International Civil Service Effectiveness (InCiSE) Index 2019

The International Civil Service Effectiveness (InCiSE) Index draws together a wealth of existing data to help countries determine how their central civil services are performing and learn from each other.

InCiSE logo

The International Civil Service Effectiveness (InCiSE) Index is the first comprehensive index of international indicators of civil service effectiveness. It aims to assess the performance of central civil services around the world.

An effective civil service can play an important role in determining a country’s progress and prosperity, and the InCiSE Index’s core objective is to help countries determine how their central civil services are performing, and to learn from each other.

Building on the lessons learned during the 2017 pilot phase, the 2019 InCiSE Index is an improved index featuring refined methodology and increased volume of metrics and range of data sources.

The 2019 InCiSE Index covers 38 countries (seven more than in the previous version) and uses 46 more metrics and 5 more data sources than previously. We have also explored ways of including non-OECD countries and developing countries. To this end, the Blavatnik School has completed two country case studies, Brazil and Nigeria, to assess potential for the InCiSE Index to be used in contexts where data availability is more challenging.

The full 2019 Index, its technical report and both technical papers on Brazil and Nigeria are available for download on the right hand-side.

The InCiSE Index can be used:

  • as a performance improvement tool for civil service leaders to find out which countries perform best in which areas and learn from them;
  • as an accountability tool which allows citizens, government officials and politicians to find out how well their civil service is performing.

We recognise the important role central civil services can play in determining a country’s progress and prosperity, and our priority is to secure long-term funding to enable the InCiSE project to expand further. We see the InCiSE Index as a learning and performance improvement tool for governments around the world, and the goal is to produce it on a regular basis and to increase country coverage while maintaining data quality.

About the Index

The InCiSE Index is a collaboration between the Blavatnik School of Government and the Institute for Government. It is supported by the UK Civil Service and has been funded by the Open Society Foundations.

The Index is focused on the central government civil service, not the public service more generally. It does not aim to be definitive and it is not possible to directly compare scores between the 2017 and 2019 results.

The InCiSE index has been designed on a framework developed in discussion with officials from national governments, international organisations, civil society partners and academics. This framework defines 17 functions and attributes that contribute to civil service effectiveness - at present, due to data availability, only 12 have been included in the index. These functions and attributes, collectively called indicators, are then disaggregated into individual metrics.

This interactive dashboard allows you to explore the results of the 2019 edition of the International Civil Service Effectiveness (InCiSE) index, including: graphs of the results, a datatable of results, and metadata about the InCiSE metrics.

Use the selection controls to choose a country and to switch between the InCiSE index and its constituent indicators.

Dashboard controls


Graphs:
  • These graphs show the results for the InCiSE index or indicator you have selected.
  • The bar graph on the left shows the overall index or indicator scores for all 38 countries.
  • The radar graph on the right shows the results for the constituent components of the score - when the index is selected it will show you the scores for the 12 InCiSE indcators, when an indicator is selected it will show you the scores for the metrics that make up that indicator.
  • The radar chart shows the selected country's scores and the InCiSE average.
  • A radar point with a white fill means that the data for that data for the indicator/metric is missing for that country and has been imputed.
Data table: This table shows the results for the selected indicator. At the bottom of the table you can use the buttons to copy this table to the clipboard, download this specific table as a CSV file, or download a CSV file containing all the data from the 2019 edition of InCISE.
Data table
Data table
Metadata: This table shows the refence metadata about the InCiSE index or the selected indicator. At the bottom of the table you can use the buttons to copy this table to the clipboard, download this specific table as a CSV file, or download a CSV file containing all the metadata from the 2019 edition of InCISE.
InCiSE metadata table

About InCiSE

The International Civil Service Effectiveness (InCiSE) Index was launched as a pilot in 2017, this dashboard shows the results from the section edition published in 2019. Recognising the important role civil services can play in helping their countries to prosper, InCiSE aims to assess how effectively civil services around the world perform and to identify in which areas their strengths lie relative to their international counterparts.

InCiSE aspires progressively to become a robust, comparative measure of civil service performance, but is not that yet – mainly because of data limitations. In addition, InCiSE does not seek to be definitive: it will be important to assess its results alongside other evidence available to leaders and citizens. The InCiSE Index should be seen as one of a range of tools available to measure civil service effectiveness globally.

InCiSE is a collaboration of the Blavatnik School of Government and the Institute for Government. It has been supported by the UK Civil Service and has recieved funding from the Open Society Foundations.

Methodology

The InCiSE index brings together data from a wide and varied range of sources. In their raw state these data not directly comparable, some data may be statistics or proportions, some might be ratings on different scales (e.g. 1 to 5 or 0 to 10), and some are categorical data. The InCiSE model processes and normalises the different types of data so that country performance in different domains can be compared. Full details of the methodology can be found in the InCiSE 2019 Technical Report.

InCiSE framework and model

The InCiSE index has been designed on a framework developed in discussion with officials from national governments, international organisations, civil society partners and academics. This framework defines 17 functions and attributes that contribute to civil service effectiveness - at present, due to data availability, only 12 have been included in the index. These functions and attributes, collectively called indicators, are then disaggregated into individual metrics.

Data sources

InCiSE draws on data from a wide range of data sources, and we are thankful to data providers for allowing the resuse of their data. A full list of data sources can be found below. InCiSE is built using the latest available data as of 30 Novmeber 2018.

Data quality and imputation

Not all sources cover all 38 countries included in the index, with data availability and quality varying across both countries and indicators. In some cases countries are missing just a few metrics for an indicator, while others are missing all data for a given indicator. In order to provide a practical tool for users, the InCiSE model uses multiple imputation to provide indicative scores for missing data points. These are clearly flagged on the graphs and tables in this dashboard.

The InCiSE model has developed a data quality assessment which assesses the level of missing data for each country and indicator, and also accounts for whether data is from a public sector proxy and the recency of the data. This data quality assessment has been used to determine country coverage.

Weighting

The scores for the InCiSE indicators are weighted sums of their constituent metrics. The InCiSE index is then calcualted as a weighted sum of the indcators. The weights for metrics and indicators can be found in the metadata table.


Data Sources
Software packages

The technical model and analysis for the 2019 edition of the InCiSE model was developed using R, a statistical programming language. This dashboard was developed using Plotly.JS, Datatables, Bootstrap with the 'Flatly' Bootswatch theme, and jQuery.

The R packages used in developing InCiSE are:

R
R Core Team (2016) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/
countrycode
Arel-Bundock V, Enevoldsen N and Yetman CJ (2018) “countrycode: An R package to convert country names and country codes”, Journal of Open Source Software 3(28): 848, https://doi.org/10.21105/joss.00848
Rilostat
Bescond D (2018) Rilostat: ILO Open Data via Ilostat Bulk Download Facility or SDMX Web Service, R package version 0.2.1, https://CRAN.R-project.org/package=Rilostat
intsuvy
Caro DH and Biecek P (2017) “intsvy: An R Package for Analyzing International Large-Scale, Assessment Data” Journal of Statistical Software 81(7): 1-44, http://doi.org/10.18637/jss.v081.i07
janitor
Firke S (2018) janitor: Simple Tools for Examining and Cleaning Dirty Data, R package version 1.1.1, https://CRAN.R-project.org/package=janitor
purrr
Henry L and Wickham H (2017) purrr: Functional Programming Tools, R package version 0.2.4, https://CRAN.R-project.org/package=purrr
magrittr
Milton Bache S and Wickham H (2014) magrittr: A Forward-Pipe Operator for R, R package version 1.5, https://CRAN.R-project.org/package=magrittr
tibble
Müller K and Wickham H (2017) tibble: Simple Data Frames, R package version 1.3.4, https://CRAN.R-project.org/package=tibble
mice
van Buuren S and Groothuis-Oudshoorn K (2011) ‘mice: Multivariate Imputation by Chained Equations in R’, Journal of Statistical Software 45(3):1-67, http://www.jstatsoft.org/v45/i03/
Plotly
Sivert C (2018) plotly for R, https://plotly-book.cpsievert.me
rvest
Wickham H (2016) rvest: Easily Harvest (Scrape) Web Pages, R package version 0.3.2, https://CRAN.R-project.org/package=rvest
scales
Wickham H (2017) scales: Scale Functions for Visualization, R package version 0.5.0, https://CRAN.R-project.org/package=scales
stringr
Wickham H (2017) stringr: Simple, Consistent Wrappers for Common String Operations, R package version 1.2.0, https://CRAN.R-project.org/package=stringr
readxl
Wickham H and Bryan J (2017) readxl: Read Excel Files, R package version 1.0.0, https://CRAN.R-project.org/package=readxl
dplyr
Wickham H, Francois R, Henry L and Müller K (2017) dplyr: A Grammar of Data Manipulation, R package version 0.7.4, https://CRAN.R-project.org/package=dplyr
tidyr
Wickham H and Henry L (2017) tidyr: Easily Tidy Data with 'spread()' and 'gather()' Functions, R package version 0.7.2, https://CRAN.R-project.org/package=tidyr
readr
Wickham H, Hester J and Francois R (2017) readr: Read Rectangular Text Data, R package version 1.1.1, https://CRAN.R-project.org/package=readr
haven
Wickham H and Miller E (2017) haven: Import and Export 'SPSS', 'Stata' and 'SAS' Files, R package version 1.1.0, https://CRAN.R-project.org/package=haven
Country codes
AUS
Australia
AUT
Austria
BEL
Belgium
BGR
Bulgaria
CAN
Canada
CHE
Switzerland
CHL
Chile
CZE
Czechia
DEU
Germany
DNK
Denmark
ESP
Spain
EST
Estonia
FIN
Finland
FRA
France
GBR
United Kingdom
GRC
Greece
HRV
Croatia
HUN
Hungary
IRL
Ireland
ISL
Iceland
ISR
Israel
ITA
Italy
JPN
Japan
KOR
South Korea
LTU
Lithuania
LVA
Latvia
MEX
Mexico
NLD
The Netherlands
NOR
Norway
NZL
New Zealand
POL
Poland
PRT
Portugal
ROU
Romania
SVK
Slovakia
SVN
Slovenia
SWE
Sweden
TUR
Turkey
USA
United States of America