Measurement of health human capital and its economic effect in China

China’s transition from factor-driven to innovation-driven growth elevates the importance of Health Human Capital (HHC) as a driver of sustained, high-quality economic development. Drawing on human capital theory, the paper emphasizes health—alongside education—as a core component that enhances individual productivity and extends working life, thereby raising aggregate output. In the context of population aging, a rising dependency ratio, and diminishing demographic dividends, improving HHC is framed as crucial to converting population pressures into human capital advantages. The study sets out to define and measure HHC comprehensively at the provincial level and to empirically evaluate its effect on economic growth, informing policies under initiatives such as Healthy China 2030 and the 14th Five-Year Plan.

Health human capital measurement: Prior studies often use single proxies such as mortality or life expectancy (e.g., Kelley and Schmidt, 1995; Bloom et al., 2004), which may not fully capture morbidity, disability, or discomfort. Broader frameworks (WHO, 1990; European Commission Public Health) consider multiple dimensions—health services, socioeconomic conditions, lifestyle, and environment. Composite indices integrating many factors (Aguayo-Rico and Guerra-Turrubiates, 2005) address multidimensionality. This paper follows that direction, selecting 18 indicators across four dimensions (basic health; preventive healthcare; medical resources and quality; healthy environment) tailored to data availability and comparability over time and across regions in China. Health human capital and economic growth: Health enhances the accumulation of education and human capital and is linked to productivity and growth (Ram and Schultz, 1979; Fogel, 2004; Weil, 2014). However, excessive health investment may crowd out other factors (Zon and Muysken, 2003) and improvements may show diminishing marginal returns, potentially increasing dependency burdens (Husain et al., 2014). Existing literature is split between macro indicators and micro survey-based measures, often lacking standardized HHC metrics. The paper contributes by constructing a comprehensive HHC index and examining its macroeconomic impact using panel data for Chinese provinces.

Measurement framework and indicators: The study builds a composite HHC index for 31 Chinese provincial-level regions over 2005–2019 using 18 indicators grouped into four dimensions: A) Basic health (A1 life expectancy [+], A2 mortality rate [−], A3 incidence of notifiable infectious diseases [−]); B) Preventive healthcare (B1 mortality from notifiable infectious diseases [−], B2 number of health examinations [+], B3 urban Engel coefficient [−], B4 rural Engel coefficient [−], B5 per capita medical consumption of urban households [+]); C) Medical resources and quality (C1 average length of stay [−], C2 hospital visits [+], C3 beds per 1000 [+], C4 health institutions per 1000 [+], C5 health technicians per 1000 [+], C6 per capita local health expenditure [+]); D) Healthy environment (D1 local environmental protection expenditure [+], D2 daily municipal sewage treatment capacity [+], D3 industrial SO2 emissions [−], D4 industrial soot emissions [−]). Weighting method: Indicators are standardized using min–max 0–1 normalization with direction alignment for positive/negative indicators. Entropy values Hi are computed to derive objective weights wi: wi = (1 − Hi)/Σ(1 − Hi). The composite HHC score Y is a linear weighted sum Y = Σ wi ri. Data sources and preprocessing: Data are drawn from the World Bank; China Statistical Yearbook (2005–2020); China Urban Statistical Yearbook; China Health Statistical Yearbook; China Environmental Statistical Yearbook; Wind and iFinD databases. Missing values for some indicators are imputed by interpolation. Monetary amounts are deflated to 2005 prices. Health Human Capital Index results: National HHC increased from 36.72 (2005) to 58.35 (2019), a 58.91% rise, with a notable jump in 2009 coinciding with the new medical reform. Provincial indices and growth rates for 2005, 2009, and 2019 are reported, showing higher levels in the east and faster growth in several western provinces from lower bases. Growth model and estimation: The study applies endogenous growth theory and a production function including physical capital, educational human capital, HHC, government expenditure, and labor force share. The empirical specification uses a log-linear panel model for province i and year t: yit = αi + α kpit + β heit + γ hhit + δ git + φ lit + εit, where y is log real GDP per capita; kp is log per capita physical capital stock (constructed via perpetual inventory and deflated; Tibet uses retail price index due to missing fixed asset investment deflator); he is average years of schooling; hh is the HHC index; g is per capita general public budget expenditure (CPI-deflated); l is the working-age population share. Estimation strategy: Baseline comparisons include pooled OLS, random effects (LM test supports RE over OLS), fixed effects (Hausman favors FE over RE), and two-way fixed effects (controlling province and year). Robustness checks replace education with the share of higher-educated population (InEduper) and winsorize the right tail (97.5%) of GDP per capita to mitigate outliers. Endogeneity is addressed by IV-2SLS using the first lag of HHC as an instrument; first-stage F-statistic is reported. Heterogeneity analyses include panel quantile regressions at the 10th, 25th, 50th, 75th, and 90th percentiles of GDP per capita and a north–south regional interaction (SOUTH) based on the Qinling–Huaihe line.

- HHC trend: National HHC index rose from 36.72 in 2005 to 58.35 in 2019 (+58.91%), with a marked increase in 2009 after healthcare reform. Eastern provinces generally have higher HHC levels than western and northeastern regions. - Provincial dynamics (Table 4): Highest growth rates 2005–2019 include Shanghai (0.88, rank 1), Tibet (0.85, rank 2), Guizhou (0.83, rank 3). Economically advanced provinces (e.g., Beijing, Guangdong, Jiangsu, Zhejiang) maintain higher HHC levels. Northeast provinces (Liaoning, Jilin, Heilongjiang) show relatively weaker growth. - Baseline two-way FE results (Table 6): InHi (HHC) coefficient 0.4809* (0.2362); InLab 0.9448*** (0.2857); InGov 0.2663*** (0.0521); InCap −0.1185** (0.0526); InEdu not significant. Interpretation: a 1% increase in HHC is associated with a 0.48% rise in real GDP per capita; labor share is the largest driver; per capita government expenditure is positively associated; physical capital shows a negative coefficient, consistent with a crowding-out mechanism. Model fit R2 = 0.8011; two-way FE preferred. - Robustness (Table 7): Replacing education with higher-education share and winsorizing GDP tails leave the positive HHC effect intact: coefficients ~0.513** and 0.443*, respectively. - Endogeneity (Table 8): IV-2SLS using lagged HHC as instrument yields InHi coefficient 0.7022*** (SE 0.1463); first-stage F = 888.70, indicating a strong instrument; results corroborate a significant positive impact of HHC on growth. - Quantile regressions (Table 9): HHC coefficients increase with GDP per capita quantiles from about 0.36* (10th) to 0.60* (90th), indicating stronger growth effects of HHC in richer provinces. - Regional heterogeneity (Table 10): Interaction InHi × SOUTH is 0.2563** (SE 0.1009), implying HHC’s growth-promoting effect is significantly larger in southern China than in the north.

The findings support the hypothesis that improving Health Human Capital enhances economic performance. A comprehensive, multidimensional HHC index captures variations across provinces, revealing both steady national improvement and persistent regional disparities. Econometrically, HHC exhibits a robust, positive association with real GDP per capita across multiple specifications, is resilient to alternative education measures and outlier treatment, and remains significant after addressing endogeneity via lagged instruments. The larger coefficients at higher income quantiles suggest complementarities between HHC and existing economic capacity—regions with stronger economies can more effectively convert health improvements into growth. The negative association with physical capital points to potential crowding-out, highlighting the importance of balanced factor accumulation. The stronger HHC effect in the south underscores geographic and institutional differences in translating health improvements into economic gains. Overall, enhancing HHC serves as both a direct productivity enhancer and an enabler of broader development, aligning with China’s shift toward innovation-driven growth.

The study constructs a comprehensive HHC index for 31 Chinese provinces (2005–2019) using an entropy-weighted, multidimensional indicator system and demonstrates that higher HHC significantly promotes economic growth, with effects stronger in wealthier provinces and in southern China. Key contributions include: (1) establishing a broad HHC measurement framework suited to China’s context; (2) documenting national upward trends and regional heterogeneity; (3) providing robust empirical evidence—via two-way FE, robustness checks, IV-2SLS, and quantile regressions—of HHC’s positive growth effect. Policy recommendations: - Strengthen both hardware and software in health systems by increasing public health expenditure, expanding high-quality beds and infrastructure, investing in medical research, training and retaining top medical talent, and improving care quality and efficiency. - Enhance operational capacity and managerial practices of medical institutions, adopting proven management approaches to raise efficiency. - Promote regional medical cooperation and technology spillovers (e.g., telemedicine), optimize resource allocation, and improve health security to reduce inter- and intra-regional disparities. - Facilitate cross-regional insurance reimbursement and build coordinated regional healthcare systems. - Improve division of labor across the medical industry chain, foster city-cluster collaboration, and upgrade health product and service quality. - Target HHC investments and policies toward underdeveloped regions while leveraging external investments to balance HHC accumulation. - Deepen domestic–international academic collaboration to strengthen medical research and technology exchange.

- Data constraints and preprocessing: Some indicator series had missing values for certain years and were imputed via interpolation; monetary variables were deflated to 2005 prices, and Tibet’s fixed asset investment deflator was unavailable (retail price index used instead). - Outliers: Per capita real GDP exhibited outliers; winsorization (97.5% right tail) was used in robustness checks, which may affect tail behavior. - Endogeneity: Potential bidirectional causality between HHC and growth was addressed using lagged HHC as an instrument; while first-stage strength is high, IV strategies rely on exclusion assumptions. - Measurement: Although the composite HHC index improves on single proxies, it is constructed from available macro indicators and may not capture all micro-level health dimensions.