Race/Ethnicity

Which ONE category best describes your racial background?

H1GI6A - H1GI6E

Income

Now think about your personal earnings. In {2006/2007/2008}, how much income did you receive from personal earnings before taxes, that is, wages or salaries, including tips, bonuses, and overtime pay, and income from self-employment?

H4EC2 (W4)

I’m going to look at variables that best approximate the reasons for racial disparities mentioned above. I’ll focus on Impulsivity, Conscientiousness, Intelligence, and Parental Marital Status. I chose Parental Marital Status because it’s so frequently offered as the reason for racial disparities. I choses impulsivity, conscientiousness, and intelligence because I think they approximate the cultural explanations – that if one works hard and is smart, then there won’t be any racial disparities.

Impulsivity:

When making decisions, you usually go with your “gut feeling” without thinking too much about the consequences of each alternative.

• H1PF16 (W1)
• H2PF15 (W2)
• H3SP18 (W3)
• H4PE34 (W4)

Conscientiousnes:

These variables are taken (from the following article)[https://www.frontiersin.org/articles/10.3389/fpsyg.2011.00158/full]

• H1PF18 (Pay attention to details)
• H1PF19 (Come up with good solutions)
• H1PF20 (Do things according to a plan)
• H1PF21 (Do more than what’s expected of me)

Intelligence

Parental Background

Construct Race

df = df %>%
  mutate(
    race = case_when(
      H1GI6B == '(1) (1) Marked' ~ 'black', 
      H1GI6C == '(1) (1) Marked' ~ 'native american', 
      H1GI6D == '(1) (1) Marked' ~ 'asian or pacific islander', 
      H1GI6E == '(1) (1) Marked' ~ 'other',
      H1GI6A == '(1) (1) Marked' ~ 'white', 
    )
  )

# fill in race for missing waves
df = df %>% group_by(AID) %>%
  mutate(
    race = if_else(any(! is.na(race)), paste0(unique(race), collapse = '|'), NA_character_)
  )

# for some reason the na detection wasnt working correctly above, so im just str_replacing the NAs
df$race = str_replace(df$race, '\\|.*', '')

construct conscientiousness

df = df %>%
 mutate_at(vars(H1PF18:H1PF21), as.numeric)

df$conscientiousness = 5 - rowMeans(df[,c('H1PF18', 'H1PF19', 'H1PF20', 'H1PF21')], na.rm = T)

Below, I make a function to plot the data.

For each plot, I am going to plot the distribution of White and Black people’s earnings, and I am going to break those distribution out by categories (e.g., parents married/not married, or the 0, 10%, 30%, 50%, 70%, 90%, and 100% quantiles of a continuous variable )

plot_it <- function(value){
    
    probs <- c(0, .1, .3, .5, .7, .9,  1)
    df.plot$quartile <- with(df.plot, cut(value, breaks = unique(quantile(value, probs, na.rm=TRUE)), 
                                include.lowest=TRUE))
    ### getting rid of outliers
    df.plot = df.plot %>% filter(H4EC2 < 100000)
    df.plot$H4EC2 = df.plot$H4EC2/10000
    ggplot(df.plot, aes(x = H4EC2, y = race, fill = race)) + 
      stat_density_ridges(quantile_lines = TRUE, quantiles = 2) +
      scale_x_continuous(breaks = seq(0, 10, by = .5)) + 
      facet_wrap(~ quartile, scales = 'free', ncol = 1)
  
}

get wave 1 variables joined in with wave 4 variables

df = df %>% 
  mutate(
    married = if_else(PA10 == '(2) (2) Married (skip to A12)', 'married', 'not married')
  )

df.wave1 = df %>% 
  filter(wave == 1 &race %in% c('black', 'white') & ! is.na(married)) %>% 
  select(AID, race, married)
df.outcome = df %>% filter(wave == 4) %>% select(AID, H4EC2) 
df.plot = left_join(df.wave1, df.outcome)
## Joining, by = "AID"

by marital status

df.plot = df.plot %>% filter(H4EC2 < 100000)
df.plot$H4EC2 = df.plot$H4EC2/10000

ggplot(df.plot, aes(x = H4EC2, y = race, fill = race)) + 
  stat_density_ridges(quantile_lines = TRUE, quantiles = 2) +
  scale_x_continuous(breaks = seq(0, 10, by = .5)) + 
  facet_wrap(~ married, scales = 'free', ncol = 1)
## Picking joint bandwidth of 0.46
## Picking joint bandwidth of 0.509

by conscientiousness

df.wave1 = df %>% 
  filter(wave == 1 &race %in% c('black', 'white') & ! is.na(conscientiousness)) %>% 
  select(AID, race, conscientiousness)
df.outcome = df %>% filter(wave == 4) %>% select(AID, H4EC2) 
df.plot = left_join(df.wave1, df.outcome)
## Joining, by = "AID"
plot_it(df.plot$conscientiousness)
## Picking joint bandwidth of 0.606
## Picking joint bandwidth of 0.566
## Picking joint bandwidth of 0.475
## Picking joint bandwidth of 0.557
## Picking joint bandwidth of 0.752

by peabody picture vocabulary

df.wave1 = df %>% 
  filter(wave == 1 &race %in% c('black', 'white') & ! is.na(AH_PVT)) %>% 
  select(AID, race, AH_PVT)
df.outcome = df %>% filter(wave == 4) %>% select(AID, H4EC2) 
df.plot = left_join(df.wave1, df.outcome)
## Joining, by = "AID"
plot_it(df.plot$AH_PVT)
## Picking joint bandwidth of 0.594
## Picking joint bandwidth of 0.498
## Picking joint bandwidth of 0.518
## Picking joint bandwidth of 0.538
## Picking joint bandwidth of 0.544
## Picking joint bandwidth of 0.684

word recall

df.wave4 <- df %>% 
  filter(wave == 4 &race %in% c('black', 'white') & ! is.na(C4WD90_1)) %>% 
  select(AID, race, C4WD90_1)
df.outcome = df %>% filter(wave == 4) %>% select(AID, H4EC2) 
df.plot = left_join(df.wave4, df.outcome)
## Joining, by = "AID"
plot_it(df.plot$C4WD90_1)
## Picking joint bandwidth of 0.545
## Picking joint bandwidth of 0.481
## Picking joint bandwidth of 0.534
## Picking joint bandwidth of 0.614
## Picking joint bandwidth of 0.686
## Picking joint bandwidth of 0.708

by impulsivity

df.wave1 = df %>% 
  filter(wave == 1 &race %in% c('black', 'white') & ! is.na(H1PF16)) %>% 
  select(AID, race, H1PF16)
df.outcome = df %>% filter(wave == 4) %>% select(AID, H4EC2) 
df.plot = left_join(df.wave1, df.outcome)
## Joining, by = "AID"
df.plot$H1PF16 = as.numeric(df.plot$H1PF16)
plot_it(df.plot$H1PF16)
## Picking joint bandwidth of 0.475
## Picking joint bandwidth of 0.531
## Picking joint bandwidth of 0.509
## Picking joint bandwidth of 0.745

In summary, it’s clear that none of the variables come close to explaining the racial disparities in income. While it is true that some variables (particularly intelligence), reduce disparities at the higher ends of the distributions, White participants in ADD Health at the middle and lower ends of the disribution outpaced their Black counterparts by approximately $5,000 - $10,000. Another important point to add is that I’m only looking at income; to eliminate racial wealth gaps, Black participants in the study would have to earn more than their White counterparts to have any hope of eliminating those disparities.