ArticleHealth services insights2026
Deconstructing Pharmaceutical Prices in a Government-Controlled Market: A Hedonic Regression Analysis of B2G Procurement in Thailand.
Article in Health services insights, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rising pharmaceutical expenditure poses a persistent governance challenge for public health systems in low- and middle-income countries, yet empirical evidence on pricing in Business-to-Government (B2G) settings remains scarce. This scarcity reflects a fundamental constraint where administrative procurement records are rarely accessible for independent analysis. This study addresses that gap by exploiting rare access to 26,652 medication transaction records from Thailand's Comptroller General's Department (CGD) for 2019-2024. Using acute pain medications, a semi-logarithmic hedonic regression decomposes price variation into product-level and institutional-level determinants. Internal product characteristics, including pharmacological class, dosage form, formulation technology, and brand status, account for the majority of price variation. Conventional market mechanisms, such as competitive pressure and procurement volume, generate no meaningful price reduction once product composition is controlled. Institutional governance factors, however, are associated with significant residual effects. Hospitals under non-Ministry of Public Health ministries are associated with price premiums of 13-29% for equivalent products, while Government Pharmaceutical Organization procurement is associated with a 9.6% price reduction. These findings indicate that product characteristics are the dominant drivers of pharmaceutical price variation in B2G markets, while institutional governance factors are associated with residual price differences after controlling for product composition. These residual differences may have relevant implications for procurement practices in fragmented public health systems. Note on causal inference: All reported associations are based on observational cross-sectional data and should not be interpreted as evidence of causal relationships. The regression coefficients reflect conditional correlations between procurement prices and their covariates, controlling for observed product and institutional characteristics.
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