Financial decision-making can be increasingly challenging for older Americans, struggling to manage variable expenses with a static budget. At a time when it faces the greatest risk, cognitive health is increasingly critical – whether it’s balancing consumption against savings, managing investment performance against risk, or reining in debt.
The Role of Cognitive Function
As people age, retirement typically forces them to depend on fixed assets rather than new income. At the same time, demands shift, especially when it comes to health care costs. Persistent trends – such as vanishing defined benefit plans, growing retirement periods, and rising care costs – add pressure to these financial choices.
But older households remain the most vulnerable to memory disorders like Alzheimer’s disease and related disorders (ADRD), which threaten cognitive function. More than 90 percent of Alzheimer’s cases first manifest after age 65. At last count, Alzheimer’s disease plagues more than 11 percent of retirement-age Americans. Worse still, researchers expect that number to more than double by 2050. And, perhaps unsurprisingly, certain groups face greater risks. Women face double the lifetime risk of Alzheimer’s compared to men, and the prevalence of ADRD is higher among Black and Hispanic populations compared to non-minority groups.
Research indicates that early-stage cognitive impairment crops up as financial decision-making deficits early on. As symptoms progress, these deficits deepen, making individuals more susceptible to financial exploitation. Cognitive changes, including memory loss and slower perceptual speed, could impair financial decision-making long before a formal diagnosis, increasing financial vulnerability.
Financial Risks Loom Larger in Retirement
Recent research has highlighted the financial risks faced by those in the early stages of ADRD. One study showed that early-stage ADRD significantly reduces household liquid assets and net wealth, with pronounced effects when the financial head of the household suffers.
Another study found that seniors living alone are at higher risk of missing a bill payment or taking a hit to their credit score years before they might receive an ADRD diagnosis.
Now, building on that research, a new study from the Federal Reserve of New York takes a more comprehensive look at the toll ADRD takes on credit outcomes in the years before a diagnosis.
This study – working off of a large, nationally representative, longitudinal dataset – investigates a range of outcomes, including credit scores, payment delinquency, and delinquency by credit type. It includes seniors regardless of household structure, applying a difference-in-differences event study model with propensity score weighting and individual fixed effects. The study also explores the roles that gender and race might play.
Critical findings reveal that credit scores deteriorate, and the probability of payment delinquency increases in the five years before an ADRD diagnosis. These financial consequences of early-stage ADRD intensify as individuals approach an official diagnosis.
Specifically, the research found that the probability of credit card delinquency is 21 percent higher and mortgage delinquency 11 percent higher two years before diagnosis.
Additionally, Black adults appear to be particularly susceptible to poor financial outcomes from early-stage ADRD, compared to their white counterparts. While effects before diagnosis are similar for men and women, post-diagnosis effects on financial outcomes are more pronounced for women.
Increasing Consequences
The study’s findings underscore the immediate financial costs, such as late fees and interest charges, and long-term consequences, such as reduced access to credit and higher interest rates. These financial tensions are particularly acute as household resource demands grow to cover substantial caregiving and related costs in the later stages of memory disorders.
Furthermore, the study authors suggest the potential utility of credit data in facilitating the early identification of at-risk individuals, paving the way for machine learning applications in credit reporting data to support early diagnosis and intervention.
Early diagnosis can help manage the disease’s progression and mitigate declining financial outcomes, improving the quality of life for those affected.
Further Reading
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