of the question. The sample skews toward upperclassmen, with juniors (31.3%), sophomores (28.9%), and seniors (26.9%) comprising the majority of responses. Freshmen are underrepresented at 4.0% (n=8), likely reflecting lower course enrollment density in survey-distributed classes. An additional 9.0% identified as "Other," which includes graduate students based on open-ended comments.

Due to subgroup sample sizes and the mid-distribution nature of this question, year-level cross-tabulations are not reported. The distribution is presented here as demographic context only.

Financial Stress

81.0% of respondents reported feeling financially stressed at least half the time. Of these, 32.5% reported feeling stressed always or most of the time, a level of chronic financial pressure that research links to reduced academic performance and elevated dropout risk. Only 7.2% reported never experiencing financial stress.

Open-ended responses reinforced the data. Representative comments included:

Budgeting Behavior

57.4% of respondents use no formal budgeting method. Among the 42.6% who do track finances, spreadsheets are the most common approach (21.2%), followed by pen and paper (17.7%) and dedicated apps (17.0%). The low adoption of automated tools, despite high stress levels, points to a structural gap between financial awareness and implementation.

Income Composition

Family support is the single largest income source, cited by 68.6% of respondents, more than those reporting employment (56.3%). Personal savings drawdown (53.9%) and student loans (22.5%) round out the income picture. The high dependence on family transfers suggests significant financial fragility: income sources that are informal, inconsistent, or debt-based dominate over earned income.

Emergency Financial Resilience

When asked how they would cover a sudden $500 emergency, 9.4% reported they would not be able to cover it at all. More telling is the dependency structure of those who could: 45.9% would borrow from family, and 21.0% would turn to credit cards. Only those with access to personal savings (73.1%) had a non-debt, non-family mechanism, and most of those responses were paired with other options, indicating savings alone are insufficient for many.

Student Loan Debt

35.2% of respondents carry some form of student loan debt. Among those with debt, the distribution skews toward lower balances: 73 respondents (45.3% of debtors) carry $10,000 or less, while 25 respondents (15.5% of debtors) carry more than $30,000. An additional 25 respondents (5.5% of all) reported not knowing their debt balance, itself an indicator of financial disengagement.

Credit Card Access & Behavior

Credit card behavior among respondents divides into four distinct groups. The largest single group, 42.4%, holds a credit card and pays the balance in full each month, reflecting either disciplined use or limited spending. A further 16.6% carry a balance month to month, either regularly (13.1%) or occasionally (3.5%). But the most notable finding is the size of the group with no credit card at all: 41.0% of respondents, on par with those who pay in full.

That 41.0% figure warrants careful interpretation. Low credit card ownership among 18–24-year-olds is not typically a sign of financial conservatism, it more often reflects limited access to credit, whether due to no credit history, insufficient income to qualify, or unfamiliarity with the application process. Credit cards, whatever their risks, function as an emergency liquidity mechanism for many households. Among WWU respondents, 21.0% said they would put a sudden $500 expense on a credit card, meaning credit access is itself a financial safety valve for a meaningful share of the population. For the 41.0% without one, that option is simply unavailable, compressing their emergency response options toward family borrowing or an inability to cover the expense at all.

App Adoption Interest

58.3% of respondents indicated they would likely or very likely use a budgeting app if provided through WWU. Combined with the 57.4% who currently use no budgeting method, this suggests substantial latent demand among the least financially engaged students, the population most likely to benefit from intervention.

Housing Costs

Note: The rent question was added after initial survey distribution. The following reflects n=201 respondents (±6.87% margin of error).

The majority of respondents (49.3%) pay between $700–$999 per month in rent. A further 23.9% pay $1,000 or more monthly, with 9.0% paying $1,201 or above. One in five respondents (20.4%) live rent-free, likely through family arrangements or campus housing. Among those paying market-rate rent, housing alone consumes a substantial share of income given Bellingham's wage environment.

Local Context: Bellingham & Whatcom County

Student financial stress does not exist in isolation. Bellingham presents a paradoxical economic environment: costs that approach metropolitan levels paired with small-city wages. Understanding the community context is essential to interpreting the survey findings.

The Income-to-Cost Gap

Bellingham's median household income is $65,821¹, approximately 55% of Seattle's $118,745². Yet housing costs in Bellingham, averaging $1,539 per month for a rental unit³, are not proportionally lower. Studio apartments in Bellingham average $1,390 per month compared to $1,492 in Seattle³, a difference of just $102 despite a median income gap exceeding $50,000. For the 18–24 demographic living independently, 95% spend between 30–50% of their income on housing alone.⁴

Basic Needs Insecurity

According to the 2024 Washington Student Experience Survey⁵, 53.4% of community and technical college students and 51.3% of four-year university students statewide experience basic needs insecurity. Both figures have risen since 2022, when rates were 50.7% and 48.8% respectively.

At the local level, Bellingham Food Bank annual reports⁶ document a dramatic rise in demand. Annual household visits grew from approximately 226,000 in 2019 to over 706,000 in 2024, a 213% increase over five years. The sharpest single-year jump occurred in 2022, coinciding with the expiration of pandemic-era federal food assistance programs, after which demand accelerated significantly. By 2024, one in four Whatcom County households relies on food assistance weekly.

Survey respondents reflected awareness of these conditions. One student offered the following perspective:

Note: This comment represents one respondent's perspective and is included as illustrative qualitative context, not as a finding.

Community Financial Fragility

The Opportunity Council's 2024 Community Needs Assessment⁷ found that 60% of Whatcom County residents cannot save for unexpected expenses, and 30% have accumulated excessive credit card debt. The poverty rate in Bellingham stands at 18.3%⁸, more than double the Seattle metropolitan area's 8.8%⁹, with young residents aged 18–34 bearing a disproportionate share of the city's affordability burden.

Student vs. Community: Direct Comparisons

Where parallel data exists, the survey findings can be placed alongside Whatcom County and Bellingham community benchmarks. Two areas allow direct comparison: emergency financial resilience and credit card debt exposure.

Emergency Expense Coverage

Among WWU students, 9.4% reported they would be entirely unable to cover a sudden $500 expense, notably lower than the 60% of Whatcom County residents who cannot save for unexpected expenses.⁷ The gap likely reflects differences in measurement and access to family transfer networks rather than stronger student financial resilience. The more telling student finding is the dependency structure: 45.9% would rely on family borrowing, suggesting fragility that is deferred rather than absent.

Credit Card Debt

The Opportunity Council found that 30% of Whatcom County residents carry excessive credit card debt.⁷ Among WWU survey respondents, 16.6% carry a credit card balance month to month (regularly or occasionally), and 41.0% have no credit card at all. The lower balance-carrying rate among students likely reflects lower credit access rather than better financial discipline, a significant share of students have not yet entered the credit system where the debt accumulation patterns documented in the broader community tend to develop.

Implications & Future Research

Taken together, the findings describe a student population under sustained financial pressure with limited structural support. Four in five WWU respondents experience financial stress at least half the time, yet fewer than half use any method to track their finances, not because they are indifferent, but because the income environment most of them operate in makes conventional budgeting difficult. The majority rely on family transfers, savings drawdown, or debt rather than earned income, sources that are irregular, finite, or accumulating in cost. When an unexpected $500 expense arises, nearly half turn to family borrowing, and one in eleven cannot cover it at all. These are not isolated data points, they describe a coherent pattern: high stress, low stability, limited tools, and income structures that leave little margin for financial error. That pattern situates WWU students within a broader Bellingham community also experiencing elevated financial fragility, suggesting the conditions driving student financial stress are environmental as much as they are behavioral.

For Financial Wellness Programming

The gap between stress prevalence (81.0%) and budgeting adoption (42.6%) indicates that awareness of financial difficulty is not translating into behavioral change. Manual tracking tools, pen and paper, spreadsheets, dominate current practice, suggesting that friction, not motivation, is the primary barrier. Institutional financial wellness programming that reduces implementation friction through automation may be more effective than financial literacy education alone.

The 58.3% app adoption interest among students, particularly concentrated among those currently using no method, represents a high-receptivity population for university-facilitated financial tools. The framing of the survey question, "WWU would only be providing it, they would not own it", indicates that institutional trust and privacy assurance are relevant adoption factors.

Designing for Student Income Realities

The income composition data, 68.6% relying on family support, 22.5% on student loans, underscores that a meaningful share of the student population operates on non-earned, non-stable income sources. Financial wellness tools and programming designed for students should account for irregular income patterns, aid disbursement cycles, and the reality that many students have limited ability to increase income on short timelines.

A Critical Research Gap

No published studies to date measure whether 18–24-year-olds save more or less, reduce financial stress more or less effectively, or improve financial literacy faster or slower than older cohorts when using automated budgeting tools. The existing evidence base for budgeting app effectiveness is drawn primarily from adult populations, leaving a significant generational gap in the literature.

This survey represents a preliminary baseline. Future research pairing longitudinal behavioral tracking with financial wellness interventions among college-aged populations would substantially advance the evidence base and contribute original findings to the personal finance and behavioral economics literature. University settings, with defined enrollment periods, consistent demographic pools, and existing wellness infrastructure, are well-positioned to host such studies.

Limitations

This study uses convenience sampling and self-reported financial data. Results should not be treated as statistically representative of the full WWU student population. The study was conducted in the context of a financial application development project; while respondents were not informed of the specific application, the survey framing may have introduced selection bias toward financially engaged students. Replication through a probability sample would strengthen generalizability.

References

Local context and community comparison figures are sourced from the following. Note: figures in this report were sourced from the original internal Kove financial wellness analysis prepared in early 2026, prior to publication of some 2025 local data updates. Source entries reflect the original cited publications.

Appendix: Full Variable Distributions

All distributions below reflect n=458 unless otherwise noted. Two questions, year in school and monthly rent, were added mid-distribution and reflect n=201 (±6.87% margin of error). Percentages are calculated against the valid response base for each question.

Multi-select questions (respondents could select more than one option; percentages sum to greater than 100%): Income Sources, Budgeting Methods, $500 Emergency Response.

Single-select questions (one response per respondent; percentages sum to 100%): Financial Stress Frequency, Credit Card Balance, Student Loan Debt, App Adoption Likelihood, Number of Banks, Monthly Rent, Year in School.

Financial Stress Frequency

Budgeting Methods

Income Sources

$500 Emergency Response

Student Loan Debt

Credit Card Balance

App Adoption Likelihood

Monthly Rent / Housing Cost (n=201, ±6.87%)

Year in School (n=201, ±6.87%)

Prepared by Maceo Campoverde, Kove Founder & Lead Researcher. Data collected February–April 2026. Study #12452EX26. The WWU HRPP determined this study to be exempt from further review in accordance with federal regulation criteria (45 CFR 46.104(d)). All data reported in aggregate; no personally identifiable information was collected or retained.