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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.

Table 1 - Benefit Categories
DPB18_0_e  DPB or WB, aged 18 - <29, youngest child aged 0 - <5, earning $1 - $200 pw
DPB18_0_f  DPB or WB, aged 18 - <29, youngest child aged 0 - <5,earning more than $200 pw
DPB18_0_no  DPB or WB, aged 18 - <29, youngest child aged 0 - <5,earning $0 pw or missing
DPB18_5+_no  DPB or WB, aged 18 - <29, youngest child aged 5+, earning $0 pw or missing
DPB18_5+_wrk  DPB or WB, aged 18 - <29, youngest child aged 5+, and earning > $0 pw
DPB18_nc_no  DPB or WB, aged 18 - <29, no dependent children, earning $0 or missing
DPB18_nc_wrk  DPB or WB, aged 18 - <29, no dependent children, earning > $0 pw
DPB30_0_e  DPB or WB, aged 30 - <60, youngest child 0 - <5, earning $1 - $200 pw
DPB30_0_f  DPB or WB, aged 30 - <60, youngest child aged 0 - <5, earning more than $200 pw
DPB30_0_no  DPB or WB, aged 30 - <60, youngest child aged 0 - <5, earning $0 pw or missing
DPB30_14_e  DPB or WB, aged 30 - <60, youngest child aged 14+, earning $1 - $200 pw
DPB30_14_f  DPB or WB, aged 30 - <60, youngest child aged 14+, earning more than $200 pw
DPB30_14_no  DPB or WB, aged 30 - <60, youngest child aged 14+, earning $0 pw or missing
DPB30_5_e  DPB or WB, aged 30 - <60, youngest child aged 5 - <14, earning $1 - $200 pw
DPB30_5_f  DPB or WB, aged 30 - <60, youngest child aged 5 - <14, earning more than $200 pw
DPB30_5_no  DPB or WB, aged 30 - <60, youngest child aged 5 - <14, earning $0 pw or missing
DPB30_nc_e  DPB or WB, aged 30 - <60, no dependent children, earning $1 - $200 pw
DPB30_nc_f  DPB or WB, aged 30 - <60, no dependent children, earning more than $200 pw
DPB30_nc_no  DPB or WB, aged 30 - <60, no dependent children, earning $0 pw or missing
DPB60_no  DPB or WB, aged 60 - <65, no dependent children, earning $0 pw or missing
DPB60_wrk  DPB or WB, aged 60 - <65, no dependent children, and earnings > $0 pw
IB18_e  IB, aged 18 - <30, earning $1 - $200 pw
IB18_f  IB, aged 18 - <30, earning more than $200 pw
IB18_no  IB, aged 18 - <30, earning $0 pw or missing
IB30_e  IB, aged 30 - <60, earning $1 - $200 pw
IB30_f  IB, aged 30 - <60, earning more than $200 pw
IB30_no  IB, aged 30 - <60, earning $0 pw or missing
IB60_no  IB, aged 60 - <65, earning $0 pw or missing
IB60_wrk  IB, aged 60 - <65, earning > $0 pw
SB18_e  SB, aged 18 - <30, earning $1 - $200 pw
SB18_f  SB, aged 18 - <30, earning more than $200 pw
SB18_no  SB, aged 18 - <30, earning $0 pw or missing
SB30_e  SB, aged 30 - <60, earning $1 - $200 pw
SB30_f  SB, aged 30 - <60, earning more than $200 pw
SB30_no  SB, aged 30 - <60, earning $0 pw or missing
SB60_no  SB, aged 60 - <65, earning $0 pw or missing
SB60_wrk  SB, aged 60 - <65, earning > $0 pw
u18  a benefit but aged under 18 years
UB18_e  UB, aged 18 - <30, earning $1 - $200 pw
UB18_f  UB, aged 18 - <30, earning more than $200 pw
UB18_no  UB, aged 18 - <30, earning $0 pw or missing
UB30_e  UB, aged 30 - <60, earning $1 - $200 pw
UB30_f  UB, aged 30 - <60, earning more than $200 pw
UB30_no  UB, aged 30 - <60, earning $0 pw or missing
UB60_no  UB, aged 60 - <65, earning $0 pw or missing
UB60_wrk  UB, aged 60 - <65, and earnings > $0 pw
Misc  other benefits, including CSI and training benefits

Table 2 - Average Entrants per Quarter
States Pre-GFC Post-GFC
DPB18_0_e 192 122
DPB18_0_f 4 4
DPB18_0_no 1,331 1,024
DPB18_5+_no 209 172
DPB18_5+_wrk 40 26
DPB18_nc_no 363 1,096
DPB18_nc_wrk 37 106
DPB30_0_e 127 83
DPB30_0_f 4 4
DPB30_0_no 789 596
DPB30_14_e 89 99
DPB30_14_f 4 7
DPB30_14_no 404 478
DPB30_5_e 282 189
DPB30_5_f 14 14
DPB30_5_no 1,106 925
DPB30_nc_e 111 246
DPB30_nc_f 5 11
DPB30_nc_no 504 1,393
DPB60_no 161 209
DPB60_wrk 46 52
IB18_e 15 11
IB18_f 1 0
IB18_no 163 162
IB30_e 44 35
IB30_f 2 2
IB30_no 714 755
IB60_no 218 297
IB60_wrk 14 14
SB18_e 107 111
SB18_f 6 8
SB18_no 2,824 3,438
SB30_e 198 189
SB30_f 19 21
SB30_no 3,887 4,608
SB60_no 401 589
SB60_wrk 30 36
u18 1,096 1,077
UB18_e 590 923
UB18_f 47 69
UB18_no 6,255 11,833
UB30_e 399 528
UB30_f 55 92
UB30_no 4,495 7,577
UB60_no 365 529
UB60_wrk 46 53
misc 1,166 1,514
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