Abstract
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.
Acknowledgements
The authors deeply thank two anonymous referees for their thoughtful and constructive comments throughout the paper.
A Appendix
Trends in the annual average number of Doctor visit for treatment and control groups (2005–2009: Raw Sample).
Trends in the annual average number of Doctor visit for treatment and control groups (2005–2013: Matched Sample).
Descriptive statistics for variables for raw sample (2005–2009).
| Treatment (n = 1,785) Meana | Control (n = 59,100) Mean | |
|---|---|---|
| Physician visits (per year) | 35.34 (45.79) | 13.34 (25.19) |
| Self-rated health | ||
| Very good/good | 0.18 | 0.65 |
| Faird | 0.17 | 0.16 |
| Poor/very poor | 0.65 | 0.19 |
| Chronic diseaseb | 0.80 | 0.37 |
| Female | 0.63 | 0.54 |
| Age | 57.17 (22.17) | 42.39 (22.69) |
| Education level | ||
| No education/primary | 0.66 | 0.38 |
| Middle school | 0.15 | 0.13 |
| High school | 0.16 | 0.27 |
| College/universityd | 0.03 | 0.22 |
| Residence 2c | 0.64 | 0.59 |
| 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.
T-tests for the mean differences between treatment and control group (2005).
| T-test Statistics | p-Value | |
|---|---|---|
| Physician visits (per year) | −3.4671 | 0.0006 |
| Self-rated health | ||
| Very good/good | −1.7632 | 0.0783 |
| Faird | 0.7056 | 0.4807 |
| Poor/very poor | 0.9409 | 0.3471 |
| Chronic diseaseb | −1.9474 | 0.0519 |
| Female | 0.4681 | 0.6399 |
| Age | 1.5764 | 0.1154 |
| Education level | ||
| No education/primary | 0.2756 | 0.7829 |
| Middle school | −0.7860 | 0.4322 |
| High school | 0.8312 | 0.4062 |
| College/universityd | −1.1035 | 0.2702 |
| Residence 2c | 0.1915 | 0.8482 |
| 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.
DID estimates of reform effect from fixed and random effects linear model.
| 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.
DID estimates of reform effect from the negative binomial with random effects.
| Random Effects Negative Binomial Model, Physician visits per year | |
|---|---|
| Post | 0.016 (0.050) |
| Treatment | 0.227*** (0.052) |
| Reform (Treatment × Post) | −0.170*** (0.056) |
| Female | 0.120*** (0.046) |
| Age | 0.002* (0.001) |
| 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) |
| Constant | −1.402*** (0.421) |
| Log-likelihood | −10,725.62 |
Notes: (a) Numbers in parentheses indicate standard errors.
(b) ***p < 0.01, **p < 0.05, and *p < 0.1.
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