Replication Data and Supplementary Materials for "Unveiling the Mezzogiorno Trap in Cultural Industries"

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

<b>Overview:</b> This repository contains the raw panel dataset and supplementary materials supporting the empirical findings of the research article titled <i>"Unveiling the Mezzogiorno Trap in Cultural Industries: Regional Disparities, Driving Factors, and Pathways to Sustainable Development from the Chinese Case"</i>.<b>Data Scope:</b> The dataset covers a balanced panel of 31 provinces, municipalities, and autonomous regions in mainland China over an 11-year period from 2013 to 2023 (N = 341 observations). All monetary variables have been adjusted for inflation to 2023 constant prices.<b>File Contents (Included in the Excel Workbook):</b><b>Table S1 (Descriptives):</b> Detailed descriptive statistics of all input/output variables for the Data Envelopment Analysis (DEA) model, explanatory variables for the Method of Moments Quantile Regression (MM-QR) model, and the macro-level Degree of Financial Support (DFS) data.<b>Table S2 (Raw Data):</b> The complete raw panel dataset used for replication. It includes macroscopic factors of labor and capital, cultural industry value-added, urbanization rate, education levels, industrial agglomeration, enterprise scale, cultural consumption ratios, and public cultural expenditure.<b>Table S3 (DEA Results):</b> Detailed annual efficiency scores (2013-2023) calculated via the global non-oriented slacks-based measure (SBM) model, including Technical Efficiency (TE), Pure Technical Efficiency (PTE), Scale Efficiency (SE), and Returns to Scale (RTS) status for all 31 provincial DMUs.<b>Table S4 (Quantile Reg):</b> Complete outputs of the panel quantile regression estimations, including both baseline models (using contemporary variables) and robust models (using one-period lagged explanatory variables to mitigate endogeneity) across the 25th, 50th, and 75th percentiles.<b>Methodology Notes:</b> These datasets are designed to perfectly replicate the identification of the "Mezzogiorno Trap" in China's cultural sector, assessing heterogeneous absorptive capacities and structural resource misallocations. For the exact mathematical formulas and theoretical background, please refer to the methodology section of the main manuscript.

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