Demand for General Practitioner and Internist Services By David S. Guzick Demand equations for general-practitioner and internist visits were estimated from 1970 CHAS-NORC survey data on health-service utilization and expenditure. Because a large proportion of respondents reported zero visits, observations were grouped according to cross-classified independent variables and regression analyses were performed using group means as data. The results showed significant differences between demand equations for generalpractitioner visits and those for internist visits. Of potential importance was an apparent substitution of internists for general practitioners as ability to pay (income or insurance coverage) increased. Own-price elasticities were low for both general practitioners and internists but were even lower for the latter (0.1 to 0.2) than the former (0.2 to 0.3). The demand for services of the two specialties also differed with respect to disability days, age, sex, residence, and race.
Previous empirical studies of demand for physician services have used data aggregated across specialties. It would be more accurate, however, to describe the "market" for physician services as several specialty markets, since although to some extent substitutable, the services provided by the various specialties are essentially distinct. Accordingly, aggregate results may obscure large differences in determinants of demand for various specialty services, and policies based on aggregate estimates might result in unforeseen distributive effects across specialties. Because of increasing recognition of the importance of disaggregating data on physiacans from different specialties and the greater availability of such data, recent studies of supply, including those of fees [1], output and productivity [2,3], and geographic distribution [4], have reported results for individual specialties. In the research reported here, I estimated functions of demand for the services of general practitioners and internists. Physicians of these two specialties provide the bulk of primary care, and their services are substitutable to some extent, allowing comparative study.
Methods Use and Expenditure Data by Specialty The source of data for this research was a national survey of health-service utilization and expenditure conducted by the Center for Health Administration Studies and the National Opinion Research WINTER 1978
Address communications and requests for reprints to David S. Guzick, Box 715, School of Medicine, New York University, 550 First Avenue, New York, NY 10016.
0017-9124/78/04035118/$02.0O/O 0 1978 Hospital Research and Educational Trust
351
GUZICK
Center (CHAS-NORC) of the University of Chicago [5-7]. The sample consisted of 3,765 families containing 11,619 individuals who were interviewed in their homes in early 1971. The poor, the aged, and persons residing in inner cities or in rural areas were overrepresented. Three files created by CHAS-NORC from this survey were combined into one containing data on respondents' social and economic characteristics and on the number and cost of visits made by each individual to general practitioners and internists during 1970. (A detailed description of the merger of the three original files is available from the author.) Use and cost data in the combined file were cross-classified by location of visit (home, office, hospital emergency room, hospital outpatient clinic, health service or other clinic, patient's room in hospital), reason for visit (obstetric, nonobstetric), and physician specialty (internal medicine, general practice). Internists were classified as board certified or uncertified, and general practitioners were classified as young or old (45 and under, and over 45, respectively).
Grouping of Data Only 25 percent of the individuals in the CHAS-NORC sample had reported one or more visits to general practitioners during 1970, and only 6 percent had reported one or more visits to internists. (These figures should not be considered estimates of the corresponding population percentages because of missing physician-specialty data for some visits and overrepresentation of some population groups in the sample.) The large proportion of persons with zero visits to physicans in the specialties under study created serious problems for the estimation of demand equations. When observations on a dependent variable are concentrated at a limiting value, errors do not behave in the manner assumed by the multiple regression model. Tobin's method for estimating relationships with limited dependent variables [8] might ordinarily be appropriate, but such an approach was confounded here by the absence of data on price for those persons reporting zero visits. One way of avoiding the problem is simply to ignore persons who report zero visits and to estimate demand equations using only data on those with one or more visits [9]. This results in estimates of demand parameters that are conditional on there having been at least one visit. However, when a large proportion of the sample reports zero visits, the validity of such estimates is questionable, since the first and subsequent visits are probably generated differently, the physician playing a greater role in subsequent visits. Furthermore, policy interest centers on the future response of the entire population to changes in factors such as insurance coverage or age composition, regardless of whether individuals do or do not currently report visits to physicians. When a substantial proportion of the sample reports zero visits, as is the case when HEALTH individual physician specialties are considered, estimates of conditional SERVICES RESEARCH demand parameters cannot be assumed to closely approximate the unconditional ones. 352 In a recent study using 1963 CHAS-NORC data, Newhouse and
PHYSICAN Phelps [10] attempted to derive unconditional estimates of demand DEMAND parameters by estimating two separate equations: a use-nonuse equation with a dummy dependent variable that equals 1 if there are any physician visits and a number-of-visits equation conditional on there being at least one visit. Newhouse and Phelps circumvented the problem of establishing visit prices for those without visits by using the coinsurance rate of policies covering physician services, but when combined with the number-of-visits equation, the overall unconditional "price" elasticity applied only to coinsurance, not gross or net price (i.e., gross price times the coinsurance rate). In addition, only a small fraction (11 percent) of respondents in Newhouse and Phelps's sample carried insurance for physician services, thus limiting the reliability of the resulting estimates. To estimate unconditional demand functions for general-practitioner and internist services, I grouped individuals according to crossclassified values of independent variables and conducted multiple regression analyses using group means as data. Variables were chosen as cross-classifiers on the basis of their potential importance in determining demand and were categorized on the basis of their joint frequency distributions and comparability with census data needed for other work. The dassifying variables and their categories were: Age: 0-4, 5-17, 18-64, and 65 and over Family income: $0-4,999, $5,000-9,999, and $10,000 and over Education of family head: 12 years or less than 12 years Residence: rural or urban Race: white or nonwhite The education variable was obtained by adding the value for education from the record of the family head to the record of each family member. Persons with missing values on any of the five dassifying variables were exduded, reducing the sample size from 11,619 to 11,481. Of the 94 cross-classified groups, 24 were collapsed over race categories because of small numbers (25 or fewer). This left 72 groups for analysis, with a mean group size of 159.5. The grouping technique used here is similar to the one used by Silver [11] in his analysis of total medical expenditure and work-loss data, but Silver's data were not as flexible as those used here. His grouping variables-region, age, and sex-were restricted to those already used by the National Center for Health Statistics and resulted in only 24 cross-classified cells. Moreover, data on price and insurance were not available to Silver, and total medical expenses were not disaggregated by object of expenditure or physician specialty. The main disadvantage of using grouped data is that the resulting estimates have greater sampling variance than those based on ungrouped data [12], and I tried to limit the drop in precision by using sufficient cross-dassification to produce a large number of fairly homoge- WINTER neous groups. To give an indication of the reliability of my uncondi- 1978 tional estimates of demand equations, I report conditional estimates 353 based on both grouped and ungrouped data.
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Measurement and Selection of Variables Demand equations for general-practitioner and internist services were estimated by regressing the average quantity of services demanded by persons in each group on several variables that were thought to account for variation in demand among the groups. Two issues that are immediately raised by such a procedure concern the method used to measure physician services and the bases on which explanatory variables are chosen. Dependent Variable. The main difficulty in measuring physician services, as in other studies of demand, is that of product heterogeneity, since it would be inappropriate to assign equal weight to all the different types of physician visits. However, restricting the analysis to demand for a particular specialty's services, as I did here, still leaves considerable disparity among visits. To approach some degree of standardization, I measured the quantity of general-practitioner or internist services demanded by a weighted index of nonobstetric visits. The weights were average prices charged for nonobstetric visits to physicians in the four categories and at each of the six locations listed in Table 1, which are assumed to reflect the relative values of these different visits. Average prices are divided by 10; thus, one weighted visit is equivalent to a home visit by an old general practitioner (9.93 *. 10 1). Average-price estimates based on very small numbers of visits are the most imprecise estimates. By definition, however, they make the smallest contribution to the overall index of weighted visits and should not substantially reduce its reliability. Average prices do not reflect relative values to the extent that market imperfections lead to a greater departure of observed from competitive prices at some locations or for some physician categories. The index that results from weighting visits by these average prices does not truly reflect standardized units of service to the extent that the quality and mix of services at each location are not homogeneous among physicians of a given category. Obstetric visits were excluded from the weighted index because most of these visits were not reported by location but for obstetric care as a whole and because demand for obstetric services is likely to be structurally different from that for nonobstetric services. The group means of weighted nonobstetric visits, averaged over persons with and without visits, were the main focus of data analysis. These means represent the medical visits of typical individuals in each group and were used to estimate unconditional demand parameters. For comparison, mean numbers of weighted visits for persons reporting at least one (unweighted) visit were used to estimate conditional parameters. Price data on individual visits, needed to calculate the weights, HEALTH were not directly available and were computed as expenditures divided REEARCEH by visits. These prices were corrected for oversampling with weights assigned by CHAS-NORC and then averaged for combinations of 354 location and physician category. Records in which expenditures at
PHYSICIAN
Table 1. Average Prices for Nonobstetric Visits During 1970, by Location and Physician Category Physican category General practitioner
Internist
Location
DEMAND
Non-
Board ertified
Younga under)
Old (over 45)
10.27
12.47 1.90 68;12
9.04 1.36
9.93 0.55
110;19
412;83
21.45 1.32 1 947;272
9.04 0.24 5 175;885
11 028;1 985
4.32 93;18
19.42 2.21 309;51
14.58 1.54 349;78
11.08 2.41
9.15 0.91
16.13 1.69
58;9
65;16
247;46
... ... ...
9.54 2.97 26;4
10.15 1.04 6;4
14.25 1.54 411;65
11.09 1.56 440;92
9.02 0.80 813;170
certified Home
Average price ($) ..........
1.40 Est. standard error ($) ...... 95;14 Wtd; unwtd sample sizes ... Office Average price ($) .......... 14.80 0.72 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 2 443;370 Hospital emergency room Average price ($) .......... 14.07 3.31 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 111;17 Hospital outpatient dept. 8.51 Average price ($) .......... 1.00 Est. standard error ($) ......
Wtd; unwtd sample sz ... Health service or other diiic Average price ($) .......... Est. standard error ($) ......
24;6
8.75 1.98 6;4
Wtd; unwtd sample sizes ... Inpatient Average price ($) .......... 16.18 2.05 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 334;56
21.52
9.12 0.21
one location were subsumed under those at another were ignored in these calculations. Estimation of standard errors of average prices was complicated by several aspects of the CHAS-NORC sampling design, indcluding their weighting scheme, cluster sampling, and stratification [5,13]. Data on dustering and stratification needed for appropriate estimation of standard errors were not available, but rough estimates were calculated by dividing the standard deviation of the weighted means by the square root of the unweighted sample size minus one (Table 1). Physician services are sometimes measured in terms of expenditures rather than visits [11,14]. The former measure allows disparate types of physician services to be combined into a single index and captures variation in quality to the extent that this is reflected in price variation WINTER of a given service. However, total expenditures for physician services 1978 understate the quantity of services used if some are paid for by government or philanthropy. And, perhaps more important, differences in
355
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price for a given service may reflect market conditions or physician price discrimination as well as quality differences. Independent Variables. The price variables Pap and PI are group means of prices for general-practitioner and internist visits, respectively. As mentioned previously, data on fees for individual visits were not available, so price was defined for each group member as total expenditures divided by weighted visits. Persons with zero visits were excluded from the mean-price calculation since price was unknown for them. To the extent that there was measurement error in both expenditures and visits, a bias of unknown direction was introduced in the estimates of price elasticity, since error in the numerator would lead to bias toward zero whereas error in the denominator would lead to bias away from zero [9]. Some studies of demand for physician services as a whole have used a "marginal price" (gross price multiplied by the coinsurance rate) variable [9,10,15]. In this study, however, market price and insurance are two separate variables because a change in Pgp, controlling for PI (or vice versa), alters the relative prices of general-practitioner and internist services, whereas a change in the insurance variable (INS) alters the net cost for both types of services equally. From the consumer' s point of view, a dedine in the price of a general-practitioner visit is not the same as an (equivalent) increase in the proportion of expenditures for physician services paid by third parties, since the former increases the relative price of internist services, whereas the latter decreases the cost of both general-practitioner and internist services by the same proportion. Moreover, from a behavioral standpoint, it is questionable whether patients calculate and use marginal rates as the decsion-making price variable. Conventional economic theory suggests that general-practitioner price should have a negative sign and internist price a positive sign in the general-practitioner equations and that the opposite should be true in the internist equations. Following Grossman's health-capital investment model [14], Newhouse and Phelps [9] showed that own-price elasticity is always negative but that cross-price elasticity is of indeterminate sign, depending on the magnitude of the elasticity of the marginal efficiency of health capital. Own-price elasticities were expected to be low, since consumers often perceive physician services as nonelective and equate price with quality. In addition, patients usually rely on their physicians' judgment since they are typically uninformed about the content and quantity of services that would benefit them most. I also expected demand for internist services to be more own-price inelastic than that for general-practitioner services, since use of the former may generally be less discretionary. The insurance variable (INS) was computed by first calculating, for each group member, the proportion of total expenditures on physican RESEARCH visits not paid out of pocket and then averaging these figures over all group members who showed positive expenditures. For persons with 356 insurance, this average rate was below that of the theoretically ap-
HEARLTCH
propriate marginal rate in the presence of a deductible. Moreover, the PHYSICIAN error in measuring the marginal rate by the average rate covaried posi- DEMAND tively with the marginal rate in these cases, producng inconsistency in the insurance parameter estimate away from zero. (This result follows directly from Eq. 4 of Newhouse and Phelps [16].) This problem of measurement error should not be confused with the one that arises when insurance is measured as the proportion of persons having insurance. The latter leads to more sizable errors, having a negative covariance with the marginal insurance rate [16]. Since an increase in the proportion of physician charges that are paid for by insurance reduces the price of physician visits, higher insurance coverage should increase demand. However, it may also cause a substitution of visits to internists for those to general practitioners if the former are believed to be of higher quality. Therefore I conjectured that internists would obtain the larger share of additional demand resulting from a rise in insurance coverage. Family income was treated as a pair of dummy variables corresponding to the categories used for group dassification-$5,000-9,999 (INC1) and $10,000 and over (INC2)-with $0-4,999 as the reference category. I expected that, other things being equal, a rise in income would shift the demand curve to the right and therefore increase utilization, a well-documented effect [171. In Grossman's health-capital investment model [14], demand is differentially affected by wage and nonwage income. However, such hypotheses cannot easily be tested without restricting the sample to wage earners or family heads [9,14] and were therefore not pursued here. In a recent attempt to estimate wage-income elasticities for a sample that induded unemployed persons, Newhouse and Phelps [10] estimated separate ordinary-least-squares (OLS) wage equations for employed males and females. They then used these equations to predict asking wages (or values of time) for persons not in the labor force by setting weeks worked equal to zero. The estimated elasticity of value of time was small (0.1) and statistically insignificant, although the authors indicated that the prediction procedure used for unemployed individuals biased the estimate toward zero. The variable for disability days, D, was computed as the mean of days reported for 1970 in each group. Disability days (or variables like it, such as work-loss days) are commonly viewed as measures of need, following Andersen [18]. Other things being equal, the greater the need for physician services, the greater the demand for them. Inclusion of disability days as an exogenous determinant of demand for physican services is a misspecification according to the healthcapital model [14], since physician services are regarded in this model as an input in the production of gross investment in the commodity "good health," along with additional inputs such as housing, diet, and other lifestyle factors. Nevertheless, I induded disability days as an independent variable in most of the physician-visit equations on 978 the assumption that individuals are probably much less able to influence the number of their disability days than their overall stock of 357
GUZICK
"sgood health" on the basis of decisions on lifestyle and medical-care expenditures. Disability days contain a highly stochastic component, even among persons of similar age and lifestyle. Therefore I suggest that disability days are partly endogenous but also contain a large exogenous element responsible for a substantial part of the variation in physician visits. For comparison, some equations that exclude disability days were also estimated. Age was entered as a set of dummy variables corresponding to the group-classification categories-0-4 (A1), 5-17 (A2), and over 64 (A8)with 18-64 as the reference category. The relation between age and utilization of physician services is generally found to be U-shaped [17]. Most physician visits made by young children are to pediatricians, however, though many are presumed to be to general practitioners. I therefore expected a somewhat U-shaped relation between age and general-practitioner visits but a monotonically increasing relation between age and internist visits. Beyond childhood, use of general-practitioner and internist services increases with age. One can think of age as a need variable, like disability days, and predict greater demand for physician services among older persons as a reflection of their greater need. In terms of Grossman's health-capital model [14], which assumes that the rate of health-capital depreciation increases with age beyond some point in the life cycle, older persons purchase more physician services to increase their production of gross investment in health capital and thereby offset part of the reduction in their health capital caused by the greater rate of depreciation. The variable for sex, S, was computed as the proportion of group members who were males. Overall, women make more visits than men to physicians [5], and I expected this difference to hold for both general practitioners and internists. Education of family head, ED, was a dummy variable equal to one if the family head had completed 12 years of schooling. Grossman [14] has argued that an increase in human capital, as measured by education, improves efficiency in the production of health. Hence the better educated are able to produce greater amounts of gross investment in health capital with given levels of direct inputs and have an incentive to offset part of the increase in health by reducing their purchase of physician services. However, the evidence to date contradicts this hypothesis, and other things being equal, physician utilization increases with education [5,9,14,18]. The variable for race, R, is the proportion of group members who are white. (For the 48 uncollapsed groups, R takes the value 0 or 1.) Whites generally show greater utilization of physician services [5,17,19], although this may reflect local supply as well as demand. Assuming that nonwhites have relatively better access to general practitioners HEALTH than to internists, the difference between races should be greater for RESEARCH use of internist services than for general practitioner services, other things being equal. 358 Residence (RES) is a dummy variable with rural residence equal
to one. Specialists are more concentrated in urban areas and general PHYSICIAN practitioners in rural areas [20]; therefore, I expected RES to have a DEMAND positive sign in the general-practitioner equations and a negative sign in the internist equations.
Equation Specification and Method of Estimation The following equations, which relate internist and general-practitioner visits to the independent variables described above, were estimated by OLS regression after multiplying through by the square root of group sample size to minimize heteroscedasticity: Fe = bo0 + b11(PGp,) + b2j(P1,) + b81(INS4) + b4j(P,p, x INS,) + b5XPI, x INS,) + b61(#NCj4) +
b7#(1NC24) + b81(D4) + b91(A,4) + bl0(A24) + bl(A84) + bL2(S4) + bul(ED4) + bL41(R4) + bj15(RES4) + e4j
where Vi, is the mean number of weighted nonobstetric visits made by persons in the ith group (i = 1, . . ., 72) to physicians in the jth category (1 = general practitioners or internists). The coefficients bo . . ., b151 are the regression parameters to be estimated, e41 are error terms, and independent variables are as defined in the previous section. Interaction terms between market price and insurance were included because the effect of market price on demand might vary for different levels of coverage. I expected the coefficents of these interaction terms to be positive, indicating that a rise in price variation leads to a smaller reduction in demand when insurance coverage is more complete. To the extent that insurance is endogenous, the above equations are misspecified; it would have been more appropriate to estimate demand for visits as part of a two-equation system, the other equation being demand for insurance. However, identification of the visit equation would require the presence of an exogenous variable in the insurance equation that would be absent in the visit equation. None of the available exogenous variables satisfied this condition (the insurance premium was an obvious candidate, but data were lacking); hence the single-equation model was used. Any endogenicity in insurance that was present was expected to bias elasticity estimates away from zero. None of the group members in 18 of the 72 groups reported an internist visit, so internist price was unknown for them. Missing prices were filled in with predicted values from a regression of PI on independent variables, exdusive of P,p and INS.
Results Unconditional Estimates The means of the weighted nonobstetric general-practitioner and WINTER internist visits for the 72 groups are shown in Table 2, and regression 1978 results are presented in Table 3. 359 Estimated parameters of the regression equation for general-practi- J
GUZICK
tioner and internist visits, exclusive of price-insurance interactions, are shown in columns 1 and 2 of Table 3. On the basis of a Chow test, I rejected the hypothesis of equality between the general-practitioner and internist equations at the 0.01 level (F(14,116) = 5.16). Differential degrees of measurement error might account for part of the observed difference between general practitioners and internists, but it is un-
Table 2. Mean Numbers of Weighted Nonobstetric Visits to General Practitioners and Intemists During 1970, by Age, Education, Income, Residence, and Race $5,000-9,999
$04,999
fagA
head's
education
Urban
Rural
Rural
$10,000. and over
Urban
Rural
Urban
NonNonNonNonNonWhite white White white White white White white White white White white
04 Less than 12 years GP visits ...... Internist visits . Sample size .... 12 years or more GP visits ...... Internist visits . Sample size ....
Non-
0.49
1.14 0.00
0.86 0.00 47
1.05 0.00 26
0.08 146
2.84 0.00 19
1.32 1.15 31
0.32 0.02 73
0.44 0.03 79
0.16
0.38
0.00
0.00 222
0.52 0.00 51
0.04 0.00 154
2.40 1.80 195
0.73 0.33 480
0.75 1.03 206
0.80 0.65 158
1.43 03.5
0.88 0.65
345
354
2.22 2.23 318
2.25
7.44
2.11
1.18 168
1.75 84
2.53 131
5-17 Less than 12 years GP visits ...... 0.68 Internist visits . 0.00 Sample size .... 134
0.49 0.08
71
12 years or more 0.42 GP visits ...... 0.00 Internist visits . 58 Sample size .... 18-64 Less than 12 years GP visits ......3.62 2.36 Internist visits . 0.39 0.11 Sample size .... 244 79 12 years or more 1.67 GP visits ...... 0.40 Internist visits . 124 Sample size .... and over 65 Less than 12 years GP visits ...... 4.19 8.53 Internist visits. 0.31 0.00 Sample size .... 253 28 12 years or more 4.03 GP visits ...... 2.29 Internist visits . 56 Sample size ....
516
2.23 2.28 137
66 2.09 0.02 74
0.32 0.00 42
1.01 0.05 198
2.16
1.86 0.07 57
0.44 357
1.86
0.51 0.01
109 0.13 0.00
0.00
0.89 0.04
67
77
95
0.00 37
0.16 150
0.38 0.01 420
0.65 0.10 186
0.40 0.02 102
0.48 0.00 101
0.61 0.09 150
0.09 0.00 125
0.83 0.13 222
0.61 0.05
0.07 0.08
286
103
1.77
0.80 0.30
1.92 2.66
476
144
1.01 1.44 260
0.98 0.80 120
0.73 0.34 252
1.71 0.69 444
0.97
1.20
0.65 0.86
709
227
0.60 1.11 30
4.06 0.11
3.21 2.29
26
64
0.99
022 73
0.39
1.09 256
3.33
2.47
0.91
6.05 69
21
0.90 0.02
1.08 0.00 21
0.00 40
1.41
3.93 0.68
21
31 0.09
1.70 5.10 56
PHYSICIAN
Table 3. Estimated Parameters of D and Equations for General-Practitioner and Internist Visits Dependent variables (mean weighted nonobstetric visits) Unconditional eitimates Independent
variable
P X INS
ARl ter
icueds exclded icuded terms
V1
VWP Pgp ....... -0.034 0.002 -0.041 VaP
V
Disability days
excluided
VW -0.045 2.22 -0.27
V1
Conditional estimates
Inpatn
Visits excluded*
Va..P -0.055
VI
ter icue icue
V0P VQ,t -0.113 -0.086
t.2.06 0.14 2.20 2.48 2.02 3.31 Elas ....020 0.03 -0.24 -OA1 -0.22 -0.17 P .-0.004 -0.007 -0.009 -0.012 -0.016 t 0.19 1.26 1.48 1.26 1.59 EIa§ . 0.02 -0.08 -0.14 -0.11 -0.19 INS . -0.590 1.073 -0.804 1.061 -0.854 1.067 -0.926 0.834 -2.149 -1.605 0.71 2.28 0.87 2.03 0.92 2.31 1.04 1.72 1.30 1.83 t* ....... 0.050 0.044 0058 0.128 0.098 P X INS .... 2.86 t* 2.69 2.94 3.47 3.79 x INS 0.010 0.007 0.009 Pz 1.61 1.44 1.56 t* INC1 42. -0.2 0.395 -0.185 O.23 -0368 0203 0.180 0.231 -0.638 -0.901 .0.86 1.68 0.78 1.08 1.42 0.91 0.76 1.32 1.54 2.66 INC . -0.021 0.670 -0.030 0.817 4.293 0.796 -0.026 0.431 -0.497 -0.385 t. 0.07 2.22 0.07 2.44 0.77 2.35 0.06 2.01 0.82 1.40 D. 0.059 0.011 0.051 0.012 0.024 0.005 0.091 0.079 4.61 0.91 4.39 0.85 t. 2.66 0.41 3.36 4.81 -1.205 -0.135 .-0269 -0224 -0.233 -0.350 -0.151 -0.230 -1.241 -1.153 Al t. 0.74 0.61 0.54 OA1 2.93 0.98 0.53 0.85 3.36 2.63 A, -0.749 -0.179 -0.782 -0.169 -1.611 -0.328 -0.628 -0.191 -1.833 -1.690 t* ......... 2.46 0.58 2.49 0.47 5.58 0.92 2.60 0.88 2.81 3.06 A, .......... 0.960 1.134 0.955 1.149 1.288 1.229 0.601 0.758 2.220 2.083 t* ......... 4.05 4.67 3.78 4.72 5.59 5.38 3.46 4.32 4.42 4.51 S .......... 2.991 -1.484 2.479 -1.116 1.816 -1.581 0.772 -0.719 2.826 0.937 t* ........ 2.30 1.13 2.06 0.64 1.40 1.19 0.81 0.75 lAl 3.14 R ......... 0.056 0.540 0.076 0.421 0.307 0.447 0.122 0.436 -0.549 -0.850 t* ......... 026 2.47 0.33 2.08 1.25 2.21 0.81 2.61 1.31 1.91 ED .......... 0.055 0.013 0.08 t ......... 0.33 RES ......... 0.808 -.413 0.847 -0.366 0.772 -0.438 0.517 -0.315 0.743 0.699 t* ......... 4.46 2.25 4.40 2.00 3.89 2.35 4.08 230 1.96 2.37 Constant .... 0.094 -0344 0.160-0.511 2.216 0.101 0.815 -0.338 5.136 6.301 0.10 0.35 0.13 0.40 2.13 0.11 1.14 0.47 2.52 4.58 t : ......... R2** ......... 0.80 0.63 0.82 0.66 0.75 0.67 0.80 0.68 0.86 0.12 F .23.42 10.41....... 18.90 8.69 13.94 8.94 17.84 7.55 22.90 58.34 72 72 72 72 72 72 72 72 72 2986 N .. .
.........
.........
.
.
.
Inpatient visits were excluded from the calculation of VaP, VI, Po,p, P., and INS. t Estimated from data on individuals. S and R are dummy variables where 1 equals male and white, respectively. * t values above 2.00 are signicant at the 0.05 level. I Price elasticity at the mean. Adjusted for deges of freedom.
WINTER 1978
361
GUZICK
HEALTH
SEARVIC 362
likely that this could fully explain differences of such magnitude. Moreover, the directions of differences for individual variables, to be discussed below, were generally consistent with a priori expectations. Price. As expected, own-price elasticities, shown in columns 1 and 2 of Table 3, were low and in the range reported for physician visits as a whole [9,10,15]. The estimate for internists was lower than that for general practitioners but it was imprecise. Changes in the price of a visit to a general practitioner had no apparent effect on demand for visits to internists, and the same was true for the opposite cross-price effect. The effect of adding price-insurance interaction terms is shown in columns 3 and 4 of Table 3. On initial estimation, cross-price coefficients were below 0.005 with t-statistics less than 0.20. In view of the apparent absence of an effect on demand and in the interest of preserving as many degrees of freedom as possible, these cross-price variables were dropped from the final equations. Similar considerations led to the exclusion of cross-interaction variables (price x insurance) and education variables from the final equations. The estimated total effect of own price in the general-practitioner equation, given by the sum of the two coefficients of Pap, b1 + b4(INS), dropped from -0.041 at zero insurance coverage to -0.016 at 50-percent average coverage and to 0.009 at 100-percent coverage. ("Total" ownprice elasticities at the mean were -0.24, -0.09, and +0.05, respectively, for 0-, 50-, and 100-percent coverage.) The result at 100-percent coverage is consistent with the expectation that variation in market price should have no effect on demand at full coverage. The estimated total effect of own price in the internist equation dropped from -0.012 (elasticity -0.14) at zero coverage to -0.002 (elasticity -0.02) at full coverage. (But the coefficents on which these estimates are based (i.e., b2 and b5 in column 4 of Table 3) are not significant at the 5-percent confidence level.) Insurance and Income. Increases in the proportion of expenditures for physician services paid by third parties were estimated to have a substantial positive effect on demand for internist visits. The estimated total effect of insurance, given by b3 + b5(PI) in column 4 of Table 3, is 1.068 at mean PI but has no effect, or perhaps even has a negative effect, on general-practitioner visits. Similarly, visits to internists increase with income, though not strikingly, whereas visits to general practitioners are not significantly affected by changes in income. Disability Days. An increase in the number of disability days is associated with a rise in both general-practitioner and internist visits (Table 3, columns 3 and 4), although the coefficient for the latter is not as large as for the former and is not significant at the 0.05 level. Estimates of unconditional demand equations that exclude disability days from the set of independent variables are shown in columns 5 and 6 of Table 3. The principal effect of excduding D was an inaease in the strength of the association between age and visits, although to a greater extent for general practitioners than for internists. None of the coefficients changed sign, and only two showed any changes in sta-
tistical significance: in the general-practitioner equation, the estimates PHYSICIAN for A1 became more negative and significant and the estimates for S DEMAND became less positive and lost statistical significance. Age. As expected, the estimated relation between general-practitioner visits and age was U-shaped, bottoming out in the 5-17 group. Internist visits appeared to rise slightly during the young and middle years and more sharply later in life. Sex. A rise of 0.10 in the proportion of males was assocated with a (significant) 0.25-unit increase in average number of general-practitioner visits and an (insignificant) 0.11-unit decrease in average number of internist visits (Table 3, columns 3 and 4). Education. According to the regression results in Table 3, education of the family head had little impact on utilization of general practitioners or internists by family members. However, Table 2 shows that among persons 18 years of age or over, those with family heads who had completed 12 years or more of schooling generally made fewer general-practitioner visits than those whose family heads had had fewer than 12 years of schooling, whereas the reverse relation between generalpractitioner visits and schooling was generally true for persons under 18. Thus, offsetting interaction effects between age and education may explain the lack of a net effect of education on demand for GP visits. (Interaction effects could not be explored with regression analysis because there were too few degrees of freedom.) Race. A rise of 0.10 in the proportion of whites was associated with a 0.05-unit increase in average number of visits to internists but had no significant effect on average number of visits to general practitioners. Residence. Rural residents showed patterns of physican utilization significantly different from those of urban residents: those in rural areas averaged an estimated 0.8 more general-practitioner visits per year and 0.4 fewer internist visits (Table 3). There were also interactions among the residence, age, and education variables. Thus rural residents 65 years of age or over with annual family incomes between $0 and $5,000 and whose family heads had fewer than 12 years of schooling made considerably more general-practitioner visits than urban residents in the same categories (4.19 vs. 2.22. for whites and 8.53 vs. 2.25 for nonwhites, although the 8.53 figure must be evaluated with caution because it is based on a sample size of only 28). Conversely, rural residents made considerably fewer internist visits than urban residents (0.31 vs. 2.23 for whites and 0.00 vs. 1.18 for nonwhites). The result for internists was particularly striking, because the number of internists visits made was the same for urban and rural residents when high school graduates were included in the elderly, low-income group. Estimates Exclusive of Inpatient Visits. Columns 7 and 8 of Table 3 contain estimates of unconditional demand equations with inpatient visits excluded from the index of weighted physician visits. The signs of the estimated coefficients were identical to those obtained when inpatient visits were included, although differences in their magnitudes 1978 were evident, reflecting different responses of inpatient and ambulatoryvisit indexes to changes in independent variables. For example, the 363
GUZICK
results indicate that demand for ambulatory visits was more own-price elastic than demand for inpatient visits and that a rise in income was associated with a greater increase in inpatient internist visits than in ambulatory intenist visits.
Conditional Estimates Conditional estimates of demand parameters for general practitioners are shown in columns 9 and 10 of Table 3. The results in column 9 are for aggregated data for those persons who reported at least one visit, with group mean weighted viSits as the dependent variable. These estimates can be compared with those in column 3 to assess the effect of specifying a conditional rather than an unconditional equation in a sample in which 75 percent of respondents reported zero visits. Price elasticity at the mean for general-practitioner visits (at zero insurance) was about the same for conditional and unconditional specifications (-0.22 vs. -0.24, respectively) even though the regrson coeffident for the conditional specification was larger as a result of the proportionately higher number of mean visits for persons in the conditional sample (4.66 vs. 1.51 for the unconditional sample). Two opposing forces may have interacted to produce this result. On the one hand, physicians may play a greater role in second and subsequent visits, thus making demand more inelastic. On the other hand, the first visit may often be perceived as more of a necessity, thus making demand for subsequent visits more elastic. In general, conditional estimates were larger than unconditional ones, especially for need variables (age and health status). Thus older or less-healthy persons were not only more likely to make a first generalpractitioner visit than younger or more-healthy persons; they were also more likely to make a second or further visit. This does not necessarily represent physician-generated demand for unnecessary visits; it may simply reflect appropriate patient management, since high-need patients may be more likely to have conditions requiring dose follow-up monitoring. The results in column 10 of Table 3 are for indivduals reporting at least one general-practitioner visit; the dependent variable was the number of weighted visits. R and S, expressed as percentages in the aggregated-data analysis, were entered as dummy variables in the individual-data equation. The estimates in column 10 are almost all within one standard error of the estimates for aggregated data reported in column 9, with the exception of the estimate for S, which is smaller than the aggregated-data estimate by slightly more than two standard deviations. Perhaps the relatively slight variation in this variable
HEALTH
RESERCH 364
among cells (mean = 0.47, SD = 0.09) contributed to the apparent imprecision in its aggregated-data estimate. On the whole, however, the comparison between aggregated-data and individual-data estimates of a conditional model for general practitioners supports my use of aggregated data in the estimation of an unconditional model, for which data on individuals cannot be used.
(Conditional estimates for internists were not made because 18 of the PHYSICUN 72 groups had zero mean visits, leaving only 54 groups for aggregated- DEMAND data estimates, which is too small a number to reliably estimate a model containing 13 parameters.)
Discussion To estimate demand equations for general-practitioner and internist visits, it would have been more appropriate to use individual consumers as units of analysis. However, a substantial proportion of respondents in the sample reported no visits to general practitioners (75 percent) or to internists (94 percent), making OLS estimation inappropriate. Excduding such a large percentage of the sample and estimating demand equations for the remainder who reported at least one visit would have seriously limited the validity and generalizability of the results. Tobit estimation was not possible because price data were not available for persons reporting no visits. Finally, estimation of separate use-nonuse and amount-of-use equations would have necessitated the inclusion of a coinsurance rate or some other proxy for price in the use-nonuse equation, and estimates for the overall effects of market price and insurance on d and were not obtainable. Therefore I aggregated the data according to five cross-classified independent variables and used the resulting groups as units of analysis, which was shown to produce little loss in precision. The specfication used allowed for separate estimates of the effects of market price and insurance on demand, which was considered important since changes in the market price of visits to a physician in one specalty alter the relative prices of other specialists' services. The results showed significant differences between demand equations for general-practitioner visits and those for internist visits. Of potential importance is an apparent substitution of visits to internists for those to general practitioners as ability to pay (income or insurance) increases. This finding goes beyond the expectation that general practitioners simply gain a smaller share of the rise in demand brought about by increased income or insurance coverage. It suggests that any direct effect of increased ability to pay on the demand for general-practitioner visits, ignoring the availability of other types of physicians, is fully or more than fully offset by substitution of visits to internists (or to other specalists such as pediatricians or obstetrician-gynecologists). Therefore, efforts to improve ability to pay by increasing insurance coverage may partly confound current attempts to increase the utilization of general primary-care physicians by expanding residency programs in family practice. The ultimate importance of this trade-off depends on whether consumers will show the same demand response to better insurance coverage for family practitioners that they did for the general practitioners of this sample. Differences in demand for general-practitioner and internist services were also found with respect to disability days, age, sex, race, and residence. The importance of health status in explaining generalpractitioner visits and in clarifying the effects of other variables such
WINTE 1978 36
JUJ
GUZICK
HEALTH
RESRICES 366
as age and sex is consistent with the results of previous research on physician services in general [9,18,21-23]. The finding that demand for general-practitioner visits is more responsive to changes in disability days than is demand for internist visits may reflect the fact that general practitioners are used more often as a primary source of care for transient conditions (the stochastic component of disability days mentioned earlier) or for long-term management of chronic illness. When variables other than sex are not taken into account, surveys such as those conducted by CHAS-NORC regularly find that females make more physician visits than males [5]. Surprisingly few studies of individual (as opposed to family) demand for physician services have examined the role of sex while controlling for other variables, and the results of these studies are not consistent. Newhouse and Phelps [9] reported that female family heads made more office visits than their male counterparts, even when other variables including health status, age, income, education, and race were controlled for. However, in Wirick's [24] analysis of Michigan survey data, sex was not found to play an important role in explaining variation in physician visits after hospital days, income, age, availability of medical care, and unmet needs (recommended medical treatment or self-perceived medical needs not received) were controlled for. Wirick suggested that the control for hospital days was particularly critical, because once the effect of hospital maternity care was removed, the difference between the sexes in numbers of physician visits disappeared. In their study of a semirural California population, Hershey, Luft, and Gianaris [23] found that sex was not an important determinant of physician visits. However, they found that females made more visits than males when health status was not controlled for but fewer visits when health status was held constant, although neither finding was statistically significant. The results of the present study add further complexity, suggesting that differences between the sexes in demand for physician services vary by specialty. Females appeared to make more visits than males to internists but fewer visits to general practitioners, perhaps because women commonly visit their gynecologists for routine primary care. In any case, the relation between sex and physician visits is not as dearcut as bivariate survey data would suggest; the relationship appears to depend on independent variables other than sex and on the particular specialty under consideration. The results for race are of some interest in view of the consistent finding in previous studies that whites make more visits to physicians [5,17]. The lack of racial differences in general-practitioner utilization, while income and other variables are controlled for, may reflect a greater concentration of general practitioners in nonwhite areas. I hypothesized that consumers' perception of services as nonelective, their ignorance of price differences, their reliance on physician judgment, and their equation of price with quality would decrease the effect of price on demand for physician services. The finding that demand for both general-practitioner and internist services was highly inelastic with respect to price is thus important because of its consistency
with (though not proof of) these behavioral assumptions. Furthermore, PHYSICIAN demand for internist services was found to be more inelastic than DEMAND demand for general-practitioner services, which is consistent with my hypothesis that demand for the services of the former is, in general, less discretionary. Perhaps more far-reaching implications of inelastic demand curves are rooted in the structure of physician-service markets. To the extent that these markets lack active price competition (for much the same reasons as those given-above to explain an inelastic demand), physicians can be expected to behave as price-setting monopolists rather than as price-taking competitors. Under these circumstances, the more inelastic the demand curve, the higher the price. To take the point one step further: in view of the large number of physicians in a given specialty market, each providing similar but not identical services (these being differentiated by the skill and appropriateness with which they are provided and by the personality, practice location, and other characteristics of the physician), one can argue that the structure of physician-service markets is that of monopolistic competition. The absence of price competition among physicians in the presence of a monopolistic-competition market structure would imply excess capacity-that is, the same total quantity of service could be provided more cheaply if it were divided up among a smaller number of physician firms, each providing a greater quantity of service. An inelastic demand curve is important in this regard because greater inelasticity implies greater excess capacity in the long run. Subject to the caveats mentioned throughout this article, the differences in the structure of demand for general-practitioner and internist services shown in this research-together with previous research showing differences among specialties in the structure of fees, output, productivity, and geographic distribution [1-4]-suggest that it is indeed appropriate to view physician services as being organized into specialty markets. Thus research on both supply and demand in physicianservice markets, as well as analyses of supply and demand forces in an overall market structure, might better focus on individual specalty markets than on a single market for physician services. AcknowledgmentLs The author wishes to thank Ronald Andersen and Virginia Daughety of the Center for Health Administration Studies and the National Opinion Research Center for graciously providing the data, Russell Blount for assitance with computer programming, and Herbert Klarman, Sanford Schwartz, Lawrence Stern, and several referees for critical comments on earlier drafts.
REFERENCES
physicans' fees. J Bus 47:493 Oct. 1974. Lorant, J.H. and L.J. Kimbell. Determinants of output in group and solo medical practice. Health Serv Res 11:6 Spring 1976. Kimbell, L.J. and J.H. Lorant. Physician productivity and returns to scale. Health Serv Res 12:367 Winter 1977. Guzick, D.S. and R. Jahiel. Distribution of private practice offices of physicians with specified characteristics among urban neighborhoods. Med Care 14:469 June 1976. Andersen, R., R.M. Greeley, J. Kravits, and O.W. Anderson. Health Services Use:
1. Steinwald, B. and F.A. Sloan. Determinants of
2. 3.
4. 5.
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National Trends and Variations, 1953-1971. DHEW Pub. No. (HSM) 73-3004. Washington, DC: U.S. Government Printing Office, 1972. 6. Andersen, R., J. Kravits, O.W. Anderson, and J. Daley. Expenditures for Personal Health Services: National Trends and Variations, 1953-1970. DHEW Pub. No. (HRA) 74-3105. Washington, DC: U.S. Govemment Printing Office, 1973. 7. Andersen, R., J. Kravits, and 0. Anderson (eds.). Equity in Health Services: Empirical Analyses in Social Policy. Cambridge, MA: Ballinger, 1976. 8. Tobin, J. Estimation of relationships for limited dependent variables. Econometrica 26:24 Jan. 1958. 9. Newhouse, J.P. and C.E. Phelps. Price and Income Elasticities for Medical Services. R-1197-NC-OEO. Santa Monica, CA: Rand, 1974. 10. Newhouse, J.P. and CE. Phelps. New Estimates of Price and Income Elasticities of Medical Care Services. In R.N. Rossett (ed.), The Role of Health Insurance in the Health Services Sector, pp. 261-313. New York: Neale Watson, 1976. 11. Silver, M. An Economic Analysis of Variations in Medical Expenses and WorkLoss Rates. In H.E. Klarman (ed.), Empirical Studies in Health EconomicsProceedings of the Second Conference on the Economics of Health, pp. 121-140. Baltimore: Johns Hopkins University Press, 1968. 12. Haitovsky, Y. Regression Estimation from Grouped Observations. New York:
Hafner, 1973. 13. Kish, L. Survey Sampling. New York: Wiley, 1965. 14. Grossman, M. The Demand for Health: A Theoretical and Empirical Investigation. New York: Columbia University Press, 1972. 15. Fuchs, V.R. and M.J. Kramer. Determinants of Expenditures for Physicians' Services in the United States, 1948-8. DHEW Pub. No. (HSM) 73-3013. Washington, DC: U.S. Government Printing Office, 1972. 16. Newhouse, J.P. and C.E. Phelps. On Having Your Cake and Eating It Too: Econometric Problems in Estimating the Demands for Health Services. R-1149NC. Santa Monica, CA: Rand, 1974. 17. Aday, L. and R.E. Eichhorn. The Utilization of Health Services: Indices and Correlates. DHEW Pub. No. (HSM) 73-3003. Washington, DC: U.S. Government Printing Office, 1972. 18. Andersen, R.A. A Behavioral Model of Families' Use of Health Services. Center for Health Administration Studies, Research Series 25, University of Chicago, 1968. 19. Newman, J. Age, Race, and Education as Predisposing Factors in Physician and Dentist Utilization. In R. Andersen, J. Kravits, and 0. Anderon (eds.), Equity in Health Services: Empirical Analyses in Social Policy, pp. 35-54. Cambridge, MA: Ballinger, 1976. 20. Haug, J.N., G.A. Roback, and B.C. Martin. Distribution of Physicians in the United States, 1970. Chicago: American Medical Association, 1971. 21. Andersen, R. and L. Benham. Factors Affecting the Relationship Between Family Income and Medical Care Consumption. In H.E. Klarman (ed.), Empirical Studies in Health Economics-Proceedings of the Second Conference on the Economics of Health, pp. 73-95. Baltimore: Johns Hopkins University Press, 1968. 22. Berki, S.E. and B. Kobashigawa. Socioeconomic and need determinants of ambulatory care use: Path analysis of the 1970 Health Interview Survey data. Med Care 14:405 May 1976. 23. Hershey, J.C., HS. Luft, and J.M. Gianaris. Making sense out of utilization data. Med Care 13:838 Oct. 1975. 24. Wirick, G.C. A multiple equation model of demand for health care. Health Serv Res 1:301 Winter 1966.
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Previous empirical studies of demand for physician services have used data aggregated across specialties. It would be more accurate, however, to describe the "market" for physician services as several specialty markets, since although to some extent substitutable, the services provided by the various specialties are essentially distinct. Accordingly, aggregate results may obscure large differences in determinants of demand for various specialty services, and policies based on aggregate estimates might result in unforeseen distributive effects across specialties. Because of increasing recognition of the importance of disaggregating data on physiacans from different specialties and the greater availability of such data, recent studies of supply, including those of fees [1], output and productivity [2,3], and geographic distribution [4], have reported results for individual specialties. In the research reported here, I estimated functions of demand for the services of general practitioners and internists. Physicians of these two specialties provide the bulk of primary care, and their services are substitutable to some extent, allowing comparative study.
Methods Use and Expenditure Data by Specialty The source of data for this research was a national survey of health-service utilization and expenditure conducted by the Center for Health Administration Studies and the National Opinion Research WINTER 1978
Address communications and requests for reprints to David S. Guzick, Box 715, School of Medicine, New York University, 550 First Avenue, New York, NY 10016.
0017-9124/78/04035118/$02.0O/O 0 1978 Hospital Research and Educational Trust
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Center (CHAS-NORC) of the University of Chicago [5-7]. The sample consisted of 3,765 families containing 11,619 individuals who were interviewed in their homes in early 1971. The poor, the aged, and persons residing in inner cities or in rural areas were overrepresented. Three files created by CHAS-NORC from this survey were combined into one containing data on respondents' social and economic characteristics and on the number and cost of visits made by each individual to general practitioners and internists during 1970. (A detailed description of the merger of the three original files is available from the author.) Use and cost data in the combined file were cross-classified by location of visit (home, office, hospital emergency room, hospital outpatient clinic, health service or other clinic, patient's room in hospital), reason for visit (obstetric, nonobstetric), and physician specialty (internal medicine, general practice). Internists were classified as board certified or uncertified, and general practitioners were classified as young or old (45 and under, and over 45, respectively).
Grouping of Data Only 25 percent of the individuals in the CHAS-NORC sample had reported one or more visits to general practitioners during 1970, and only 6 percent had reported one or more visits to internists. (These figures should not be considered estimates of the corresponding population percentages because of missing physician-specialty data for some visits and overrepresentation of some population groups in the sample.) The large proportion of persons with zero visits to physicans in the specialties under study created serious problems for the estimation of demand equations. When observations on a dependent variable are concentrated at a limiting value, errors do not behave in the manner assumed by the multiple regression model. Tobin's method for estimating relationships with limited dependent variables [8] might ordinarily be appropriate, but such an approach was confounded here by the absence of data on price for those persons reporting zero visits. One way of avoiding the problem is simply to ignore persons who report zero visits and to estimate demand equations using only data on those with one or more visits [9]. This results in estimates of demand parameters that are conditional on there having been at least one visit. However, when a large proportion of the sample reports zero visits, the validity of such estimates is questionable, since the first and subsequent visits are probably generated differently, the physician playing a greater role in subsequent visits. Furthermore, policy interest centers on the future response of the entire population to changes in factors such as insurance coverage or age composition, regardless of whether individuals do or do not currently report visits to physicians. When a substantial proportion of the sample reports zero visits, as is the case when HEALTH individual physician specialties are considered, estimates of conditional SERVICES RESEARCH demand parameters cannot be assumed to closely approximate the unconditional ones. 352 In a recent study using 1963 CHAS-NORC data, Newhouse and
PHYSICAN Phelps [10] attempted to derive unconditional estimates of demand DEMAND parameters by estimating two separate equations: a use-nonuse equation with a dummy dependent variable that equals 1 if there are any physician visits and a number-of-visits equation conditional on there being at least one visit. Newhouse and Phelps circumvented the problem of establishing visit prices for those without visits by using the coinsurance rate of policies covering physician services, but when combined with the number-of-visits equation, the overall unconditional "price" elasticity applied only to coinsurance, not gross or net price (i.e., gross price times the coinsurance rate). In addition, only a small fraction (11 percent) of respondents in Newhouse and Phelps's sample carried insurance for physician services, thus limiting the reliability of the resulting estimates. To estimate unconditional demand functions for general-practitioner and internist services, I grouped individuals according to crossclassified values of independent variables and conducted multiple regression analyses using group means as data. Variables were chosen as cross-classifiers on the basis of their potential importance in determining demand and were categorized on the basis of their joint frequency distributions and comparability with census data needed for other work. The dassifying variables and their categories were: Age: 0-4, 5-17, 18-64, and 65 and over Family income: $0-4,999, $5,000-9,999, and $10,000 and over Education of family head: 12 years or less than 12 years Residence: rural or urban Race: white or nonwhite The education variable was obtained by adding the value for education from the record of the family head to the record of each family member. Persons with missing values on any of the five dassifying variables were exduded, reducing the sample size from 11,619 to 11,481. Of the 94 cross-classified groups, 24 were collapsed over race categories because of small numbers (25 or fewer). This left 72 groups for analysis, with a mean group size of 159.5. The grouping technique used here is similar to the one used by Silver [11] in his analysis of total medical expenditure and work-loss data, but Silver's data were not as flexible as those used here. His grouping variables-region, age, and sex-were restricted to those already used by the National Center for Health Statistics and resulted in only 24 cross-classified cells. Moreover, data on price and insurance were not available to Silver, and total medical expenses were not disaggregated by object of expenditure or physician specialty. The main disadvantage of using grouped data is that the resulting estimates have greater sampling variance than those based on ungrouped data [12], and I tried to limit the drop in precision by using sufficient cross-dassification to produce a large number of fairly homoge- WINTER neous groups. To give an indication of the reliability of my uncondi- 1978 tional estimates of demand equations, I report conditional estimates 353 based on both grouped and ungrouped data.
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Measurement and Selection of Variables Demand equations for general-practitioner and internist services were estimated by regressing the average quantity of services demanded by persons in each group on several variables that were thought to account for variation in demand among the groups. Two issues that are immediately raised by such a procedure concern the method used to measure physician services and the bases on which explanatory variables are chosen. Dependent Variable. The main difficulty in measuring physician services, as in other studies of demand, is that of product heterogeneity, since it would be inappropriate to assign equal weight to all the different types of physician visits. However, restricting the analysis to demand for a particular specialty's services, as I did here, still leaves considerable disparity among visits. To approach some degree of standardization, I measured the quantity of general-practitioner or internist services demanded by a weighted index of nonobstetric visits. The weights were average prices charged for nonobstetric visits to physicians in the four categories and at each of the six locations listed in Table 1, which are assumed to reflect the relative values of these different visits. Average prices are divided by 10; thus, one weighted visit is equivalent to a home visit by an old general practitioner (9.93 *. 10 1). Average-price estimates based on very small numbers of visits are the most imprecise estimates. By definition, however, they make the smallest contribution to the overall index of weighted visits and should not substantially reduce its reliability. Average prices do not reflect relative values to the extent that market imperfections lead to a greater departure of observed from competitive prices at some locations or for some physician categories. The index that results from weighting visits by these average prices does not truly reflect standardized units of service to the extent that the quality and mix of services at each location are not homogeneous among physicians of a given category. Obstetric visits were excluded from the weighted index because most of these visits were not reported by location but for obstetric care as a whole and because demand for obstetric services is likely to be structurally different from that for nonobstetric services. The group means of weighted nonobstetric visits, averaged over persons with and without visits, were the main focus of data analysis. These means represent the medical visits of typical individuals in each group and were used to estimate unconditional demand parameters. For comparison, mean numbers of weighted visits for persons reporting at least one (unweighted) visit were used to estimate conditional parameters. Price data on individual visits, needed to calculate the weights, HEALTH were not directly available and were computed as expenditures divided REEARCEH by visits. These prices were corrected for oversampling with weights assigned by CHAS-NORC and then averaged for combinations of 354 location and physician category. Records in which expenditures at
PHYSICIAN
Table 1. Average Prices for Nonobstetric Visits During 1970, by Location and Physician Category Physican category General practitioner
Internist
Location
DEMAND
Non-
Board ertified
Younga under)
Old (over 45)
10.27
12.47 1.90 68;12
9.04 1.36
9.93 0.55
110;19
412;83
21.45 1.32 1 947;272
9.04 0.24 5 175;885
11 028;1 985
4.32 93;18
19.42 2.21 309;51
14.58 1.54 349;78
11.08 2.41
9.15 0.91
16.13 1.69
58;9
65;16
247;46
... ... ...
9.54 2.97 26;4
10.15 1.04 6;4
14.25 1.54 411;65
11.09 1.56 440;92
9.02 0.80 813;170
certified Home
Average price ($) ..........
1.40 Est. standard error ($) ...... 95;14 Wtd; unwtd sample sizes ... Office Average price ($) .......... 14.80 0.72 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 2 443;370 Hospital emergency room Average price ($) .......... 14.07 3.31 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 111;17 Hospital outpatient dept. 8.51 Average price ($) .......... 1.00 Est. standard error ($) ......
Wtd; unwtd sample sz ... Health service or other diiic Average price ($) .......... Est. standard error ($) ......
24;6
8.75 1.98 6;4
Wtd; unwtd sample sizes ... Inpatient Average price ($) .......... 16.18 2.05 Est. standard error ($) ...... Wtd; unwtd sample sizes ... 334;56
21.52
9.12 0.21
one location were subsumed under those at another were ignored in these calculations. Estimation of standard errors of average prices was complicated by several aspects of the CHAS-NORC sampling design, indcluding their weighting scheme, cluster sampling, and stratification [5,13]. Data on dustering and stratification needed for appropriate estimation of standard errors were not available, but rough estimates were calculated by dividing the standard deviation of the weighted means by the square root of the unweighted sample size minus one (Table 1). Physician services are sometimes measured in terms of expenditures rather than visits [11,14]. The former measure allows disparate types of physician services to be combined into a single index and captures variation in quality to the extent that this is reflected in price variation WINTER of a given service. However, total expenditures for physician services 1978 understate the quantity of services used if some are paid for by government or philanthropy. And, perhaps more important, differences in
355
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price for a given service may reflect market conditions or physician price discrimination as well as quality differences. Independent Variables. The price variables Pap and PI are group means of prices for general-practitioner and internist visits, respectively. As mentioned previously, data on fees for individual visits were not available, so price was defined for each group member as total expenditures divided by weighted visits. Persons with zero visits were excluded from the mean-price calculation since price was unknown for them. To the extent that there was measurement error in both expenditures and visits, a bias of unknown direction was introduced in the estimates of price elasticity, since error in the numerator would lead to bias toward zero whereas error in the denominator would lead to bias away from zero [9]. Some studies of demand for physician services as a whole have used a "marginal price" (gross price multiplied by the coinsurance rate) variable [9,10,15]. In this study, however, market price and insurance are two separate variables because a change in Pgp, controlling for PI (or vice versa), alters the relative prices of general-practitioner and internist services, whereas a change in the insurance variable (INS) alters the net cost for both types of services equally. From the consumer' s point of view, a dedine in the price of a general-practitioner visit is not the same as an (equivalent) increase in the proportion of expenditures for physician services paid by third parties, since the former increases the relative price of internist services, whereas the latter decreases the cost of both general-practitioner and internist services by the same proportion. Moreover, from a behavioral standpoint, it is questionable whether patients calculate and use marginal rates as the decsion-making price variable. Conventional economic theory suggests that general-practitioner price should have a negative sign and internist price a positive sign in the general-practitioner equations and that the opposite should be true in the internist equations. Following Grossman's health-capital investment model [14], Newhouse and Phelps [9] showed that own-price elasticity is always negative but that cross-price elasticity is of indeterminate sign, depending on the magnitude of the elasticity of the marginal efficiency of health capital. Own-price elasticities were expected to be low, since consumers often perceive physician services as nonelective and equate price with quality. In addition, patients usually rely on their physicians' judgment since they are typically uninformed about the content and quantity of services that would benefit them most. I also expected demand for internist services to be more own-price inelastic than that for general-practitioner services, since use of the former may generally be less discretionary. The insurance variable (INS) was computed by first calculating, for each group member, the proportion of total expenditures on physican RESEARCH visits not paid out of pocket and then averaging these figures over all group members who showed positive expenditures. For persons with 356 insurance, this average rate was below that of the theoretically ap-
HEARLTCH
propriate marginal rate in the presence of a deductible. Moreover, the PHYSICIAN error in measuring the marginal rate by the average rate covaried posi- DEMAND tively with the marginal rate in these cases, producng inconsistency in the insurance parameter estimate away from zero. (This result follows directly from Eq. 4 of Newhouse and Phelps [16].) This problem of measurement error should not be confused with the one that arises when insurance is measured as the proportion of persons having insurance. The latter leads to more sizable errors, having a negative covariance with the marginal insurance rate [16]. Since an increase in the proportion of physician charges that are paid for by insurance reduces the price of physician visits, higher insurance coverage should increase demand. However, it may also cause a substitution of visits to internists for those to general practitioners if the former are believed to be of higher quality. Therefore I conjectured that internists would obtain the larger share of additional demand resulting from a rise in insurance coverage. Family income was treated as a pair of dummy variables corresponding to the categories used for group dassification-$5,000-9,999 (INC1) and $10,000 and over (INC2)-with $0-4,999 as the reference category. I expected that, other things being equal, a rise in income would shift the demand curve to the right and therefore increase utilization, a well-documented effect [171. In Grossman's health-capital investment model [14], demand is differentially affected by wage and nonwage income. However, such hypotheses cannot easily be tested without restricting the sample to wage earners or family heads [9,14] and were therefore not pursued here. In a recent attempt to estimate wage-income elasticities for a sample that induded unemployed persons, Newhouse and Phelps [10] estimated separate ordinary-least-squares (OLS) wage equations for employed males and females. They then used these equations to predict asking wages (or values of time) for persons not in the labor force by setting weeks worked equal to zero. The estimated elasticity of value of time was small (0.1) and statistically insignificant, although the authors indicated that the prediction procedure used for unemployed individuals biased the estimate toward zero. The variable for disability days, D, was computed as the mean of days reported for 1970 in each group. Disability days (or variables like it, such as work-loss days) are commonly viewed as measures of need, following Andersen [18]. Other things being equal, the greater the need for physician services, the greater the demand for them. Inclusion of disability days as an exogenous determinant of demand for physican services is a misspecification according to the healthcapital model [14], since physician services are regarded in this model as an input in the production of gross investment in the commodity "good health," along with additional inputs such as housing, diet, and other lifestyle factors. Nevertheless, I induded disability days as an independent variable in most of the physician-visit equations on 978 the assumption that individuals are probably much less able to influence the number of their disability days than their overall stock of 357
GUZICK
"sgood health" on the basis of decisions on lifestyle and medical-care expenditures. Disability days contain a highly stochastic component, even among persons of similar age and lifestyle. Therefore I suggest that disability days are partly endogenous but also contain a large exogenous element responsible for a substantial part of the variation in physician visits. For comparison, some equations that exclude disability days were also estimated. Age was entered as a set of dummy variables corresponding to the group-classification categories-0-4 (A1), 5-17 (A2), and over 64 (A8)with 18-64 as the reference category. The relation between age and utilization of physician services is generally found to be U-shaped [17]. Most physician visits made by young children are to pediatricians, however, though many are presumed to be to general practitioners. I therefore expected a somewhat U-shaped relation between age and general-practitioner visits but a monotonically increasing relation between age and internist visits. Beyond childhood, use of general-practitioner and internist services increases with age. One can think of age as a need variable, like disability days, and predict greater demand for physician services among older persons as a reflection of their greater need. In terms of Grossman's health-capital model [14], which assumes that the rate of health-capital depreciation increases with age beyond some point in the life cycle, older persons purchase more physician services to increase their production of gross investment in health capital and thereby offset part of the reduction in their health capital caused by the greater rate of depreciation. The variable for sex, S, was computed as the proportion of group members who were males. Overall, women make more visits than men to physicians [5], and I expected this difference to hold for both general practitioners and internists. Education of family head, ED, was a dummy variable equal to one if the family head had completed 12 years of schooling. Grossman [14] has argued that an increase in human capital, as measured by education, improves efficiency in the production of health. Hence the better educated are able to produce greater amounts of gross investment in health capital with given levels of direct inputs and have an incentive to offset part of the increase in health by reducing their purchase of physician services. However, the evidence to date contradicts this hypothesis, and other things being equal, physician utilization increases with education [5,9,14,18]. The variable for race, R, is the proportion of group members who are white. (For the 48 uncollapsed groups, R takes the value 0 or 1.) Whites generally show greater utilization of physician services [5,17,19], although this may reflect local supply as well as demand. Assuming that nonwhites have relatively better access to general practitioners HEALTH than to internists, the difference between races should be greater for RESEARCH use of internist services than for general practitioner services, other things being equal. 358 Residence (RES) is a dummy variable with rural residence equal
to one. Specialists are more concentrated in urban areas and general PHYSICIAN practitioners in rural areas [20]; therefore, I expected RES to have a DEMAND positive sign in the general-practitioner equations and a negative sign in the internist equations.
Equation Specification and Method of Estimation The following equations, which relate internist and general-practitioner visits to the independent variables described above, were estimated by OLS regression after multiplying through by the square root of group sample size to minimize heteroscedasticity: Fe = bo0 + b11(PGp,) + b2j(P1,) + b81(INS4) + b4j(P,p, x INS,) + b5XPI, x INS,) + b61(#NCj4) +
b7#(1NC24) + b81(D4) + b91(A,4) + bl0(A24) + bl(A84) + bL2(S4) + bul(ED4) + bL41(R4) + bj15(RES4) + e4j
where Vi, is the mean number of weighted nonobstetric visits made by persons in the ith group (i = 1, . . ., 72) to physicians in the jth category (1 = general practitioners or internists). The coefficients bo . . ., b151 are the regression parameters to be estimated, e41 are error terms, and independent variables are as defined in the previous section. Interaction terms between market price and insurance were included because the effect of market price on demand might vary for different levels of coverage. I expected the coefficents of these interaction terms to be positive, indicating that a rise in price variation leads to a smaller reduction in demand when insurance coverage is more complete. To the extent that insurance is endogenous, the above equations are misspecified; it would have been more appropriate to estimate demand for visits as part of a two-equation system, the other equation being demand for insurance. However, identification of the visit equation would require the presence of an exogenous variable in the insurance equation that would be absent in the visit equation. None of the available exogenous variables satisfied this condition (the insurance premium was an obvious candidate, but data were lacking); hence the single-equation model was used. Any endogenicity in insurance that was present was expected to bias elasticity estimates away from zero. None of the group members in 18 of the 72 groups reported an internist visit, so internist price was unknown for them. Missing prices were filled in with predicted values from a regression of PI on independent variables, exdusive of P,p and INS.
Results Unconditional Estimates The means of the weighted nonobstetric general-practitioner and WINTER internist visits for the 72 groups are shown in Table 2, and regression 1978 results are presented in Table 3. 359 Estimated parameters of the regression equation for general-practi- J
GUZICK
tioner and internist visits, exclusive of price-insurance interactions, are shown in columns 1 and 2 of Table 3. On the basis of a Chow test, I rejected the hypothesis of equality between the general-practitioner and internist equations at the 0.01 level (F(14,116) = 5.16). Differential degrees of measurement error might account for part of the observed difference between general practitioners and internists, but it is un-
Table 2. Mean Numbers of Weighted Nonobstetric Visits to General Practitioners and Intemists During 1970, by Age, Education, Income, Residence, and Race $5,000-9,999
$04,999
fagA
head's
education
Urban
Rural
Rural
$10,000. and over
Urban
Rural
Urban
NonNonNonNonNonWhite white White white White white White white White white White white
04 Less than 12 years GP visits ...... Internist visits . Sample size .... 12 years or more GP visits ...... Internist visits . Sample size ....
Non-
0.49
1.14 0.00
0.86 0.00 47
1.05 0.00 26
0.08 146
2.84 0.00 19
1.32 1.15 31
0.32 0.02 73
0.44 0.03 79
0.16
0.38
0.00
0.00 222
0.52 0.00 51
0.04 0.00 154
2.40 1.80 195
0.73 0.33 480
0.75 1.03 206
0.80 0.65 158
1.43 03.5
0.88 0.65
345
354
2.22 2.23 318
2.25
7.44
2.11
1.18 168
1.75 84
2.53 131
5-17 Less than 12 years GP visits ...... 0.68 Internist visits . 0.00 Sample size .... 134
0.49 0.08
71
12 years or more 0.42 GP visits ...... 0.00 Internist visits . 58 Sample size .... 18-64 Less than 12 years GP visits ......3.62 2.36 Internist visits . 0.39 0.11 Sample size .... 244 79 12 years or more 1.67 GP visits ...... 0.40 Internist visits . 124 Sample size .... and over 65 Less than 12 years GP visits ...... 4.19 8.53 Internist visits. 0.31 0.00 Sample size .... 253 28 12 years or more 4.03 GP visits ...... 2.29 Internist visits . 56 Sample size ....
516
2.23 2.28 137
66 2.09 0.02 74
0.32 0.00 42
1.01 0.05 198
2.16
1.86 0.07 57
0.44 357
1.86
0.51 0.01
109 0.13 0.00
0.00
0.89 0.04
67
77
95
0.00 37
0.16 150
0.38 0.01 420
0.65 0.10 186
0.40 0.02 102
0.48 0.00 101
0.61 0.09 150
0.09 0.00 125
0.83 0.13 222
0.61 0.05
0.07 0.08
286
103
1.77
0.80 0.30
1.92 2.66
476
144
1.01 1.44 260
0.98 0.80 120
0.73 0.34 252
1.71 0.69 444
0.97
1.20
0.65 0.86
709
227
0.60 1.11 30
4.06 0.11
3.21 2.29
26
64
0.99
022 73
0.39
1.09 256
3.33
2.47
0.91
6.05 69
21
0.90 0.02
1.08 0.00 21
0.00 40
1.41
3.93 0.68
21
31 0.09
1.70 5.10 56
PHYSICIAN
Table 3. Estimated Parameters of D and Equations for General-Practitioner and Internist Visits Dependent variables (mean weighted nonobstetric visits) Unconditional eitimates Independent
variable
P X INS
ARl ter
icueds exclded icuded terms
V1
VWP Pgp ....... -0.034 0.002 -0.041 VaP
V
Disability days
excluided
VW -0.045 2.22 -0.27
V1
Conditional estimates
Inpatn
Visits excluded*
Va..P -0.055
VI
ter icue icue
V0P VQ,t -0.113 -0.086
t.2.06 0.14 2.20 2.48 2.02 3.31 Elas ....020 0.03 -0.24 -OA1 -0.22 -0.17 P .-0.004 -0.007 -0.009 -0.012 -0.016 t 0.19 1.26 1.48 1.26 1.59 EIa§ . 0.02 -0.08 -0.14 -0.11 -0.19 INS . -0.590 1.073 -0.804 1.061 -0.854 1.067 -0.926 0.834 -2.149 -1.605 0.71 2.28 0.87 2.03 0.92 2.31 1.04 1.72 1.30 1.83 t* ....... 0.050 0.044 0058 0.128 0.098 P X INS .... 2.86 t* 2.69 2.94 3.47 3.79 x INS 0.010 0.007 0.009 Pz 1.61 1.44 1.56 t* INC1 42. -0.2 0.395 -0.185 O.23 -0368 0203 0.180 0.231 -0.638 -0.901 .0.86 1.68 0.78 1.08 1.42 0.91 0.76 1.32 1.54 2.66 INC . -0.021 0.670 -0.030 0.817 4.293 0.796 -0.026 0.431 -0.497 -0.385 t. 0.07 2.22 0.07 2.44 0.77 2.35 0.06 2.01 0.82 1.40 D. 0.059 0.011 0.051 0.012 0.024 0.005 0.091 0.079 4.61 0.91 4.39 0.85 t. 2.66 0.41 3.36 4.81 -1.205 -0.135 .-0269 -0224 -0.233 -0.350 -0.151 -0.230 -1.241 -1.153 Al t. 0.74 0.61 0.54 OA1 2.93 0.98 0.53 0.85 3.36 2.63 A, -0.749 -0.179 -0.782 -0.169 -1.611 -0.328 -0.628 -0.191 -1.833 -1.690 t* ......... 2.46 0.58 2.49 0.47 5.58 0.92 2.60 0.88 2.81 3.06 A, .......... 0.960 1.134 0.955 1.149 1.288 1.229 0.601 0.758 2.220 2.083 t* ......... 4.05 4.67 3.78 4.72 5.59 5.38 3.46 4.32 4.42 4.51 S .......... 2.991 -1.484 2.479 -1.116 1.816 -1.581 0.772 -0.719 2.826 0.937 t* ........ 2.30 1.13 2.06 0.64 1.40 1.19 0.81 0.75 lAl 3.14 R ......... 0.056 0.540 0.076 0.421 0.307 0.447 0.122 0.436 -0.549 -0.850 t* ......... 026 2.47 0.33 2.08 1.25 2.21 0.81 2.61 1.31 1.91 ED .......... 0.055 0.013 0.08 t ......... 0.33 RES ......... 0.808 -.413 0.847 -0.366 0.772 -0.438 0.517 -0.315 0.743 0.699 t* ......... 4.46 2.25 4.40 2.00 3.89 2.35 4.08 230 1.96 2.37 Constant .... 0.094 -0344 0.160-0.511 2.216 0.101 0.815 -0.338 5.136 6.301 0.10 0.35 0.13 0.40 2.13 0.11 1.14 0.47 2.52 4.58 t : ......... R2** ......... 0.80 0.63 0.82 0.66 0.75 0.67 0.80 0.68 0.86 0.12 F .23.42 10.41....... 18.90 8.69 13.94 8.94 17.84 7.55 22.90 58.34 72 72 72 72 72 72 72 72 72 2986 N .. .
.........
.........
.
.
.
Inpatient visits were excluded from the calculation of VaP, VI, Po,p, P., and INS. t Estimated from data on individuals. S and R are dummy variables where 1 equals male and white, respectively. * t values above 2.00 are signicant at the 0.05 level. I Price elasticity at the mean. Adjusted for deges of freedom.
WINTER 1978
361
GUZICK
HEALTH
SEARVIC 362
likely that this could fully explain differences of such magnitude. Moreover, the directions of differences for individual variables, to be discussed below, were generally consistent with a priori expectations. Price. As expected, own-price elasticities, shown in columns 1 and 2 of Table 3, were low and in the range reported for physician visits as a whole [9,10,15]. The estimate for internists was lower than that for general practitioners but it was imprecise. Changes in the price of a visit to a general practitioner had no apparent effect on demand for visits to internists, and the same was true for the opposite cross-price effect. The effect of adding price-insurance interaction terms is shown in columns 3 and 4 of Table 3. On initial estimation, cross-price coefficients were below 0.005 with t-statistics less than 0.20. In view of the apparent absence of an effect on demand and in the interest of preserving as many degrees of freedom as possible, these cross-price variables were dropped from the final equations. Similar considerations led to the exclusion of cross-interaction variables (price x insurance) and education variables from the final equations. The estimated total effect of own price in the general-practitioner equation, given by the sum of the two coefficients of Pap, b1 + b4(INS), dropped from -0.041 at zero insurance coverage to -0.016 at 50-percent average coverage and to 0.009 at 100-percent coverage. ("Total" ownprice elasticities at the mean were -0.24, -0.09, and +0.05, respectively, for 0-, 50-, and 100-percent coverage.) The result at 100-percent coverage is consistent with the expectation that variation in market price should have no effect on demand at full coverage. The estimated total effect of own price in the internist equation dropped from -0.012 (elasticity -0.14) at zero coverage to -0.002 (elasticity -0.02) at full coverage. (But the coefficents on which these estimates are based (i.e., b2 and b5 in column 4 of Table 3) are not significant at the 5-percent confidence level.) Insurance and Income. Increases in the proportion of expenditures for physician services paid by third parties were estimated to have a substantial positive effect on demand for internist visits. The estimated total effect of insurance, given by b3 + b5(PI) in column 4 of Table 3, is 1.068 at mean PI but has no effect, or perhaps even has a negative effect, on general-practitioner visits. Similarly, visits to internists increase with income, though not strikingly, whereas visits to general practitioners are not significantly affected by changes in income. Disability Days. An increase in the number of disability days is associated with a rise in both general-practitioner and internist visits (Table 3, columns 3 and 4), although the coefficient for the latter is not as large as for the former and is not significant at the 0.05 level. Estimates of unconditional demand equations that exclude disability days from the set of independent variables are shown in columns 5 and 6 of Table 3. The principal effect of excduding D was an inaease in the strength of the association between age and visits, although to a greater extent for general practitioners than for internists. None of the coefficients changed sign, and only two showed any changes in sta-
tistical significance: in the general-practitioner equation, the estimates PHYSICIAN for A1 became more negative and significant and the estimates for S DEMAND became less positive and lost statistical significance. Age. As expected, the estimated relation between general-practitioner visits and age was U-shaped, bottoming out in the 5-17 group. Internist visits appeared to rise slightly during the young and middle years and more sharply later in life. Sex. A rise of 0.10 in the proportion of males was assocated with a (significant) 0.25-unit increase in average number of general-practitioner visits and an (insignificant) 0.11-unit decrease in average number of internist visits (Table 3, columns 3 and 4). Education. According to the regression results in Table 3, education of the family head had little impact on utilization of general practitioners or internists by family members. However, Table 2 shows that among persons 18 years of age or over, those with family heads who had completed 12 years or more of schooling generally made fewer general-practitioner visits than those whose family heads had had fewer than 12 years of schooling, whereas the reverse relation between generalpractitioner visits and schooling was generally true for persons under 18. Thus, offsetting interaction effects between age and education may explain the lack of a net effect of education on demand for GP visits. (Interaction effects could not be explored with regression analysis because there were too few degrees of freedom.) Race. A rise of 0.10 in the proportion of whites was associated with a 0.05-unit increase in average number of visits to internists but had no significant effect on average number of visits to general practitioners. Residence. Rural residents showed patterns of physican utilization significantly different from those of urban residents: those in rural areas averaged an estimated 0.8 more general-practitioner visits per year and 0.4 fewer internist visits (Table 3). There were also interactions among the residence, age, and education variables. Thus rural residents 65 years of age or over with annual family incomes between $0 and $5,000 and whose family heads had fewer than 12 years of schooling made considerably more general-practitioner visits than urban residents in the same categories (4.19 vs. 2.22. for whites and 8.53 vs. 2.25 for nonwhites, although the 8.53 figure must be evaluated with caution because it is based on a sample size of only 28). Conversely, rural residents made considerably fewer internist visits than urban residents (0.31 vs. 2.23 for whites and 0.00 vs. 1.18 for nonwhites). The result for internists was particularly striking, because the number of internists visits made was the same for urban and rural residents when high school graduates were included in the elderly, low-income group. Estimates Exclusive of Inpatient Visits. Columns 7 and 8 of Table 3 contain estimates of unconditional demand equations with inpatient visits excluded from the index of weighted physician visits. The signs of the estimated coefficients were identical to those obtained when inpatient visits were included, although differences in their magnitudes 1978 were evident, reflecting different responses of inpatient and ambulatoryvisit indexes to changes in independent variables. For example, the 363
GUZICK
results indicate that demand for ambulatory visits was more own-price elastic than demand for inpatient visits and that a rise in income was associated with a greater increase in inpatient internist visits than in ambulatory intenist visits.
Conditional Estimates Conditional estimates of demand parameters for general practitioners are shown in columns 9 and 10 of Table 3. The results in column 9 are for aggregated data for those persons who reported at least one visit, with group mean weighted viSits as the dependent variable. These estimates can be compared with those in column 3 to assess the effect of specifying a conditional rather than an unconditional equation in a sample in which 75 percent of respondents reported zero visits. Price elasticity at the mean for general-practitioner visits (at zero insurance) was about the same for conditional and unconditional specifications (-0.22 vs. -0.24, respectively) even though the regrson coeffident for the conditional specification was larger as a result of the proportionately higher number of mean visits for persons in the conditional sample (4.66 vs. 1.51 for the unconditional sample). Two opposing forces may have interacted to produce this result. On the one hand, physicians may play a greater role in second and subsequent visits, thus making demand more inelastic. On the other hand, the first visit may often be perceived as more of a necessity, thus making demand for subsequent visits more elastic. In general, conditional estimates were larger than unconditional ones, especially for need variables (age and health status). Thus older or less-healthy persons were not only more likely to make a first generalpractitioner visit than younger or more-healthy persons; they were also more likely to make a second or further visit. This does not necessarily represent physician-generated demand for unnecessary visits; it may simply reflect appropriate patient management, since high-need patients may be more likely to have conditions requiring dose follow-up monitoring. The results in column 10 of Table 3 are for indivduals reporting at least one general-practitioner visit; the dependent variable was the number of weighted visits. R and S, expressed as percentages in the aggregated-data analysis, were entered as dummy variables in the individual-data equation. The estimates in column 10 are almost all within one standard error of the estimates for aggregated data reported in column 9, with the exception of the estimate for S, which is smaller than the aggregated-data estimate by slightly more than two standard deviations. Perhaps the relatively slight variation in this variable
HEALTH
RESERCH 364
among cells (mean = 0.47, SD = 0.09) contributed to the apparent imprecision in its aggregated-data estimate. On the whole, however, the comparison between aggregated-data and individual-data estimates of a conditional model for general practitioners supports my use of aggregated data in the estimation of an unconditional model, for which data on individuals cannot be used.
(Conditional estimates for internists were not made because 18 of the PHYSICUN 72 groups had zero mean visits, leaving only 54 groups for aggregated- DEMAND data estimates, which is too small a number to reliably estimate a model containing 13 parameters.)
Discussion To estimate demand equations for general-practitioner and internist visits, it would have been more appropriate to use individual consumers as units of analysis. However, a substantial proportion of respondents in the sample reported no visits to general practitioners (75 percent) or to internists (94 percent), making OLS estimation inappropriate. Excduding such a large percentage of the sample and estimating demand equations for the remainder who reported at least one visit would have seriously limited the validity and generalizability of the results. Tobit estimation was not possible because price data were not available for persons reporting no visits. Finally, estimation of separate use-nonuse and amount-of-use equations would have necessitated the inclusion of a coinsurance rate or some other proxy for price in the use-nonuse equation, and estimates for the overall effects of market price and insurance on d and were not obtainable. Therefore I aggregated the data according to five cross-classified independent variables and used the resulting groups as units of analysis, which was shown to produce little loss in precision. The specfication used allowed for separate estimates of the effects of market price and insurance on demand, which was considered important since changes in the market price of visits to a physician in one specalty alter the relative prices of other specialists' services. The results showed significant differences between demand equations for general-practitioner visits and those for internist visits. Of potential importance is an apparent substitution of visits to internists for those to general practitioners as ability to pay (income or insurance) increases. This finding goes beyond the expectation that general practitioners simply gain a smaller share of the rise in demand brought about by increased income or insurance coverage. It suggests that any direct effect of increased ability to pay on the demand for general-practitioner visits, ignoring the availability of other types of physicians, is fully or more than fully offset by substitution of visits to internists (or to other specalists such as pediatricians or obstetrician-gynecologists). Therefore, efforts to improve ability to pay by increasing insurance coverage may partly confound current attempts to increase the utilization of general primary-care physicians by expanding residency programs in family practice. The ultimate importance of this trade-off depends on whether consumers will show the same demand response to better insurance coverage for family practitioners that they did for the general practitioners of this sample. Differences in demand for general-practitioner and internist services were also found with respect to disability days, age, sex, race, and residence. The importance of health status in explaining generalpractitioner visits and in clarifying the effects of other variables such
WINTE 1978 36
JUJ
GUZICK
HEALTH
RESRICES 366
as age and sex is consistent with the results of previous research on physician services in general [9,18,21-23]. The finding that demand for general-practitioner visits is more responsive to changes in disability days than is demand for internist visits may reflect the fact that general practitioners are used more often as a primary source of care for transient conditions (the stochastic component of disability days mentioned earlier) or for long-term management of chronic illness. When variables other than sex are not taken into account, surveys such as those conducted by CHAS-NORC regularly find that females make more physician visits than males [5]. Surprisingly few studies of individual (as opposed to family) demand for physician services have examined the role of sex while controlling for other variables, and the results of these studies are not consistent. Newhouse and Phelps [9] reported that female family heads made more office visits than their male counterparts, even when other variables including health status, age, income, education, and race were controlled for. However, in Wirick's [24] analysis of Michigan survey data, sex was not found to play an important role in explaining variation in physician visits after hospital days, income, age, availability of medical care, and unmet needs (recommended medical treatment or self-perceived medical needs not received) were controlled for. Wirick suggested that the control for hospital days was particularly critical, because once the effect of hospital maternity care was removed, the difference between the sexes in numbers of physician visits disappeared. In their study of a semirural California population, Hershey, Luft, and Gianaris [23] found that sex was not an important determinant of physician visits. However, they found that females made more visits than males when health status was not controlled for but fewer visits when health status was held constant, although neither finding was statistically significant. The results of the present study add further complexity, suggesting that differences between the sexes in demand for physician services vary by specialty. Females appeared to make more visits than males to internists but fewer visits to general practitioners, perhaps because women commonly visit their gynecologists for routine primary care. In any case, the relation between sex and physician visits is not as dearcut as bivariate survey data would suggest; the relationship appears to depend on independent variables other than sex and on the particular specialty under consideration. The results for race are of some interest in view of the consistent finding in previous studies that whites make more visits to physicians [5,17]. The lack of racial differences in general-practitioner utilization, while income and other variables are controlled for, may reflect a greater concentration of general practitioners in nonwhite areas. I hypothesized that consumers' perception of services as nonelective, their ignorance of price differences, their reliance on physician judgment, and their equation of price with quality would decrease the effect of price on demand for physician services. The finding that demand for both general-practitioner and internist services was highly inelastic with respect to price is thus important because of its consistency
with (though not proof of) these behavioral assumptions. Furthermore, PHYSICIAN demand for internist services was found to be more inelastic than DEMAND demand for general-practitioner services, which is consistent with my hypothesis that demand for the services of the former is, in general, less discretionary. Perhaps more far-reaching implications of inelastic demand curves are rooted in the structure of physician-service markets. To the extent that these markets lack active price competition (for much the same reasons as those given-above to explain an inelastic demand), physicians can be expected to behave as price-setting monopolists rather than as price-taking competitors. Under these circumstances, the more inelastic the demand curve, the higher the price. To take the point one step further: in view of the large number of physicians in a given specialty market, each providing similar but not identical services (these being differentiated by the skill and appropriateness with which they are provided and by the personality, practice location, and other characteristics of the physician), one can argue that the structure of physician-service markets is that of monopolistic competition. The absence of price competition among physicians in the presence of a monopolistic-competition market structure would imply excess capacity-that is, the same total quantity of service could be provided more cheaply if it were divided up among a smaller number of physician firms, each providing a greater quantity of service. An inelastic demand curve is important in this regard because greater inelasticity implies greater excess capacity in the long run. Subject to the caveats mentioned throughout this article, the differences in the structure of demand for general-practitioner and internist services shown in this research-together with previous research showing differences among specialties in the structure of fees, output, productivity, and geographic distribution [1-4]-suggest that it is indeed appropriate to view physician services as being organized into specialty markets. Thus research on both supply and demand in physicianservice markets, as well as analyses of supply and demand forces in an overall market structure, might better focus on individual specalty markets than on a single market for physician services. AcknowledgmentLs The author wishes to thank Ronald Andersen and Virginia Daughety of the Center for Health Administration Studies and the National Opinion Research Center for graciously providing the data, Russell Blount for assitance with computer programming, and Herbert Klarman, Sanford Schwartz, Lawrence Stern, and several referees for critical comments on earlier drafts.
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