OBJECTIVES: Drug price elasticity of demand could provide a key signal for market competition and government regulation, but there is yet limited researches on this issue in China. Our study was to estimate the price elasticity of demand for the drugs among different pharmaco-therapeutic classes and quality layers in China.

METHODS: Within a theoretical demand framework, a dynamic panel empirical model controlling the physician’s behavioural inertia and preference was employed for estimating the price-demand elasticity by using product-level data from Tianjin Urban Employee Basic Medical Insurance database (2008-2010). System Generalized Method of Moments (SYS-GMM) was used to estimate the model. Three disease areas include anti-tumor, cardiovascular disease (CVD) and antimicrobial drugs, and two subgroups of generic and originator in each area were focused in our study. Lagged dependent variable was identified to estimate the behavioural inertia effect and lagged market share was identified as proxy variable for the behavioural preference. Defined Daily Dose was standard unit of measurement to calculate price and demand.

RESULTS: The results show that price elasticity for these three disease areas have huge differences. These elasticities were ranked from low to high among anti-tumor, antimicrobial drugs and CVD (-0.096 vs. -0.731 vs. -1.330, P<0.01). In the same treatment area, the value of price elasticity for the generics was greater than the originators (anti-tumor: -0.105, P<0.01 vs. -1.000; antimicrobial drugs: -0.774 vs. -0.426, P<0.01; circulatory system: -1.458 vs. -1.255, P<0.01). In addition, the physician’s behavioural inertia and preference were significant correlated with the demand (P<0.01), respectively.

CONCLUSIONS: The price elasticities of demand are variant among different pharmaco-therapeutic classes in China. The price elasticity of demand for the generics is larger than that for the originators even under the same disease area. The physician’s behavioural inertia and preference are significant predictors of the demand in China.