Table 3 Recent examples of Indices
Aggregation | Index Methodology |
|---|---|
Social Health of the States (2008) [42] | |
The Institute for Innovation in Social Policy (Vassar College, Poughkeepsie, NY, USA) has a long running interest in tracking health and social equality in the United States. It uses 16 indicators, which are scaled so that the worst possible average is 50. The differences between a state’s actual average and the worst possible is expressed as a percentage of 50; the larger the percentage, the higher the social health score. States are ranked and the 50 states are grouped in quintiles, with ranks 1–10 deemed excellent, and ranks 41–50 deemed poor. Thus this project provides a set of indicators (see their table that is built on the life cycle, and has many points of concordance with those already discussed). In addition it calculates an Index from these that permits ranking and tracking of states. This is obviously not specific to urban areas, but is of interest in development of a metric for urban areas. | |
Michigan index of urban prosperity [45] | www.landpolicy.msu.edu (The initiative on Michigan prosperity is active, but the Index is not currently available, highlighting the evanescent nature of some of these enterprises.) |
This index, developed by the group that created the Michigan Critical Health Indicators (see above), combines multiple components: crime rate; property value change; median household income; employment rate; employment change; graduation rate; MEAP passing rate; young adults; population change. These indices are measurements that are compared to the overall state measurement, taken to be 1. They do not explicitly state how the 9 measures are combined to produce an overall index, though it would appear to be a simple average. They apply this index to a number of urban areas within the state, showing that Ann Arbor (a University town) is faring much better than its more gritty urban counterparts (such as Detroit). This effort is a good example of the attempt to construct an index (and could be classified as well as a Unitary Indicator), though it is not clear that it would be applicable in developing areas where much of the data might not be available. The initiative on Michigan prosperity is active, but the Index is not currently available, highlighting the evanexcent nature of some of these enterprises. | |
Index of Resident Economic Well Being [46] | In this older study, the authors developed a 5-component index that includes: unemployment rate; poverty rate; labour force participation; median household income; per capita income. These are combined using N-scores (like z-scores but use deviations from the median), but the details are not provided. It is another attempt to use a few indicators to form a unitary metric that can be used to compare areas. |
Noted also in their discussion: City Distress Index (city poverty rate, unemployment rate, per capita income growth, and population change); James, F. (1990) City needs and distress in the United State : 1970s to the mid 1980s, in: M. Kaplan and F. James (Ed.) The Future of National Urban Policy. Durham, NC: Duke University Press. Other measures are reviewed there as well, but all seem to follow a similar pattern. These metrics are specifically urban, but not specifically health-related. | |
UNDP: Human Development Index and new associated measures [27, 47, 77] | |
The Human Development Index focuses on three fundamental measures: life expectancy at birth; mean years of schooling and expected years of school; gross national income per capita. Each of these measures is “normalized” by taking the country value as a percent of the range of the most extreme values ([country value – minimum value]/[maximum value – minimum value]). The two measures of education are then combined by taking their geometric mean, and this combined value is further combined with the other two measures using the geometric mean. The result is a value between 0 and 1.0 that reflects the relative place of each country in the overall ranking of nations. The measure is thus complex in its creation but simple in its interpretation. It serves as an interesting model for a possible Urban Health Index, which could be constructed from a small number of constantly recurring measures, or a possible Urban Health Disparities Index, with similar characteristics. | |
The HDI has now been augmented by a number of similarly-constructed measures whose characteristics have considerable ramifications for the development of an Urban Health Index: | |
Inequality-adjusted Human Development Index (http://hdr.undp.org/en/statistics/ihdi/) | |
Gender Inequality Index (http://hdr.undp.org/en/statistics/gii/) | |
Multidimensional Poverty Index (http://hdr.undp.org/en/statistics/mpi/) | |
Each of these requires considerable mathematical manipulation and might not be readily accessible by health workers in country, but they provide a sophisticated example of an approach to a unitary index that may have value. | |
Bertelsmann Transformation Index [49] | |
Though not directly related to health and urbanicity, the BTI is an interesting example of index construction by a somewhat different route. A total of 17 criteria subdivided into 52 “questions” are provided in a country report for each of the 129 participating nations. Experts on subject matter and from the participating countries review and “calibrate” the numbers which are then subjected to a final review by the BTI board. The scores are combined using linear aggregation (the exact method is not reported on the website) and an overall score for a number of domains is assigned. The major difference between this Index and many of the others is that it employs a modified Delphi technique rather than simply aggregating available statistics. An approach of this sort may be of value in developing an index, but might not be workable on the local level. | |
Corruption Perception Index [50] | http://www.transparency.org/policy_research/surveys_indices/cpi/2010 |
Transparency International has created an index that ranks countries according to their perception of corruption in the public sector. It uses a minimum of three sources for each country, carried out by “independent and reputable institutions” (their web materials cite 12 different surveys from 11 listed organizations, of which the BTI, above, is one). The data are standardized using matching percentiles, then undergo beta transformation for which the cumulative distribution function is used. The final CPI is the linear average of the transformed values. This complex process, involving two important transformations is thus based on an amalgamation of impressions from many sources, and rests heavily on a Delphi process as well. The final product is a rank ordering of nations, with attendant political ramifications. |