Wooldridge Source: K. Graddy (1997), “Do Fast-Food Chains Price Discriminate on the Race and Income Characteristics of an Area?” Journal of Business and Economic Statistics 15, 391-401. Professor Graddy kindly provided the data set. Data loads lazily.
data('discrim')A data.frame with 410 observations on 37 variables:
psoda: price of medium soda, 1st wave
pfries: price of small fries, 1st wave
pentree: price entree (burger or chicken), 1st wave
wagest: starting wage, 1st wave
nmgrs: number of managers, 1st wave
nregs: number of registers, 1st wave
hrsopen: hours open, 1st wave
emp: number of employees, 1st wave
psoda2: price of medium soday, 2nd wave
pfries2: price of small fries, 2nd wave
pentree2: price entree, 2nd wave
wagest2: starting wage, 2nd wave
nmgrs2: number of managers, 2nd wave
nregs2: number of registers, 2nd wave
hrsopen2: hours open, 2nd wave
emp2: number of employees, 2nd wave
compown: =1 if company owned
chain: BK = 1, KFC = 2, Roy Rogers = 3, Wendy's = 4
density: population density, town
crmrte: crime rate, town
state: NJ = 1, PA = 2
prpblck: proportion black, zipcode
prppov: proportion in poverty, zipcode
prpncar: proportion no car, zipcode
hseval: median housing value, zipcode
nstores: number of stores, zipcode
income: median family income, zipcode
county: county label
lpsoda: log(psoda)
lpfries: log(pfries)
lhseval: log(hseval)
lincome: log(income)
ldensity: log(density)
NJ: =1 for New Jersey
BK: =1 if Burger King
KFC: =1 if Kentucky Fried Chicken
RR: =1 if Roy Rogers
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
If you want to assign a common final project, this would be a good data set. There are many possible dependent variables, namely, prices of various fast-food items. The key variable is the fraction of the population that is black, along with controls for poverty, income, housing values, and so on. These data were also used in a famous study by David Card and Alan Krueger on estimation of minimum wage effects on employment. See the book by Card and Krueger, Myth and Measurement, 1997, Princeton University Press, for a detailed analysis.
Used in Text: pages 112, 166, 699-700
str(discrim)
#> 'data.frame': 410 obs. of 37 variables:
#> $ psoda : num 1.12 1.06 1.06 1.12 1.12 ...
#> $ pfries : num 1.06 0.91 0.91 1.02 NA ...
#> $ pentree : num 1.02 0.95 0.98 1.06 0.49 ...
#> $ wagest : num 4.25 4.75 4.25 5 5 ...
#> $ nmgrs : num 3 3 3 4 3 4 3 3 4 3 ...
#> $ nregs : int 5 3 5 5 3 4 2 5 4 5 ...
#> $ hrsopen : num 16 16.5 18 16 16 15 16 17 17 18 ...
#> $ emp : num 27.5 21.5 30 27.5 5 17.5 22.5 18.5 17 27 ...
#> $ psoda2 : num 1.11 1.05 1.05 1.15 1.04 ...
#> $ pfries2 : num 1.11 0.89 0.94 1.05 1.01 ...
#> $ pentree2: num 1.05 0.95 0.98 1.05 0.58 ...
#> $ wagest2 : num 5.05 5.05 5.05 5.05 5.05 ...
#> $ nmgrs2 : num 5 4 4 4 3 3 3 3 4 6 ...
#> $ nregs2 : int 5 3 5 5 3 4 2 5 4 5 ...
#> $ hrsopen2: num 15 17.5 17.5 16 16 15 16 16 18 17 ...
#> $ emp2 : num 27 24.5 25 NA 12 28 18.5 17 34 22 ...
#> $ compown : int 1 0 0 0 0 0 0 1 0 1 ...
#> $ chain : int 3 1 1 3 1 1 1 3 1 3 ...
#> $ density : num 4030 4030 11400 8345 720 ...
#> $ crmrte : num 0.0529 0.0529 0.036 0.0484 0.0616 ...
#> $ state : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ prpblck : num 0.1712 0.1712 0.0474 0.0528 0.0345 ...
#> $ prppov : num 0.0366 0.0366 0.0879 0.0591 0.0254 ...
#> $ prpncar : num 0.0788 0.0788 0.2694 0.1367 0.0738 ...
#> $ hseval : num 148300 148300 169200 171600 249100 ...
#> $ nstores : int 3 3 3 3 1 2 1 1 5 5 ...
#> $ income : num 44534 44534 41164 50366 72287 ...
#> $ county : int 18 18 12 10 10 18 10 24 10 10 ...
#> $ lpsoda : num 0.1133 0.0583 0.0583 0.1133 0.1133 ...
#> $ lpfries : num 0.0583 -0.0943 -0.0943 0.0198 NA ...
#> $ lhseval : num 11.9 11.9 12 12.1 12.4 ...
#> $ lincome : num 10.7 10.7 10.6 10.8 11.2 ...
#> $ ldensity: num 8.3 8.3 9.34 9.03 6.58 ...
#> $ NJ : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ BK : int 0 1 1 0 1 1 1 0 1 0 ...
#> $ KFC : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ RR : int 1 0 0 1 0 0 0 1 0 1 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"