Stata .dta to CSV/JSON Converter

Convert Stata .dta data files to CSV or JSON online. Preserves variable labels, value labels, and metadata. No Stata license needed.

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About Stata .dta to CSV/JSON Converter

Stata .dta is the dominant data format in econometrics, health economics and certain branches of political science. It's also a binary proprietary format that only Stata (paid), pandas.read_stata (free but requires Python), haven (R) or the bare-bones reader in a few statistical packages can open. If a coauthor sends you a regression-ready .dta from the World Bank, IPUMS, or a faculty collaborator and your toolchain is Excel or Tableau rather than Stata, you hit a conversion wall.

This Stata .dta converter reads the file and emits CSV (Excel-ready) or JSON (for scripts), with variable labels and value labels preserved. Variable labels are the full descriptions ("Gross hourly wage, deflated to 2010 USD") attached to terse variable names like wage_def; value labels are the categorical mappings (1 = "Married", 2 = "Single") that turn a survey's coded responses into readable text. Without them, a panel-study dataset is a wall of integers; with them, the CSV opens in Excel and is immediately understandable. Optional include_labels toggle swaps between human-readable labels (great for reading) and the underlying numeric codes (great for re-importing into a different statistical pipeline). Files up to 50 MB and Stata format versions 14 through 18 are supported.

Use it to drop an IPUMS extract into Excel without buying Stata, share a World Bank panel dataset with a pandas collaborator, audit a coded survey's value labels before regression, prep a CSV for Tableau or Power BI, or just open a .dta someone emailed without setting up a Python toolchain. Files are processed in a stateless serverless function and discarded immediately after the response.

Stata .dta to CSV/JSON Converter Use Cases

  • Econometrics students opening an IPUMS or World Bank dataset without buying Stata
  • Researchers sharing .dta panels with pandas or R coauthors who don't run Stata
  • Auditing value-label mappings in a coded panel survey before regression
  • Loading World Bank or DHS .dta data into Tableau or Power BI dashboards
  • Migrating regressions from Stata to Python statsmodels or R lme4
  • Quick decoder for a .dta a coauthor emailed without booting up Python
  • Data engineers ingesting .dta extracts into a modern ETL pipeline

Stata .dta to CSV/JSON Converter Features

  • Reads Stata .dta data files (format versions 14, 15, 16, 17, 18) and emits CSV or JSON in one pass
  • Variable labels preserved — the full descriptions attached to terse variable names
  • Value labels preserved — categorical mappings like 1='Married' for survey or panel coded data
  • include_labels toggle — emit human labels for readability or underlying codes for re-import
  • Shares the underlying conversion engine with the SPSS converter — predictable behaviour across labelled survey formats
  • CSV output uses UTF-8 BOM so Excel renders non-ASCII labels correctly on double-click
  • Files up to 50 MB processed in a stateless serverless function and discarded immediately after the response

How to Use Stata .dta to CSV/JSON Converter

Upload your .dta file

Drag-and-drop or click to select a Stata .dta data file (up to 50 MB). Files written by Stata 14 onwards are supported — that covers the vast majority of files currently circulating in academic and policy data.

Pick CSV or JSON

CSV opens in Excel, Sheets, LibreOffice, R via read.csv, pandas via read_csv. JSON is the right choice for direct script loading or tools that prefer structured input.

Decide on labels

Tick include_labels (default) to emit human-readable value labels — '1' becomes 'Married', '2' becomes 'Single'. Untick to keep underlying integer codes — required when the destination is another statistical pipeline that expects raw codes.

Click Convert

The serverless parser reads the .dta header, extracts variable metadata (name, type, label, value labels), and iterates over the data records. A 100-variable, 100,000-observation panel converts in a few seconds.

Download and load

CSV: open in Excel, or pandas.read_csv('file.csv'), or read.csv('file.csv') in R. JSON: pandas.read_json or json.load(). Variable labels show as the first comment row in the CSV or as a metadata block in the JSON.

Stata .dta to CSV/JSON Converter FAQ

No. The file is uploaded to a stateless serverless function, parsed, and discarded immediately after the response. Nothing is logged to durable storage. For highly confidential health-economics or financial-panel data, the same conversion runs locally with pandas.read_stata or haven::read_dta in one line — that's the safer path when transit is a concern.

Yes. Variable labels (the human-readable description attached to each variable name) and value labels (the categorical mapping for coded responses) are both read from the .dta header. With include_labels on (default), the CSV uses the value labels. With it off, raw codes are emitted — better for re-importing into a statistical pipeline that expects the numeric coding.

Format versions 14, 15, 16, 17 and 18 — the formats written by Stata releases from 2015 onwards. Earlier formats (Stata 13 and below, format versions 113 and older) sometimes work but aren't officially tested. If you have a very old .dta, opening it in Stata or PSPP first and re-saving as the latest format is the safest path.

50 MB. This covers most labelled academic datasets — a 100-variable 100,000-observation panel is typically well under 10 MB. Long longitudinal panels (DHS, IPUMS multi-year) can exceed this — chunk by year or use pandas.read_stata in a local script for arbitrary sizes.

Yes. String variables come through with their original values and encoding (UTF-8 in the output regardless of the .dta's internal encoding). Stata's missing-value codes (system missing '.', extended missing '.a' through '.z') are preserved as empty cells in CSV (Excel convention) and as null in JSON — which is what pandas, R and most downstream tools expect.

pandas.read_stata is the right answer when you're building a statistical pipeline and want .dta read directly. This is the bridge tool for everyone else — open the .dta once, get a CSV or JSON, use that downstream. Particularly useful when coauthors don't have Python set up, or when the destination is Excel or a BI tool rather than a script.

No — this only handles .dta data files. Stata do-files are code, not data, and converting them to another language (R, Python) requires semantic translation. The closest path is to read your .dta into pandas or R and rewrite the analysis steps in that language; regression syntax in Python's statsmodels and R's lme4 map closely to Stata's reg and xtreg commands.

Upload your Stata .dta file

.dta format (Stata 8–18) • Max 50MB

Requires login • 1 credit

Stata .dta to CSV/JSON Converter Tutorial

What is Stata .dta?

Stata .dta is the binary data format used by Stata, a statistical software package widely used in economics, political science, sociology, epidemiology, and biostatistics. A .dta file stores not just the data but also variable labels, value labels, and dataset notes that plain CSV cannot capture.

Why Convert?

  • Open Stata data without a Stata license ($195+/year for students, $1395+/year for commercial)
  • Import into Excel, Google Sheets, R, Python, or SPSS
  • Share data with collaborators who don't use Stata
  • Archive research data in a universal, long-term readable format
  • Old Stata versions can't always open newer .dta files — CSV is forever

What Gets Extracted?

  • All data rows and columns
  • Variable labels — human-readable descriptions (the label variable attribute)
  • Value labels — coded values mapped to labels (e.g. 1→"Male", 2→"Female")
  • Data types — byte, int, long, float, double, str
  • Missing value counts
  • File encoding — important for non-ASCII data (Chinese, accented characters, etc.)

CSV vs JSON Output

  • CSV — Best for Excel/Sheets/R. Flat table, opens anywhere.
  • JSON — Includes full metadata (variable labels, value labels, file info). Best for programmatic use and preserving all Stata metadata.

Value Labels Option

"Apply labels" replaces coded numbers with their labels (e.g. 1→"Strongly Agree"). This is what you see when running list in Stata with label display on. "Keep raw codes" preserves the original numeric values — needed if you want to run statistical analysis in R or Python.

Supported Stata Versions

Stata 8 through Stata 18 (.dta format versions 113–119). Older pre-Stata-8 files are rare but may also work.