Upjohn Institute Working Papers Upjohn Research home page 2016 The Impact of Nurse Turnover on Quality of Care
and Mortality in Nursing Homes: Evidence from
the Great Recession
Yaa Akosa Antwi
Indiana University - Purdue University Indianapolis John R. Bowblis
Miami University Upjohn Institute working paper ; 16-249 Citation
Akosa Antwi, Yaa, and John R. Bowblis. 2016. "The Impact of Nurse Turnover on Quality of Care and Mortality in Nursing Homes:
Evidence from the Great Recession." Upjohn Institute Working Paper 16-249. Kalamazoo, MI: W.E. Upjohn Institute for Employment
Research. https://doi.org/10.17848/wp15-249 This title is brought to you by the Upjohn Institute. For more information, please contact ir@upjohn.org. The Impact of Nurse Turnover on Quality of Care and Mortality
in Nursing Homes: Evidence from the Great Recession
Upjohn Institute Working Paper 16-249
Yaa Akosa Antwi
Indiana University-Purdue University Indianapolis
and
John R. Bowblis
Miami University
January 2016 ABSTRACT
We estimate the causal effect of nurse turnover on mortality and the quality of nursing
home care with a fixed effect instrumental variable estimation that uses the unemployment rate
as an instrument for nursing turnover. We find that ignoring endogeneity leads to a systematic
underestimation of the effect of nursing turnover on mortality and quality of care in a sample of
California nursing homes. Specifically, 10 percentage point increase in nurse turnover results in
a facility receiving 2.2 additional deficiencies per annual regulatory survey, reflecting a 19.3
percent increase. Not accounting for endogeneity of turnover leads to results that suggest only a
1 percent increase in deficiencies. We also find suggestive evidence that turnover results in lower
quality in other dimensions and may increase mortality. An implication of our mortality results is
that turnover may be a mechanism for the procyclicality of mortality rates.
JEL Classification Codes: I11, J21, E24
Key Words: Employee turnover, unemployment rate, quality of care, nursing home
Acknowledgments
We also thank Padmaja Ayyagari, Christopher. S. Brunt, Laura Dague, Sarah Hamersma,
Daniel Hammermesh, Christina Marsh, Elizabeth Munnich, Erik Nesson, Lauren Nicholas, and
Owen Thompson and conference participants at the 11th World Congress on Health Economics
in Milan for helpful comments. We acknowledge generous funding from the Upjohn Institute’s
Early Career Research Grant, the IUPUI School of Liberal Art Summer grant program, and
Miami University’s Farmer School of Business summer grant program. New employees can be costly, as new hires need to be trained to become familiar with
the procedures and operations of a firm. Thus, excessive employee turnover can be a source of
concern for a firm. The health care industry is one profession in which turnover is potentially an
important determinant of firm output, but it has received little attention from economists.
Turnover in health facilities reduces the effectiveness and productivity of delivering care, and
may also increase operating cost (Squillace et al. 2008). In addition, when nurses are assigned to
the same patients, they can form personal bonds, which may lead to better health outcomes
(Thomas et al. 2013). For this reason, policymakers and trade associations have made efforts to
identify and address turnover, particularly in the nursing home industry. For example, in 2012,
the American Health Care Association (2012) announced a three year goal to reduce staff
turnover in nursing homes by 15 percent. And in the state of Ohio, the state legislature passed the
Long-Term Care Quality Initiative, which pays nursing homes higher Medicaid reimbursement
rates for meeting certain quality goals, including reducing staff turnover. 1
While there are many calls and efforts made to improve healthcare worker turnover, it is
not fully understood if turnover directly impacts quality. Most research on turnover in the health
care sector has focused on the determinants of staff retention (Elliott et al. 2009; Frijters, Shields,
and Price 2007) or cites turnover as a potential explanation for a result, but it does not directly
examine turnover. For example, Propper and Van Reenan (2010) suggest that turnover may be a
reason for poor hospital quality. And more recently, turnover of staff in nursing homes has been
suggested as a mechanism for why mortality rates are procyclical. Specifically, Miller et al.
(2009) find that most of the improvement in health during recessions occur among those older 1 See Ohio Senate Bill Number 264, available at
http://archives.legislature.state.oh.us/bills.cfm?ID=129_SB_264. (accessed May 19, 2015), and the Staff Retention
section of Ohio’s Department of Aging Nursing Home Quality Incentive website, available at
https://aging.ohio.gov/ltcquality/nfs/qualityincentives.aspx (accessed May 19, 2015). 1 than 85, with much of the variation coming from the elderly in nursing homes (Stevens et al.
2015). While not directly explored, the results from these two papers suggests that recessions
lead to poor job prospects for low-skilled direct care workers in nursing homes, which then
results in lower turnover rates. These lower turnover rates may translate into better quality and
mortality outcomes for nursing home residents.
While there are a number of studies outside the economics literature that have examined
whether staff turnover in nursing homes is associated with quality of care, most of these studies
report results that are not statistically significant but suggest an association between turnover and
health outcomes (see Castle and Anderson 2011; Castle and Engberg, 2005; Castle, Enberg, and
Men 2007; Lerner et al. 2014; Thomas et al. 2013). More importantly, the existing literature does
“not convincingly establish causality running from turnover to outcomes” (Stevens et al. 2015, p.
301). The methods employed in these studies are generally not designed to find causal
relationships, as many studies use only one year of data, use data from self-collected surveys
with low response rates, econometrically dichotomize turnover and quality outcomes, and/or
ignore unobserved heterogeneity. Of greatest concern is unobserved heterogeneity. Failing to
account for unobserved factors that influence quality and are correlated with turnover can result
in biased estimates of the effect of turnover on quality. To illustrate, nursing homes with poor
quality of care may have bad management or be poor places to work, which are variables that are
unobserved to the researcher and can be correlated with turnover, leading to omitted variable
bias. A few studies have used multiple years of data and employed fixed effects to handle timeinvariant omitted variable bias (Castle and Anderson 2011; Thomas et al. 2013), but the current 2 literature that examines quality outcomes has ignored the endogeneity of turnover that may arise
due to simultaneity or time-varying omitted variable bias. 2
This paper directly assesses whether employee turnover in nursing homes impact patient
quality and mortality after accounting for the endogeneity of turnover. We utilize administrative
data for all nursing homes in California. We chose California because it had available
information on turnover for various types of nursing home staffing, and it has a large nursing
home industry, with about 8 percent of all nursing home facilities in the United States. We
examine from the period 2005 to 2011, during which California’s economy saw significant
growth and contraction. We use this variation in the economy’s strength over time and
geographically across the state as our exclusion restriction in an instrumental variables (IV)
approach. Specifically, the exclusion restriction is the unemployment rate in the nursing home’s
county. Identification relies on the assumption that changes in county unemployment rates affect
quality of care only through turnover. As Shapiro and Stiglitz (1984) note, when the
unemployment rate is high, the threat of firing improves the quality through lower turnover.
Because many nursing home residents are on Medicaid or are expected to remain in a nursing
home for the rest of their lives, their personal health and hence quality of care is unlikely to be
impacted by the state of the local economy once other factors are accounted for in the model. 3
Using panel data constructed by merging data from the Online Survey Certification and
Reporting System (OSCAR), Office of Statewide Health Planning and Development (OSHPD)
in California, the Bureau of Labor Statistics (BLS) and the Area Health Resource File, we find
that ignoring endogeneity leads to a systematic underestimation of the effect of nursing turnover
2 One paper outside of the economics literature used IVs to examine how turnover impacts nurse staffing
levels (Kash et al. 2006). The paper used training expense ratio, benefits expense ratio, professional staff ratio and
contracted staff ratio as instruments. These ratios are likely to impact turnover but may also influence staffing levels,
potentially undermining these ratios as valid instruments.
3
We test this formally in the section titled “Exclusion Restriction Variable.” 3 on the quality of care and mortality. We find that a 10 percentage point increase in nursing
turnover leads to an additional 2.2 deficiency citations to a nursing home per annual regulatory
survey. This represents a 19.3 percent increase in deficiency citations. Not accounting for
endogeneity leads to results that suggest that nurse turnover leads to a facility receiving 0.12
more deficiency citations, or a 1 percent increase in citations. For most of our other quality
measures, we find that nursing turnover leads to worse quality of care, though this effect is not
statistically significant at conventional levels in some specifications. We also find suggestive
evidence that as turnover increases, a greater percentage of discharged nursing home residents
are discharged because of patient death.
This paper contributes to our understanding of the relationship between turnover and
outcomes. First, to the best of our knowledge the existing literature on the impact of turnover on
outcomes in nursing homes is noncausal. While some studies use panel data with fixed effects to
account for any unobserved heterogeneity, fixed effects cannot handle omitted time-invariant
factors. The changing policies and advocate efforts to improve the quality of nursing homes,
along with personal hiring/firing decisions that align with nursing home quality, make turnover
endogenous even though fixed effects are included in a model. By using fixed effect panel IV
regression, endogeneity bias from a number of factors is accounted for in our regressions.
Second, this paper expands the existing literature on the business cycle and health (Ruhm 2000).
With recent work (Stevens et al. 2015) finding that elderly mortality in nursing homes are
driving the procyclical nature of mortality, nurse turnover may be a leading causal factor driving
this result. And finally, the nursing home industry is large, with revenues equivalent to nearly 2
percent of GDP, and much of the turnover is among lower-skilled workers. Therefore,
understanding turnover in this industry may provide insight into other industries. 4 CONCEPTUAL MODEL OF TURNOVER AND NURSING HOME QUALITY
Prior research outside of the health care sector has found that high employee turnover can
lead to lower productivity, diminished profits, and poor customer service (Eckardt, Skaggs, and
Youndt e2014; Siebert and Zubanov 2009; Ton and Huckman 2008). One argument for these
results is that a lack of room for promotion or higher wages from outside options may encourage
workers with desirable traits to seek outside employment (Mas 2006; Munasinghe 2006). When
motivated workers and those with desirable traits leave, the quality of employees who remain
employed is lower. In contrast, firing workers may improve outcomes by enhancing the average
traits of employees that are retained (Jovanovic 1979; Weiss 1980). This implies that turnover of
employees can be a positive or negative for outcomes depending on the economics of the
particular industry.
In the case of the nursing home industry, the primary caregivers and those most
responsible for resident outcomes are nurses and nurse aides, which are collectively referred to
as nurses. These nurses come in three types based on the level of education, training, and
licensure: 1) registered nurses (RNs), 2) licensed practical nurses (LPNs), and 3) certified nurse
aides (CNAs). RNs and LPNs are considered licensed nurses because they have some
postsecondary education and are required to pass licensing exams. Licensed nurses coordinate
care, administer medicines and treatment ordered by physicians, and ensure professional
oversight of care directly provided to residents. In contrast to licensed nurses, CNAs provide the
majority of direct care to residents. Federal standards only require CNAs to have at least 75
hours of training, which includes 35 hours of classroom instruction and 50 hours of clinical
training. 5 The economics of employment in nursing homes lend the industry to experience high
turnover, over 50 percent annually, and in some facilities exceed 100 percent (Banaszak-Holl
and Hines 1996). Broken down by type of nurse, annualized turnover rates for RNs, LPNs, and
CNAs are estimated to be as high as 56, 51, and 75 percent, respectively (Donoghue 2009). One
of the key drivers of turnover is that wages at nursing homes tend to be lower than in other health
care settings, and often the job is not considered as “glamorous” as those in other health care
industries, such as working in hospitals. For example, the hourly mean wage for an RN in a
nursing home in 2013 was $29.81 compared to $33.94 for similar work in hospital. In fact, RN
wages in nursing homes were the lowest wage among the five settings where the Bureau of
Labor and Statistics (BLS) measured RN wages. 4 This drives licensed nurses to look for
employment in other health care settings. For CNAs, who are often considered unskilled or lowskilled workers, the average wage at nursing homes ($12.01 on average in 2013) is similar to
employment in similar skill-level jobs in retail, tourism, or other growing industries (Grabowski
et al. 2011). Additionally, these other jobs do not have the same mental cost of caring for
individuals who are physically dependent or have severe cognitive impairment.
We expect nurse turnover to be countercyclical, as poor economic environments make it
harder for existing employees to find jobs in other industries. This implies that economic
conditions may indirectly impact nursing home quality and mortality outcomes through turnover
for a number of reasons. First, when the economy is strong it may be harder to fill each
additional vacancy. This implies that for each subsequent nurse hired, the nursing home may
need to look deeper into their applicant pool and may be required to hire individuals that have
less desirable traits (e.g., less reliable, less caring, less experienced). Second, nurse staffing
4 Based on May 2013 BLS data for occupation 29-1141 – registered nurses, mean hourly wages are as
follows: nursing homes, $29.81; physician offices, $30.22; home health care services, $32.17; general medical and
surgical hospitals, $33.94; and outpatient care centers, $35.62. 6 levels are known to be associated with higher nursing home quality (Cohen and Spector 1995;
Lin 2014), and lower turnover can lead to more consistent staffing levels. Third, when turnover
rates are lower, each nurse has more experience in knowing how to provide highquality, meet
regulatory standards, and build stronger personal relationships with residents (Thomas et al.
2013). Such familiarity might decrease the likelihood of using less evasive care practices, such as
catheters or physical restraints.
Overall, these mechanisms suggest that reducing turnover should result in improved
health outcomes, and that higher unemployment rates would impact outcomes through reductions
in nurse turnover. While a few studies have found that higher nurse turnover can lead to worse
quality, the vast majority of studies do not find a statistically significant relationship (Castle and
Anderson 2011; Castle and Engberg 2005; Castle et al. 2007; Lerner et al. 2011; Thomas et al.
2013). The lack of using causal identification by the current literature may explain why most
studies find statistically insignificant effects. Our contribution to the literature is to use causal
identification techniques, specifically to use the local unemployment rate as an instrument to
determine how turnover impacts nursing home outcomes. DATA AND METHOD
Data Sources and Sample Selection
This study uses data from four sources for nursing homes in the state of California. The
first is utilization and financial information on long-term care facilities obtained from the
California OSHPD. On an annual basis, OSPHD collects information on various measures such
as patient census, patient demographics, major capital expenditures, wages and salaries, casemix, and most importantly for this study labor turnover. We merge OSHPD data with data from 7 the OSCAR data set. OSCAR, maintained by the Centers for Medicare and Medicaid Services
(CMS), is a uniform database of yearly regulatory reviews of all nursing homes that receive
payments from Medicare or Medicaid. These reviews are completed by a government survey
team that assesses nursing home quality and validates all the data reported in OSCAR. Reviews
of nursing homes are completed every 9–15 months with an average of 12 months between
reviews. OSCAR contains data on the number of regulatory deficiencies each nursing home
receives, staffing levels, case-mix, and multiple measures of quality. Finally, these two data
sources are supplemented with information about the annual county unemployment level and
demographic information from the Bureau of Labor Statistics and Area Health Resource File,
respectively.
The sample used in this analysis is free-standing nursing homes in the state of California
from 2005 through 2011. The resulting sample consists of 5,992 facility-year observations of 980
unique nursing homes. 5 We examined the state of California because OSHPD data contain
multiple measures of nursing and employee turnover, the key variable in this analysis. We
selected the study period 2005—2011 for three reasons. First, the study period includes
economic growth and contraction associated with the Great Recession. This provides temporal
and regional variation in the economic growth that aids in the identification of the effect of
turnover in nursing homes. Second, California implemented a minimum nurse staffing ratio in
hospitals that became effective in January 2004. Many hospitals were required to increase nurse
staffing levels (Cook et al. 2012), potentially impacting turnover in nursing homes. By starting 5 While the vast majority of nursing homes have data for all years, some nursing homes may only have
partial data because they entered or exited the market. To determine if entry or exit is a concern, we estimated
models for nursing homes that appear in the sample each year. Our results are not overly sensitive to entry or exit
and are discussed in the “Robustness Tests” section. 8 the study in 2005, any impact of this change would have worked its way through the system. 6
Finally, the Medi-Cal Long Term Care Reimbursement Act of 2004 (Act AB1629) increased
reimbursement to nursing homes for the state’s Medicaid program starting in 2005 (California
Assembly Bill 1629). Since all nursing homes are affected by this legislation, using data starting
in 2005 minimizes the potential impact that the changes in reimbursement might have on
turnover and quality of care by examining a study period that traverses 2005. 7
Key Dependent and Explanatory Variables
The OSHPD provides data on the key explanatory variable of interest, staff turnover.
Staff turnover is available for three types of employees: 1) all employees; 2) all nursing staff
(RNs, LPNs, and CNAs); and 3) CNAs. While historically most turnover in nursing homes is
among CNAs, our main focus is on turnover rates for all nurses as the three available turnover
rates are highly correlated as shown in Figure 1. 8 Turnover rates are measured annually and are
defined as the number of times an employee is replaced in a year divided by the average number
of people employed during the year. All turnover rates are measured as percentages with 0
percent indicating no turnover during the year and 100 percent indicating the average employee
was replaced once during the year. The average annual turnover rate regardless of the measure
used is approximately 50 percent, though some facilities report zero turnover in some years and
others have turnover rates of over 200 percent (See Table 1).
The dependent variables are a series of quality measures and two mortality measures.
Information on quality is obtained from OSCAR, which is considered one of the most reliable 6 We also examined slightly later starting years and found little difference in our results.
We also conducted a robustness check that accounts for this change in reimbursement. Results are
qualitatively identical to our main results and are discussed in the “Robustness Tests” section with other robustness
checks.
8
The correlation between the three measures of turnover ranges from 0.80 to 0.89. In the robustness check
section, we present results for the other turnover measures.
7 9 sources of quality of care in nursing homes and has been used in studies on the nursing home
industry in California (Harrington et al. 2000; Matsudaira 2014). The first measure utilized is the
number of regulatory deficiencies a facility received during their federal regulatory inspection.
We also follow the research of Harrington et al. (2000) by classifying each deficiency into three
mutually exclusive categories: quality of care, quality of life, and other deficiencies. 9 As per the
State Operations Manual, surveyors examine whether the facility is meeting each of over 180
federal regulatory standards. 10 If the facility is found to fail to meet a standard, the inspection
team will issue a deficiency indicating that a quality problem exists. For example, under
regulation §483.13, residents have the right to be free from physical restraints unless medically
necessary. If a nursing home uses physical restraints for discipline or convenience, then the
nursing home would receive a deficiency for improper physical restraint use. For our study
period, the average nursing home received 11.5 deficiencies, though the range is 0–51 (See Table
1).
The second and third set of quality measures are resident outcomes and care practices
utilized by the nursing home. The two resident outcome measures we examine are the percentage
of residents with bedsores and the percentage of residents with contractures. Bedsores are an
injury to the skin and tissue caused by lack of blood supply induced by constant pressure. A
contracture is a shortening of the soft tissue caused by lack of movement of a joint. These two
measures are good measures of quality of care because both conditions are preventable and
treatable (Bowblis, Meng, and Hyer 2013; Grabowski 2001). Two measures of care practices are
9 Quality of care included 72 specific items in the following federal survey categories: resident assessment,
quality of care, nursing services, dietary services, physician services, rehabilitative services, dental services,
pharmacy services, and infection control. The quality of life category included 77 specific items on resident’s rights;
admission, transfer, and discharge rights (including resident rights); resident behavior and facility practices (includes
resident rights); quality of life; and physical environment. Other deficiencies included 30 specific items on
administration, lab services and other activities.
10
The State Operations Manual is available at: http://www.cms.gov/Regulations-andGuidance/Guidance/Manuals/downloads/som107ap_pp_guidelines_ltcf.pdf. 10 also utilized: the percentage of residents with catheters and percentage physically restrained.
Care practices are associated with quality of life and may impact the physical and emotional
health of residents (Bowblis and Lucas 2012). For instance, the insertion of catheters places the
resident at greater risk for urinary tract infection (Cawley, Grabowski, and Hirth 2006; Park and
Stearns 2009). Physical restraints, on the other hand, may increase the risk of bedsores,
depression, mental and physical deterioration, and mortality (Park and Stearns 2009; Zinn 1993).
For both resident outcomes and care practice quality measures, higher values imply lower
quality. Additionally, some residents may have had the und...

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