Panel data for digital economy, agricultural new quality productive forces, and farmers' prosperity in China (2014–2023)

Research Hypothesis
This dataset tests whether China’s digital economy (DE) , agricultural new quality productive forces (ANQPF) , and farmers’ prosperity (FP) exhibit a coupling coordinated relationship. It posits that DE enables agricultural transformation, ANQPF mediates the link to FP, and the three subsystems co-evolve with regional variations shaped by infrastructure, policy, and human capital.

Data Overview
A balanced panel of 31 Chinese provinces (2014–2023) with 49 indicators across three subsystems:

DE (14 indicators) : Informatization, Internet, and digital transaction development (e.g., optical cable density, software income, e‑commerce).

ANQPF (18 indicators) : New-quality means of production, laborers, and objects of labor (e.g., Taobao villages, agri‑tech patents, R&D input, digital finance).

FP (17 indicators) : Income, living standards, consumption, and social security (e.g., net income, Engel coefficient, housing, pension expenditure).

Data sources: China Statistical Yearbook, China Rural Statistical Yearbook, provincial yearbooks, Peking University Digital Financial Inclusion Index, and ministry reports.

Processing
Data were cleaned (missing values <2% interpolated), normalized (min‑max), weighted (entropy method), and used to compute coupling coordination degrees (three‑system model, equal weights).

Key Findings
National average coupling coordination rose from 0.31 (2014, mild imbalance) to 0.48 (2023, imminent imbalance). Eastern provinces lead; western regions lag.

Significant spatial clustering: Global Moran’s I = 0.257–0.286. “High‑high” clusters in the east, “low‑low” in the west.

Main obstacles: Digital infrastructure and e‑commerce indicators—especially Taobao villages, software income, and online retail.

Positive drivers: Rural human capital and agricultural marketization. Negative effects from current rural investment, agricultural structure, and fiscal support suggest inefficiencies.

Usage Notes
Interpretation: Higher values = better development (except reverse‑coded items).

Applications: Replicate analysis, apply alternative methods (AHP, TOPSIS), spatial econometrics, or policy targeting.

Limitations: Provincial data mask local heterogeneity; entropy weights are sample‑dependent.

File Structure
Excel sheets: Raw_Data, Normalized_Data, Weights, Coupling_Coordination, README (with variable definitions).