1. The 2016 Pobal HP Deprivation Index for Small Areas (SA) Introduction and Key Findings
Welcome to the launch of the new Pobal HP Deprivation Index
Dublin, 9th November, 2017

2. The 2016 Pobal HP Deprivation Index - Relative Index Scores 2016
Most of you will be familiar with this map of relative affluence and deprivation in Ireland in one form or another
Maps depicting the Pobal HP Deprivation Index have become common place in almost every development plan of the country’s 34 local authorities, and, before that, had long been prominent in the work of the 50 plus Local Development Companies currently supported under the LCDP, the Local and Community Development Programme, the LEADER Programme, and other national and EU programmes aimed at promoting social inclusion.
The maps are distinctive in the their colour scheme, ranging from a deep blue which depicts locations of greatest affluence, to a deep red, which identifies locations with the highest level of deprivation.
The maps can be produced at almost every geographical scale, ranging from the most fine-grained unit level, the new Census Small Areas to Counties and Regional Authorities.
To see just how accurately the maps identify known areas of deprivation, let us briefly look at the Dublin map to the left-hand side of the slide, which depicts a map most of you will be very familiar with: Starting in the top right and moving anti-clock-wise, we can clearly identify the known disadvantaged area of Dublin, starting with Coolock-Darndale, then Ballymun, Finglas, and Cabra , parts of Blanchardstown, Clondalkin, Kilmainham, Cherry Orchard, West-Tallaght and even smaller areas in Dublin’s South-side, like Nutgrove, and Ballybrack and Sallynoggin as we move towards Bray.
However, the main point about confirming what we already know is that once you see an area that you know being accurately represented, you start trusting the HP Index to provide an equally accurate picture for the rest of the country – and there are very few people who can claim that they know the socio-economic make-up of every single street or townland in the whol country.
There are two major ways in which which the Pobal HP Deprivation Index is being used:
Firstly, as an Index of Relative Affluence and Deprivation
In this way, the index is used used for the mapping of deprivation, the identification of specific Target Areas of multiple and deeply ingrained deprivation, and the development of Resource Allocation Models
Secondly, the Pobal HP Deprivation Index represents a sophisticated measure of Well-being which allows the assessment of changes in society’s well-being well-being over time
In my presentation, I will briefly address key findings with respect to both of these uses of the Pobal HP Deprivation Index in turn.

3. Major Applications of the Relative Pobal HP Deprivation Index
Local Development:
Used by Pobal in the Resource Allocation Model for Local Development Companies under successive Local Development Programmes.
Used by Pobal in the roll-out of early-years education initiatives as part of the free Pre-school year.
Used by DECLG in the Resource Allocation Model for Local Development Companies funded under LEADER.
Used by BIM in the Resource Allocation Model for Fisheries Local Action Groups supported through the European Fisheries Support Fund.
Education:
Used by the DES for the designation and funding of schools under the disadvantaged schools scheme (DEIS).
Will be used by the HEA to monitor the affect of social class on access to and progression in third-level education.
In particular, we:
use information on the area of residence of drug treatment recipients (where available) to derive count data for EDs
identify risk factors or proxy variables that allow us to make ED estimates outside the coverage of existing data sources
verify these estimates using available count data at county level
map out the resulting estimates
We will now describe this process in greater detail and illustrate the results.
I will start my presentation with a brief overview of where the Pobal HP Deprivation Index has nowadays gained major application.
The number of policy arenas in which the Pobal HP Deprivation Index has gained political influence over the past 5 years marks a significant departure from its origin.
The Index was originally developed to serve the local development arena and the budgets affected by its application were comparatively small, amounting to some €40m per annum, predominantly in form of targeted supports for Ireland’s most disadvantaged communities under successive local development programmes managed by Pobal.
However, the Index’ wider potential was gradually realised and its application gathered momentum as it was successfully deployed in a number of key government departments.
Today, the Pobal HP Deprivation Index has become a major tool to support the development of evidence-based policy making in mainstream policy arenas such as health, education and the environment, with a potential of affecting the distribution of hundreds of millions of Euro every year.
How could such advance happen?
The index quickly established itself as a reliable tool to identify areas of relative affluence and deprivation.
From the onset, the Index had broad popular support helped by its origin in the local development arena with many community-based stakeholders having participated in extensive consultations with the authors during its formative years. Local communities are highly aware of the Index and trust the characterisation of their areas relative to other areas and the country as a whole. The Index has thus served as a major contributor to the development of a political consensus with regard to where investments need to be targeted at place to address social inequalities and social exclusion.
Of considerable importance is also the high quality of the Index’ conceptual design and its exclusive reliance on the 5-yearly Census, both of which instil confidence amongst key policy decision makers to use it as a decision support tool in their respective policy arenas.
As part of this process, the Index has undergone critical appraisal by many statisticians in key government departments and the CSO, which now itself is an ardent user and supporter of the Pobal HP Deprivation Index.

4. Major Applications of the Pobal HP Deprivation Index
Health:
Used to support research into the predictors of population health (Irish Health Survey, Healthy Ireland, GUI, TILDA, National Drug Treatment Reporting System (NDTRS), National Cancer Registry Ireland, etc.).
Central to the Resource Profiler currently being developed by the HSE Health Intelligence Unit and Health Atlas.
The Resource Profiler is currently being used to analyse existing resource distributions across the HSE and will increasingly be used in the future to facilitate evidence-based resource allocations across the entire health system.
Will be used in the Resource Allocation Model and Performance Measurement Framework for Local and Regional Drug Task Forces (DATF).
Used to support strategic management and resource allocations within TUSLA.

5. Major Applications of the Pobal HP Deprivation Index
Other important uses:
Used by An Garda Síochána in the profiling of local areas to support community-based policing.
Used by the National Transport Authority to optimise the social benefits of alternative route and services design.
Used by the Central Statistics Office in the survey design of all of its household surveys (QNHS EU-SILC, etc.).
Used by the Central Statistics Office as a predictor for the Residential Property Price Index (RPPI).
Used by the Revenue Commissioners to independently assess Local Property Tax.
Used by a myriad of research applications in the universities and research centres, as well as private-sector spatial data applications (e.g. used by several health and other insurance companies for their risk asessment models.

6. Starting Point for the POBAL hp deprivation INdex: A Comprehensive Definition of Poverty
“The fundamental implication of the term deprivation is of an absence – of essential or desirable attributes, possessions and opportunities which are considered no more than the minimum by that society.”
(Coombes et al., DoE – UK, 1995)
To understand why the Pobal HP Deprivation Index has become so important as a tool for developing evidence-based policies towards greater social inclusion , we have to take a brief look at how the Index is constructed.
We start with the definition of deprivation that underlies the Pobal HP Deprivation index.
“The fundamental implication of the term deprivation is of an absence – of essential or desirable attributes, possessions and opportunities which are considered no more than the minimum by that society.”
This definition was first advanced by Mike Coombes in 1995 as part of a review of existing deprivation indices at that time.
he importance in this definition lies in the three dimensions of attributes, possessions and opportunities, attributes, and you will quickly appreciate why this is such powerful definition.

7. The Underlying Dimensions of The Pobal HP Deprivation Index
Demographic Decline (predominantly rural)
population loss and the social and demographic effects of emigration (age dependency, low education of adult population)
Social Class Deprivation (equally applying in rural and urban and rural areas)
social class composition, education, housing quality
Labour Market Deprivation (predominantly urban)
unemployment, lone parents, low skills base
When developing the Pobal HP Deprivation index for Ireland, it was paramount for the authors to define deprivation in such a way that it would not only capture the well-established features of urban deprivation, but would equally account for the specific forms of rural deprivation.
This is because deprivation in rural areas is largely linked to the concept of opportunity deprivation, which cannot be captured in measurements that rests with individuals alone. [explain].
Instead, opportunity deprivation - and hence rural deprivation - is an interaction term that must be constructed.
Any Deprivation Index which does not explicitly include a measure of opportunity deprivation, will inevitably be urban-biased and fail to get broad support in both urban and rural areas alike.
Indeed, most deprivation indices that exist in other countries – and particularly throughout the have a significant urban bias; i.e. they fail to give sufficient attention to the conceptualisation and measurement of opportunity or rural deprivation.
[explain the three dimensions shown in slide]

8. The Measurement Model of the Pobal HP Deprivation Index
Age Dependency Rate 1
Demographic d
Population Change
Growth 2 d
Primary Education only 3 d
Third Level Education 4 d
Persons per Room 5
Social Class
Composition d
Professional Classes 6 d
Semi- and Unskilled Classes 7 d
Lone Parents 8
Labour Market d
Male Unemployment Rate
Situation 9 d
Female Unemployment Rate 10
The Pobal HP Deprivation Index is estimated using a multiple group means and covariance structure model
This makes it possible to measure the change that occurred in the mean of the latent variables between 2006, 2011 and 2016

9. Mapping Deprivation most disadvantaged most affluent
The degree of affluence and deprivation is given by the relative Pobal HP Deprivation Score, and can be displayed at various geographical levels, ranging from Small Areas, to Electoral Divisions, Local Electoral Areas, counties, or indeed any other aggregation of SAs, such as, for example, Primary Health Care Areas, Community Health Office Areas, Education Training Board Areas, Drug Task Force Areas and many more.
The distribution of HP Index Scores follows a normal curve, and covers the complete affluence to disadvantage spectrum.
there are several reasons as to why the HP Deprivation Index is constructed in this manner:
Firstly, it allows one to express the relative affluence/deprivation of any one area to be expressed in terms of a real measurement which signifies the relative distance to the mean
This, indeed, remains the most frequent use of the Pobal HP Deprivation Index, as shown in the map at the beginning of this presentation applied in many social inclusion contexts.
Secondly, it allows one to construct both an absolute deprivation score (which takes a fixed time period as its reference point and thus identical measurements at different points in time which facilitate the monitoring of changes in relative affluence/deprivation over time.
In this, the Pobal HP Deprivation Index profoundly differs from most other deprivation indices, notably the UK indices ( IMD, NIMDM NZ Deprivation, all of which are only able to rank areas in terms of their relative deprivation, but do not allow true comparison over time.
As already said, whilst the most frequent use of the Pobal HP DEPRIVATION Index lies in the mapping of relative deprivation, our first and major finding I want to report on today in the context of the launch of the 2016 Pobal HP Deprivation Index lies with this latter aspect; i.e. the ability of the Index to provide us with some real insights into the development of affluence and deprivation over the past ten years.
ents as to how an area has changed over time.
marginally below the average
marginally above the average
disadvantaged
affluent
very disadvantaged
very affluent
extremely disadvantaged
extremely affluent

10. (Average Health Risks)
Moving towards evidence-based Resource Allocations: The HSE Resource Profiler
Deprived
Affluent
Above Average Risks
Health
Risks
Below Average Risks
(Average Health Risks)
We all know of the extreme inequalities that exist in various key policy arenas such a health, education, housing etc.
To demonstrate just why the Pobal HP Deprivation Index is making such rapid in-roads into ever more policy arenas, let me use an example from the health arena and simply asks the question: what would a distribution of health services look like which gives people more equal access to health services?
To start off, the slide again depicts the various levels of affluence and deprivation as experience throughout Irish society and, as before, we are showing this as a normal distribution ranging from extremely disadvantaged on the left, to extremely affluent on the right.
Poorer people experience poorer health and thus require more health services. In addition, they have less resources to meet the burden of ill-health. The level of health risks, and thus health service needs is shown in the graphic by the dotted blue line. If we were to spend identical amounts of money per person on health services for each unit of population or community, the outcome would be highly unequal, as poorer people with greater health service needs would have to make do with the same amount of services in their area as better-off people with less health service needs in their respective area.
Unfortunately, there is a long tradition in Ireland to allocate major public spending on a simple per-capita basis. This tradition has resulted in the increasingly unacceptable social inequalities that we now observe. Accompanying this is an equally long tradition by Government of failing to actually monitor the social gradients as they prevail in key health and education outcomes, though this question has recently started to receive a little bit more attention (see e.g. Healthy Ireland and the designation of DEIS schools and the reporting of CSO household survey data by HP deprivation quintiles to name some prominent examples only).
A more equitable outcome would clearly be achieved if we were to allocate resources according to the underlying risk of ill-health and hence health service needs in any one area.
The idea of adjusting health or education expenditure by the underlying risks of ill-health or poor educational outcomes is thus conceptually quite simple and straight forward, but requires the political will to actually do so.
Technically, the systems that can calculate an evidence-based distribution are well-developed and some departments have started to actually use them. In practice, the question becomes primarily one of firstly identifying the exact shape of the blue curve; in other words, an answer to the question of what the exact social gradient is with regard to specific health or education outcomes and, secondly one of how to account for the geographical distribution of affluence and deprivation within the population .
But this is of course exactly provided by using the Pobal maps based on the HP Deprivation Index data. SD
0.1% % % % % % % %
High
Moderate
Low
Source: Haase, Pratschke (2017)

11. Distribution of Absolute HP Index Scores, 2006, 2011 and 2016
Having gone through the rapidly expanding usage of the Relative HP Deprivation Index, I now want to turn to the lesser known use of the Absolute HP Deprivation Scores
The Absolute HP Deprivation scores are specifically designed to facilitate inter-temporal comparisons. To this end, the measurement scale of the deprivation index model is fixed to a particular Census (in this case the 2006 Census, as this is the first wave for which we have small area data. By applying the same measurement scale to the 2011 and 2016 indices, we are for thus able to evaluate the movement of any area, or indeed the country as a whole through the 2008/9 depression, as well as being able to shed light on the extent to which there has been a recovery over the past five years ( ).
The emphasis here is on an actual measurement, using a multidimensional measure of community well-being as opposed to measurements based on productive output.
The graphic in this slide shows the distribution of the absolute HP index cores for the three census waves of 2006, 2011 and 2016.
The blue curve for 2006 shows the same curve as shown in the previous slide.
The pink curve shows the distribution of the absolute HP Index scores for 2011 and one can clearly see the leftward shift in the curve from a mean of -0.4 in 2006 to a mean of -6.4 in 2016; i.e. a drop of 6.8 HP Scores which of course reflects the deep recession in 2008/9
Between 2011 and 2016, the curve again shifted to the right , regaining a mean of -4.2.
most disadvantaged
most affluent
The Figure shows the decline by 6.8 points in the mean of the Absolute HP Index Scores between 2006 and 2011 (or 0.68 of a standard deviation) and the subsequent recovery by 2.8 Index Scores (or 0.28 of a standard deviation) between 2011 and 2016.

12. The Urban-Rural Dimension: Changes in Absolute Deprivation, 2006-2016
Urban Rural Category HP
absolute
2006
2011
2016
Change
in HP absolute
Dublin City
-1.2
-4.5
-1.4
-.2
Other County Boroughs & Environs
1.6
-3.5
-1.0
-2.7
Large Towns (10,000 and over)
-1.7
-7.9
-6.0
-4.3
Medium-sized Towns (5 to10,000)
3.6
-3.6
-4.6
Small Towns (1 to5,000)
-2.1
-9.3
-6.9
-4.8
Mixed Urban/Rural
-.1
-7.3
-4.7
Rural
-8.2
-4.9
Ireland
0.4
-6.4
-4.0

13. Relative Index Scores 2006

14. Relative Index Scores 2011
The most basic pattern of affluence and disadvantage has remained broadly intact over the past ten years: affluence is highest in the urban peripheries and gradually declines as one moves towards more rural locations.
There is some indication that the reach of affluent commuter belts has diminished, particularly in the years following the recession.
Within the Greater Dublin Area, there has been a shift in the location of the most affluent areas.
In 2006 the most affluent areas were in the Western part of the GDA. In 2011 and 2016, by contrast, affluence is again most pronounced in the local authority area of Dun Laoghaire/Rathdown (which includes the Local Electoral Areas of Rathgar-Rathmines, Blackrock, Glencullen, Sandyford, Stillorgan and Pembroke –South Dock.

15. Change in Relative Index Scores 2006-2011

16. The Urban-Rural Dimension: Changes in Relative Deprivation, 2006-2016
Urban Rural Category HP
relative
2006
2011
2016
Change
In HP relative
Dublin City
-1.2
2.2
3.1
4.4
Other County Boroughs & Environs
1.6
3.2
3.3
Large Towns (10,000 and over)
-1.7
-1.4
-1.6 .1
Medium-sized Towns (5 to10,000)
3.6
-.4
Small Towns (1 to5,000)
-2.1
-2.9
-2.6
-.5
Mixed Urban/Rural
-.1
-.7
-.6
Rural
-1.1 .3
There is no consistent pattern emerging to answer the question whether either urban or rural areas fared better over the past decade.
All seven categories across the CSO’s urban-rural classification first experienced a significant decline following the 2007/8 recession but also experienced a partial recovery between 2011 and 2016.
The best-faring urban/rural category for the 10-year period from 2006 to 2016 is Dublin City, which declined by only 0.2 HP scores. This is followed by the other four cities and their environs, which declined by 2.7 HP scores.
This clearly points to the advantageous performance of Ireland’s major cities during the recession, but also their ability to benefit faster during the subsequent period of partial recovery.
Large towns (with over 10,00 population) declined by 4.3 HP scores over the ten-year period, followed by medium sized towns and mixed urban/rural areas, each of which declined by 4.6 HP scores.
The worst-affected areas over the ten year period are small towns, which declined by 4.8 HP scores.

17. The Urban-Rural Dimension: Changes in Absolute Deprivation, 2006-2016

18. The Urban Rural Dimension: Changes in Deprivation, 2006, 2011 and 2016
There is no consistent pattern emerging to answer the question whether either urban or rural areas fared better over the past decade.
All seven categories across the CSO’s urban-rural classification first experienced a significant decline following the 2007/8 recession but also experienced a partial recovery between 2011 and 2016.
The best-faring urban/rural category for the 10-year period from 2006 to 2016 is Dublin City, which declined by only 0.2 HP scores. This is followed by the other four cities and their environs, which declined by 2.7 HP scores.
This clearly points to the advantageous performance of Ireland’s major cities during the recession, but also their ability to benefit faster during the subsequent period of partial recovery.
Large towns (with over 10,00 population) declined by 4.3 HP scores over the ten-year period, followed by medium sized towns and mixed urban/rural areas, each of which declined by 4.6 HP scores.
The worst-affected areas over the ten year period are small towns, which declined by 4.8 HP scores.

19. Most Affluent and Most Disadvantaged Local Electoral Areas, Relative HP Scores, 2006-2016
Local Authority HP
rel.
2006
2011
2016
Change in
HP rel.
Rathgar - Rathmines
Dublin City
10.8
14.1
14.3
3.4
Blackrock
Dun Laoghaire/Rathdown
11.8
15.1
13.3
1.4
Glencullen/Sandyford
11.6
13.2
1.6
Stillorgan
12.6
14.7
12.9 .3
Pembrooke - South Dock
9.1
13.9
12.7
3.6
Longford
-6.3
-6.2
-8.0
-1.7
Stranorlar
Donegal
-8.8
-9.0
-9.4
-.6
Waterford City South
County Waterford
-11.2
-9.8
-9.7
1.5
Glenties
-12.1
-10.6
Cork City North-West
Cork City
-13.9
-11.8
-12.0
1.9
The most affluent Local Electoral Area is Rathgar-Rathmines (HP relative of 14.3), followed by Blackrock (13.3), Glencullen-Sandyford (13.2), Stillorgan (12.9), and Pembroke-South Dock (12.7).
The most disadvantaged Local Electoral Area is Cork City North-West (-12.0), followed by Glenties (-10.6), Waterford City South (-9.7), Stranorlar (-9.4), and Longford (-8.0).
Interestingly, the five most disadvantaged LEAs include both urban and rural areas, thus coroborating that the Pobal HP Deprivation Index is equally sensitive to both urban and rural forms of deprivation.
It may come as some surprise to see Cork City North-West and Waterford City being identified as the most disadvantaged LEAs, as there doesn’t exist the same narrative around these areas compared to other more well-known disadvantaged areas in Dublin and Limerick.
The reason for this has probably to do with comparing areas of similar population size. The analysis by Local Electoral Area appears to be particularly useful in this respect, as LEAs are broadly of similar size.
It is also worth noting that there is no systemic effect of the gaps in affluence/deprivation either narrowing or widening over the 10-year period.

20. The Urban Rural Dimension: Changes in Deprivation, 2006, 2011 and 2016
The most affluent Local Electoral Area is Rathgar-Rathmines (HP relative of 14.3), followed by Blackrock (13.3), Glencullen-Sandyford (13.2), Stillorgan (12.9), and Pembroke-South Dock (12.7).
The most disadvantaged Local Electoral Area is Cork City North-West (-12.0), followed by Glenties (-10.6), Waterford City South (-9.7), Stranorlar (-9.4), and Longford (-8.0).
Interestingly, the five most disadvantaged LEAs include both urban and rural areas, thus coroborating that the Pobal HP Deprivation Index is equally sensitive to both urban and rural forms of deprivation.
It may come as some surprise to see Cork City North-West and Waterford City being identified as the most disadvantaged LEAs, as there doesn’t exist the same narrative around these areas compared to other more well-known disadvantaged areas in Dublin and Limerick.
The reason for this has probably to do with comparing areas of similar population size. The analysis by Local Electoral Area appears to be particularly useful in this respect, as LEAs are broadly of similar size.
It is also worth noting that there is no systemic effect of the gaps in affluence/deprivation either narrowing or widening over the 10-year period. .

21. Most Affluent and Most Disadvantaged Local Electoral Areas: Selective Indicators , 2016
Local Authority
Low
Educ. %
High Educ.
Hi Low Prof
Low Skill
LA rented
Rathgar - Rathmines
Dublin City
4.6
71.5
57.1
7.9
3.3
Blackrock
Dun Laoghaire/Rathdown
3.9
68.2
63.4
5.0
2.9
Glencullen/Sandyford
4.7
59.6
55.2
7.3
6.0
Stillorgan
2.6
65.6
61.8
4.5
Pembrooke - South Dock
7.8
67.2
48.4
10.2
11.5
Longford
6.7
50.2
54.5
8.1
2.8
Stranorlar
Donegal
16.9
26.3
23.5
25.4
19.3
Waterford City South
County Waterford
24.6
22.1
26.0
22.0
9.5
Glenties
17.9
21.4
18.6
29.7
22.7
Cork City North-West
Cork City
25.2
24.9
26.5
8.7
So far, this presentation has entirely focused on the multivariate measures of both relative and absolute deprivation, as presented by the Pobal HP Deprivation Index.
I want to conclude the presentation with some descriptive data that demonstrates what it means to live in an area of extreme affluence or deprivation and just how divided our society has become.
It is time to acknowledge that such inequalities are morally and, ultimately, politically unsustainable. It is time that we develop evidence-based policies in all major policy arenas hat acknowledge these inequalities and move towards targeting resources towards those in our society who have greater needs and require and deserve society’s support.