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Financial Activity and Debt The scores were derived from a variety of data sources. Debt, for example is positively correlated with a number of measures of deprivation such as unemployment, County Court Judgements, the proportion of households headed by a single parent, poor housing conditions, type of housing etc. It is negatively correlated with the proportion of the work force in professional and higher managerial type occupations, detached housing, low unemployment and so on. These patterns of correlation have be used to derive a score which predicts debt likelihood. The measure on financial activity is built in a similar way. Each predictor is held in two formats. The first is a standardised numeric score with a mean of zero and a standard deviation of one. Thus a large positive score for the postcode denotes the likelihood of debt, whilst a large negative score denotes the opposite - the absence of debt. The numeric score tells you exactly how far up or down the scale any given postcode falls. A debt score of +1.96 or more, for example, would tell you that this postcode comes within the top 2.5% of the country in terms of the debt rating. All the scores can be interpreted in exactly the same way. These scores can be used directly in analysis and statistical modeling. The second format in which the information is held is as a banded ranking. Each postcode score is grouped into one of twenty possible bands so that approximately 5% of all households in the country fall within each band. A band one ranking for debt, for example, denotes that the postcode in question contains some of the most indebted households in the country, whilst a band 20 ranking shows the opposite. Using the banded rankings it is possible to overlay these onto any file of names and addresses and generate a profile by simply comparing the proportion of the file in each of the bands against the expected 5%. For further information Download
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