black_politicians: Data from "Black Politicians are More Intrinsically Motivated...

In causaldata: Example Data Sets for Causal Inference Textbooks

Data from "Black Politicians are More Intrinsically Motivated to Advance Blacks' Interests"

Description

The black_politicians data contains data from Broockman (2013) on a field experiment where the author sent fictional emails purportedly sent by Black people to legislators in the United States. The experiment sought to determine whether the effect of the email being from "out-of-district" (someone who can't vote for you and so provides no extrinsic motivation to reply) would have a smaller effect on response rates for Black legislators than for non-Black ones, providing evidence of additional intrinsic motivation on the part of Black legislators to help Black people.

Usage

black_politicians

Format

A data frame with 5593 rows and 14 variables

leg_black

Legislator receiving email is Black

treat_out

Email is from out-of-district

responded

Legislator responded to email

totalpop

District population

medianhhincom

District median household income

black_medianhh

District median household income among Black people

white_medianhh

District median household income among White people

blackpercent

Percentage of district that is Black

statessquireindex

State's Squire index

nonblacknonwhite

Legislator receiving email is neither Black nor White

urbanpercent

Percentage of district that is urban

leg_senator

Legislator receiving email is a senator

leg_democrat

Legislator receiving email is in the Democratic party

south

Legislator receiving email is in the Southern United States

Details

This data is used in the Matching chapter of The Effect.

Source

Broockman, D.E., 2013. Black politicians are more intrinsically motivated to advance blacks’ interests: A field experiment manipulating political incentives. American Journal of Political Science, 57(3), pp.521-536.

References

Huntington-Klein. 2021. The Effect: An Introduction to Research Design and Causality. https://theeffectbook.net.

causaldata documentation built on Oct. 25, 2024, 1:07 a.m.