2026-03-07

Introduction

Today I will be exploring the question: Does education level relate to occupational prestige?

I will work with the Duncan dataset.

Variables:

  • education
  • income
  • prestige
  • occupation type

Occupational Prestige

In this analysis we define occupational prestige as the status or social respect associated with a job.

Duncan dataset: - Prestife is the percentage of answers that rated an occupation “good” or better in prestige.

Examples:

  • Data sceintist: often high prestige
  • Manual Labor jobs: often lower prestige

I will be able to form some conclusion whether a persons educatoin relates to social status, not purely income.

Data

##            type income education prestige
## accountant prof     62        86       82
## pilot      prof     72        76       83
## architect  prof     75        92       90
## author     prof     55        90       76
## chemist    prof     64        86       90
## minister   prof     21        84       87

The duncan dataset tracks 45 different occupations in the US from 1950. The important variables to note:

  • education: percentage of workers with high school level education
  • income: percentage of workers earning over $3500 a year
  • prestige: percentage of responses raing the occupation as prestigous For reference, $3500 in 1950 is roughly $48,000 today.

Simple Linear Regression Model

I use the model: Prestige = m + x * education + b

  • m: intercept
  • x: change in prestife for one unit of education
  • b: error

This tests how education predicts prestige.

Education vs Prestige

## Adding a Second Predictor: Income I believe income may also play a role in perceived prestige. To test this I fit a second simple linear regression model: Prestige = m + x * income + b This allows me to compare how prestige is related to both education and income.

Income vs Prestige

This plot shows that occupations with higher income tend to be perceived as higher prestige.

Plot with Occupational Focus

This plot allows a closer look at the different occupational groups.

R code

ggplot(duncan, aes(x = education, y = prestige)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(
    title = "Education vs Prestige",
    x = "Education",
    y = "Prestige"
  )

Model Results

Model Slope R-squared p-value
Prestige vs Education 0.902 0.726 0
Prestige vs Income 1.080 0.702 0

Here we see both the R squared and P values for the models.

Conclusion

Based on the data, I can conclude that both education and income have a noticeable positive relationship with occupational prestige. Simple linear regression models prove to be useful in relating social patterns to data. This data poses the future question: How has occupational prestige evolved in relation to education and income in our society today?