class: center, middle, inverse, title-slide # IDS 702: Module 6.8 ## The minimum wage analysis ### Dr. Olanrewaju Michael Akande --- ## The minimum wage analysis - Let's see how causal inference with propensity scores works using the minimum wage data. -- - The data is in the file `MinimumWageData.csv` on Sakai. .small[ Variables | Description :------------- | :------------ NJ.PA | indicator for which state the restaurant is in (1 if NJ, 0 if PA) EmploymentPre | measures employment for each restaurant before the minimum wage raise in NJ EmploymentPost | measures employment for each restaurant after the minimum wage raise in NJ WagePre | measures the hourly wage for each restaurant before the minimum wage raise BurgerKing | indicator for Burger King KFC | indicator for KFC Roys | indicator for Roys Wendys | indicator for Wendys ] --- ## The minimum wage analysis ```r MinWage <- read.csv("data/MinimumWageData.csv",header=T, colClasses=c("factor","numeric","numeric","numeric", "factor","factor","factor","factor")) str(MinWage) ``` ``` ## 'data.frame': 372 obs. of 8 variables: ## $ NJ.PA : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... ## $ EmploymentPost: num 18 29.5 24 30.5 9 6.5 13.5 25 26.5 23 ... ## $ EmploymentPre : num 30 19 67.5 18.5 6 7 12.5 55 21.5 25.5 ... ## $ WagePre : num 5 5.5 5 5 5.25 5 5 5 5 5.5 ... ## $ BurgerKing : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 2 2 2 ... ## $ KFC : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 1 1 1 1 ... ## $ Roys : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ... ## $ Wendys : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ... ``` ```r head(MinWage) ``` ``` ## NJ.PA EmploymentPost EmploymentPre WagePre BurgerKing KFC Roys Wendys ## 1 0 18.0 30.0 5.00 0 0 0 1 ## 2 0 29.5 19.0 5.50 0 0 0 1 ## 3 0 24.0 67.5 5.00 1 0 0 0 ## 4 0 30.5 18.5 5.00 1 0 0 0 ## 5 0 9.0 6.0 5.25 0 1 0 0 ## 6 0 6.5 7.0 5.00 0 1 0 0 ``` --- class: center, middle # In-class analysis: move to the R script [here](https://ids-702-f20.github.io/Course-Website/slides/MinimumWage.R) --- class: center, middle # What's next? ### Move on to the readings for the next module!