3.1 The Data and Construction of Matrices

The data used here were obtained from the Benefit Dynamics Dataset (BDD) maintained by MSD. The dataset captures the key variables required for the analysis of individual benefit histories. The BDD includes information on all people who received any main working-age social welfare benefit in the period of study, from February 2005 to February 2011. It provides basic information on their demographic characteristics, and traces their changing benefit status and other circumstances from the beginning of the study period (for benefits current at that date) or from the date they are first granted benefit in that period (for new grants). It also traces the benefit histories of partners and dependent children included in benefits.

The first stage involved constructing the relevant flow matrices and vectors for each quarter over the period, resulting in 24 sets of accounts. At this stage, benefit recipients were divided into 63 categories. After examination of these matrices, the number of benefit categories was cut down to 47 separate types, largely by amalgamating different age groups within a category type: some age groups were found to contain very small numbers of individuals.[7] The final set of categories is described in Table 1.

Examination of the many matrices of transitions between quarters showed a relative stability over the pre-global financial crisis (pre-GFC) periods. There are clearly fewer observations for the post-global financial crisis (post-GFC) period, but again the flows showed little change. Hence, a dividing line was drawn between pre-GFC and post-GFC flows. For this reason, the many matrices were reduced to only two sets of flow matrices and vectors, by computing average flows in the two periods. In view of this averaging process, no explicit allowance is made here for seasonality (particularly regarding unemployment benefit inflows). The full details of the two matrices and their flows coefficients are given in Appendix C, where it can be seen that there are substantial 'off diagonal' movements.

However, it is important to recognise that the coefficients are expected to change over time as a result of policy changes, as discussed earlier, as well as extraneous factors. There have in fact been some policy changes over the relevant periods. In considering a practical policy context the nature of the changes over time in particular transitions would be the focal point of analysis. For present purposes the large changes observed for many flows following the GFC provide useful illustrations of the major benefits of the general approach and the potential value of recognising explicitly that changes take place to a system that is not in equilibrium, so that the consequences of any change can be much wider than anticipated.

A difficulty arises in dealing with exits from benefits. Instead of having a single vector of 'outflows', d, many reasons are recorded. In addition, this part of the dataset has a significant number of missing entries. Appendix A describes the method used to divide the exits into just four categories, involving extraneous information from LEED/MSD Feasibility Study[8]. In particular, it is most useful to have information about the flows of individuals off benefits and into employment.

The average number of entrants into each benefit category, before and after the GFC, are reported in Table 3. The largest increases in average entrants are for those with no earnings in all of the basic categories; these are Domestic Purposes Benefit (DPB), Invalid's Benefit (IB), Sickness Benefit (SB) and Unemployment Benefit (UB). Not surprisingly, the largest increases by far are for UBs, particularly in 18_no and 30_no categories. However, increases in the corresponding SB categories are also substantial.