discrim: discrim

Description Usage Format Notes Source Examples

Description

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.

Usage

1
data('discrim')

Format

A data.frame with 410 observations on 37 variables:

Notes

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

Source

https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041

Examples

1

Example output

'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(*, "datalabel")= chr ""
 - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"
 - attr(*, "formats")= chr  "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
 - attr(*, "types")= int  254 254 254 254 254 251 254 254 254 254 ...
 - attr(*, "val.labels")= chr  "" "" "" "" ...
 - attr(*, "var.labels")= chr  "price of medium soda, 1st wave" "price of small fries, 1st wave" "price entree (burger or chicken), 1st wave" "starting wage, 1st wave" ...
 - attr(*, "version")= int 10

wooldridge documentation built on Nov. 8, 2021, 9:07 a.m.