Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16

ABSTRACT This paper presents the results of a pilot study conducted in Ghana that utilized citizen data approaches for monitoring a governance indicator within the SDG framework, focusing on indicator 16.6.2 citizen satisfaction with public services . This indicator is a crucial measure of governance quality, as emphasized by the UN Sustainable Development Goals (SDGs) through target 16.6 Develop effective, accountable, and transparent institutions at all levels . Indicator 16.6.2 specifically measures satisfaction with key public services, including health, education, and other government services, such as government‐issued identification documents through a survey. However, with only 5 years remaining to achieve the SDGs, the lack of data continues to pose a significant challenge in monitoring progress toward this target, particularly regarding the experiences of marginalized populations. Our findings suggest that well‐designed citizen data initiatives can effectively capture the experiences of marginalized individuals and communities. Additionally, they can serve as valuable supplements to official statistics, providing crucial data on population groups typically underrepresented in traditional surveys.

Authors

Institutions

Publication Details

Journal
Sustainable Development
Published
2025-03-26
DOI
https://doi.org/10.1002/sd.3441
Citations
3
Primary Topic
E-Government and Public Services
Type
article
Field-Weighted Citation Impact
25.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16

Dilek Fraisl, Omar Seidu, Linda See, Mariana Neves et al.
3 citations
Sustainable Development
E-Government and Public Services
25.57
article

Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16

Dilek Fraisl, Omar Seidu, Linda See, Mariana Neves, Arvinn Gadgil, Cyrus Kwesi Darpoh, Aparna Basnyat, Fatma Usheva, Basil Tungbani, Dieudonnee Ankamah
article en
3 citations

Abstract

ABSTRACT This paper presents the results of a pilot study conducted in Ghana that utilized citizen data approaches for monitoring a governance indicator within the SDG framework, focusing on indicator 16.6.2 citizen satisfaction with public services . This indicator is a crucial measure of governance quality, as emphasized by the UN Sustainable Development Goals (SDGs) through target 16.6 Develop effective, accountable, and transparent institutions at all levels . Indicator 16.6.2 specifically measures satisfaction with key public services, including health, education, and other government services, such as government‐issued identification documents through a survey. However, with only 5 years remaining to achieve the SDGs, the lack of data continues to pose a significant challenge in monitoring progress toward this target, particularly regarding the experiences of marginalized populations. Our findings suggest that well‐designed citizen data initiatives can effectively capture the experiences of marginalized individuals and communities. Additionally, they can serve as valuable supplements to official statistics, providing crucial data on population groups typically underrepresented in traditional surveys.

Sustainable DevelopmentVol. 33(4)
United Nations Development Programme (US), International Institute for Applied Systems Analysis (AT), Ghana Education Service (GH)
Openalex Percentile: Top 1%
E-Government and Public Services
25.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.