OECDscaleup

OECDscaleup builds the firm-level “high-growth firm” (HGF) / scale-up indicators used in the OECD Scale Up project from confidential national firm microdata, and produces the disclosure-checked aggregate tabulations (characteristics, transitions/evolutions, contribution, predictors) shared back with the OECD.

It is a behaviour-preserving translation of the OECD’s original Stata pipeline (MASTER_OECD_p2.do and 5 worker .do files, version 1.0) — see Changelog - OECDscaleup 0.1.0 for what changed in the translation and why.

Firm-level microdata is confidential and never leaves the country whose national statistical institute holds it, so no real data ships with this package — every input this package needs is documented in variable_dictionary_v1.xlsx from the original Stata package.

Installation

This package isn’t on CRAN — it’s distributed internally to the OECD Scale Up project’s participating national statistical institutes. Install from source:

# from a local clone/copy of the package source
devtools::install_local("path/to/OECDscaleup")

# or, from a git remote your organisation hosts internally
devtools::install_git("https://your-internal-git-host/OECDscaleup.git")

The package uses renv to pin its exact dependency versions (renv.lock); run renv::restore() after cloning the source repository if you’re developing on it directly.

Usage

library(OECDscaleup)

cfg <- config_scaleup(
  country = "FR",
  input_dir = "path/to/input",   # base_financials_FR.dta, etc.
  output_dir = "path/to/output"
)

# reads the raw .dta inputs, runs the full pipeline, writes every output CSV
outputs <- run_scaleup_pipeline(cfg)

Every pipeline stage is also available as a pure function (data frame in, data frame out) if you already have your data loaded in R:

accounts_indicators <- build_accounts_indicators(cfg, base_financials)
characteristics     <- compute_characteristics(cfg, accounts_indicators)
persistence         <- compute_persistence(cfg, accounts_indicators)
contribution        <- compute_contribution(cfg, accounts_indicators)
predictors          <- compute_predictors(cfg, accounts_indicators)

See vignette("OECDscaleup") for a complete walkthrough with a synthetic worked example, including how to use the dominance confidentiality-check statistics.

Development

devtools::test()      # run the test suite
devtools::document()  # regenerate NAMESPACE/man pages after editing roxygen comments
devtools::check()     # full package check

See CONTRIBUTING.md for more.

License

MIT — see LICENSE.md.