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AAEP Meeting 1999

MORAL HAZARD AND PRICES

IN ARGENTINA’S HEALTH MARKETS

Fabio M. Bertranou (*)

Department of EconomicsUniversidad Nacional de Cuyo

FCE-UNCuyo, Casilla de Correo 594, Mendoza 5500, ArgentinaE-mail: fbertran@fcemail.uncu.edu.ar

This version: August 24, 1999

AbstractThis paper empirically studies two issues in health insurance provision for wage and salaryworkers in Argentina and their utilization of health services: (1) the moral hazard problem; and(2) the role of prices in utilization. I examine the relationship between insurance and utilizationboth by estimating how insurance type affects utilization and by parameterizing characteristicsof seven major Obras Sociales (social insurance organizations) using out-of-pocketexpenditures. Results from two-part count data models of utilization show that social and privateinsurance matter in the decision of contacting physicians and hospital services but not in thefrequency choice. Evidence that could be interpreted as supply-induced demand for physicianvisits is also found.Key Words: health services utilization, health insurance, two-part models, Argentina.JEL classification: I1, C4

ResumenEste artículo estudia dos temas relacionados con la provisión de seguros de salud paratrabajadores asalariados y su utilización de servicios de salud: (1) el problema de moral hazard,y (2) el rol de los precios en la utilización. Se examina la relación entre seguros y utilización através de la estimación de cómo diferentes tipos de seguro afectan la utilización, yparametrizando características de siete grandes Obras Sociales usando el gasto de bolsillo delos afiliados. Los resultados de los modelos de utilización de dos partes muestran que losseguros sociales y privados son importantes en explicar la desición de contacto con losservicios médicos y hospitalarios pero no en la frecuencia de uso. También se encuentraevidencia que puede interpretarse como oferta inducida por la demanda para las visitas con elmédico.Palabras claves: utilización de servicios de salud, seguros de salud, modelos de dos partes,Argentina.Clasificación JEL: I1, C4.

______________________________________________________________(*) I thank the Ministry of Health and Social Action of Argentina for making available the data used in thispaper. Lara Shore-Shepard and Hide Ichimura provided helpful comments. All remaining errors are mine.

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1. Introduction1

The purpose of this paper is to examine the relationship between insurance coverageand the use of health care services among wage and salary workers in Argentina. This paperis an extension of my previous work (Bertranou 1998a) in which I study the interdependencebetween the demand for health insurance and the demand for health care. In this work, I use alarger sample from the Module of Health Care Utilization and Expenditures (MHCUE) to extendthe study in two important directions.2

(1). The traditional approach to health care demand, which is the basis for theestimations carried out for Argentina in Bertranou (1998a), is based on consumer theory, andconsiders medical care demand to be primarily determined by the patient, although conditionedby the health care delivery system. An alternative theoretical approach indicates that medicaldemand is determined within a principal-agent context. In this framework, there is a two-partdecision-making process where the patient (principal) initially chooses whether to seek treatmentbut, once in treatment, the physician (agent) determines the number of visits. This is potentiallya more realistic approximation because of the existence of large asymmetries of informationbetween physicians and patients about the type and amount of medical treatment necessary.I use a two-step econometric method to test which approach more accurately describesArgentina's health utilization data. As recent research has shown that accounting for the fact thatmeasures of medical care utilization (visits to the doctor, length of stay, and number of medicinesconsumed) are discrete and non-negative, I use count data regression models instead of linearregression to implement these extensions.

(2). Economists are usually interested in estimating how responsive consumption is tochanges in prices. The question of how individuals change their consumption of medical carewhen the price of health care changes is an important one for public policy, particularly asgovernments (such as Argentina's) consider changes to the health insurance sector. Healthdata surveys, however, are unable to collect all price related information. Previous studies haveproxied price in the utilization equation by using dummy variables for individual health insurancestatus. A drawback of this strategy is that the estimates offer no information on the effects ofchanging the marginal price of care that individuals face. Therefore, in order to have a moremeaningful interpretation, I calculate “pseudo-price” variables for workers from seven socialinsurance organizations (or Obras Sociales) using information on out-of-pocket expenses andsocio-demographics. I then impute the pseudo-prices to the entire sample, including those whowere not health services users, to capture the price effect on consumption of health careservices. This alternative method is compared with the traditional approach of using dummyvariables for health insurance status.

The plan for the paper is as follows. In section 2, I review the literature and discuss theissues of adverse selection and moral hazard looking at Argentina’s health care sectorcharacteristics. Other institutional features, the data used and variable specifications arepresented in section 3. In sections 4 and 5, I discuss alternative demand/utilization models andtheir empirical implementation, and in section 6, the results using Argentine data. Section 7discusses the two alternative methods to impute price effects in utilization models. Section 8concludes.

2. Health insurance and utilization of health services: literature review and issuesIn general, when using non-experimental data, the observed differences in utilization

among individuals with different insurance types are due to both selectivity in health insurancemarkets and the response to the net prices of health services under the chosen insurancecoverage. Adverse selection arises because of information asymmetries. In health insurancemarkets these asymmetries mean that there are characteristics unobservable to insurers suchas health risks and tastes for medical care that would cause individuals to purchase insurancein anticipation of intensive use of health care. Therefore, by observing health status, it can beindirectly assessed whether there is evidence of selectivity in health insurance markets.

Empirical evidence on health insurance market failure for Latin American countries islimited. The health economics literature for European and former British Commonwealthcountries usually contains little discussion about this type of failure in health insurance markets.

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Because the main source of health insurance in these countries is social insurance, and theextent of private health insurance is relatively limited, selectivity is not an important issue as itis for countries such as the United States. In some cases, empirical models do not even includeinsurance variables because there is no variation as a consequence of high coverage by well-developed public health care systems. Studies for European countries concentrate more onissues relative to access or inequalities on utilization rather than in the failures of healthinsurance markets. This is the case of Gertham (1997) for Sweden, and Geil et al. (1997) andPohlmeier and Ulrich (1995) for Germany.

The effect of insurance on utilization is identified as moral hazard; i.e. the presence ofinsurance coverage and the existence of more comprehensive coverage modifies the net pricesof health care services producing both a higher rate of service use and a larger utilizationfrequency. Moral hazard may occur either before (ex-ante) or after (ex-post) a health problemevent. The former case arises when because of the presence of insurance, individuals do nothave an incentive to avoid risky activities or unhealthy environments. The latter case occurswhen because of the existence of health insurance, individuals do not bear the full cost of usinghealth care services.

Empirical studies have tried to distinguish between the overall moral hazard effect andthe residual moral hazard effect. The former refers to differences in health care servicesutilization between those who are covered by any insurance arrangement and the uninsured.The latter refers to differences in utilization across insurance types or policies; this residual effecthowever, is sometimes too small and cannot be detected (Cameron et al. 1988).

As noted by Geil et al. (1997) in their study of the German insurance system, the moralhazard problem is aggravated when insurance is provided through public schemes financedbased on social insurance principles of solidarity. While in private schemes premiums reflectindividual risks, social insurance usually does not adjust contribution rates to individualcharacteristics such as gender and age; nor does social insurance require health checks beforeinsuring new workers or family dependents. As in the case of Germany, in Argentina, wage andsalary workers are covered by social insurance schemes, therefore, individual risks are typicallynot reflected in contribution levels. Instead, contributions are only related to earnings up to adefined limit. Consequently, the moral hazard problem should be more serious in public than inprivate health insurance.

The last hypothesis motivates the empirical study of economic incentives andhospitalization in Germany done by Geil et al. (1997). They focus on the impact of insurance onthe number of hospital trips using a set of dummy variables for private, public, voluntary andfamily insurance, which are also interacted with supplementary insurance, and two types ofpublic insurance. The presence of chronic conditions which could result in changes in healthinvestment, and consequently in the insurance scheme chosen, is studied by estimatingseparate models for individuals with and without chronic conditions. Their results show thatchronic status seems not to be related to insurance choice and that the type of insurancecoverage does not play an important role in the hospitalization decision. They conclude thatmoral hazard stemming from differences across insurance types is not an important issue,probably because of the German institutional setting (relatively uniform and widespreadcoverage), thus contrasting with evidence found for the U.S. and Australia.

3. Institutions, data and variable specificationSalary and wage earners in Argentina are mandatory covered by the social health

insurance organization of their occupation/industry, identified generically as Obras Sociales.3 Ingeneral, insured workers have access to ambulatory health care and pay small fees or nothing;this includes the freedom to choose any physician registered under the social health insuranceorganization. Premiums consist of payroll taxes, which are independent of individual medicalconditions and provide coverage for the worker’s family dependents. There are no deductibles,and co-payments are limited to some services and prescription drugs (between 20 and 50percent). Private physicians may charge a “plus” which varies across Obra Sociales andphysicians; this “plus” means that the insured may have to pay additional out-of-pocketexpenses. Obras Sociales contract the bulk of health services provided to their members with

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private providers. Private physicians and clinics are paid on a fee-for-service basis; the pricesare negotiated between the Obras Sociales (normally at regional offices) and the correspondinglocal physician and clinic organizations.

The data used in this study is the Module of Health Care Utilization and Expenditure(MHCUE) included in the May 1989 wave of the Argentine Permanent Household Survey. TheMHCUE collected health related data for eight major urban areas: City of Buenos Aires, GreaterBuenos Aires, Mendoza, La Plata, Córdoba, Bahía Blanca, Tucumán, Chaco, and Neuquén. Thelast urban area was dropped because the demographic data were missing. These data containthe first comprehensive set of health and socio-demographic variables jointly collected at thehousehold level for Argentina. This study uses a sample of wage and salary workers age 16 to65, which consists of 9085 observations (436 additional observations were dropped from thesample because of the presence of missing values). Summary statistics on insurance status areshown in Table 1. For the total sample of wage and salary workers, 79.9 percent have some typeof coverage (69.4 percent only Obra Social coverage, 4.6 percent Obra Social and privateinsurance coverage, and 5.8 percent only private insurance coverage). Single women havehigher coverage than single men, but married men show higher coverage than married women.Because a worker’s coverage can be extended to dependent family members (basically spouseand children), married individuals experience higher coverage. Insurance coverage alsoincreases with age and income.

Table 2 reports sample means for utilization variables by health insurance status. Thethree health care utilization measures contained in this table (visits to a physician, days ofhospitalization, and consumption of medicines) show that the mean differences in rates ofutilization are significantly different from zero across insurance status, with insured individualsbeing more likely to use any service. However, the frequencies of utilization, i.e. the utilizationcounts conditional on having positive utilization, show that only for the consumption of medicinesis the difference in means significantly different from zero at a 1 percent level. Therefore,insurance status seems to matter in the contact but not in the frequency decision.

4. Alternative health care demand models: literature reviewDuan et al. (1983) examine alternative models for the demand for medical care using

experimental data from the Rand Health Insurance Study. They focus on five different modelsto fit the dependent variable medical expenses, which has a highly skewed distribution with alarge number of non-spenders. The models studied are: (1) analysis of variance (ANOVA); (2)analysis of covariance (ANACOVA); (3) one-part model, which has a two-parameter Box-Coxtransformation of expenses; (4) two-part model, which has two separate equations to estimatethe probability of positive expenses and the level of positive expenses; and (5) four-part modelwith separate equations to estimate the probability of positive expenses, the probability ofinpatient expenses conditional on having positive medical expenses, the level of positiveexpenses for those individuals with only ambulatory expenses, and the level of positiveexpenses for persons who have positive inpatient utilization. Their results show that analysis ofvariance and covariance on untransformed expenditures yield imprecise results. The one- andtwo-part models improve precision, however, the one-part model produces inconsistent resultsbecause of the problems in handling both the large number of non-spenders and the proportionof patients with inpatient services utilization. The two-part model corrects the problem causedby the high proportion of individuals with zero expenses, but the four-part model is the one thatmost accurately reflects the distribution of medical expenses distinguishing ambulatory andinpatient expenses.

Kenkel (1990) uses a similar model to study the role of consumer health information onthe demand for medical care. He addresses the controversial hypothesis of physician createdor induced demand based on the idea that medical professionals can exploit informationalasymmetries. The distinctive characteristic of his two-part model is that information is includedas an endogenous explanatory variable in both equations: the probability of positive physician

visits, P( V > 0 )i ; and the demand for number of visits, conditional on any use of medical

services, {V | V > 0}i i . The first equation is estimated using maximum likelihood probit,

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treating information as an endogenous variable which is estimated in a first stage by ordinaryleast squares. The second equation is estimated using a simultaneous equation version of asample selection model, where first a reduced form probit equation for the probability of usingmedical services is estimated to calculate Heckman’s lambda. Unlike Duan et al. (1983), whoconsider an empirical specification where the two error terms are independent, Kenkel assumesthat they are correlated because there are common omitted variables that explain both thedecision to use medical care and the demand conditional on use.

There is a vast literature with empirical studies that use the traditional one-step approachbased on the idea that individuals decide the utilization of health care services under a utilitymaximizing framework, i.e., the patient is the one who primarily determines medical demand.See for example Cauley (1987) and Cameron et al. (1988) who use count data regressionmodels; or Coulson et al. (1995) who use a non-linear least squares model.

More recent studies depart from the traditional one-part approach and use two-partmodels. The first part consists of a logit or probit regression for the probability of using the healthcare service studied. This step is followed by different regression models conditioned on positiveutilization; for instance Pohlmeier and Urich (1995) and Gertham (1997) use count dataregression; Hurd and McGarry (1997) and Grana and Stuart (1996) use ordinary least squaresregression; and Kenkel (1990) uses a Heckman-type sample selection model.

5. Count data regression models of utilization

5.1. One-part modelsThe demand for health care services is measured as the number of times that an

individual used the service during the month prior to the interview. This means that thedependent variable has an underlying discrete probability function or count data process. Thebasic count data model is based on the Poisson distribution, which is characterized by a single

parameter indicating equi-dispersion. LetYbe the random variable defined on

N {0} = {0,1,2,3,...}� . Then, under the Poisson assumption, the probability that y counts

is observed is given by:

P (Y = k) = P = exp(- )

k!k

k↔ ↔

where, ↔ � �Η

is the Poisson distribution parameter; and k = 0, 1, 2, 3, ....

The specification of the univariate Poisson regression model implies three importantassumptions (Winkelmann 1997). First, the conditional mean of y, or regression function, is

specified as a log-linear function of x and ϒ : E(Y |x ) = exp(x ) = i i i iϒ ↔ ; i = 1, 2, 3, ....n. This

implies that the marginal effect depends on the value of xϒ ; i.e., ⌡

⌡

E(Y|x)

x = exp(x )ϒ ϒ .

Second, the conditional distribution of iY given the realization of ix is Poisson distributed with

parameter i↔ . Therefore, the conditional probability of Y is given by

P (Y = k | x ) = ( exp(- exp(x )) (exp(x ) ) ) / k!i i i

kϒ ϒ This further implies that the conditional

variance of iY is equal to the conditional mean, i.e. Var(Y |x ) =i i E(Y |x ) = exp(x )i i iϒ (the

equi-dispersion condition). The final assumption is that (y , x )i i are independently and

identically distributed, therefore allowing for a straightforward application of the maximumlikelihood method.

A Poisson regression specification has two advantages. First, it captures the discreteand non-negative nature of the data, and second, it allows inference to be drawn on theprobability of event occurrence. Therefore, the model accounts for the heteroscedastic andskewed distribution inherent in non-negative data, and attributes a non-negligible probability tothe outcome zero (Winkelmann and Zimmermann 1995). An important feature of this model isthat the heterogeneity of y is modeled as a deterministic function of the explanatory variables,therefore the randomness is intrinsic and not due to other factors as in the classical regressionmodel.

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An important assumption of the Poisson regression model is that events occurindependently over time; however, there may exist a form of dynamic dependence between theoccurrence of successive events. In the case of health care services, a visit to a doctor becauseof an accident or illness may raise the probability of subsequent visits. This has also beenstudied under the name of contagious probability models in the biometric literature (Cameronand Trivedi 1986).

The equi-dispersion condition turns out to be very restrictive, especially for utilization ofhealth care measures. Consequently, the conditional mean-variance equality restriction may

produce spuriously small standard errors of the estimated ϒ s . This has previously been shown

by Cameron et al. (1988) using Australian data; Phomeier and Ulrich (1995) using German data;and Gertham (1997) using Swedish data, among others. A less restrictive model assumes thatY follows a negative binomial distribution, which is derived as a compound Poisson with

parameter ↔ distributed as a gamma random variable:

f (y | , ) = ( + y )

( ) (y +1) (

+) (

+)i i

i

i

yi

∼ ↔∼

∼

∼

↔ ∼

↔

↔ ∼

∼

δ

δ δ

The negative binomial regression model specifies i i i = exp(X + )↔ ϒ ⁄ = exp(X ) ui iϒ .

The negative binomial distribution allows the introduction of a stochastic error term; thereforeheterogeneity and measurement error are captured such as in the classical regression model

(the Poisson model only allows heterogeneity with respect to the observed characteristics, ix ).

Without loss of generality, and as long as the model includes an intercept, the expected value

of the error term can be set as E(u ) = 1i ; therefore, the conditional mean becomes

E(Y | , ) =i i i i∼ ↔ ↔ and the conditional variance Var(Y | , ) = + i i i i u i2

i∼ ↔ ↔ ″ ↔ .

Two non-nested parametrizations of iu

″ are common in the literature. The negative

binomial model identified as NEGBIN I sets iu

2

i

-1 = ″ ″ ↔ , where

2″ is a constant. The

negative binomial model NEGBIN II sets iu

-1 = ″ ∼ , which is constant across individuals

meaning that iY is a quadratic function of the conditional mean i↔ . A more general model

specifies a = 1 / 2

i

r-1″ ↔ , which leads to the NEGBIN I when r = 0, and to the NEGBIN II when

r = 1 (Winkelmann and Zimmermann 1995).

Var(Y |x ) = E(Y |x ) + E(Y |x ) = E(Y |x ) (1 + )i i i i

2

i i i i

2″ ″ NEGBIN I

Var(Y |x ) = E(Y |x ) + E(Y |x ) = E(Y |x ) (1 + E(Y |x ))i i i i

-1 2

i i i i

-1

i i∼ ∼ NEGBIN II

Notice that the negative binomial log-likelihood converges to the Poisson log-likelihood

for 2 0″ , therefore the validity of the equi-dispersion restriction can be tested by a likelihood

ratio test.

5.2. Two-part modelsThe basic idea behind two-part models for health care demand is that the contact and

frequency decisions are treated as different stochastic processes. Even when the two processesare driven by the same set of explanatory variables, the interpretation may differ depending onthe stage of the decision process. Hurdle models allow for a systematic difference in thestatistical process governing observations with zero counts and observations with one or morecounts. These models combine a dichotomous model governing the binary outcome of the countbeing zero or positive with a truncated-at-zero model for strictly positive outcomes. This modelis particularly suitable when there are a sizable number of zero outcomes.4

Mullahy (1986) and Winkelman and Zimmermann (1995) provide a general formulation

of the hurdle model. Assume that 1P is a probability distribution function which governs the

hurdle process, and that 2P is any other probability distribution function which governs the

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process once the hurdle has been passed. The probability distribution of the hurdle model isgiven by:

P(Y = 0) = P (y = 0)1 i

P(Y = y) = [ 1 - P (y = 0) ] P (y )

P (y 1) y = 1, 2, 3,......1 i

2 i

2 i∫

where [1 - P (y = 0)]1 i indicates the probability of crossing the hurdle. Then, the log-likelihood

for the hurdle model is:

Log- L = P (y = 0| x ) [ 1 - P (y = 0| x ) ] P (y | x )

P (y 1| x )i=1

N U

1 i i

i=1

U

1 i i

2 i i

2 i i

+ � �∫

where NU indicates the subsample of non-users and U the subsample of users of a particularhealth care service. The first sum shows the probability of a zero count; the second sum is theprocess which governs the utilization pattern truncated-at-zero (the first term indicates theprobability of contact, while the second term indicates the probability of positive counts

conditional on a count). If id is a variable that takes the value of one if contact was made, and

zero otherwise, the log-likelihood can be re-written as:

Log- L = [P (y = 0| x )] [1 - P (y = 0| x ] P (y | x )

P (y 1| x )i=1

N U + U

1-d

1 i i

d

1 i i

i=1

U

2 i i

2 i i

i i + � �∫

where the first sum is the log-likelihood for the binary process which captures the contact versusnon-contact decision for the entire sample (non-users and users), and the second sum is the log-likelihood for the negative binomial model truncated-at-zero defined over the sub-sample ofindividuals who used of the health care service (i.e., the frequency decision). Therefore, theresults can be obtained by separate log-likelihood maximization problems.

A likelihood ratio test (LRT) can be used to test the standard one-part Negative Binomialmodel against the hurdle two-part Negative Binomial model. Because the hurdle model isestimated by separate log-likelihood maximization processes, the LRT is defined as follows:

↔ = 2 ( ln L + ln L - ln L )PROBIT NEG.BIN.| Y>0 NEG.BIN. .

6. Utilization models: specification and results using Argentine dataThe model specification assumes that the utilization functions are the solution to a utility

maximization process with a health production function (Grossman 1972). My major interest isthe impact of health insurance status on utilization of health care services. The insurance statuschoice is taken as given and utilization is estimated using one- and two-part frameworks. Themeasures of health care utilization used as dependent variables are three: the number of visitsto a physician in a four week period, the number of hospitalization days in a year, and thenumber of different medicines consumed in a four week period. Table 3 shows the samplefrequency distributions for the utilization counts of these variables. The characteristics of thesedistributions justify the utilization of count data regression models (non-negative integer valuesand a large proportion of non-users during the time surveyed). For instance, 74.4 percent of therespondents had zero visits to the physician in the previous four weeks, 14.9 percent had onevisit, 10.1 had between 2 and 5 visits, and less than 1 percent had 6 or more visits. Other healthservices also show a high proportion of non-users: 94.1 percent had no hospitalizations in theprevious 12 months, and 53.5 percent had not consumed medicines in the previous four weeks.

In the empirical literature, it is common to find that utilization is modeled as a function ofenabling, predisposing and need factors. Enabling factors include characteristics such asincome, insurance status and accessibility to health care services. Predisposing factors consistof socio-demographic characteristics such as age, gender, education and marital status. Finally,medical needs include factors like self-assessed health status, the existence of physician-

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diagnosed conditions and other measures of health status, for instance, the capacity toundertake normal activities of daily living (Grana and Stuart 1996). The count data regressionmodels estimated in this study, therefore, control for the three variable groups: enabling(insurance and regional dummies), predisposing (age, age square, family income, marital status,education, and occupational dummies), and need factors (health status dummies). There arethree dummy variables for different categories of health insurance status (the omitted one isbeing uninsured).

Tables 4, 5 and 6, which will be discussed separately below, present the estimationresults for the three health care services studied. In the three tables, columns (1) and (2) are theresults from maximum likelihood estimations of a one-part model assuming the Poisson and theNegative Binomial distributions respectively (the NEGBIN II model is assumed). Columns (3) and(4) correspond to the two-part model: binary probit for the first part, and Negative Binomial forthe second part (the data show over-dispersion therefore this assumption is preferred over thePoisson distribution

LRTs for the Poisson models against the Negative Binomial models lead me to reject thePoisson assumption for the three cases studied (visits to physicians, days of hospitalization, andconsumption of medicines). Because of the over-dispersion problem, which is caused byunobserved heterogeneity, the more flexible Negative Binomial assumption is used to allow aconditional variance larger than the conditional mean. The evaluation of the one-part against thetwo-part models also using a LRT enables me to reject the null hypothesis of no differencebetween the statistical parameters driving the contact and frequency decisions. Pohlmeier andUlrich (1995) and Gertham (1997) found similar results for their studies of physician visits andnumber of hospital care weeks in Germany and Sweden respectively. Next, I separately discussfindings from the two-part models for physician visits, hospitalization days and consumption ofmedicines (columns 3 and 4 in Tables 4, 5 and 6).

6.1. Findings from the visits to the physician modelsThe three insurance status dummies shown in Table 4 (the omitted category is being

uninsured) indicate that there is a moral hazard problem. The coefficients are positive, andsizable at least for the contact decision (i.e., the binary probit results). The estimatedcoefficients, however, are statistically significant only for the contact decision and not for thefrequency decision. Income, which is another enabling factor, is neither significant in the contactnor in the frequency decision. Regional and occupational dummies, which are not reported inTable 4, are jointly significant for both parts of the model. The contact and frequency decisionsin physician visits are strongly responsive to need factors (health status/morbidity conditions).The results show (not reported in the Table) that the four short-term health status measures arepositive and significant at the 0.1 percent level.

Among the enabling factors, the inclusion of variables that reflect different levels ofschooling is justified by the idea that education may be eventually correlated with medicalknowledge. Another hypothesis is that more highly educated people can improve their healthmore efficiently and therefore visit a physician less often (Wagstaff 1986). The results show thatdummies for educational levels are not significant in any decision stage. The estimatedcoefficient for the female dummy is positively significant in both stages of the decision-makingprocess corroborating that women are not only more likely to use medical care than men, butthey do it more intensively. Marital status (married=1) is positive and significant in the contactdecision but not significant in the second stage. The inclusion of this variable is justified due tothe possible impact that illness of one spouse may have on the utility of the other. Finally, thecoefficient obtained for age is positive and significant, but only in the contact decision, i.e., theprobability of visiting a doctor increases with age because older people are more likely to presentdisease conditions and a deterioration of health.

Do physicians act as agents of patients?It is argued that because of the existence of asymmetric information between physicians

and patients, doctors may act as agents for patients. Taking advantage of this situation,physicians may be able to increase their own demand by inducing patients to visit them morefrequently. This has been the basic idea for the controversial supply-induced demand

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hypothesis. In a similar two-part count data framework, Pohlmeier and Urich (1995) and Gertham(1997) try to find evidence to support such hypothesis. Pohlmeier and Urich first argue thatcompetition among physicians is a major determinant of multiple visits, however they can onlyproxy this effect by variables such as community size and physician density. When they usephysician density, in the first stage this variable proxies an availability effect (pure demandeffect), while in the second stage, it captures both a demand and a supplier response. Eventhough the frequency is an outcome from the joint decisions of physicians and patients,Pohlmeier and Ulrich are inclined to interpret their findings as some evidence of supplier-induceddemand since the physician density roughly measures the degree of competition. Their resultshowever, are very weak; one of the reasons is that their data do not contain direct supply-sideinformation.

Gertham’s data also lack information on the supply side of the health care sector (forinstance physician and hospital type). He also uses dummies for big and small cities in order tocapture level of competition, and shows that the probability of having a physician visit is similareverywhere in Sweden but the frequency is higher in big cities. A problem with this result is thatit cannot be ruled out that the positive and significant coefficient for big cities is due to an overproportional supply of specialists in those cities, thus reflecting only an availability effect.

The data I use allows me to identify the type of physician visited by the patient (privatepractitioner, or practitioner in the public sector or in a social health insurance organization).Insurers and social health insurance organizations pay private physicians on a fee-for-servicebasis; on the other hand, physicians who work in the public sector or in social securityorganizations are salaried professionals. Therefore, private practitioners could have strongincentives to induce higher utilization. The specification for the second stage of the hurdle modelreported in column (5) of Table 4, shows that the dummy for private physicians is positive andstatistically significant at 10 percent level. This indicates that even after controlling for healthconditions, insurance status and other enabling and predisposing variables, individuals whovisited a private physician are more likely to have higher utilization frequency of medical services.

I am inclined to interpret this result as a consequence of the reimbursement methodrather than an outcome of a supply-induced demand story. In such a case, under a fee-for-service scheme there are incentives for excessive and unnecessary treatment, therefore doctorstend to over-produce. The data I use however, does not allow me to rule out that public andsocial security doctors under-produce. This means that it may be the case that private doctorsare the ones who are providing the optimal amount of health services which could be explainedby their necessity for building and maintaining a reputation.

Alternative specifications were carried out (not reported in this paper, see Bertranou1998b) for the second part of the negative binomial model where the dummy variable indicatingprivate physician is interacted with health and insurance status. When these interactions areincluded the coefficient for private physician losses some statistical significance but it is stillpositive. The likelihood ratio statistics for testing the models with interactions against the modelswithout private physician interactions are not statistically significant indicating that the lattercannot be rejected.

6.2. Findings from the hospitalization days modelsThe utilization models for hospitalization days also yield some distinctive results.

Findings from Table 5 show that being covered by an Obra Social or by both an Obra Social andPrivate Insurance are positively correlated with the probability of being admitted to a hospital.The coefficients in this first stage are smaller than those obtained for visits to the doctor meaningthat the moral hazard effect associated with having insurance coverage seems to be lower forhospital services than for physician services. The second stage, which evaluates the frequencydecision, shows that none of the insurance status dummies are significant (column 4). Thecoefficient for income is negative and significant only for the second stage, meaning that thepoor are more likely to stay longer in hospitals. The reason may be due to a lower opportunitycost of time, or because a lower income reflects inferior accumulation of health stock and lowerchances of a rapid recovery once hospitalized.

10

Another relevant difference with respect to models for physician visits is that thecoefficient on the dummy variable for women is positive for the contact decision (women aremore likely to be hospitalized) and negative for the frequency decision (women are likely to haveshorter stays longer than men once hospitalized). Furthermore, the coefficients are significantin both stages. The reason for a positive sign in the contact decision and negative in thefrequency decision is that a significant number of women’s hospitalizations are for childbirth,which have a relatively short stay (from 2 to 3 days for normal deliveries). Therefore, the negativecoefficient in the second stage is capturing that effect. Finally, the coefficient for the variable ageis not statistically significant for the contact decision but is positive and significant for thefrequency decision. This means that older individuals, other things constant, have a higherprobability of using longer hospital services.

6.3. Findings from the consumption of medicine modelsThe model for counts of different medicines consumed during the four weeks previous

to the survey interview (Table 6, columns 3 and 4) show that all enabling, predisposing and needfactors are statistically significant for both the use and frequency decisions. The only factorswhich are not significant are income (use and frequency) and marital status (frequency). Usinga one-part utilization model and Australian data, Cameron et al. (1988) also found that incomewas not relevant in explaining variations in the use of total number of prescribed and non-prescribed medications.

Contrary to what the physician visits and hospitalization days models show, insurancestatus matters not only for the first but for the second part as well. The size of the coefficientsand their standard errors indicate that the moral hazard effect seems to be larger whenindividuals are covered by private insurance. The coefficient for age is also positive andsignificant for both stages, a result not found in the other two models of utilization. Theprobability of taking medicines has a monotonic increasing relationship with schooling, however,once past the hurdle the relation between frequency and schooling has an inverted U-shape.Those with primary and college education are less likely to consume a higher number ofmedicines than those with secondary education.

7. Utilization and response to prices: an alternative approachIt has been largely acknowledged that the demand for health care is not completely

inelastic but is price sensitive. The utilization responsiveness to different measurements of pricehas been documented in several studies using both experimental and non-experimental data.For instance, Wagstaff (1986) and Newhouse et al. (1993) summarize some of the results in theliterature.

Because of the existence of different health insurance arrangements, non-experimentalpopulation surveys usually are unable to collect complete information about prices. Thetraditional approach in demand/utilization studies using non-experimental data has been to proxyprice by using health insurance status (health insurance type or plan). This approach is used,for example, by Cameron et al. (1988); Grana and Stuart (1996), Geil et al. (1997) and Hurd andMcGarry (1997), as well as previously in this study. Other categorical variables have been alsoused to capture part of the price variation given that health care demand is strongly influencedby waiting time and distance. For instance, regional dummies may capture community travelcosts, while other socio-demographics, such as income, may also reflect the opportunity costof time (therefore the value that individuals assign to waiting time).

The question of how individuals change their consumption of medical care when theprice of health care changes is an important one for public policy, particularly as governments(such as Argentina's) consider changes to the health insurance sector. A drawback of usinginsurance status as a proxy for price is that the estimates offer no information on the effects ofchanging the marginal price of care that individuals face. Therefore, in order to have a moremeaningful interpretation, for users of health care services I calculate pseudo-price variables forworkers from seven Obras Sociales using information on out-of-pocket expenses and socio-demographics. This allows me to impute the pseudo-prices to the entire sample, including thosewho were not users, thus capturing the price effect on consumption of health care services. This

11

alternative method is compared with the traditional approach of using dummy variables for healthinsurance status.

There are three reasons why I restrict the analysis to seven major Obras Sociales. Thefirst reason is data driven. The data base for the MHCUE has coded information for type ofinsurer, however the information for the specific name of the Obra Social or the private insureris contained in a field formatted as a descriptive label. Moreover, the exact name of the ObraSocial was not always reported. Instead, the field frequently contained the industry/occupationor the name of the labor union linked to the Obra Social. The second reason is that due to thelarge number of Obras Sociales and private plans, there are few observations in each of theother plans. Therefore, I coded the information for the largest social health insuranceorganizations in the sample. The last reason for choosing only Obras Sociales ruled by federallegislation is because current health care reforms focus on workers enrolled in theseorganizations. The sub-sample contains 2176 observations and the seven Obras Socialeschosen correspond to the following industry/occupations: retailing (OSECAC), metallurgic(UOM), construction (UOCRA), financial (BANC), rail-road (FERR), state oil (YPF), and teachers(OSPLAD).

This section focuses only on one dependent variable: utilization of physician services.My strategy is to parameterize out-of-pocket expenditure functions for physician services andconsumption of medicines by estimating separate censored-regression models for users ofhealth care services in each Obra Social. Parameters for out-of-pocket expenditures inmedicines are also estimated to study any complementarity or substitutability with physicianservices. Therefore, the following two censored regression models or tobit equations areestimated for each Obra Social,

(OPEP | NVDOC > 0 ) = + (NVDOC - 1 ) + ij 1P j 2P j ij ij∂ ∂ ⁄ (2)

(OPEM | NCMEDICI > 0 ) = + (NCMEDICI - 1 ) + ij 1M j 2M j ij ij∂ ∂ ⁄ (3)

where i indexes individuals from 1 to N, j indexes Obras Sociales from 1 to 7; OPEP is out-of-pocket expenditures in physician services in the last four weeks, OPEM is out-of-pocketexpenditures in medicines in the last four weeks, NVDOC is the number of visits to the doctor,NCMEDICI is the number of different medicines consumed in the last four weeks. Notice thatequations (2) and (3) are estimated for users of health services (i.e., for all individuals withNVDOC > 0, or NCMEDICI > 0), and that the tobit specification is chosen because many usersshow zero out-of-pocket expenditure.

After estimating the tobit-type equations (results are reported in Bertranou 1998b), Irecover the constant and slope parameters for each Obra Social, and then I use them aspseudo-price variables for the entire seven Obras Sociales sample of users and non-users.These two parameters are likely to reflect, in the case of physician services, the cost of contactwith doctors and the marginal cost of a visit. The goal therefore, is to compare the model thatuses the standard approach, i.e. to proxy price by using dummies for different Obras Sociales (model A or equation (4)), against the model that proxies price by using pseudo-prices obtainedfrom the parameterization of Obras Sociales characteristics (model B or equation (5)).

Model A:

i i

j=1

7

i j jNVDOC = f ( X + OS ) ƒ ↓� (4)

Model B:

i i

j=1

7

s=1

2

i j sNVDOC = f ( X + ) ƒ ∂ ♠�� (5)

12

where i indexes individuals, j indexes Obras Sociales, and s indexes price parameters;

iNVDOC is number of visits to a physician, iX are socio-demographics, iOS are dummy

variables indicating the Obra Social which provides health coverage to the insured, ∂ are Obras

Sociales’ expenditure function parameters (pseudo-prices); ƒ is the vector of socio-

demographic parameters to be estimated in both models; ↓ is the vector of price parameters

to be estimated in model A, and ♠ is the vector of price parameters to be estimated in modelB. Notice that model A is more general but with a more complicated interpretation than modelB because differences between two insurance plans may be due to either price variations orother unobserved characteristics. On the other hand, model B is more specific because it onlydifferentiates insurance plans by using price attributes.

Results for one-part count data negative binomial regression estimations for models Aand B are presented in Table 7 in columns (1) and (2) respectively. Besides the price-relatedvariables (i.e., dummy variables for Obras Sociales and pseudo-prices), both specificationscontrol for the same individual characteristic variables: income, age, gender, marital status,schooling, region, health status, and private insurance status. The coefficients on the price-related variables have the expected signs (negative), indicating that an increase in prices is likelyto affect negatively the utilization of health care services.5 The coefficients of the other controlvariables look quite robust to changes from model A to model B.

A natural test to compare the estimates of the price-related variables for both models canbe constructed using the Wald principle. The null hypothesis of interest is whether differencesin utilization are explained by characteristics associated with the prices that individual face ineach Obras Sociales (Model B), or whether differences are also explained by other healthinsurance organization characteristics (i.e., Model A). Formally, I want to test whether

Α [ Α ]↓ ♠ τ , where Α↓ is a 7x1 vector, τ is a 7x2 matrix of pseudo-prices variables (i.e., 1∂

and 2∂ ), and Α♠ is a 2x1 vector of price parameters. Consequently,

0H : D ( ) = - ϒ ↓ ♠τ

= ( I ) 7 τ↓

♠

�

��

�

��

= C = 0ϒ

where C is a 7x9 parameter restrictions matrix and ϒ is a 9x1 matrix of coefficients. Therefore,

the Wald test is defined as W = [ C ] [ C V C ] [ C ]' ' -1ϒ ϒ and it is asymptotically

distributed as 2

(r)′ , where r is the number of restrictions (see in Bertranou 1998b Appendix B

for derivation of W). The W statistic calculated is 117.1 and rejects the null hypothesis of no

difference at 0.01 percent [2

(7) = 18.5′ ]. This result indicates that the specification which

uses dummy variables not only captures price-variation across Obras Sociales but otherdimensions. Therefore, the parameterization of Obras Sociales characteristics using pseudo-price variables obtained from out-of-pocket expenditure functions is not enough to capture allthe variation, and an alternative way of describing plans in a more completely way should befound. Moreover, in terms of goodness of fit based on R-squared measures for count dataregression models, Model A performs better than Model B (see in Bertranou 1998b AppendixC for the R-squared formulas proposed by Cameron and Windmeijer (1996) and the valuesobtained for the data used for the estimations in this section). The method of using pseudo-prices offers the possibility of doing inference on the effects of changing marginal prices,however, using characteristics from out-of-pocket expenditures are not sufficient to accuratelydescribe the data on physician services utilization used in this study.

8. Conclusions

13

I used count data hurdle (two-part) regression models to empirically study factorsaffecting the utilization of health care services. The use of two-part models allows me toseparate the factors influencing individuals’ decisions to seek care from the factors influencingthe amount of care. The separation of both processes is relevant because, for instance inmedical services, individuals are usually responsible for the initial decision to seek care, whereasphysicians play a more significant role in follow-up visits and referrals. Empirical studies thatignore the two decision making stages not only yield inconsistent estimates but also may leadto misinterpretations.

The empirical findings from utilization models of three health care variables (physicianvisits, hospitalization days, and consumption of medicines) allowed me to reject the nullhypothesis that the same stochastic processes govern contact and frequency decisions. Fromseparate estimations for both processes, the following conclusions can be drawn:(1) The first part for the three models of utilization yields coefficients that are in generalstatistically significant. Because this part of the decision making process is mostly patient driven,socio-demographics characteristics explain most of the variation.(2) The second stage, however, yields too many imprecise coefficients. The reason may beunobserved heterogeneity from factors related to the supply-side, which are not well capturedby the type of population survey used in this study. The second part in these utilization modelsseems to be driven by supply factors, in particular for visits to the doctor and hospitalizationdays. This seems not to be the case for consumption of different medicines where most of theparameters in the second stage are estimated more precisely.(3) Evidence for visits to physicians that could be interpreted as supply-induced demand wasfound using as a regressor a dummy for type of practitioner (private physician=1). The estimatedcoefficient was positive and significant, and it could be argued that such result is explained bythe fact that private physicians are paid on a fee-for-service basis (contrary to public and socialhealth insurance organization physicians who are salaried practitioners), therefore they mayhave incentives to induce patients to do more visits.

Finally, I used an alternative method to test the price sensitivity of utilization/demand forhealth services. The traditional approach proxies prices by using insurance status dummieswhen they are not directly observed. The alternative approach consists of parameterizinginsurance plan characteristics from out-of-pocket expenditure functions obtaining a pseudo-pricevector, which is used as a control in the utilization equation. This method provides a betterinformation for inference about the effect of changing marginal prices of care that individualsface, however it does not capture the total variation in characteristics across health insuranceorganizations.

14

Table 1Health Insurance Coverage: Obras Social (Social Insurance)

and Private Insurance 1/Wage and Salary Workers

Insured

Type of InsuranceAny healthinsurance

(Obra Socialand/orPrivate)

ObraSocialOnly

ObraSocial andPrivate

OnlyPrivate

Uninsured N

All .799 .694 .046 .058 .201 9085

single .703 .597 .044 .061 .297 1308Women

married .822 .708 .054 .059 .178 2246

single .696 .593 .038 .065 .304 1530men

married .858 .758 .046 .053 .142 4001

16-30 .717 .621 .034 .061 .283 3605

31-45 .853 .742 .061 .048 .147 3327

Age

46-65 .855 .743 .045 .066 .145 2153

1st. Q .636 .556 .030 .050 .364 1557

2nd. Q .749 .676 .035 .037 .251 2469

3rd. Q .836 .730 .044 .062 .164 2702

Income2/

4th. Q .912 .762 .070 .079 .088 2568

BA .756 .684 .032 .039 .244 2864

CA .880 .735 .068 .075 .120 1101

MZ .783 .675 .044 .063 .217 790

LP .857 .692 .071 .093 .143 870

CO .772 .638 .024 .109 .228 883

TU .788 .628 .106 .054 .212 969

CH .769 .716 .006 .046 .231 847

Region3/

BH .856 .786 .037 .032 144 762

1/ Private insurance includes mutuales and pre-pagas, and excludes emergency plans and other non- comprehensive coverage. 2/ Per-capita family income. 3/ Region: BA=Greater Buenos Aires, CA= City of Buenos Aires (Capital Federal); MZ=Mendoza; LP=La Plata; CO=Córdoba; TU=Tucumán; CH=Chaco; and BH=Bahía Blanca.

15

Table 2Sample Characteristics of Insured and Uninsured Wage and Salary Workers

Utilization Measures

Insured Test on theEqualityof Means

Type of InsuranceAny healthinsurance

(1)

ObraSocialOnly(2)

ObraSocial andPrivate(3)

PrivateOnly

(4)

Un-insured

(5)

(1)-(5)

1/

(2), (3)and (5)

2/

rate 0.285 0.279 0.360 0.299 0.182 0.103a b

DoctorVisits

frequency3/

1.813 1.810 1.861 1.812 1.802 0.011

rate 0.058 0.056 0.089 0.056 0.039 0.019a

a

HospitalUtilization4/ frequency

3/7.883 7.824 8.149 8.272 9.333 -1.450

rate 0.525 0.513 0.604 0.602 0.447 0.078a

a

Medicine5/

frequency3/

1.456 1.430 1.729 1.499 1.340 0.116a

a

N 7244 6292 420 532 1841 --- ---

a Significant at 0.1 percent;

b Significant at 1 percent;

c Significant at 5 percent;

d Significant at 10 percent.

1/ Two sample t-test of the null hypothesis that the mean of variable x is the same for the insured and the uninsured sample. 2/ Tests for differences among the means of variable x across categories of health insurance (only Obra Social, Obra Social and private, and only private). 3/ Conditional on having counts > 0. 4/ Length of stay in last 12 months (hospital days). 5/ Number of different medicines consumed in last four weeks.

16

Table 3Health Care Services Utilization by Wage and Salary Workers

Frequency DistributionN=9085

Visits Doctor(NVDOC)

Hospitalizations Length of Stay(LOSSUM)

Medicines(NCMEDICI)

y=0 74.45 94.15 94.15 53.48

y=1 14.98 5.31 0.72 31.23

y=2 5.92 0.39 0.76 10.67

y=3 2.32 0.09 1.06 3.10

y=4 1.16 0.01 0.66 0.89

y=5 0.68 0.02 0.42 0.41

y=6 0.14 0.02 0.13 0.15

y=7 0.06 0.01 0.51 0.03

y=8 0.10 0.22 0.03

y=9 0.02 0.04

y=10 0.09 0.24

y>10 0.07 1.78

UnconditionalMean

0.47 0.06 0.46 0.69

UnconditionalVariance

1.15 0.09 11.22 0.88

Uncond. Var./Uncond. Mean

2.45 1.34 24.39 1.28

17

Table 4One- and Two-Part Utilization Models: Physician Visits

(standard errors in parenthesis)

One-Part Two-Part

Poisson

(1)

Neg. Bin.

(2)

Binary Probit

(3)

TruncatedNeg.Bin.

(4)

TruncatedNeg.Bin.

(5)

Constant -1.8647 a

(.0805)-2.0771

a

(.1046)-1.5595

a

(.0797)-1.2196

a

(.2473)-1.2527

a

(.2477)

Obra Social .4301 a

(.0481).3748

a

(.0617).3643

a

(.0469).0438(.1114)

.0222(.1117)

Obra Social andPriv. Insurance

.2888 a

(.0802).4101

a

(.1090).3989

a

(.0856).0519(.1870)

.0070(.1882)

PrivateInsurance

.3716 a

(.0779).3309

a

(.1034).2982

a

(.0796).0649(.1823)

.0289(.1829)

Income -.0032(.0043)

-.00003(.0057)

.0041(.0042)

-.0141(.0105)

-.0155(.0105)

Age .0031 b

(.0015).0046

b

(.0020).0038

b

(.0015).0027(.0033)

.0027(.0033)

Women .3105 a

(.0375).3875

a

(.0492).2567

a

(.0379).2889

a

(.0858).2806

a

(.0858)

Married .1043 a

(.0391).1349

a

(.0518).0951

a

(.0402).0052(.0891)

.0007(.0890)

School1 -.0547 (.0399)

-.0654 (.0529)

-.0595 (.0412)

.0159 (.0917)

.0311 (.0919)

School3 -.0161(.0429)

-.0175(.0579)

-.0190(.0449)

-.0116(.0992)

-.0152(.0919)

Private Physician - - - - .1536 b

(.0758)

Health Status Yes Yes Yes Yes Yes

Region Yes Yes Yes Yes Yes

continued ///

18

Table 9-A (cont’d)

One-Part Two-Part

Poisson

(1)

Neg. Bin.

(2)

Binary Probit

(3)

TruncatedNeg.Bin.

(4)

TruncatedNeg.Bin.

(5)

Occupation Yes Yes Yes Yes Yes

- 1.0133 - 2.1765 2.1463

n 9085 9085 9085 2318 2318

Log-L -7516.3 -6997.2 -3834.2 -2781.7 -2779.6

Chi2 1/ 3593.4 a

2004.4 a

2650.7 a

114.5 a

118.6 a

Chi2 2/ 1038.1 a

726.6 a

Chi2 3/ 81.32 a

37.53 a

61.41 a

0.19 0.05

a Significant at 1 percent;

b Significant at 5 percent;

c Significant at 10 percent.

1/ LRT joint significance regressors. 2/ Column (2): LRT Neg.Bin against Poisson; Column (4): One-Part Neg. Binomial against Two-Part Probit-Neg.Bin. 3/ LRT three insurance dummies = 0.

19

Table 5One- and Two-Part Utilization Models: Hospitalization Days

(standard errors in parenthesis)

One-Part Two-Part

Poisson

(1)

Neg. Bin.

(2)

Binary Probit

(3)

Truncated Neg.Bin. (4)

Constant -1.8612 a

(.0815)-2.2194

a

(.4016)-2.0304

a

(.1115)-1.7132

a

(.2987)

Obra Social .1686 a

(.0452).1482(.2269)

.2104 a

(.0644)-.2068(.1627)

Obra Social andPriv. Insurance

.2580 a

(.0785).5071(.4384)

.2665 b

(.1118)-.0408(.2779)

PrivateInsurance

.3122 a

(.0727).3150(.4022)

.1248(.1108)

.0560(.2817)

Income -.0574 a

(.0059)-.0147(.0199)

-.0012(.0059)

-.0313 b

(.0134)

Age .0185 a

(.0014).0165

b

(.0078)-.0032(.0021)

.0183 a

(.0053)

Women .2304 a

(.0380).5721

a

(.1979).4108

a

(.0518)-.4383

a

(.1367)

Married .3919 a

(.0435).5150

a

(.1877).2097

a

(.0544).1805(.1310)

School1 -.0849 b

(.0380).1203 (.2042)

.0203 (.0560)

-.0836(.1380)

School3 -.3385 a

(.0486)-.4059(.2203)

.0141(.0597)

-.5064 (.1428)

Health Status Yes Yes Yes Yes

Region Yes Yes Yes Yes

Occupation Yes Yes Yes Yes

- 47.8184 a

- 1.1744

n 9085 9085 9085 530

continued ///

20

Table 10 (cont’d)

One-Part Two-Part

Poisson

(1)

Neg. Bin.

(2)

Binary Probit

(3)

Truncated Neg.Bin. (4)

Log-L -14047.1 -3565.2 -1911.5 -1500.3

Chi2 1/ 2107.3 a

86.7 a

222.9 a

127.9 a

Chi2 2/ 20963.7 a

306.8 a

Chi2 3/ 23.71 a

1.54 11.17 a

3.12

a Significant at 1 percent;

b Significant at 5 percent;

c Significant at 10 percent.

1/ LRT joint significance regressors. 2/ Columnd (2): LRT Neg.Bin against Poisson; Column (4): One-Part Neg. Binomial against Two- Part Probit- Neg.Bin. 3/ LRT three insurance dummies = 0.

21

Table 6One- and Two-Part Utilization Models: Medicines

(standard errors in parenthesis)

One-Part Two-Part

Poisson(1)

Neg. Bin. (*)(2)

Binary Probit(3)

TruncatedNeg.Bin. (4)

Constant -1.5493 a

(.0658)-1.5493

a

(.0658)-1.0401

a

(.0690)-1.7258

a

(.1342)

Obra Social .1208 a

(.0373).1208

a

(.0373).0659

c

(.0389).1363

c

(.0727)

Obra Social andPriv. Ins.

.2452 a

(.0614).2452

a

(.0614).1890

a

(.0759).4035

a

(.1067)

PrivateInsurance

.2510 a

(.0595).2510

a

(.0595).1924

a

(.0684).3095

a

(.1065)

Income .0133 a

(.0029) .0133

a

(.0029) .0140

a

(.0037) .0073(.0053)

Age .0111 a

(.0012).0111

a

(.0012).0100

a

(.0013).0136

a

(.0021)

Women .2019 a

(.0302).2019

a

(.0302).1761

a

(.0338).2593

a

(.0546)

Married .0960 a

(.0321).0960

a

(.0321).1296

a

(.0352)-.0065(.0579)

School1 -.1161 a

(.0331)-.1161

a

(.0331)-.1472

a

(.0359)-.0762 (.0608)

School3 .1445 a

(.0340).1445

a

(.0340).0974

a

(.0400) .2369

a

(.0600)

Health Status Yes Yes Yes Yes

Region Yes Yes Yes Yes

Occupation Yes Yes Yes Yes

- - - .1498

n 9011 9011 9085 4184

continued ///

22

Table 11 (cont’d)

One-Part Two-Part

Poisson(1)

Neg. Bin. (*)(2)

Binary Probit(3)

TruncatedNeg.Bin. (4)

Log-L -8975.1 -8975.1 -5206.1 -3630.4

Chi2 1/ 2570.2 a

2570.2 a

2146.5 a

555.3 a

Chi2 2/ 277.20 a

Chi2 3/ 25.36 a

20.91 a

11.35 a

18.27 a

a Significant at 1 percent;

b Significant at 5 percent;

c Significant at 10 percent.

(*) The one-part Neg.Bin. specification had convergency problems. Poisson model results are reported in this column. 1/ LRT joint significance regressors. 2/ Columnd (2): LRT Neg.Bin against Poisson; Column (4): One-Part Neg.Binomial against Two- Part Probit-Neg.Bin. 3/ LRT three insurance dummies = 0.

23

Table 7Utilization Models for Insured Enrolled in Seven Major Obras Sociales:

Physician Visits (NVDOC)Negative Binomial Models (standard errors in parenthesis) (*)

Neg. Binomial(1)

Neg. Binomial (2)

Neg.Binomial(3)

OS1 (OSECAC) -1.7136 a

(.1764)- -

OS2 (UOM) -1.6388 a

(.1956)- -

OS3 (UOCRA) -1.6873 a

(.2316)- -

OS4 (BANC) -1.3510 a

(.2141)- -

OS5 (FERR) -1.3708 a

(.2250)- -

OS6 (YPF) -1.1195 a

(.2647)- -

OS7 (OSPLAD) -1.4853 a

(.2215)- -

1∂ Physician - -.0005 c

(.0003)-.0005(.0005)

2∂ Physician - -.0010 (.0010)

.0010(.0023)

1∂ Medicine - - -.0007(.0022)

2∂ Medicine - - -.0007(.0016)

PrivateInsurance

.6494 a

(.1735).6416

a

(.2639).6410

a

(.2617)

Income .0215 b

(.0114).0260

b

(.0129).0259

b

(.0127)

Age -.0023(.0042)

-.0012(.0053)

-.0014(.0053)

continued ///

24

Table 12 (cont’d)

Neg. Binomial(1)

Neg. Binomial (2)

Neg. Binomial(3)

Women .1847 b

(.0917).1865

c

(.1026).1914

c

(.1048)

Married .1974 c

(.1080).2122

c

(.1263).2102

c

(.1263)

School1 .0153 (.1109)

-.0359 (.1149)

-.0325 (.1150)

School3 -.1207(.1182)

-.0678(.1400)

-.0817(.1449)

HS1 (General ill-healthassessment in last fourweeks=1)

1.2992 a

(.1152)1.3043

a

(.1267)1.3023

a

(.1268)

HS2 (Illness in last fourweeks=1)

1.8901 a

(.1103)1.8896

a

(.1086)1.8915

a

(.1085)

HS3 (Accident in last fourweeks=1)

2.2390 a

(.2055)2.2283

a

(.1698)2.2207

a

(.1722)

HS4 (Other healthproblems in last fourweeks=1)

2.2733 a

(.2700)2.2574

a

(.3375)2.2559

a

(.3338)

Region Yes Yes Yes

Constant - -1.6817 a

(.1926)-1.5851

a

(.3004)

Dispersion 1.1531 1.1598 1.1585

N 2176 2176 2176

Log-L -1785.3 -1789.7 -1789.3

Chi2 1/ 716.0 a

494.8 a

495.5 a

Chi2 2/ 114.6 a

5.8 b

6.4

Chi2 3/ 126.5 a

21.3 a

22.0 a

(*) Huber/White/sandwich heteroscedastic-consistent standard errors in columns (2) and (3). 1/ Test joint significance regressors.

2/ Test joint significance: Column (1) seven OS dummy variables; Column (2) 1∂ and 2∂ physician;

Column (3) 1∂ and 2∂ physician and medicine.

3/ Test joint significance: Column (1) seven OS dummy variables and Private Insurance; Column (2)

1∂ and 2∂ physician and Private insurance; Column (3) 1∂ and 2∂ physician and medicine and

Private Insurance.

25

BIBLIOGRAPHY

Bertranou, F. (1998a). “Health Care Services Utilization and Health Insurance Coverage:Evidence from Argentina,” Revista de Análisis Económico, Vol.13, No.2, November, pp.25-52.

Bertranou, F. (1998b). “Argentina’s Health Care Sector and The Demand for HealthServicies: Three Essays on Health Economics,” Ph.D. Dissertation, University of Pittsburgh.

Cameron, A. C., P. Trivedi, F. Milne and J. Piggott (1988). “A Microeconometric Modelof the Demand for Health Care and Health Insurance in Australia,” Review of Economics andStatistics, Vol. 55, pp.85-106.

Cameron, A. C. and P. K. Trivedi (1986). “Econometric Models Based on Count Data:Comparisons and Applications of Some Estimators and Tests,” Journal of Applied Econometrics,Vol. 1, pp.29-53.

Cameron, A. C. and F. Windmeijer (1996). “R-Squared Measures for Count DataRegression Models with Applications to Health-Care Utilization,” Journal of Business andEconomic Statistics, Vol.14, No.2, April, pp.209-220.

Cauley, S. D. (1987). "The Time Price of Medical Care," Review of Economics andStatistics, Vol.13, pp.56-66.

Coulson, N. E., J. V. Terza, C. A. Neslusan and B. Stuart (1995). "Estimating the MoralHazard Effect of Supplemental Medical Insurance in the Demand for Prescription Drugs by theElderly," American Economic Review (Papers and Proceedings), Vol.85, No.2, May, pp.112-126.

Duan, N., W. G. Manning (Jr.), C. N. Morris and J. P. Newhouse (1983). “A Comparisonof Alternative Models for the Demand for Health Care,” Journal of Business and EconomicStatistics, Vol.1, No.2, April, pp.115-126.

Geil P. et al. (1997). "Economic Incentives and Hospitalization in Germany," Journal ofApplied Econometrics, Vol.12, pp.295-311.

Gerdtham, Ulf-G. (1997). "Equity in Health Care Utilization: Further Test Based on HurdleModels and Swedish Micro Data," Health Economics, Vol.6, pp.303-319.

Gertler, P. and R. Sturm (1997). "Private Insurance and Public Expenditures in Jamaica,"Journal of Econometrics, 77, pp.237-257.

Grana, J. and B. Stuart (1996). “The Impact of Insurance on Access to PhysicianServices for Elderly People with Arthritis,” Inquiry, 33, Winter, pp.326-338.

Grossman, M. (1972). The Demand for Health: A Theoretical and Empirical Investigation.New York: Columbia University Press.

Hopkins, S. and M. P. Kidd (1996). "The Determinants of the Demand for Private HealthInsurance under Medicare," Applied Economics, 28, pp.1623-1632.

Hurd, M. D. and K. McGarry (1997). "Medical Insurance and the Use of Health CareServices by the Elderly," Journal of Health Economics, 16, pp.129-154.

Kenkel, D. (1990). “Consumer Health Information and the Demand for Medical Care,”Review of Economics and Statistics, Vol.72, No.4, November, pp.587-595.

Mullahy, J. (1986). “Specification and Testing of some Modified Count Data Models,”Journal of Econometrics, 33, pp.341-365.

Newhouse, J. P. and The Insurance Experiment Group (1993). Free for All? Lessonsfrom the RAND Health Insurance Experiment. Cambridge, MA: Harvard University Press.

Pohlmeier, W. and V. Ulrich (1995). “An Economic Model of the Two-Part Decisionmaking Process in the Demand for Health Care,” Journal of Human Resources, Vol.30, 2,pp.339-361.

Wagstaff, A. (1986). “The Demand for Health: Some New Empirical Evidence,” Journalof Health Economics, Vol.5, No.3, pp.195-233.

Winkelmann, R. (1997). Econometric Analysis of Count Data. Second Edition. Berlin:Springer.

-------------- and K. F. Zimmermann (1995). “Recent Developments in Count DataModeling: Theory and Evidence,” Journal of Economic Surveys, Vol.9, No.1, pp.1-24.

26

Notes

1 This paper is a shortened version of Bertranou (1998b).

2 A third issue explored in Bertranou (1998b) is selection in health markets, but technically

selection should not exist for wage and salary workers, for whom social insurance is mandatory.

3 Self-employed workers are not mandatory covered by social insurance. They can either

voluntarily join an Obra Social under the name of a family member who is salaried or a wage earner, orpurchase private health insurance.

4 Duan et al. (1983), Kenkel (1990), Grana and Stuart (1996), Hurd and McGarry (1997), among

others, apply hurdle models for continuous data to health care services utilization measures such as visitsto the doctor and length of stay. Mullahy (1986) uses count data hurdle models for the first time, whereasamong recent applications of these models to health data we find Pohlmeier and Urich (1995) andGertham (1997).

5 Notice that in Model A, dummy variables do not have the traditional interpretation because the

specification includes all seven Obras Sociales and excludes the constant term.