*! version 1.5 26Nov2025

Title

province_devcat -- Generate development level variable for Chinese A-share listed companies based on provincial GDP per capita

Syntax

province_devcat [using filename] [, options]

using specify the dataset to analyze (default: asure.dta)

save(filename) save the processed dataset to filename

replace overwrite existing file when using save()

Description

province_devcat is a specialized Stata program designed to analyze the development level of Chinese A-share listed companies based on provincial GDP per capita data. The program performs the following operations:

1. Loads and validates the A-share listed company dataset (default: asure.dta) and provincial GDP per capita dataset (pcgdp2010-2024.dta)

2. Standardizes province names across datasets by creating unified province codes

3. Merges the datasets using province_code and year as matching variables

4. Generates an ordinal development level variable (province_devcat) with values 1, 2, 3 representing "developed", "less developed", and "underdeveloped" respectively

5. Provides comprehensive descriptive statistics and distribution analysis

The classification is based on terciles (33rd and 66th percentiles) of GDP per capita within each year, ensuring dynamic classification standards over time.

Important Notes

Data Source Disclaimer:

The datasets used in this program contain simulated/synthetic data for demonstration and testing purposes only.

asure.dta (or user-specified listed company dataset) contains simulated data of Chinese A-share listed companies.

pcgdp2010-2024.dta contains simulated provincial GDP per capita data.

These are NOT real datasets and should not be used for actual economic analysis, investment decisions, or policy recommendations.

For research applications, users should replace these datasets with actual data from official sources.

Options

using specifies an alternative dataset to analyze. If not specified, the default dataset "asure.dta" will be used.

save(filename) specifies the filename to save the processed dataset containing the development level variable.

replace allows overwriting an existing file when used with the save() option.

Examples

Basic usage (using default dataset)

. province_devcat

Using a custom dataset

. province_devcat using "my_companies.dta"

Save results to a new dataset

. province_devcat, save("listed_companies_development.dta") replace

Using custom dataset and saving results

. province_devcat using "custom_data.dta", save("results.dta") replace

Expected output:

- Data quality checks and validation results

- Province name standardization process

- Merge statistics showing matched observations

- Distribution of development level variable by year and province

- Descriptive statistics for each development category

Stored results

province_devcat does not store formal results in r() but generates the following variables in the dataset:

province_devcat Development level ordinal variable:

1 Developed (top 33% of GDP per capita within year)

2 Less developed (middle 33% of GDP per capita within year)

3 Underdeveloped (bottom 33% of GDP per capita within year)

Variable labels and value labels are applied for clarity in output tables.

Authors

Wu Lianghai

School of Business, Anhui University of Technology (AHUT)

Ma'anshan, China

agd2010@yeah.net

Chen Liwen

School of Business, Anhui University of Technology (AHUT)

Ma'anshan, China

2184844526@qq.com

Wu Hanyan

School of Economics and Management

Nanjing University of Aeronautics and Astronautics (NUAA)

Nanjing, China

2325476320@qq.com

Development date: 26 November 2025

Last updated: Version 1.5 - Added comprehensive data source disclaimers for both datasets

Also see

merge, egen, tabulate, tabstat, area

Technical notes

Required data files:

asure.dta (or user-specified dataset): Simulated Chinese A-share listed company annual panel dataset with province variable (string)

pcgdp2010-2024.dta: Simulated provincial GDP per capita dataset with province (string), year, and pcgdp variables

Data validation:

- Checks for missing values in key variables

- Standardizes province names by creating unified province codes

- Verifies variable types and dataset compatibility

Province name standardization:

- Converts province names to standardized full administrative names (e.g., "北京" to "北京市")

- Handles both complete administrative names and abbreviated forms

- Ensures consistent matching across datasets with different naming conventions

Merging strategy:

- Uses many-to-one merge (m:1) matching on province_code and year

- Keeps all observations from the master dataset

- Includes only matching observations from the using dataset (pcgdp2010-2024.dta)

Classification methodology:

- Development levels are calculated dynamically by year

- Uses tercile cutoffs (33rd and 66th percentiles) within each year

- Relative classification ensures comparability across time periods

This help file was updated for version 1.5 on 26 November 2025.