Donors: Dynamics of Charitable Giving

Description

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

Usage

data(Donors)

Arguments

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

Run this code

# 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 {
# }

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