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