DOI: 10.5281/zenodo.15394438
title: Russian Financial Statements Database: A firm-level collection of the universe of financial statements

description: The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms:

- 🔓 First open data set with information on every active firm in Russia.

- 🗂️ First open financial statements data set that includes non-filing firms.

- 🏛️ Sourced from two official data providers: the Rosstat and the Federal Tax Service.

- 📅 Covers 2011-2023 initially, will be continuously updated.

- 🏗️ Restores as much data as possible through non-invasive data imputation, statement articulation, and harmonization.

The RFSD is hosted on 🤗 Hugging Face and Zenodo and is stored in a structured, column-oriented, compressed binary format Apache Parquet with yearly partitioning scheme, enabling end-users to query only variables of interest at scale. 

The accompanying paper provides internal and external validation of the data: https://doi.org/10.1038/s41597-025-05150-1.

Here we present the instructions for importing the data in R or Python environment. Please consult with the project repository for more information: http://github.com/irlcode/RFSD.

Importing The Data

You have two options to ingest the data: download the .parquet files manually from Hugging Face or Zenodo or rely on 🤗 Hugging Face Datasets library. 

Python

🤗 Hugging Face Datasets

It is as easy as:

from datasets import load_dataset
import polars as pl

# This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder
RFSD = load_dataset('irlspbru/RFSD')

# Alternatively, this will download ~540MB with all financial statements for 2023# to a Polars DataFrame (requires about 8GB of RAM)
RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')

Please note that the data is not shuffled within year, meaning that streaming first n rows will not yield a random sample.

Local File Import

Importing in Python requires pyarrow package installed.

import pyarrow.dataset as ds
import polars as pl

# Read RFSD metadata from local file
RFSD = ds.dataset("local/path/to/RFSD", partitioning="hive")

# Use RFSD_dataset.schema to glimpse the data structure and columns' classes
print(RFSD.schema)

# Load full dataset into memory
RFSD_full = pl.from_arrow(RFSD.to_table())

# Load only 2019 data into memory
RFSD_2019 = pl.from_arrow(RFSD.to_table(filter=ds.field('year') == 2019))

# Load only revenue for firms in 2019, identified by taxpayer id
RFSD_2019_revenue = pl.from_arrow(
    RFSD.to_table(
        filter=ds.field('year') == 2019,
        columns=['inn', 'line_2110']
        )
)

# Give suggested descriptive names to variables
renaming_df = pl.read_csv('local/path/to/descriptive_names_dict.csv')
RFSD_full = RFSD_full.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})

R

Local File Import

Importing in R requires arrow package installed.

library(arrow)
library(data.table)

# Read RFSD metadata from local file
RFSD <- open_dataset("local/path/to/RFSD")

# Use schema() to glimpse into the data structure and column classes
schema(RFSD)

# Load full dataset into memory
scanner <- Scanner$create(RFSD)
RFSD_full <- as.data.table(scanner$ToTable())

# Load only 2019 data into memory
scan_builder <- RFSD$NewScan()
scan_builder$Filter(Expression$field_ref("year") == 2019)
scanner <- scan_builder$Finish()
RFSD_2019 <- as.data.table(scanner$ToTable())

# Load only revenue for firms in 2019, identified by taxpayer id
scan_builder <- RFSD$NewScan()
scan_builder$Filter(Expression$field_ref("year") == 2019)
scan_builder$Project(cols = c("inn", "line_2110"))
scanner <- scan_builder$Finish()
RFSD_2019_revenue <- as.data.table(scanner$ToTable())

# Give suggested descriptive names to variables
renaming_dt <- fread("local/path/to/descriptive_names_dict.csv")
setnames(RFSD_full, old = renaming_dt$original, new = renaming_dt$descriptive)

Use Cases



🌍 For macroeconomists: Replication of a Bank of Russia study of the cost channel of monetary policy in Russia by Mogiliat et al. (2024) — interest_payments.md

🏭 For IO: Replication of the total factor productivity estimation by Kaukin and Zhemkova (2023) — tfp.md

🗺️ For economic geographers: A novel model-less house-level GDP spatialization that capitalizes on geocoding of firm addresses — spatialization.md


FAQ

Why should I use this data instead of Interfax's SPARK, Moody's Ruslana, or Kontur's Focus?hat is the data period?

To the best of our knowledge, the RFSD is the only open data set with up-to-date financial statements of Russian companies published under a permissive licence. Apart from being free-to-use, the RFSD benefits from data harmonization and error detection procedures unavailable in commercial sources. Finally, the data can be easily ingested in any statistical package with minimal effort.

What is the data period?

We provide financials for Russian firms in 2011-2023. We will add the data for 2024 by July, 2025 (see Version and Update Policy below).

Why are there no data for firm X in year Y?

Although the RFSD strives to be an all-encompassing database of financial statements, end users will encounter data gaps:



We do not include financials for firms that we considered ineligible to submit financial statements to the Rosstat/Federal Tax Service by law: financial, religious, or state organizations (state-owned commercial firms are still in the data).

Eligible firms may enjoy the right not to disclose under certain conditions. For instance, Gazprom did not file in 2022 and we had to impute its 2022 data from 2023 filings. Sibur filed only in 2023, Novatek — in 2020 and 2021. Commercial data providers such as Interfax's SPARK enjoy dedicated access to the Federal Tax Service data and therefore are able source this information elsewhere.

Firm may have submitted its annual statement but, according to the Uniform State Register of Legal Entities (EGRUL), it was not active in this year. We remove those filings.


Why is the geolocation of firm X incorrect?

We use Nominatim to geocode structured addresses of incorporation of legal entities from the EGRUL. There may be errors in the original addresses that prevent us from geocoding firms to a particular house. Gazprom, for instance, is geocoded up to a house level in 2014 and 2021-2023, but only at street level for 2015-2020 due to improper handling of the house number by Nominatim. In that case we have fallen back to street-level geocoding. Additionally, streets in different districts of one city may share identical names. We have ignored those problems in our geocoding and invite your submissions. Finally, address of incorporation may not correspond with plant locations. For instance, Rosneft has 62 field offices in addition to the central office in Moscow. We ignore the location of such offices in our geocoding, but subsidiaries set up as separate legal entities are still geocoded. 

Why is the data for firm X different from https://bo.nalog.ru/?

Many firms submit correcting statements after the initial filing. While we have downloaded the data way past the April, 2024 deadline for 2023 filings, firms may have kept submitting the correcting statements. We will capture them in the future releases.

Why is the data for firm X unrealistic?

We provide the source data as is, with minimal changes. Consider a relatively unknown LLC Banknota. It reported 3.7 trillion rubles in revenue in 2023, or 2% of Russia's GDP. This is obviously an outlier firm with unrealistic financials. We manually reviewed the data and flagged such firms for user consideration (variable outlier), keeping the source data intact.

Why is the data for groups of companies different from their IFRS statements?

We should stress that we provide unconsolidated financial statements filed according to the Russian accounting standards, meaning that it would be wrong to infer financials for corporate groups with this data. Gazprom, for instance, had over 800 affiliated entities and to study this corporate group in its entirety it is not enough to consider financials of the parent company.

Why is the data not in CSV?

The data is provided in Apache Parquet format. This is a structured, column-oriented, compressed binary format allowing for conditional subsetting of columns and rows. In other words, you can easily query financials of companies of interest, keeping only variables of interest in memory, greatly reducing data footprint.

Version and Update Policy

Version (SemVer):  1.0.1.

We intend to update the RFSD annualy as the data becomes available, in other words when most of the firms have their statements filed with the Federal Tax Service. The official deadline for filing of previous year statements is April, 1. However, every year a portion of firms either fails to meet the deadline or submits corrections afterwards. Filing continues up to the very end of the year but after the end of April this stream quickly thins out. Nevertheless, there is obviously a trade-off between minimization of data completeness and version availability. We find it a reasonable compromise to query new data in early June, since on average by the end of May 96.7% statements are already filed, including 86.4% of all the correcting filings. We plan to make a new version of RFSD available by July.

Changelog

All notable changes to this project will be documented below. The format is based on Keep a Changelog.

[1.0.1] - 2025-05-13

Fixed



Fixed a bug in summation of negative lines when calculating line 2400 (net profit). The reason for the incorrect values was the sign with which the profit tax (line 2410) enters the calculation for adjusting net profit (2400). In the reporting forms, there are lines with values enclosed in parentheses: this means that they can only be negative, but they need to be entered as positive. However, it sometimes happens that companies enter negative values in these fields. To correct this, during the data preparation stage, we make all values of the ()-ed lines positive. At first, we thought it was there that the problem occurred, because we missed line 2410 while compiling the list of ()-ed lines. But this was not the case: the list is correct, and line 2410 can indeed be both negative and positive — but only in the full statement form. In simplified form it is strictly negative, in parentheses. This is a useful side finding: now the correction of the ()-ed lines occurs separately for the firms that use simplified and non-simplified forms for their statements. The error was further down the process, in the values adjustment part, where we check summary lines like 2400 against their recalculated values and change them if the difference is more than 4000 rubles. To subtract the profit tax from profit, we multiplied line 2410 by -1, as if it were ()-ed while it wasn't. We corrected this and changed the logic for adjusting line 2410 itself: previously, we adjusted it based on lines 2411 and 2412 only starting from 2020, when its content changed, but now the logic is expanded to all years. This should help with companies that were the first to implement the changes back in 2019, and it does not break anything for others.

Fixed a bug in adjustment of line 1300 (total capital and reserves) and 2500 (result of the period).


The updated lines 2400 are quite different from the original values. The value of line 2400 changed in 6-11% of observations in 2011-2018 and in about 25% observations in 2019-2023, the summed difference in the original and new values ranges from 5% to 110% depending on year. Fix for signs inconsistency implies revising scripts for all calculations where negative-only, those ()-ed in statement forms, variables were used.

Licence

Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright © the respective contributors.

Citation

Please cite as:

@article{bondarkov2025rfsd,  title={{R}ussian {F}inancial {S}tatements {D}atabase},  author={Bondarkov, Sergey and Ledenev, Victor and Skougarevskiy, Dmitriy},  journal={Scientific Data},  doi={10.1038/s41597-025-05150-1},  year={2025},

  volume="12",

  issue="1"

}

Acknowledgments and Contacts

Data collection and processing: Sergey Bondarkov, sbondarkov@eu.spb.ru, Viktor Ledenev, vledenev@eu.spb.ru

Project conception, data validation, and use cases: Dmitriy Skougarevskiy, Ph.D., dskougarevskiy@eu.spb.ru