In this study we will be looking at the Unemployment of Blue Collor Workers dataset. The focus is on comparing the data for the two genders and asking whether one is affected disproportionatly than the other.

uemp <- data.frame(read.csv("https://raw.githubusercontent.com/Patel-Krutika/Bridge_2021/main/Benefits.csv"))
f <- subset(uemp, uemp$sex == "female")
m <- subset(uemp, uemp$sex == "male")

fcount <- nrow(f)
mcount <- nrow(m)

mcount/fcount
## [1] 3.24087

The ratio of male to female individuals in the study is approximatly 3:1.

hist(f$age, main = "Female Age")

hist(m$age, main = "Male Age")

print("Female Age Summary")
## [1] "Female Age Summary"
summary(f$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   20.00   28.00   35.00   37.37   46.00   61.00
print("")
## [1] ""
print("Male Age Summary")
## [1] "Male Age Summary"
summary(m$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   20.00   27.00   34.00   35.75   42.00   61.00

The age distribution between female and male is nearly identical.

hist(f$yrdispl, xlab = "Year (1982-1991)",  main = "Female Job Displacement")

hist(m$yrdispl, xlab = "Year (1982-1991)", main = "Male Job Displacement")

## The spread of umepmployment accross the time frame is similar for both genders.

scatter.smooth(x=f$age, y = f$rr, xlab = "Age", ylab = "Replacement Rate", main = "Female: Age v. Replacement Rate")

scatter.smooth(x=m$age, y = m$rr, xlab = "Age", ylab = "Replacement Rate", main = "Male: Age v. Replacement Rate")

summary(f)
##        X           stateur          statemb          state      
##  Min.   :   3   Min.   : 2.400   Min.   : 84.0   Min.   :11.00  
##  1st Qu.:1220   1st Qu.: 5.600   1st Qu.:148.0   1st Qu.:32.00  
##  Median :2458   Median : 7.000   Median :175.0   Median :56.00  
##  Mean   :2449   Mean   : 7.333   Mean   :178.2   Mean   :51.51  
##  3rd Qu.:3673   3rd Qu.: 8.900   3rd Qu.:203.8   3rd Qu.:72.00  
##  Max.   :4873   Max.   :18.000   Max.   :291.0   Max.   :95.00  
##       age            tenure         joblost             nwhite         
##  Min.   :20.00   Min.   : 1.000   Length:1150        Length:1150       
##  1st Qu.:28.00   1st Qu.: 2.000   Class :character   Class :character  
##  Median :35.00   Median : 3.000   Mode  :character   Mode  :character  
##  Mean   :37.37   Mean   : 5.676                                        
##  3rd Qu.:46.00   3rd Qu.: 8.000                                        
##  Max.   :61.00   Max.   :40.000                                        
##    school12             sex              bluecol              smsa          
##  Length:1150        Length:1150        Length:1150        Length:1150       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##    married             dkids              dykids             yrdispl      
##  Length:1150        Length:1150        Length:1150        Min.   : 1.000  
##  Class :character   Class :character   Class :character   1st Qu.: 3.000  
##  Mode  :character   Mode  :character   Mode  :character   Median : 5.000  
##                                                           Mean   : 5.211  
##                                                           3rd Qu.: 8.000  
##                                                           Max.   :10.000  
##        rr             head                ui           
##  Min.   :0.1564   Length:1150        Length:1150       
##  1st Qu.:0.4639   Class :character   Class :character  
##  Median :0.5000   Mode  :character   Mode  :character  
##  Mean   :0.4838                                        
##  3rd Qu.:0.5200                                        
##  Max.   :0.6689
c <- f$school12[f$school12=="no"]

The line of best fit of the female replacement rate is steady as the age increases. The line of best fit representing the male replacement begins high at 0.5, but decreases .10 between the 20 to 30 age group, then growing steady. Both genders start is the same replacement rate, but with the progression of age only the male rate decreases.

Conclusion

This analysis looked at the age, year of job displacement and replacement rate as factors to determine the affects of unemployment on each gender. The ratio of males to females was 3 to 1. The findings show that the age group spread is nearly identical for boh genders as well as the job displacement year spread. The replacement rate is the only factor in which there was a slight difference. Females had a steady rate 0f 0.5 while males had a .10 dip between the 20 to 30 age group and steadied at 0.4 from there on. Overall, we can concluded that there were not any major differences in the data between males and females.