Wages Data of High School Dropout from the National Longitudinal Survey of Youth (NLSY79)

A data set contains longitudinal data of mean hourly wages along with several demographic variables from the National Longitudinal Survey of Youth (NLSY79) held by the U.S. Bureau of Labor Statistics from Round 1 (1979 survey year) to Round 28 (2018 survey year). The cohort provided in this data set is high school dropouts, i.e., the male aged between 14 and 17 years and only completed either 9th, 10th, 11th grade, or aged at least 19 years old when completed high school and participated in at least five rounds of surveys.

Format

A tsibble contains 97,087 rows and 15 variables:

#'

id

A unique individual's ID number. This is the key of the data.

year

The year the observation was taken. This could be the index of the data.

wage

The mean of the hourly wages the individual gets at each of their different jobs. The value could be a weighted or an arithmetic mean. The weighted mean is used when the information of hours of work as the weight is available. The mean hourly wage could also be a predicted value if the original value is considered influential by the robust linear regression as part of data cleaning.

age_1979

The age of the subject in 1979.

gender

Gender of the subject, FEMALE and MALE.

race

Race of the subject, NON-BLACK,NON-HISPANIC; HISPANIC; BLACK.

hgc

Highest grade completed.

hgc_i

Integer of highest grade completed.

yr_hgc

The year when the highest grade completed.

njobs

Number of jobs that an individual has.

hours

The total number of hours the individual usually works per week.

is_wm

Whether the mean hourly wage is weighted mean, using the hour work as the weight, or regular/arithmetic mean. TRUE = is weighted mean. FALSE = is regular mean.

is_pred

Whether the mean hourly wage is a predicted value or not.

Source

The U.S. Bureau of Labor Statistics. (2021, January 6). National Longitudinal Survey of Youth 1979. https://www.nlsinfo.org/content/cohorts/nlsy79/get-data

Examples

# saving the data into a new object
library(yowie)
library(tsibble)
wages_hs_do %>% key_data()
#> # A tibble: 1,188 × 2
#>    id          .rows
#>    <fct> <list<int>>
#>  1 7            [17]
#>  2 31           [22]
#>  3 36           [25]
#>  4 42           [23]
#>  5 53           [14]
#>  6 84            [6]
#>  7 87           [24]
#>  8 93           [25]
#>  9 145          [23]
#> 10 146          [23]
#> # … with 1,178 more rows