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WAGE1

WAGE1

WAGE1 installed course data for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

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Structure

cross-sectional

File type

DTA

Module

Course catalog

Visibility

public

Preview table

Preview rows are shown when safely installed in the app. External or licensed files may need to be opened from the source link.

No local preview table is installed for this dataset. Use the download/source link, then load it with the Python example below.

Python loading code

import pandas as pd

# Download WAGE1 from the dataset link first.
data = pd.read_stata("WAGE1.DTA")
print(data.head())

Check the variable dictionary and source documentation before dropping or recoding missing values.

Dataset AI assistant

Get help with variables, graphs, regression questions, limitations, and Python loading code.

Variables dictionary

wagenumeric

wage used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

lwagenumeric

lwage used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

educnumeric

educ used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

expernumeric

exper used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

tenurenumeric

tenure used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

femalenumeric

female used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

marriednumeric

married used for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.WAGE1 is catalogued for Module 7 wage group gaps, binary regressors, and log-wage dummy interpretation. Installed original synthetic CSV; estimates are practice results, not real empirical findings.
  2. 2.Create one summary table and one graph that support an econometric question.
  3. 3.Write one limitation about measurement, missing data, or omitted variables.