ExecutiveSummary

Executive Summary

MEDICAL LEAVE INSURANCE (FAMLI) PROGRAM- PHASE II

This report provides a comprehensive analysis of expected program claims and administration experience for the Maryland Family and Medical Leave Insurance (FAMLI) program. It studies and projects the expected volumes, costs, and durations of claims by leave types for 2026-2030, and expected employer opt-out and the opt-in of self-employed individuals. The findings underscore the significance of ongoing monitoring, targeted initiatives, and stakeholder engagement through surveys to shape evidence-based policies that optimize program effectiveness and inclusivity.

 

First, we conducted three independent studies to make predictions on the number, cost, and duration of claims, based on econometric modeling (Chapter 1), the USDOL Worker PLUS model (Chapter 2), and an actuarial study by Milliman Inc. (Chapter 6).

The three studies:

1. Projected the expected volumes of claims by month or quarter for 2026 and by year for 2027-2030 and their breakdowns by leave types (medical, family care, birth of a child, military exigency).
2. Projected the expected costs by leave types (medical, family care, birth of a child, military exigency).
3. Projected the duration of claims with details by claim type (medical, family care, birth of a child, military exigency in two of the three studies).

The data from all the states where the monthly claims data are available (California, Rhode Island, and Washington) exhibit no significant seasonality on the number of claims. Our estimation of the volume of first-year monthly claims in Maryland in Chapters 1 & 2 is predicted based on the first-year data of California, Rhode Island, and Washington. Annual claims and costs over 2027-2030 are projected based on updated policy parameters, wage inflation adjustment, employment growth, and the growth of take-up rates over the years. A slight increase in the number of claims from 2026 to 2030 is forecasted. The duration of different types of leaves varies based on factors like gender, age, marital status, and education. For example, women typically take longer leaves for medical reasons and for new childcare, while factors like being married influence the duration of family leaves. The expected leave durations are simulated from FMLA-based distribution with no wage replacement, and the actual durations with paid leave in the FAMLI program should be longer than the simulation results. The table below summarizes the expected benefit expenses estimated with three different models presented in Chapters 1, 2, and 6. 

 

Table 1. Summary of Projected Benefit Costs of FAMLI Claims ($ Millions)

Second, an empirical analysis of the growth of leave lengths is performed in Chapter 3. A wide variation of leave durations is found across both the leave types and states, regardless of whether the leave durations are measured by raw length or a ratio of the raw length to the maximum state-allowed duration. Despite the wide variations of leave durations, medical leaves tend to be longer than family leaves presumably reflecting their generally much longer state-allowed maximum durations. In terms of the ratio of the leave length to the maximum state-stipulated leave length, however, the pattern is reversed. In fact, we also observe a general negative relationship between the maximum state-stipulated leave duration and the ratio of the leave length to this maximum duration, presumably reflecting a less than proportional increase in the leave duration with an increase in the maximum leave length. Our most important result in this chapter, however, is that the leave durations do not exhibit a consistent temporal trend over the years after the implementation of the state paid FAMLI programs, either based on the graphic or regression analysis. As a result, we do not consider the growth of the leave durations in our cost analyses.

 

Lastly, the expected behaviors of the opt-out of employers and the opt-in of self-employed individuals are discussed in Chapter 4 and 5, respectively. Chapter 4 explores the landscape of businesses opting out of state-paid family leave programs across the U.S., analyzing factors influencing their decisions. The study delves into opt-out rates and examines each state’s policies. Overall, opt-out rates range from 3% in California to 33% in Massachusetts, presumably driven by policy differences across states, including whether government agencies can opt out and the division of the FAMLI contribution rate between employers and employees. The impact of employer size, industry dynamics, salary structures, and public perception on participation rates is also investigated. Strategies to reduce business opt-out rates are proposed, including financial incentives, administrative simplification, and collaborative efforts between government agencies and business associations. The importance of educating businesses about state-paid family leave is underscored, with recommendations for government initiatives to support businesses, including online tools, training programs, and helplines. An overview of various state approaches to paid leave programs, along with their impacts and reasons for opting in or out is provided.

 

Chapter 5 examines self-employed workers’ opt-in behavior and access to FAMLI programs in the United States. As of January 2024, 13 states and Washington D.C. have implemented FAMLI 4 initiatives allowing self-employed individuals to voluntarily participate. However, opt-in rates remain very low with low awareness and high costs deterring participation: only around 1.8% averagely across existing state programs and based on available data. Several factors contribute to this trend, including that the costs of opt-in are disproportionately higher for self-employed individuals versus salaried workers in some states, eligibility barriers like waiting periods, and limited awareness of FAMLI programs. An analysis of survey data indicates that only 11% of self-employed workers taking leave for family/medical reasons had paid leave coverage, compared to 47% of employees. Addressing the low opt-in rate is crucial to expand access and ensure self-employed individuals can balance work with personal and family needs. Recommended strategies include enhancing affordability, raising awareness through outreach and education, and implementing regulatory measures to promote equity.

 

In addition to the analysis of expected claim costs for the FAMLI program, the actuarial study by Milliman Inc., Chapter 6 also provides a summary of the expected opt-out and opt-in behaviors by employers and self-employed individuals, respectively, based on publicly available information from states that have mandated FAMLI benefits.