So far we have shown that, when used cross-sectionally, UMBR is in line with other widely used measured of area deprivation, but unlike the IMDs it can help describe patterns of neighbourhood change. This section addresses two further questions. First, how well does UMBR reflect residents’ perceptions of their neighbourhoods? This relates to the concern that objective measures may not correspond to subjective perceptions of neighbourhood characteristics. The second question relates to residential mobility. More specifically, can change as described by UMBR enrich our understanding of location choices? We answer both questions by matching UMBR data to the individual responses to the MCS.
The MCS is a nationally representative sample of births covering around 19,000 children born in the UK between September 2000 and January 2002. Families were interviewed for the first time when the children were aged 9 months (MCS1), mainly in 2001. They were followed up when children were 3 years old, with interviews mainly in 2004 (MCS2), and when the children were aged 5, in 2006 (MCS3). Since then, MCS children and their families have been interviewed more, in 2008, 2012 and 2015. In this paper we concentrate on early childhood, before compulsory schooling, so use data from just the first three surveys.
An important feature of MCS is its clustered sample design (Plewis 2007). It oversamples children living in areas with high rates of child poverty or high minority ethnic populations. Thus MCS offers the opportunity to look closely at families living in poor areas. Furthermore, the MCS sample also over-samples in Scotland, Wales and Northern Ireland. As UMBR is unfortunately available only for Great Britain, we excluded the Northern Ireland sample from the MCS data.
Also by design MCS targets a specific demographic group—families with small children. The characteristics of the immediate surroundings are likely to be very relevant to children, whose regular interactions with people and institutions, such as day centres or playgrounds, tend to occur within a limited distance from their home. And indeed, parents of young children who have the possibility to do so tend to be particularly careful to select areas offering good conditions and resources for bringing up a family (Shonkoff and Phillips 2000). In addition, families with young children have often only recently moved to an area, and therefore have made their residential choice fairly recently. Indeed, among all families interviewed in Great Britain at MCS1, only 32 % had lived at their address for more than 4 years. By the time the cohort child was five, more families had settled, with around 60 % staying put between first and third surveys. Thus, MCS is an ideal dataset to study residential mobility. Moreover, comparing the subjective opinions of neighbourhood in the MCS with the UMBR score of the statistical area provides a particularly strong test of whether an objective poverty measure can reflect the “neighbourhood quality” perceived by residents.
We use the LSOA/DZ of residence at interview to match MCS interviews to UMBR data. Our analytical sample is confined to those families observed in Great Britain at all three sweeps. Footnote 5 To define movers and stayers, we used two sources of information—self-reported moves and the geocodes attached to the place of residence. We defined as movers all those who explicitly reported a move, dropping from our analytical sample 786 observations who appeared to have changed LSOA/DZ between sweeps but did not report a move. This is because information on the reasons for moving could only be asked if moving was reported and because we did not want to treat as ‘stayers’ those whose moving status was not clear. After also discarding observations with incomplete information on their views about the neighbourhood we were left with 10,240 observations. Throughout the analysis we used weights that take into account the sample design and attrition up to MCS3 (Plewis 2007).
MCS allows respondents both to define subjective neighbourhood boundaries and to record their subjective views of the neighbourhood. At all three surveys, MCS recorded mothers’ (or main respondents’) views of the area they lived in. It was indicated that areas would be “within 20 min walk” from the respondents’ home. Such a definition of area obviously varies by person to person depending on their mobility. It also allows a degree of flexibility for respondents to refer to their immediate surrounding or larger neighbourhood (Kearns and Parkes 2003). Such a definition does not necessarily coincide with the LSOA/DZ or other administrative boundaries. In densely populated areas, a 20 min walk will cover more than one LSOA/DZ. Hereafter we use the term “locality” or “neighbourhood” to indicate the area subjectively defined by respondents, while we continue to use the term “area” to refer to LSOAs and DZs. The difference in terminology, although rather arbitrary, serves as a reminder that when comparing UMBR with residents’ views of their locality we cannot be sure that geography covered by the two definitions is the same.
The survey questions differed across sweeps. In MCS1 and MCS2 mothers were asked about their general satisfaction with their locality using a five point scale, from “very satisfied” to “very dissatisfied”. At MCS2 and MCS3 respondents were asked whether the locality was good “to bring up children”. With no question about general neighbourhood satisfaction in MCS3, there is no consistent information across all three sweeps.
Table 4 reports the average level of UMBR of the areas where MCS families lived in 2001, 2004 and 2006. UMBR levels in MCS are in line with the overall average for Great Britain, which, in 2001, was 21 %. Among areas falling in the top three deciles of UMBR, its average level was 40 %, with, in 2001, a minimum of 25 % and a maximum of 88 %. Also in line with the evidence reported in the previous section, there was no visible variation in the average level of UMBR over time. Table 5 reports the distribution of MCS families across areas falling in the top 30 % of UMBR distribution in Great Britain. At MCS1, 30 % of families were in living in such high poverty areas. That percentage had hardly changed by MCS3. Because MCS data allow looking at family-level poverty as well, we examined to what extent poor families were concentrated in high poverty areas.Footnote 6 At MCS1, 60 % of the families who were below the poverty line were living in the 30 % poorest areas. And, similarly, 55 % of the MCS families living in the 30 % poorest areas were themselves below the poverty line. These data provide a useful reminder of the fact that area poverty and individual poverty are not synonymous. Not everyone who lives in a high poverty area is poor, and not all people who are poor live in high poverty areas (Townsend 1979).
Table 6 presents subjective opinions on neighbourhood. At both sweep 1 and sweep 2, the great majority of respondents reported being either very or fairly satisfied with their locality. However, views on whether the neighbourhood was good for bringing up children were less positive, with 71 and 73 % of respondents considering it good or very good. Families who did not move (“stayers”) constituted around 60 % of our sample. For them, any neighbourhood change happened “around” them. Among those who moved, 35 % mentioned wanting a “better area” among the reasons for moving. This incudes those who explicitly said “better area”, and also those who reported moving for “children’s education”, “school catchment area”, or because they had “problems with neighbours” or because they wanted to “move away from crime”. Other reasons reported in the survey comprised both positive reasons, such as wanting to be closer to families, as well as negative reasons, such as relationship breakdown or money problems (Ketende and McDonald 2008).
Were those expressing more positive opinions about their neighbourhood concentrated in areas with lower poverty rates? We start by looking at “stayers”. Figures 4 and 5 (and similar plots, not shown, for the two measures of satisfaction from MCS2) suggest that those with the most positive views were more likely to be in areas with low poverty. At high levels of poverty negative views outweighed positive and neutral responses. We compared residents of the poorest 30 % areas with those who living in the other 70 %. In the poorer areas, only 10 % of residents said that their neighbourhood was excellent for bringing up children, in contrast with 41 % of residents in the less poor areas. Seventeen per cent of mothers in areas with high levels of UMBR considered their locality as very poor for bringing up children, but less than 2 % in the less poor areas took this view.
Movers provided further evidence on whether UMBR correlated with subjective opinions on neighbourhood. First of all, we find that families living in areas with higher UMBR were more likely to move (Table 7), and that the majority of them moved to areas that were less poor (Table 8). This is in line with the mobility literature, which suggests poverty is a “push factor”, as people tend to move out of deprived areas and to improve their situation by moving (Rabe and Taylor 2010). By using UMBR, we are also able to quantify the magnitude of the changes. In case of moves to less poor areas, the area of destination was on average 12 percentage points less poor. In case of moves to poorer areas, 11 points poorer.
We then looked at those who said—retrospectively—that they had moved for reasons related to the new locality, implicitly suggesting that their current neighbourhood was better than the previous one. We expected this group would have gone to areas with lower UMBR than the original area. Table 9 (first row) confirms this: at the time of the first interview in 2001 these families had been living in areas with an average UMBR level of 22.5 %, while by 2006, they had moved to areas with an average UMBR of 16.8 %. By contrast, movers who did not say they moved for a better neighbourhood, went to areas with, on average, a similar level of UMBR (Table 9 second row). That residents’ opinions were in line with UMBR levels suggests that UMBR captures aspects of an area relevant to residents; this pattern emerges among stayers and movers alike.
The second part of the analysis explores what extent the dynamic description of areas afforded by UMBR can enrich our understanding of residential mobility. We start by looking at the type of area stayers and movers are found at in 2006, where we classify areas on the basis of their change in UMBR since 2001. Are movers flocking to areas that have been improving? We do not find any evidence of that. Instead, there is no significant difference in the distribution movers and stayers across areas with different trends in UMBRFootnote 7 (Table 10). This suggests that movers do not select their area of destination on the basis of what is happening to that area. Instead, they appear to simply compare it to their area of origin.
We test the usefulness of UMBR further, by focusing on the group of movers who had reached a poorer area than their original one. This is a sizable group: 38 %, as Table 8 indicates. Information on area changes can help us check whether these families were nevertheless better off in terms of area poverty by moving than by staying put. Perhaps they were escaping rapidly deteriorating areas? In fact that does not seem to be the case: 96.5 % of the families who moved to poorer areas would have had less of an increase in UMBR if they had stayed at the original address (Table 11). Finally we examine whether these families were moving to improving areas. Perhaps they were choosing to live in a poorer area with the expectation that things were nonetheless improving. Again, that does not seem to be the case: only 19 % of those moving to higher UMBR than at origin were found in areas with a falling UMBR (Table 12). Instead, 40 % of these families appeared to be at double disadvantage: not only had they moved areas that were poorer than their area of origin but also to areas where poverty had been on the rise (Table 12).