Intended and unintended consequences of minimum staffing standards for nursing homes.

HEALTH ECONOMICS Health Econ. 24: 822–839 (2015) Published online 22 May 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3063

INTENDED AND UNINTENDED CONSEQUENCES OF MINIMUM STAFFING STANDARDS FOR NURSING HOMES MIN M. CHENa,* and DAVID C. GRABOWSKIb a

College of Business, Florida International University, Miami, FL, USA Harvard Medical School, Department of Health Care Policy, Boston, MA, USA

b

ABSTRACT Staffing is the dominant input in the production of nursing home services. Because of concerns about understaffing in many US nursing homes, a number of states have adopted minimum staffing standards. Focusing on policy changes in California and Ohio, this paper examined the effects of minimum nursing hours per resident day regulations on nursing home staffing levels and care quality. Panel data analyses of facility-level nursing inputs and quality revealed that minimum staffing standards increased total nursing hours per resident day by 5% on average. However, because the minimum staffing standards treated all direct care staff uniformly and ignored indirect care staff, the regulation had the unintended consequences of both lowering the direct care nursing skill mix (i.e., fewer professional nurses relative to nurse aides) and reducing the absolute level of indirect care staff. Overall, the staffing regulations led to a reduction in severe deficiency citations and improvement in certain health conditions that required intensive nursing care. Copyright © 2014 John Wiley & Sons, Ltd. Received 16 April 2013; Revised 1 April 2014; Accepted 14 April 2014 JEL Classification: KEY WORDS:

I11; I18; L51; L88

staffing; health care quality; nursing input; nursing home; regulation

1. INTRODUCTION Minimum nurse staffing regulations in hospitals, nursing homes, and home health agencies have become more common in recent years. With the objective to increase the quality of health care services, these regulations set a minimum ratio of staff to patients or minimum nursing hours per patient day (Harrington and Carrillo, 1999; Spetz, 2001; GAO, 2003). However, no consensus exists among researchers, medical professionals, and policymakers on the effectiveness of such regulations. The debate is of particular relevance to nursing homes, given that staffing is the dominant input in the production of nursing home services. Proponents emphasize the legislation’s potential to improve the quality of care provided to residents. They argue that the current nurse staffing levels are so low as to jeopardize the well-being of residents. Some evidence supports this argument for nursing homes, but very little evidence suggests that regulations improve quality in hospitals (Donaldson and Shapiro, 2010). Indeed, two separate literature reviews have concluded that higher nursing home staffing is associated with better quality of care (Bostick et al., 2006; Collier and Harrington, 2008). Nursing homes with low staffing levels, especially low registered nurse (RN) levels, tend to have higher rates of poor resident outcomes such as pressure ulcers, catheterization, lost ability to perform daily living activities, and depression. Staffing standards may also improve working conditions, which would increase job satisfaction and reduce nursing turnover and burnout. Critics of minimum staffing standards, on the other hand, have raised several issues with these policies. First, economists are generally against policies that do not allow providers to choose the most efficient mix of inputs *Correspondence to: College of Business, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA. E-mail: min. chen2@fiu.edu

Copyright © 2014 John Wiley & Sons, Ltd.

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NURSING HOME STAFFING STANDARDS

in the production of care (Buchan, 2005; Buerhaus et al., 2009). If a nursing home can more efficiently produce particular outcomes with fewer staff, they should not be required to hire more staff to meet a minimum standard. Second, because prices in health care are often set administratively, providers cannot necessarily raise output prices to account for the increased labor costs under minimum staffing standards (Walshe, 2001; Gaynor 2006). As such, these regulations may cause providers to substitute away from other inputs to care in order to pay the higher labor costs. Importantly, minimum staffing standards may take resources away from other areas such as indirect care staff (e.g., activities) or facility infrastructure (Bowblis and Hyer, 2013). A final criticism of these policies is that they are often not adequately enforced because of the cost burden the regulation places on providers and the severe nursing shortage in many local markets (GAO, 1999; Wiener, 2003). For example, using cost report data from the California Office of Statewide Health Planning, Harrington and O’Meara (2006) estimated that 27% of nursing homes failed to comply with the state’s minimum staffing standards in 2003. The purpose of this paper is to explore the causal relationship between the imposition of minimum nurse staffing standards in nursing homes and outcomes including nursing home staffing and quality of care. We focus on two states, California and Ohio, that changed their staffing regulations for nursing homes in 2000 and 2002. We analyzed a panel data set of 45,738 nursing home-year observations in California, Ohio and control states1 from 1996 through 2006, including detailed information on nursing home characteristics, resident census, payment source, and quality indicators measuring different dimensions of quality. We analyzed the effect of nurse staffing laws on the nursing home staffing level and quality of care using a difference-in-differences (DID) research design and several different specifications. The results show that total nursing hours per resident day (HPRD) in the treated group of nursing homes increased by about 5% relative to comparison facilities— nursing homes in other states that have no state-level staffing standards. However, because the staffing standards were broadly applied based on total direct care hours, the increased nursing inputs were certified nursing assistants (CNAs) and licensed practical nurses (LPNs) rather than RNs. As a result, nursing skill mix decreased under the minimum staffing standards. Moreover, we also found another consequence of these staffing standards, which was a substitution away from indirect care staff. In terms of quality, our results suggested a positive and statistically significant impact of staffing standards on health output quality as measured by a reduction in deficiency citations—both total counts and presence of severe deficiency citations. Other quality measures were generally not impacted by the minimum staffing standard. The effects of the regulations, however, depended on a nursing home’s staffing level and market competition at baseline. Nursing homes that ranked in the bottom quartile for staffing prior to regulation were more likely to increase LPNs and CNAs, substitute away from indirect care staff, and improve in quality. Similarly, nursing homes facing greater competition had a stronger response to the regulations.

2. BACKGROUND AND CONCEPTUAL FRAMEWORK 2.1. Background Poor nursing home quality has been a longstanding problem (Institute of Medicine, 1986). Given the importance of nurse staffing in resident safety and quality of care, minimum staffing standards have been a major subject of debate among policymakers (Harrington, 2005a, 2005b; Park and Stearns, 2009). The Omnibus Budget Reconciliation Act of 1987 (OBRA-87) created a national set of standards of care (including staffing standards) for people living in Medicare- and Medicaid-certified nursing facilities. The staffing regulations established under OBRA-87 required that certified nursing homes have LPNs on duty 24 hours a day; an RN on duty at least 8 hours a day, 7 days a week; and an RN director of nursing in place (OBRA-87, 1987). Importantly, OBRA-87 did not mandate a specific staff-to-resident ratio or minimum nurse HPRD. 1

The control states are Alabama, Kentucky, Nebraska, Nevada, New Hampshire, New York, North Dakota, South Dakota, Virginia, and Washington.


Health Econ. 24: 822–839 (2015) DOI: 10.1002/hec

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M. M. CHEN AND D. C. GRABOWSKI

Following OBRA-87, a number of states introduced legislation to establish or increase minimum staffing standards using either minimum staffing levels or minimum staff-to-resident ratios. Some bills were passed and signed into law, while others were stalled or failed.2 In 2000, California AB 1731 (Shelley) was signed into law (Chapter 451, Statutes of 2000). In addition to raising the minimum nursing staff requirement from 3.0 to 3.2 hours of direct resident care per day, the law eliminated the policy of allowing RN or LPN hours to be counted double toward meeting the prior staffing standard. Before the policy change, more than 97% of nursing homes met the then-current 3.0 staffing standard (Klutz, 2001) set in 1990 (Chapter 502, Statutes of 1990). Similarly, Ohio increased its minimum total direct care hours from 1.6 to 2.75 in 2002.3 Given that the prior minimum staffing standards were longstanding in both states, the large ‘one-time’ regulatory change eliminated potential complicating factors from frequent policy changes4 and improved identification. Together, these two states accounted for about 15%5 of all US nursing homes. A remarkable feature of the laws passed by both California and Ohio is that the minimum nursing staff requirements were specified as total direct care hours, giving the same weight to RN, LPN, and CNA hours. RNs, LPNs, and CNAs have different educational requirements and scopes of practice. To become an RN, an individual must obtain an Associate or a Bachelor degree of Science in Nursing (which normally takes 3–4 years to complete) or complete a 3-year diploma program in registered nursing. RNs have a significantly expanded scope of practice compared with that of LPNs, and they often delegate tasks to LPNs and CNAs and assume a supervisory role. LPNs are required to complete an LPN program provided by vocational schools or community colleges, which generally takes 1–2 years, and pass a licensing exam. LPN scope of practice varies across states rather significantly (Seago et al., 2004). LPNs can perform some complex procedures but not to the extent of RNs. CNAs constitute the bulk of the direct care workforce, assisting residents with daily tasks such as bathing, dressing, transferring, and feeding (Bureau of Labor Statistics, 2012). The training and qualifications to become a CNA vary across states, but the minimum requirements generally include 75 hours of training, among which 16 hours must be supervised clinical training as mandated by the Nursing Home Reform Act, part of OBRA-97. The cost to a nursing home of complying with the minimum standards depends on the extent of compliance before the regulation, the composition of the nurse skill mix, and the wages of nursing personnel. In our data, the percentage of nursing homes complying with the minimum standards in California was quite stable at around 35% from 1996 to 1999. It then increased abruptly to 45% in 2000 and kept increasing to almost double at 70% by 2004. This is consistent with the findings from a California Department of Health Services (2001) report to the legislature that used a sample of 111 nursing homes. In Ohio, more than 70% of nursing homes met the requirement prior to the regulation because of a less stringent standard, and the compliance rate increased by about 20 percentage points to 90% after the adoption of the regulation. State health departments conduct on-site licensure inspections to ensure that nursing homes meet the requirements for licensure and certification. A nursing facility that is not in compliance with the minimum staffing level or any other specific requirements may receive a deficiency citation in that dimension and must submit a plan of correction. If the harm is serious or the problem persists, a severe deficiency citation can be issued, and the nursing home may be subject to penalties. The most common penalty imposed is a civil monetary penalty. Depending on the scope and the severity of the substantiated violations, the amount of the fine can range from $50 to $10,000 per day or $1,000 to $10,000 per violation. Nursing homes with repeated or especially severe violations may be subjected to more severe sanctions, such as license suspension and revocation of the Medicare and/or Medicaid reimbursement for services provided (Chen and Serfes, 2012). 2

By 2007, 37 states had established their own staffing standards as a part of their state nursing facility licensing requirements. Among them, 13 states established their current standards in the year 2000 or later. States without their own minimum standards follow the federal guidelines. Details of the state standards are presented in Appendix tables A.1 and A.2. 3 Refer to Ohio H.B.No.78 for specifics of the regulation. The previous regulation was implemented in 1974 and amended in 1992 (Harrington, 2010). 4 For instance, Minnesota had relevant regulation changes on 1996, 2000, and 2001. Similarly, Maine’s current standards took effect in 2001, but it had changes to the previous standards in 1999 and 2000. 5 According to authors’ calculation based on the Online Survey, Certification, and Reporting (OSCAR) Database. Copyright © 2014 John Wiley & Sons, Ltd.



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According to a 1999 survey by the Bureau of Labor Statistics (Wiatrowski, 2000), the average hourly wage rates for RNs, LPNs, and CNAs were $19, $14, and $8, respectively. To increase 1 h of direct care nursing to meet the standard, a typical nursing home with 100 residents would need to pay $292,000 in wages if they hired only CNAs, while the total additional wage costs would be $594,000 if they hired only RNs. Based on the Online Survey, Certification, and Reporting (OSCAR) Database, the total additional annual wage cost to meet the higher minimum nurse staffing standards would be $202m in California and $24.4m in Ohio if nursing homes hired only RNs. By comparison, if nursing homes hired only CNAs to meet the increased staffing standards, the additional cost would be roughly $85m in California and $10.3m in Ohio. Although both states increased the minimum total direct care hours under their staffing laws, the California law also disallowed the double counting of RNs and LPNs toward meeting its staffing threshold. Moreover, the California standard of 3.2 HPRD is slightly higher than the Ohio standard of 2.75. As a result, the pressure to employ low cost staff—both new CNAs and a reallocation from RNs to LPNs—is expected to be magnified in California relative to Ohio. 2.2. Conceptual framework Nursing home administrators choose the combination of inputs to maximize the objective of the nursing home; however, minimum staffing requirement might require the facility to change the amount of nursing staff regardless of the facility’s objective function. In this context, the staffing requirements are based on total staff, making no distinction for RNs, LPNs, and NAs. Because Medicaid and Medicare nursing home payments are set administratively, providers cannot necessarily raise output prices for these public-pay residents to account for the increased labor costs under minimum staffing standards. How nursing homes respond to staffing regulations depends on both their initial staffing level and the market environment (Chen, 2008). Nursing homes that fall short of the minimum requirements will have a strong incentive to increase their staffing if the requirements are binding. On the other hand, the increased cost of complying with the requirements may lead to reallocation of their resources and substitute inputs away from other dimensions that are not directly regulated such as indirect staff. Higher nurse staffing has generally been found to have a positive effect on quality, while the decrease in unregulated dimensions may have detrimental effects. It is theoretically ambiguous whether and how nursing homes with staffing exceeding the standards will respond to the standards. They may not change staffing in a perfectly competitive market in which consumers cannot observe product quality prior to purchase (Leland, 1979; Shapiro, 1983). As other nursing homes respond to the regulation by hiring more nursing staff, which raises wages, the nursing homes above the minimum standards may respond to the higher wages by lowering their staffing ratio. If this happens, quality in a nursing home that is above the standards could get worse. On the other hand, Ronnen (1991) and Crampes and Hollander (1995) note that in an imperfectly competitive market with complete quality information, when facilities that fall short of the standards increase quality, other facilities also increase their quality in order to soften the intensified price competition from their improved competitors. These models all assume that providers compete on both price and quality. However, a large share of nursing home payments are set administratively by Medicaid and Medicare, and nursing homes can only set the price for private-pay residents. Although the Medicaid rate is generally 20 to 30% lower than private-pay price, nursing home quality is assumed to be a public good across long-stay residents regardless of the disparity in payment rates (Grabowski et al., 2008). The main incentive for nursing homes to compete on quality is to attract private-pay residents who are willing to pay more for better quality of care (Cohen and Spector, 1996). Gaynor (2006) concludes that when prices are regulated, greater competition will lead to higher quality. Quality competition has potentially become stronger in recent years because of ‘Nursing Home Compare’, a web-based nursing home report card initiative introduced by the Centers for Medicare and Medicaid Services (CMS) that started to report data on various dimensions of quality since 2002 (Grabowski and Town, 2011). The standard economic models suggest that the competitiveness of the market may influence the strategies and behaviors that the nursing homes pursue to maximize their objectives. However, the effects of staffing Copyright © 2014 John Wiley & Sons, Ltd.


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standards on quality might be complicated, depending on a decision maker’s quality elasticity of demand and quality information. There could also be a dynamic effect in which the staffing standards cause changes in factors that are not directly observable such as improved organizational process, higher productivity, and greater quality information. Based on this framework, these regulations lead to a series of testable implications: Hypothesis 1: Nursing homes below the staffing standard will respond to the minimum staffing requirements by employing greater numbers of low cost CNAs. Hypothesis 2: The increase in staffing in response to the minimum requirements will be strongest in those facilities that are most deficient prior to the regulation. Hypothesis 3: Nursing homes below the staffing standard will substitute away from other unregulated inputs such as indirect care staff (e.g., activities) in order to pay the higher nurse staff labor costs under the minimum staffing regulation. Hypothesis 4: Nursing homes above the staffing standard will decrease staffing if the price of staffing is increased by the minimum staffing regulation. Hypothesis 5: The increase in staffing in response to the minimum requirements will improve the quality of care. Hypothesis 6: The response to the staffing regulation will be strongest in more competitive markets.

2.3. Prior literature Several early studies (e.g., Janelli et al. (1994), Moseley (1996)) investigated the impact of federal staffing standards in selected states and found a decrease in restraint use and catheter use among nursing home residents after the implementation of federal standards. Zhang and Grabowski (2004) used national data and stronger methods to examine the effects of the Nursing Home Reform Act staffing requirements and found both a significant increase in nursing home staffing levels and quality improvements from 1987 to 1993. Several studies used cross-sectional data to evaluate state-level staffing standards and found that states with higher staffing standards generally had higher staffing levels (e.g., Harrington (2005a, 2005b), Mueller et al., 2006). The most recent generation of studies has used panel data methods to investigate the impact of the state minimum staffing standards. Park and Stearns (2009) examined the effect of state-level staffing policy changes between 1998 and 2001. Using a national sample, they found that the implementation of mandated standards led to small staffing increases for facilities with staffing initially below the new standards and also an improvement in selected health outcomes. Lin (2010) explored the differential impacts of minimum staffing requirements on licensed nurses and direct-care nurses respectively and found that while the former reduced deficiency citations, the latter had no significant effect on quality. Using national facility-level data for the period 1999 through 2004, Bowblis (2011) found that higher minimum staffing requirements increased nurse staffing levels, although the effect on skill mix depended on the Medicaid share within the facility. He found that minimum standards had a mixed effect on care practices but generally improved resident outcomes and lowered survey deficiencies. Hyer and colleagues (2009) found a substitution away from RNs and toward LPNs and NAs following the adoption of the Florida minimum staffing standards. In a study specific to California, Tong (2011) used whether each facility was constrained by the minimum staffing change at baseline as an instrument for a subsequent staffing increase. She found that the minimum staffing standard increased direct Copyright © 2014 John Wiley & Sons, Ltd.



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care staffing among lower skilled workers, which led to a decline in on-site mortality. Matsudaira (2014) found a similar increase in nurse aide hours proportional to the gap between their initial staffing level and legislated minimum threshold in California but no corresponding increase in the quality of care at these facilities. The difference in results across these two California studies is likely related to the use of mortality as an outcome in the Tong study, as compared with a broader mix of quality measures in the Matsudaira study. In terms of the unintended consequences of minimum staffing standards, Bowblis and Hyer (2013) found substitution away from support staff (e.g., housekeeping) in the context of increased minimum staffing standards for direct care workers. Thomas and colleagues (2010) found a similar decline in the indirect workforce following the adoption of a more stringent staffing standard in Florida. In summary, the existing literature generally suggests that overall nurse staffing has increased in response to minimum staffing standards, but this response has largely resulted in a lower nursing skill mix as nursing homes have responded to these broad staffing standards by hiring more NAs. Evidence also suggests that nursing homes decrease indirect care staff in the context of minimum direct care staffing standards. Finally, the majority of the studies show a modest positive quality response to the staffing standards. 2.4. Our contribution Previous studies either use a national sample (which may include states with frequent policy changes and lower nonbinding staffing standards) or use a single state and identify the effects using instrumental variable approach. Our research design is different from both of these approaches in that we identify effects in two large states that experienced a major, one-time policy change, and we compare these states against nursing homes in other states that did not employ a minimum staffing standard at any point over the study period. As such, our study is the first in this literature to combine the strength of the national approach (nonadopting comparison states) with the strength of the single-state approach (one-time, binding regulation). This paper further contributes to this literature by using a long panel (from 1996 to 2006) to identify not only the immediate impact but also the intermediate and long-term causal effects of minimum staffing standards on staffing levels and a large number of quality of care measures. It may be the case that the effect of these regulations dissipates or magnifies over time. Moreover, the detailed data on differentiated input and various quality measures before and after the policy change can provide insight on heterogeneous responses to regulation. Specifically, we examine whether the response is stronger among those nursing homes that are most deficient prior to the regulation and also whether the response is stronger in more competitive markets.

3. DATA AND SAMPLE To estimate the effects of minimum staffing standards on nursing home quality, we employed the 1996–2006 OSCAR Database, in conjunction with the state regulation data.6 OSCAR is an administrative database collected by CMS, which provides information on nursing home operations, resident census, and regulatory compliance status. Every Medicare- and Medicaid-certified nursing home is required to be surveyed at least once during a 15-month period, usually once a year,7 to determine their eligibility for maintaining certification 6

State regulation data are collected from the University of California at San Francisco 2000–2001 and 2007 Survey of the Nursing Home Staffing Standards (Harrington, 2010), and a Department of Health and Human Services (DHHS) 2003 report on state case studies of minimum nursing staff ratios (DHHS, 2003). When there was ambiguity or missing information, we reviewed the state government’s website and checked the relevant state codes and senate or assembly bills. We also contacted the state licensing and certification offices and ombudsman programs to ensure the accuracy of the timing and nature of the regulation. 7 The statewide average interval for these surveys must not exceed 12 months. Based on concerns that facilities could mask certain deficiencies if they could predict the survey timing, the Health Care Financing Administration (now the Centers for Medicare and Medicaid Services) directed states in 1999 to (i) avoid scheduling a home’s survey for the same month of the year as the home’s previous standard survey and (ii) conduct at least 10% of standard surveys outside the normal work day (either on weekends, early in the morning, or late in the evening) (GAO, 2003). Copyright © 2014 John Wiley & Sons, Ltd.


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status. Because approximately 96% of nursing homes are certified by Medicare or Medicaid (Grabowski et al., 2008), OSCAR covers almost the entire universe of nursing homes. We used staffing measures from OSCAR to construct overall nursing HPRD and also RN, LPN, and CNA HPRD. According to CMS (2004), HPRD means ‘the average hours worked by the licensed nurses or nursing assistants divided by total number of residents’. Staffing measures in OSCAR were reported in full time equivalents (FTEs)8 over a 2-week period. The FTEs were converted to HPRD using this formula: HPRDk ¼ ðFTEk *70Þ=14 N residents , k = RN, LPN, and CNA (Harrington et al., 1998). We converted FTEs to total staffing hours by taking the total nursing staff FTEs and multiplying by 70 work hours for the period. We then divided the total staffing hours by 14 days in the reporting period and then by the total number of residents. For example, if there are four CNAs, two LPNs, and one RN working full time and another two CNAs each working 14 h a week, this nursing home would report 4.8 = 4 + 2 * (14 / 35) FTEs for CNAs, 2 FTEs for LPNs, and 1 FTE for RN. The total hours of nursing care is (4.8 + 2 + 1) * 5 = 39. Suppose the total number of residents (totres) equals 10, then dividing 39 total hours of nursing care by 10, we obtain 3.9 HPRD. This example would comply with California law. We also investigated the impact of the minimum staffing standards on indirect care staff. Specifically, we constructed measures of housekeeping, food service, and activity staff HPRD in the same way as the direct care staffing HPRD described in the preceding texts. We also summed these three indirect care staff types together to construct an overall indirect staff HPRD. We measured overall nursing home quality by the total number of assigned survey deficiencies and an indicator that a severe health deficiency was assigned. We also constructed five facility-level quality measures expressed as the share of total residents living in the nursing home at the time of the OSCAR survey. The five quality measures, the share of residents with contractures, physical restraints, psychoactive medications, pressure ulcers, and urethral catheters, are standard measures of nursing home quality (Abt Associates, 2004; Castle and Engberg, 2005). Contractures and pressure ulcers have been frequently used in the medical care literature to measure adverse resident health outcomes (Grabowski et al., 2008; Cowles, 2002). The rates of catheter use and physical restraint use were considered to be indicators of care process quality (Zinn, 1993; Cawley et al., 2006; Park and Stearns, 2009). Antipsychotic medications are commonly prescribed to nursing home residents despite their possible side effects (Avorn, et al., 1989; Gellad et al., 2012). Also, these measures are likely to be closely related to nurse staffing. Conditions such as contractures and pressure ulcers can be prevented or significantly reduced with adequate nursing care. More medical supervision or a better understanding by staff members of the purpose and side effects of commonly used psychoactive drugs can reduce inappropriate use and adverse events. Finally, they pass the identification test, which will be detailed in the next section, showing similar trends in the experiment states versus the control states prior to the regulation. To control for heterogeneity associated with quality changes over time, we constructed covariates employing time-varying facility characteristics contained in OSCAR. These variables include bed size, number of residents, occupancy rate, acuity index,9 payer mix, ownership status (for profit, nonprofit, and government), whether the facility is hospital based, and whether the facility is part of a chain. We also constructed a countylevel Herfindahl–Hirschman Index (HHI) as a proxy for market competition and estimated the impact of minimum staffing standards on various outcomes conditional on market competition at baseline. This index was constructed by summing the squared market shares based on the number of beds of all facilities in a county. The index ranges from 0 to 1, with lower values signifying a lower concentration of facilities and thus more competitive market.

8 9

The full-time equivalents are reported by full time, part time, and contract labor, respectively. The acuity index is the sum of average activities of daily living (ADL) index and a special treatments index. The special treatments index is defined as the sum of the proportion of residents receiving respiratory care, suctioning, intravenous therapy, tracheotomy care, and intravenous feeding. ADL index is the sum of the proportion of residents with certain characteristics times their associated weights. Cowles (2002) provides the formula.



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We identified and excluded occasionally misreported staffing values using the following rules in Bowblis (2011): (i) observations with greater than 24 h of staffing per resident day; (ii) observations with zero staffing (except for activities staff); and (iii) among nursing homes that do not fall into the first two categories, those that are outside four standard deviations of the mean staffing value. The original database contains 48,799 observations from 5599 facilities from the 12 states studied during the period from 1996 to 2006. One hundred thirteen facilities (2%) were excluded based on the exclusion criteria. The resulting sample consists of 5486 unique nursing facilities with a total of 45,738 survey observations.10

4. METHODS We used a DID design to identify effects of minimum staffing standards, by contrasting outcomes in nursing homes that were subject to state-level minimum staffing standards relative to outcomes in nursing homes located in ten states that did not have a minimum staffing regulation in place at any point over our study period. Specifically, the control states are Alabama, Kentucky, Nebraska, Nevada, New Hampshire, New York, North Dakota, South Dakota, Virginia, and Washington.11 The identifying assumption underlying our model is that the experiment states (OH and CA) are affected by the same unobservable shocks as these ten control states. We will discuss how we test this assumption later in this section. We began by using a straightforward two-way fixed-effects framework to estimate the average effect of minimum nurse staffing standards on the outcomes of interest. Specifically, we used the standard DID model of the following form: Yjst ¼ α þ Xjstβ þ λMSSst þ Yeart þ Facilityj þ t * vs þ εjst

(1)

where Xjst is a vector of observable nursing home characteristics, including certification status, ownership type, size, occupancy rate, and resident acuity index (refer to the complete list in Table I). YEARt is a vector of year dummies that controls for unobserved impacts that are common to all facilities but vary by year such as changes in federal laws and changes in technology. Facilityj is a vector of nursing home dummies to control for the unobserved, time-invariant differences across nursing homes that might be correlated with variation in minimum staffing standards. MSSst is an indicator variable that equals 1 if nursing facility j in state s and year t is subject to minimum staffing standards and 0 otherwise. If nursing homes do respond to the minimum staffing standard on average, we expect λ > 0. λ is identified by the relationship between within-facility variations over time in the outcomes of interest and the variation of minimum staffing regulations across states. To address concerns about unobserved variations across states and over time that might be correlated with minimum staffing policy changes, we added state-specific trends (t * vs) to our specification. This specification allows each state to have its own time trend related to the specific measure of quality. It also controls for any additional source of state-specific heterogeneity over time such as unobserved demographic changes. We estimated Eqn (1) using a series of different outcome variables Y. First, we estimated the model with total hours of direct staff per resident day. Second, the detailed staffing information contained in OSCAR enables us to investigate the change in staff mix, which is the HPRD delivered by RNs, LPNs, and CNAs, respectively. Third, we estimated the effect of the minimum staffing standard on indirect care staff HPRD collectively and then separately for housekeeping, food service, and activity staff. Fourth, we used total count of deficiency citations, an indicator of the presence of severe deficiency, and five health outcome measures (proportion of residents with contractures, pressure ulcers, physical restraints, catheterization, and psychoactive medications) to study the effect on nursing home quality of care. Finally, we estimated each of the regressions conditional on 10

Regressions conditional on staffing or competition at baseline contain 44,108 observations because 1630 observations appear in the database after baseline date. 11 In a set of sensitivity analyses, all the results that we present in the next section are robust to limiting the control states to Kentucky, which borders Ohio, and Nevada, which borders California. Copyright © 2014 John Wiley & Sons, Ltd.


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Table I. Summary statistics for staffing, nursing home characteristics, and quality measures by regulation status California and Ohio a

Full sample

Before regulation

After regulation

t-test

Dependent variables Nurse staffing hours per resident day RN HPRD LPN HPRD NA HPRD Total HPRD

0.42 [0.46] 0.74 [0.41] 2.13 [0.71] 3.29 [1.11]

0.48 [0.55] 0.74 [0.42] 2.08 [0.66] 3.30 [1.16]

0.42 [0.51] 0.78 [0.45] 2.23 [0.77] 3.43 [1.21]

***

0.5 [0.24] 0.68 [0.37] 0.08 [0.07] 1.27 [0.53]

0.5 [0.25] 0.68 [0.39] 0.09 [0.08] 1.27 [0.56]

0.5 [0.25] 0.67 [0.38] 0.08 [0.07] 1.26 [0.55]

*

6.74 [6.40] 0.22 [0.42]

7.89 [7.28] 0.30 [0.46]

8.60 [6.92] 0.12 [0.32]

***

7.25 [5.81] 34.43 [24.78] 7.18 [7.24] 11.12 [13.67] 54.01 [17.94] 113.18 [87.70] 96.07 [71.86] 0.87 [0.17] 0.11 [0.31] 0.51 [0.50] 10.54 [1.77] 63.92 [23.93] 12.03 [16.67] 24.06 [19.51]

7.38 [6.21] 33.05 [24.55] 8.02 [8.06] 17.14 [17.27] 48.17 [19.40] 110.33 [89.01] 82.35 [49.84] 0.81 [0.20] 0.10 [0.31] 0.57 [0.50] 10.50 [1.87] 63.12 [26.86] 11.91 [19.87] 24.96 [20.57]

7.99 [6.58] 31.53 [23.76] 7.85 [8.25] 12.22 [12.55] 57.97 [18.81] 103.51 [79.54] 83.02 [50.81] 0.84 [0.18] 0.09 [0.28] 0.57 [0.49] 10.71 [2.03] 62.78 [26.16] 13.05 [16.93] 24.17 [20.80]

*** *** ***

Support staff hours per resident day Housekeeping Food services Activities Combined

*** *** *

Overall quality of care variables Total deficiency count Indicator of severe deficiency

***

Percentage of resident with Pressure ulcers Contractures Urethral catheterization Physical restraint Psychoactive medication Number of beds Total residents Occupancy rate Hospital based Chain-affiliated Acuity index Percentage of Medicaid residents Percentage of Medicare residents Percentage of private pay/other

*** ***

*** *** ***

*** ***

***

*** ***

(Continues)



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Table I. (Continued) California and Ohio a

For profit Nonprofit Government Herfindahl–Hirschman Index Medicaid rate

2

No. of facility-year observations

Full sample

Before regulation

After regulation

t-test

0.67 [0.47] 0.27 [0.45] 0.06 [0.23] 0.19 [0.25] 127.45 [28.66] 45,738

0.75 [0.43] 0.22 [0.41] 0.03 [0.18] 0.09 [0.12] 114.29 [16.29] 9778

0.78 [0.41] 0.18 [0.39] 0.03 [0.17] 0.08 [0.12] 132.34 [16.11] 12,548

*** ***

*** ***

a

The full sample includes California, Ohio, and ten control states: Alabama, Kentucky, Nebraska, Nevada, New Hampshire, New York, North Dakota, South Dakota, Virginia, and Washington. These control states did neither have any direct care regulation in effect nor b any changes in such regulation from 1996 to 2006. Medicaid daily rate is CPI-adjusted using 2004 dollars and covers from 1996 to 2004. Standard deviations are reported in brackets. *Significant at 10%; **significant at 5%; ***significant at 1%.

staffing and competition at baseline, respectively. We ranked facilities into different quartiles based on their total HPRD (TOTHPRD) or the county-level HHI where the facility was located.12 We then included the interactions between MSS and indicators of which quartile the facility ranked in terms of both staffing and market competitiveness at baseline. These interaction terms allow us to assess the differential impact of staffing policy changes across nursing homes. To examine the dynamics in the timing of the impact of the minimum staffing standards, we enriched the basic specification to allow for separate short-term, intermediate-term, and long-term effects of the regulations as follows: TOTHPRDjst ¼ α þ Xjstβ þ λ1MSSst; 1 þ λ2MQSst; 2 þ λ3MQSst; 3 þ YEARt þ FACILITYj þ t * vs þ εjst

(2)

We broke the postperiod into short term (within 1 year after the regulation, measured by MSSst, 1), intermediate term (within 2 years after the regulation, measured by MSSst, 2), and long term (3 or more years after the regulation, measured by MSSst, 3). The omitted reference category is the year prior to the regulation (period 0). The underlying identifying assumption for estimating Eqn (1) is that, absent the regulation, outcomes would have similar trends in the treatment and control states. If this assumption does not hold, that is, if, for example, nursing homes anticipate the implementation of the regulations or the regulations come into place when nursing intensity is increasing anyway, then our estimates would be biased. We conducted a partial test of this identifying assumption by examining whether the experiment states had significant changes in TOTHPRD in the periods prior to the regulation. We therefore estimated the following model: TOTHPRDjst ¼ α þ Xjstβ þ λ1MSSst; 1 þ λ2MSSst; 2 þ λ3MSSst; 3 þ λ1MSSst; 1 þλ2MSSst; 2 þ λ3MSSst; 3 þ YEARt þ FACILITYj þ t * vs þ εjst

(3)

MSSst, 1 is an indicator variable equal to 1 if it is 2 years prior to the regulation enforcement, MSSst, 2 is an indicator variable equal to 1 if it is 3 years prior to the regulation enforcement, and MSSst,3 is an indicator variable equal to 1 if it is 4 or more years prior to the enforcement. The standard errors in all the regression analyses were clustered at the level of the facility to allow for an arbitrary covariance matrix within the clusters (Bertrand et al., 2004).

12

The total HPRD and HHI at baseline are defined by the last value of total HPRD and HHI prior to the implementation of staffing requirements.



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Table II. Effects of minimum staffing standards on total staff hours per resident day

Staffing standards (all post years)

(1)

(2)

0.095*** (0.018)

0.175*** (0.023)

(3)

(4)

0.181*** (0.027)

Staffing standards (4 or more years prior to regulation)

0.004 (0.038)

Staffing standards (3 years prior to regulation)

0.015 (0.030)

Staffing standards (2 years prior to regulation) Staffing standards (year of the regulation)

0.136*** (0.025)

0.017 (0.025) 0.125*** (0.026)

Staffing standards (Post 2 years)

0.217*** (0.027) 0.191*** (0.036)

0.209*** (0.029) 0.179*** (0.039)

N Y

N Y

Staffing standards (Post 3 or more years) Includes market variables Includes market variables R-squared N

(5)

N N

N Y

Y Y

0.64

0.64

0.64

0.64

0.68

45,738

45,738

45,738

45,738

37,440

All models include year fixed effects and facility fixed effects. Standard errors are clustered at the level of the facility. *Significant at 10%; **significant at 5%; ***significant at 1%.

5. RESULTS Table I presents summary statistics of the key dependent and independent variables used in the specifications. The top panel shows an increase in the mean HPRD of LPN, CNA, and TOTHPRD in California and Ohio after the regulation. We observe a decrease in mean RN HPRD. On average, about 7.9 deficiencies were found in nursing homes before the regulation, and the total count slightly increased to 8.6 after the regulation was implemented. However, the percentage of nursing homes with a severe deficiency decreased by 60%. A high prevalence of psychoactive drug usage was present in nursing homes, with about half of residents receiving psychoactive medications. Nearly one third of the residents had contractures, and about 7% of the residents had pressure ulcers. The bottom panel presents summary statistics for independent variables and other nursing home characteristics including size (measured by the number of beds and total residents), occupancy rate, acuity level, payer source, and ownership status. The first analysis examines the relationship between the minimum staffing requirements and staffing levels under different model specifications (Table II). Column 1 presents benchmark estimates of specification (1) without adding state specific trends. Column 2 presents the DID estimates of specification (1), in which the prechange and postchange in TOTHPRD in the treatment states are compared with the prechange and postchange in TOTHPRD in the control states. The minimum staffing regulation was associated with a 0.175 increase in direct care HPRD; a 5% increase given that the mean TOTHPRD in California and Ohio before regulation was around 3.3. Column 3 presents estimated coefficients of specification (2) when the basic specification is enriched to allow for separate short-term, intermediate-term, and long-term effects of the regulations. The estimates show a positive and statistically significant impact of minimum staffing standards on total direct nursing care hours, and the impact persists after it has been in place for 3 or more years. The policy changes were associated with a 13

Although Assembly Bill 1107 addressing nursing homes in California became effective on January 1, 2000, facilities were notified that enforcement of the new standards would begin in April of 2000 (Matsudaira, 2014).



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0.14 increase in TOTHPRD during the first year of regulation and a 0.19 increase after 3 or more years after the regulation was issued. The first year phase-in may be because of a lag in enforcement.13 Column 4 presents the results of estimating specification (3) when both preregulation and postregulation dummies are included. The small and insignificant lead coefficients confirm that there is no significant difference in changes of total direct care HPRD between the treatment and control states prior to the regulation relative to period 0 (the year right before the regulation). This provides support for the identifying assumption that, absent the minimum staffing regulation, the treatment and control states would follow the same trend in TOTHPRD. In results not reported here, we found that the other coefficient estimates also make intuitive sense. For example, total nurse staffing level increased the most in nursing homes with a higher share of Medicarefinanced residents, while it increased the least in nursing homes with a higher share of Medicaid-financed residents. This result likely relates to the greater demand responsiveness and higher payment levels associated with Medicare relative to Medicaid. Column 5 presents the results when we added market and state variables such as a county-level HHI and the state Medicaid per-diem rate to our basic specification as a robustness check. The results of our specification are robust to those generated by including the market variables. Given that the Medicaid rate data were not available for years after 2004 and the lack of within-facility variation in the HHI over time (within-facility SD = 0.025), we opted to use our original specification (1) with state specific time trends to control for additional source of heterogeneity. We examined the components of the change in total direct care staffing. The results reported in Table III reveal that 71% (0.125 out of 0.175) of the increase in total direct care staffing HPRD came from the increase in CNA hours, while the rest came from the increase in LPN input intensity. Given the double counting of RNs and LPNs in California prior to the regulations, we also examined the effect of the regulations in California and Ohio in separate regressions (results available upon request). Interestingly, the results suggest that the shift in Ohio was largely because of an increase in CNA hours, while the shift in California was because of both an increase in NA and LPN hours. The results in Table III also indicate that nursing homes responded quite differently to the minimum staffing standards based on their initial staffing level and the market competitiveness. Nursing homes that ranked in the bottom quartile at baseline on total staffing significantly increased all three types of nursing staff HPRDs. On the contrary, nursing homes that ranked in the top quartile at baseline saw no change in RN and LPN and even reduced NA HPRDs—the type of staffing hours that could be adjusted most easily. The results conditional on competition at baseline show a parallel pattern. Nursing homes that were located in the most competitive markets (i.e., ranked in the bottom quartile at baseline on HHI) increased their LPN and NA staffing significantly, while nursing homes located in the least competitive markets saw no impact on all three types of nursing staff. Table III. Effects on nursing skill mix: measured by staffing hours per resident day RN MSST (all postyears)

LPN

0.008 (0.007)

Conditional on staffing at baseline Bottom quartile Top quartile

NA

0.042*** (0.009)

0.125*** (0.018)

0.023*** (0.009) 0.002 (0.016)

0.053 (0.012) 0.002 (0.018)

0.013 (0.008) 0.012 (0.017)

0.048*** (0.012) 0.010 (0.019)

0.302*** (0.027) 0.067** (0.031)

Conditional on competitiveness at baseline Bottom quartile Top quartile

0.161*** (0.024) 0.093** (0.042)

All regressions include facility characteristics, year fixed effects, facility fixed effects, and state-specific time trends. Robust standard errors clustered by state are reported in parentheses. *Significant at 10%; **significant at 5%; ***significant at 1%. Copyright © 2014 John Wiley & Sons, Ltd.


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Table IV. The impact of nursing home staffing standards on indirect care staffing hours per resident day Housekeeping MSST (all post years)

0.015*** (0.005)

Conditional on staffing at baseline Bottom quartile Top quartile

Food service 0.019*** (0.007)

0.008 (0.008) 0.031*** (0.011)

Activities staff

Combined

0.003** (0.001)

0.037*** (0.009)

0.014* (0.008) 0.041** (0.019)

0.002 (0.002) 0.0001 (0.003)

0.024* (0.013) 0.073*** (0.025)

0.013 (0.009) 0.015 (0.014)

0.001 (0.002) 0.001 (0.004)

0.029** (0.013) 0.051** (0.023)


0.015** (0.007) 0.034** (0.013)

*Significant at 10%; **significant at 5%; ***significant at 1%.

In order to analyze whether nurse staffing requirements caused nursing facilities to divert resources from other dimensions, we estimated the effects of the minimum staffing standard on indirect staff HPRD (Table IV). We observed a statistically significant decrease in all three types of indirect staff HPRD and the combined indirect staff HPRD, ranging from 2.8 to 3.3% (when compared with the corresponding mean HPRD at baseline). Nursing homes that initially ranked in the top quartile of total staffing had the largest decrease in support staff HPRD.14 Moreover, nursing homes with greater market power on average reduced their support staff HPRD more. In order to deal with the nonlinearity of count variables, we estimated a negative binomial model with facility fixed effects, year fixed effects, and state time trends. Table V reports the average marginal effects. We found that the increased minimum staffing standards significantly decreased the total number of deficiencies by 2.8% and severe deficiencies by 24%. In terms of the other five quality measures, we observed a significant decline in the rate of contractures (5.7%) following the adoption of the minimum staffing standards (Table VI). The other four measures—physical restraint use, pressure ulcers, urethral catheter use, and psychoactive medication use—were not found to be statistically significant at conventional levels. Nursing homes that were located in more competitive markets consistently had significantly fewer deficiency citations and lower incidence of contractures and psychoactive mediation use. Similarly, the facilities that were in the bottom staffing quartile at baseline experienced the strongest improvement in reducing deficiency citations, pressure ulcers, and contractures. The use of urethral catheterization had no significant change in both the top and bottom staffing quartile. The use of physical restraints increased among facilities in the bottom quartile but not the top. There was a significant drop in the psychoactive medication use among facilities in the top quartile of staffing but not the bottom. We further investigated the validity of the identifying assumption by estimating model (3) with other outcomes in addition to total hours of direct staff per resident day. Appendix Table B reports results from these additional robustness checks. We found no evidence of significant differences in nursing home quality measured by process of care and certain health outcomes in periods prior to the regulations relative to period 0.15 This partially confirms the validity of the identifying assumption that absent the regulations, states would have had similar trends in staffing and other quality measures. It also suggests that the regulations do not appear to have been implemented in response to preexisting trends in nursing home quality (Finkelstein, 2004).

14 15

Except for activities staff. In results not reported here, we do not find significant difference in other nursing home quality measures (e.g., direct care HPRD, support staff HPRD, and lack of severe deficiency citations) in periods prior to the regulations relative to period 0.



835


Table V. The impact of nursing home staffing standards on overall quality: measured by deficiency citations Total count of deficiency citations 0.221*** (0.013)

MSST (all post years)

0.071*** (0.013)

0.139*** (0.205) 0.159*** (0.019) 0.287*** 0.015

Staffing standard (year of the regulation) Staffing standard (post 2 years) Staffing standard (post 3 or more years)

Indicator of severe citations

0.050*** (0.015) 0.103*** (0.014) 0.145*** (0.018)

Conditional on staffing at baseline Bottom quartile Top quartile Conditional on competitiveness at baseline Bottom quartile Top quartile

0.253*** (0.020) 0.202*** (0.021)

0.097*** (0.017) 0.037** (0.017)

0.141*** (0.016) 0.029 (0.037)

0.099*** (0.015) 0.041 (0.025)


Table VI. The impact of nursing home staffing standards on process of care and health outcomes Pressure ulcers MSST 0.064 (all postyears) (0.156) Conditional on staffing at baseline Bottom quartile Top quartile

Contractures 1.885*** (0.604)

0.431** (0.204) 0.264 (0.266)

Urethral catheterization 0.112 (0.154)

Physical Restraint

Psychoactive medication 0.221 (0.417)

0.373 (0.356)

2.608*** (0.911) 1.857** (0.911)

0.319 (0.215) 0.201 (0.275)

1.441*** (0.556) 0.242 (0.606)

0.189 (0.624) 2.780*** (0.656)

2.529*** (0.755) 0.190 (1.393)

0.272 (0.196) 0.214 (0.348)

0.077 (0.468) 1.179 (0.863)

1.172** (0.514) 0.493 (0.896)


0.125 (0.190) 0.215 (0.360)


6. DISCUSSION In this paper, we examined nursing homes’ response to minimum staffing standards, an important regulatory tool often used to raise product quality in health care. The main findings can be summarized as follows. First, we observed a significant increase in nursing homes’ total direct care HPRD after the imposition of a minimum staffing standard. Second, because the minimum staffing standard included all direct care workers, the regulation led to the hiring of additional CNAs and LPNs rather than higher wage RNs. Third, we found that nursing homes responded to the minimum staffing standard for direct care workers by employing fewer indirect care workers such as housekeeping, food service, and activities staff. Fourth, the staffing regulations were found to improve quality of care as measured by survey deficiencies and contractures, but other quality measures (physical restraints, antipsychotic medications, pressure ulcers, and catheters) remained unchanged. Finally, Copyright © 2014 John Wiley & Sons, Ltd.


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the results were strongest in those nursing homes that were particularly deficient in staffing at baseline or were located in more competitive markets. These results offer several important contributions to this literature. We have selected two treatment states with relatively ‘clean’ policy changes. That is, these states made a single minimum staffing standard change, with long pre-period and post-period with no other changes in the policy. We were also able to construct a comparison group of ten states that made no policy change over this period of study. As a result, we were able to examine pre-trend and post-trend in our outcomes of interest. Importantly, we did not observe any trends in staffing or deficiencies in the years prior to the adoption of the staffing regulation. This suggests that we have identified a valid experiment and our empirical results are unbiased. Similarly, with our relatively long followup period, we were able to examine how staffing and deficiencies evolve in the years following adoption of the policy. Like many policies, we observed a slight lag in the response to the staffing standard, perhaps because of some leeway that facilities were allowed initially under the new regulations. From a policy perspective, these results suggest that staffing regulations have had a positive impact on certain nursing home quality measures. However, the crude nature of the direct care staffing requirements has important policy implications for the composition of the nursing home workforce. Not surprisingly, nursing homes have largely increased LPN and CNA staff in response to the minimum staffing laws that employ a total direct care HPRD threshold. The cost of an LPN is 74% as much as an RN, while the cost of an NA is 42% of an RN (Wiatrowski, 2000). Simple economics suggest that nursing homes will largely buy cheaper labor when faced with these crude staffing requirements. Nevertheless, the increased hiring of LPNs and CNAs led to fewer deficiencies and contractures. This result is somewhat different from other studies suggesting that RN staffing is particularly important for nursing home quality (Bostick et al., 2006). However, this result may reflect the problem of ‘local average treatment effects’, whereby our estimates reflect the true answer for a particular population but not for others. That is, additional CNAs or LPNs may be quite productive in nursing homes with low staff levels, but in order to improve quality in nursing homes with average or higher staffing levels, more RNs may be necessary. Moreover, we did not observe a statistically meaningful effect on other quality measures such as pressure ulcers. It may be the case that greater RNs are necessary to improve these outcomes. This is an issue to consider in future research, especially in a state that specifically mandated a higher number of RN HPRD under a new staffing regulation. Our study also adds value to the nursing home minimum staffing literature by considering heterogeneous treatment effects by staffing at baseline and also by the competitiveness of the market. In both cases, we found support for the idea that the effects are strongest in those facilities that had the lowest staffing at baseline and also in those more competitive markets. From a policy perspective, the finding that these regulations, as intended, are targeting those lowest staffed facilities at baseline is reassuring. However, policymakers may need to consider additional policies in those less competitive markets in which facilities did not respond as strongly to the regulations. Moreover, the finding that facilities in the top quartile reduced NA staffing and experienced no increase in RN and LPN staffing suggests that the increased demand for nursing personnel from the nursing homes below the staffing standards may have led to an increase in the price of labor for facilities already in compliance with the minimum. In response to this higher wage price, these facilities may have decreased NA staffing. This is consistent with the previous finding by Mark et al. (2009) where RNs in California metropolitan areas experienced real wage growth after California’s RN staffing law was implemented. The general nature of the staffing regulations may also cause what is known in the economics literature as the multitasking problem. If the goal of the regulator is multidimensional and not all dimensions are ‘regulated’, then the regulation will distort effort away from unregulated objectives that may be important to patient wellbeing (Holmstrom and Milgrom, 1991). Previous nursing home research has found evidence of the multitasking problem in the context of quality report cards (e.g., Konetzka et al., 2013; Mukamel et al., 2010). Our research offers further support for multitasking in that nursing homes decreased indirect care staff in the context of higher mandated direct care staff standards. Although we ultimately found fewer deficiencies and contractures under the minimum staffing standards, lower indirect care staff HPRD may have implications for nursing home quality of life, an important yet difficult to measure output. Copyright © 2014 John Wiley & Sons, Ltd.



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Some states have attempted to avoid this multitasking problem through the use of a wage pass-through policy, which earmarks additional Medicaid payments to nursing homes for the explicit purpose of increasing compensation for direct-care workers. Feng and colleagues (2010) found that wage pass-through policies increased CNA staffing by 3–4%. However, a wage pass-through policy requires state Medicaid programs to target new dollars to nurse staffing, whereas a staffing regulation leaves it open to the provider to determine how to allocate resources. In a period of state budget shortfalls in which the available financing for wage pass-through policies may be limited, policymakers may be willing to tolerate the unintended consequences of a minimum direct care staff standard in order to increase direct staffing and improve certain measures of quality. This study is limited in several ways. First, although we undertook a series of checks to ensure the validity of our identification strategy, we could not rule out unobservables that were present in those states adopting minimum standards and those facilities that were below the staffing threshold at baseline. Second, our quality measures (e.g., pressure ulcers) were aggregated at the facility level, and we cannot rule out the sorting of different residents across nursing homes following the regulations. Finally, the standard limitations apply to our DID model in that there may be different trends in those facilities subject to the regulations relative to the comparison facilities. In summary, this paper has found that minimum staffing standards had a positive impact on both direct care staffing and certain measures of nursing home quality of care. However, we have also found evidence that these regulations caused nursing homes to decrease the number of indirect care staff. Further, these effects were found to differ based on the staffing level and market competition at baseline. Given these trade-offs, future research will need to study the complex relationship between regulation, staffing, competition, input use, and quality. It is also important to continue to monitor both the intended and unintended consequences of these policies.

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SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at the publisher’s website.



The Need for Higher Minimum Staffing Standards in U.S. Nursing Homes.

The intended and unintended consequences of performance monitoring in colonoscopy.

Targets and the emergency medical system--intended and unintended consequences.

Staffing subsidies and the quality of care in nursing homes.

Comparing the nurse staffing in Korean and U.S. nursing homes.

Wolbachia re-replacement without incompatibility: potential for intended and unintended consequences.

Nursing Staffing and Quality of Life in Western New York Nursing Homes.

Health Human Resources Guidelines: Minimum Staffing Standards and Role Descriptions for Canadian Cystic Fibrosis Healthcare Teams.

Intended and Unintended Consequences of Constraining Clinician Prescribing: The Case of Antipsychotics.

From methods to policy: the intended and unintended consequences of comparative effectiveness research.

Unintended Consequences of Screening for Ebola.

Nurse staffing impact on quality of care in nursing homes: a systematic review of longitudinal studies.

Alternative settings: unintended consequences.

Nurse Staffing Hours At Nursing Homes With High Concentrations Of Minority Residents, 2001-11.

Citalopram in depression--meta-analysis of intended and unintended effects.

Intended and unintended pregnancies worldwide in 2012 and recent trends.

On the intended and unintended consequences of enhanced U.S. Border and interior immigration enforcement: evidence from Mexican deportees.

Safety guidance is no substitute for mandatory minimum staffing.

Nurses' views sought for law on minimum staffing ratios.

Evaluation of nursing homes.

The selection and usage of information for perceiving and remembering intended and unintended object properties.

Good Intentions, Unintended Consequences, and Unrealized Benefits.

Unintended Immunological Consequences of Biologic Therapy.

Revisiting the relationship between nurse staffing and quality of care in nursing homes: an instrumental variables approach.