Education Quality and Human Capital Development in Rural China: A Time-Series Analysis
DOI:
https://doi.org/10.66348/hsr.26.v1.n1.a36Keywords:
Human capital development; education quality; rural China; time-series analysis; ARDL model; literacy rate; education expenditure; pupil–teacher ratioAbstract
Human capital development is a fundamental driver of long-term economic growth and structural transformation, particularly in developing economies. This study examines the impact of education quality on human capital development in rural China using annual time-series data from the World Development Indicators (WDI). Grounded in Human Capital Theory, the study conceptualizes education quality through key indicators including education expenditure, literacy rate, and pupil–teacher ratio. An Autoregressive Distributed Lag (ARDL) approach is employed to capture both long-run equilibrium relationships and short-run dynamics among the variables. The empirical results reveal that education expenditure and literacy rate have significant positive effects on human capital development, while a higher pupil–teacher ratio negatively affects it. The existence of cointegration confirms a stable long-run relationship among the variables, and the error correction term indicates a rapid adjustment toward equilibrium following short-run shocks. Robustness checks using alternative estimators further validate the findings. The study concludes that improving education quality is essential for strengthening human capital development in rural China. Policy implications emphasize increased education investment, improved teacher allocation, and enhanced literacy programs to reduce rural development disparities.
Received: 2026-04-13 | Revised: 2026-06-02 | Accepted: 2026-06-23 | Published: 2026-06-30
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Ethics and Guidelines: Not applicable.
Consent to participate: Not applicable.
Consent to publish: The authors have provided consent to publish.
Competing interests: The authors declare no competing interests.
Data availability statement: Data will be made available on reasonable request from the corresponding author.
Funding: This research received no external funding.
Clinical Trial Number: Not Applicable.
Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.
Author Contributions: Conceptualization, S.A.; methodology, S.A.; formal analysis, S.A.; writing—original draft preparation, S.A.; writing—review and editing, S.A. All authors have read and agreed to the published version of the manuscript.
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Data will be made available on reasonable request from the corresponding author.
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