Add Asia internationalization-performance thesis umbrella

data/analysis/p7/p7_model_fit.csv

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data/analysis/p7/p7_summary_focal.csv

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data/asia/README.md

@@ -0,0 +1,93 @@
+# WBES Asia Dataset
+
+Pooled World Bank Enterprise Surveys (WBES) microdata for **48 Asian/Pacific economies**, 6 regions (UN M.49 Asia + World Bank "East Asia & Pacific" extension).
+
+> **Region definition note**: We follow the World Bank's *East Asia & Pacific* (EAP) regional grouping, which includes Pacific Island economies (Fiji, Samoa, Kiribati, PNG, Tonga, Solomon Islands, Vanuatu) alongside East and Southeast Asia. UN M.49 classifies these as Oceania separately; readers preferring strict Asia-only analysis can filter `src_region != "Pacific"` in the pooled file.
+
+## Contents
+
+```
+data/asia/
+├── README.md                 # this file
+├── manifest.csv              # 1 row per file: country, year, n_obs, n_vars, size, md5
+├── schema.json               # per-file variable list (first 50 vars + core hits)
+├── raw/                      # 110 deduplicated country-year .dta files
+│   ├── Afghanistan/
+│   ├── Armenia/
+│   ├── ...
+│   └── Yemen/
+├── pooled/
+│   ├── asia_pooled_core.dta  # 122,858 firm-obs × 41 cols, core WBES variables only
+│   └── asia_pooled_core.csv  # same data, CSV format
+└── scripts/
+    ├── build_asia_dta.py     # dedupe + canonicalize raw files
+    └── pool_asia_dta.py      # enrich manifest + build pooled file
+```
+
+## Coverage
+
+- **120 country-year files** (deduplicated from 298 raw uploads — many duplicates from re-uploads)
+- **124,229 firm-observations** total
+- **48 economies** across 6 regions:
+
+| Region          | Files | Economies                                                                                              |
+| --------------- | ----- | ------------------------------------------------------------------------------------------------------ |
+| East Asia       | 12    | China, HongKong, Korea, Mongolia, Taiwan                                                               |
+| Southeast Asia  | 33    | Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, TimorLeste, Vietnam |
+| South Asia      | 24    | Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, SriLanka                            |
+| Central Asia    | 16    | Kazakhstan, KyrgyzRepublic, Tajikistan, Turkmenistan, Uzbekistan                                       |
+| West Asia       | 25    | Armenia, Bahrain, Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, SaudiArabia, Yemen |
+| Pacific         | 10    | Fiji, Kiribati, PapuaNewGuinea, Samoa, SolomonIslands, Tonga, Vanuatu                                  |
+
+## Pooled file: variable set
+
+`asia_pooled_core.dta` keeps only WBES variables that appear consistently across waves (Enterprise Surveys Indicator Descriptions, World Bank). Per-row coverage shown below.
+
+| Var      | Meaning                              | Coverage |
+| -------- | ------------------------------------ | -------- |
+| src_country, src_year, src_region, src_file | metadata (added) | 100%     |
+| idstd    | Standard firm ID                     | 95.6%    |
+| a1       | Country name                         | 99.4%    |
+| a2       | City/region                          | 86.9%    |
+| a6a      | ISIC sector                          | 77.1%    |
+| b1       | Legal status                         | 99.0%    |
+| b2a      | Sector                               | 97.3%    |
+| b3       | Year of operation                    | 64.4%    |
+| b4       | Female ownership share               | 98.8%    |
+| b6a      | Foreign ownership %                  | 52.3%    |
+| b7       | Top manager experience               | 83.7%    |
+| d2       | Total annual sales                   | 83.7%    |
+| n3       | Sales last fiscal year               | 95.9%    |
+| l1       | Full-time permanent workers          | 83.7%    |
+| l2       | Full-time temporary workers          | 99.0%    |
+| d3a/b/c  | Direct/indirect exports, domestic %  | 83.7%    |
+| h1       | New product/service introduced       | 66.8%    |
+| h5       | R&D spending                         | 66.0%    |
+| k3a/k3bc | Working capital financing            | 80%      |
+| wstrict, wmedian | Sample weights               | 90/94%   |
+
+For variables NOT in the core list, load the raw country-year `.dta` directly.
+
+## Caveats
+
+1. **Variable harmonization is partial.** WBES question wording, value labels, and code definitions shift across waves (especially 2009→2013→2019 vs. 2022→2025 redesign). Pooled comparisons require additional cleaning per variable.
+2. **Encoding.** 22 files required `latin1`/`cp1252` fallback (legacy waves); column labels may contain non-ASCII residue.
+3. **Survey weights.** `wstrict` / `wmedian` are within-country weights — NOT comparable across countries without re-stratification.
+4. **Year semantics.** `src_year` is the survey wave year (filename), not necessarily the firm's reporting year. The WBES `year` variable (present in 9.6% of rows) is the firm's reporting year.
+5. **Panel files.** `Pakistan_panel_2007-2013-2022.dta`, `Mongolia_panel_2009-2013-2019.dta`, etc. stack multiple waves with a panel ID — treat separately from cross-section.
+
+## Reproducibility
+
+```bash
+# 1. Place all raw .dta uploads in /tmp/*/ or /root/.claude/uploads/
+# 2. Run inventory
+python3 data/asia/scripts/build_asia_dta.py
+# 3. Build pooled file
+python3 data/asia/scripts/pool_asia_dta.py
+```
+
+Requires: `python3.9+`, `pyreadstat>=1.3`, `pandas>=2.0`.
+
+## Source
+
+World Bank Enterprise Surveys, https://www.enterprisesurveys.org. Microdata access subject to WBES terms (free academic use, attribution required).