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WAGE2

WAGE2

WAGE2 for Module 3 omitted-variable comparison.

Open LINK data

Structure

cross-sectional

File type

LINK

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 WAGE2 from the dataset link first.
data = pd.read_stata("WAGE2.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 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

educnumeric

educ used for Module 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

lwagenumeric

lwage used for Module 4 linear combinations and wage inference.

Missing values: Review source documentation before recoding missing values.

IQnumeric

IQ used for Module 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

sibsnumeric

sibs used for Module 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

expernumeric

exper used for Module 4 linear combinations and wage inference.

Missing values: Review source documentation before recoding missing values.

meducnumeric

meduc used for Module 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

tenurenumeric

tenure used for Module 4 linear combinations and wage inference.

Missing values: Review source documentation before recoding missing values.

feducnumeric

feduc used for Module 3 omitted-variable comparison.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.WAGE2 is catalogued for Module 3 omitted-variable comparison. Open the linked Drive source to install the file before validating output.
  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.