Dynamics of International Giving: How Heuristics Shape Individual Donor Preferences

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. “Dynamics of International Giving: How Heuristics Shape Individual Donor Preferences,” Nonprofit and Voluntary Sector Quarterly (forthcoming), doi: 10.1177/0899764020971045.

All this project’s materials are free and open:

• Download the data
• See the analysis notebook website

   

---

Abstract

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 donors’ 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. “Dynamics of International Giving: How Heuristics Shape Individual Donor Preferences”". 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. “Dynamics of International Giving: How Heuristics Shape Individual Donor Preferences,” Nonprofit and Voluntary Sector Quarterly (forthcoming), doi: 10.1177/0899764020971045.

How to cite

Please cite this compendium as:

Suparna Chaudhry and Andrew Heiss. 2020. Compendium of R code and data for “Dynamics of International Giving: How Heuristics Shape Individual Donor Preferences”. Accessed November 17, 2020. Online at https://dx.doi.org/10.17605/OSF.IO/FG53W

How to download and install

You can either download the compendium as a ZIP file or use GitHub to clone or fork the compendium repository (see the green “Clone or download” button at the top of the GitHub page).

In order to reproduce this project, you’ll need to install the compendium as an R package. After downloading the compendium, do the following:

1. Open the compendium as a project in RStudio (double click on crackdownsphilanthropy.proj).

2. Install the pkgbuild, devtools, rstan, and rstantools packages, either with RStudio’s “Packages” panel or by running install.packages(c("pkgbuild", "devtools", "rstan", "rstantools"))

3. 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()
   

4. 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 RStudio’s “Terminal” 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.

Data

This project includes the following data files:

• data/raw_data/crackdowns_philanthropy_raw.csv: Original results from the Qualtrics survey
• data/derived_data/results_clean.csv: Final version of the data, cleaned with analysis/01_clean-data.Rmd
• data/derived_data/results_clean.yaml: YAML metadata describing the syntax of the cleaned data
• data/derived_data/results_clean.rds: .rds version of cleaned data
• data/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 in analysis/01_clean-data.Rmd
• raw_data/ipums-cps/: Data extracts from the US Census’s Current Population Survey (CPS) via the Minnesota Population Center’s 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 by ASECWT):
    • AGE
    • SEX
    • EDUC
    • INCTOT
  • cps_09_2015.dat.gz: September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted by VLSUPPWT):
    • VLSTATUS
    • VLDONATE

Licenses

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 compilation—R/stanmodels.R and tools/make_cc.R—are licensed as GPL 3 only, and we can’t 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 we’ve 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)

Contributions

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.