New estimates of elasticity of demand for healthcare in rural China.
BACKGROUND
Only limited empirical studies reported own-price elasticity of demand for health care in rural China. Neither research on income elasticity of demand for health care nor cross-price elasticity of demand for inpatient versus outpatient services in rural China has been reported. However, elasticity of demand is informative to evaluate current policy and to guide further policy making.


OBJECTIVE
Our study contributes to the literature by estimating three elasticities (i.e., own-price elasticity, cross-price elasticity, and income elasticity of demand for health care based on nationwide-representative data. We aim to answer three empirical questions with regard to health expenditure in rural China: (1) Which service is more sensitive to price change, outpatient or inpatient service? (2) Is outpatient service a substitute or complement to inpatient service? and (3) Does demand for inpatient services grow faster than demand for outpatient services with income growth?


METHODS
Based on data from a National Health Services Survey, a Probit regression model with probability of outpatient visit and probability of inpatient visit as dependent variables and a zero-truncated negative binomial regression model with outpatient visits as dependent variable were constructed to isolate the effects of price and income on demand for health care. Both pooled and separated regressions for 2003 and 2008 were conducted with tests of robustness.


RESULTS
Own-price elasticities of demand for first outpatient visit, outpatient visits among users and first inpatient visit are -0.519 [95% confidence interval (-0.703, -0.336)], -0.547 [95% confidence interval (-0.747, -0.347)] and -0.372 [95% confidence interval (-0.517, -0.226)], respectively. Cross-price elasticities of demand for first outpatient visit, outpatient visits among users and first inpatient visit are 0.073 [95% confidence interval (-0.176, 0.322)], 0.308 [95% confidence interval (0.087, 0.528)], and 0.059 [95% confidence interval (-0.085, 0.204)], respectively. Income elasticities of demand for first outpatient visit, outpatient visits among users and first inpatient visit are 0.098 [95% confidence interval (0.018, 0.178)], 0.136 [95% confidence interval (0.028, 0.245)] and 0.521 [95% confidence interval (0.438, 0.605)], respectively. The aforementioned results are in 2008, which hold similar pattern as results in 2003 as well as results from pooled data of two periods.


CONCLUSIONS
First, no significant difference is detected between sensitivity of outpatient services and sensitivity of inpatient services, responding to own-price change. Second, inpatient services are substitutes to outpatient services. Third, the growth of inpatient services is faster than the growth in outpatient services in response to income growth. The major findings from this paper suggest refining insurance policy in rural China. First, from a cost-effectiveness perspective, changing outpatient price is at least as effective as changing inpatient price to adjust demand of health care. Second, the current national guideline of healthcare reform to increase the reimbursement rate for inpatient services will crowd out outpatient services; however, we have no evidence about the change in demand for inpatient service if insurance covers outpatient services. Third, a referral system and gate-keeping system should be established to guide rural patients to utilize outpatient service.
School of Public Policy and Administration, Xi'an Jiaotong University, 76 Yanta West Road, Xi'an 710061, China. zzliang1981@gmail.com
Zhongliang
Z
Zhou
Yanfang
Y
Su
Jianmin
J
Gao
Ling
L
Xu
Yaoguang
Y
Zhang
D000368:Aged; D000553:Ambulatory Care / Q000706:statistics & numerical data; D002681:China; D005260:Female; D017048:Health Care Costs; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012044:Regression Analysis; D012424:Rural Population / Q000706:statistics & numerical data*