# Supplementary Materials

Data, code, and documentation accompanying the article *Beyond Access: The
Third-Level Digital Divide in Adolescents' Digital Self-Efficacy in Latin
America and the Caribbean*.

These materials allow full reproduction of every result, table, and figure
reported in the article. No author-identifying information is included.

## Contents

```
Supplementary_Materials/
├── README.md                          this file
├── requirements.txt                   software dependencies
├── code/
│   ├── 01_extract_lac_sample.py       extract the Latin American subsample from PISA 2022
│   ├── 02_build_analysis_dataset.py   recodes, merges, system-level data, centering
│   ├── 03_fit_models.py               null models, ICCs, country-specific and pooled HLM
│   └── 04_make_figures.py             Figures 1 and 2 (300 dpi)
├── data/
│   ├── codebook.md                    variable codes, labels, sources, recodes
│   ├── raw/README.md                  instructions to obtain the PISA 2022 source files
│   └── processed/
│       ├── analysis_dataset.csv       analytic sample (18,761 students, 1,377 schools)
│       └── system_level_data.csv      system-level descriptors
├── docs/
│   ├── methods.md                     model equations, centering, estimation notes
│   └── data_sources.md                provenance of all data used
└── output/
    ├── figures/
    │   ├── Figure1_gradients.png      Figure 1 in the article
    │   └── Figure2_school_escs.png    Figure 2 in the article
    └── results/
        ├── res_icc.json               unconditional models (Table 3)
        ├── res_pooled.json            pooled models M1–M3 (Table 4)
        ├── res_percountry.json        country-specific models (Table 5)
        └── table2_desc.csv            weighted descriptive statistics (Table 2)
```

## Data

`data/processed/analysis_dataset.csv` is the complete-case analytic sample:
18,761 students in 1,377 schools in six education systems (Argentina, Brazil,
Chile, the Dominican Republic, Panama, and Uruguay). It contains the outcome
(ICTEFFIC), all student- and school-level predictors, their centered
transformations, final student weights, the 80 balanced repeated replication
weights, and system-level descriptors. Records carry the PISA anonymous
identifiers `CNTSCHID` and `CNTSTUID`; no individual can be identified.

`data/processed/system_level_data.csv` contains GDP per capita (PPP, 2022),
fixed broadband subscriptions per 100 people (2022), and the coding of
national one-to-one computing programs.

The raw PISA 2022 microdata are not redistributed here. They are publicly and
freely available from the OECD; `data/raw/README.md` gives the download
location and the two file names required. All variable definitions are in
`data/codebook.md`.

## Reproduction

Requirements: Python 3.10 or later.

```bash
pip install -r requirements.txt
```

To reproduce all models, tables, and figures from the analytic dataset
provided (no download required):

```bash
python code/03_fit_models.py
python code/04_make_figures.py
```

To rebuild the analytic dataset from the original PISA files, place
`CY08MSP_STU_QQQ.SAV` and `CY08MSP_SCH_QQQ.SAV` in `data/raw/` and run the
full pipeline:

```bash
python code/01_extract_lac_sample.py
python code/02_build_analysis_dataset.py
python code/03_fit_models.py
python code/04_make_figures.py
```

Scripts write to `output/results/` and `output/figures/`, overwriting the
files supplied here with identical content.

## Correspondence between materials and article exhibits

| Article exhibit | File |
|---|---|
| Table 1 (measures) | `data/codebook.md` |
| Table 2 (descriptives) | `output/results/table2_desc.csv` |
| Table 3 (variance components, ICC) | `output/results/res_icc.json` |
| Table 4 (pooled models) | `output/results/res_pooled.json` |
| Table 5 (country-specific models) | `output/results/res_percountry.json` |
| Figure 1 | `output/figures/Figure1_gradients.png` |
| Figure 2 | `output/figures/Figure2_school_escs.png` |

## Licensing and terms of use

PISA microdata are made publicly available by the OECD under its terms of use;
the processed extract is redistributed here for verification and replication.
System-level indicators are drawn from the World Bank World Development
Indicators (public domain, CC BY 4.0) and from ECLAC (2022). The analysis code
may be reused for research and teaching with attribution to the article.
