Tuesday, December 5, 2017

Does Paid Family Leave Reduce Nursing Home Use? The California Experience | JPAM Featured Article

Paid family leave (PFL) is intended to make it financially easier for eligible workers to take time off to care for young children or seriously ill family members. Concurrently, the receipt of informal care from family members has been shown to lower the probability of institutionalization among elderly Americans. This raises an important question: Do PFL policies influence nursing home use among older adults?

PRINT PAGE
JPAM_stack_horizontal

Does Paid Family Leave Reduce Nursing Home Use? The California Experience | JPAM Featured Article

December 5, 2017 01:43 PM

By Kanika Arora, Department of Health Management and Policy at the University of Iowa, and Douglas Wolf, Maxwell School of Citizenship and Public Affairs at Syracuse University.

Article Introduction

Paid family leave (PFL) is intended to make it financially easier for eligible workers to take time off from work in order to care for young children or seriously ill family members. Concurrently, the receipt of informal care from family members has been shown to lower the probability of institutionalization among elderly Americans. This raises an important question: Do PFL policies influence nursing home use among older adults?

In this article, Arora and Wolf address this question for California, which in 2004 became the first state in the United States to implement a PFL law. The authors employ a multivariate difference in differences design in their analysis of state-level aggregate data over the 1999-2008 period. Because the passage of a PFL law is not randomly assigned, the authors use cluster analysis to construct their primary comparison group. They also investigate the sensitivity of their findings by using alternative sets of comparison group states as well as through other placebo tests. Their inferential approach accounts for the fact that there is only one treatment state, while also adjusting for heteroscedastic errors.

The authors estimate that across alternative comparison groups, the PFL law is consistently shown to produce a decline in nursing home occupancy among the 65-and-older population in California by 0.5 to 0.7 percentage points. Their preferred estimate, employing an empirically-matched group of control states, finds that PFL reduced nursing home usage by about 0.65 percentage points. For California this represents an 11% relative decline in elderly nursing home utilization. The authors note that by nature of their outcome measure, the PFL estimate in this study understates the true policy impact. At the same time, because nursing-home episodes averted as a consequence of PFL are likely to be quite short on average, the implementation of PFL might ultimately lead to a modest reduction in the state’s nursing home use. This is the first study that empirically examines the relationship between paid leave and long-term care outcomes. The findings of this study have implications for Medicaid spending, a large share of which goes toward long-term care services and supports.

This article preview is from the Winter 2018 issue of the Journal of Policy Analysis and Management (JPAM). APPAM invites authors from each issue to asnwer a few questions about their research to further promote the quality work in the highly-ranked research journal. Check out this and other JPAM articles online.


Author Interview

What spurred your interest in this research on how Paid Family Leave (PFL) impacts nursing home use, or the impacts of PFL more broadly?

This paper was an unexpected offshoot of a project that focusses on ways in which a large range of state-level policies influence family members’ caregiving behavior. As part of that project, we wanted to predict whether a potential caregiver’s parent was a nursing home resident. We investigated numerous policies and noticed that living in a paid-leave state was associated with a lower prevalence of parental nursing home usage. That led us to focus on nursing home usage as a policy outcome, with paid leave as a key explanatory factor.

How do you recommend further research address the differences in post-acute and chronic care, as well as origin and endpoints of care?

That would require going to a detailed individual-level data source – possibly the Minimum Data Set (MDS) – and tabulating the count of nursing home episodes according to origin and endpoint – assuming that those details are recorded in the data. The MDS is a very large data base, so that would be a big job. There are other potential data sources (such as the National Nursing Home Survey) but they have much smaller samples. It would take a large sample to find differences due to family leave policy changes.

Based on your research, would you recommend that states expand PFL benefits rather than expanding Medicaid eligibility? How does Medicaid funding impact this decision for states?

It would require a true cost-benefit study (which has not yet been done) to make an informed recommendation on this issue. Expanding Medicaid eligibility would raise costs for all sorts of services, not just nursing homes. Expanding PFL benefits might reduce Medicaid costs, but there are limits given that the duration of paid leave eligibility is not likely to rise above 6 to 12 weeks. And, we think that some of the benefits of expanded PFL would show up as reduced Medicare costs, which is a Federal rather than a state issue. In addition, expanding paid leave may also influence employers, and those costs may need to be taken into account.

What challenges, if any, did you find when conducting this research? How can further studies overcome these challenges?

There were two – assembling the data and using appropriate statistical methods. We used a combination of publicly- and privately-available (i.e., supplied by helpful colleagues) and somewhat obscure and hard-to-locate sources (for example, several of the CMS Nursing Home Compendium reports are offline, but relevant excerpts were supplied by CMS staff). A comprehensive repository of relevant state-level data, extending back over a long time period, would be a wonderful public resource, but one that is unlikely to be undertaken without a very substantial investment of funds.

Fortunately, bits and pieces of such a resource do exist already (for example, Brown University’s “LTC Focus” database). Statistical problems arose because the “treatment”—implementation of PFL—was not assigned randomly, as it would be in a biomedical research trial, and it was only adopted in one state during the time period of our analysis. Both problems are commonly encountered in attempts to determine the impacts of policy changes, and a rapidly-evolving set of statistical tools is available for addressing those problems. Over the course of our research we tried a number of alternative approaches, many of which are included in the paper.

Are there other unanticipated consequences of PFL polices that you want to study? What holes are in there in the existing knowledge base on PFL or LTC?

It would be useful to further understand the mechanism(s) for how paid family leave influences nursing home use. In our paper, we note that the most likely pathway for this is through an increase in hours of care provided by family caregivers. Future research should confirm if the provision of paid family leave influences the extent of informal caregiving. In addition, it would be important to understand whether the policy has heterogeneous impacts – i.e., whether it influences certain kinds of caregivers differently than others.

Some unanticipated consequences worth examining include: Does a PFL-induced increase in care hours raise any non-financial costs of caregiving on caregivers, such as the physical and emotional burden of caregiving? Does the provision of PFL influence how individuals use other types of leave, such as paid sick leave, which sometimes also allow employees to take time off to provide care to family members? Beyond nursing homes, does PFL influence the use of other formal long-term care services and supports such as home and community-based services?

How does this study impact or add to the existing research that can inform PFL policies? What would be the ideal next step for your research findings? How would you like to see your findings implemented?

This is the first study to examine long-term care outcomes associated with a state-level policy on paid family leave. In doing so, it has demonstrated that the provision of this leave reduces nursing home use among older adults. While the current administration has proposed a federal paid family leave program, it is only focused on providing paid leave to families after the birth or adoption of a child. The results of this study suggest that the Trump administration should consider expanding the benefits of such a program to individuals with a seriously ill family member.


About the Authors

Kanika_Arora Kanika Arora joined the Department of Health Management and Policy at the University of Iowa in 2015. She completed her PhD in Public Administration from the Maxwell School, Syracuse University in 2015. Her research focuses on understanding the motivations and implications of supplying long-term care services to the elderly in the U.S. Her recent work examines: time tradeoffs associated with informal care, parental dementia and wealth accumulation among adult children, and the effect of formal care services on Medicaid expenditures. A second stream of her research studies evaluation design for cooperative extension programs. Before starting her PhD at Syracuse, Kanika was a Monitoring and Evaluation Specialist for Orbis International – an international non-profit working to prevent and treat blinding eye disease in developing countries.
Douglas_Wolf Douglas Wolf is a demographer and gerontological researcher with many years of experience studying the economic, demographic, and social aspects of aging and long-term care. He is currently a Professor of Public Administration and International Affairs and the Gerald B. Cramer Professor of Aging Studies at Syracuse University. Previously he was a Senior Research Associate at the Urban Institute and a Research Scholar at the International Institute for Applied Systems Analysis in Laxenburg, Austria where he worked with and was Acting Director of its Population Program. Wolf’s research areas include several topics in the well-being and life course-patterns of the older population, such as disability dynamics and active life expectancy; household composition and parent-child coresidence; family caregiving patterns and their consequences; and the spatial distribution of kin and migration choices. A recurring theme of Wolf’s research is the role of family and kinship patterns in shaping the choices facing older people and their immediate kin with respect to living and care arrangements.

Submit Your Research to JPAM

JPAM seeks contributions that span a broad range of policy analysis and management topics with an emphasis on research that conveys methodologically sophisticated findings to policy analysts and other experts in the field. Both domestic and international contributions in public management are welcome, as well as a broad range of policies related to social well-being, health, education, science, environment, and public finance. JPAM strives for quality, relevance, and originality. An interdisciplinary perspective is welcome as are articles that employ the tools of a single discipline.

Find information for prospective authors and submit manuscripts via Editorial Express.

 

« Back

 
 
 
Association for Public Policy Analysis and Management (APPAM)
NEW ADDRESS! 1100 Vermont Avenue, NW, Suite 650 Washington, DC 20005
Phone: 202.496.0130 | Fax: 202.496.0134
Twitter Facebook LinkedIn Subscribe to me on YouTube

Home|About APPAM|Membership|Public Policy News|Conference & Events|Publications| Awards|Careers & Education|Members Only

Web site design and web site development by Americaneagle.com

© 2017 Association for Public Policy Analysis & Management. All Rights Reserved.
Site Map | Privacy Policy | Terms of Use | Events | Add Your Event