Using the WGI data: Cross country comparisons

WGI data sources

Using the WGI data: Over time comparisons

WGI aggregation methodology

Further reading

Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.

The Worldwide Governance Indicators report on six broad dimensions of governance for over 200 countries and territories over the period 1996-:

Follow the links above for a definition of each of the six dimensions of governance, and a list of the individual indicators on which each aggregate indicator is based.  The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an Unobserved Components Model (UCM).  A key feature of the methodology is that it generates margins of error for each governance estimate.  These margins of error need to be taken into account when making comparisons across countries and over time.  An overview of the WGI methodology and use of the WGI data is provided below.  For a full description of the WGI methodology, click here. A complete replication package for the WGI calculations is available here.

The six composite WGI measures are useful as a tool for broad cross-country comparisons and for evaluating broad trends over time.  However, they are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts.  Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances.

The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country.  Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.

The WGI are a research dataset initiated by Daniel Kaufmann (President Emeritus, Natural Resource Governance Institute (NRGI) and Brookings Institution) and Aart Kraay (World Bank, Development Economics) in 1999.  Pablo Zoido and Massimo Mastruzzi also made major contributions to the development and updating of the WGI.  The WGI authors are grateful to the Knowledge for Change Program of the World Bank for financial support, to Jacquelyn Pavilon for outstanding research assistance, to Anna Kojzar, Sebastian Dolber, and Parisa Nazarijam for web support, and to Luiza Cardoso De Andrade, Luis Eduardo San Martin and Avnish Singh for code review.

The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms. The WGI do not reflect the official views of the Natural Resource Governance Institute, the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources.

WGI Data Sources

The WGI compile and summarize information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private and NGO sectors from around the world, on the quality of various aspects of governance.

The WGI draw on four different types of source data:

  • Surveys of households and firms, including the Afrobarometer surveys, Gallup World Poll, and Global Competitiveness Report survey,
  • Commercial business information providers, including the Economist Intelligence Unit, IHS Markit, Political Risk Services,
  • Non-governmental organizations, including Global Integrity, Freedom House, Reporters Without Borders, and
  • Public sector organizations, including the CPIA assessments of World Bank and regional development banks.

A summary table listing the data sources used in the current update of the WGI, and the years they are included in the WGI can be found here.  To access data from the individual indicators used in the WGI project, please click here.

The 2022 WGI update includes significant revisions to the underlying source data that affect the data for earlier years in the WGI dataset (for details please click here). This release of the WGI supersedes previous releases.  To access previous versions of the WGI dataset, please click here.  Please click here for further description of changes in the methodology and/or availability of three data sources that have contributed to large changes in the aggregate WGI indicators for a few countries between 2013 and 2014.

WGI Aggregation Methodology

Each of the six aggregate WGI measures are constructed by averaging together data from the underlying sources that correspond to the concept of governance being measured.  This is done in the three steps described below.

STEP 1:  Assigning data from individual sources to the six aggregate indicators.  Individual questions from the underlying data sources are assigned to each of the six aggregate indicators.  For example, a firm survey question on the regulatory environment would be assigned to Regulatory Quality, or a measure of press freedom would be assigned to Voice and Accountability.  A full description of the individual variables used in the WGI and how they are assigned to the six aggregate indicators, can be found by clicking on the names of the six aggregate indicators listed above.  Note that not all of the data sources cover all countries, and so the aggregate governance scores are based on different sets of underlying data for different countries.

STEP 2:  Rescaling of the individual source data to run from 0 to 1.  The questions from the individual data sources are first rescaled to range from 0 to 1, with higher values corresponding to better outcomes.  If, for example, a survey question asks for responses on a scale from a minimum of 1 to a maximum of 4, we rescale a score of 2 as (2-min)/(max-min)=(2-1)/3=0.33.  When an individual data source provides more than one question relating to a particular dimension of governance, we average together the rescaled scores.

The 0-1 rescaled data from the individual sources are available interactively through the WGI website here, and in the data files for each individual source.  Although nominally in the same 0-1 units, this rescaled data is not necessarily comparable across sources.  For example, one data source might use a 0-10 scale but in practice most scores are clustered between 6 and 10, while another data source might also use a 0-10 scale but have responses spread out over the entire range.  While the max-min rescaling above does not correct for this source of non-comparability, the procedure used to construct the aggregate indicators does (see below).

STEP 3:  Using an Unobserved Components Model (UCM) to construct a weighted average of the individual indicators for each source.   A statistical tool known as an Unobserved Components Model (UCM) is used to make the 0-1 rescaled data comparable across sources, and then to construct a weighted average of the data from each source for each country.  The UCM assumes that the observed data from each source are a linear function of the unobserved level of governance, plus an error term.  This linear function is different for different data sources, and so corrects for the remaining non-comparability of units of the rescaled data noted above.  The resulting estimates of governance are a weighted average of the data from each source, with weights reflecting the pattern of correlation among data sources.  Click here for the weights applied to the component indicators.

The UCM assigns greater weight to data sources that tend to be more strongly correlated with each other.  While this weighting improves the statistical precision of the aggregate indicators, it typically does not affect very much the ranking of countries on the aggregate indicators.  The composite measures of governance generated by the UCM are in units of a standard normal distribution, with mean zero, standard deviation of one, and running from approximately -2.5 to 2.5, with higher values corresponding to better governance.  We also report the data in percentile rank terms, ranging from 0 (lowest rank) to 100 (highest rank).

Details on the aggregation procedure can be found in the WGI methodology paper. A full replication package is available here.

Using the WGI Data: Cross-Country Comparisons

The six aggregate WGI indicators, and the underlying source data, are available interactively here.  The full dataset can be downloaded as an Excel file here.  The six aggregate indicators are reported in two ways:  (1) in their standard normal units, ranging from approximately -2.5 to 2.5, and (2) in percentile rank terms from 0 to 100, with higher values corresponding to better outcomes.

A key feature of the WGI is that all country scores are accompanied by standard errors.  These standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors).  These standard errors reflect the reality that governance is difficult to measure using any kind of data.  In most measures of governance or the investment climate they are however left implicit or ignored altogether.

In our graphical presentation of the data we transform these standard errors into 90 percent confidence intervals or "margins of error".  These indicate the statistically-likely range for governance based on the available data for a country (see figure below).  If when comparing two countries these margins of error overlap, the difference between the two countries should not be interpreted as signaling a statistically significant difference.  For details on the interpretation of the composite indicators and margins of error, please refer to Section 5 of the methodology paper .

Using the WGI: Over-Time Comparisons

Changes in governance over short year-to-year periods are difficult to measure with any kind of data, and are typically quite small.  Recognizing this, users of the WGI should in most cases not focus on short-run year-to-year changes but rather in trends over longer periods.

As with comparisons across countries, comparisons of governance scores for one country over time need to take into account margins of error: if confidence intervals for governance estimates in two periods overlap, the WGI data should not be interpreted as signaling meaningful changes in governance.  The vast majority of year-to-year changes in the WGI are too small relative to margins of error to be viewed as statistically, or practically, significant, and so should not be over-interpreted as indicating a significant change in governance performance.  However, over longer periods of time such as a decade, the WGI data do show significant trends in governance in a number of countries.

Changes over time in a country‚Äôs score on the WGI reflect a combination of three factors (i) changes in the underlying source data, (ii) the addition of new data sources for a country that are only available in the more recent period, and (iii) changes in the weights used to aggregate the individual sources.  For large and statistically significant changes over long periods of time, changes in the underlying source data are most often the most important of these three factors.  For the many smaller and often insignificant changes over shorter periods, a combination of all three factors contributes to changes in country scores.  Full access to the individual underlying data sources can help users better understand the reasons for changes in the aggregate indicators for a particular country.