6,188 rated or chosen profiles from 6,188 respondents, in up to 1 tasks of 1 profile varying on 6 attributes.

About this table

Description	Rural respondents in Malawi and Zambia (LGPI 2019 face-to-face survey) each read one vignette about a hypothetical neighbour with randomized ethnic group, gender, age, migration status, income and land title, then rated expected cooperation (trust to repay a loan, help collecting donations, follow the headman, observe the local way of life, share a well). Non-co-ethnic arm only: 6,188 respondents.
Reference	Harris, A. S., & Honig, L. (2023). Mutual dependence and expectations of cooperation. The Journal of Politics, 85(1).
DOI	10.1086/720646
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/K0BFAG

Notes

Conjoint table (irw_conjoint). Single neighbour vignette; ratings kept raw: rating = 'Say you had loaned this neighbor some money, would you trust them to pay it back?' (1 No, 2 Yes), rating_donations / rating_follow_headman / rating_share_well 1 Very likely to 4 Not at all likely (higher = less likely), rating_way_of_life 1 Agree / 2 Disagree; don't know and refusals NA. Only the non-co-ethnic arm (6,209 of 12,362) is kept because the deposit did not save the ethnic group shown to the 6,153 co-ethnic-arm respondents, so the co-ethnic contrast cannot be estimated (the arm was randomized, so the rest is a random subset); the title text's pronoun follows the neighbour's gender; 21 respondents with no answer omitted.

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Design

Topic	social identity interpersonal
A profile represents	person
Country	MW, ZM
Language shown	unknown
Language of stored levels	en
Randomization restrictions	observed in the data (not documented by the source): attr_title text carries a pronoun (her / his, him) that always matches attr_gender, so its four texts are 2 title levels x gender; ethnic-group levels come from a country-specific list of 8 (Malawi: Chewa, Tumbuka, Ngoni, Lambya, Yao, Lomwe, Senga, Ndali; Zambia: Chewa, Tumbuka, Ngoni, Namwanga, Bemba, Senga, Bisa, Nyika).
Task order	inferred from row order
Profile position	inferred from row order

Because the attribute levels were not randomized independently, an estimator has to account for the restriction; a plain difference in means across levels can mislead. Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

rating	Say you had loaned this neighbor some money, would you trust them to pay it back? [rating, 1 (No) to 2 (Yes)]
rating_donations	Imagine you are collecting donations to [add a new classroom to the local school OR repair the local health clinic], how likely do you think this person would be to help you collect donations? [rating, 1 (Very likely) to 4 (Not at all likely)]
rating_follow_headman	If there were a conflict in the village that involved this neighbor, and people went to the headman/woman to resolve it, how likely do you think it is that this neighbor would follow the headman/woman's orders? [rating, 1 (Very likely) to 4 (Not at all likely)]
rating_share_well	Imagine that there was a well on this neighbor's land. How likely do you think it is that they would let you use their well? [rating, 1 (Very likely) to 4 (Not at all likely)]
rating_way_of_life	To what extent do you agree or disagree with the following statement: This neighbor is observing our way of life in this village/neighborhood. [rating, 1 (Agree) to 2 (Disagree)]

Columns

attr_ageattr_ethnic_groupattr_genderattr_incomeattr_migrationattr_titlecov_agecov_countrycov_ethnic_groupcov_gendercov_have_gov_titlecov_localityidprofileratingrating_donationsrating_follow_headmanrating_share_wellrating_way_of_lifetask

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("harris_2023_neighbor_cooperation", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("harris_2023_neighbor_cooperation", source="conj")

How to cite

@article{Harris_2023, title={Mutual Dependence and Expectations of Cooperation}, volume={85}, ISSN={1468-2508}, url={http://dx.doi.org/10.1086/720646}, DOI={10.1086/720646}, number={1}, journal={The Journal of Politics}, publisher={University of Chicago Press}, author={Harris, Adam S. and Honig, Lauren}, year={2023}, month=Jan, pages={192–208} }

Respondents	6,188
Rows (one per respondent, task and profile)	6,188
Tasks per respondent (most)	1
Profiles per task	1
Attributes	6

IRW version	v584
Redivis dataset	irw_conjoint v4.2
Redivis dataset DOI	10.57761/qkye-q530
Manifest pin for this IRW version	v4.2
Metadata source	irw_meta v44.0

harris_2023_neighbor_cooperation