Like U.S. population, the workforce is aging faster

Didier Malagies • June 3, 2024




The share of older workers in the U.S. has grown significantly since the turn of the century, with 29.5% of workers in 2023 at least 65 years old, compared to 23% in 2000, according to a new report from the Employee Benefit Research Institute (EBRI).


This occurred as labor force participation by older Americans between the ages of 55 and 64 has surpassed pre-pandemic levels, while the rate of those 65 and older did not change, the data suggested.


“The movement of the Baby Boom generation out of the age groups younger than 65 has made the composition of the older workforce even older,” Craig Copeland, director of wealth benefits research at EBRI, said in the report. “At the same time, the older workforce is becoming more diverse, as a smaller share of White Americans comprise the ages 55 or older population.


“These are important considerations for employers to understand, as older workers and a more diverse workforce calls for additional or new answers to the optimal design of employee benefit plans,” he added.

Key findings of the full report include that the labor force participation rates of men ages 60 to 64 increased in 2022 and 2023 while falling among those ages 75 and older. Similar increases among women between the ages of 55 to 59 and 70 to 74 were also observed at that time, but they decreased for women in the 60-64 bracket in 2023.


“After rising to its highest point since 2001, in 2022, the male share of the labor force ages 55 or older decreased in 2023,” according to the report. “The female share of the labor force ages 55 or older has generally fallen since 2010, though it did increase slightly in 2023. Despite this, females ages 55 or older are still a higher share of the labor force than they were in the late 1990s.”


Despite the increases in 2022 and 2023, labor force participation among those ages 70 to 74 did not quite reach the pre-pandemic threshold observed in 2019, the research explained. But it did surpass pre-pandemic levels last year among the 55-64 group.


“In contrast, the labor force participation rate of those ages 75 or older in 2023 stayed at its 2021 level, below its 2019 level, while the labor force participation rate of those ages 65-69 decreased in 2023 to below its 2022 and 2019 levels,” the report explained.


Among demographic cohorts, Hispanic Americans had the highest rate of labor force participation across all older age groups when compared with Black and white Americans, despite having among the lowest such participation rate in 2000.


White Americans apparently switched places with Hispanic Americans during that time. They now occupy “some of the lowest rates compared with Hispanic and Black Americans by 2023,” the research stated.

The data used in the report was sourced from the U.S. Census Bureau’s Current Population Survey, which is jointly sponsored by the U.S. Bureau of Labor Statistics.




Have A Question?

Use the form below and we will give your our expert answers!

Reverse Mortgage Ask A Question

Start Your Loan with DDA today
Your local Mortgage Broker

Mortgage Broker Largo
See our Reviews

Looking for more details? Listen to our extended podcast! 

Check out our other helpful videos to learn more about credit and residential mortgages.

Now offering a 3rd Mortgage

By Didier Malagies • October 6, 2025
A third mortgage is an additional loan secured by the same property after a first and second mortgage already exist. It’s essentially a third lien on the property, which means it’s in third place to be repaid if the borrower defaults — making it riskier for lenders. Because of this higher risk, third mortgages typically: Have higher interest rates, Offer smaller loan amounts, and Require strong borrower profiles or solid property equity. 🤖 How AI Is Transforming 3rd Mortgage Lending AI tools can make offering third mortgages much more efficient and lower-risk by handling the data-heavy analysis that used to take underwriters days. Here’s how: 1. AI-Powered Lead Generation AI platforms identify homeowners with significant equity but limited cash flow — ideal candidates for third liens. Example: AI scans property databases, loan records, and credit profiles to spot someone with 60–70% total combined LTV (Loan-to-Value). The system targets those borrowers automatically with personalized financing offers. 2. Smart Underwriting AI underwriters use advanced algorithms to evaluate: Combined LTV across all liens, Income stability and payment history, Real-time credit behavior, Local property value trends. This allows the lender to make quick, data-backed decisions on small, higher-risk loans while keeping default rates low. 3. Dynamic Pricing AI adjusts rates and terms based on real-time risk scoring — similar to how insurance companies use predictive pricing. For example: Borrower A with 65% CLTV might get 10% APR. Borrower B with 85% CLTV might see 13% APR. 4. Automated Servicing and Risk Monitoring Post-funding, AI tools can monitor the borrower’s financial health, detect early signs of distress, and even suggest restructuring options before default risk rises. 💡 Why It’s Appealing Opens a new revenue stream for lenders and brokers, Meets demand for smaller equity-tap loans without refinancing, Uses AI automation to keep costs low despite higher credit risk, Attracts tech-savvy borrowers seeking quick approvals. tune in and learn https://www.ddamortgage.com/blog didier malagies nmls#212566 dda mortgage nmls#324329

Always look at the 10 year treasury

By Didier Malagies • September 29, 2025
Great question — the 10-year U.S. Treasury Note (T-Note) is one of the most important benchmarks in finance, and it’s tightly linked to interest rates. Here’s a breakdown of how it works and why it matters: 1. What the 10-Year Treasury Is It’s a bond issued by the U.S. government with a maturity of 10 years. Investors buy it, loaning money to the government in exchange for: Semiannual coupon payments (interest), and The face value back at maturity. Because it’s backed by the U.S. government, it’s considered one of the safest investments in the world. 2. Yield vs. Price The yield is the effective return investors earn on the bond. The yield moves inversely with the bond’s price: If demand is high and price goes up → yield goes down. If demand falls and price goes down → yield goes up. 3. Connection to Interest Rates The 10-year Treasury yield reflects investor expectations about: Future Federal Reserve policy (Fed funds rate). Inflation (higher inflation expectations push yields higher). Economic growth (slower growth often pushes yields lower). While the Fed directly controls only the short-term Fed funds rate, the 10-year yield is market-driven and often moves in anticipation of where the Fed will go. 4. Why It’s So Important Mortgage rates & lending costs: 30-year mortgage rates generally move in step with the 10-year yield (plus a spread). If the 10-year goes up, mortgage rates usually rise. Benchmark for global finance: Companies, governments, and banks often price loans and bonds based on the 10-year yield. Risk sentiment: Investors flock to Treasuries in times of uncertainty, driving yields down (“flight to safety”). 5. Practical Example Suppose the Fed raises short-term rates to fight inflation. Investors expect tighter policy and possibly lower inflation later. If they believe inflation will fall, demand for 10-years might rise → yields drop. But if they fear inflation will stay high, demand falls → yields rise. Mortgage rates, business loans, and even stock valuations all adjust accordingly. ✅ In short: The 10-year Treasury is the bridge between Fed policy and real-world borrowing costs. It signals market expectations for growth, inflation, and Fed moves, making it a crucial guide for interest rates across the economy. tune in and learn https://www.ddamortgage.com/blog didier malagies nmls#212566 dda mortgage nmls#324329 

AI underwriting to human underwriting and this is what happened

By Didier Malagies • September 24, 2025
Speed & Efficiency AI Underwriting: Processes applications in seconds to minutes. 1.Can instantly pull data from multiple sources (credit reports, bank statements, income verification, property valuations, etc.). Ideal for high-volume, standardized cases. Human Underwriter: Takes hours to days, depending on complexity. Manually reviews documents, contacts third parties, and applies professional judgment. Slower, especially for complex or edge cases. 2. Data Handling AI: Uses algorithms and machine learning to analyze massive datasets. Can detect patterns humans might miss (e.g., spending behavior, alternative data like utility payments, even digital footprints in some markets). Human: Relies on traditional documentation (pay stubs, tax returns, appraisals). Limited by human bandwidth—can’t process as much raw data at once. 3. Consistency & Bias AI: Decisions are consistent with its rules and training data. However, if the data it’s trained on is biased, the system can replicate or even amplify those biases. Human: Brings subjective judgment. Can weigh special circumstances that don’t fit a neat rule. Risk of inconsistency—two underwriters might interpret the same file differently. May have unconscious bias, but also flexibility to override rigid criteria. 4. Risk Assessment AI: Excels at quantifiable risks (credit scores, loan-to-value ratios, historical claim data). Weak at unstructured or nuanced factors (e.g., a borrower with an unusual income stream, or a claim with unclear circumstances). Human: Strong at contextual judgment—understanding unique borrower situations, exceptions, or “gray areas.” Can pick up on red flags that an algorithm might miss (e.g., forged documents, conflicting information). 5. Regulation & Accountability AI: Regulators are still catching up. Requires transparency in decision-making (explainable AI). Hard to appeal an AI decision if it can’t explain its reasoning clearly. Human: Provides a clear chain of accountability—borrower can request explanations or escalate. Easier for compliance teams to audit decision-making. 6. Cost & Scalability AI: Scales cheaply—one system can process thousands of applications simultaneously. Lower ongoing labor costs once implemented. Human: Labor-intensive, costs grow with volume. Better suited for complex, high-value, or unusual cases rather than mass processing. ✅ Bottom line: AI underwriting is best for speed, scale, and straightforward cases. Human underwriters are best for nuanced judgment, exceptions, and handling edge cases. Most modern institutions use a hybrid model: AI handles the bulk of simple files, while humans step in for complex or flagged cases. tune in and learn https://www.ddamortgage.com/blog didier malagies nmls#212566 dda mortgage nmls#324329
Show More