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Uncompensated hospital care payment and access for the uninsured: evidence from New Jersey.
Abstract:
Objective. We assess the impacts of New Jersey's payment for hospital uncompensated care on access for the uninsured. Data Sources. Uncompensated care charges and other data were obtained from audited reports maintained by the New Jersey State Department of Health. Other data sources include the AHA Annual Survey and the Bureau of Labor Statistics. The sample includes 80 of 88 acute care hospitals in the state for 1979 to 1987. Study Design. This study used a pre- and postdesign to assess the impacts of the introduction of uncompensated care payment. Both descriptive and multivariate analyses were used. Key variables include hospital ownership and teaching characteristics; the labor force composition; and the level of government funding for public health insurance. Principal Findings. The overall level of uncompensated hospital care increased markedly in New Jersey during the period 1979 through 1987. However, this trend can be attributed to variables other than the new payment system, including increased demand for uncompensated care. The program did result in a more even distribution of uncompensated care across hospitals. The financial condition of hospitals providing the largest share of this care also improved, ensuring continued access. Conclusions. Funding of uncompensated care via hospital payment regulation did not increase its overall provision. However, improved access was achieved as opportunities for the uninsured to receive care were made more widely available. Keywords. Uncompensated care; uninsured; hospital access

Subject:
Hospitals (Prices and rates)
Medically uninsured persons (Services)
Authors:
Dunn, Daniel L.
Chen, Michael
Pub Date:
04/01/1994
Publication:
Name: Health Services Research Publisher: Health Research and Educational Trust Audience: Trade Format: Magazine/Journal Subject: Business; Health care industry Copyright: COPYRIGHT 1994 Health Research and Educational Trust ISSN: 0017-9124
Issue:
Date: April, 1994 Source Volume: v29 Source Issue: n1
Geographic:
Geographic Scope: New Jersey Geographic Name: New Jersey; New Jersey
Accession Number:
15443993
Full Text:
The issue of access to hospital care by uninsured persons has increased in importance in the United States over the past decade. Between the late 1970s and 1987, persons without insurance grew markedly, from 13.0 to 17.5 percent of the nonelderly population. By 1987, the number of uninsured Americans had reached 37 million (Swartz 1989). Currently, a significant fraction of the population continues to be uninsured (Families USA Foundation 1993). At the same time, the ability of hospitals to provide uncompensated care has been impaired by the spread of cost containment and other procompetitive forces.

Concern for the uninsured and access to medical care has led to the formulation of a number of policy options including the expansion of public health insurance coverage; the. advancement of private insurance alternatives such as risk pools or employer-mandated insurance; and state regulation of hospital payment to finance care for the uninsured. While the first two options increase coverage and target individuals, the latter approach uses direct payments to hospitals and targets providers.

In 1980, as part of a wider reform of hospital payment, the state of New Jersey chose payment regulation as a way to promote care for the uninsured and assist hospitals who provide this care. Under this provision, payment rates for all payers, public and private, were set to include the costs of the uncompensated care provided by a hospital. In this way, patients who paid wholly subsidized the care of those who did not pay.

In this article, we evaluate three effects of New Jersey's uncompensated care provision: (1) Has the level of uncompensated care changed in response to the payment reform? (2) Has the provision of uncompensated care been shared to a greater extent across hospitals in the state? and (3) Are the hospitals that provide a disproportionately larger share of uncompensated care better off financially since implementation of the reform? The answers to these questions provide useful insights into the effects of New Jersey's approach on access to hospital care by the uninsured.

We begin by providing background on uncompensated care and hospital payment in New Jersey. We then review evidence from previous studies of the impacts of uncompensated care payment in the state. Next, we describe a conceptual model and discuss the data used in the study. Finally, we present our results and offer some general conclusions.

UNCOMPENSATED HOSPITAL CARE AND THE UNINSURED IN NEW JERSEY

Uncompensated hospital care can be separated into two components: charity and bad debt. Charity care includes services to persons who are "medically indigent" at the time of admission and not expected to pay their hospital bills. Bad debt is that accrued by persons who are expected to pay but do not. It is estimated that uninsured patients account for about 75 percent of the uncompensated care provided by hospitals (Coye 1989).

In 1986, about 11 percent or 843,000 of New Jersey's 7.6 million residents were without health insurance. This compares with an estimate of 15 percent nationwide (State of New Jersey Department of Health 1989). As is the case nationally, New Jersey's uninsured are young: more than half are below age 25, and about one-fourth are children under age 18. Two-thirds of the state's uninsured are employed, or are in a family with at least one working adult. Following national trends, the number of persons without health insurance in the state rose over the past two decades (State of New Jersey Department of Health 1989).

1980 REFORM OF HOSPITAL PAYMENT IN NEW JERSEY

In 1980, in response to rising hospital costs, the deteriorating financial condition of inner-city hospitals, and a growing volume of uncompensated care, New Jersey undertook a major reform of hospital payment. The new system, based on prospective diagnostic-related group (DRG)-based per case payment, replaced a prospective per diem payment regulation known as the Standard Hospital Accounting and Rate Evaluation (SHARE) program. SHARE had covered Blue Cross and Medicaid patients, while the new payment system covered all payers. Hospitals were phased onto the system over a three-year period beginning in 1980; 26, 36, and 35 hospitals entered the system in 1980, 1981, and 1982. The SHARE and the 1980 DRG-based payment systems are compared in Figure 1.(1)

A key element of the payment reform was the introduction of a provision to fund hospitals' charity and bad debt care by setting payment rates to explicitly include the cost of this care. This system replaced one where the costs of uncompensated care were either not covered, financed through philanthropy or government grants, or reimbursed through increased charges to commercial insurers and self-pay patients. Between 1980 and 1986, institutions in the state received a hospital-specific markup to their payment rates to cover the reasonable costs, verified by audit, of their own uncompensated care. These markups ranged from 1 to 25 percent across hospitals, varying with the amount of uncompensated care provided by individual institutions (Rosko 1990). In 1987, to assist hospitals with disproportionately high numbers of uninsured and low-income patients, a statewide markup of payment rates was established.(2)

UNCOMPENSATED CARE PAYMENT IN NEW JERSEY: PREVIOUS STUDIES

Two studies have focused on how uncompensated hospital care payment in New Jersey has affected the level of this care provided. Using inpatient data for the years 1979 to 1982, Mann, Melnick, and Kominski (1989) found that the number and proportion of self-pay admissions increased following the introduction of uncompensated care payment. Their results indicated that the concentration of self-pay admissions across hospitals also diminished. Since they did not control for other factors, it is unclear whether the payment program, changes in the hospitals' market conditions, or the underlying distribution of the uninsured population, or some combination, was responsible for their results.

Rosko used hospital level data for the years 1979 to 1985 and multivariate regression to examine the payment program's effects on inpatient discharges (Rosko 1990). Controlling for unemployment, population, recipients of Aid to Families with Dependent Children (AFDC), and teaching status, he found that payment for uncompensated care increased the number of self-pay discharges in the state. Rosko also concluded that the payment program promoted the financial status of hospitals that typically provided a disproportionately high level of uncompensated care. He did not examine changes in the distribution of this care across hospitals in the state.

METHODS AND DATA

Our investigation builds on this previous research. We extend the period of study beyond the initial years of the payment program and include outpatient hospital care in our analysis. Further, we model more fully the factors that influence the overall amount of uncompensated care provided by hospitals in the state. We also provide a more detailed analysis of the distribution of uncompensated care across hospitals.

We analyze uncompensated care in New Jersey over the period 1979 to 1987. We use descriptive statistics to summarize trends in important variables as well as a multivariate regression analysis to isolate effects. To evaluate changes in the distribution of this care across hospitals, we employ a Herfindahl index, a measure of concentration. Finally, to gauge hospitals' financial status before and after the program, we examine their surplus of revenues over expenses.

As the measure of uncompensated care, we use total inpatient and outpatient hospital charges attributed to charity and bad debt care.(3,4) This variable incorporates two components: the number of episodes (inpatient admissions or outpatient visits) and the service volume per episode, as measured by charges. This allows us to test for the effect of factors on not only the level, but also the intensity, of uncompensated care services. This approach contrasts with other studies that have employed inpatient discharges or admissions as the dependent variable.

We measured the effect of uncompensated care payment on services using the following reduced-form model:

Uncompensated [Care.sub.it] = f ([NJREG.sub.it], [DEMVARS.sup.it], [SUPPVARS.sup.it]) where i indexes hospitals and t indexes time. NJREG is a binary variable measuring when a hospital entered the regulated system and began receiving uncompensated care payments. For each hospital, NJREG is equal to 0 prior to the year of entry (1980, 1981, or 1982) and 1 for all subsequent years. DEMVARS and SUPPVARS describe vectors of variables reflecting the supply and demand factors influencing the amount of uncompensated care provided. Definitions and descriptive statistics for the variables included in the model are given in Table 1. We discuss these variables and their expected signs in the following text.

[TABULAR DATA 1 OMITTED]

We would expect payment for uncompensated care to affect both the supply and demand for this care. For hospitals, these payments greatly increase expected revenues from providing care to the uninsured. Prior to the new system, hospitals financed indigent care, when possible, through philanthropy, grants, or increased charges to other patients. Further, prior to 1980, the patient base to which hospitals could increase charges was limited by the SHARE regulation, which set prospective payment rates for Blue Cross and Medicaid patients. The new system covered all payers and built the full costs of uncompensated care into each hospital's rates.

For patients, as their perception of hospitals' willingness to provide free care increases, they will expect to be able to pay less. As a result, they may increasingly seek this care.

Factors other than uncompensated care payment are likely to affect the amount of this care provided. In addition to expected price, the demand for this care is a function of the number of uninsured or underinsured persons in a hospital's area. Since there are no detailed measures of the health insurance coverage for a particular area over time, we approximate demand using several variables: the level of government funding for public health insurance (e.g. Medicaid), as measured by the population receiving AFDC and general assistance payments; the share of the labor force employed in sectors historically providing different levels of insurance coverage (low coverage, high coverage, and unemployed); per capita income; the area's elderly population; and the hospital's location (inner-city, urban, suburban, or rural). We would expect AFDC coverage, employment in high-coverage sectors, and real per capita income to decrease demand for uncompensated care. This demand is also expected to fall with an increased proportion of elderly persons, through their eligibility for Medicare coverage. Hospitals' location may further describe the characteristics of the area population.

Studies have found that hospital ownership, teaching status, and size are important in explaining the level of uncompensated care provided by an institution (Thorpe and Spencer 1991, Sloan, Morrisey, and Valvona 1988, and Feder, Hadley, and Mullner 1984). We model these supply attributes using hospital ownership (private, public, or owned by a religious organization); teaching status (major, minor, or nonteaching); and hospital bed size. Historically, public-owned and teaching hospitals have provided a disproportionate share of the uncompensated care in an area, while hospitals owned by religious organizations may also be more likely to provide such care.

The presence of particular types of institutions in a hospital's area may also affect the uncompensated care provided (Thorpe and Brecher 1987). We include the share of beds in a hospital's area in public-owned and teaching hospitals. We expect the supply of free care provided by a hospital to decline with the increased presence of these alternative sources.

Finally, the characteristics of physicians in the hospital's area may influence the amount of uncompensated hospital care provided. We model these effects using physician supply and primary care mix. As the physician supply increases, we expect that the overall demand for hospital care, including uncompensated care, will increase. Since services provided by primary care physicians may substitute for hospital outpatient care, we expect the supply of uncompensated care to decrease with the increased presence of these physicians.

We use data from a sample of New Jersey hospitals for the years 1979 through 1987 to complete our analysis. This sample includes 80 of the 88 short-term, acute care hospitals in the state during this time. We excluded the remaining hospitals due to incomplete data. All 88 hospitals considered were nonprofit institutions. An examination of the key variables for the omitted hospitals suggests that the results were not sensitive to their exclusion. To remove the effects of general price inflation, all financial variables were adjusted to 1987 dollars using the general consumer price index (U.S. Department of Labor 1980-1988).

The pooled cross-section-time series nature of the data necessitates that we examine different model specifications for our multivariate analysis. We evaluated three approaches: (1) ordinary least squares (OLS) estimation without fixed effects; (2) OLS with fixed effects for hospitals and years (Kmenta 1971); and (3) a random effects model estimated using generalized least squares (Wallace and Hussain 1969). We use Hausman's test to select the proper specification (Hausman 1978).

RESULTS

DESCRIPTIVE ANALYSES

Uncompensated care costs in New Jersey during the 1980s were substantial, totaling more than $282 million in 1984, or about 7 percent of total gross hospital revenue (Coye 1989). Table 2 shows the mean levels of uncompensated care and other important variables for the years 1979 through 1987. As shown, total uncompensated care has increased significantly during this period, growing at an annual compound rate of 10 percent in constant dollars. Both inpatient and outpatient uncompensated care grew, at similar rates. Table 2 also shows that this care increased as a percentage of total revenues.

[TABULAR DATA 2 OMITTED]

Variables likely to affect the amount of uncompensated care include public insurance coverage and the sectoral composition of the labor force. Table 2 shows that participation in programs related to public insurance, as measured by those receiving AFDC or general assistance payments, decreased during the study period. Further, the share of the labor force employed in sectors historically providing lower levels of health insurance coverage increased, while the share in high-coverage sectors decreased. These trends would be expected to increase demand for uncompensated care.

Table 2 also presents the Herfindahl index of total uncompensated care charges for hospitals in the state for the years 1979 through 1987. The index is a measure of the evenness of a distribution; the lower the index, the more even the distribution. As shown, the distribution of uncompensated care became markedly less concentrated. The Herfindahl index decreased from 0.071 in 1979 to 0.028 in 1987. In comparison, the same index for gross charges, compensated and uncompensated, grew slightly from 0.016 to 0.017 over the same period.

Table 3 summarizes changes in uncompensated care over the study period by hospital ownership, teaching status, location, and bed size. With the exception of public-owned hospitals, the level of uncompensated care increased for all hospital groups. Further, this care increased at a greater rate after 1981.(5) The results for public-owned versus other hospitals and teaching versus nonteaching hospitals suggest that the volume of uncompensated care was redistributed between hospitals during this time.

[TABULAR DATA 3 OMITTED]

MULTIVARIATE ANALYSES

Table 4 presents the results of the multivariate regression analysis. In estimating our models, we corrected for heteroscedasticity by taking natural logarithms of all continuous independent and dependent variables.(6) This approach also allowed the estimated coefficients to be interpreted as constant elasticities. Based on Hausman's specification test, we selected the fixed effects model, using a separate binary variable for each hospital, as the most appropriate specification. An analysis evaluating the contribution of fixed-effects for time in the model indicated that they were not necessary.(7)

Table 4 (Model I) shows that the implementation of the 1980 payment reform had little effect on the overall amount of uncompensated care. The coefficient on NJREG is small and insignificant. Hospitals did not provide more uncompensated care in the aggregate than would have been predicted without the payment regulation.

The estimates of the remaining coefficients in Model I show some interesting results. Table 2 indicated a shift in the labor force over the study period to sectors with historically low levels of medical insurance. The negative coefficients for HIGHCOV and UNEMRT in Table 4 suggest that this shift is associated with increased uncompensated care.(8) In other words, employment in low-coverage industries has a more positive effect on this care than employment in high-coverage industries or unemployment. The finding for HIGHCOV is as expected. The result for UNEMRT may be due to the availability of public insurance programs for the unemployed poor or to COBRA (Consolidated Omnibus Budget Reconciliation Act) provisions that often allow workers to retain health insurance benefits for a limited period after becoming unemployed. These coefficients support our model: the amount of uncompensated care provided by New Jersey hospitals is sensitive to demand factors in the local economy.

The negative coefficient for the share of public hospital beds in an area (PUBSHR) is as expected; the increased presence of public institutions decreases uncompensated care in other area hospitals. The coefficient on the minor teaching hospital share variable (MINSHR) suggests a similar effect. However, the coefficient for the share of major teaching hospital beds (MAJSHR) is opposite from that expected, and is insignificant. Finally, the coefficients on AFDC and general assistance coverage (AFDCGAP) are of the expected sign (negative) but insignificant.

We used the multivariate model to explore further any redistribution of uncompensated care across hospital groups. To do this, we included variables in our model that represent the relative postregulation experience of hospitals by ownership and teaching status. These results are presented as Model II in Table 4. The variables PUBLIC*REG, CHURCH* REG, MAJOR*REG, and MINOR*REG are the interaction between the variable NJREG and the hospital ownership and teaching status variables. These variables are measured relative to private, non-religious-owned, non-teaching hospitals (the reference group), which are represented by the result for the variable NJREG in Model II. A positive coefficient for these variables suggests a greater response to the regulation than the reference hospitals. As shown, public-owned and minor teaching hospitals experienced a relative decrease in uncompensated care following the regulation, while hospitals owned by religious organizations experienced a relative increase. Major teaching hospitals, although maintaining their high level of uncompensated care, showed no significant relative change.(9)

ANALYSIS OF HOSPITAL FINANCIAL CONDITION

We assessed the effects of uncompensated care payment on hospitals' financial condition using the margin of total revenues over total expenses. We first divided the hospitals into groups based on the percentage of their total revenue composed of uncompensated care in 1979. We selected three groups: light uncompensated care burden (< 4% of revenues); medium burden (4-8% of revenues) and heavy burden (> 8% of revenues). We identified 25, 30, and 25 hospitals in the three groups, respectively.

Figure 2 shows that uncompensated care payment had a positive effect on the financial condition of hospitals providing a disproportionately larger share of this care. The surplus margins of these hospitals increased following the regulation and were similar to those of other hospitals after 1983.

DISCUSSION

Our analysis provides answers to the three questions we raised. First, although uncompensated care in New Jersey increased following the introduction of payment for this care, our multivariate analysis suggests that the increase cannot be attributed to the payment provision. Other trends in the state,

including changes in the composition of the labor force and in public insurance programs, appear to have added to the number of uninsured persons and resulted in increased demand for free care. Given these trends, hospitals collectively have not provided more care than that predicted without the payment regulation.

Second, the adoption of payment for uncompensated care has resulted in a more even distribution of this care across hospitals in New Jersey. In particular, the burden of uncompensated care has been shifted from publicly owned and teaching hospitals to other institutions in the state. This result suggests that some barriers to care existed at certain types of hospitals prior to the regulation.

Finally, uncompensated care payment has improved the financial condition of hospitals providing the largest share of this care. Surplus margins for the most heavily burdened hospitals became more like those of other hospitals following the regulation.

The state of New Jersey cited two objectives in adopting payment for uncompensated hospital care: (1) to promote access to hospital care for uninsured persons and (2) to ensure the financial viability of hospitals providing this care. We conclude that the program has met these objectives. Our findings suggest that, although hospitals did not provide collectively more free care, opportunities for the uninsured to receive such care were more widely available than before the regulation. Further, the financial stability of hospitals providing the bulk of this care improved, ensuring that patients would have continued access to these institutions.

State regulation of hospital payment to finance care for the uninsured has had some success. Our conclusions support those of previous evaluations of the New Jersey payment program. Further, Thorpe and Spencer (1991), in analyzing a similar program in New York state, concluded that it assisted hospitals financially and resulted in an increased volume of uncompensated care. These programs appear to have improved access to hospital care by the uninsured.

Despite this success, programs like New Jersey's must be compared with alternative ways of caring for the uninsured. Compared to insurance, direct payments to hospitals do not permit patients to choose care in the most effective and efficient manner. Inefficiencies result as the uninsured forgo primary and preventive services and seek care only when a medical condition becomes acute and more expensive. Further, by focusing solely on hospital services, these programs provide patients with incentives to initiate care through hospital emergency rooms or outpatient units--expensive alternatives to a physician's office. As others have argued (Thorpe and Spencer 1991), expansion of insurance coverage appears to be a more effective way of providing medical care for the uninsured.

ACKNOWLEDGMENTS

The authors thank William Hsiao, Eric Latimer, Judy Dernburg, and Kathleen Adams for their helpful comments on earlier drafts of this article. We also acknowledge the comments of two anonymous reviewers.

NOTES

(1.) Several studies have examined the impacts of the New Jersey SHARE and all-payer DRG payment regulations on overall hospital costs and utilization (Hsiao et al. 1986, Hsiao and Dunn 1987, Rosko and Broyles 1986). This article focuses solely on the effects of the uncompensated care provision of the 1980 payment reform. (2.) Two events related to uncompensated care payment in New Jersey occurred after 1987. First, Medicare stopped contributing payments to hospitals for outpatient and inpatient uncompensated care in 1988 and 1989, respectively. Second, for the first half of 1991 (January to June), the payment method for uncompensated care shifted back to hospital-specific markup rates. In 1992, the statewide markup was 19 percent. The uncompensated care provision was eliminated in January 1993 (Frankel 1993). These changes are outside the scope of this article, but highlight the potential political and financial instability of this approach to dealing with hospital care for the uninsured. (3.) Discounts on charges were not considered as part of uncompensated care for the New Jersey regulation. As a result, only charity and bad debt care were included in this analysis. (4.) It can be argued that hospitals have an incentive under the provision to more "accurately" or "inaccurately" code cases as charity or bad debt care, or to let up on collection procedures. In order to curb this incentive and standardize the coding process, New Jersey adopted a definition for charity care and a collection procedure for bad debt (how many times a hospital must try to collect a bill over a period of time). If hospitals do not follow this procedure, they cannot claim the unpaid bill as bad debt. There is no evidence that hospitals' coding of charity cases or efforts to collect bills have changed, or that these factors have influenced the level of uncompensated care reported. (5.) Since hospitals were phased onto the new payment system over a three-year period beginning in 1980, the year 1981 was chosen as marking the "before" and "after" period for the analysis shown in Table 3. (6.) The OLS results are available from the authors. (7.) Subsequent use of the White test failed to reject the null hypothesis of homoscedasticity (White 1980). (8.) Since the logarithms of the share of the labor force in low- and high-coverage sectors, and unemployed, sum to some relatively constant amount (the unlogged values sum to 100 percent for an area), we omitted the variable LOWCOV from the regression. The parameters for HIGHCOV and UNEMRT can be interpreted as the effects of these variables on uncompensated care relative to that of LOWCOV. (9.) We also considered a potential caveat to our findings using the multivariate regression model. In addition to payment for uncompensated care, the 1980 reform of hospital payments in New Jersey included other provisions, such as the introduction of prospective all-payer DRG-based payment. It can be argued that if the new payments provide a strict financial constraint to hospitals, the level of uncompensated and other care provided could be affected. As a result, the response to the new payment rates will be confounded with that from the introduction of uncompensated care payment.

We examined the potential effect of fiscal pressure on the hospitals' postregulation response using our multivariate regression model. For each of the years following the introduction of the regulation, we included in regression Model I a hospital's previous year's surplus margin. This approach assumes that the hospitals make decisions about the provision of uncompensated care based on the previous year's financial experience. The estimated coefficient for this variable can be interpreted as the effect of the interaction between the financial constraint and the new payment regulation on the provision of uncompensated care. We found the coefficient for this variable to be .001 with a t-statistic of .50. The sign, significance, and magnitude of the other variables in the model, including NJREG, were not affected. We conclude that our results are not confounded by this potential effect of the new payment regulation.

REFERENCES

Coye, M. J. The Uncompensated Care Trust Fund: Assuring Universal Access to Hospital Care in New Jersey. Trenton, NJ: State of New Jersey Department of Health, November 1989. Families USA Foundation. "Half of Us: Families Priced Out of Health Protection." Washington, DC: Families USA Foundation, April 1993. Feder, J., J. Hadley, and R. Mullner. "Falling Through the Cracks: Poverty, Insurance Coverage, and Hospital Care for the Poor, 1980 and 1982." Milbank Memorial Fund Quarterly 62, no. 4 (1984): 544-66. Frankel, D. H. "USA: Health Care Reform in New Jersey." Lancet 269 (1993): 153. Hausman, J. A. "Specification Tests in Econometrics." Econometrica 46, no. 6 (November 1978): 1251-71. Hsiao, W. C., and D. L. Dunn. "The Impact of DRG Payment on New Jersey Hospitals." Inquiry 24, no. 3 (1987): 203-11. Hsiao, W. C., H. M. Sapolsky, D. L. Dunn, and S. C. Weiner. "Lessons of the New Jersey Payment System." Health Affairs 5, no. 2 (1986): 32-45. Kmenta, J. Elements of Econometrics. New York: McMillan Publishing Co., Inc. 1971. Mann, J. M., G. A. Melnick, and G. F. Kominski. Uncompensated Care Payments to Hospitals: Does Access for the Uninsured Improve? Santa Monica, CA: RAND Corporation, November 1989. Morrisey, M. A., F. Sloan, and J. Valvona. "Defining Geographic Markets for Hospital Care." Law and Contemporary Problems 314 (February 1986): 522-57. Rosko, M. D., and R. W. Broyles. "The Impact of the New Jersey All-Payer DRG System." Inquiry 23, no. 1 (1986): 67-75. Rosko, M. D. "All-Payer Rate-Setting and the Provision of Hospital Care to the Uninsured: The New Jersey Experience." Journal of Health Politics, Polity and Law 15, no. 4 (Winter 1990): 815-31. Sales and Marketing Management. Survey of Buying Power. New York: Bill Publications, 1980-1988. Sloan, F. A., M. A. Morrisey, and J. Valvona. "Hospital Care for the |Self-Pay' Patients." Journal of Health Politics, Polity and Law 13 (Spring 1988): 83-102. State of New Jersey Department of Health. Access to Health Care for the Uninsured in New Jersey: Issues and Answers. Trenton: New Jersey Department of Health, 1989. Swartz, K. Strategies for Assisting the Medically Uninsured. Washington, DC: The Urban Institute, August 1989. Thorpe, K. E., and C. Brecher. "Improved Access to Care for the Uninsured Poor in Large Cities: Do Public Hospitals Make a Difference?" Journal of Health Politics, Policy and Law 12, no. 2 (1987): 313-24. Thorpe, K. E., and C. Spencer. "How Do Uncompensated Care Pools Affect the Level and Type of Care? Results from New York State." Journal of Health Politics, Policy and Law 16, no. 2 (Summer 1991): 363-81. U.S. Department of Labor, Bureau of Labor Statistics. Handbook of Labor Statistics. Washington, DC: U.S. Government Printing Office, 1980-1988. U.S. Department of Labor, Bureau of Labor Statistics. Employment and Earnings. Establishment Data: State and Area Employment. Washington, DC: Government Printing Office, 1980-1988. U.S. General Accounting Office. Health Insurance: An Overview of the Working Uninsured. Report No. GAO/HRD-89-45. Washington, DC: GAO, February 1989. Wallace, T. D., and A. Hussain. "The Use of Error Components Model in Combining Cross Section with Time Series Data." Econometrica 37 (January 1969): 55-72. White, H. "A Heteroscedasticity Consistent Covariance Matrix and a Direct Test for Heteroscedasticity." Econometrica 48 (May 1980): 817-38.

This research was supported by a grant from the Robert Wood Johnson Foundation. Address correspondence and requests for reprints to Daniel L. Dunn, Ph.D., Research Economist, Department of Health Policy and Management, Harvard School of Public Health, 1350 Massachusetts Avenue, Cambridge, MA 02138. Michael Chen, Ph.D. is Associate Professor, Department of Social Welfare, National Chung-Cheng University, Chia-yi, Taiwan. This article, submitted to Health Services Research on November 30, 1992, was revised and accepted for publication on August 26, 1993.
Table 4: Multivariate Regression Results
Dependent Variable. UC      Model I                  Model II
Constant                    11.017(**)               12.772(**)
                            (5.342)([dagger])        (5.322)
NJREG                        0.015                   (0.038
                            (0.042)                  (0.045)
AFDCGAP                     -0.085                   (0.109
                            (0.155)                  (0.154)
HIGHCOV                     -3.417(*)                -3.559(*)
                            (0.523)                  (0.519)
UNEMRT                      -0.203(*)                -0.211(*)
                            (0.060)                  (0.059)
RCAPINC                     0.550(**)                 0.483(**)
                           (0.238)                   (0.239)
POP65                       2.652(*)                  2.435(*)
                           (0.777)                   (0.774)
BEDS                        0.507(*)                  0.453(*)
                           (0.141)                   (0.141)
PUBSHR                     -1.118(**)                -1.043(**)
                           (0.484)                   (0.482)
MAJSHR                      0.416                     0.469
                           (0.369)                   (0.368)
MINSHR                     -0.921(*)                 -0.902(*)
                           (0.297)                   (0.294)
SPMD                       -0.169                    -0.204
                           (0.676)                   (0.669)
PHYSPOP                    -0.351                    -0.351
                           (0.237)                   (0.235)
PUBLIC(*)REG                                         -0.316(*)
                                                     (0.122)
CHURCH(*)REG                                          0.149(**)
                                                     (0.071)
MAJOR(*)REG                                           0.013
                                                     (0.068)
MINOR(*)REG                                          -0.219(*)
                                                     (0.076)
No. observations              720                       720
Adjusted [R.sup.2]          0.923                     0.925
Note: All continuous variables were transformed using natural logarithms. Due to
 the fixed
effects specification of the final models, the binary variables for hospital cha
racteristics
(CHURCH, MAJOR, MINOR, INNER, URBAN, SUBUR, and RURAL) were omitted. Results
for these variables for the OLS specification are available from the authors.
(*) p [less than or equal to] .01.
(**) p [less than or equal to] .05.
(***) p [less than or equal to].10.
([dagger] Standard errors of parameter estimates shown in parentheses.
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Copyright 1994 Gale, Cengage Learning. All rights reserved.