dta

A .dta file is the native data format used by Stata, whereas a .csv file is a plain-text, tool-agnostic format. They serve similar purposes (tabular data storage) but differ significantly in structure and capabilities.

1. Core differences

Aspect	.dta (Stata)	.csv
Format	Binary	Plain text
Primary use	Stata statistical analysis	General data exchange
Metadata	Rich (labels, types, formats)	None (data only)
Data types	Preserved explicitly	Inferred on load
Missing values	Multiple typed missing values ($.a$–$.z$)	Single convention (e.g. empty, NA)
File size	Typically smaller	Larger
Human-readable	No	Yes

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2. Metadata and semantics

.dta

A .dta file stores:

• Variable names and variable labels
• Value labels (e.g. 1 = "Yes", 0 = "No")
• Explicit data types (int, float, string, date)
• Display formats (dates, numeric precision)
• Multiple missing value categories ($.,.a,.b,\dots$)

This makes .dta self-describing, which is important in statistical workflows.

.csv

A .csv file stores:

• Raw values only
• No schema, labels, or typing information

All structure is reconstructed at load time via inference or user-specified rules.

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3. Interoperability

• .csv is universally supported (Excel, Python, R, SQL, BI tools).

• .dta is primarily for Stata, but can be read by:

  • pandas.read_stata()
  • R (haven::read_dta())

However, some Stata-specific features (e.g. value labels) may be partially flattened depending on the tool.

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4. Practical implications

When .dta is preferable

• You rely on labels and metadata for interpretation
• You want reproducibility in statistical analysis
• You work mainly in Stata or with Stata users

When .csv is preferable

• You need maximum portability
• Data is part of a pipeline (ETL, databases, APIs)
• You expect many tools or users to consume the data

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5. Example (Python)

import pandas as pd
 
# Load Stata file (metadata-aware)
df_dta = pd.read_stata("survey_data.dta")
 
# Load CSV (types inferred)
df_csv = pd.read_csv("survey_data.csv")

With .dta, pandas can retain labels and Stata missing values; with .csv, everything must be inferred or reconstructed manually.

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6. Summary

• .dta = analysis-oriented, metadata-rich, Stata-native
• .csv = simple, portable, schema-less

A common pattern is:

• Authoritative data stored as .dta
• Exchange and integration done via .csv

If you want, I can also compare .dta with .parquet or discuss best practices for converting .dta into analytics pipelines.