Jordan D. Matsudaira is an assistant professor in the Department of Policy Analysis and Management at Cornell University. He thanks Damon Clark, Heather Royer, and seminar participants at RAND for helpful
comments. He is grateful for grants from the Cornell Population Program and the National Science Foun- dation SES- 0850606, and to the Robert Wood Johnson Foundation’s Scholar in Health Policy Program
for supporting the early stages of this work. Margaret Jones provided excellent research assistance. The data used in this article can be obtained beginning August 2014 through July 2017 from the author at
jdm296cornell .edu. [Submitted August 2011; accepted December 2012]
ISSN 0022- 166X E- ISSN 1548- 8004 © 2014 by the Board of Regents of the University of Wisconsin System
T H E J O U R N A L O F H U M A N R E S O U R C E S • 49 • 1
Government Regulation and the Quality of Healthcare
Evidence from Minimum Staffi ng Legislation for Nursing Homes
Jordan D. Matsudaira
A B S T R A C T
This paper investigates the effect of a regulation mandating a minimum nurse- staffi ng level on the quality of healthcare in nursing homes. To comply
with the regulation, fi rms increase employment of nurses in proportion to the gap between their initial staffi ng level and the legislated minimum threshold.
If higher nurse staffi ng causes better quality, then the changes in quality outcomes should mirror these changes. Despite inducing increases in nurse
aide hours of 10 percent on average and up to 30 percent for some fi rms, I fi nd no impact of the regulation on patient outcomes or overall facility
quality.
I. Introduction
In 2000, the Institute of Medicine reported that nearly 100,000 Ameri- cans die each year due to medical errors in hospital settings National Academy Press
[NAP] 2000. A report by the U.S. Government Accounting Offi ce GAO found that 29 percent of long- term care facilities were cited for safety and care violations that
caused actual harm or immediate jeopardy to their residents in mid- 2000 GAO 2003, reinforcing concerns over quality of care in that industry documented since long be-
fore NAP 2001. In response to this perceived crisis in the quality of healthcare, policymakers have responded with calls for increases in the level of nurse staffi ng
in all healthcare settings. California was fi rst to adopt minimum nurse- patient staff-
ing legislation for hospitals beginning in 2004, and as of early 2008 13 other states were considering such legislation Thrall 2008. Staffi ng standards for nursing homes
have existed since the early 1980s, but they are perceived to be too lenient and many states have increased their staffi ng standards in the last ten years Park and Stearns
2009. Meanwhile, calls for more aggressive national standards have intensifi ed NAP 2004.
At least since Arrow 1963, economists have recognized that government involve- ment in the medical care industry might be desirable to overcome market failures
stemming from imperfect and asymmetric information. For example, in a model similar to Akerlof’s 1970 market for lemons, Leland 1979 shows that minimum
quality standards licensing requirements for physicians in his context can improve welfare by preventing low quality providers from driving out high quality ones. Regu-
lation carries its own hazards, however, including the possibility that rent- seeking through the political process may result in a quality standard set ineffi ciently high or
low. Moreover, since it is diffi cult to regulate the quality of medical care through its outcomes, policymakers are left to prescribe limits on the inputs of a complex and
poorly understood production function. For both reasons, there is a risk that regula- tion may increase healthcare costs without generating commensurate improvements
in health.
Calls to raise minimum nurse staffi ng ratios have been motivated by a number of studies showing cross- sectional links between higher nurse to patient ratios and bet-
ter patient outcomes across healthcare facilities Aiken, Clarke, Sloane, Sochalski, and Silber 2002; Needleman, Buerhaus, Mattke, Stewart, and Zelevinsky 2002; Har-
rington, Zimmerman, Karon, Robinson, and Beutel 2000. Standard concerns about omitted variables and patient sorting based on unobserved patient acuity suggest,
however, that these correlations may not refl ect a causal relationship Dobkin 2003. Recent studies by Evans and Kim 2006 and Dobkin 2003 better isolate exogenous
variation in nurse staffi ng levels and fi nd little evidence of a causal effect of nurse staffi ng on patient outcomes. But the variation they isolate is temporary—due, for
example, to unexpectedly high admissions shocks over a weekend—and therefore may not identify the effect of a permanent increase in nurse staffi ng that would be the
goal of government intervention.
This study directly assesses the case for minimum nurse- to- patient ratios by evalu- ating the impact of an actual instance of such legislation on patient health and quality
of care outcomes. In 2000, the California legislature enacted a large increase in mini- mum nurse staffi ng standards for long- term care facilities nursing homes, requiring
that all facilities employ at least 3.2 hours of direct care nurse labor for each resident, every day HPRD, for “hours per resident- day”. While an older staffi ng law had been
in place, the new standard was aggressive: Nearly 70 percent of all facilities had staff- ing levels below 3.2 HPRD in 1999 with an average staffi ng level of fi rms below the
threshold of about 2.7 HPRD.
1
This setting lends itself to a transparent research design for estimating the impact
1. This is not a full analysis in the sense that I make no effort to compare minimum staffi ng ratios to other policy levers that might be attempted, and may well dominate staffi ng policy in cost- benefi t terms. Instead, I
focus only on establishing what health benefi ts may result from such a policy.
of minimum staffi ng legislation on patient outcomes, providing arguably the clean- est evidence to date. If facilities mechanically comply with the law, then we would
expect changes in staffi ng to mirror the gap between their staffi ng levels before the law passed and the mandated threshold of 3.2. That is, the fi rms with the lowest initial
staffi ng levels should increase their nurse employment the most and fi rms with staffi ng levels already above 3.2 should increase staffi ng levels little, if at all. To the extent
that nurse- staffi ng levels positively affect patient and quality of care outcomes, then changes in these outcomes across facilities should mirror the staffi ng trends. Firms
with low initial staffi ng levels should see large improvements in these outcomes, and fi rms already above the threshold should see little to no change. Causal inference relies
on assuming that in the absence of the legislation, fi rms with different initial staffi ng levels would have had similar trends in staffi ng and quality of care outcomes. The
similarity of trends in the years leading up to passage of the law suggests this assump- tion is supported by the data available.
Two points about what effects are identifi ed in the analysis below are worth empha- sizing up front. While the research design identifi es the effect of the law on quality of
care, making inferences about the structural parameters of a patient health production function—for example, the marginal effects of particular types of nurse labor on pa-
tient health—rests on exclusion restrictions that are hard to verify. Many inputs into the quality production function might be affected by the law, including each of the dif-
ferent types of nursing staff, but also other inputs such as materials use of restraints or catheters, for example, treatment protocols, and so on. With only one instrument the
pressure to raise staffi ng produced by the law, we cannot hope to isolate the effects of each of these inputs. I use the data to shed light on which mechanisms might be most
important in effecting observed changes in quality of care, but the possibility remains that changes in unobserved inputs are part of the story. Relatedly, the effects estimated
below are identifi ed from fi rms whose nurse staffi ng levels are initially low. This is arguably a more policy- relevant parameter than an average treatment effect because
public policies are clearly targeting nursing homes with low staffi ng levels rather than increases in staffi ng in fi rms with already high staffi ng levels. Regardless, the results
may not generalize to facilities with higher staffi ng levels.
I fi nd that the law was quite effective in increasing staffi ng levels among facilities initially out of compliance with the law. Firms complied with the law primarily by
increasing their employment of nurse aides, who provide primarily custodial care and earn the lowest wages of various nurse occupations, by an average of about 10 percent
for fi rms initially out of compliance. Facilities with the lowest levels of staffi ng had legislation induced increases in nurse aide employment of over 20 percent. In contrast,
I fi nd no evidence of a correlation between initial staffi ng levels and changes in patient and quality of care outcomes. Overall, the results cast doubt on the supposition that
mandated increases in staffi ng levels for facilities with low initial levels will improve quality of care. Moreover, total costs rose by an average of 4 percent among affected
fi rms suggesting the regulation may have had adverse effi ciency consequences.
The remainder of this paper is organized as follows. After discussing the past litera- ture on the nexus between nurse staffi ng and patient outcomes, I discuss the minimum
nurse staffi ng legislation passed in California in 1999 and the research design used in the paper in Section III. Section IV describes the data used in the paper, and presents
a descriptive overview of nurses and nursing homes in California. The results are presented in Section V, and the fi nal Section concludes.
II. Background
