Suparna Chaudhry ⢠Department of
International Affairs ⢠Lewis & Clark College
Andrew Heiss ⢠Andrew Young School of
Policy Studies ⢠Georgia State University
Suparna Chaudhry and Andrew Heiss. 2020. âDynamics of International Giving: How Heuristics Shape Individual Donor Preferences,â Nonprofit and Voluntary Sector Quarterly (forthcoming), doi:
10.1177/0899764020971045.
All this projectâs materials are free and open:
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State restrictions on non-governmental organizations (NGOs) have become increasingly pervasive across the globe. While this crackdown has been shown to have a negative impact on public funding flows, we know little about how it impacts private philanthropy. How does information about crackdown abroad, as well as organizational attributes of nonprofits affect individual donorsâ willingness to donate internationally? Using a survey experiment, we find that learning about repressive NGO environments increases generosity in that already-likely donors are willing to donate substantially more to legally besieged nonprofits. This generosity persists when mediated by two organizational-level heuristics: NGO issue areas and main funding sources. We discuss the implications of our results on how nonprofits can use different framing appeals to increase fundraising at a time when traditional public donor funding to such organizations is decreasing.
This repository contains the data and code for our paper. Our pre-print is online here:
Suparna Chaudhry and Andrew Heiss. 2020. âDynamics of International Giving: How Heuristics Shape Individual Donor Preferencesâ". Accessed November 17, 2020. Online at https://dx.doi.org/10.17605/OSF.IO/FG53W
The paper is published at Nonprofit and Voluntary Sector Quarterly:
Suparna Chaudhry and Andrew Heiss. 2020. âDynamics of International Giving: How Heuristics Shape Individual Donor Preferences,â Nonprofit and Voluntary Sector Quarterly (forthcoming), doi:
10.1177/0899764020971045.
Please cite this compendium as:
Suparna Chaudhry and Andrew Heiss. 2020. Compendium of R code and data for âDynamics of International Giving: How Heuristics Shape Individual Donor Preferencesâ. Accessed November 17, 2020. Online at https://dx.doi.org/10.17605/OSF.IO/FG53W
You can either download the compendium as a ZIP file or use GitHub to clone or fork the compendium repository (see the green âClone or downloadâ button at the top of the GitHub page).
In order to reproduce this project, youâll need to install the compendium as an R package. After downloading the compendium, do the following:
-
Open the compendium as a project in RStudio (double click on
crackdownsphilanthropy.proj). -
Install the pkgbuild, devtools, rstan, and rstantools packages, either with RStudioâs âPackagesâ panel or by running
install.packages(c("pkgbuild", "devtools", "rstan", "rstantools")) -
You need to compile the Stan C++ binary DLLs before building and installing the actual crackdownsphilanthropy compendium package (see here for details about why). Run these two lines in R:
pkgbuild::compile_dll() # This will probably take a few minutes roxygen2::roxygenize() -
Finally, build the compendium package by running this in R:
devtools::install(".")
You should now be able to run library(crackdownsphilanthropy) and
reproduce all the findings in the paper (as well as the paper itself).
To reproduce the analysis, run make build from RStudioâs âTerminalâ
panel. Open analysis/_site/ to see the results. Run make serve to
serve the site at
http://localhost:7000.
To repdocuce the paper, run make html or make tex or make docx or
make paper (for all three output formats) from the terminal. Open
manuscript/ (or manuscript/tex_out/ for PDFs) to see the results.
This project includes the following data files:
data/raw_data/crackdowns_philanthropy_raw.csv: Original results from the Qualtrics surveydata/derived_data/results_clean.csv: Final version of the data, cleaned withanalysis/01_clean-data.Rmddata/derived_data/results_clean.yaml: YAML metadata describing the syntax of the cleaned datadata/derived_data/results_clean.rds:.rdsversion of cleaned datadata/derived_data/codes_to_exclude.csv: Unique IDs of participants who failed the attention checks or took the survey outside of Mechanical Turk. These are excluded from the final data inanalysis/01_clean-data.Rmdraw_data/ipums-cps/: Data extracts from the US Censusâs Current Population Survey (CPS) via the Minnesota Population Centerâs Integrated Public Use Microdata Series (IPUMS):cps_2017.dat.gz: 2017 ASEC, with the following variables selected (in addition to whatever IPUMS preselects by default) (and weighted byASECWT):AGESEXEDUCINCTOT
cps_09_2015.dat.gz: September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted byVLSUPPWT):VLSTATUSVLDONATE
Text and figures: All prose and images are licensed under Creative Commons (CC-BY-4.0)
Code: With only two exceptions, all the code is dual-licensed under
the MIT License and GPL
3. Two dependencies
related to Stan compilationâR/stanmodels.R and
tools/make_cc.Râare licensed as GPL 3 only, and we
canât backwards-license them as only MIT. Additionally, any binary files
generated by those files (i.e. the compiled Stan code) is inherently
GPL-licensed, which also makes this entire project GPL-licensed in the
end. But since weâve dual-licensed pretty much everything else, you
treat everything as MIT unless a file is explicitly GPL-only.
(Here are a bunch of resources about the vagaries of mixing MIT and GPL licenses: link; link; link)
We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.