«The Effect of Competition on Nursing Home Expenditures under Prospective Reimbursement John A. Nyman Thefor-profit nursing home's incentive to ...»
The Medicaid reimbursement rate was included as another explanatory variable. The 1983 reimbursement rates for Wisconsin nursing homes were based on the 1981 costs of the home, adjusted forward to account for inflation (State of Wisconsin 1983, 1984).
Therefore, the rate is prospective and exogenous, since the individual nursing home cannot influence the size of its rate in the present period.
The reimbursement rate is induded in order to determine what proportion of an additional Medicaid dollar is spent on patient care.
The tenth explanatory variable is the percentage of Medicaid patients in the home. Under excess-demand conditions, there are two reasons why costs are expected to be lower in homes with more Medicaid patients. First, nursing homes can choose among patients when the bed supply is tight; given that choice, they will admit the more lucrative private patients first, then the less dependent (and therefore less costly) of the Medicaid patients, until they run out of beds. A home with a larger percentage of Medicaid patients will, therefore, exhibit relatively lower costs. Second, with excess demand, nursCompetition and Nursing Home Expenditures 561 ing homes need to compete only for the higher-paying private patients.
They can attract Medicaid patients with only minimal-quality care.
Homes with a higher percentage of private patients will, therefore, exhibit higher quality. To the extent that differences in expenditures reflect differences in quality, homes with a greater percentage of Medicaid patients will spend less on patient care.2 Three additional variables were included in the regression: the percentage of SNF patients in the home, a dummy representing whether or not the home was located in the Milwaukee area, and a final dummy representing whether the nursing home was operated in conjunction with a hospital. SNF patients have greater staffing requirements than other patients. As a result, we would expect that as the percentage of SNF patients increases, so will average costs. Homes located in urban areas such as Milwaukee face higher wage and other input prices; therefore, they are likely to have higher costs. Finally, homes operated in conjunction with hospitals may use upgraded inputs (e.g., registered nurses instead of licensed practical nurses); thus, their costs are expected to be higher than those operated without a hospital connection.
The regression results from the 6.5 empty beds subsample show that the average excess capacity variable is now insignificant. This is also true of the analogous cases for the 5.5 and 7.5 cutoff subsamples.
This may indicate that there are homes located in counties where the average excess capacity is so great that differences in that variable do not reflect differences in the homes' need to compete. For example, the need to compete for patients may be virtually the same for homes located where the average excess capacity is 20 as where the average excess capacity is 15. Because the 2 6.5 subsample is likely to contain these homes, a weaker relationship exists. This would seem to indicate that a threshhold level of competition can be reached, beyond which increases in the average number of empty beds in a county have a Competition and Nursing Home Expenditures 565
negligible effect on costs. This analysis, however, gives us only sketchy information regarding the location of that threshhold.
The average ADL score variable shows an interesting reversal of signs across the two regressions. In the regression where the bed supply is tight, an increase in the average ADL scores of the homes leads to significantly lower costs, while the same coefficient where the availability of beds is greater indicates that increases in the dependency of patients leads to the expected finding of significantly increased costs.
One possible explanation is that nursing homes can choose among patients when the bed supply is tight. Those homes that choose high ADL cases may choose the exceptional cases that are not as costly. For example, a comatose patient may require less attention than one who has the same number of dependencies (or even fewer), but who is also HSR: Health Services Research 23:4 (October 1988) conscious. Homes located in areas where beds are available cannot pick and choose among the more dependent patients; therefore, their costs reflect the generally positive relationship between ADLs and cost that would be expected on average.
The percentage Medicaid variable shows a similarly interesting loss of significance in the regression where beds are generally available.
This is again consistent with the hypothesis that homes can choose among Medicaid patients where the bed supply is tight, choosing the patients that require the least costly care. When there are many beds available, the nursing home cannot be as selective and must take Medicaid patients in the order that they apply for admission. This means that the relationship between cost and percent Medicaid patients, though again negative, is apparently less systematic than when the nursing homes are able to choose. This explanation would hold only to the extent that other variables do not adequately control for the effects on costs of case-mix differences.
The loss of significance in the percent Medicaid variable is also consistent with the hypothesis that, when nursing homes do not need to compete for patients, it is the Medicaid patients who suffer most from the lack of competitive quality care. Since private patients are a more lucrative source of revenue, nursing homes prefer them to Medicaid patients. When there is a shortage of beds, nursing homes will only compete for the private patients. As a result, nursing homes catering to a more private clientele will have higher expenditures than homes catering primarily to Medicaid patients. When there is a surplus of beds, however, nursing homes must compete for both types of patients and, in that case, lower expenditures per patient day will not necessarily be associated with Medicaid-dominated homes.3 It should also be noted that the number of weighted violations is directly related to costs when the bed supply is tight, and that the expected inverse relationship exists when there are beds available. Perhaps the direct relationship indicates that homes with more violations have greater costs because of fines and the costs associated with corrective measures. This pattern again obtained for the two other pairs of subsetted regressions.
Finally, the size of the Medicaid reimbursement rate coefficient changed across the two regressions. When the bed supply is tight, an additional dollar of reimbursement results in an additional S.81 spent on patient care; however, when ample beds are available, an additional dollar of reimbursement results in $1.04 more of expenditures, ceteris paribus.4 If this difference is significant, it indicates that the taxpayer's Medicaid dollars are more likely to be spent on patient care when there Competition and Nursing Home Expenditures 567 is competition for patients. A Chow test (Kmenta 1971, 373) was performed and showed that the structure of the two regressions is significantly different at the 5 percent level, indicating that differences in any of the coefficients across the two regressions must be taken seriously.
The declining coefficients of the average excess capacity variables in the less than 5.5, 6.5, and 7.5 average empty-bed subsetted regressions may indicate a nonlinear relationship between costs and the average number of empty beds. Accordingly, the natural logarithm of the average empty-beds variable (plus 1) was substituted into the allobservation regression. The resulting coefficient for that variable was 1.70, significant at the 1 percent level. Although the fit was slightly better and the average excess-capacity variable was more significant, the results of this regression were not appreciably different from the original all-observation results and are therefore not reported.
INTERPRETATION OF RESULTSThese findings appear to support the hypothesis that a lack of competition caused by an excess demand for beds was an important reason why costs were allowed to drop low enough to jeopardize the quality of nursing home care under prospective and flat-rate reimbursement systems. Indeed, the importance of the effect of excess demand on costs rivals that of the profit-maximization incentive itself. It was found that for-profit nursing homes have $4.15 lower costs than nonprofits. If excess demand were to decrease from a level represented by 0 average empty beds to a lower level represented by 6.5 average empty beds, and if a linear relationship between costs and average empty beds is assumed, each nursing home would have about $4.03 ($.62 x 6.5) greater costs.5 The magnitude of the effect of reducing excess demand to this extent ($4.03), therefore, is similar to the magnitude of the cost reduction from having a for-profit rather than a nonprofit charter ($4.15).
Although similar in magnitude, the lower expenditures in forprofit nursing homes may have less sinister implications for quality than the lower expenditures of homes located in underbedded markets.
Lower costs in for-profit firms may stem from differences in efficiency between them and nonprofit firms. To the extent that reduced expenditures in proprietary firms represent efficiency gains, they are desirable.
568 HSR: Health Services Research 23:4 (October 1988) Although this only represents a possibility, it is difficult to ascribe any similarly positive reason for nursing homes in underbedded markets to have lower costs. Therefore, the effect of excess demand on quality may be greater than the effect of the firm's charter status on quality, even though the effect of both on firm expenditures is the same.
Furthermore, the effect of the excess demand is not simply reflected in the size of the average excess-capacity coefficient. The subsetted regressions are distinguished by the kvel of the average excess-capacity variable. Therefore, differences in the coefficients across the two subsetted regressions may also reflect the effect of excess demand through other variables. For example, the Medicaid reimbursement variable coefficients show that, for every additional dollar increase in the rate, homes in underbedded markets spend about S.23 less ($1.04 - $.81) on patient care than homes located where the average excess capacity is higher. Also, homes with 100 percent Medicaid patients spend about $3.80 less ($7.92 - $4.12) on patient care in underbedded markets than do 100 percent Medicaid homes in markets with more empty beds. The differences in the coefficients of these two variables are consistent with the theoretical expectations about the effect of excess demand on their relationship with costs. Consequently, these differences would seem to indicate that the effect of excess demand on costs is considerably greater than the effect captured in the average excess-capacity variable's coefficient alone. Therefore, even the magnitude of the effect of excess demand on costs may be significantly greater than the effect of the home's charter status on costs.
One way to estimate the entire effect of excess demand on expenditures is to compare average costs across the two groups. The mean average cost for the underbedded observations is $41.25, while the nursing homes located where more empty beds are available had expenditures of $43.62 per patient day. This comparison, however, does not control for the differences in the characteristics of nursing homes across the two subsamples. To hold these constant, the characteristics of the average home in the underbedded sample (i.e., the mean levels of the regression variables for these homes) were multiplied by the regression coefficients of the homes in the surplus bed sample.
The sum of these products is $46.37; it represents the costs per patient day of the average nursing home in the sample where the mean average excess capacity is 4.4 beds if this home were located in a market where the mean average excess capacity is 10.3 beds. In other words, by relocating the average home in a market with excess demand to a market where there is more competition for patients, this nursing home would be forced to spend $5.12 (or about 12.4 percent) more per day Competition and Nursing Home Expenditures 569 for every patient in the home. Since the average number of patients in a home is about 128, this amounts to an average annual increase in total expenditures of almost $240,000 ($5.12 x 128 patients x 365 days, or $239,206 in 1983 dollars) per nursing home.
EXPENDITURES AND QUALITYThis article has implied throughout that a relationship exists between the need to compete as measured by excess demand and the quality of care provided by a home. Clearly, the empirical findings of an association between average excess capacity and expenditures found are consistent with this relationship. Other studies (Nyman 1985, 1988) have found evidence of a connection between the excess demand and a direct measure of quality. In those studies, the weighted number of violations was shown to be greater (and sometimes significantly so) for homes located in counties with smaller average excess capacities. These earlier studies have an advantage over this work in that they use Wisconsin data from 1979, the first year that Wisconsin collected violation data.
It is likely that, because of unfamiliarity with the new quality assurance system, violations data from 1979 were more representative of all possible quality-defining characteristics of the homes. Therefore, the 1979 violation variable may represent the true "quality" of care provided by the nursing homes better than the 1983 variable, since homes may have learned by 1983 to pay closer attention to those quality-defining characteristics for which they could be cited and fined.
The disadvantage of the earlier studies, however, is their reliance on a direct measure of quality, a construct that is difficult to measure.
Clearly, no consensus has yet been achieved within the health services research community regarding the identity of those characteristics that constitute "quality" nursing home care. Consequently, any measure of quality is open to criticism. This is not a problem here because the meaning of the dependent variable is not in doubt.