Overview of all QoG datasets accessible through pyqog, with codebook links.
The Quality of Government (QoG) Institute provides several datasets, each with a different scope and focus. All datasets are available in time-series (panel) and cross-sectional (snapshot) formats. All data is downloaded in CSV format.
| Dataset | Prefix | pyqog name | Description |
|---|---|---|---|
| Basic | bas |
"basic" |
Curated selection of key governance and institutional quality indicators. Good starting point for most analyses. |
| Standard | std |
"standard" |
The most comprehensive QoG dataset with approximately 2,000 variables from hundreds of sources. |
| OECD | oecd |
"oecd" |
Focused on OECD member countries with indicators relevant to developed economies. |
| Environmental | ei |
"environmental" |
Environmental indicators including emissions, resources, and sustainability metrics. |
| Social Policy | soc |
"social_policy" |
Social policy data covering health, education, welfare, and social protection. |
The Basic dataset contains a curated subset of the Standard dataset, focusing on the most commonly used indicators of governance quality, democracy, corruption, and economic development. It is ideal for teaching, quick explorations, and analyses that do not require the full breadth of the Standard dataset.
Usage:
df = pyqog.read_qog(which_data="basic")
The Standard dataset is the flagship QoG product. It contains approximately 2,000 variables compiled from hundreds of data sources including the World Bank, United Nations, V-Dem, Transparency International, Freedom House, and many others. It covers governance, economics, health, education, environment, conflict, and more.
Usage:
df = pyqog.read_qog(which_data="standard")
The OECD dataset focuses on OECD member countries and includes indicators particularly relevant to developed economies and OECD policy analysis. It contains additional variables from OECD-specific data sources not found in the Standard dataset.
Usage:
df = pyqog.read_qog(which_data="oecd")
The Environmental Indicators dataset compiles environmental data including CO2 emissions, deforestation, biodiversity, water resources, energy use, and sustainability metrics from various international sources.
Usage:
df = pyqog.read_qog(which_data="environmental")
Each dataset version has a corresponding codebook that describes every variable in detail. Below are direct links to codebook PDFs for all datasets and versions.
| Dataset | Codebook PDF |
|---|---|
| Basic | codebook_bas_jan26.pdf |
| Standard | codebook_std_jan26.pdf |
| OECD | codebook_oecd_jan26.pdf |
| Environmental | codebook_ei_jan26.pdf |
| Social Policy | codebook_soc_jan26.pdf |
Codebooks for older dataset versions are available from the QoG data archive.
Use the year parameter in read_qog() to download the
corresponding data.
| Version | Year | Codebook PDF |
|---|---|---|
| jan26 | 2026 | codebook_bas_jan26.pdf |
| jan25 | 2025 | codebook_bas_jan25.pdf |
| jan24 | 2024 | codebook_bas_jan24.pdf |
| jan23 | 2023 | codebook_bas_jan23.pdf |
| jan22 | 2022 | codebook_bas_jan22.pdf |
| jan21 | 2021 | codebook_bas_jan21.pdf |
| jan20 | 2020 | codebook_bas_jan20.pdf |
| jan19 | 2019 | codebook_bas_jan19.pdf |
| jan18 | 2018 | codebook_bas_jan18.pdf |
| jan17 | 2017 | codebook_bas_jan17.pdf |
| jan16 | 2016 | codebook_bas_jan16.pdf |
| jan15 | 2015 | codebook_bas_jan15.pdf |
| jan14 | 2014 | codebook_bas_jan14.pdf |
| 30aug13 | 2013 | codebook_bas_30aug13.pdf |
| 21may12 | 2012 | codebook_bas_21may12.pdf |
| 6apr11 | 2011 | codebook_bas_6apr11.pdf |
| 27may10 | 2010 | codebook_bas_27may10.pdf |
| 17sep09 | 2009 | codebook_bas_17sep09.pdf |
| 15may08 | 2008 | codebook_bas_15may08.pdf |
| Version | Year | Codebook PDF |
|---|---|---|
| jan26 | 2026 | codebook_std_jan26.pdf |
| jan25 | 2025 | codebook_std_jan25.pdf |
| jan24 | 2024 | codebook_std_jan24.pdf |
| jan23 | 2023 | codebook_std_jan23.pdf |
| jan22 | 2022 | codebook_std_jan22.pdf |
| jan21 | 2021 | codebook_std_jan21.pdf |
| jan20 | 2020 | codebook_std_jan20.pdf |
| jan19 | 2019 | codebook_std_jan19.pdf |
| jan18 | 2018 | codebook_std_jan18.pdf |
| jan17 | 2017 | codebook_std_jan17.pdf |
| jan16 | 2016 | codebook_std_jan16.pdf |
| jan15 | 2015 | codebook_std_jan15.pdf |
| jan14 | 2014 | codebook_std_jan14.pdf |
| 30aug13 | 2013 | codebook_std_30aug13.pdf |
| 21may12 | 2012 | codebook_std_21may12.pdf |
| 6apr11 | 2011 | codebook_std_6apr11.pdf |
| 27may10 | 2010 | codebook_std_27may10.pdf |
| 17sep09 | 2009 | codebook_std_17sep09.pdf |
| 15may08 | 2008 | codebook_std_15may08.pdf |
| Version | Year | Codebook PDF |
|---|---|---|
| jan26 | 2026 | codebook_oecd_jan26.pdf |
| jan25 | 2025 | codebook_oecd_jan25.pdf |
| jan24 | 2024 | codebook_oecd_jan24.pdf |
| jan23 | 2023 | codebook_oecd_jan23.pdf |
| jan22 | 2022 | codebook_oecd_jan22.pdf |
| jan21 | 2021 | codebook_oecd_jan21.pdf |
| jan20 | 2020 | codebook_oecd_jan20.pdf |
| jan19 | 2019 | codebook_oecd_jan19.pdf |
| jan18 | 2018 | codebook_oecd_jan18.pdf |
| jan17 | 2017 | codebook_oecd_jan17.pdf |
| jan16 | 2016 | codebook_oecd_jan16.pdf |
| jan15 | 2015 | codebook_oecd_jan15.pdf |
| jan14 | 2014 | codebook_oecd_jan14.pdf |
| 30aug13 | 2013 | codebook_oecd_30aug13.pdf |
| 21may12 | 2012 | codebook_oecd_21may12.pdf |
| 6apr11 | 2011 | codebook_oecd_6apr11.pdf |
| 27may10 | 2010 | codebook_oecd_27may10.pdf |
| 17sep09 | 2009 | codebook_oecd_17sep09.pdf |
| 15may08 | 2008 | codebook_oecd_15may08.pdf |
| Version | Year | Codebook PDF |
|---|---|---|
| jan26 | 2026 | codebook_ei_jan26.pdf |
| jan25 | 2025 | codebook_ei_jan25.pdf |
| jan24 | 2024 | codebook_ei_jan24.pdf |
| jan23 | 2023 | codebook_ei_jan23.pdf |
| jan22 | 2022 | codebook_ei_jan22.pdf |
| jan21 | 2021 | codebook_ei_jan21.pdf |
| jan20 | 2020 | codebook_ei_jan20.pdf |
| jan19 | 2019 | codebook_ei_jan19.pdf |
| jan18 | 2018 | codebook_ei_jan18.pdf |
| jan17 | 2017 | codebook_ei_jan17.pdf |
| jan16 | 2016 | codebook_ei_jan16.pdf |
| jan15 | 2015 | codebook_ei_jan15.pdf |
| jan14 | 2014 | codebook_ei_jan14.pdf |
| 30aug13 | 2013 | codebook_ei_30aug13.pdf |
| 21may12 | 2012 | codebook_ei_21may12.pdf |
| 6apr11 | 2011 | codebook_ei_6apr11.pdf |
| 27may10 | 2010 | codebook_ei_27may10.pdf |
| 17sep09 | 2009 | codebook_ei_17sep09.pdf |
| 15may08 | 2008 | codebook_ei_15may08.pdf |
| Version | Year | Codebook PDF |
|---|---|---|
| jan26 | 2026 | codebook_soc_jan26.pdf |
| jan25 | 2025 | codebook_soc_jan25.pdf |
| jan24 | 2024 | codebook_soc_jan24.pdf |
| jan23 | 2023 | codebook_soc_jan23.pdf |
| jan22 | 2022 | codebook_soc_jan22.pdf |
| jan21 | 2021 | codebook_soc_jan21.pdf |
| jan20 | 2020 | codebook_soc_jan20.pdf |
| jan19 | 2019 | codebook_soc_jan19.pdf |
| jan18 | 2018 | codebook_soc_jan18.pdf |
| jan17 | 2017 | codebook_soc_jan17.pdf |
| jan16 | 2016 | codebook_soc_jan16.pdf |
| jan15 | 2015 | codebook_soc_jan15.pdf |
| jan14 | 2014 | codebook_soc_jan14.pdf |
| 30aug13 | 2013 | codebook_soc_30aug13.pdf |
| 21may12 | 2012 | codebook_soc_21may12.pdf |
| 6apr11 | 2011 | codebook_soc_6apr11.pdf |
| 27may10 | 2010 | codebook_soc_27may10.pdf |
| 17sep09 | 2009 | codebook_soc_17sep09.pdf |
| 15may08 | 2008 | codebook_soc_15may08.pdf |
You can also get codebook URLs programmatically:
import pyqog
# Get codebook URL for any dataset and version
url = pyqog.get_codebook_url("standard", 2026)
print(url)
# https://www.qogdata.pol.gu.se/data/codebook_std_jan26.pdf
For more information about QoG data, including detailed variable descriptions, methodology, and additional datasets, visit the official QoG resources:
Interactive tool to search and explore QoG variables across all datasets.
Open Data Finder →
Social Policy Dataset
QoG Social Policy soc
The Social Policy dataset focuses on social protection, health systems, education, welfare spending, inequality, and labor market indicators across countries and over time.
Usage: