The results for the Pobal HP Deprivation Index are contained in a number of PDF documents, PowerPoint presentations and Excel datafiles, all of which can be downloaded below.

The 2011 Pobal HP Deprivation Index is available as an interactive mapping tool at  www.maps.pobal.ie 

Access to the SA-level dataset is subject to a license agreement. For further details see Register of Use

 

Substantive Findings (August 2017)

Analysis by Small Area (SA) Level

• Analysis at the level of Small Areas (SA) provides the backbone of the HP Deprivation Index. Unlike the Index of Multiple Deprivation (IMD) for the UK or the Multiple Deprivation Measures for Northern Ireland (NIMDM), both of which use different sets of indicators for their construction at different geographical levels, the HP Deprivation Index is constructed as a truely multidimensional index at the lowest geographical level for which data from the Census of Population are released by the CSO.
• Any spatial aggregations are subsequently calculated as the population-weigthted aggregates of the underlying SA level scores or percentage rates in the case of the key indicators.
• For the ordinary user, access  to the SA-level HP Deprivation Index dataset is generally not useful, as the data cannot be interpreted on its own but requires mapping before an appropriately-scaled background map of the Ordnance Survey of Ireland. Access to the SA level data is provided free of charge by way of the online platforms of Pobal Maps and AIRO, but do not facilitate the download of the complete HP Deprivation Index dataset at SA level.
• In addition to the online access via Pobal Maps and AIRO, a number of Government departments and State agencies, as well as some academic institutions have developed their own GIS environments to make full use of the SA-level HP Index data. Access to the complete SA-level HP dataset is subject to a license agreement. For further details see Register of Use.
• Possibly one of the most important uses of the SA-level HP Deprivation Index data has been for the purpose of highly sophisticated Resource Allocation Models. Pioneered by Pobal for the distribution of resources under the Social Inclusion Community Activation Programme (SICAP), the most important applications of  formal Resource Allocation Models are now in the health and education arenas, including the recent designation of disadvantagd schools under the DEIS scheme.

Analysis by Enumerative Areas (EDs)

• The ED-level data of the HP Derivation Index remains one of the most-frequently used datasets. This is the case for a number of reasons:
• EDs have remained unchanged for at least ten years and the ED-level HP Deprivation Index data provides an important reference point which can now be used for longitudinal analyses spanning six census waves.
• EDs have names and their location can thus easily be identified.
• The ED-level dataset of the HP Deprivation Index has always been made available by its authors free of charge and thus provides ready access to the HP Deprivation Index data for local community groups, students etc.
• For most purposes, the ED-level data of the HP Deprivation Index provides sufficient detail to support the spatial targeting of social inclusion initiatives and even the development of formal Resource Allocation Models.

Analysis by Urban Rural Contrast

• One question raised again and again with every new edition of the Pobal HP Deprivation Index is whether the Index adequately captures the experience of rural deprivation. Considerable care has been taken in the construction of the HP Index to account for the specific forms of rural deprivation which are captured by the loss in population, particularly amongst the key working-age cohorts and the consequent loss in an area’s skills base and its unfavourable demographic composition as captured in higher age-dependency rates. Analysis of the HP Index data using the CSO Urban-Rural Classification demonstrates that social disadvantage is greatest in the small towns category (towns with between 1,000 and 5,000 population, followed by entirely rural areas. More importantly, the geographical clustering of affluence is a predominantly urban phenomenon. As already highlighted throughout our previous analyses (Pratschke and Haase, 2011),  affluence is most pronounced in the peripheries of the large urban centers, notably of Dublin and the other four cities, as well as the mid-sized towns with between 5,000 and 10,000 population.
• The 2016 data highlights that the most dramatic increase in affluence appears to have taken place in Dublin City itself, which has moved from a relative HP Deprivation score of 2.2 in 2011 to a score of 3.1 in 2016, the single largest percentage change pertaining to any of the urban-rural categories. This reinforces our impression that the economic recovery after 2011 has been lead by the recovery of the capital and its continued ability to attract high-skilled labour.
• By contrast, the relatively unfavourable position in the affluent to disadvantaged spectrum of small towns, mixed urban/rural areas and remote rural areas has remained largely unchanged since the previous Census, although the most rural areas appear to have marginally reduced their degree of relative disadvantage.

Analysis by 2015 Local Electoral Areas

• This is a new level of geographical analysis which we have not previously reported upon. Local Electoral Areas where defined in 2015 by the Electoral Boundaries Commission in an attempt to define a new set of intermediate geographical areas which more closely reflect actual local communities. Small Areas only have a numerical identifier comprising a 65 digit code which, unless overlayed with an Ordnance Survey Map as in Pobal Maps or AIRO, cannot be interpreted on its own. Electoral Divisions (EDs) do have names, but are not easily related to the expand of actual communities. EDs have widely disparate populations, ranging from as few as fifty households to over 10,000 households (e.g. in Blanchardstown). Communities can thus be represented by multiple EDs and it is not always self-evident from the ED names which EDs, when taken together, make up actual communities. This problem is overcome when utilising the 2015 LEA definitions, a map of which can be accessed on the website of the Local Boundaries Commission.
• The 2015 LEA definitions provide a convenient intermediate geography which are particularly useful if one wants to compare the relative affluence or deprivation of one or other community as such undertaking requires one not only to define the delineation of the particular community in question, but also the definition of all other communities along similar considerations. The 2015 LEA definitions define 137 Local Electoral Areas with a more or less standardised population size of about 10,000 households.
• Not surprisingly the vast majority of the more affluent LEAs are located in the Dublin Region. The most affluent LEA is Rathgar-Rathmines (HP2016rel Index Score of 14.3), followed by Blackrock (13.3), Stillorgan (12.7) and Pembroke – South Dock (also 12.7).
• At the opposite end of the spectrum, the most disadvantaged LEA is Cork City North-West which comprises the neighbourhoods of Knocknaheeny, Fair Hill,  Gurranebraher and Mayfield with a HP score of -12.0 in 2016. Other particularly disadvantaged urban LEAs include Tallaght South (-7.3) and Waterford City South (-9.9).
• The most disadvantaged rural LEAs are Glenties (-10.6) and Stranorlar (-9.0) in Donegal.

Analysis by Deprivation Decile or Quintile

• Analysis by Deprivation Decile or Quintile provides a convenient way by which to contrast the socio-economic composition of the two deciles or quintiles at the extremes of the distribution
• Concentrating in commenting on the insights from the analysis by deprivation decile, the most affluent decile has an average HP score of 16.9., whilst the most disadvantaged decile has an average HP score of -18.5. This is a pure design effect and reflects how deciles are defined under a normal distribution and is thus of no substantive interest.
• More interesting and insightful are the contrasts with respect to the key socio-economic indicators. Comparing the most disadvantaged decile versus the most affluent decile:
• There are nearly four (3.6) times as many lone parent (43.1% v. 11.9%) in the most disadvantaged decile compared to the most affluent decile. Furthermore, the relative ratio has increased from 2.9 in 2006 to 3.6 in 2016 indicative of the increasing clustering of such families in particular geographical areas.
• The proportion of the adult population with primary education only in the most disadvantaged decile is about ten times that in the most affluent decile (29.1% v. 3.0%).
• Again, this ratio has dramatically increased  from 6.4 in 2006 to 9.8 in in 2016.
• The same relationship can be observed with regard to the proportions with high education. In the most affluent decile, exactly two thirds of the adult population has acquired third level education, this compares to just one in ten in the most disdvantaged decile (66.2% v. 11.3%).
• Social class composition, as measured by occupations again exhibits similar relationships as education, with higher and lower professionals accounting for nearly five times the share in the most affluent as opposed to the most disadvantaged decile (56.8% v. 12.4%)  and the reverse relationship applying for semi and unskilled workers which are about four times more prominent in the most disadvantaged  decile (33.7 % v. 8.1%).
• Male unemployment is more than seven five times as prominent in the most disdvantaged decile (35.8% v. 4.9%) and female unemployment is about five times as prominent (28.5% v. 5.7%)
• With regard to all of the above observations, we can further observe that the relative ratios have increased between the 2006 and  2016 census waves, which appears to indicate that social gradients – or social inequality -  has consistently and significantly widened over the past ten years.
• The extent to which these increases in the social gradients are also clustered in specific geographical neighbourhoods can best be judged from the analysis by Local Electoral Area (LEA).
• The best way to do so is by comparing the 2006, 2011 and 2016 relative HP deprivation scores for the most affluent LEAs v. the most disdvantaged LEAs. Indeed, for the five most affluent LEAs the HP scores have further increased; i.e the LEAs are experiencing in 2016 an even greater relative affluence than in 2006. By contrast, the movement of the relative HP scores for the five most disadvantaged LEAs is slightly more diverse, with some areas having improved their relative position and others having experienced even further marginalisation.

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