Analysis

Generally, two types of analyses will be produced from a wealth survey: those oriented to a general audience, and more in-depth analyses of interest to academics or policy makers. The more general analysis is often made available at the time of the initial release of the wealth data set in order to publicise the release and highlight key findings. In-depth analysis usually takes more time and is often conducted by the more sophisticated users once the data are released by the organisation that conducted the survey. These analyses will be produced by a variety of users in various organisations and may be made available to the general public depending on the mandate of the individual or group sponsoring the analysis. In addition, the broad distribution of analysis based on the wealth data can be used to educate users on the correct interpretation of the wealth data.

Data tables

Data tables are one way to make a variety of data available to users who may not have the analytical skills, resources or data access required to produce their own output from the micro-data file of wealth data. Often the tabulated data are produced in a publication or in an on-line database to allow users to browse the data tables and choose those statistics that are of more interest to them. This is a way of providing broad access to a wide variety of data to a large number of users.

As noted in Chapter 3, there are many ways in which household wealth data can be grouped to look at different sub-sets of the population. Table 8.1 provides an example of how data for various groups can be presented in a table to provide a general picture of one aspect of wealth data, in this case net worth.

Table 8.1. Family net worth by selected characteristics of families in the United States

Thousands of 2010 US dollars

Table 8.1. Family net worth by selected characteristics of families in the United States

(cont.)

..: Less than 0.05 (USD 50).

Source: United States Federal Reserve Bulletin, June 2012 article, “Changes in US Family Finances from 2007 to 2010”, Table 4, pp. 17-18.

As the power of desktop computing increases, so has the potential for statistical organisations to provide users with the ability to customise the tabular output to meet their own specific requirements through the use of self-help web-based table-builder products. Often the starting point of such products is aggregate data at the lowest level of detail possible from the micro-data file. Then users are provided with options on how to build their own tables based on the themes or variables of interest. One of the advantages of starting with aggregate data, rather than a micro-data file, is that less computing power is needed to group aggregate data than to produce tabulations for large micro-data files. Another advantage to this approach is that the detailed aggregate data can be screened for confidentiality prior to being made available to the general user population. An example of this type of product is the OECD.Stat web browser, which provides a single online platform for access to statistical databases from the OECD (http://stats.oecd.org).