Beauty: Beauty and Wages
In pder: panel data econometrics with R

Description Usage Format Source References See Also

observations of 165 individuals

number of observations : 825

country : argentina

economic topic : labor economics

econometrics topic : fixed effects model

1	data(Beauty)

A dataframe containing :

employer: employer index
worker: worker index
beauty: beauty score
treatment: a factor with levels 'B' (baseline), 'V' (visual), 'O' (oral), 'VO' (visual and oral), 'FTF' (face to face)
setwage: a factor with levels 'yes' or 'no' indicating whether the employer set the wage of the worker
practice: the time used to solve the preliminary maze
estimated: worker's estimation of the number of mazes he'll solve during the 15 minutes 'employement' period
actual: worker's actual number of mazes solved during the 15 minutes 'employement' period
wage: the wage set by the employer
univ: the university of the worker, one of 'nac' (Universidad National de Tucuman), 'stho' (Universidad del Norte Santo Tomas de Aquino), 'tecno' (Universidad Tecnologica Nacional)
faculty: actual major for the workers, one of 'other', 'science', 'computers', 'humanities' and 'arts'
age: the age of the worker
sex: the gender of the worker
teamsport: is the worker participate in team sports ?
internet: has the worker an internet connection at home ?
prevjobs: number of previous jobs
jobeduc: employment in education
jobit: employment in information technology
jobretail: employment in retail sales
jobbiz: employment in business
jobgov: employment in public sector
jobart: employment in art
jobfood: employment in food production and service
jobind: employment in industry
jobinter: intensity of interpersonal interactions required in each job on a 0 to 5 scale
hobbyit: hobbies in computers
hobbyrec: hobbies in recreation
hobbycrea: hobbies in arts
hobbysport: hobbies in sports