Mid-Atlantic Wage Data#

Wage and other data for a group of 3000 male workers in the Mid-Atlantic region.

  • year: Year that wage information was recorded

  • age: Age of worker

  • maritl: A factor with levels ‘1. Never Married’, ‘2. Married’, ‘3. ‘3. Widowed’, ‘4. Divorced’ and ‘5. Separated’ indicating marital status

  • race: A factor with levels ‘1. White’, ‘2. Black’, ‘3. Asian’ and ‘4. Other’ indicating race

  • education: A factor with levels ‘1. < HS Grad’, ‘2. HS Grad’, ‘3. Some College’, ‘4. College Grad’ and ‘5. Advanced Degree’ indicating education level

  • region: Region of the country (mid-atlantic only)

  • jobclass: A factor with levels ‘1. Industrial’ and ‘2. Information’ indicating type of job

  • health: A factor with levels ‘1. <=Good’ and ‘2. >=Very Good’ indicating health level of worker

  • health_ins: A factor with levels ‘1. Yes’ and ‘2. No’ indicating whether worker has health insurance

  • logwage: Log of workers wage

  • wage: Workers raw wage

Source#

Data was manually assembled by Steve Miller, of Inquidia Consulting (formerly Open BI). From the March 2011 Supplement to Current Population Survey data.

See also: re3data.org/repository/r3d100011860

from ISLP import load_data
Wage = load_data('Wage')
Wage.columns

Index(['year', 'age', 'maritl', 'race', 'education', 'region', 'jobclass',
       'health', 'health_ins', 'logwage', 'wage'],
      dtype='object')

Wage.shape

(3000, 11)

Wage.columns

Index(['year', 'age', 'maritl', 'race', 'education', 'region', 'jobclass',
       'health', 'health_ins', 'logwage', 'wage'],
      dtype='object')

Wage.describe()
year age logwage wage
count 3000.000000 3000.000000 3000.000000 3000.000000
mean 2005.791000 42.414667 4.653905 111.703608
std 2.026167 11.542406 0.351753 41.728595
min 2003.000000 18.000000 3.000000 20.085537
25% 2004.000000 33.750000 4.447158 85.383940
50% 2006.000000 42.000000 4.653213 104.921507
75% 2008.000000 51.000000 4.857332 128.680488
max 2009.000000 80.000000 5.763128 318.342430