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 recordedage
: Age of workermaritl
: A factor with levels ‘1. Never Married’, ‘2. Married’, ‘3. ‘3. Widowed’, ‘4. Divorced’ and ‘5. Separated’ indicating marital statusrace
: A factor with levels ‘1. White’, ‘2. Black’, ‘3. Asian’ and ‘4. Other’ indicating raceeducation
: A factor with levels ‘1. < HS Grad’, ‘2. HS Grad’, ‘3. Some College’, ‘4. College Grad’ and ‘5. Advanced Degree’ indicating education levelregion
: Region of the country (mid-atlantic only)jobclass
: A factor with levels ‘1. Industrial’ and ‘2. Information’ indicating type of jobhealth
: A factor with levels ‘1. <=Good’ and ‘2. >=Very Good’ indicating health level of workerhealth_ins
: A factor with levels ‘1. Yes’ and ‘2. No’ indicating whether worker has health insurancelogwage
: Log of workers wagewage
: 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 |