Rural E-commerce, Ecological Value Conversion, and Common Prosperity: Cleaned County-Year Panel (2014–2022)

The panel covers 2{,}725 Chinese counties across 30 provincial units from 2014 to 2022 and supports three nested analysis samples used in the paper. All variables have been harmonised to a single county identifier scheme, Winsorised at the 1% level where appropriate, and documented for reproducibility. When citing the underlying study, please also cite the manuscript that uses this panel. ## File Inventory The archive `cleaned_data.zip` contains 23 files organised into five functional groups. ### Master Panel | File | Rows | Unit | Years | Purpose | |---|---:|---|---|---| | `master_panel_county_2014_2022.csv` | 24,525 | county-year | 2014–2022 | Primary panel with 44 curated analytic variables | | `master_panel_full.csv` | 24,525 | county-year | 2014–2022 | Wide version with all merged fields (87 columns) | ### Analytic Samples | File | Rows | Counties | Notes | |---|---:|---:|---| | `baseline_sample.csv` / `baseline_sample.dta` | 9,247 | 1,600 | Listwise deletion on Y/X/Q/controls, 1% Winsor, ready for fixed-effect regressions | | `did_sample.csv` / `did_sample.dta` | 12,498 | 1,929 | Loose sample preserving post-treatment years for staggered DID | ### Component Sub-Tables | File | Rows | Unit | Years | |---|---:|---|---| | `taobao_prov_panel.csv` | 279 | province | 2014–2022 | | `taobao_city_panel.csv` | 1,701 | prefecture | 2014–2022 | | `taobao_county_panel.csv` | 5,994 | county | 2014–2022 | | `taobao_unmatched.csv` | 189 | county name | — | | `yearbook_county_panel_2014_2022.csv` | 24,525 | county-year | 2014–2022 | | `county131_panel.csv` | 51,742 | county-year | 2000–2019 | | `m_county_panel.csv` | 24,525 | county-year | 2014–2022 | | `gini_county_panel.csv` | 69,048 | county-year | 2000–2023 | | `dfiic_county_panel.csv` | 26,091 | county-year | 2014–2023 | | `did_digital_village_panel.csv` | 64,345 | county-year | 2000–2023 | | `gtfp_city_panel.csv` | 5,076 | city-year | 2006–2023 | | `rural_revit_city_panel.csv` | 10,104 | city-year | 2000–2023 | | `internet_prov_panel.csv` | 403 | province-year | 2011–2023 | | `internet_city_panel.csv` | 6,196 | city-year | 2003–2022 | | `common_prosperity_prov_panel.csv` | 682 | province-year | 2000–2021 | ### Classification Labels | File | Rows | Purpose | |---|---:|---| | `county_groupings.csv` | 2,725 | Time-invariant county labels for heterogeneity analysis: region (east/middle/west), staple-grain vs cash-crop, eco-sensitive indicators | ### Quality Report | File | Purpose | |---|---| | `QC_report.md` | Missing-rate diagnostic, per-year and per-province coverage for the master panel | ## Variable Naming Conventions Variables in the analytic samples follow a consistent prefix system. Variables starting with `y_` are outcomes. Variables starting with `x_` are treatment measures of Taobao village density. Variables starting with `m_` are mediators related to agricultural land-based value conversion efficiency. Variables starting with `q_` are moderators derived from the Peking University Digital Financial Inclusion Index. Variables starting with `ctrl_` are control variables. The fields `treat`, `post`, and `did` encode the staggered treatment indicators of the digital-village pilot launched in 2020. The primary outcome variables are `y_sen_welfare` (Sen social welfare function on rural income and Gini), `y_rural_inc_log` (natural logarithm of rural per-capita disposable income), and `y_gini` (nightlight-based county Gini). The primary treatment is `x_taobao_density` (Taobao villages per ten thousand residents) with its quadratic `x_taobao_density_sq`. The primary mediator is `m_primary`, defined as the natural logarithm of agricultural output value divided by administrative land area. ## Raw Data Provenance The panel integrates administrative and platform data from the following sources. County-level statistics come from the *China County Statistical Yearbook* compiled through the 2022 edition. Taobao village counts come from the annual rosters published by the AliResearch Institute between 2014 and 2022, with the 2022 roster providing a twelve-digit village code that anchors cross-year county identification. Digital financial inclusion indicators are from the Peking University Digital Financial Inclusion Index (2014–2023 version). Nightlight-based Gini coefficients follow the county-level construction by Chen et al. (2015). The digital-village pilot roster was obtained from the Ministry of Agriculture and Rural Affairs and the Cyberspace Administration of China announcements. Prefecture-level green total factor productivity is from published panel estimates covering 2006–2023. ## Reproducibility The cleaning pipeline is available as a set of Python scripts in the accompanying repository. The pipeline runs end to end with pandas, numpy, and openpyxl. Statistical analyses use pyfixest (primary), linearmodels, statsmodels, and the differences package for staggered DID. The ordered build is documented in the main paper's online appendix. Stata dta exports are provided for users who prefer to re-estimate specifications in Stata. The character encoding of all CSV files is UTF-8 with BOM, which renders correctly in Microsoft Excel. ## Known Limitations Roughly 58 percent of county-year observations in `m_primary` carry missing values, a result of coverage gaps in the underlying agricultural output series. Multiple imputation is used in the paper as a robustness check. The matching between Taobao village names and national county codes succeeds for 96.9 percent of village-year rows, with 189 unmatched rows covering 21 distinct county names preserved in `taobao_unmatched.csv` for transparency. The baseline sample loses 2022 observations because a small number of control variables are not yet released for that year in the current yearbook edition, and users who require a strict 2022 balanced panel should consult the DID sample instead. County-level gross ecosystem product measures are not yet publicly available for a sufficiently broad set of counties, and the mediator used here relies on administrative agricultural output as a proxy. ## License The data are released under the Creative Commons Attribution 4.0 International license (CC-BY 4.0), which permits reuse and redistribution provided attribution is given to the authors. Downstream users should comply with the license terms of the original raw sources where onward redistribution is concerned.