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The Effects of Copayments on Healthcare Utilization in Korea’s Medical Aid Program

  • Yong-Woo Lee and Yong-Ju Lee EMAIL logo


This study empirically tests the effects of the introduction of copayments on healthcare utilization in the Korea’s medical aid program (MAP). Due to a growing concern about overutilization of public healthcare and government’s financial burden, the Korean government reformed the MAP in 2007 and introduced copayments for outpatient care of Type 1 enrollees who had borne no medical treatment cost until the reform. Exploiting the natural experiment of 2007 reform, we perform a difference-in-differences (DID) analysis with propensity score matching and conclude that the introduction of copayments reduces healthcare utilization in the short-run through heterogeneous effect on the outpatient services consumption distribution, but this effect rapidly disappears over time.

JEL Classification: I11; I18; C60; C50


The authors deeply thank two anonymous referees for their thoughtful and constructive comments throughout the paper.

A Appendix

Figure 4: Trends in the annual average number of Doctor visit for treatment and control groups (2005–2009: Raw Sample).
Figure 4:

Trends in the annual average number of Doctor visit for treatment and control groups (2005–2009: Raw Sample).

Figure 5: Trends in the annual average number of Doctor visit for treatment and control groups (2005–2013: Matched Sample).
Figure 5:

Trends in the annual average number of Doctor visit for treatment and control groups (2005–2013: Matched Sample).

Table 9:

Descriptive statistics for variables for raw sample (2005–2009).

Treatment (n = 1,785)

Control (n = 59,100)

Physician visits (per year)35.34 (45.79)13.34 (25.19)
Self-rated health
Very good/good0.180.65
Poor/very poor0.650.19
Chronic diseaseb0.800.37
Age57.17 (22.17)42.39 (22.69)
Education level
No education/primary0.660.38
Middle school0.150.13
High school0.160.27
Residence 2c0.640.59
Log household income6.73 (0.58)7.85 (0.82)
  1. Notes: (a) For the categorical variables, data are % in the category; for continuous variables, data include standard deviation in brackets.

  2. (b) 1 if the person suffers from at least one chronic illness on the questionnaire list such as diabetes, hypertension, asthma, and so on, and has taken medications prescribed by a doctor.

  3. (c) 1 if the person resides outside the Seoul metropolitan area.

  4. (d) Reference category in the empirical estimation.

Table 10:

T-tests for the mean differences between treatment and control group (2005).

T-test Statisticsp-Value
Physician visits (per year)−3.46710.0006
Self-rated health
Very good/good−1.76320.0783
Poor/very poor0.94090.3471
Chronic diseaseb−1.94740.0519
Education level
No education/primary0.27560.7829
Middle school−0.78600.4322
High school0.83120.4062
Residence 2c0.19150.8482
Log household income1.36000.1743
  1. Notes: (a) Null hypothesis is that difference is zero and the alternative hypothesis is that the difference is not zero.

  2. (b) The degrees of freedom is 643.

Table 11:

DID estimates of reform effect from fixed and random effects linear model.

Fixed effectsRandom effects
Post−3.584 (4.593) a0.642 (2.131)
Treatmentb10.49*** (2.498)
Reform (Treatment × Post)−6.218** (2.500)−6.480*** (2.490)
Femaleb9.692*** (2.279)
Age1.305 (1.347)0.109* (0.061)
No education/primary7.395 (14.91)9.261** (5.897)
Middle school5.470 (14.21)0.732 (6.250)
High school2.606 (12.28)8.253 (6.036)
Log household income3.724** (1.702)3.034** (1.327)
Residence 20.031 (14.00)6.992*** (2.256)
Unemployment rate−4.308 (4.171)−3.354 (3.922)
Chronic disease12.50*** (2.501)16.12*** (2.174)
Good health−3.509 (2.606)−4.189* (2.396)
Poor health6.713*** (2.107)8.391*** (1.936)
Constant−70.38 (73.29)−22.88 (19.04)
  1. Notes: (a) Numbers in parentheses indicate standard errors.

  2. (b) These dummy variables were dropped because they were constant within the group.

  3. (c) ***p < 0.01, **p < 0.05, and *p < 0.1.

Table 12:

DID estimates of reform effect from the negative binomial with random effects.

Random Effects Negative Binomial Model, Physician visits per year
Post0.016 (0.050)
Treatment0.227*** (0.052)
Reform (Treatment × Post)−0.170*** (0.056)
Female0.120*** (0.046)
Age0.002* (0.001)
No education/primary0.152 (0.126)
Middle school0.013 (0.134)
High school0.076 (0.131)
Log household income0.051* (0.029)
Residence 20.142*** (0.045)
Unemployment rate0.057 (0.088)
Chronic disease0.908*** (0.057)
Good health−0.244*** (0.061)
Poor health0.122*** (0.043)
Constant−1.402*** (0.421)
  1. Notes: (a) Numbers in parentheses indicate standard errors.

  2. (b) ***p < 0.01, **p < 0.05, and *p < 0.1.


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Published Online: 2019-06-07

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