Sage Journals: Discover world-class research

Abstract

Nursing staff including registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs) are critical to nursing home (NH) operations but account for approximately 27% of net revenues. Understanding how nursing staff wages affect financial performance is particularly important as policy efforts seek to expand NH minimum staffing hours. Drawing from efficiency wage theory, which posits that employers may pay above-market wages to enhance worker productivity and retention, this study examined the relationship between nursing staff wages and NH financial performance. We used secondary datasets, including Payroll-Based Journal data and Medicare cost reports (N = 37 933 facility-year observations, 2020-2022). The dependent variable was operating margin, while the independent variables were facility-level RN, LPN, and CNA wages. An instrumental variable (IV) approach was used to address potential endogeneity in RN wages, with county-level average wages (excluding the index facility) serving as the instrument. The first stage modeled RN wages as a function of the instrument, and the second stage estimated the effect of predicted wages on operating margin. Ordinary least squares models were used for LPN and CNA wages, for which endogeneity was not detected. A $1 increase in RN wages was associated with a 0.70 percentage-point decrease in operating margin (P = .01, 95% CI [−1.27, −0.14]). For LPNs, a $1 increase was associated with a 0.17-point decrease (P < .001, 95% CI [−0.20, −0.13]), and for CNAs, a 0.31-point decrease (P < .001, 95% CI [−0.37, −0.26]). These findings underscore the tension between workforce investment and financial sustainability in an industry that operates in a resource-constrained environment. Policy interventions such as wage subsidies or higher Medicaid reimbursements may be necessary to balance staffing investments with financial viability.

Keywords

nursing homes nursing staff wages instrumental variable analysis financial performance efficiency wage theory

What do we already know about this topic?

● Nursing homes face financial challenges in balancing competitive nursing staff wages with maintaining financial sustainability.

● Higher nursing staff wages are linked to lower staff turnover and better quality, but their association with financial performance remains unclear.

● Many nursing homes, especially those reliant on Medicaid, operate with tight profit margins, which may limit their ability to increase wages.

How does your research contribute to the field?

● Shows that higher nursing staff wages may be associated with lower operating margins, clarifying the direct financial impact that prior research left unresolved.

● Extends the literature by quantifying wage impacts across all three nursing staff cadres (RNs, LPNs, and CNAs), highlighting variation in financial consequences and potential substitution pressures.

● Uses an instrumental variable approach potential endogeneity; a significant concern when quantifying wage-financial performance relationship.

What are your research’s implications toward theory, practice, or policy?

● Theory: Findings complicate efficiency wage theory in the NH context, suggesting that anticipated long-run benefits of higher pay (eg, quality improvements) may be offset by financial strain at least in the short-term.

● Practice: Administrators must balance wage increases with operational efficiencies and non-monetary incentives to stabilize the workforce to ensure financial viability.

● Policy: Wage subsidies, targeted reimbursement adjustments (particularly for Medicaid-dependent facilities), and explicit investment in direct care services may help NHs absorb higher labor costs while supporting staffing mandates and quality improvement initiatives.

Introduction

Nursing staff, including registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs), are crucial to delivering high-quality care in nursing homes (NHs).^1
-4 RNs provide clinical oversight, infection control, and resident assessments; LPNs deliver bedside nursing care, medication administration, and monitoring; and CNAs deliver the majority of direct resident care.³ However, NHs face 2 concurrent realities: chronic staff shortages^3,5 and a financially precarious operating environment.⁵ This combination highlights the challenge NHs face in balancing adequate workforce investment with fiscal sustainability.

An important strategy for attracting and retaining qualified staff is to provide competitive wages and benefits.⁶ Past research has shown that higher wages are associated with lower turnover⁷ and improved retention,^8
-10 as well as with better NH quality outcomes—including lower rates of preventable conditions¹¹ and higher ratings on formal quality measures such as the Five-Star Quality Rating System (Five-Star QRS).^12,13 These findings suggest that competitive compensation not only benefits the workforce but may also strengthen NH quality of care and resident outcomes.

At the same time, implementing wage increases can be financially challenging for NHs, given the labor-intensive nature of their operations. Overall spending for direct care in U.S. NHs typically accounts for over 66% of net revenues, including 27% on nursing-related costs.¹⁴ These financial pressures are exacerbated by the industry’s narrow profit margins, particularly following the exhaustion of temporary public health emergency-related funding.¹⁵ Facilities heavily reliant on Medicaid reimbursement or located in rural areas may be particularly resource-constrained.^16,17 A recent industry report, based on a survey of 450 facilities, reported that nearly 90% were operating in the red, defined as having a negative operating margin.⁵ Although these findings may not fully represent the broader NH industry, they underscore the difficult economic environment in which NHs must balance workforce investment with financial sustainability. Consistent with this environment, approximately 800 nursing homes have closed since 2020, highlighting the severity of ongoing financial pressures.¹⁸

In light of these financial pressures, understanding how wage decisions affect NH financial health is important. Nursing home administrators (NHAs) face the twin challenge of navigating a financially challenging environment in the post-COVID-19 environment^19,20 while ensuring competitive wages to attract and retain nursing staff. However, facilities must still offer adequate compensation to attract and retain staff, particularly as many facilities are limiting admissions due to labor shortages⁵ and complying with evolving regulatory requirements.

To date, only one study has examined the effect of NH wages on financial performance. Ruffini¹¹ analyzed minimum wage increases for CNAs and found that while labor costs rose, profitability remained largely stable as facilities offset the additional costs by attracting more private-pay residents and raising prices.

Building on prior work, we examine the association between RN, LPN, and CNA wages and NH financial performance utilizing an instrumental variable approach. Because clinical roles, labor markets, and pay scales differ across these staff types, we analyze them separately; pooling wages risks obscuring heterogeneous financial effects, substitution responses, and role-specific policy levers.²¹

Findings from this study could inform wage decisions and help policymakers and administrators balance wage increases with financial sustainability. These insights are particularly timely given growing financial pressures facing NHs and the continued emphasis on staffing to ensure high-quality care for residents.

Conceptual Model

Efficiency wage theory posits that wages are not solely determined by market supply and demand but can be strategically set by employers to enhance worker productivity, loyalty, and retention.^22,23 It argues that paying higher wages may boost efficiency to a degree that offsets the additional costs, making it profitable for firms to pay wages above the prevailing market rate.²⁴ This theoretical approach is particularly relevant to the NH industry, where attracting and retaining skilled nursing staff is essential for delivering high-quality care and maintaining financial stability.

One of the most significant challenges facing the NH industry is the high level of nursing staff turnover.²⁵ Higher turnover can disrupt care delivery, increase recruitment and training costs, and negatively impact resident outcomes.^26,27 Empirical studies have shown that paying higher wages can lower turnover^7,28 and reduce recruitment and training costs.²⁹ In turn, lower turnover limits costly service disruptions, improves operational efficiency,³⁰ and is linked to higher employee satisfaction.³¹

Furthermore, by offering competitive wages, NHs may avoid penalties tied to non-compliance with staffing regulations and poor quality ratings,¹² support higher occupancy and a more favorable payer mix, and enhance eligibility for quality-linked incentives. In addition, higher wages foster a sense of fairness and satisfaction among staff, which may lead to lower absenteeism and improved team dynamics.²² In labor-intensive industries like NHs, such improvements in employee morale and productivity may enhance financial performance.³ A stable, adequately compensated, and qualified workforce may contribute to better operations by delivering care more efficiently, reducing errors, and increased compliance with staffing mandates.³²

In summary, efficiency wage theory highlights the strategic role of wages in shaping nursing staff behavior and, consequently, organizational outcomes. Within this framework, higher wages are viewed as an investment that can enhance productivity, retention, and organizational efficiency.³³ At the same time, higher wages require substantial upfront investment and may generate short-term financial strain in resource-constrained settings such as NHs. Nevertheless, efficiency wage theory suggests that the productivity and retention gains associated with higher wages can offset these costs. As such, we hypothesize that:

Hypothesis: Higher nursing staff (RN, LPN, and CNA) wages will be positively associated with better nursing home financial performance.

Methods

Our study utilized the following secondary datasets: Payroll Based Journal (PBJ), Medicare Cost Reports, LTCFocus.org, and the Area Resource Files (AHRF) for the study period from 2020 to 2022. The PBJ provides detailed auditable NH staffing data, while the Medicare Cost Reports were used to calculate nursing staff wages. LTCFocus.org provides detailed data on NH organizational and market factors. The AHRF was used to capture environmental variables. The CMS Certification Number (CCN) and county FIPS code were used to merge the different datasets. The final analytic data file comprised 37 933 facility-year observations with an average of 12 644 unique NHs per year (see Figure 1 in Appendix 1 for data merge steps).

Variables

Dependent Variable: Profitability is an important metric since it reflects the efficiency with which a firm turns its business activities into profits.³⁴ We measured profitability utilizing the operating margin, which is a commonly used financial performance measure in NH literature. Operating margin focuses on core business functions of NHs and excludes the influence of non-operating income such as endowments and non-operating expenses such as interest.³⁵ We calculated the operating margin as follows: (Net Patient Revenue − Operating Expenses)/Net Patient Revenue.

Independent variables: The independent variables were average RN, LPN, and CNA wages calculated at the facility level. The Medicare Cost Reports provide the total wage costs for directly employed RNs, LPNs, and CNAs. We calculated the average hourly cost of RNs, LPNs, and CNAs by dividing total wages by the number of hours worked.²¹ The wages were adjusted for inflation using the Consumer Price Index to ensure comparability over time.

Control variables: We controlled for facility-level and community-level characteristics of NHs that may affect financial performance.²⁰ Facility-level control variables included the following: RN, LPN, and CNA hours per resident day (PRD), size (resident count), acuity index (resident complexity), chain affiliation (yes/no), ownership (for-profit, not-for-profit, government), occupancy rate (percentage of occupied beds), NH payer mix (percentages of Medicare, Medicaid, and private-pay residents), and the resident racial/ethnic mix (% White, % Black, % Hispanic, % Asian, and % Other). Finally, we also included a COVID-19 indicator to account for the potential impact of the peak of the pandemic on NH operations and financial outcomes in 2020 (1 = 2020; 0 = 2021, 2022).

Community-level control variables were calculated at the county level: percentage of the population 65 years and older, household income, Medicare Advantage (MA) penetration (percentage of Medicare beneficiaries in MA), and competition. Competition refers to the degree to which NHs in a market compete for residents and resources. It was assessed using the Herfindahl-Hirschman Index (HHI), a metric that evaluates market concentration within an industry. HHI was calculated by summing the squares of each NH’s market share based on residents-days, with values ranging between 0 and 1. Higher values indicate more concentrated markets (less competition); lower values reflect less concentrated markets (higher competition).

Analysis

The analysis was conducted at the facility level. Descriptive statistics were used to summarize the dependent, independent, and control variables: means and standard deviations for continuous variables and frequencies and percentages for categorical variables.

Endogeneity, particularly reverse causality, is a significant concern in examining the relationship between wages and financial performance. NHs with stronger financial resources can offer higher wages to their nursing staff; in such cases, better financial status could enable facilities to afford higher compensation biasing estimates and obscuring the direction of effect. To address this concern, we adopted an instrumental variable (IV) approach to provide a more accurate assessment of the wage-quality relationship. Adapting Mukamel et al’s approach,² the IVs used in this study were the county-level average wages for RNs, LPNs, and CNAs (calculated separately for each cadre), excluding the index facility.

NHs within the same county operate in a shared labor market influenced by local economic conditions and labor supply. Therefore, the county-level average NH wages are likely correlated with the wages set by the index facility due to these shared local labor market conditions and prevailing wage norms (instrument relevance). Importantly, the average wages at competing facilities should not directly affect the financial performance at the index facility except through their impact on the facility’s own wages (instrument exogeneity). By isolating the exogenous variation in wages attributable to external labor market conditions, this approach addresses endogeneity concerns from potential reverse causality and unobserved confounders.

We modeled the data using both ordinary least squares (OLS) regression models with RN, LPN, and CNA wages as the main predictors, and 2-stage least squares (2SLS) models to account for potential endogeneity. In the first stage, facility wages (for RNs, LPNs, and CNAs, respectively) were modeled as functions of the corresponding county-level instruments and covariates. In the second stage, we examined the impact of the predicted wages on operating margin. Two-way fixed effects (state and year) were included in all regression models.

We compared OLS and IV estimates using the Durbin–Wu–Hausman test³⁶ separately for RNs, LPNs, and CNAs. The test indicated endogeneity for RN wages; therefore, we report 2SLS estimates for RNs. For LPNs and CNAs, the test did not reject exogeneity; accordingly, we report OLS as the primary specification. To ensure the validity of our findings, we performed several robustness checks, including testing different model assumptions and exploring variations in county-level labor market conditions. These checks help confirm that our conclusions are robust and not driven by specific model choices.

Results

The descriptive statistics (Table 1) provide an overview of the key variables analyzed in this study. The mean operating margin was 3.28%. Average hourly wages were $37.58 (RNs), $29.13 (LPNs), and $17.59 (CNAs). Average staffing intensity was 0.45 RN, 0.83 LPN, and 2.09 CNA hours per resident day (PRD). Facilities averaged 80.71 residents with an occupancy rate of 73.04%. Ownership was 75.06% for-profit, 18.27% not-for-profit, and 6.67% government. Medicaid comprised 55.82% of residents. Markets were generally competitive (lower HHI). Approximately 21% of residents were racial/ethnic minorities.

Table 1.

Descriptive Summary of Dependent and Independent Variables (N = 37 936).

Variable	Mean/frequency	Standard deviation/percentage
Operating margin	3.28	19.03
RN wages per hour	37.58	9.65
LPN wages per hour	29.13	7.52
CNA wages per hour	17.59	5.19
COVID-19 indicator
2020	12 720	33.53%
2021-2022	25 216	66.47%
RN nurse hours per resident day	0.45	0.32
LPN nurse hours per resident day	0.83	0.33
CNA nurse hours per resident day	2.085	0.59
Resident count (size)	80.71	46.68
Acuity Index	12.15	1.98
Chain affiliation
Affiliated	20 807	45.15%
Independent	17 129	54.85%
Ownership
For profit	28 476	75.06%
Not-for-profit	6931	18.27%
Government	2529	6.67%
Occupancy rate	73.04	45.83
Nursing home payer mix
% of Medicare residents	14.03	12.34
% of Medicaid residents	55.82	25.54
% of Private pay residents	30.14	22.64
Nursing home R/E Mix
%White residents	79.45	23.22
% Black residents	8.55	16.85
% Hispanic residents	3.16	10.18
% Asian residents	0.74	5.56
% Other residents	8.84	13.54
County population 65 and over	17.94	4.18
Household income (United States Dollar)	63 732.21	17 373.56
Medicare advantage penetration	40.75	13.52
Competition (Herfindahl Hirschman Index)	0.20	0.24

Note. RN = registered nurse; N = number of facility-year observations; Ref = reference; RN = registered nurse; LPN = license practical nurse; CNA = certified nurse assistant; R/E = racial/ethnic.

Table 2 presents the results of a 2SLS analysis. Stage 1 (Supplementary Table 1) modeled RN wages as a function of the IV (average RN wages for the county excluding the index facility). The IV demonstrated strong relevance, with a statistically significant positive association with RN wages (coefficient = 0.16, P < .01, 95% CI [0.13, 0.19]). Stage 2 used operating margin as the dependent variable and the predicted RN wages from Stage 1 as the key independent variable. RN wages were significantly negatively related to operating margin (coefficient = −0.70, P = .01, 95% CI [−1.27, −0.14]), indicating that a 1-dollar increase in RN wages was associated with a 0.70 percentage-point decrease in operating margin.

Table 2.

Two-Stage Least Squares Regression Results: Operating Margin with RN Wages Instrumented by County-Level RN Wages (N = 37 936).

Variable	Coefficient	P-value	95% Confidence interval
RN wages	−0.70	.01	−1.27	−0.14
COVID-19 indicator (Ref = Before Covid)	−1.03	.15	−2.43	0.38
RN hours per resident day	−7.10	<.001	−8.05	−6.16
Size (resident count)	0.09	<.001	0.08	0.10
Acuity Index	−0.01	.92	−0.15	0.13
Chain affiliated (Ref = Independent)	5.68	<.001	5.10	6.27
Ownership (Ref = For Profit)
Not-for-profit	−11.09	<.001	−12.14	−10.03
Government	−11.90	<.001	−13.21	−10.60
Occupancy rate	0.02	<.001	0.02	0.03
Nursing home payer mix (Ref: Private Pay)
% of Medicare residents	0.11	<.001	0.09	0.13
% of Medicaid residents	0.31	<.001	0.28	0.33
Race/Ethnicity (Ref = White)
Black	0.01	.41	−0.02	0.04
Hispanic	0.11	<.001	0.08	0.14
Asian	0.12	<.001	0.07	0.17
Other	−0.05	<.001	−0.08	−0.03
County population 65 and over	0.13	<.001	0.06	0.21
Household income (United States Dollar)	0.00	.04	0.00	0.00
Medicare advantage penetration	0.12	<.001	0.09	0.14
Competition (Herfindahl Hirschman Index)	−0.01	.30	−0.02	0.01

Note. RN = registered nurse; N = number of facility-year observations; Ref = reference.

Compared to the OLS model (Table 3), the IV estimates imply a larger effect. In OLS, a $1 increase in RN wages was associated with a 0.12 percentage-point reduction in operating margin, versus 0.70 percentage points in the IV model.

Table 3.

Ordinary Least Squares Regression Results between Operating Margin and RN Wages (N = 37 936).

Variable	Coefficient	P-value	95% Confidence interval
RN wages	−0.12	<.001	−0.15	−0.09
Covid 19 (Ref = Before Covid)	−2.35	<.001	−2.93	−1.76
RN hours per resident day	−7.23	<.001	−8.14	−6.31
Size (resident count)	0.08	<.001	0.07	0.09
Acuity Index	−0.01	.85	−0.15	0.12
Chain affiliated (Ref = Independent)	5.38	<.001	4.89	5.88
Ownership (Ref = For Profit)
Not-for-profit	−11.93	<.001	−12.59	−11.27
Government	−12.64	<.001	−13.71	−11.57
Occupancy rate	0.02	<.001	0.02	0.03
Nursing home payer mix (ref: Private Pay)
% of Medicare residents	0.12	<.001	0.11	0.13
% of Medicaid residents	0.31	<.001	0.29	0.33
Race/Ethnicity (Ref = White)
Black	−0.01	.10	−0.03	0.00
Hispanic	0.09	<.001	0.06	0.12
Asian	0.13	<.001	0.08	0.18
Other	−0.06	<.001	−0.08	−0.04
County population 65 and over	0.14	<.001	0.07	0.21
Household income (United States Dollar)	0.00	.48	0.00	0.00
Medicare advantage penetration	0.12	<.001	0.09	0.14
Competition (Herfindahl Hirschman Index)	−0.02	.01	−0.03	0.00

Note. RN = registered nurse; N = number of facility-year observations; Ref = reference.

The validity of the instrumental variable approach was supported by 3 key criteria: instrument relevance (Stage 1 results), the Durbin-Wu-Hausman test confirming RN wages endogeneity, and correction for endogeneity yielding consistent estimates of the RN wage effect on financial performance (F statistic = 132.08).

Tables 4 and 5 report only OLS estimates for LPNs and CNAs, respectively. In these models, a $1 increase in LPN wages was associated with a 0.17 percentage-point decrease in operating margin (P < .001, 95% CI [−0.20, −0.13]), and a $1 increase in CNA wages was associated with a 0.31 percentage point decrease (P < .001, 95% CI [−0.37, −0.26]).

Table 4.

Ordinary Least Squares Regression Results between Operating Margin and LPN Wages (N = 37 936).

Variable name	Coefficient	P-value	95% Confidence interval
LPN wages	−0.17	<.001	−0.2	−0.13
Covid 19 (Ref = Before Covid)	−1.937	<.001	−2.51	−1.34
LPN hours per resident day	−5.18	<.001	−5.98	−4.36
Size (resident count)	0.08	<.001	0.07	0.09
Acuity Index	−0.04	.6	−0.17	0.09
Chain affiliated (Ref = Independent)	8.055 < 0.001 7.56 8.556
Ownership (Ref = For Profit)
Not-for-profit	−12.44	<.001	−13.08	−11.79
Government	−11.72	<.001	−12.79	−10.65
Occupancy rate	0.024	<.001	0.02	0.03
Nursing home payer mix (Ref: Private Pay)
% of Medicare residents	0.3	<.001	0.28	0.32
% of Medicaid residents	0.13	<.001	0.12	0.15
Race/Ethnicity (Ref = White)
Black	−0.004	.6	−0.02	0.01
Hispanic	0.07	<.001	0.04	0.09
Asian	−0.04	<.001	−0.06	−0.02
Other	−0.03	<.001	−0.05	−0.03
County population 65 and over	0.15	<.001	0.08	0.22
Household income (United States Dollar)	−0.0001	.54	−0.0002	0.0001
Medicare advantage penetration	0.12	<.001	0.09	0.14
Competition (Herfindahl Hirschman Index)	−0.01	.06	−0.02	0.0005

Note. LPN = license practical nurse; Ref = reference.

Table 5.

Ordinary Least Squares Regression Results Between Operating Margin and CNA Wages (N = 37 936).

Variable	Coefficient	P-value	95% Confidence interval
CNA wages	−0.31	<.001	−0.37	−0.26
Covid 19 (Ref = Before Covid)	−3.09	<.001	−3.67	−2.50
CNA hours per resident day	−8.59	<.001	−9.07	−8.10
Size (resident count)	0.08	<.001	0.07	0.08
Acuity Index	0.04	.54	−0.09	0.17
Chain affiliated (Ref = Independent)	6.82	<.001	6.33	7.32
Ownership (Ref = For Profit)
Not-for-profit	−9.55	<.001	−10.21	−8.90
Government	−9.61	<.001	−10.67	−8.55
Occupancy rate	0.02	<.001	0.02	0.03
Nursing home payer mix (Ref = Private Pay)
% of Medicare residents	0.32	<.001	0.29	0.34
% of Medicaid residents	0.11	<.001	0.10	0.12
Race/Ethnicity (Ref = White)
Black	−0.03	<.001	−0.04	−0.01
Hispanic	0.07	<.001	0.04	0.10
Asian	−0.04	<.001	−0.06	−0.03
Other	−0.02	<.001	−0.04	−0.02
County population 65 and over	0.12	<.001	0.04	0.19
Household income (United States Dollar)	0.00	.72	0.00	0.00
Medicare advantage penetration	0.11	<.001	0.09	0.14
Competition (Herfindahl Hirschman Index)	−0.01	.03	−0.03	0.00

Note. CNA = certified nurse assistant; Ref = reference.

In terms of control variables, broadly speaking, chain affiliation was associated with higher operating margins, while not-for-profit and government ownership were associated with lower operating margins (relative to for-profit ownership). Higher occupancy and larger facility size were associated with higher operating margins, whereas higher nursing hours per resident day were associated with lower operating margins. Payer mix variables (% Medicare and % Medicaid) were positively associated with operating margins. At the county level, a higher share of the population aged 65 and older and higher Medicare Advantage penetration were associated with higher operating margins.

As a sensitivity analysis, we re-estimated all models excluding 2020 to account for the atypical operating environment during the first year of the COVID-19 pandemic, when some facilities may have raised wages to retain staff for extended periods. The results were broadly similar in magnitude and direction to the main analyses, and our substantive conclusions were unchanged.

Discussion

The purpose of this study was to examine the relationship between nursing staff wages and NH financial performance, with operating margin as the primary indicator. Grounded in efficiency wage theory, we hypothesized that higher wages would be positively associated with improved NH financial performance. Contrary to our expectations, higher wages were associated with lower operating margins consistently across RNs, LPNs, and CNAs. To address endogeneity, we estimated IV models for RNs; for LPNs and CNAs, the Durbin–Wu–Hausman test did not reject exogeneity, so we report OLS estimates rather than IV.

Our findings contrast with Ruffini,¹¹ who leveraged statutory minimum wage reforms that primarily affected lower-wage direct care workers and reported that NHs maintain profitability despite higher wages by shifting towards a more renumerative payer mix and exercising greater pricing power. In contrast, our study examines facility-level wages across the nursing staff hierarchy, including higher-paid staff such as licensed nurses. Because wage increases for higher-skilled staff can represent larger absolute labor-cost changes and may be more difficult to offset through payer-mix shifts or price adjustments in a Medicaid-reliant, tight-margin sector, the broader scope of wage exposure in our analysis likely contributes to the negative association observed for operating margins. Consistent with this interpretation, the estimated impact was largest for RNs, who are paid significantly higher wages than direct care staff.

At the same time, an efficiency wage framework suggests that wage increases may influence not only labor costs but also staffing strategies. For instance, higher wages may reduce turnover, vacancies, and reliance on overtime or temporary staff, potentially allowing facilities to operate with lower staffing intensity within a cadre or to reallocate resources across nursing roles. Although RNs, LPNs, and CNAs serve distinct clinical functions, staffing decisions are made jointly under a common budget constraint, and efficiency gains in one category may affect staffing composition elsewhere. Our analysis treats wages and staffing intensity as separate inputs and estimates wage effects by cadre, but it does not explicitly model dynamic staffing adjustments or cross-cadre reallocation following wage changes. Moreover, such staffing optimization processes typically unfold over longer time horizons and are difficult to observe within a short, 3-year panel. As a result, the estimated associations likely reflect net, near-term financial effects rather than longer-run staffing optimization that may occur as facilities adapt.

Given the larger estimated impact of RN wages on operating margins, these results may incentivize facilities to substitute RNs with LPNs³⁷ wherever scope-of-practice permits in an effort to contain costs. Limited task shifting (eg, routine monitoring, medication administration) is possible, but this is concerning because RNs are required for clinical oversight, assessment, care planning, infection control, and supervision; excessive substitution risks undermining quality and jeopardizing compliance with supervisory requirements and staffing standards.³⁸ Any short-run savings may also be offset if reduced RN oversight increases survey deficiencies or erodes Five-Star ratings, exposing facilities to potential penalties and revenue losses. However, this difference should be interpreted alongside the methods: the RN results come from IV models, whereas the LPN/CNA results rely on OLS given no evidence of endogeneity.

Several factors may explain why the observed wage–margin relationship runs counter to our hypothesized positive association. First, although research suggests that competitive wages can lead to better NH outcomes, such as improved care quality, fewer errors, and improved workforce retention,^7,12,13,39 these benefits often take time to fully materialize.⁴⁰ Given that our observation window (2020-2022) captures primarily short-run dynamics amid COVID-era shocks, the estimates likely reflect near-term cost impacts more than longer-run efficiency gains. Initially, the increased labor costs associated with higher wages may outweigh the financial savings from these potential long-term benefits.

From an efficiency wage perspective, higher wages can foster greater workforce loyalty and enhanced performance over time,^41,42 but in heavily regulated, tight-margin environments like NHs, cost pressures related to raising wages can dominate short-term financial outcomes—particularly when reimbursement structures fail to adequately compensate for staffing investments.

Another possible explanation is the NH sector’s financial structure, which relies heavily on Medicaid (56% of residents in our analytic sample were Medicaid beneficiaries). Because Medicaid reimbursement rates are substantially lower than Medicare and private pay, NHs have limited flexibility to absorb higher labor costs. This structural imbalance makes the negative effect of higher wages on operating margins particularly pronounced.

Adding to these challenges, NHs operate in a highly competitive environment, where higher labor costs are often difficult to offset through better reimbursement rates or payer contracts. As a result, wage increases can strain financial resources, highlighting the challenge of balancing competitive pay strategies with long-term operational sustainability.

Finally, the organizational structure of the NH industry, dominated by for-profit (FP) chain facilities (three-quarters of facilities in our sample), may further complicate the relationship between wages and financial performance.⁴³ Corporate chains may maintain centralized wage structures, limiting facility-level flexibility in adjusting compensation to local market conditions. Unlike not-for-profit facilities, FPs often lack access to additional financial resources, such as endowments or charitable contributions, which could help offset the higher costs associated with raising wages. This resource limitation may make it more challenging for FP facilities to adopt wage increases without negatively affecting their operating margins.

Our study has several limitations. First, unmeasured facility-level confounders may still influence the observed relationship, including leadership characteristics, organizational culture, and human resource management practices. Second, the 2020 to 2022 study window coincides with COVID-19 shocks that may leave residual confounding despite inclusion of a COVID indicator. Third, the short horizon (2020-2022) likely emphasizes near-term cost impacts, while longer-run efficiency-wage benefits may not be fully captured; future research using longer panels is needed to assess whether these wage investments translate into improved financial performance over time. Fourth, defining markets at the county level may imperfectly proxy the true NH labor market, raising standard caveats for the RN instrument’s exclusion restriction. Fifth, while operating margin is a commonly used measure of operational efficiency in NH research, it may not fully capture the financial health of not-for-profit and government-owned facilities. It may be important to consider using other metrics of financial performance such as liquidity or reinvestment capacity. Finally, future research should also investigate compensation and staffing considerations for other significant NH roles, including administrators.

Policy and Managerial Implications

For NH administrators, the core challenge lies in balancing financial performance with workforce investment. As previously discussed, a significant body of literature has linked higher wages with better NH performance metrics.^7,12 However, with nursing costs already accounting for over 27% of NH revenues,¹⁴ raising wages can jeopardize profitability—an especially pressing concern in the post-COVID-19 environment.^15,20 Administrators may need to pair wage increases with operational efficiencies and strategic cost management to mitigate short-term margin pressures. In addition to competitive pay, leveraging non-monetary incentives—such as professional development, career advancement pathways, and an engaging work culture—can further enhance retention and performance.^3,44

Moreover, prior research has indicated that higher NH quality may be linked to improved financial performance.⁴⁵ Thus, investing in higher nursing staff wages to bolster quality¹³ may serve as a strategic long-term approach, potentially strengthening both workforce stability and overall financial outcomes.

Given the critical role nursing staff play in NHs³ and emerging staffing mandates,⁴⁶ policymakers face a significant challenge in balancing improved staffing levels with ensuring NH financial viability. Policymakers should address these structural issues by reevaluating reimbursement formulas to reflect the actual costs associated with providing high-quality care. Our estimates provide initial benchmarks that policymakers may use to inform policy design; for example, the finding that a $1 increase in RN wages is associated with a 0.70 percentage-point reduction in operating margin offers a reference point for approximating the scale of reimbursement increases or targeted wage subsidies that may be needed to offset staffing-related cost pressures. Cadre-specific wage estimates similarly allow policymakers to calibrate reimbursement adjustments to the differential financial impact of RN, LPN, and CNA staffing. For instance, the National Academies of Sciences, Engineering, and Medicine⁴⁴ has recommended designating a specific percentage of Medicare and Medicaid payments for direct-care services, including staffing levels, wages, and benefits, to ensure adequate investment in NH workforce development. Incentives could also be offered to facilities meeting specific criteria, such as those located in rural or other underserved areas.³ By alleviating some of the fiscal pressure on NHs, policymakers can better address workforce challenges, retain skilled caregivers, and improve overall care quality.

Conclusion

Grounded in efficiency wage theory and employing an IV approach, the primary purpose of this study was to examine the relationship between nursing staff wages and NH financial performance. The findings highlight the inherent tension between workforce investments and profitability. Although raising wages may initially strain NH finances, operational efficiencies and appropriate policy measures can help mitigate these short-term pressures. However, it will require concerted efforts from both policymakers and industry stakeholders to ensure that NHs can prioritize their workforce while still remaining financially viable.

Supplemental Material

sj-docx-1-inq-10.1177_00469580261420709 – Supplemental material for Higher Nursing Staff Wages Are Associated With Lower Operating Margins in Nursing Homes: An Instrumental Variable Analysis

Supplemental material, sj-docx-1-inq-10.1177_00469580261420709 for Higher Nursing Staff Wages Are Associated With Lower Operating Margins in Nursing Homes: An Instrumental Variable Analysis by Akbar Ghiasi, Rohit Pradhan, Gregory Orewa and Robert Weech-Maldonado in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Appendix 1 ORCID iDs

Rohit Pradhan

Gregory Orewa

Robert Weech-Maldonado

Ethical Considerations

Prior studies of this type have been reviewed by the Texas State University Research Integrity and Compliance (RIC) office. According to the provisions in 45 CFR § 46.102 pertaining to “human subject” research, the RIC has determined that studies of this type exclusively involve the examination of anonymous originally collected from the public domain. Therefore, the RIC has concluded that research of this type does not use human subjects and is not regulated by the provisions in 45 CFR § 46.102 and therefore an IRB review of the study is not required.

Author Contributions

Akbar Ghiasi led the analytic strategy and conducted the statistical analyses, contributed to study design, and assisted with manuscript revision. Rohit Pradhan conceptualized the study, drafted the initial version of the manuscript, contributed to interpretation of the findings, and led manuscript revisions. Gregory Orewa contributed to data preparation, statistical analysis, and review of the manuscript. Robert Weech-Maldonado contributed to study conceptualization, interpretation of results, and critical revision of the manuscript for important intellectual content. All authors reviewed and approved the final manuscript and agree to be accountable for all aspects of the work.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a Texas State University Research Enhancement Program (REP) grant.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Publicly available datasets were analyzed for this study.

a) Payroll-based Journal:

b) Five-Star Quality Rating System:

c) LTFocus.org:

d) Area Health Resources File:

Supplemental Material

Supplemental material for this article is available online.

References

Mukamel

Saliba

Ladd

Konetzka

RT.

Associations between daily nurse staffing levels and daily hospitalizations and ED visits in nursing homes. J Am Med Dir Assoc. 2022;23(11):1793-1799.e3.

Mukamel

Saliba

Ladd

Konetzka

RT.

The relationship between nursing home staffing and health outcomes revisited. J Am Med Dir Assoc. 2024;25:105081.

Abt Associates. Nursing Home Staffing Study. Abt Associates; 2023.

Chen

Grabowski

DC.

Intended and unintended consequences of minimum staffing standards for nursing homes. Health Econ. 2015;24(7):822-839.

American Health Care Association. State of the Nursing Home Sector. American Health Care Association; 2024.

Heiks

Sabine

Long term care and skilled nursing facilities. Dela J Public Health. 2022;8(5):144-149.

Sharma

Association between wages and nursing staff turnover in Iowa nursing homes. Innov Aging. 2022;6(4):igac004.

Vadean

Saloniki

E-C.

Job quality and job separation of direct care workers in England. Innov Aging. 2023;7(2):igad009.

Kennedy

Applebaum

Bowblis

Straker

JK.

Organizational factors associated with retention of direct care workers: a comparison of nursing homes and assisted living facilities. Gerontologist. 2021;61(4):530-539.

10.

Gleason

Miller

EA.

Maximizing home health aide retention: the impact of control and support on the job. Gerontologist. 2021;61(4):517-529.

11.

Ruffini

Worker earnings, service quality, and firm profitability: evidence from nursing homes and minimum wage reforms. Rev Econ Stat. 2024;106(6):1477-1494.

12.

Allan

Vadean

The impact of wages on care home quality in England. Gerontologist. 2023;63(9):1428-1436.

13.

Pradhan

Ghiasi

Weech-Maldonado

The impact of nursing staff wages on nursing home quality: an instrumental variable approach. J Am Med Dir Assoc. 2025;26(8):105705.

14.

Harrington

Mollot

Braun

Williams

United States’ nursing home finances: spending, profitability, and capital structure. Int J Soc Determinants Health Health Serv. 2024;54(2):131-142.

15.

American Health Care Association. Economic State of Skilled Nursing Facility (SNF) Industry. American Health Care Association; 2023.

16.

Weech-Maldonado

Lord

Pradhan

, et al. High Medicaid nursing homes: organizational and market factors associated with financial performance. INQUIRY. 2019;56:0046958018825061.

17.

Mor

Zinn

Angelelli

Teno

Miller

SC.

Driven to tiers: socioeconomic and racial disparities in the quality of nursing home care. Milbank Q. 2004;82(2):227-256.

18.

American Health Care Association. Access to Care Report. American Health Care Association; 2024.

19.

Brunt

Bowblis

Applebaum

Loss of public health emergency funds challenges the financial viability of nursing homes, especially not-for-profit facilities: article examines nursing home viability following the loss of public health emergency funds. Health Aff. 2024;43(11):1578-1586.

20.

Orewa

Weech-Maldonado

Lord

Davlyatov

Becker

Feldman

SS.

COVID-19 pandemic impact on nursing homes financial performance. INQUIRY. 2024;61:00469580241240698.

21.

Bowblis

Brunt

Applebaum

Grabowski

DC.

Nursing homes increasingly rely on staffing agencies for direct care nursing: study examines nursing home staffing. Health Aff. 2024;43:327-335. doi:10.1377/hlthaff. 2023.01101

22.

Weiss

Efficiency Wages: Models of Unemployment, Layoffs, and Wage Dispersion, Vol. 1192. Princeton University Press; 2014.

23.

Morris Morant

Jacobs

DC.

Frontiers of efficiency wages: unconventional wisdom?

J Manag Hist. 2018;24(3):300-315.

24.

Katz

LF.

Efficiency wage theories: a partial evaluation. NBER Macroecon Annu. 1986;1:235-276.

25.

Gandhi

Grabowski

DC.

High nursing staff turnover in nursing homes offers important quality information: study examines high turnover of nursing staff at US nursing homes. Health Aff. 2021;40(3):384-391.

26.

Pradhan

Ghiasi

Weech-Maldonado

Leadership matters: investigating the association between nursing home administrator turnover and quality. Gerontologist. 2024;64(9):gnae066.

27.

Shen

McGarry

Gandhi

AD.

Health care staff turnover and quality of care at nursing homes. JAMA Intern Med. 2023;183(11):1247-1254.

28.

Dube

Naidu

Reich

The economic effects of a citywide minimum wage. ILR Rev. 2007;60(4):522-543.

29.

Mowday

RT.

Strategies for adapting to high rates of employee turnover. Hum Resour Manage. 1984;23(4):365-380.

30.

Mukamel

Saliba

Ladd

Konetzka

RT.

Association of staffing instability with quality of nursing home care. JAMA Network Open. 2023;6(1):e2250389.

31.

McHugh

Aiken

LH.

Organization of hospital nursing and 30-day readmissions in Medicare patients undergoing surgery. Med Care. 2015;53(1):65-70.

32.

Castle

Hyer

Harris

Engberg

Nurse aide retention in nursing homes. Gerontologist. 2020;60(5):885-895.

33.

Campbell

CM.

Do firms pay efficiency wages? Evidence with data at the firm level. J Labor Econ. 1993;11(3):442-470.

34.

Gapenski

Pink

GH.

Understanding Healthcare Financial Management. Health Administration Press Chicago; 2007.

35.

Vélez-González

Pradhan

Weech-Maldonado

The role of non-financial performance measures in predicting hospital financial performance: the case of for-profit system hospitals. J Health Care Finance. 2011;38(2):12-23.

36.

Wooldridge

JM.

Econometric Analysis of Cross Section and Panel Data. MIT Press; 2010.

37.

Mueller

Duan

Vogelsmeier

Anderson

McConnell

Corazzini

Interchangeability of licensed nurses in nursing homes: perspectives of directors of nursing. Nurs Outlook. 2018;66(6):560-569.

38.

Yang

Carter

Trinkoff

Nelson

HW.

Nurse staffing and skill mix patterns in relation to resident care outcomes in US nursing homes. J Am Med Dir Assoc. 2021;22(5):1081-1087.e1.

39.

Steinmetz

de Vries

Tijdens

KG.

Should I stay or should I go? The impact of working time and wages on retention in the health workforce. Hum Resour Health. 2014;12(1):12.

40.

Akerlof

GA.

Labor contracts as partial gift exchange. Q J Econ. 1982;97(4):543-569.

41.

McNulty

Brady

KP.

How efficiency wage theory helps explain differences in bank financial performance. Q J Financ Account. 2023;61(1/2):1-30.

42.

Levine

DI.

Can wage increases pay for themselves? Tests with a production function. Econ J. 1992;102(414):1102-1115.

43.

Welch

Ruhter

Bosworth

Lew

Sommers

BD.

Changes of Ownership of Hospital and Skilled Nursing Facilities: An Analysis of Newly-Released CMS Data. ASPE; 2022.

44.

National Academies of Sciences Engineering and Medicine. The National Imperative to Improve Nursing Home Quality: Honoring Our Commitment to Residents, Families, and Staff. National Academies Press; 2022.

45.

Weech-Maldonado

Pradhan

Dayama

Lord

Gupta

Nursing home quality and financial performance: is there a business case for quality?

INQUIRY. 2019;56:0046958018825191.

46.

Centers for Medicare & Medicaid Services. Medicare and Medicaid Programs: Minimum Staffing Standards for Long-Term Care Facilities and Medicaid Institutional Payment Transparency Reporting Final Rule (CMS 3442-F). Centers for Medicare & Medicaid Services; 2024.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB