Nursing assistants (NAs) deliver the majority of direct care to nursing home (NH) residents, and the presence of inadequate staffing levels and high turnover of NAs has direct implications for the quality of resident care (Castle, 2008; Castle & Engberg, 2005; Harrington, Zimmerman, Karon, Robinson, & Beutel, 2000). NAs hold approximately 1.1 million jobs in NHs (Smith & Baughman, 2007), yet vacancy rates indicate that this number is insufficient to meet the current demand (American Health Care Association, 2003). Furthermore, historically high turnover rates, measuring 71% annually, exacerbate the unmet need (American Health Care Association, 2003). As the demand for paraprofessional staff is projected to increase with the aging of the population (Institute of Medicine, 2008; U.S. Department of Health and Human Services, 2003), attention to recruitment and retention of NAs is of importance.
The availability of employment-based benefits may be one important strategy to recruit and retain NAs. For example, recent studies have found that benefits were associated with fewer intentions to leave the job (Stearns & D'Arcy, 2008) and lower turnover rates (Temple, Dobbs, & Andel, 2009) among NAs employed in NHs. In addition, employee benefits have been positively associated with job commitment (Bishop et al., 2008) and job satisfaction (Ejaz, Noelker, Menne, & Bagaka's, 2008) among direct care workers in long-term care settings. At the national level, a recent report by the Institute of Medicine (2008) highlighted the importance of benefits for the recruitment and retention of direct care workers and recommended increasing the availability of benefits. However, little is known about the range of benefits offered to NAs and the variation of benefits across the NH industry. The objectives of this study were to describe the employment-based benefits offered to NAs and to explore the relationship between NH organizational characteristics and the availability of NA staff benefits.
For decades, critical perspectives on social policy and aging have been used to explain healthcare delivery to older adults, including care provided in NHs (e.g., Estes, 1979, 1999a, 2001). The political economy of aging perspective emphasizes how political and economic factors within a market-driven healthcare system have facilitated the creation of the "aging enterprise" (Estes, 1979, 1999b; Estes, Harrington, & Pellow, 2001). The aging enterprise refers to burgeoning industries that have had a considerable financial interest in serving the needs of older adults (Estes, 1979, 1999b). Thus, healthcare, including long-term care, has become a profitable commodity through cost containment measures, growth in the for-profit sector, and corporate consolidation (Estes, 1999b; Estes et al., 2001; Polivka & Zayac, 2008). These market-driven changes in the industry have influenced the organizational context and operations of many NHs, which may provide insight into the variation of employment-based benefits offered to staff.
Nursing Home Characteristics and the Availability of Benefits
Aging policies in the United States have reinforced market-driven systems over the years, which has created the aging enterprise and resulted in an NH industry that is mostly profit motivated (Estes et al., 2001). The NH industry experienced a significant increase in for-profit and chain membership from the 1970s to 1990s (Estes et al., 2001). For-profit facilities are primarily concerned with profit maximization and as a result may not invest as much in employee compensation as do not-for-profit facilities. Using 54 NHs from Wisconsin, Haley-Lock and Kruzich (2007) found that public and not-for-profit NHs offered better wages and health insurance contributions to NAs. Similarly, Kash, Castle, and Phillips (2007) found that expense ratios related to nursing staff benefits were significantly higher in not-for-profit versus for-profit NHs. In contrast, earlier work by Hunter (2000) did not find an association between profit status and an index of NA wages and benefits in a sample of 149 NHs in Massachusetts. With the increase in chain membership, NHs belonging to a multifacility chain overseen by the same corporate organization may benefit from greater economies of scale and sources of capital. This may explain the finding that those facilities belonging to a chain are more likely to provide better NA wages and benefits (Haley-Lock & Kruzich, 2007).
With the goal of profit maximization present in the aging enterprise (Estes, 1979, 1999b), sources of revenue may also affect the capacity of NHs to invest in benefits for NAs. Facilities with larger numbers of beds and higher occupancy rates may be indicative of greater revenue and better performance, and research has shown that these characteristics are associated with better NA wages and benefits (Haley-Lock & Kruzich, 2007). Occupancy rates by payer source may also influence profits and expenditures on staffing. Medicaid is the lowest third-party payer of NH care, in contrast to Medicare and private pay, and research has shown that greater proportions of private-pay residents have been associated with higher job quality of NAs in terms of wages and benefits (Hunter, 2000).
Other organizational factors influenced by the goal of profit maximization that could potentially affect the availability of NA benefits include staffing levels, union involvement, and education of the NH administrator. NHs with a market-driven philosophy may have practices that reflect just the minimum required level of nurse staffing, which may be indicative of NHs that offer fewer benefits to maintain lower staffing costs. Collective bargaining of employee unions may also have an important influence on minimum wages and benefits offered to staff. For example, union presence was associated with higher job quality of NAs in one study (Hunter, 2000). In addition, NH administrators with better training and education may be better able to balance the competing demands of profit maximization and investment in human capital. For example, studies have found that NH administrators who completed a standardized training program (Hunter, 2000) or earned a master's degree (Haley-Lock & Kruzich, 2007) provide better wages and benefits to NAs.
Although some research suggests that NH organizational characteristics may play a role in the availability of benefits offered to NAs, previous studies (e.g., Haley-Lock & Kruzich, 2007; Hunter, 2000) have been limited by small samples from a single state and the inclusion of only a few benefits in the outcome measure. In this study, we examined the presence of a full range of NA benefits and explored the relationship between NH organizational characteristics and benefits offered to NAs using the political economy of aging as a theoretical framework. We utilized a large, nationally representative sample of NHs, allowing for generalizability of the findings. In addition, we included several benefits in the outcome measure that are likely considered the most important to employees. Consistent with the framework of the political economy of aging (Estes, 1979, 1999a), we expected that the NH organizational context would be related to the provision of benefits. Specifically, we hypothesized that organizational characteristics including ownership status, occupancy by payer source, and union membership would be associated with the availability of benefits offered to NAs.
Data were derived from the 2004 National Nursing Home Survey (NNHS), a publicly available data set provided by the National Center for Health Statistics that surveys a nationally representative sample of NHs in the United States. The 2004 NNHS was administered between August and December 2004 and includes components on staffing and facility characteristics at the organizational level. The staffing questionnaire is a self-administered questionnaire completed by the NH administrator. It contains data on education, staffing levels, staff turnover and tenure, staffing practices, and wages and benefits of various categories of staff within the facility. The facility questionnaire is completed by an in-person, computer-assisted interview with the administrator. This component contains organizational characteristics and services provided by the facility. Both of these components are included together in one publicly available data set.
For this survey, a sampling frame of 16,628 NHs was stratified by bed size and metropolitan area and then sorted by certification status, hospital relationship, ownership, geographic region, state, county, and zip code. NHs included in the study were selected using systematic sampling based on probabilities proportional to bed size. A total of 1,500 NHs were selected from the sampling frame. Of these, 283 refused to participate, and 43 were ineligible for the study. The final sample yielded 1,174 participating NHs included in the NNHS for a response rate of 81%. Participating facilities had at least three beds and were certified by Medicare or Medicaid or had a State license to operate.
The analytic sample for this study consisted of 944 of the 1,174 NHs (80%) from the NNHS after 230 facilities were excluded due to missing data. The 230 omitted facilities did not differ significantly (p > .05) on bed size, profit status, or occupancy rate, although those omitted were more likely to belong to an NH chain (χ2 = 4.45, p = .04).
For the main analysis, benefits were measured as a composite variable based on the following five weighted benefits: health insurance, retirement, paid vacation, paid sick days, and paid personal days. These benefits, which are among the most important in employee compensation (Christensen, 2002), were selected based on availability of weightings. The weightings were derived from the hourly cost of these benefits to employers in the private industry available in the Employer Costs for Employee Compensation (ECEC; U.S. Bureau of Labor Statistics, 2007a). Costs for private industry were selected because NHs in the sample are largely based in the private industry. In contrast to simply summing the total number of available benefits, weighting these benefits in terms of their respective costs to NH employers allowed for assessing the relative value of each benefit and capturing greater variation in the outcome.
For health insurance, facilities received a corresponding weight based on the most expensive level of health coverage offered to NAs: (a) partially paid health insurance for the employee, (b) fully paid health insurance for the employee, (c) partially paid health insurance for the employee's family, and (d) fully paid health insurance for the employee's family. The ECEC includes only an average hourly cost to employers for all four health plans combined; however, monthly employer costs of the four levels of health insurance coverage are included in the National Compensation Survey (U.S. Bureau of Labor Statistics, 2007b), from which the ECEC is derived. Using this information, the hourly costs of the four plans in this study were estimated to equal the average hourly cost of all health insurance plans presented in the ECEC ($1.85; U.S. Bureau of Labor Statistics, 2007a) and were calculated in the same relative proportions as the monthly costs in the National Compensation Survey.
The estimated weights for the health insurance benefits are presented in Table 1. For example, the least expensive plan, partial employee coverage at $265.74 per month, is approximately 33% of the monthly cost of the most expensive plan, full family coverage at $814.44. Therefore, the estimated hourly cost of partial employee coverage ($0.94) is also 33% of the estimated hourly cost of full family coverage ($2.86). Correspondingly, full employee coverage and partial family coverage account for 47% and 79%, respectively, of the costs of full family coverage. The estimated hourly costs of all four health insurance plans average to equal $1.85 as presented in the ECEC (U.S. Bureau of Labor Statistics, 2007a), from which all other weights were derived.
Facilities received the corresponding weight in Table 1 if they offered the benefit to NA employees. The total benefits score for each facility was calculated as the sum of the weights of available benefits. For example, a facility offering full family health insurance (2.86) plus retirement benefits (0.92) and paid personal days (0.06) would obtain a benefits score of 3.84. The benefits score had a range of 0.06 to 4.96 and a mean of 3.27 (SD = 1.11).
There were additional benefits surveyed in the NNHS that could not be included in the benefits score due to lack of cost data to generate weights: child daycare, transportation allowance, employee assistance, and career promotion. However, these benefits are still presented in the descriptive statistics (Figure 1) to explore the range of NA benefits across the NH industry. Overall, the most important benefits from an employee's perspective were included in the benefits score, including health insurance, retirement, and paid time off (Christensen, 2002).
A total of eight organizational characteristics of NHs were included in this study. All of these were ordinal-level variables in the NNHS that needed to be recoded to attain adequate data distribution across levels of measurement. Facility ownership was dummy coded into four variables by profit status and chain affiliation: for-profit/chain; not-for-profit/chain; for-profit/nonchain; and not-for-profit/nonchain. Not-for-profit included both government and private not-for-profit facilities. Facility bed size was measured as 3-49, 50-99, 100-199, and >200 beds and was dichotomized for this study into <100 beds (0) and ≥100 beds (1). Total facility occupancy was measured as <70%, 70%-79%, 80%-89%, 90%-94%, and ≥95%. For this analysis, it was dummy coded into three variables to represent low (<70%), moderate (80%-94%), and high (≥95%) facility occupancy levels. Medicaid census, or the percentage of Medicaid residents in the facility, was measured as <19%, 20%-39%, 40%-59%, 60%-79%, and ≥80%. For this analysis, it was dummy coded into three variables to represent low (0%-59%), moderate (60%-79%), and high (≥80%) levels of Medicaid occupancy. Medicare census was measured as <9%, 10%-19%, and ≥20% and was recoded into <9% (0) versus ≥10% (1). The level of total nurse staffing (i.e., registered nurse, licensed practical nurse, and NA) was also included. This variable was measured as <1.0, 1.0-1.99, 2.0-2.99, 3-3.99, and ≥4.0 hours per patient day and was dichotomized to reflect <4.0 (0) versus ≥4.0 (1) based on a minimum of 4.1 hours per patient day recommended by the Centers for Medicare and Medicaid Services (2001). Involvement of NAs in a labor union was included as a dichotomous variable. Last, the education of the NH administrator was dichotomized as having a graduate degree (1) versus an undergraduate degree or lower level of education (0).
To account for the stratified probability design of the NNHS, descriptive statistics were calculated for all study variables using the procedure SURVEYFREQ in SAS (SAS Institute Inc., 2003). Bivariate correlations among variables were also examined before the multivariate analysis to assess the possibility of a multicollinearity bias. The variables showed no substantial indication of collinearity. The highest correlation was found between Medicaid and Medicare census (r = −.34).
To examine associations between organizational characteristics and the benefits score, a linear regression model was calculated using the SAS program procedure SURVEYREG (SAS Institute Inc., 2003). This linear regression procedure is most suitable in the analysis of stratified sample designs with sample weights in survey data. All available organizational characteristics were entered into the regression model, and two-tailed tests of significance (p < .05) were used. The results are presented as unstandardized regression coefficients and standard errors for each independent variable.
Descriptive statistics for the facility sample are presented in Table 2. Most of the facilities were for-profit/chain (40%) followed by not-for-profit/nonchain (29%); for-profit/nonchain (20%); and not-for-profit/chain (11%). Nearly half of the facilities surveyed had more than 100 beds, and three fourths had occupancy rates exceeding 80%. These facilities served predominately Medicaid residents, with the majority having greater than 60% occupancy of Medicaid recipients. About half had greater than 10% occupancy of residents reimbursed under Medicare. Only 20% provided at least 4.0 hours per patient day of total nursing care, and NA union involvement was present in 16% of facilities. Approximately one third of NH administrators had a graduate degree.
In relation to the availability of NA benefits (Figure 1), more than half of the facilities (56%) offered partially paid health insurance for family coverage as the most comprehensive health plan followed by partially paid health insurance for the individual employee (19%). Few facilities offered fully paid health insurance for the employee (9%) or family (3%), and approximately 13% offered no form of health insurance. Nearly all facilities offered paid vacation days (96%), and the majority offered retirement benefits (60%), paid sick days (79%), paid personal days (64%), and career promotion opportunities (72%). Relatively few NHs offered access to employee assistance programs (30%), child daycare assistance (5%), or transportation allowance (5%).
Table 3 presents the results of the linear regression model examining associations between organizational characteristics and NA benefits. In comparison with for-profit/chain facilities, the not-for-profit facilities (chain and nonchain) were associated with a higher benefits score, whereas for-profit, nonchain facilities were associated with a lower benefits score. Facility size and occupancy levels were positively associated with the NA benefits score. The highest Medicaid occupancy level compared with the lowest was associated with a lower benefits score for NA staff, whereas the association with Medicare occupancy was not significant. Nurse staffing above 4.0 hours per patient day, the presence of NA union involvement, and graduate-level education of the NH administrator were all associated with a higher NA benefits score. The model accounted for 17% of the variation in the benefits score (adjusted R2 = 0.17).
Recruitment and retention of a direct care workforce to provide long-term care to the increasing population of older adults have been persistent challenges. On the basis of previous research indicating that employee benefits may be important for NA job satisfaction (Ejaz et al., 2008), commitment (Bishop et al., 2008), and retention (Stearns & D'Arcy, 2008; Temple et al., 2009), we examined the availability of NA staff benefits and explored the relationship between NH organizational characteristics and NA benefits in a large, nationally representative sample of NHs. We used the political economy of aging, which suggests that the organizational context and operations of NHs are influenced by a market-driven healthcare system, to explain why certain organizational characteristics may be associated with the availability of NA benefits. We found that ownership status, facility size, occupancy levels, Medicaid occupancy, nurse staffing level, unionization of NAs, and education of the NH administrator were associated with the availability of benefits. These findings are consistent with theoretically based tenets within the political economy of aging framework (Estes, 1999a) that organizational factors related to the goal of profit maximization may influence the availability of benefits for NA staff.
The results of this study indicate that not-for-profit facilities, belonging to a chain or not, offered greater benefits to NA staff. Previously, Haley-Lock and Kruzich (2007) found that public and not-for-profit NHs provide better compensation to NAs in terms of wages and health insurance contributions. In the context of the political economy of aging framework (Estes, 1979, 1999a; Estes et al., 2001), the provision of fringe benefits may be counter to profit maximization present in the aging enterprise. Moreover, not-for-profit facilities may invest more in staff compensation due to ideological differences in mission and favorable tax exemptions (Estes, Alford, & Egan, 2001).
Similar to the findings of Haley-Lock and Kruzich (2007), this study provides evidence that chain membership may be positively associated with NA benefits. Among the for-profit facilities, nonchain facilities offered fewer benefits compared with chain facilities. Not-for-profit facilities, whether belonging to a chain or not, provided a higher level of benefits than did for-profit facilities that also belonged to a chain. Multifacility chains are more likely to take advantage of greater economies of scale and sources of capital, which may enable them to offer more generous benefits than do freestanding facilities while still maintaining desirable profit levels, which is inherent in the aging enterprise (Estes et al., 2001).
Within the political economy of aging, NHs generally target clients and services toward those that generate the highest profits (Estes et al., 2001). In this study, we found that larger bed size and higher occupancy levels were positively associated with the level of NA benefits. In contrast, facilities with the highest levels of Medicaid occupancy (≥80%) offered fewer NA benefits compared with facilities with the lowest Medicaid occupancy rates (<60%), although no significant difference in benefits provision was found among NHs with other Medicaid occupancy rates (60%-79%). Others have also found these relationships between size and occupancy measures and the wages and benefits of NAs (Haley-Lock & Kruzich, 2007; Hunter, 2000). It may be that larger facilities and highly occupied facilities bring in greater sources of revenue, which in turn could be used to invest in staff benefits. Contrastingly, higher proportions of Medicaid residents may be an indicator of facilities that bring in lower revenues, thus not able to offer as many fringe benefits to staff.
This study found that a high level of nurse staffing was associated with greater benefits for NAs. Kash et al. (2007) also found that spending on employee benefits was associated with higher levels of nurse staffing. NHs that spend fewer resources on staffing levels and employee benefits to simply meet minimum requirements may reflect the market-driven philosophy of NHs that are part of the aging enterprise (Estes et al., 2001). Results from this study also provide evidence that NHs in which NAs are members of a union offer higher levels of benefits, which is likely due to the fact that unions tend to improve pay and benefits. Similarly, Hunter (2000) found that unionization was associated with better job quality of NAs in terms of wages and benefits. In relation to the profit-driven delivery of care in the aging enterprise (Estes et al., 2001), collective bargaining from unions may be one of few alternatives to increase spending on NA staff benefits.
Finally, education of the NH administrator was positively associated with NA benefits controlling for all other characteristics. Specifically, facilities with administrators earning a graduate level of education were associated with a higher level of benefits. This association was also found in a study by Haley-Lock and Kruzich (2007), although the relationship existed only among administrators in not-for-profit facilities. In a similar study, Hunter (2000) found a positive association between job quality of NAs and NHs with administrators who completed a state-approved training program. Administrators who are highly educated may possess additional knowledge in business or human resource management that allows them to recognize the importance of employee compensation in addition to earning profits.
There are a number of key implications for practitioners and policy makers based on findings from this study. First, for-profit facilities seem to offer fewer benefits than do not-for-profit facilities yet both compete for limited numbers of NAs. Thus, it may be advantageous for NHs with proprietary ownership to model not-for-profit facilities in their provision of benefits because benefits may be an important strategy to recruit and retain NAs (Institute of Medicine, 2008; Temple et al., 2009). Although the addition of benefits may initially be detrimental to profits, providers may be able to save some of the expenses associated with NA turnover at an estimated $3,500 per worker (Seavey, 2004). In addition, NH providers that offer benefits such as health insurance or pension plans may be able to take advantage of tax deductions to minimize the additional expense. Administrators of for-profit NHs may need to give more consideration to the cost-effectiveness of the addition of benefits.
Second, the findings from this study suggest that NH chains offer more benefits to staff than do freestanding facilities. This relationship appears to be particularly important among for-profit facilities. For example, for-profit facilities belonging to a chain may be able to negotiate better group rates on health insurance plans due to economies of scale. Administrators of freestanding facilities may gain greater negotiating power by pooling together with other affiliated or professional organizations to increase the affordability and availability of staff benefits. On a broader level, current healthcare reform may help small businesses, which includes many freestanding NHs, to purchase competitively priced insurance coverage for their employees.
Third, facilities with the highest Medicaid occupancy levels are likely to offer fewer benefits to NA staff. Medicaid reimbursement rates are the lowest among payers of NH care, which may make it difficult for facilities with a high Medicaid occupancy to absorb the cost of additional benefits for its employees. In light of these findings, policies to increase Medicaid reimbursement for direct care staffing may allow NHs with high proportions of Medicaid residents to offer comparable benefits to their staff. A number of states have successfully increased Medicaid reimbursements designated for wages or benefits of direct care staff (Seavey & Salter, 2006). Another promising approach is to link increases in Medicaid reimbursement with better staffing outcomes including lower turnover and higher retention (Better Jobs Better Care, 2005), which may be achieved with a competitive benefits package.
There are several limitations to this study. First, the study is cross-sectional, so only associations between organizational characteristics and NA benefits can be identified. In addition, many of the variables in the NNHS were measured at the ordinal level when a continuous measure may have been preferable. There may also be other important organizational characteristics related to NA benefits that were not available in the data, such as financial ratios or measures of quality. We also could not control for local market conditions due to lack of information about facility location, which may play a role in a facility's decision to offer benefits; however, it is unlikely that these controls would have explained away the findings. Finally, we could not include all of the available benefits in the benefits score due to lack of data to generate weightings, although we feel we included the most important NA benefits in the outcome measure based on previous research (Christensen, 2002). Future research efforts are needed to examine additional organizational and market characteristics associated with the provision of a comprehensive benefits package.
It is also important to note that this study examined the availability of benefits offered to NAs working in NHs. We did not examine eligibility for or enrollment in any of the benefits, and low participation rates may influence whether these benefits are made widely available to NA staff. For example, some NAs cannot afford to participate in their employer's health plan (Squillace et al., 2009), and high turnover rates suggest that some may not be employed long enough to earn paid time off. Future research is needed to examine the use or importance of various benefits among NAs.
Despite these limitations, this study also has important strengths. To the best of our knowledge, this is the first study to explore the prevalence of a range of benefits offered to NAs, which could be useful for future comparisons. The study also provides a rationale of why particular organizational characteristics potentially make a difference in the availability of NA benefits using the political economy of aging theory. In addition, this study utilized a large, nationally representative sample of NHs from the NNHS and included the most important employee benefits in the outcome measure. Finally, the weighting of the benefits score by cost to employers represents a novel way to capture the relative worth of various benefits offered to NA staff from an organizational perspective.
Inadequate staffing levels and high turnover of NAs have direct implications for the quality of resident care (Castle, 2008; Castle & Engberg, 2005; Harrington et al., 2000). Increasing the availability of employment-based benefits is receiving greater attention as one possible solution to address the ongoing shortage and high turnover of NAs (Institute of Medicine, 2008). The results of this study suggest that specific organizational characteristics within a political economy of aging framework may explain some of the variations in the availability of benefits offered to NAs. Attention to these systemic differences may be informative for providers and policy makers to increase employment-based benefits for NA staff.
The authors thank Drs. Cathy McEvoy, Jennifer Salmon, and Elizabeth Bass for their assistance in the preparation of this article.
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