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Data

Country-level estimates of negative reciprocity

About this data

Source
Global Preferences Survey (2018) – processed by Our World in Data
Last updated
November 6, 2018
Date range
2012–2012

Sources and processing

This data is based on the following sources

Global Preferences Survey

The Global Preferences Survey (GPS) measures preferences for nationally representative samples taken from each of the 76 countries covered. This includes 15 countries from the Americas, 25 from Europe, 22 from Asia and the Pacific, and 14 African countries - 11 of which are Sub-Saharan. In total, this covers 90% of both the world population and total income.

The median sample size was 1,000 participants per country. The sample includes preference measures for over 80,000 participants altogether.

Country level averages for each economic preference were calculated using individual-level data weighted by the sampling weights provided by the Gallup World Poll. Each preference is normalized to have mean 0 and standard deviation 1 in the individual-level data.

For more information on the construction of the GPS dataset, see Section II.A of the paper by Falk et al. (2018).

Retrieved on
November 6, 2018
Retrieved from
https://www.briq-institute.org/global-preferences/downloads
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., and Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics (forthcoming). Published by briq - Institute on Behavior and Inequality.
Falk, A., Becker, A., Dohmen, T. J., Huffman, D., and Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper No. 9674.

The Global Preferences Survey (GPS) measures preferences for nationally representative samples taken from each of the 76 countries covered. This includes 15 countries from the Americas, 25 from Europe, 22 from Asia and the Pacific, and 14 African countries - 11 of which are Sub-Saharan. In total, this covers 90% of both the world population and total income.

The median sample size was 1,000 participants per country. The sample includes preference measures for over 80,000 participants altogether.

Country level averages for each economic preference were calculated using individual-level data weighted by the sampling weights provided by the Gallup World Poll. Each preference is normalized to have mean 0 and standard deviation 1 in the individual-level data.

For more information on the construction of the GPS dataset, see Section II.A of the paper by Falk et al. (2018).

Retrieved on
November 6, 2018
Retrieved from
https://www.briq-institute.org/global-preferences/downloads
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., and Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics (forthcoming). Published by briq - Institute on Behavior and Inequality.
Falk, A., Becker, A., Dohmen, T. J., Huffman, D., and Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper No. 9674.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Country-level estimates of negative reciprocity”. Our World in Data (2026). Data adapted from Global Preferences Survey. Retrieved from https://archive.ourworldindata.org/20260512-085513/grapher/cross-country-variation-in-negative-reciprocity.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Global Preferences Survey (2018) – processed by Our World in Data

Full citation

Global Preferences Survey (2018) – processed by Our World in Data. “Country-level estimates of negative reciprocity” [dataset]. Global Preferences Survey, “Global Preferences Survey” [original data]. Retrieved May 13, 2026 from https://archive.ourworldindata.org/20260512-085513/grapher/cross-country-variation-in-negative-reciprocity.html (archived on May 12, 2026).

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Data API

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Data URL (CSV format)
https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/cross-country-variation-in-negative-reciprocity.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear