Replication materials

Replication package for:

Gomez, R. E. (2026). Where knowledge drives digital sophistication and where it does not: institutional contingency of the second-level divide in subnational software production. Working paper.

Structure

contingency/
├── data/
│   ├── departamentos_master.csv         # 511 departments × 277 variables (integrated dataset)
│   └── departamentos_polygons.geojson   # Department boundary polygons for mapping
├── scripts/
│   ├── 00_build_knowledge_institutions.py  # Knowledge infrastructure integration (MinCyT, SPU)
│   ├── 01_extract_satellite_indicators.py  # Satellite proxies (VIIRS nighttime radiance)
│   ├── 02_integrate_economic_indicators.py # CEP-XXI wages, employment, ENACOM internet
│   ├── 03_analysis.py                     # Main analysis: OLS, Chow test, type-specific models
│   └── 04_regenerate_figures.py           # Standalone figure regeneration (all 8 body + 2 supp)
├── figures/                               # Output figures (PNG, 300 DPI)
├── tables/                                # Output tables (CSV)
├── requirements.txt
├── LICENSE
└── README.md

Quick start

pip install -r requirements.txt

# Run the full analysis (generates all figures and tables)
python scripts/03_analysis.py

# Or regenerate figures only (standalone, improved formatting)
python scripts/04_regenerate_figures.py

Data

The integrated dataset (data/departamentos_master.csv) contains 511 Argentine departments with 277 variables from the following sources:

Source	Variables	Period
GitHub Argentina	Developer counts, language portfolios, ECI	2008–2026
MinCyT (CVar)	STEM researchers per 10,000 population	2022
SPU	University locations, STEM programme offerings	2023
INDEC Census	Population, education, employment, poverty	2010, 2022
CEP-XXI	Formal wages, sectoral employment	2020–2022
ENACOM	Internet household penetration	2022
VIIRS	Nighttime radiance composites	2014, 2022

Scripts 00–02 document the pipeline from raw sources to the master dataset. Since raw source files are not redistributed (census microdata, MinCyT personnel records), these scripts serve as methodological documentation. The analysis is fully reproducible from departamentos_master.csv alone using 03_analysis.py.

Territorial typology

The six territorial types used in this study derive from a Multiple Correspondence Analysis (MCA) and hierarchical clustering computed in a companion study:

Gomez, R. E. (2026). The spatiality of software: subnational economic complexity from GitHub data in Argentina. Working paper. Replication materials: https://doi.org/10.5281/zenodo.18674718

The MCA cluster assignments are included in departamentos_master.csv (variable cluster_6). The MCA estimation code is available in the companion repository linked above.

Key results

Model	N	R²	Key finding
Pooled OLS	221	0.468	STEM β = 0.220*** (knowledge > wealth)
Chow test	221	—	F = 2.14, p < 0.001 (reject coefficient homogeneity)
Metro-Diversified	45	0.453	STEM β = 0.467***
Pampeana-Educated	58	0.326	STEM β = 0.225***
Metro-Core	53	0.456	STEM β = 0.236*
Intermediate-Urban	49	0.172	STEM n.s.; wages β = 0.301*

Output mapping

Figures

Article	File	Content
Fig. 1	fig_08_choropleth_typology.png	Choropleth: six territorial types
Fig. 2	fig_01_distributions.png	Histograms: ECI, developers, STEM, wages
Fig. 3	fig_09_choropleth_eci_stem.png	Dual choropleth: ECI and STEM density
Fig. 4	fig_05_mca_knowledge.png	MCA factorial plane with knowledge gradient
Fig. 5	fig_06_forest_plot.png	Forest plot: standardised betas by type
Fig. 6	fig_03_horse_race_scatter.png	Scatter: ECI vs STEM and wages by type
Fig. S1	fig_S1_robustness.png	Coefficient stability across specifications
Fig. S2	fig_02_correlation_heatmap.png	Correlation heatmap

Tables

Article	File	Content
Table 2	table_01_descriptive.csv	Descriptive statistics
Table 3	table_03_models.csv	Pooled OLS (knowledge-only, wealth-only, full)
Table 4	table_05_type_specific.csv	Type-specific OLS coefficients
Table S1	table_02_correlations.csv	Bivariate correlations
Table S1	table_02b_partial_correlations.csv	Partial correlations
Table S2	table_07_robustness.csv	Robustness checks
Table S3	table_S3_mca_coordinates.csv	MCA modality coordinates and contributions
Table S4	table_08_logit_type.csv	Type-specific logit (participation)

Colour palette

Paul Tol Bright (colorblind-friendly):

Type	Hex
Peripheral-Deprived	#CC3311
Metro-Core	#0077BB
Metro-Diversified	#009988
Pampeana-Educated	#AA3377
Semi-Rural-Active	#EE7733
Intermediate-Urban	#BBBBBB

Author

Raimundo Elias Gomez CONICET / Universidad Nacional de Misiones, Argentina Institute of Sociology, University of Porto, Portugal ORCID: 0000-0002-4468-9618

License

This work is licensed under the MIT License.