This page provides answers to the most relevant and most commonly raised issues about the Indices of Social Development.
How do I cite the Indices of Social Development?
How reliable are the scores?
We updated with data until 2015. More data for the years 2020 and beyond will become available every 5 years. Every time we add new data, standard errors of the Indices will decrease and hence become more reliable.
Why no standard errors (s.e.) for the Clubs and Associations measure?
For the Clubs and Associations measure this is because there are some countries for which the only available source is the World Values Survey. For other indices, an estimate is not produced if there was only one source but for this measure, the range of data is so limited these are included anyway.
Why do you present data for five year-averages?
Not all data (e.g. household survey data) is available for every single year. Hence, taking averages overcomes sample bias errors and outliers caused by extreme scores for one point in time.
What about the variable names and sources?
For background information there is a list of variable names and sources that are used to compile the indices of Social Development.
What options do I have to access the data?
On the access to the data part of this site you can make a tailor-made selection of the countries, years and the indices.
Why use so many variables rather than selecting a few ‘key’ indicators?
First, there are few data sources that cover a fully representative range of the world’s countries, and thus without combining indicators, it would be impossible to gain scores for more than a small sub-sample of nations.
Second, we assume that every indicator has some amount of error. Error can be due to several causes: observational error may exist because of unreliability in the instrument used to record a particular phenomenon: surveys, for example, may be subject to reporting biases or sampling error, while official statistics on the other hand may have been compiled using different methodologies.
There is also error that is attributable to the use of indicators with low concept validity, that is, when the selected indicator, however reliably gathered, only imperfectly corresponds to the latent variable under consideration. One way to reduce error is to employ greater scrutiny in the selection and consideration of indicators. Yet this presumes a high degree of knowledge on the part of the analyst: it can be difficult to assess the reliability of any given measure in isolation, especially in the absence of familiarity with the method used to generate those values. Validity is easier to determine, though here again we often have to rely on complex assumptions regarding the causal relationship between what we are measuring and what we seek to measure.
For example, it may be open to contention whether civic capacity is best measured by features of the institutional environment (the number of media organizations, freedom of information), features of citizen behavior (engagement in local civic groups, participation in voting, petitions and demonstrations), or some other feature of that society (e.g. the number of international NGOs).
Moreover, with social phenomena we often face a trade-off between reliability, validity, and representativeness: a given indicator, such as the income ratio between different ethnic groups, may be a valid and reliable measure of social exclusion, but available for very few countries; a survey item on attitudes toward other ethnic groups is certainly valid and may be widely available, but subject to survey response bias. There is, in short, rarely a single indicator that adequately measures the concept we are trying to quantify.
Combining multiple indicators, on the other hand, is another means to reduce aggregate error. If one assumes that errors are uncorrelated between data sources and that the size of the error is constant across items, then the combination of multiple sources will progressively reduce error as the number of indicators increases. We supplement these estimates with the calculated margin of error for each country, which is based on how many sources there were and the extent to which these sources agreed.
How reliable are perceptions-based indicators?
The institutions which drive social development are by nature difficult to detect, given that they rest upon tacit norms, beliefs, and practices which lack explicit formalization. Previous quantitative studies of social institutions have therefore largely relied upon using either proxies based upon causes or consequences (such as using daily newspaper circulation as a proxy for the extent to which citizens take an active interest in local politics, or linguistic fractionalization as a proxy for cohesion or otherwise between social groups), or survey responses to questions regarding social attitudes.
Not all survey data is perceptionsbased, however, and can often be behavioral, as when respondents are asked whether they have been the victim of crime, whether they have signed a petition, or whether they have contacted a local representative. Both proxy variables and survey items are used in these indices, and both correlate to an exceptionally large extent.
For example, a country’s reported level of social trust is strongly predicted by a country’s homicide rate, while the correlation between the proportion of managers who say men have a greater priority than women to a job, and the ratio between male and female labor force participation, is likewise high. To some extent, this reflects the fact that perceptions and attitudes are not simply the result of social institutions, but are the institution, to a substantial degree.
What are the advantages of using matching percentiles?
The matching percentiles method brings with it several advantages for creating a set of indices of this nature.
First, the matching percentiles method overcomes the problem of sampling bias. This is pervasive when a new data source only covers a limited and unrepresentative sample of countries, as country scores on the new indicator will reflect not only a difference in scaling (β) but also a difference in the constant (α). A further advantage of the matching percentiles technique is that it allows us to keep adding successive waves of indicators, even with very small samples, that can be used to continually ‘refine’ the country scores simply by using information on relative rankings. Whereas regression based techniques of aggregation encounter difficulties in incorporating small sample sources due to difficulties estimating α and β when the sample size is very low, no such difficulties affect the matching percentiles technique. This is critically important for a set of indices of this nature, where the present data remain incomplete, such that it will be necessary to keep adding new indicators in future years as successive data source become available, even where such sources cover relatively few countries.
What does ‘Matching Percentiles’ mean?
The matching percentiles method, used by the Indices of Social Development, was first deployed by Lambsdorff et al. (1999) to construct the Corruptions Perceptions Index. In the matching percentiles process, values are matched across indicators based on country rankings. The ranks of successive indicators included in the index are used to assign equivalent values to countries based on their position on each additional measure. Variables are iteratively added to produce the index.
What is meant by social development?
‘Social’ development can be broadly defined as the social, or informal, institutions that empower individuals to make the most of their skills and resources and live a full and complete life. Examples include the way in which norms of non-discrimination help allow women and minorities to access the labour market, or in which community policing can help prevent the threat of violence. By highlighting the role that such institutions play in the development process, the objective of the indices of social development is to track global progress in building and sustaining the social institutions that contribute to human well-being.
What are the 6 dimensions of the Indicators of Social Development?
Civic Activism refers to the social norms, organizations, and practices which facilitate greater citizen involvement in public policies and decisions. These include access to civic associations, participation in the media, and the means to participate in civic activities such as nonviolent demonstration or petition. Civic engagement is essential in ensuring that public institutions function in an accountable and transparent manner, with participation and representation for all.
Clubs and Associations measures the extent to which there is a rich local associative life within towns, neighborhoods and villages. Such ties are essential in ensuring that individuals who fall on hard times do not also ‘fall through the cracks’, and in securing individuals’ wellbeing through a system of social relations and a community of identity.
Intergroup Cohesion measures the extent to which there is social cohesion between defined religious, ethnic, and linguistic groups, without degeneration into civil unrest or intergroup violence.
Interpersonal Safety and Trust measures the extent to which there is social cohesion between strangers, as manifested through bonds of trust, reciprocity, and absence of criminal intent. In a society with high interpersonal safety and trust, violent crime is rare and people do not have to fear the violation of their personal and property rights.
Gender Equality measures the extent to which women have equal opportunities as men in the fields of education, employment, in the home, and in political life.
Inclusion of Minorities measures level of discrimination against vulnerable groups such as indigenous people, migrants, refugees, or lower caste groups.