mditools

Microdata Infrastructure Tools: Analysis Tools for Firm-Level Microdata Research

mditools supports the full analysis pipeline for researchers working with firm-level microdata.

Start with the data tools to prepare your panel: import raw files, detect outliers, and harmonize classifications over time. Then run your analysis — estimate production functions and capital stock, compute markups, intensity measures, and distributions, or run regressions and clustering. Once results are ready, use the disclosure tools to tag outputs with dominance and observation counts, aggregate to industry or country level, and apply suppression rules before publication.

Installation

Once on CRAN:

install.packages("mditools")

Development version from GitHub:

# install.packages("remotes")
remotes::install_github("Secretariat-CompNet/mditools")

Main features

Area	Functions
Data tools	`mdi_import_data()`, `mdi_outlier()`, `mdi_make_conc()`
Aggregation	`mdi_aggregate()`, `mdi_hier_apply()`
Disclosure control	`mdi_disclose_crit()`, `mdi_disclose_reg_tab()`
Production functions	`mdi_estimate_prodfun()`, `mdi_acf_prodest()`, `mdi_lp_prodest()`, `mdi_ols_prodest()`, `mdi_wdrg_prodest()`, `mdi_cs_prodest()`, `mdi_dpgmm_prodest()`
Analysis functions	`mdi_regress()`, `mdi_clustering()`, `mdi_estimate_markup()`, `mdi_pim_capital()`, `mdi_intensity()`, `mdi_jointdist()`, `mdi_transition()`

Usage example

library(mditools)
library(data.table)

DT <- data.table(
  firmid = rep(1:10, each = 2),
  year   = rep(2020:2021, 10),
  nace   = rep(c("A", "B"), 10),
  emp    = sample(10:100, 20)
)

# Aggregate employment by industry, with disclosure criteria
agg <- mdi_aggregate(DT, var_list = "emp", bygroups = c("nace", "year"),
                     agg_type = "sum", disclosure = TRUE)

# Check disclosure criteria (dominance and observation counts)
disc <- mdi_disclose_crit(agg, domVar = "var", domNr = 2L,
                          bygroups = c("nace", "year"), var_list = "emp")

License

GPL-3.