Donors
Dynamics of Charitable Giving
a pseudo-panel of 32 individuals
number of observations : 1039
number of individual observations : 4-80
country : United States
JEL codes: C93, D64, D82, H41, L31, Z12
Chapter : 08
- Keywords
- datasets
Usage
data(Donors)
Format
A dataframe containing:
- id
the id of the sollicitor
- solsex
the sex of the sollicitor
- solmin
does the sollicitor belongs to a minority ?
- beauty
beauty rating for the sollicitor
- assertive
assertive rating for the sollicitor
- social
social rating for the sollicitor
- efficacy
efficacy rating for the sollicitor
- performance
performance rating for the sollicitor
- confidence
confidence rating for the sollicitor
- age
age of the individual
- sex
sex of the individual
- min
does the individual belongs to a minority
- treatment
the treatment, one of "vcm", "sgift" and "lgift"
- refgift
has the individual refused the gift ?
- donation
the amount of the donation
- prior
has the individual been visited during the previous campaign ?
- prtreat
the treatment during the previous campaign, one of "none", "vcm", and "lottery"
- prcontr
has the individual made a donation during the previous campaign ?
- prdonation
the amount of the donation during the previous campaign
- prsolsex
the sex of the sollicitor during the previous campaign
- prsolmin
did the sollicitor of the previous campaign belong to a minority ?
- prbeauty
beauty rating for the sollicitor of the previous campaign
References
Landry, Craig E.; Lange, Andreas; List, John A.; Price, Michael K. and Nicholas G. Rupp (2010) “Is a Donor in Hand Better Than Two in the Bush ? Evidence From a Natural Field Experiment”, American Economic Review, 100(3), 958--983, 10.1257/aer.100.3.958 .
Examples
# NOT RUN {
#### Example 8-5
## ------------------------------------------------------------------------
# }
# NOT RUN {
data("Donors", package = "pder")
library("plm")
T3.1 <- plm(donation ~ treatment + prcontr, Donors, index = "id")
T3.2 <- plm(donation ~ treatment * prcontr - prcontr, Donors, index = "id")
T5.A <- pldv(donation ~ treatment + prcontr, Donors, index = "id",
model = "random", method = "bfgs")
T5.B <- pldv(donation ~ treatment * prcontr - prcontr, Donors, index = "id",
model = "random", method = "bfgs")
# }
# NOT RUN {
# }