The Impact of Drug Price Reduction on Healthcare System Sustainability: A CGE Analysis of China’s Centralized Volume-Based Procurement Policy

Abstract

China’s healthcare expenditure tripled during 2010–2019, prompting the nationwide implementation of centralized volume-based procurement (CVBP). While effective in reducing drug prices, CVBP introduces sustainability challenges including supply chain vulnerabilities and welfare trade-offs. This study develops a pharmaceutical sector-embedded computable general equilibrium (CGE) model to quantify CVBP’s multidimensional sustainability impacts. Using China’s 2020 Social Accounting Matrix (SAM) with simulated 10–50% price reductions, key findings reveal that (1) >40% price reductions trigger sectoral output reversal; (2) GDP exhibits an inverted U-shape; (3) household income declines despite corporate/government gains; and (4) industrial contraction impairs innovation capacity and employment stability. Our analysis identifies potential sustainability risks, emphasizing the need for rigorous empirical validation prior to implementing aggressive price reduction policies, and underscores the importance of integrating supply chain considerations into procurement policy design. This approach maximizes resource allocation efficiency while advancing socioeconomic resilience in healthcare systems.

1. Introduction

1.1. Research Background

Between 2010 and 2019, China’s healthcare expenditure increased more than threefold [1]. To address rising medical costs, the CVBP policy has been widely implemented [2,3,4]. However, the policy’s outcomes present a dual effect: It effectively reduces supplier costs, enhances purchaser bargaining power, and alleviates patient financial burdens [5,6,7], while simultaneously introducing challenges such as pharmaceutical quality control issues and limited improvements in social welfare [8,9,10]. These factors impact the sustainability of the public healthcare system. Since its initial pilot as group purchasing in 2006, China’s CVBP policy has evolved into a routine mechanism [11], aiming to enhance medicine accessibility through price regulation. As a significant case study, research into its policy effects can offer valuable insights for optimizing international drug price regulation frameworks. This study employs a CGE model to analyze the multisectoral economic transmission effects of the policy, providing practical implications for promoting sustainable development within public healthcare systems.

1.2. Literature Review

1.2.1. CVBP Policy’s Impact on Pharmaceutical Markets and Macroeconomy

Evidence indicates that CVBP policy can effectively reduce procurement costs for medical institutions [8,12,13]. However, some studies suggest that such policies may enhance buyer dependence on specific suppliers through reinforced economies of scale, potentially creating market entry barriers [14]. This dependence could diminish market competition, increase industry concentration [15], and ultimately elevate costs for both healthcare systems and patients [16], while also introducing risks to drug supply security [8,17].

Empirical analyses demonstrate that CVBP policy can stimulate intensified enterprise competition and incentivize investments in technological R&D, thereby enhancing domestic production capacity and contributing to GDP growth [18,19]. Conversely, other scholars argue that the heightened regulatory signals conveyed by such policies may impact the long-term stability of investment decisions within the pharmaceutical industry, potentially suppressing capital allocation towards high-risk innovation projects [20].

Current empirical research on the effects of CVBP policy primarily relies on methods such as literature analysis [17], difference-in-differences [19], and regression analysis [18]. However, these approaches have yet to fully investigate cross-sectoral linkage effects, limiting a comprehensive assessment of the policy’s overall impact on the sustainability of public healthcare systems. In contrast, CGE models offer the capability to capture feedback mechanisms within complex systems.

1.2.2. CVBP Policy’s Impact on Social Welfare

Empirical studies demonstrate that pharmaceutical price regulation policies can enhance medicine accessibility, potentially translating improved short-term access into long-term health gains [21]. However, some research suggests that such policies may intensify market competition, potentially triggering involuntary enterprise exit and supply chain disruption risks [10,22], thereby exerting negative impacts on citizen social welfare.

Current discussions regarding social welfare impacts predominantly focus on partial equilibrium indicators, such as supply chain profit distribution and medicine accessibility [23]. Less attention has been given to the dynamic interlinkages between pharmaceutical prices and the broader economic system, limiting a comprehensive assessment of the policy’s system-wide impact on overall social welfare.

1.2.3. Research on the Health Policy Based on CGE Model

CGE models exhibit versatile analytical applications in health policy assessment. Research employing integrated healthcare and non-healthcare sectoral frameworks demonstrates that pharmaceutical price inflation generates significant negative welfare effects [24]. Multiregional CGE specifications further quantify how health and education infrastructure investments positively influence regional economic development trajectories [25]. Methodologically, econometric calibration techniques provide essential parameterization for evaluating health-related ecosystem service valuations [26]. Multisectoral CGE simulations empirically establish that optimized tobacco excise taxation concurrently enhances population health outcomes and macroeconomic efficiency [27]. These applications collectively underscore CGE modeling’s capacity to elucidate economic transmission pathways in pharmaceutical and health policy systems.

Existing scholarship has employed CGE methodologies to examine medical expenditure and drug price volatility impacts, yet systematic evaluation of pharmaceutical price regulation’s multifactorial socioeconomic consequences—particularly within China’s CVBP policy regime—remains underdeveloped. This study addresses this research gap through a CGE framework calibrated to China’s institutional context. We quantify supply-chain transmitted impacts of pharmaceutical price reductions on industrial composition, factor allocation efficiency, and macroeconomic indicators. The analysis generates empirical evidence to optimize synergistic policy-ecosystem coordination mechanisms, advancing sustainable development objectives under the Healthy China Initiative.

2. Model Methods and Data

2.1. Model Methods

The CGE model is based on general equilibrium theory and constructs the model through the SAM, depicting the behavior of producers achieving profit maximization under technological constraints and consumers achieving utility maximization under budget constraints, ultimately reaching equilibrium in various markets under the influence of market mechanisms [28], thereby simulating and analyzing the real economic structure and economic operation. Drug prices are treated as exogenous shock variables under classical economic theory, simulating the impact of price volatility on the pharmaceutical sector and other industries. The CGE model in this paper consists of five modules: the production module, trade module, main institution module, equilibrium closure module, and social welfare module. The variables and parameters in the model are shown in the

Table 1. Variables and parameters in the model.

Variable or Parameter	Variable Definition
${\mathrm{Q}\mathrm{A}}_{\mathrm{a}}$	Output of sector a
$\mathrm{Q}{\mathrm{V}\mathrm{A}}_{\mathrm{a}}$	Factor inputs for production activities in sector a
$\mathrm{Q}{\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}$	Non-factor intermediate inputs
$\mathrm{Q}{\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}$	Intermediate input nec
${\mathrm{P}\mathrm{A}}_{\mathrm{a}}$	Output price of sector a
$\mathrm{P}{\mathrm{V}\mathrm{A}}_{\mathrm{a}}$	Price of factor inputs
$\mathrm{P}{\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}$	Price of non-factor intermediate inputs
$\mathrm{P}{\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}$	Price of intermediate input nec
${\mathrm{Q}\mathrm{D}\mathrm{A}}_{\mathrm{a}}$	Quantity of output supply from sector a domestically
${\mathrm{Q}\mathrm{E}}_{\mathrm{a}}$	Export amount of output from sector a
${\mathrm{Q}\mathrm{Q}}_{\mathrm{c}}$	Domestic total demand for good c
${\mathrm{Q}\mathrm{M}}_{\mathrm{c}}$	Import volume of commodity c
${\mathrm{Q}\mathrm{D}}_{\mathrm{c}}$	Domestic production and domestic sales of commodity c
EXR	Exchange rate
${\mathrm{P}\mathrm{Q}}_{\mathrm{c}}$	Price of commodity c
${\mathrm{P}\mathrm{D}\mathrm{A}}_{\mathrm{a}}$	Domestic price of output supply from sector a
${\mathrm{P}\mathrm{E}}_{\mathrm{a}}$	Export price of output from sector a
${\mathrm{P}\mathrm{M}}_{\mathrm{c}}$	Import price of commodity c
${\mathrm{P}\mathrm{D}}_{\mathrm{c}}$	Price of commodity c produced and sold domestically
YH	Household income
QLS	Labor supply
$\mathrm{W}\mathrm{L}$	Labor price
QKS	Capital supply
$\mathrm{W}\mathrm{K}$	Capital price
${\mathrm{Q}\mathrm{H}}_{\mathrm{c}}$	Total consumption of commodity c by residents
${\mathrm{Q}\mathrm{G}}_{\mathrm{c}}$	Total consumption of commodity c by the government
$\mathrm{Y}\mathrm{E}\mathrm{N}\mathrm{T}$	Corporate income
YG	Government revenue
EINV	Corporate investment
HSAV	Household savings
GSAV	Government savings
FSAV	Foreign savings
${\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{c}}$	Investment in good c
${\mathrm{P}\mathrm{Q}0}_{\mathrm{c}}$	Pre-shock price of commodity c
${\mathrm{Q}\mathrm{H}0}_{\mathrm{c}}$	Pre-shock total consumption of commodity c by residents
${\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{a}}$	Scale parameter of the first-level nest
${\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{a}}$	Exponent parameter of the first-level nest
${\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{a}}$	Share parameter of the first-level nest
${\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{v}\mathrm{a}}$	Share parameter of the second-level nest
${\mathsf{\rho }}_{\mathrm{a}}^{kfpc}$	Share parameter of the third-level nest
${\mathsf{\rho }}_{\mathrm{a}}^{fpc}$	Share parameter of the fourth-level nest
${\mathsf{\rho }}_{\mathrm{a}}^{pc}$	Share parameter of the fifth-level nest
${\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}$	Share parameter of the CET function
${\mathsf{\rho }}_{\mathrm{a}}^x$	Share parameter of the Armington aggregation function
${\mathrm{i}\mathrm{c}\mathrm{a}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}$	Input-output coefficient of the intermediate input module
${\mathrm{t}\mathrm{a}\mathrm{x}}_{\mathrm{a}}$	Production tax rate
${\mathrm{t}\mathrm{m}}_{\mathrm{c}}$	Import tariff on good c
${\mathrm{t}\mathrm{e}}_{\mathrm{a}}$	Export tariff on output from sector a
${\mathrm{p}\mathrm{w}\mathrm{e}}_{\mathrm{a}}$	Foreign price of output from sector a
${\mathrm{p}\mathrm{w}\mathrm{m}}_{\mathrm{c}}$	Foreign price of imported good c
${\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}\mathrm{e}\mathrm{r}}_{\mathrm{g}\mathrm{h}}$	Government transfer payments to residents
${\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{k}}$	Proportion of capital income held by residents
${\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{h}\mathrm{k}}$	Proportion of capital income held by residents
${\mathrm{m}\mathrm{p}\mathrm{c}\mathrm{h}}_{\mathrm{c}}$	Marginal propensity to consume good c by residents
${\mathrm{m}\mathrm{p}\mathrm{c}\mathrm{g}}_{\mathrm{c}}$	Government’s marginal propensity to consume good c
${\mathrm{t}\mathrm{i}}_{\mathrm{h}}$	Personal income tax rate
${\mathrm{t}\mathrm{i}}_{\mathrm{e}\mathrm{n}\mathrm{t}}$	Corporate income tax rate
${\mathrm{s}\mathrm{a}\mathrm{v}\mathrm{e}}_{\mathrm{h}}$	Resident savings rate
${\mathrm{s}\mathrm{a}\mathrm{v}\mathrm{e}}_{\mathrm{g}}$	Government savings rate

below:

2.1.1. Production Module

In the production function module, this paper adopts a five-layer nesting, with the fifth layer being pharmaceutical manufacturing, and the cultivation and harvesting of traditional Chinese medicinal materials; the fourth layer is the pharmaceutical manufacturing–traditional Chinese medicine cultivation synthesis, and drug distribution; the third layer is the capital and pharmaceutical manufacturing–drug distribution synthesis; the second layer is the labor and the capital–pharmaceutical manufacturing-drug distribution synthesis; and the first layer is the capital–pharmaceutical manufacturing–drug distribution-labor synthesis and non-factor intermediate products, with non-factor intermediate inputs determined by Leontief’s input–output relationship. The input structure of factors and related products in the production function is detailed in Figure 1, and the synthesis of inputs at each level adopts the constant elasticity of substitution (CES) production function form.

The first-layer production nesting structure is defined by Equations (1)–(3): Equation (1) represents the profit maximization function of total output; Equation (2) represents the demand functions for capital–pharmaceutical manufacturing–drug distribution-labor synthesis; and Equation (3) represents non-factor intermediate products.

Production structures in subsequent levels follow the same constructive logic.

$${\mathrm{Q}\mathrm{A}}_{\mathrm{a}}={\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{a}}{\left({\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{a}}{\mathrm{Q}\mathrm{V}\mathrm{A}}_{\mathrm{a}}^{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{a}}}+(1-{\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{a}}){\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}^{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{a}}}\right)}^{{\displaystyle \frac{1}{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{a}}}}}$$

(1)

$${\displaystyle \frac{{\mathrm{P}\mathrm{V}\mathrm{A}}_{\mathrm{a}}}{{\mathrm{P}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}}}={\displaystyle \frac{{\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{a}}}{1-{\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{a}}}}{\left({\displaystyle \frac{{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}}{{\mathrm{Q}\mathrm{V}\mathrm{A}}_{\mathrm{a}}}}\right)}^{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{a}}-1}$$

(2)

$${{\mathrm{P}\mathrm{A}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{A}}_{\mathrm{a}}=\left(1+{\mathrm{t}\mathrm{a}\mathrm{x}}_{\mathrm{a}}\right)\times \left({{\mathrm{P}\mathrm{V}\mathrm{A}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{V}\mathrm{A}}_{\mathrm{a}}+{{\mathrm{P}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}\right)$$

(3)

The second-level production combination function includes the CES production function and the intermediate input production function, where the intermediate input production function is represented by Equations (4) and (5):

$${\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}={\mathrm{i}\mathrm{c}\mathrm{a}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}{ \times \mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}$$

(4)

$${{\mathrm{P}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}={\sum }_{\mathrm{n}\mathrm{e}\mathrm{c}}{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}} \times {\mathrm{P}\mathrm{Q}}_{\mathrm{n}\mathrm{e}\mathrm{c}}$$

(5)

2.1.2. Trade Module

Domestic product demand functions are specified through Equations (6)–(9): Equation (6) represents the domestic market demand function; Equation (7) represents the domestic production and import optimization function and imported goods under the Armington assumption; Equation (8) represents the product demand function; and Equation (9) represents the import price function.

$${\mathrm{Q}\mathrm{Q}}_{\mathrm{c}}={\mathsf{\alpha }}_{\mathrm{c}}^{\mathrm{q}}{\left({\mathsf{\delta }}_{\mathrm{c}}^{\mathrm{q}}{\mathrm{Q}\mathrm{M}}_{\mathrm{c}}^{{\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}}+(1-{\mathsf{\delta }}_{\mathrm{c}}^{\mathrm{q}}){\mathrm{Q}\mathrm{D}\mathrm{C}}_{\mathrm{c}}^{{\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}}\right)}^{{\displaystyle \frac{1}{{\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}}}}$$

(6)

$${\mathrm{P}\mathrm{M}}_{\mathrm{a}}\times \left(1-{\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{q}}\right)\times {\mathrm{Q}\mathrm{M}}_{\mathrm{c}}^{{1-\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}}={\mathrm{P}\mathrm{D}\mathrm{C}}_{\mathrm{c}}\times {\mathsf{\alpha }}_{\mathrm{c}}^{\mathrm{q}}\times {\mathrm{Q}\mathrm{D}\mathrm{C}}_{\mathrm{c}}^{{1-\mathsf{\rho }}_{\mathrm{c}}^{\mathrm{q}}}$$

(7)

$${{\mathrm{P}\mathrm{Q}}_{\mathrm{c}}\times \mathrm{Q}\mathrm{Q}}_{\mathrm{c}}={{\mathrm{P}\mathrm{M}}_{\mathrm{c}}\times \mathrm{Q}\mathrm{M}}_{\mathrm{c}}+{{\mathrm{P}\mathrm{D}\mathrm{C}}_{\mathrm{c}}\times \mathrm{Q}\mathrm{D}\mathrm{C}}_{\mathrm{c}}$$

(8)

$${\mathrm{P}\mathrm{M}}_{\mathrm{c}}={\mathrm{p}\mathrm{m}\mathrm{c}}_{\mathrm{c}}\times \left(1+{\mathrm{t}\mathrm{m}}_{\mathrm{c}}\right)\times \mathrm{E}\mathrm{X}\mathrm{R}$$

(9)

Equations (10)–(13) collectively define the CET allocation framework for domestic production: Equation (10) represents the domestic output allocation function; Equation (11) represents the domestic sales and export optimization function; Equation (12) represents the product supply function; and Equation (13) represents the export price function.

$${\mathrm{Q}\mathrm{A}}_{\mathrm{a}}={\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{x}}{\left({\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{x}}{\mathrm{Q}\mathrm{E}}_{\mathrm{a}}^{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{x}}}+(1-{\mathsf{\delta }}_{\mathrm{a}}^{\mathrm{x}}){\mathrm{Q}\mathrm{D}\mathrm{A}}_{\mathrm{a}}^{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{x}}}\right)}^{{\displaystyle \frac{1}{{\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{x}}}}}$$

(10)

$${\mathrm{P}\mathrm{E}}_{\mathrm{a}}\times \left(1-{\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{x}}\right)\times {\mathrm{Q}\mathrm{E}}_{\mathrm{a}}^{{1-\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{x}}}={\mathrm{P}\mathrm{D}\mathrm{A}}_{\mathrm{a}}\times {\mathsf{\alpha }}_{\mathrm{a}}^{\mathrm{x}}\times {\mathrm{Q}\mathrm{D}\mathrm{A}}_{\mathrm{a}}^{{1-\mathsf{\rho }}_{\mathrm{a}}^{\mathrm{x}}}$$

(11)

$${{\mathrm{P}\mathrm{Q}}_{\mathrm{c}}\times \mathrm{Q}\mathrm{A}}_{\mathrm{c}}={{\mathrm{P}\mathrm{E}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{E}}_{\mathrm{a}}+{{\mathrm{P}\mathrm{D}\mathrm{A}}_{\mathrm{a}}\times \mathrm{Q}\mathrm{D}\mathrm{A}}_{\mathrm{a}}$$

(12)

$${\mathrm{P}\mathrm{E}}_{\mathrm{a}}={\mathrm{p}\mathrm{w}\mathrm{e}}_{\mathrm{a}}\times \left(1+{\mathrm{t}\mathrm{e}}_{\mathrm{a}}\right)\times \mathrm{E}\mathrm{X}\mathrm{R}$$

(13)

The production and activity transformation equations are represented by Equations (14) and (15):

$${\mathrm{Q}\mathrm{D}\mathrm{A}}_{\mathrm{a}}={\sum }_{\mathrm{c}}{\mathrm{s}\mathrm{a}\mathrm{x}}_{\mathrm{a},\mathrm{c}}\times {\mathrm{Q}\mathrm{D}\mathrm{C}}_{\mathrm{c}}$$

(14)

$${\mathrm{P}\mathrm{D}\mathrm{C}}_{\mathrm{a}}={\sum }_{\mathrm{a}}{\mathrm{s}\mathrm{a}\mathrm{x}}_{\mathrm{a},\mathrm{c}}\times {\mathrm{P}\mathrm{D}\mathrm{A}}_{\mathrm{a}}$$

(15)

2.1.3. Main Institution Module

The main institution module describes the sources of income and expenditure of various entities based on their income and expenditure, including three entities: households, enterprises, and the government.

Household income is obtained through labor, capital, and government-transferring payments. The utility function of households is a Cobb–Douglas function, and the share of disposable income allocated to each good is fixed, represented by Equations (16)–(18):

$$\mathrm{Y}\mathrm{H}=\mathrm{W}\mathrm{L}\times \mathrm{Q}\mathrm{L}\mathrm{S}+{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{h}\mathrm{k}}\times \mathrm{W}\mathrm{K}\times \mathrm{Q}\mathrm{K}\mathrm{S}+{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}\mathrm{e}\mathrm{r}}_{\mathrm{g}\mathrm{h}}$$

(16)

$${\mathrm{Q}\mathrm{H}}_{\mathrm{c}}={\mathrm{m}\mathrm{p}\mathrm{c}\mathrm{h}}_{\mathrm{c}}\times \left(1-{\mathrm{t}\mathrm{i}}_{\mathrm{h}}-{\mathrm{s}\mathrm{a}\mathrm{v}\mathrm{e}}_{\mathrm{h}}\right)\times \mathrm{Y}\mathrm{H}$$

(17)

$$\mathrm{H}\mathrm{S}\mathrm{A}\mathrm{V}={\mathrm{s}\mathrm{a}\mathrm{v}\mathrm{e}}_{\mathrm{h}}\times \mathrm{Y}\mathrm{H}$$

(18)

The income of enterprises is the retained portion of capital income, represented by Equations (19) and (20):

$$\mathrm{Y}\mathrm{E}\mathrm{N}\mathrm{T}={\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{k}}\times \mathrm{W}\mathrm{K}\times \mathrm{Q}\mathrm{K}\mathrm{S}$$

(19)

$$\mathrm{E}\mathrm{I}\mathrm{N}\mathrm{V}=\left(1-{\mathrm{t}\mathrm{i}}_{\mathrm{e}\mathrm{n}\mathrm{t}}\right)\times \mathrm{Y}\mathrm{E}\mathrm{N}\mathrm{T}$$

(20)

Equation (21) represents government revenue composition, include production taxes, personal income taxes, and corporate income taxes. Equation (22) represents government consumption expenditure, assuming fixed expenditure shares across commodity categories. Equation (23) represents the household consumption function without an autonomous consumption component, establishing identical marginal and average propensities to consume (MPC = APC).

$$\begin{gathered} \mathrm{Y}\mathrm{G}={\sum }_{\mathrm{a}}{[\mathrm{t}\mathrm{a}\mathrm{x}}_{\mathrm{a}} \times {(\mathrm{P}\mathrm{V}\mathrm{A}}_{\mathrm{a}}\times {\mathrm{Q}\mathrm{V}\mathrm{A}}_{\mathrm{a}}+{\mathrm{P}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}\times {\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{A}}_{\mathrm{a}}\left)\right]+{\mathrm{t}\mathrm{i}}_{\mathrm{h}}\times \mathrm{Y}\mathrm{H}+ \\ {\mathrm{t}\mathrm{i}}_{\mathrm{e}\mathrm{n}\mathrm{t}}\times \mathrm{Y}\mathrm{E}\mathrm{N}\mathrm{T} \end{gathered}$$

(21)

$${\mathrm{Q}\mathrm{G}}_{\mathrm{c}}={\mathrm{m}\mathrm{p}\mathrm{c}\mathrm{g}}_{\mathrm{c}}\times (\mathrm{Y}\mathrm{G}-\mathrm{G}\mathrm{S}\mathrm{A}\mathrm{V}-{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}\mathrm{e}\mathrm{r}}_{\mathrm{g}\mathrm{h}})$$

(22)

$$\mathrm{G}\mathrm{S}\mathrm{A}\mathrm{V}={\mathrm{s}\mathrm{a}\mathrm{v}\mathrm{e}}_{\mathrm{g}}\times \mathrm{Y}\mathrm{G}$$

(23)

2.1.4. Equilibrium Closure Module

The Equilibrium closure module mainly includes product market equilibrium, the balance of international payments, and savings–investment balance.

The clearing equation for the factor market is expressed by Equations (24) and (25):

$$\mathrm{Q}\mathrm{L}\mathrm{S}={\sum }_{\mathrm{a}}{\mathrm{Q}\mathrm{L}}_{\mathrm{a}}$$

(24)

$$\mathrm{Q}\mathrm{K}\mathrm{S}={\sum }_{\mathrm{a}}{\mathrm{Q}\mathrm{K}}_{\mathrm{a}}$$

(25)

Foreign savings calculated in local currency is expressed by Equation (26):

$${\sum }_{\mathrm{c}}{\mathrm{p}\mathrm{w}\mathrm{m}}_{\mathrm{c}}\times {\mathrm{Q}\mathrm{M}}_{\mathrm{c}}={\sum }_{\mathrm{a}}{\mathrm{p}\mathrm{w}\mathrm{e}}_{\mathrm{a}} \times {\mathrm{Q}\mathrm{E}}_{\mathrm{a}}+\mathrm{F}\mathrm{S}\mathrm{A}\mathrm{V}$$

(26)

The savings–investment equation and other equations related to the model system are expressed by Equation (27):

$$\mathrm{H}\mathrm{S}\mathrm{A}\mathrm{V}+\mathrm{E}\mathrm{I}\mathrm{N}\mathrm{V}+\mathrm{G}\mathrm{S}\mathrm{A}\mathrm{V}+\mathrm{F}\mathrm{S}\mathrm{A}\mathrm{V}={\sum }_{\mathrm{c}}{\mathrm{P}\mathrm{Q}}_{\mathrm{c}} \times {\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{c}}+\mathrm{w}\mathrm{a}\mathrm{l}\mathrm{r}\mathrm{a}\mathrm{s}$$

(27)

The market clearing equation for goods is based on the balance of goods accounts in the SAM table. In the domestic market, total supply equals total demand, as expressed in Equation (28). The market clearing equations for the primary, secondary, and tertiary sectors are similar to Equation (28):

$${\mathrm{Q}\mathrm{Q}}_{\mathrm{n}\mathrm{e}\mathrm{c}}={\sum }_{\mathrm{a}}{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{T}}_{\mathrm{n}\mathrm{e}\mathrm{c},\mathrm{a}}+{\mathrm{Q}\mathrm{H}}_{\mathrm{n}\mathrm{e}\mathrm{c}}+{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{n}\mathrm{e}\mathrm{c}}+{\mathrm{Q}\mathrm{G}}_{\mathrm{n}\mathrm{e}\mathrm{c}}$$

(28)

GDP is expressed in Equation (29):

$$\mathrm{G}\mathrm{D}\mathrm{P}={\sum }_{\mathrm{c}}{(\mathrm{Q}\mathrm{H}}_{\mathrm{c}}+{\mathrm{Q}\mathrm{G}}_{\mathrm{c}}+{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{c}}-{\mathrm{Q}\mathrm{M}}_{\mathrm{c}})+{\sum }_{\mathrm{a}}{\mathrm{Q}\mathrm{E}}_{\mathrm{a}}$$

(29)

2.1.5. Social Welfare Module

Social welfare is measured using the residents’ utility function. The social utility function can determine the increase or decrease in social welfare, as well as the degree of increase or decrease in social welfare, as expressed in Equation (30):

$$\mathrm{E}\mathrm{V}={\sum }_{\mathrm{c}}{(\mathrm{P}\mathrm{Q}}_{\mathrm{c}}\times {\mathrm{Q}\mathrm{H}}_{\mathrm{c}})-{\sum }_{\mathrm{c}}{(\mathrm{P}\mathrm{Q}0}_{\mathrm{c}}\times {\mathrm{Q}\mathrm{H}0}_{\mathrm{c}})$$

(30)

2.2. Construction of the SAM Table, Parameter, and Scenario Settings

2.2.1. Construction of the SAM Table

This study employs China’s 2020 national input–output table as its empirical foundation. Consistent with industrial structure characteristics and research objectives, the extraction coefficient method aggregates original sectors into six consolidated categories: three pharmaceutical-related sectors and primary/secondary/tertiary industry sectors. Pharmaceutical sector delineation primarily adapts established methodologies from China’s health industry research [29,30], ensuring logical coherence while maintaining data accessibility and analytical tractability. Following the ‘Health Industry Statistical Classification (2019)’, we identify three pharmaceutical core sectors. Their input–output subsector mappings appear in

Table 2. Classification and corresponding codes for pharmaceutical and related industries.

Sector of SAM Table	Sector of Input–Output Table	Code
Distribution services for pharmaceuticals and other health products	Wholesale Trade	51,105
	Retail Trade	52,106
	Leasing Services	71,130
Pharmaceutical manufacturing	Pharmaceutical Manufacturing	27,048
	Other Specialized Equipment	35,074
Cultivation and harvesting of traditional Chinese medicinal materials	Agricultural Products	1001
	Forestry Products	2002
	Livestock Products	3003
	Nonferrous Metal Mining and Dressing	9009

, with extraction coefficients derived from industry asset proportions [31].

The data for extraction coefficient estimation primarily derive from the ‘China Economic Census Yearbook’, ‘China Statistical Yearbook’, and ‘China Health Statistics Yearbook’. Given the relative stability of industrial structures over short-term periods, economic census data provide essential benchmarks for estimating structural parameters. This study employs China’s 2020 input–output table as its core empirical foundation, representing the most recent authoritative official source currently available. Selecting this annual dataset satisfies temporal relevance requirements while aligning with the research objective of examining economic structural impacts.

SAM utilizes the consolidated six-sector input–output table generated through the extraction coefficient method as its foundational framework. Supplementary data from the ‘2021 Statistical Yearbook’ (https://www.stats.gov.cn/sj/ndsj/2021/indexch.htm, accessed on 11 August 2025), ‘China Tax Yearbook 2021’ (https://www.las.ac.cn/front/book/detail?id=045c1995fd0c46844c9dea81563d33c4, accessed on 11 August 2025), and ‘2020 Fiscal Revenue’ (http://gks.mof.gov.cn/tongjishuju/202101/t20210128_3650522.htm, accessed on 11 August 2025) and Expenditure Report inform the subsequent SAM compilation.

2.2.2. Parameter Settings

The elasticity of substitution parameter captures the degree of substitutability between production factors. Although some scholars estimate this parameter econometrically using historical data, its value in CGE models primarily draws upon established research. This study parameterizes the substitution elasticity coefficient $\mathsf{\rho }$ based on references [24,25,26], with specific values presented in

Table 3. The elasticity of substitution parameter.

	${\mathsf{\rho }}_{\mathbf{a}}^{\mathbf{a}}$	${\mathsf{\rho }}_{\mathbf{a}}^{\mathbf{v}\mathbf{a}}$	${\mathsf{\rho }}_{\mathbf{a}}^{\mathit{k}\mathit{f}\mathit{p}\mathit{c}}$	${\mathsf{\rho }}_{\mathbf{a}}^{\mathit{f}\mathit{p}\mathit{c}}$	${\mathsf{\rho }}_{\mathbf{a}}^{\mathit{p}\mathit{c}}$	${\mathsf{\rho }}_{\mathbf{c}}^{\mathbf{q}}$
Sector 1	0.3	0.4	0.4	0.45	0.45	0.6
Sector 2	0.3	0.4	0.4	0.45	0.45	0.6
Sector 3	0.3	0.4	0.4	0.45	0.45	0.6
Sector 4	0.3	0.4	0.4	0.45	0.45	0.6
Sector 5	0.3	0.4	0.4	0.45	0.45	0.6
Sector 6	0.3	0.4	0.4	0.45	0.45	0.6

. Scale and share parameters are calibrated using production function specifications and SAM data.

2.2.3. Scenario Settings

CVBP policy induces pharmaceutical price reductions through scale effects [6], competition effects [32], and transaction cost savings [5], though the magnitude of reduction is not constant. Empirical evidence indicates average price reductions for centrally procured drugs range between 15% and 36.24% relative to pre-policy prices [6,14]. Reflecting real-world observations, this study parameterizes pharmaceutical price reductions within a 10% to 50% interval, with 10-percentage-point increments between adjacent reduction levels. This discrete reduction interval configuration objectively simulates actual price reduction distributions while facilitating comparative analysis of differential impacts between low and high reduction scenarios, thereby establishing an analytical framework to examine economy-wide effects of pharmaceutical price changes.

We incorporate CVBP-induced pharmaceutical price reductions as exogenous shocks within the model, with sectoral definitions aligned with the ‘Health Industry Statistical Classification (2019)’. Specifically, this study simulates the impact of drug price reduction by impacting the output price ${\mathrm{P}\mathrm{A}}_{\mathrm{a}}$ of the ‘drug and other health product circulation service sector’ in the production module.

3. Results and Analysis

This study employs an established CGE model for pharmaceutical pricing analysis. Drug prices are defined as exogenous variables within the computational framework. Numerical solutions are derived using GAMS 46.3.0 software to process SAM data.

The analysis examines CVBP policy impacts through price transmission mechanisms, quantifying effects on pharmaceutical supply chain sectors and non-pharmaceutical industries.

Comparative evaluation of macroeconomic indicators and social welfare metrics is conducted between pre-policy and post-policy implementation states.

3.1. The Impact of Drug Prices on the Pharmaceutical Industry

The changes in actual output and output prices of drug-related industries under various scenarios are shown in

Table 4. Changes in actual output and output prices in the pharmaceutical-related industry.

	Scenario 1: 10%		Scenario 2: 20%		Scenario 3: 30%		Scenario 4: 40%		Scenario 5: 50%
	Output	Price	Output	Price	Output	Price	Output	Price	Output	Price
Distribution services for pharmaceuticals and other health products	−4.20%	−10.00%	−5.47%	−20.00%	−7.32%	−30.00%	−9.23%	−40.00%	−2.35%	−50.00%
Pharmaceutical manufacturing	−1.95%	−0.09%	−1.68%	0.98%	−3.73%	−1.45%	−1.98%	3.85%	1.01%	0.33%
Cultivation and harvesting of traditional Chinese medicinal materials	−0.50%	−0.36%	−0.92%	−1.49%	−2.84%	3.51%	−4.54%	−7.72%	−3.96%	−0.59%

.

Pharmaceutical price reductions of 10–30% generally decrease output across pharmaceutical-related sectors, with the magnitude of decline positively correlated with each sector’s input share in pharmaceutical distribution services. However, a threshold effect emerges: When price reductions reach ≥40%, output decline in Chinese medicinal materials cultivation surpasses that in pharmaceutical manufacturing. At 50% price reductions, pharmaceutical manufacturing output shifts to positive growth. Given the high capital intensity of pharmaceutical manufacturing, profit compression from abrupt price reductions induces firms to substitute capital for labor [33]. Capital price rigidity proves less pronounced than wage adjustment flexibility in the short term, facilitating increased capital investment that partially offsets cost pressures and underpins output expansion. Furthermore, pharmaceutical price reductions significantly enhance international competitiveness, with 1.58% export growth in Scenario 5 contributing to net output expansion in pharmaceutical manufacturing.

Pharmaceutical manufacturing exhibits price reductions at 10% and 30% exogenous discount levels, while demonstrating price increases in other scenarios. This pattern directly reflects the tension between upstream cost transmission and downstream bargaining power. Model parameterization specifies low input substitution elasticity in pharmaceutical manufacturing, indicating that winning bidders face constrained supplier switching capacity when confronting raw material price increases. Consequently, firms absorb costs or transfer them to purchasers in the short term [22].

Chinese medicinal materials cultivation shows price reductions at 10–20% and 40–50% discount levels but displays a significant anomalous increase at 30% reduction. China’s chemical pharmaceutical industry exhibits relative upstream concentration and downstream competition intensity [34]. When raw material supply is concentrated and demand inelastic, upstream producers maintain profitability through price increases—consistent with the imperfect competition market structure.

3.2. The Impact of Drug Prices on Macroeconomics

The changes in actual output and output prices of the primary, secondary, and tertiary industries under each scenario are shown in

Table 5. Changes in actual output and output prices of primary, secondary, and tertiary industries.

	Scenario 1: 10%		Scenario 2: 20%		Scenario 3: 30%		Scenario 4: 40%		Scenario 5: 50%
	Output	Price	Output	Price	Output	Price	Output	Price	Output	Price
Primary industry	0.14%	0.00%	−0.22%	0.02%	0.06%	0.00%	−0.33%	0.07%	0.23%	−0.01%
Secondary industry	−0.01%	0.00%	0.03%	0.00%	−0.04%	0.01%	0.03%	−0.01%	−0.01%	0.00%
Tertiary industry	0.22%	−0.01%	0.37%	−0.01%	0.34%	−0.01%	0.25%	0.01%	−0.06%	0.00%

, and the changes in GDP are shown in

Table 6. Changes in GDP.

Decline	Scenario 1: 10%	Scenario 2: 20%	Scenario 3: 30%	Scenario 4: 40%	Scenario 5: 50%
GDP	0.0053%	0.2082%	−0.1442%	−0.0626%	−0.0514%

.

Pharmaceutical price variations exert significant output effects on primary and tertiary industries. Secondary industry output demonstrates negligible sensitivity to these price fluctuations. This is because drug prices influence labor costs, which, in turn, have a certain impact on labor-intensive industries, while capital and technology-intensive industries are relatively less affected. Primary industry output exhibits measurable variations following pharmaceutical price reductions. The most significant fluctuation occurs under Scenario 4, manifesting as a 0.33% output contraction. The output of the tertiary industry shows an initial increase followed by a decrease with the decline in drug prices; the slight decrease in drug prices helps reduce labor costs, thereby benefiting the development of the tertiary industry. However, the continuous decline in labor costs is not conducive to transforming the economic development and does not help the traditional production characterized by low wages, low costs, and low profits.

The prices of the primary, secondary, and tertiary industries are less affected by fluctuations in drug prices. In Scenario 4, when drug prices exogenously decrease by 40%, the price of the primary industry only fluctuates by an increase of 0.07%. Sectoral prices respond to multiple determinants. Pharmaceutical price volatility exhibits relatively constrained influence on these price levels [10,22].

In terms of GDP changes, as drug prices decline, GDP initially rises before experiencing a downturn, with the largest decrease of 0.1442% observed under Scenario 3. Labor cost advantages have played an important role in booming China’s economic growth [35]. In Scenarios 1–2, pharmaceutical price reductions stimulate GDP in the short term through reduced labor costs. However, China’s demographic transition entails persistent attenuation of labor quantity advantages, diminishing the effectiveness of traditional cost transmission mechanisms [36]. Simultaneously, pharmaceutical price reductions lack direct stimulative effects on health human capital accumulation. Consequently, Scenario 5 demonstrates attenuated GDP impacts from pharmaceutical price shocks as reduction magnitude increases, indicating an inflection point in policy effectiveness during demographic transition.

3.3. The Impact of Drug Prices on Social Welfare Levels

The changes in social welfare under various scenarios are shown in

Table 7. Changes in social welfare.

Decline	Scenario 1: 10%	Scenario 2: 20%	Scenario 3: 30%	Scenario 4: 40%	Scenario 5: 50%
EV (billion CNY)	−0.42	−132.43	3.48	−42.76	−76.582
YH	0.00%	−0.36%	0.01%	−0.12%	−0.30%
YENT	0.30%	0.99%	1.07%	0.98%	0.35%
YG	0.06%	0.30%	0.25%	0.32%	0.17%
EINV	0.30%	0.99%	1.06%	0.97%	0.35%
HSAV	0.00%	−0.40%	−0.03%	−0.12%	−0.39%
GSAV	0.06%	0.39%	0.26%	0.25%	0.62%
FSAV	−0.30%	5.61%	−7.82%	−5.08%	−4.39%

. When drug prices decrease exogenously by 10–20% and 40–50%, social welfare levels decline; in Scenario 3, a 30% exogenous decrease in drug prices leads to an increase in social welfare of CNY 3.485 billion. At the 30% price reduction level approaching the price elasticity threshold for pharmaceutical demand, two parallel effects emerge: enhanced household real income and optimized corporate cost structures. Given pharmaceuticals’ higher budget share in low-income households, the exogenous 30% price reduction in Scenario 3 unleashes purchasing power that stimulates non-pharmaceutical goods demand, indirectly boosting tertiary sector output by 0.34%. Concurrently, reduced labor costs elevate corporate savings by 1.07% and investment by 1.06%, both potentially contributing to welfare improvements through distinct transmission mechanisms.

Household income declines across most scenarios result from labor market segmentation and factor allocation imbalances [37]. Pharmaceutical price shocks compress profits in upstream labor-intensive industries, triggering wage reductions for low-skilled workers and increased unemployment [38]. Concurrently, capital substitution effects in downstream pharmaceutical manufacturing further suppress demand for high-skilled labor. This cross-sectoral employment shock induces aggregate labor compensation contraction. Meanwhile, 0.35% corporate investment growth and 0.17% government revenue increase demonstrate redistribution toward capital returns and fiscal expansion. In the absence of compensatory transfer mechanisms such as medical insurance rebates [39], these dynamics ultimately manifest as an overall contraction in household disposable income.

4. Conclusions and Prospects

4.1. Conclusions

This paper utilizes the extraction coefficient method to compile a SAM for China in 2020, focusing primarily on the pharmaceutical industry. From a general equilibrium perspective, a CGE model is constructed to study the impact of drug price reduction on drug-related industries, macroeconomics, and social welfare. While the findings offer preliminary insights into potential effects, they necessitate empirical validation due to inherent constraints in the modeling methodology.

4.1.1. Sectoral Impacts

The output changes in the pharmaceutical manufacturing industry and the cultivation and harvesting of traditional Chinese medicinal materials are generally downward in response to the exogenous decline in drug prices. In terms of the degree of impact, the pharmaceutical manufacturing industry is affected to a greater extent when the level of exogenous decline in drug prices is relatively low; when the level of exogenous decline in drug prices is relatively high, the cultivation and harvesting of traditional Chinese medicinal materials are affected to a greater extent. In terms of the impact on prices in drug-related industries, in response to the exogenous decline in drug prices, the price changes in the pharmaceutical manufacturing industry and the cultivation and harvesting of traditional Chinese medicinal materials show mixed fluctuations, indicating that the decline in drug prices does not necessarily lead to a decrease in the prices of upstream raw material pharmaceutical companies, thereby reducing costs. To address the ‘upstream monopolization, downstream competition’ structure characteristic of China’s pharmaceutical sector, CVBP policy design should incorporate two strategic enhancements:

• Integrate production costs and reasonable profit margins of active pharmaceutical ingredients (APIs) as essential parameters in winning-bid price negotiations, facilitating transparent price transmission while curbing monopolistic practices.
• Increase weighting coefficients for supply chain stability, quality control metrics, and process innovation indicators within CVBP scoring mechanisms, displacing exclusive reliance on low-price selection.

4.1.2. Macroeconomic Effects

The exogenous decline in drug prices causes fluctuations in the output of the primary and tertiary industries, while the impact on the secondary industry is relatively low. The effect of drug prices on labor-intensive industries is greater than that on capital and technology-intensive industries. In terms of the impact on macroeconomic prices, the impact of drug prices on the prices of the primary, secondary, and tertiary industries is relatively limited. Regarding the impact on GDP, it shows a trend of first rising and then falling with the exogenous decline of drug prices, indicating that persistent drug price reductions cannot sustainably drive economic growth. Initial pharmaceutical price reductions functionally resemble an increase in household real purchasing power coupled with reduced production costs across other sectors, generating short-term demand stimulus and output expansion in non-pharmaceutical industries. However, sustained price reductions may trigger adverse outcomes including diminished investment, production capacity downsizing, and employment/income losses, ultimately impairing GDP and sectoral output. The short-term demand stimulus effect becomes surpassed by medium-to-long-term supply-side contraction, underscoring the dynamic complexity of price shocks and inherent fragility within industrial ecosystems.

4.1.3. Welfare Consequences

The exogenous decline in drug prices leads to an overall decrease in social welfare levels. The decline in drug prices results in an overall decrease in household income, household savings, and foreign savings, while corporate income, government revenue, corporate investment, and government savings all increase overall. However, the observed decline in social welfare does not imply that increasing pharmaceutical prices would enhance welfare. Rather, it indicates that suppressing drug prices through overreliance on administrative measures without complementary policies may undermine the pharmaceutical industry’s supply capacity and sustainability. Such approaches could consequently exert negative impacts on the broader economy and household incomes, potentially offsetting or even outweighing the immediate benefits to patients.

4.1.4. Sustainability Risks

Pharmaceutical output contraction signifies industrial scale reduction and production capacity erosion. Concurrently, diminished firm revenues and declining investment weaken technological innovation capabilities, impeding future drug R&D and process upgrades. This trajectory ultimately compromises China’s pharmaceutical industry competitiveness, strategic autonomy, and long-term supply security—contravening economic sustainability imperatives. Industry contraction directly induces employment losses and income reductions among workers, adversely affecting individual/family welfare while potentially exacerbating income inequality and social instability.

Collectively, these output and welfare effects demonstrate that administratively driven price suppression without complementary measures generates systemic risks threatening industrial vitality, eroding employment foundations, and undermining population health security capacities. Such outcomes fundamentally compromise long-term socioeconomic sustainability. To mitigate such systemic risks, complementary measures are proposed:

• Establish a cost auditing mechanism for critical APIs to incorporate reasonable profit margins into centralized procurement price negotiations, thereby mitigating price transmission distortions caused by upstream monopolies.
• Create a dedicated R&D innovation fund using savings from centralized procurement to support targeted investments in pharmaceutical R&D, counteracting innovation disincentives arising from profit margin compression.
• Implement workforce reskilling programs for labor-intensive segments, facilitating labor transition towards higher value-added processing or manufacturing sectors.
• Allocate savings from drug expenditure reductions towards expanding reimbursement for chronic disease medications and establishing out-of-pocket caps for low-income patients, directly compensating for social welfare losses.

4.2. Limitations

While the CGE analysis provides systematic insights into pharmaceutical pricing impacts, methodological constraints necessitate critical reflection. This section delineates three limitations—data limitations, model limitations, and scenario specification constraints—that qualify the findings while establishing pathways for subsequent refinement.

4.2.1. Data Limitations

The SAM construction relies on integrating, consolidating, and imputing data from input–output tables, taxation records, and fund flow accounts. This process entails statistical discrepancies and information loss due to sectoral/institutional aggregation, potentially resulting in inadequate granularity within pharmaceutical subsectors. Critical parameter values significantly influence CGE outcomes, yet these parameters—typically derived from literature or historical estimates—exhibit inherent uncertainty and context-dependent nature.

4.2.2. Model Limitations

This study employs a static CGE model simulating post-shock equilibrium states, inherently incapable of capturing dynamic adjustment pathways. Moreover, the model inadequately quantifies demand-side welfare gains from pharmaceutical price reductions, such as enhanced healthcare accessibility, reduced financial burdens, and indirect economic benefits from health improvements—core policy objectives of drug price regulation constituting vital welfare components. Furthermore, standard CGE frameworks conventionally assume perfect or monopolistic competition, providing inadequate depth in modeling oligopolistic or strong monopoly behaviors.

4.2.3. Scenario Specification Constraints

The ‘exogenous price reduction’ scenario constitutes a simplified representation of CVBP policy effects. It fails to fully incorporate essential CVBP characteristics including the volume-for-price tradeoff and complementary policy measures.

4.2.4. Confidence Level and Policy Implementation

The confidence level of this study’s conclusions necessitates integrated assessment across qualitative and quantitative dimensions. Qualitative trend findings—such as short-term output suppression in pharmaceutical-related industries post-price reductions, the inverted U-shaped GDP response, and the overall decline in social welfare—exhibit moderate to high confidence. Their core logic aligns with industrial interconnections and economic laws, remaining unaffected by data or scenario simplifications. In contrast, quantitative estimates—including precise output growth rates under specific price reductions and magnitudes of GDP decline—demonstrate lower confidence. This is attributed to data aggregation bias, parameter uncertainty, and the constraints of static modeling, which may introduce deviations from actual values.

Given the established confidence levels and inherent complexity of policy impacts, practical implementation should prioritize the following:

• Adopt phased policy adjustments. Aligning with the study’s qualitative finding that price reductions stimulate consumer demand in the short term but suppress industrial output in the long run, mitigate risks through incremental implementation. Concurrently monitor pharmaceutical industry output and quality metrics to calibrate policy intensity progressively.
• Establish dynamic monitoring with feedback mechanisms. To compensate for static modeling limitations, institute regular tracking of core indicators—including GDP fluctuations, household healthcare expenditure burdens, and pharmaceutical R&D investment. Conduct quarterly policy impact assessments to enable timely optimization and prevent the accumulation of adverse long-term effects.

4.3. Research Contributions

This study makes distinct contributions to the literature on the impacts of the CVBP policy and healthcare sustainability modeling through three key dimensions.

• It develops the first CGE model specifically focused on the pharmaceutical sector. This model addresses constraints inherent in difference-in-differences and regression analyses for capturing cross-sectoral linkages. The framework systematically traces price fluctuation transmission mechanisms throughout industrial chains and the broader macroeconomy, thereby addressing a critical gap in multi-sectoral economic feedback analysis of CVBP policies.
• Extending beyond the existing literature’s emphasis on short-term cost reductions or localized market effects, it provides the first integrated quantification of systemic impacts stemming from pharmaceutical price reductions. These impacts span industrial output, GDP dynamics, and social welfare, revealing potential long-term sustainability risks associated with price-centric regulatory approaches.
• The study simulates graded price reduction scenarios consistent with CVBP’s core ‘volume-for-price’ mechanism. These simulations provide quantitative evidence to inform the optimization of CVBP negotiation design and enhance industrial sustainability.

4.4. Future Research Directions

While our quantitative results carry uncertainty as detailed in Section 4.2, the conceptual framework and identification of potential transmission mechanisms represent valuable contributions to understanding the complex dynamics of pharmaceutical market interventions. Future research directions aimed at addressing current limitations involve developing dynamic CGE models to capture adjustment pathways over time, conducting an econometric estimation of China-specific substitution parameters, incorporating actual CVBP mechanisms such as volume guarantees and quality requirements, and performing empirical validation using real-world policy variations across regions and time periods.