US Earnings Data (CPS MORG)

Overview

Current Population Survey (CPS) Merged Outgoing Rotation Groups data for analyzing earnings patterns, gender gaps, and returns to education in the US labor market. Read more on CPS here.

Used in: Week 6 (From Data to Report)

Source: Data Analysis Textbook Case Study

Data Files

File	Description
`morg-2014-emp-state5.csv`	CPS MORG 2014 data filtered to 5 states
`cpsx.pdf`	Codebook and variable documentation

Browse data files

Code

File	Description
`earnings.R`	R script for earnings analysis

Browse code files

Key Variables

earnwke - Weekly earnings
sex - Gender
age - Age
grade92 - Education level
weight - Survey weight

Learning Objectives

Working with survey data
Analyzing earnings distributions
Creating visualizations with AI assistance
Understanding gender wage gaps

Sample Research Questions

Is there a gender gap in earnings?
How do earnings vary by education level?
What is the age-earnings profile?

Downloads

morg-2014-emp-state5.csv or the full dataset from OSF - Gabors Data Analysis / CPS MORG
Codebook (PDF)

---
title: "US Earnings Data (CPS MORG)"
---

## Overview

Current Population Survey (CPS) Merged Outgoing Rotation Groups data for analyzing earnings patterns, gender gaps, and returns to education in the US labor market. Read [more on CPS here](https://gabors-data-analysis.com/datasets/#cps-earnings).

**Used in:** Week 6 (From Data to Report)

**Source:** [Data Analysis Textbook Case Study](https://gabors-data-analysis.com/casestudies/#ch09a-estimating-gender-and-age-differences-in-earnings)

## Data Files

| File | Description |
|------|-------------|
| `morg-2014-emp-state5.csv` | CPS MORG 2014 data filtered to 5 states |
| `cpsx.pdf` | Codebook and variable documentation |

[Browse data files](data/)

## Code

| File | Description |
|------|-------------|
| `earnings.R` | R script for earnings analysis |

[Browse code files](code/)

## Key Variables

- `earnwke` - Weekly earnings
- `sex` - Gender
- `age` - Age
- `grade92` - Education level
- `weight` - Survey weight

## Learning Objectives

- Working with survey data
- Analyzing earnings distributions
- Creating visualizations with AI assistance
- Understanding gender wage gaps

## Sample Research Questions

- Is there a gender gap in earnings?
- How do earnings vary by education level?
- What is the age-earnings profile?

## Downloads

- [morg-2014-emp-state5.csv](data/morg-2014-emp-state5.csv) or the full dataset from [OSF - Gabors Data Analysis / CPS MORG](https://osf.io/g8p9j/files/4ay9x)
- [Codebook (PDF)](data/cpsx.pdf)