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.
The authors deeply thank two anonymous referees for their thoughtful and constructive comments throughout the paper.
|Treatment (n = 1,785)|
|Control (n = 59,100)|
|Physician visits (per year)||35.34 (45.79)||13.34 (25.19)|
|Age||57.17 (22.17)||42.39 (22.69)|
|Log household income||6.73 (0.58)||7.85 (0.82)|
Notes: (a) For the categorical variables, data are % in the category; for continuous variables, data include standard deviation in brackets.
(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.
(c) 1 if the person resides outside the Seoul metropolitan area.
(d) Reference category in the empirical estimation.
|Physician visits (per year)||−3.4671||0.0006|
|Log household income||1.3600||0.1743|
Notes: (a) Null hypothesis is that difference is zero and the alternative hypothesis is that the difference is not zero.
(b) The degrees of freedom is 643.
|Fixed effects||Random effects|
|Post||−3.584 (4.593) a||0.642 (2.131)|
|Treatment||― b||10.49*** (2.498)|
|Reform (Treatment × Post)||−6.218** (2.500)||−6.480*** (2.490)|
|Female||― b||9.692*** (2.279)|
|Age||1.305 (1.347)||0.109* (0.061)|
|No education/primary||7.395 (14.91)||9.261** (5.897)|
|Middle school||5.470 (14.21)||0.732 (6.250)|
|High school||2.606 (12.28)||8.253 (6.036)|
|Log household income||3.724** (1.702)||3.034** (1.327)|
|Residence 2||0.031 (14.00)||6.992*** (2.256)|
|Unemployment rate||−4.308 (4.171)||−3.354 (3.922)|
|Chronic disease||12.50*** (2.501)||16.12*** (2.174)|
|Good health||−3.509 (2.606)||−4.189* (2.396)|
|Poor health||6.713*** (2.107)||8.391*** (1.936)|
|Constant||−70.38 (73.29)||−22.88 (19.04)|
Notes: (a) Numbers in parentheses indicate standard errors.
(b) These dummy variables were dropped because they were constant within the group.
(c) ***p < 0.01, **p < 0.05, and *p < 0.1.
|Random Effects Negative Binomial Model, Physician visits per year|
|Reform (Treatment × Post)||−0.170*** (0.056)|
|No education/primary||0.152 (0.126)|
|Middle school||0.013 (0.134)|
|High school||0.076 (0.131)|
|Log household income||0.051* (0.029)|
|Residence 2||0.142*** (0.045)|
|Unemployment rate||0.057 (0.088)|
|Chronic disease||0.908*** (0.057)|
|Good health||−0.244*** (0.061)|
|Poor health||0.122*** (0.043)|
Notes: (a) Numbers in parentheses indicate standard errors.
(b) ***p < 0.01, **p < 0.05, and *p < 0.1.
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