ARE EDUCATION LEVEL AND SPORTS ACTIVITIES DETERMINANTS OF HEALTH CARE UTILIZATION IN GERMANY? AN...

8/6/2019 ARE EDUCATION LEVEL AND SPORTS ACTIVITIES DETERMINANTS OF HEALTH CARE UTILIZATION IN GERMANY? AN EC

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ECONOMETRICSECONOMETRICSECONOMETRICSECONOMETRICS Microeconometric Final ExamMicroeconometric Final ExamMicroeconometric Final ExamMicroeconometric Final Exam

Italo Raul A. ArbulItalo Raul A. ArbulItalo Raul A. ArbulItalo Raul A. Arbul VillanuevaVillanuevaVillanuevaVillanueva

1111

MASTER IN TOURISM AND ENVIRONMENTALECONOMICS

(MTEE)

Econometrics

Final Exam Microeconometric Part

ARE EDUCATION LEVEL AND SPORTS

ACTIVITIES DETERMINANTS OF HEALTH CAREUTILIZATION IN GERMANY?AN ECONOMETRIC APPROACH WITH A HURDLEMODEL

ByItalo Arbul Villanueva

January 2010


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INDEX

1. INTRODUCTION ....................................................................... 3

2. LITERATURE REVIEW ............................................................. 42.1. The Grossman Model ............................................................. 4

2.2. The Zweifel Model .................................................................. 6

3. ECONOMETRIC SPECIFICATION ........................................... 83.1. The Hurdle Specification ...................................................... 10

4. DATA ...................................................................................... 12

5. RESULTS ............................................................................... 15

6. MAIN CONLUSIONS .............................................................. 23

7. BIBLIOGRAPHY AND SOURCES .......................................... 25


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1. INTRODUCTION

This paper models the demand for health services in Germany measured as the number

of visits to a doctor. At the present time, it is important to understand the decision-

making process to obtain a better evaluation of the socioeconomic forces that cause the

increase in health care utilization (POHLMEIER and ULRICH, 1995).

Germany has a highly state-financed health-care system like most nations, and

subsequently the results obtained with this work will be appropriate to use in other

countries with policy purposes. Specifically, this work seeks to determine if citizens

actively engaged in sports and the level of education are driven forces in the health

service demand.

Having a clear idea of both, health care demand determinants and the magnitude of their

effects will help to give some advices to public authorities in order to achieve a healthier

society.

In the Health Economics literature the first health care demand model was formulated by

GROSSMAN (1972). It is based on the traditional consumer theory and considers the

individual as a sole agent or a prime decision maker in the process of health care

utilization.

The second approach of this process is to assume that the demand for health services is

made in two stages: the contact decision and the intensity of use decision. This model

developed by ZWEIFEL (1981) is based on the principal-agent framework.

This work will take into account these two approaches through the use of specific

instruments, in this sense, the Hurdle Model is appropriate for the purpose of this study.


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2. LITERATURE REVIEW

The theoretical approaches capable of application to the analysis of health care

utilization are two: (i) The traditional consumer theory (Grossman, 1972), which

considers the individual as the primary agent to determine the demand for health

services, but conditioned by the organization of the health system and, (ii) principal-

agent models, in which the physician as agent of the patient, determines the amount of

medical services used on behalf of the patient (principal) once it has produced the first

visit. (Zweifel, 1981).

The contribution of Pohlmeier and Ulrich (1995) is the combination of both approaches to

tackle the demand for health services as a process consisting of two stages.

2.1. The Grossman Model

Grossman (1972) presents a model as a result of his concern about the demand for

health services and the distinction between the concepts of health and medical services,

considering the first as a basic good in consumer demand, while medical services are

inputs, the result of a derived demand, to produce more health.

Grossman believes that each individual tries to maximize an inter-temporal utility

function, based on a set of consumer goods Zt, and the total consumption of "health

services", ht, understood as the time variable produced by healthy level health, Ht:

The stock of health changes over time depending on the investment I t, and the rate of

depreciation of health t, as shown by the following relationship:


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Consumers can be considered as producers of those services that increase their utility

levels, buying inputs from the market, and combining them with their own time. These

production functions are expressed as:

where M is a vector of inputs that, according to Grossman, contribute to the gross

investment in health care; TH is the time taken to improve health; X are market inputs

for the production of good Z, T represents the free time spent on produce those goods,

and E is the exogenous level of education, which operates as an efficiency factor in the

production function.

The consumer faces two constraints: time and budget constraints. As regards the first,

the total amount of time (t) is divided into time spent on: production of health (THt) and

other assets (Tt), work (TWt) and time lost due to illness (TSt). Regarding to the second

constraint, that income must match the costs they incurred for the production of health

and other goods:

where ptx y pt

m are prices of inputs X and healthcare M, respectively, w t is the wage rate

per hour, i is the interest rate for present value, and A0 is the discounted value of

unearned income (non-labor income) or endowments.


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The quantities of balance between goods Ht and Zt are obtained by maximizing the

function utility and production functions subject to the restrictions already mentioned.

The optimal value is reached when the marginal benefits are equal to the marginal costs

of gross investment in health.

It is expected that wage increases lead to an increase of income realized by healthy

days that will encourage individuals to invest more in health care and demand more

health care services. The price of health services will have special significance in the

demand for private services, so that increasing them will have negative effects onconsumption. In the case of analyzing public health systems demand, the price is

replaced by variables representing the cost of time or access.

With respect to education, people with more education increases the marginal product of

the inputs used and, therefore, reduce the necessary amount of them to produce the

same amount of health. On the other hand, the age will reduce the health status and,

consequently, increase medical costs.

2.2. The Zweifel Model

The economic model of physician behavior developed by Zweifel (1981), unlike the

Grossman Model, is characterized by the fact that is the physician, as agent of the

patient, who determines the necessary amount of health services on behalf of the patient

(principal) once it has produced the first visit.

This model states that the physician not only determines the treatment according to

clinical and ethical criteria (I), but also stems from economic incentives, such as income

(Y) or leisure (L). The utility function is represented by the following expression:


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Where s is an unknown parameter that represents the average level of symptoms.

While it is difficult to specify conduct that would constitute an unethical behavior, this is

set (approximated) according to whether the compensation in terms of income and

leisure is large enough.

Note that only the demand for first visits is under the control of patients. Once the first

contact occurs, the physician is free to choose the number of visits, either by visits of

longer or shorter duration, or varying the frequency of the visits on patient or refer the

patient to another level of assistance.

The usefulness of this theoretical model for empirical applications is specified in

separate analysis of the use, depending on whether a decision taken by the patient or

determined by the healthcare professional in accordance with that agency relationship.


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3. ECONOMETRIC SPECIFICATION

Count data models represent a natural starting point for estimating the demand for

health services, measured as the number of visits to the doctor during a given time

interval.

Unlike more popular approaches for qualitative endogenous variables such as logit and

probit, count data approaches assume a dependent variable resulting from a discrete

probability function. For our specific application this implies that we are mainly interested

in an explanation of the number of visits to a doctor per se.

A widely used count data model is based on the Poisson distribution. This model can be

characterized by a single parameter, implying the equality of the conditional mean and

the conditional variance. However, the equidispersion property turns out to be too

restrictive for most empirical applications. In the following, we assume that our

dependent variable stems from a negative binomial data generating process.

Assuming a random variable Y, which can take only nonnegative integer values, the

probability that exactly y counts are observed is given under the Poisson assumption by:

The negative binomial distribution for Y can be derived as a compound Poisson process

where the parameter of the Poisson distribution is specified as a gamma distributed

random variable:


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Integration over yields the negative binomial distribution for Y (see Cameron and

Trivedi 1986):

Since and v are positive it is clear that the variance exceeds the mean. Hence, the

model allows for overdispersion, a fact that characterizes many data sets.

This model has been used by Cameron et al. (1988) to explain the number of visits to

the doctor, number of hospital admissions, days of stay and number of drugs consumed

in Australia; Pohlmeier and Ulrich (1995) have applied this model to analyze the number

of visits to general practitioners and specialists in Germany; and Gerdtham (1997) used

the model to analyze medical visits and weeks of hospital stay in Sweden.


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3.1. The Hurdle Specification

Health economic considerations suggest that the decision to contact a doctor and the

decision about the length of treatment are based on different decision-making

processes, since the contact decision solely depends on the individual, while the

frequency of visits also reflects the supply of health services given by the doctor.

The main attractiveness of this model is given in part by a fundamental characteristic of

the demand for health care services, which is the high percentage of non-use. In addition

to its connection with the principal-agent models (Zweifel, 1981), in which the doctor (

agent) determines the utilization on behalf of the patient (principal) once the initial

contact has been made.

The contribution of Pohlmeier and Ulrich (1995) to empirical modeling lies in the

consideration of decision making as a two-step process. The first is the patient who

decides to visit the doctor but once the doctor comes in, it is he who determines the

intensity of treatment in the second stage.

The two-part models distinguish between "users" and "non-users" of the health

service. The "non-users" are those that show a zero use in the study period. In this

context, the first stage models the division between "non-users" (zero) and "users"

(positive) based on binary regression models, this means that at this stage estimates the

probability of accessing the service.

The second stage models the use or frequency for those with a positive level of use byemploying a model "count data" for truncated data. The structure of the models in two

parts is applied to both discrete and continuous variables. When applied to the first kind

of variables, the model is often called "Hurdle model."

A Hurdle model suggests that the processes generating the zeros (not going to the

doctor) and positive values (visit the doctor) are different. The model combines the zeros

of a distribution function with positive values of another distribution function.


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In this work we use to model the first stage a probit model and, for the second stage, the

negative binomial model with truncated data (positive values).

The specification of the two processes of decision is made with the same explanatory

variables, but the results must be interpreted differently, depending on the stage. The

likelihood function for this model is expressed:

The first factor is estimated using a binomial probability model and indicates that there is

no any contact with health services. The first term of the second factor represents the

probability of making one or more visits, while the ratio measures the likelihood that a

positive event occurs conditional on the completion of a contact.


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4. DATA

Our data source is the German Socioeconomic Panel Survey (SOEP) realized in 1999.

The SOEP is a household based study which started in 1984 and which reinterviews

adult household members annually. The annual surveys are conducted by the German

Institute for Economic Research (DIW Berlin). The survey is funded by the German

Federal Government and the State of Berlin via the Bund-Lnder Commission for

Educational Planning and Research Promotion

The size of the sample is 6,231 individuals. The survey was conducted by direct

interviews. The dependent variable of our study is the number of visits to a doctor in the

three months before the interview.

The panel includes a wide range of micro- level information on socioeconomic

characteristics of individuals and households, including specific variables on working and

living conditions as well as variables on health conditions and health care utilization.

The variables are shown in the following tables which contains the definition and a

summary of the main statistics of those variables.


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Data organization

doctco number of doctor visits in last three months

age age in years

male male = 1; female = 0

educ years of schooling

married married = 1; otherwise = 0

hsize number of people living in household

sport actively engaged in sports = 1; otherwise = 0

goodh good health (self assessment) = 1; otherwise = 0

badh bad health (self assessment) = 1; otherwise = 0

sozh individual receives welfare payments = 1; otherwise = 0

loginc logarithm of monthly gross income

ft full time work = 1; otherwise = 0

pt part time work = 1; otherwise = 0

unemp unemployed = 1; otherwise 0

winter interview in winter quarter = 1; otherwise = 0

spring interview in spring quarter = 1; otherwise = 0

fall interview in fall quarter = 1; otherwise = 0

doctcod 1 = did visit doctor in last three months; 0 otherwise.

age2 age - squared


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VARIABLE Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

DOCTCO 2.39 1.00 60.00 0.00 3.94 4.98 45.18

AGE 38.92 37.00 60.00 20.00 11.23 0.27 1.99

MALE 0.47 0.00 1.00 0.00 0.50 0.14 1.02

EDUC 11.33 11.00 18.00 7.00 2.36 0.94 4.38

MARRIED 0.65 1.00 1.00 0.00 0.48 -0.62 1.39

HSIZE 3.09 3.00 11.00 1.00 1.33 0.73 4.37

SPORT 0.27 0.00 1.00 0.00 0.44 1.06 2.12

GOODH 0.58 1.00 1.00 0.00 0.49 -0.32 1.10

BADH 0.13 0.00 1.00 0.00 0.34 2.21 5.89

SOZH 0.03 0.00 1.00 0.00 0.18 5.25 28.58

LOGINC 6,982,553.00 7,476,104.00 9,420,332.00 8.57 1,878,661.00 -2.91 10.05

FT 0.54 1.00 1.00 0.00 0.50 -0.15 1.02

PT 0.11 0.00 1.00 0.00 0.32 2.42 6.88

UNEMP 0.07 0.00 1.00 0.00 0.26 3.23 11.45

WINTER 0.32 0.00 1.00 0.00 0.47 0.79 1.62

SPRING 0.53 1.00 1.00 0.00 0.50 -0.13 1.02

FALL 0.01 0.00 1.00 0.00 0.12 8.44 72.19

DOCTCOD 0.65 1.00 1.00 0.00 0.48 -0.65 1.42

AGE2 1640.81 1369.00 3600.00 400.00 915.65 0.64 2.26


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5. RESULTS

The first equations (first-stage decision) which represent the decision process made by

the principal (patient) to visit the doctor were estimated through a probit model, the

following table shows the results.

Dependent Variable: DOCTCODMethod: ML - Binary Probit (Quadratic hill climbing)Date: 01/17/10 Time: 02:05

Sample: 1 6231Included observations: 6231Convergence achieved after 12 iterationsCovariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob.

C 1.203106 0.252049 4.773301 0.0000AGE -0.026998 0.012675 -2.129953 0.0332AGE2 0.000328 0.000155 2.116699 0.0343EDUC 0.014512 0.007462 1.944741 0.0518

MARRIED 0.122406 0.043448 2.817321 0.0048HSIZE -0.064512 0.014172 -4.552100 0.0000SPORT 0.116645 0.039531 2.950688 0.0032GOODH -0.488356 0.039590 -12.33530 0.0000BADH 0.559279 0.067412 8.296379 0.0000SOZH 0.016492 0.098910 0.166736 0.8676

LOGINC 6.55E-09 9.01E-09 0.727023 0.4672FT -0.275771 0.042508 -6.487591 0.0000PT 0.011880 0.062702 0.189468 0.8497

UNEMP -0.297365 0.070312 -4.229238 0.0000WINTER -0.027029 0.054082 -0.499779 0.6172SPRING 0.015579 0.050587 0.307972 0.7581

FALL 0.340272 0.162125 2.098821 0.0358

Mean dependent var 0.653988 S.D. dependent var 0.475735S.E. of regression 0.456797 Akaike info criterion 1.208380Sum squared resid 1296.633 Schwarz criterion 1.226762Log likelihood -3747.709 Hannan-Quinn criter. 1.214751Restr. log likelihood -4018.639 Avg. log likelihood -0.601462LR statistic (16 df) 541.8595 McFadden R-squared 0.067418Probability(LR stat) 0.000000

Obs with Dep=0 2156 Total obs 6231Obs with Dep=1 4075


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As we can appreciate in the table, the McFadden R-squared is very low (0.067), this

means that the variables included in the model are not enough to explain the probability

of visiting the doctor. In this sense, there are other variables that should be taken into

account but are not included on the survey, for example, the fact that the individual has a

private health insurance.

The second equation (second-stage decision) represents the decision process made by

the agent (doctor) about the number of necessary visits that the principal (patient) should

do. This model was estimated through a count data model which used a negative

binomial distribution, the following table shows the results.

It is important to remember that in count data models, in which the dependant variables

takes integers positive values, the most useful methods to estimate the coefficients are

the use of the Poisson distribution and the negative binomial distribution. However, the

use of Poisson distribution imposes some restrictions; the most important restriction is

the equality of the (conditional) mean and variance. However, this assumption is often

violated in empirical applications. When this his restriction does not hold, it is better touse the negative binomial distribution.

E-Views estimate the logarithm of the measure of the extent to which the conditional

variance exceeds the conditional mean, and labels this parameter as the "SHAPE"

parameter in the output. Therefore, is this parameter is statistically equal to zero (accept

the null hypothesis) the Poisson distribution should be used; otherwise, the negative

binomial distribution would be a better choice.

As we can see in the estimation output of the second stage, the parameter SHAPE is

statistically different from zero; this means that the choice of the negative binomial

distribution was a correct choice in order to estimate the number of visits to the doctor.

It is important to consider that in this regression the adjustment coefficient (measured by

the Pseudo-R2) is also low (0.24) which also leads to the conclusion that the number of

visits to the doctor have other determinants that maybe are not related with


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socioeconomic variables but to variables related with the necessary treatment of the

patient (for example, a patient with flu should have less number of visits than a patient

whit a broken leg or heart diseases).

Dependent Variable: DOCTCO

Method: ML - Negative Binomial Count (Quadratic hill climbing)

Date: 01/16/10 Time: 19:56

Sample: 1 6231 IF DOCTCOD=1

Included observations: 4075

Convergence achieved after 12 iterations

Covariance matrix computed using second derivatives

Coefficient Std. Error z-Statistic Prob.

C 1.563108 0.197797 7.902606 0.0000

AGE 0.003744 0.009606 0.389800 0.6967

AGE2 -4.73E-05 0.000115 -0.410315 0.6816

EDUC -0.013015 0.005733 -2.270132 0.0232

MARRIED 0.023284 0.033088 0.703712 0.4816

HSIZE -0.042016 0.011141 -3.771298 0.0002

SPORT 0.023491 0.030199 0.777874 0.4366

GOODH -0.349505 0.030214 -11.56781 0.0000

BADH 0.605303 0.035395 17.10127 0.0000

SOZH 0.139548 0.070566 1.977549 0.0480

LOGINC 5.45E-09 6.87E-09 0.793319 0.4276

FT -0.175504 0.031013 -5.659066 0.0000

PT -0.225013 0.044650 -5.039540 0.0000

UNEMP -0.153783 0.053371 -2.881392 0.0040

WINTER 0.009389 0.041715 0.225063 0.8219

SPRING -0.023857 0.039093 -0.610246 0.5417

FALL 0.013600 0.106748 0.127406 0.8986

Mixture Parameter

SHAPE:C(18) -1.023684 0.035730 -28.65024 0.0000

R-squared 0.140521 Mean dependent var 3.655706Adjusted R-squared 0.136920 S.D. dependent var 4.376656

S.E. of regression 4.066006 Akaike info criterion 4.491873

Sum squared resid 67071.97 Schwarz criterion 4.519757

Log likelihood -9134.192 Hannan-Quinn criter. 4.501748

Restr. log likelihood -12072.05 Avg. log likelihood -2.241519

LR statistic (17 df) 5875.716 LR index (Pseudo-R2) 0.243360

Probability(LR stat) 0.000000


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In order to examine the marginal effects of a variable, we must examine the change in

the unconditional medical visits mean given a change in an explanatory variable which

is:

The first part of the previous equation is the change in the conditional mean of medical

visits weighted by the probability of making a visit to the doctor and the second part is

the change in the probability of taking a non-zero health care demand weighted by the

conditional mean of medical visits. The following chart shows the marginal effects for

the first stage (probit model), the second stage (count data model using a negative

binomial distribution) and for the whole estimation (Hurdle model).

Marginal Effect of Explanatory Variables

Variable PROBIT NEG-BIN HURDLE

C 0.784296 7.461712 7.730783

AGE -0.017600 0.000000 -0.064326

AGE2 0.000214 0.000000 0.000781

EDUC 0.009460 -0.011233 0.027254

MARRIED 0.079795 0.000000 0.291647

HSIZE -0.042055 -0.036992 -0.177824

SPORT 0.076040 0.000000 0.277921

GOODH -0.318356 -0.293832 -1.355117

BADH 0.364590 0.673028 1.771292

SOZH 0.000000 0.140180 0.091382

LOGINC 0.000000 0.000000 0.000000

FT -0.179773 -0.160743 -0.761846

PT 0.000000 -0.218765 -0.142611

UNEMP -0.193850 -0.152126 -0.807679

WINTER 0.000000 0.000000 0.000000

SPRING 0.000000 0.000000 0.000000

FALL 0.221821 0.000000 0.810740


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It is important to mention that in this work we used a 10% confidence level, in this sense,

the variables that which coefficients were not statistically significant for this confidence

level were replaced by zeros.

We find a quadratic relationship between the decisions of visit the doctor (DOCTORD)

and the age of the agent since this variable and the age-squared are significant variables

in this model, this means that for the first stage, age is a significant variable. However,

for the second stage this variable does not explain the decision of the number of visits.

Furthermore, Education (measured as the number of years of schooling) is a significantvariable for both stages1, however, for the first stage this variable has a positive effect

while for the second stage, this variables has a negative effect. It is possible to find in

the literature arguments for both effects.

Grossman explained that educated people increases the marginal product of the inputs

used and, therefore, reduce the necessary amount of them to produce the same amount

of health. In this sense, as Pohlmeier and Ulrich mentioned, once the patient has

visited the doctor and know which is his health problem and the necessary treatment, he

can take care of himself in a better way and can improve their health more efficiently

than less educated patients, this leads to less number of visits in the future, as the

marginal effect of this variable measures for the Hurdle model.

Marital status (MARRIED) is also an explanatory variable for the first stage, this means

that it is more probably that married people decide to visit a doctor; this could be

explained by the fact that married couples tend to promote the visits to the doctor when

the husband or wife fell any kind of illness. However, the marital status is not a

statistically significant variable on the second stage; this means that marital status has

no effect over the number of visits to the doctor. On the other hand, referring also to the

family characteristics, we found that for both stages, the family size have a statistically

negative impact.

1At a 10% confidence level.


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In the case of people actively engage with sports activities it is interesting to appreciate

that this group are more likely to visit the doctor as a first stage decision. However, this

variable (SPORTS) is not a significant variable on the second stage. This could be

explained by the fact that people who are actively engaged in sports tend to care more

about their health status.

The variables GOODH and BADH capture the incidence of an illness in the previous

periods and the perception of the agent over his health status. It is not surprising that

individuals who feel ill increase the probability of visiting a doctor while individuals who

feel that their health status is good are less probably to visit a doctor in the first-stage

(probit model). This relationship also holds on the second-stage (count data model

using a negative binomial distribution), this means that the perception of the health

status is also an indicator for the doctor at the moment of setting the visit schedule for

the patient.

There is no evidence that welfare payments (SOZH) have a positive impact over the

probability of taking the decision to visit a doctor for the first time. However individuals

who receive welfare payments tend to have more visits to the doctor. As Zweifel

mentioned, there is an incentive to promote more visits because it generates more

income to the doctor, in this sense, doctors tend to increase the number of visits of this

group.

An important issue revealed by the hurdle model is that income (LOGINC) is not a

significant variable. Germany has a highly state-financed health-care system like most

European nations, and subsequently the results obtained in the first stage reveal that asthe cost of medical care is very low citizens with low income does not see the cost as a

problem at the moment to take the decision to visit a doctor. On the other hand, in this

kind of systems, the doctors do not know the income level of the patient, furthermore, the

system gives revenues to the doctor based on the number of patients but not over the

income of them, in this sense, doctors have no incentive to increase the number of visits

based on this variable.


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The dummy variables used to measure the differences in labor status showed that the

reference group (retired or former workers) tends to visit more times a doctor than

unemployed workers (UNEMP), part-time workers (PT) or full time workers (FT). This

can be appreciated by the fact that all the dummy coefficients are negatives.

Seasonal dummies (WINTER, SPRING and FALL) are not significant to explain the

number of visits to the doctor. However, in the first stage only FALL is statistically

significant, this means that the probability to visit the doctor for the first time increases in

FALL over the rest of seasons.

Finally, it is important to mention that the expected number of visits to the doctor is 3.65

approximately. This was estimated forecasting the Hurdle model which results are

shown in the following graphs:

1

2

3

4

5

6

7

89

10

1000 2000 3000 4000 5000 6000

DOCTCO_F

Forecast: DOCTCO_F

Actual: DOCTCO_Forecast sample: 1 6500 IF DOCTCOD=1

Included observations: 4075

Root Mean Squared Error 4.057016

Mean Absolute Error 2.304492


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0

100

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8 9

Series: DOCTCO_FSample 1 6500 IF DOCTCOD=1

Observations 4075

Mean 3.654933

Median 3.084520

Maximum 9.583826

Minimum 1.853764

Std. Dev. 1.631899

Skewness 1.451277

Kurtosis 3.947390

Jarque-Bera 1582.860

Probability 0.000000

As we can see, the variables has more concentration over lower values, in this case,

mean and median are values between 3 and 4 visits which are values near to the

minimum (approximately 2 visits) while the maximum number of visits is almost 10. This

is also confirmed by the Jarque-Bera test which is a test statistic for testing whether the

series is normally distributed (the null hypothesis of a normal distribution), in this sense,

this test showed that the expected number of visits does not have a normal distribution.


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6. MAIN CONLUSIONS

The main objective of this work was to understand the determinants of health care

utilization in Germany and determine if citizen actively involved in sports activities and

with higher level of education tend to demand more health care services. This objective

was successfully achieved through the use of specific methods that account for the

characteristics of our dependent variable.

On the basis of a Hurdle model using a negative binomial distribution, the determinants

of the demand for medical services as measured by the number of visits to the doctor in

the last 3 months was estimated. Moreover, the Hurdle model approach allows us to

separate and quantify the determinants of medical demand regarding contact and

frequency decisions. Without repeating all empirical findings, at this point we would like

to emphasize only the main results:

For the German case, people actively engage with sports activities are more

likely to visit the doctor as a first stage decision. This could be explained by the

fact that this group tend to take care more about their health status. However,there is no evidence that relate this to the number of visits, since this is a medical

decision which is not involved with the sports activities (second stage).

Education is a significant variable for both stages, however, for the first stage this

variable has a positive effect while for the second stage, this variables has a

negative effect. As a whole, this variable has a positive marginal effect for the

Hurdle model.

The expected number of visits to the doctor is approximately 3.65, but the

distribution of this series showed more concentration over lower values (the

median is approximately 3 visits) than higher number of visits.

Finally, it is important to mention that behind this formulation, there are some variables

not included in the survey that could improve the results for future research and obtain

even more accurate estimations such as variables related to the illness or the use of


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private health insurance. Formulations that take into account interdependence between

demands for health insurance and health care or demand for health specialist (for

example cardiologists) are also improvements that could be done in the future.


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7. BIBLIOGRAPHY AND SOURCES

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