Evidence Centre seminar: May 2018
Published: June 19, 2018
Our May seminar featured presentations about current multiple disadvantage and vulnerable transience using data from the Integrated Data Infrastructure (IDI).
IDI Insights: Understanding Multiple Disadvantage and Vulnerable Transience
The seminar featured two presentations 'What is the scale of vulnerable transience in New Zealand' and 'Multiple disadvantage and government spending'.
People experiencing difficulties across several life areas at the same time eg. health, housing, employment, can be described as having 'multiple disadvantage' while 'vulnerable transience' is defined as moves that lead to worse outcomes for people.
The studies used government data (from the Integrated Data Infrastructure) to look at how the wellbeing of people is affected by multiple disadvantage and vulnerable transience.
Eric Krassoi Peach: Multiple disadvantage and government spending
Eric gave a short background on his work exploring multiple disadvantage using General Social Survey data linked to the IDI.
Multiple disadvantage and government spending – video transcript
Eric Krassoi Peach - senior analyst and researcher at the Ministry of Social Development (formerly of Superu):
Kia ora tatou, good morning and welcome. I spend a lot of my time in dimly lit rooms looking at rows of data, so it's a real pleasure to be able to be with you this morning, and share some of our work, and I want a mihi to Abby and Sarah and James and Vasantha for making this happen, and for inviting me to be with you today.
So, as Abby said, I'm going to be speaking a bit about a piece of work that we've done, looking at using some data from the General Social Survey that's been linked to the IDI and trying to do an exploratory piece of work to understand how our Government spending matches to people with different levels of disadvantage.
So, before I get into that I have a bit of housekeeping, this is a disclaimer that I'm required by my agreement with Stats New Zealand to show you, you'll see it in Jason's presentation as well. It essentially says that this is not official statistics, that Stats New Zealand has endeavoured to hide the identity of all the people that we look at, and that any mistakes in the analysis are entirely my own and Stats cannot be held responsible. So they're off the hook.
I should explain a bit, oh, so I didn't say that I am the lead of the family stream of the Families and Whānau Wellbeing Research Programme, and I wanted to tell you but about the history of that programme. It was originally a creation of the Families Commission which became a Superu in 2014 as a result of the change of its legislation, and then as Superu prepares to be wound down at the end of this financial year, at the end of June, it has moved to the Ministry of Social Development as of November last year. So, that's to explain why you'll see Superu logos on some of the slides and, you know, one of the things that that research programme does is, since it became Superu, we've published an annual report on the status of families and whānau in New Zealand, so here's some pictures of the covers that we've had over the years. You can see we finally coalesced on one colour scheme. And the work that I'm going to be presenting is going to form part of a chapter that's coming out in this year's report, at the end of June.
So if I may shamelessly plug that report, and just tell you a little bit about the chapters that are in it, I think you'll find that interesting there's a family stream and a whanau stream. In the family stream we have three chapters. For the first time, we've worked with the Ministry of Health and Statistics New Zealand to apply family type to the New Zealand Health Survey, we show some analysis from a family perspective on that data for the for the first time in our first chapter. Then we have a chapter that extends some of the work that we started on multiple disadvantage last year and will be some of the content that I'll be presenting today.
We have done a deep dive into looking at the wellbeing of sole parents because they are disproportionately affected by disadvantage and particular multiple disadvantage. And on the whānau side we have another three chapters. The first is an overview of the history and circumstances of housing for whanau dating from before the signing of the Treaty of Waitangi to the present day. We have a second chapter that explores the connection between housing quality and whānau wellbeing using data from the Te Kupenga survey. And the last chapter is looking at how the Whānau Rangatiratanga Framework which was developed by Superu can inform the evaluation of the E Tū Whānau initiative at the Ministry of Social Development.
So, thanks for your patience there, I hope that you'll see some things you'll be interested in reading at the end of June. And because this is a Superu product and we're transitioning to being an MSD product thereafter, you'll will find it on the Superu website at the end of June, so I hope you go and look at it then. Now, I've said that this presentation is about multiple disadvantage so I should probably explain what I mean by that.
Simply put, multiple disadvantage is the situation of facing a number of difficulties or challenges across several life areas all at the same time. So, historically we've had some idea about the population who are in poor health, or who are living in poor quality housing, or who are unemployed. But our interest is in understanding the co-occurrence or the overlap of these things, so who is sick, and in a poor quality house, and unemployed. And we're interested in that group of people because we know that the most vulnerable people in society are not those who have one or two difficulties, but have many. And if we think that trying to resolve a complex interplay of a number of different difficulties across many different areas of people's lives requires different kinds of services, for people who just have one or two issues, then it's important to understand what size a population is experiencing multiple disadvantage, what kinds of combinations of disadvantage do they face, does it vary for different groups, and so this is how we got into this topic.
The way we began was to try to create a measure for multiple disadvantage in New Zealand, because we don't have one, we haven't historically, and so we began with our family wellbeing framework which, if you're interested in knowing more about that, is in previous status reports. So, we looked at what we had there and also the literature on measuring multiple disadvantage.
That got us to a list of life domains and indicators for measuring multiple disadvantage, and we took that to a reference group composed of a number of representatives from different Government agencies, I think seven in total, and we also looked at the General Social Survey which if you don't know is a survey that's been going on since 2008 every two years and it looks at a number of different areas of people's lives and it's pretty much the best option that we have for this kind of work, because remember we need to know about the same person in a number of the different areas of life, so the GSS is a great source of data for that. After doing a bit of testing, we've ultimately landed on a measure that we felt comfortable using.
Now, the fancy diagram that I have in the report on this, looked terrible in this presentation so I've come up with another one, so I apologize it's not all that slick. But what we've done is we've looked at eight different life domains and that's what you see there in the purple. So income, material wellbeing, employment, education, health, housing, safety, and connectedness. We have identified some indicators that will help us identify whether those domains are in disadvantage or not, and if we see three or more of those purple boxes and disadvantage we'll say that somebody is multiply disadvantaged. And clearly, if you have more of those domains and disadvantage it's a more serious form of that. So, okay so far?
Okay, thank you. So okay, we then used that measure and applied it to the General Social Survey and I'll just give you a few of the high level results. So, first we found that 18% or a little more than one in six adults in New Zealand experienced three or more life domains in disadvantage, and that if you flip that around and look at all of the incidences of disadvantage that we examined, about half occur with two or more other disadvantages, which is to say that half of all disadvantage comes in the form of multiple disadvantage. Yet another way of putting it is you could say that 18% of all adults bear about half of all the disadvantage in New Zealand. So, those are some findings. We also looked at the prevalence of multiple disadvantage across family types, it ranges for sort of between 5% and 13% for many family types, but in the case of sole parents it's half. So, 50% of sole parents have three or more of their life domains in disadvantage. And much of that is a more extreme form, four or more, which is why we did a whole chapter on sole parents this year.
The other thing is that the type of disadvantage is faced for people who are multiply disadvantaged in different family types, if you're still following me. So, for younger people, younger couples without children, or those who do have children, housing and income and material wellbeing were some of the main ones if they had three or more, whereas older couples without children tended to have health, education, and income as the ones that were most prevalent if they had three or more domains in disadvantage. So if you want to know more at that, again the Superu website is a good place to go. It will continue after its disestablishment for a year, so you can find all of that work there. Okay, so this kind of explains all of the General Social Survey work and a bit of what we did on multiple disadvantage. So, we were able to identify where we think people are experiencing difficulties or challenges in their life areas.
We got very excited when we heard that the General Social Survey would be moved into and linked to the IDI, because the IDI is a collection of administrative data that has a lot in there but one of the main things we were interested in is understanding how the provision of services kind of matched to people's needs, and how well we were doing in that. And so we've kind of used a rough measure of the dollars that have been spent in delivering services to individuals in the 12 months following their taking the General Social Survey. So you're going to see some numbers, it relates to the respondents and what can be attributed to them in terms of government spending in the year after the survey. So, what is in that? We've looked at five different areas of spending, so the first is income support, so this includes all of the tier 1, 2, 3 benefits and ACC income support, we have pensions, they're such a big source of Government spending that we've concluded them on their own, public health care, this includes anything you might imagine, inpatient, outpatient, laboratory tests, pharmaceutical reimbursements, ACC injury payments, and there's also mental health to a greater or lesser degree in there as well, Formal education, so this General Social Survey interviews people who are 15 plus so there's not any ECE or Early Childhood Education in there, but we do have industry training enrollments, tertiary education, and secondary education, and then some some information about the court and corrections costs, which don't really accrue to the person with the problem, but anyway they're in there as well.
So, I've got three graphs that I think kind of highlight the results that we found. So, on the Y axis here are dollars, so this is the spend that people received in the 12 months after the survey, by the number of domains and disadvantage. So, the points here that you see are the average spend for different groups, at different levels of disadvantage. So, it ranges from about 3,000 for those who have zero domains and disadvantage, up to 15 or so for those with 5, 6, or 7. And you can see that the confidence intervals which are these black bars begin to overlap because they get to be quite small numbers, so we can't see a statistical difference once you get up to five but there's a couple features of this graph you'll note that it's more or less linear, it's upward sloping, so that's good news. I think if we saw something different, that people with lots of disadvantages weren't receiving as much or equal to those with lower numbers, we might be concerned, so I think that's one finding. We also see that there's a bit of a jump between those with four and five, that that spending really kicks in at that point. So, the next thing we did is we tried to decompose this line into the different spending areas that I presented earlier, just to see in terms of those who have zero domains and disadvantage what proportion on average of that is, say, pension spending versus income support. So, remember this line? That's what it looks like broken down.
So, you can see that the the flatness of the slope earlier, in large part is driven by the fact that for those who have 0, 1, or 2, about half of the government spending comes in the form of pensions, so it's a bit skewed, there's a lot of folks who will be receiving relatively little at that end, but because it's kind of a universal benefit for those of a certain age, you kind of see this, oh, so I should point out this blue line with the circles is the pension spend, and the darker blue line with the stars is income support. So, we see that a lot of the shape of that line toward those who have many domains in disadvantage is driven by the change in income support. The other three lines are more flat, although you do see an upward slope with the health spending, because those are more population driven spending we don't see a lot of difference across those who have different levels of disadvantage in terms of the amount of spending they would receive on average, so we didn't interrogate these data much further, and I'll explain why in a moment, but the the last thing that we did was to have a look at incidences where we saw people who had demonstrably high levels of disadvantage but didn't seem to be receiving a lot of Government spending, for one reason or another. This is trying to understand who might be in need of Government services, but doesn't seem to be getting them. So, I'll take you through column by column, I've been told this is a complicated table.
So, in thinking about multiple disadvantage having three or more domains in disadvantage, what I've done is I've looked at the proportion who have different levels of spending, and I've picked zero to 3,000 as a point of interest, because the average for those who have zero domains in disadvantage is $3,000 So I would categorise that as a relatively low level of spend. And we see that for those who have three or more domains in disadvantage – almost a third, are receiving a low level of spend. If you look at a more extreme form multiple disadvantage, four or more, the number decreases slightly, but about one in four people are receiving very low spend. And then, at five or more, it's about one or six. So, that's a bit disturbing on the face of it, but I should very quickly say that there are a lot of reasons why we may have gotten this result, that don't necessarily mean that those people are receiving nothing. The first is that when Stats New Zealand did the linking, they only linked the responses for the respondent of the General Social Survey and not the other members of their family or household, so because we deliver income support especially to families and to many members of the family, we're not properly capturing all of the support that those people are receiving. So, that's one source. They may also be receiving support from friends or family, and have not gone to the Government, or they may be receiving support from an NGO that's been funded by a block grant and is unable, or doesn't have the data, to be able to attribute the spending that they are doing for any particular individual. And that's why we fail to capture them. It may also be that we don't provide services for some of the disadvantages that they have. We provide relatively few housing support services compared to income support, for example.
So, you know, it's possible you have a combination of disadvantages that there's just not a lot of Government programmes for and that's why you've ended up in my figures. However, it's hard for me to believe that 100% of these numbers are related to the data, or to these other artifacts, so I think that this shows that there's some power in being able to identify folks who have needs but are not being addressed. And I think that what we'd hope to do with the future of this work -- and I think this shows the power of that linked data -- often people discuss the pros and cons of administrative data versus survey data, but clearly they complement each other and are very powerful, and so we're hoping that this linked data continues to be a feature of our data ecosystem and improves over time. And we'd like to do a bit more work on that in the future.
So, that's where I'm going to leave you, but I think, you know, there's some final conclusions: Well, one -- this is an exploratory piece of work, so we can only say what we can say, but I think there's some good news in that. We can see that those trends sort of show what we would hope or expect, that people with more disadvantages are receiving more support, and I think you can also see that there are clearly probably some people -- and a non-trivial number -- who are needing support but are not getting any, and we need to look into that further. And as I've said, I think that this linked data is a great place to be doing that. So I don't know where I am on time, but I think I'd better hand it over. So, thanks!
(End of transcript.)
Jason Timmins: What is the scale of vulnerable transience in New Zealand?
Jason's presentation looked at 'transience' – something that can lead to poorer outcomes in education, health and wellbeing.
Jason reported on research, commissioned by the Social Policy Evaluation and Research Unit, that used the IDI to estimate how many New Zealanders are transient and at risk of poor life outcomes.
What is the scale of vulnerable transience in New Zealand? – video transcript
Jason Timmins - principal analyst at the Education Review Office (formerly of Superu):
Good morning, thank you for inviting me along, Oranga Tamariki crew, it's great to come along and talk, it's great to talk to my friend Eric, and to also continue to showcase the the work that we did at Superu, where we no longer work.
And so first of all, I just need to make very clear that I didn't do this research, I get the lovely job of talking about it, but we contracted the great people, Gail Pacheco was the lead from Auckland University of Technology – they have a NZ Work Research Institute. So, they did all the hard work. I read and reviewed it, and helped design the research, and hopefully I'll be able to answer all of your questions about it. But I'll give some contact details if you'd like to follow up with the researchers.
I guess what we're interested in with this piece of research is this link between being transient and, for one or a better word, impacts on your wellbeing and in particular, negative impacts on your wellbeing. So, one way to think about it, or one way I think about it, is that in my mihi earlier I told you that I was born and raised in England, I was in Essex. Now I live in in Berhampore, a suburb in Wellington, so clearly I've moved to New Zealand. And I've moved a few times while I've been in New Zealand. And those moves have been positive for me, and I'd say they've enhanced my wellbeing.
I moved from New Zealand to a small town in Te Awamutu – which is in the Waikato, for love, as I tell everybody, to be my partner Karen (who I'm still together with so it's gone really well!) and then we moved from Te Awamutu to Wellington for jobs that we got, and so again that was an improvement for me. And, in all that time we've been able to pick and choose a little bit where we moved to, and when we've moved, so that we can minimise disruption to our lives But what this paper is really interested in, is really trying to identify people where the movements are causing disruption to their lives, that they are potentially leading to worse outcomes for them or impacting on their wellbeing.
So, just a bit of a background. This research was commissioned by the former Minister of Finance the Rt Hon Bill English and it was through the Social Sector Research Fund which we managed at Superu, and so very quickly this was just a fund available to Ministers to ask questions, and this was one of the questions that got asked. There's a couple of motivations you can think about in terms of these kind of questions.
The one that came from Bill English's office was they were really interested in whether Government services are harder to deliver to people. They had this assumption that Government services might be harder to deliver to people that moved around a lot. And, as I remember, the discussion went along the lines of: Well, if we have support services that could help people, it's a real concern if they're moving around a lot and we're not getting those services to those people. And so that was kind of where that question came from.
The other motivation, which I kind of alluded to in my introduction, is that potentially moving a lot can lead to poor outcomes, both in terms of, well, in the literature education is a common one because often it's the impact on children having to start midway through the school year, moving to a different school every year, but there's also other evidence around health impacts, particularly mental health impacts.
So, the first step with this question was really that classic measurement kind of policy question: Well, how big is this issue? And, so the question was, well first of all the Minister's office was interested in the number of people who were transient, but in particular they wanted to try an identify of those who were transient which ones might be experiencing poor outcomes from that transience. And then a final trigger was that measuring frequent movements in New Zealand has been difficult, at least using surveys or data, and some of the linked data that
Eric talked about as potentially openin up an opportunity for doing that, and so they wants to know whether that was also feasible.
Here's my disclaimer. It's the same as Eric's, I think. And he did a very good job of explaining why it's important but essentially the key part of it is that the data was used in such way that the identities of individuals and their data was not revealed.
So here's my outline for the talk today: We'll talk about a little bit about what is vulnerable transience, so you know how did the researchers approach trying to measure this concept. I'll take you through some of the data sources they considered. We'll then go through the definition they used, and then we will look at some results and we will look at kind of three types of results if we have time. The first one is
how many people are vulnerable transient in New Zealand so this kind of group of transient people who might be at risk of poor outcomes or experiencing poor outcomes who are they and then the final question about who might be at risk of being vulnerable transient in the future, which is a question they had looked at in the paper.
So, while I'm giving this talk I want you to think about Hamilton and I want you to think about Hamilton because Hamilton's about 200,000 people which is about the number of people that this research identified as being transient. So, these are people who moved frequently over a period and we'll talk about those definitions in a moment and then the other number I want you to think about is that, most of those, 80% of that 200,000, were identified in this research as being vulnerable transient. And I'm going to come back to that, but essentially what the research is suggesting is that for people who move around a lot, most of them may not be experiencing good outcomes from that move, unlike the story I told you, where my movement I felt led to positive outcomes for these people that might not be the case. So, they're the figures, so remember Hamilton city and we'll come back and talk about that 200,000 in a moment.
So, we commissioned this piece of research on the open market and so we thought we should have a definition. And because coming from the Minister's office we had this definition around repeated disruption of key social support mechanisms, including residence, which is associated with negative impacts on social education, employment, and you can add in their health outcomes as well, but some idea about the kind of
transience that we were interested in trying to measure.
The researchers came at it from two dimensions: the first was this idea of transience and I guess what they hoped was that they'd go to the literature, and the literature would have a nice definition for them to use, and they could just apply it here. But, of course, that's often never the case.
So what they did find in the literature is that transience is something that's temporary or short-lived, but what they also found was that there wasn't really an accepted definition of transience in the literature. And I think really what they concluded is that it very much depends on the question that you're asking. And so transience might be a particular issue for one particular outcome, defined in, let's say, ten moves in five years, but actually for another outcome that might not be the correct definition. And then the other dimension they thought about or had to incorporate was identifying, for people who were transient, were those moves were leading to worse outcomes.
So, I've got a table just to talk through those two different definitions. So, on the transient side which is the first column, you've got people with low movement and high movement and that's reasonably relatively clear. In low movement we're talking about people who don't move over a particular period, and people who might move once or twice. And then high movement is people who move frequently in a given period, and I guess it's trying to decide what that frequent looks like. It is a challenge and I'll show you what they did it shortly.
On the vulnerable definition the research has kind of approached it from thinking about people who made voluntary
moves versus people who might be making involuntary moves. Voluntary moves might lead to better homes, because you're able to choose them, better neighborhoods that work for you. So, for example, you're able to choose two neighborhoods where you can still easily get to your job, where you can still keep in contact with friends and family, and also you can time the move, so it reduces the disruption to your life.
So, I gave the example of children being able to start school at beginning of the school year and not starting during the school year. And then they thought about involuntary moves where you're not in control as much, and so you might end up in worse housing and worse neighborhoods. Maybe you're forced or you have to move to a neighborhood where it's much harder to get to work, you've got a longer commute and it's not as easy to keeping contact with friends and family. And the other dimension is that you're unable to control the timing, so there's more disruption to your life, your child has to move schools during the school year and it's more disruption
So, in terms of the data set sources available, the researchers looked at a couple: they looked at the Census which is a survey of all New Zealanders, that many of you will be familiar with, and the other advance of that data set is that it clicks a lot of socio-economic information so the question about "who are these people?" can be easily answered using the Census. It's main disadvantage though is that it doesn't measure frequent movement. It does ask you, and hopefully all of you have answered this question in the last month or two, where you're currently living, and where you were living five years ago. And so from that, you can work out whether someone's moved.
There's also an additional question that asked you how long you've been at your current residence, and so you can work out whether people have recently moved. But you can't work out whether they've moved once every year in that five year period or just compared to once.
So, then they looked at the administration data, the Integrated Data Infrastructure (IDI), that Statistics New Zealand have been creating. And so the advantage of that is that you're better able to measure frequent movement, based on the administration data that's collected. This was quite difficult about five years ago because there wasn't much information in there, but now there's so many bits of administration data, in particular the health data, they reckon they see everyone in New Zealand about once every two years. But what stats can do is that they scan across all the data sets they have and at a point in time they work out the address for an individual, and then you can use that information to look at the number of address changes someone has experienced over time.
Another advantage of that one is that it might contain measures of involuntary movement. It's a longitudinal data set, now it turns out it doesn't, but that was one of the hunches. But over time increasingly we might be able to identify triggers of explaining movement so, for example, one trigger you see mentioned in the literature is eviction. A common trigger of an involuntary move, but there might be others, so for example a relationship ending, or the birth of a new child - you suddenly need a bigger place. In terms of the disadvantages of these two datasets, so I talked about Census, it can't measure frequent movement. Because it's a cross-sectional data it doesn't really have any measures of involuntary movement. You do know whether somebody is divorced, I think it's a question that's asked in the Census, but you don't know whether that happened last week or last year, or ten years.
And then, it's not all up sides with administration data, unfortunately it never is with data. So, one of the problems with administration data is that it excludes people who do not receive Government services, or it doesn't collect a lot of information on individuals who don't have a lot of interaction with Government. And the other thing as well is, because it's administration data, address changes may not be captured. Either you don't inform someone of an address change, I've never told IRD where I live but they've always found me, but I've never consciously told them that I've changed address; they might get it from other sources but that might not get captured. And there's just the fact that people might change address and not let anyone know. So that's some of the data sources.
So for this piece of work, mainly because of the need to measure frequent movement, the researchers went with the administration data but they had to think of another way of trying to separate people that may have been making moves that might be enhancing their outcomes, their wellbeing, versus moves that might be not enhancing their wellbeing. So, this is where they got to. So, the first thing they do on that on the top is that they divided people into the amount of time they moved over a three-year period, that was the period they took, there's not a huge defense for choosing a three-year period, but based on some of the periods were chosen in the literature was one of the reasons why they did that, and if you look in the paper they do a little bit of sensitivity testing about whether, if we chose a slightly different cut off, does it change a bit? And so if you're interested in it that, that's in the
paper. They then describe people who are low or medium movers, where they moved one or two times over a three year period. And then the
group that we were most interested in are these higher movement people. So these are people who've moved three or more times in three years. So they're moving on average once a year, there are people in there who moved, well I don't know, it's unbelievable how much some people moved. There's a lot of people know they're moving six or seven times over this period, and more. So that was the relatively easy bit, measuring the movement. The next bit was and how do we separate the moves where we think they're moving to moves that might be enhancing their outcomes, or at least not making them worse off, versus those where they might not be. So, what the researchers did was they tried to use socio-economic information and the direction of movement to do that.
So, they came up with three definitions. The first were people who move three or more times in three years, but they were moving to less deprived neighborhoods or suburbs or they were moving within the least deprived neighborhoods or suburbs in New Zealand. And they used that using the New Zealand deprivation index, so they classified all areas in New Zealand using the index, and then they tried to identify whether people seemed to be moving from a more deprived to a less deprived neighborhood or whether they were moving within the least deprived, so the top 30% of neighborhoods.
They then came up with a definition of transience, they called this middle group transient, where they were moving to a more deprived neighborhood, but not all the way to the bottom, so kind of in the middle middle deprived, I guess that's how you might describe that. And how they moved within these, so the NZDep is on the scale of one to ten I'm working in the education sector now so I always get confused the decile sector...So I remember that one is the least deprived neighborhood in New Zealand and ten is the most deprived neighborhoods. So, these people are kind of moving in the in the 4-6 range, I think, as I remember.
And then their definition of vulnerable transience was people who were moving again to a more deprived neighborhood, but they were moving into the bottom and the most deprived sort of 30% of neighborhoods, or they were moving within the most deprived neighborhoods. so we've got the we've got these high movement people but they've described them as "upward," they seem to be moving around the least deprived neighborhoods in New Zealand. We've got this kind of middle group that are actually moving to a more deprived neighborhood but it's not the most deprived neighborhood in New Zealand. And then we've got our vulnerable transience who are who are moving to and within the most deprived neighborhoods in New Zealand.
Did everyone follow that? Did that kind of make sense? So, the key thing here is that it's the vulnerable transient group that we're keeping an eye on. We've got a little bit of concern about the potentially about the middle transient group, based on this definition, and the high movement ones are the ones where we think that, you know, for those people the moves are positive, even though they're moving around a lot. And you can think of, you know, particular jobs, military is a common example where people do move around a lot, but they probably have a lot of support and control over those movements to minimize some of the disruption.
Okay, so here's some results. So, this is where we come back to Hamilton. So, we had 3.8 million people in the data set over that
three years. When you look at the characteristics of that population it's pretty representative on some basic demographics, and as we have a lot of other studies, including the Census, what we find is that most people don't move, so 70 percent of them didn't move over that three year period. I gave you a bit of my movement history at the beginning of the talk but most that movement happened 10 years ago and actually in the last 10 years I haven't moved so it's not an uncommon thing there's a few people that move once or twice over that period about 25% ish between 20 to 25% and it's the bottom of three categories that we're going to focus on so the first thing to take away is about five and a half percent of this 3.8 million they moved three or more times over those three years that's our population of Hamilton so it's quite a big number I think the real takeaway though is that the majority of them 80% of them are moving to or were they the most deprived communities in New Zealand so now I'm not saying that all of them are experiencing poor outcomes but I'm saying that I think what the researchers are pushing is that we might be concerned about some of that, that a lot of that movement may not be welfare or wellbeing enhancing. That's kind of the headline figures. Not surprisingly, when we have a look at the characteristics (not going to do that [slide] it's terrible, very difficult to describe and I was cutting and pasting from the publication, so they don't work as well in a presentation). So, the key thing here is that 75% of that vulnerable transient people receive a benefit, so that interaction with Government does exist at a high rate for this group.
One thing I would also say is that if you compared to the non-movers who are on the far right, look at the blue bar, which is basically being in a family and receipt of the benefit. Movers are just generally are more likely to be on a benefit, with the exception of that high movement category, so that gives us a bit of confidence that what we're picking up there are people whose moves are a bit more positive, but you can see for the vulnerable transient who are on this far right, there's quite a big difference in terms of benefit receipt rates for that group.
The other thing I'll just add is that that group also tend to be a lot younger. When I look at the high movement group, that's quite an older age profile that might be a lot of transitions into retirement that come with a move, but they do tend to be to be a lot younger. And so they're a big group, they're a large number of people, they're most of the population of Hamilton, and also there's a high proportion of them receiving a benefit. So, they've got that interaction with Government.
The final result I'll show you before I wrap up is the researchers wanted to see whether there were certain people who were at risk of becoming vulnerably transient, so they used longitudinal information and some characteristics of those individuals, both in terms of their sort of demographic social characteristics, as well as their receipt of Government services, to see whether there were certain groups of people whowere more likely to become vulnerably transient in the future. And that's what I'll show you in this final one. So, what they found is that for the high movement group in general, that this group, people who were having some engagement with mental health services, or had had recent engagement with visiting emergency departments, so if you like these are proxies for potentially not having good health outcomes,
they were more likely to become, they were more likely to become high movers or, you know, moving three or more times over a three year period in the future.
When we look at, specifically, at vulnerable transience as an outcome, we find that the poor health factors are there as well, but also there's some justice factors in terms of people who had had a recent court charge were more like to become vulnerably transient in the future, and
people who were being supported by the Government through some sort of economic income support, both the work of the families and an income support benefit were also more likely to be become vulnerably transient in the future.
So, they were looking at the preceding five years to the three year period that we talked about, or was presenting this presentation. And so just to summarise, I'm going to talk about Hamilton again. So, what the research found was that they estimated around 200,000 people out of the 3.8 million people moved three or more times over a three year period. And the three year period, I don't think I actually mentioned, was, and I'll just refer to my notes, it's relatively recent, but the period that they covered was between 1 August 2013 to the 31 July 2016.
They found that of that 200,000, 80% of them could be defined as vulnerably transient, so 80% of that movement is occurring towards or within the most deprived communities in New Zealand. When we talk about the characteristics of that group, I mean a key one is that 75 % of vulnerable transient people received a benefit during that reference period, so if we're thinking about a potential impact on delivering services to transient people, this group is a high user, or is a relatively high user, of Government services, in terms of a benefit.
If you look at some of those other ones I showed you around health and justice you also see similar patterns for those. And then finally I showed you that the receiving welfare supporters are associated with being vulnerably transient in the future as well as working for families. Some of the health measures that they managed to include were associated with moving a lot in the future but not necessarily being vulnerably transient, and so I just raised a question there for discussion about, you know, can government services be used to reduce the amount of movement occurring in the future. Is there an ability to bring a bit more stability to people's lives, or to maintain stability in people's lives, before they become transient.
I have to say that there's a very thin line between preventing someone from moving to avoid worse outcomes in the future than stopping someone from moving to enhance their wellbeing in the future. So, you know, that would be a tricky thing to do, but there's a greater focus on maybe linking up Government services and it may be that people at risk, you know, of being vulnerably transient there might be something that can be bolted onto this service delivery before that happens.
So, just to finish and to thank the Auckland University researchers for the research from Superu, there's some contact details for you, it's also
in the paper, and just to acknowledge Gail Pacheco who and led the research for us and did a great job. Thank you.
(End of transcript.)