THE 2011 POBAL HP DEPRIVATION INDEX
FOR SMALL AREAS (SA)
Conceptual Underpinnings
Trutz Haase & Jonathan Pratschke
Dublin, August 2012
THE PURPOSE OF COMPOSITE DEPRIVATION INDICES
1. It is difficult to simultaneously comprehend the spatial distribution of multiple indicators
at multiple points in time
2. For practical purposes, there is a need for a single indicator which draws a variety of
observations together
3. Such indices can provide the basis for the effective targeting of the most
disadvantaged areas
4. Such indices can provide a means by which to assess changes over time, and facilitate
monitoring and evaluation
5. However, it is important that such indices enjoy broad support amongst all key
stakeholders, including government departments, state agencies, community
representatives and the broader public
THE PURPOSE OF DEPRIVATION INDICES
Deprivation Index
Small Area Data in General
To provide insights into the spatial distribution
of poverty and deprivation
To identify the specific needs of localities
To provide a basis for consensus-building on
targeting need in particular areas
To improve specific services or the integration of
multiple services at local level
To facilitate inter-temporal comparison
To inform policies that address poverty and
deprivation at local level
As a proxy for socio-economic status (SES)
when modelling health and other outcomes
n/a
REQUIREMENTS
Deprivation Index
Small Area Data in General
Data ought to be concise (i.e. brief but
comprehensive)
Should be more comprehensive
Data need to be consistent for all spatial units
Greater emphasis on domains (to inform
sectoral policies)
Data needs to be consistent over time
May include data which are not available for all
areas
Data ought to be timely
Does not necessarily have to be consistent over
time
Ought to have precise statistical properties
(ideally normally distributed)
n/a
MEASUREMENT CONSIDERATIONS
Deprivation Index
Small Area Data in General
Data have to be available at identical units of
analysis
May comprise data at different levels of spatial
aggregation
Near-normal distribution of input variables
Overall less restrictive
May require transformations
n/a
Requires dimensional analysis to avoid double
counting
n/a
Requires methods and weights for combining
into single index scores
n/a
A COMPREHENSIVE DEFINITION OF POVERTY
 Relative Poverty
“People are living in poverty if their income and resources
(material, cultural and social) are so inadequate as to preclude
them from having a standard of living which is regarded as
acceptable by Irish society generally.”
(Government of Ireland, NAPS, 1997)
 Relative Deprivation
“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)
TRADITIONAL APPROACH: EXPLORATORY FACTOR
ANALYSIS (EFA)
 Ordinary Factor Analysis (EFA) reduces variables to a smaller number of underlying
Dimensions or Factors
V1
F1
V2
V3
V4
V5
F2
V6
 EFA is essentially an exploratory technique; .i.e. data-driven
 all variables load on all factors
 the structure matrix is the (accidental) outcome of the variables available
 EFA cannot be used to compare outcomes over time
NEW APPROACH: CONFIRMATORY FACTOR ANALYSIS
(CFA)
 Confirmatory Factor Analysis also reduces observations to the underlying Factors, however
d1
V1
d2
V2
d3
V3
d4
V4
d5
V5
d6
V6
L1
L2
 CFA requires a strong theoretical justification before the model is specified
 the researcher decides which of the observed variables are to be associated with which of
the latent constructs
 variables are conceptualised as the imperfect manifestations of the latent concepts
 CFA model allows the comparison of outcomes over time
 CFA facilitates the objective evaluation of the quality of the model through fit statistics
STRENGTHS OF CFA-BASED DEPRIVATION INDICES
 true multidimensionality, based on theoretical considerations
 provides for an appropriate treatment of both urban and rural deprivation
 no double-counting
 rational approach to indicator selection
 uses variety of alternative fit indices to test model adequacy
 identical structure matrix across multiple waves
 identical measurement scale across multiple waves
 true distances to means are maintained (i.e. measurement, not ranking)
 distinguishes between measurement of absolute and relative deprivation
 allows for true inter-temporal comparisons
OVERVIEW OF SUCCESSIVE DEPRIVATION INDICES,
HAASE & PRATSCHKE 1996 - 2012
06
SA
n=18,488
ED
n = 3,409
91
96
86
91
96
91
96
02
91
96
02
06
06
NUTS 4 n = 34
91
96
86
91
96
91
96
02
91
96
02
06
06
NUTS 3 n = 8
91
96
86
91
96
91
96
02
91
96
02
06
06
NUTS 2 n = 2
91
96
86
91
96
91
96
02
91
96
02
06
06
NUTS 1 n = 1
91
96
86
91
96
91
96
02
91
96
02
06
06
01
NI
01
NI
01
NI
01
NI
01
NI
01
NI
06
11
06
11
91
96
02
06
11
06
11
91
96
02
06
11
06
11
91
96
02
06
11
06
11
91
96
02
06
11
06
11
91
96
02
06
11
06
11
Haase et al., 1996
Haase, 1999
Pratschke & Haase, 2001
Pratschke & Haase, 2004
Haase & Pratschke, 2005
Level at which model is estimated
Level to which data is aggregated
Haase & Pratschke, 2008
Haase & Pratschke, 2010
Haase & Pratschke, 2011
Haase & Pratschke, 2012
THE UNDERLYING DIMENSIONS OF SOCIAL
DISADVANTAGE
 Demographic Decline (predominantly rural)

population loss and the social and demographic effects of emigration
(age dependency, low education of adult population)
 Social Class Deprivation (applying in rural and urban areas)

social class composition, education, housing quality
 Labour Market Deprivation (predominantly urban)

unemployment, lone parents, low skills base
THE BASIC MODEL OF THE
POBAL HP DEPRIVATION INDEX
d1
Age Dependency Rate
d2
Population Change
d3
Primary Education only
d4
Third Level Education
d5
Persons per Room
d6
Professional Classes
d7
Semi- and Unskilled Classes
d8
Lone Parents
d9
Male Unemployment Rate
d10
Female Unemployment Rate
Demographic
Growth
Social Class
Composition
Labour Market
Situation
SOLUTION 2:
A LONGITUDINAL SEM MODEL
2006
2011
1
d7
Age Dependency Rate 2006
d8
Population Change 2002-06
d10
Primary Education only 2006
d11
Third Level Education 2006
-0.61
0.46
2006
Demographic
Growth
2011
Demographic
Growth
0.89
0.36
d16
Persons per Room 2006
d12
Professional Classes 2006
d13
Semi- and Unskilled Classes 2006
d9
0.03
d14
-0.17
-0.58
2006
Social Class
Composition
0.95
0.10
0.92
0.97
-0.76
-0.89
2
Female Unemployment Rate 2006
Third Level Education 2011
d21
Persons per Room 2011
d26
Professional Classes 2011
d22
Semi- and Unskilled Classes 2011
d23
Lone Parents 2011
d 19
Male Unemployment Rate 2011
d 24
Female Unemployment Rate 2011
d 25
0.01
0.35
2006
Labour Market
Situation
0.61
2011
Labour Market
Situation
0.63
-0.64
-0.86
-0.74
-0.68
d15
d20
0.18
0.82
0.14
Male Unemployment Rate 2006
-0.51
0.04
0.20
-0.86
-0.65
Primary Education only 2011
0.73
2011
Social Class
Composition
0.03
Lone Parents 2006
d18
0.49
0.17
-0.57
Population Change 2006-11
0.46
-0.06
0.53
0.69
d17
-0.59
-0.63
-0.51
0.24
Age Dependency Rate 2011
-0.54
3
COMPARISON OF MODELS
• Both the means model and the longitudinal model rely on the same
factor model
• Using the means model, it is possible to measure the change that
occurred in the mean of the latent variables between 2006 and 2011
• Both the means model and the longitudinal model impose equality
constraints on all factor loadings
• The Pobal HP Deprivation Index is estimated using a multiple group
means and covariance structure model
DISTRIBUTION OF HP INDEX SCORES, 2006 AND 2011
Number of SAs
4000
3500
3000
2500
2000
1500
1000
500
0
-40
-35
-30
-25
most disadvantaged
-20
-15
-10
-5
0
5
10
15
20
25
30
35
most affluent
The Figure shows the distribution of the 2006 and 2011 Absolute HP Index
Scores in 5-point ranges (one half of a standard deviation)
40
SMOOTHED DISTRIBUTION OF ABSOLUTE HP INDEX
SCORES, 2006 AND 2011
Number of SAs
4000
3500
2006
3000
2011
2500
2000
1500
1000
500
0
-40
-35
-30
-25
most disadvantaged
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
most affluent
The Figure shows the decline by 7.0 points in the mean of the Absolute HP
Index Scores between 2006 and 2011 (or 0.7 of a standard deviation)
SMOOTHED DISTRIBUTION OF RELATIVE HP INDEX
SCORES, 2006 AND 2011
Number of SAs
4000
3500
2006
3000
2011
2500
2000
1500
1000
500
0
-40
-35
-30
-25
most disadvantaged
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
most affluent
The Figure shows the distribution of the 2006 and 2011 Relative HP Index
Scores, after de-trending the absolute scores by the difference in means
MAPPING DEPRIVATION
most disadvantaged
most affluent
marginally below the average
disadvantaged
very disadvantaged
extremely disadvantaged
marginally above the average
affluent
very affluent
extremely affluent
COMPARISON OF 2006 AND 2011 ABSOLUTE INDEX
SCORES
COMPARISON OF 2006 AND 2011 RELATIVE INDEX
SCORES
ABSOLUTE
INDEX SCORES
2006
Absolute Index Score 2006
Haase & Pratschke 2012
30 to 50
20 to 30
10 to 20
0 to 10
-10 to 0
-20 to -10
-30 to -20
-60 to -30
(22)
(293)
(2513)
(6857)
(5925)
(2294)
(564)
(20)
ABSOLUTE
INDEX SCORES
2011
Absolute Index Scores 2011
Haase & Pratschke 2012
30 to 50
20 to 30
10 to 20
0 to 10
-10 to 0
-20 to -10
-30 to -20
-60 to -30
(2)
(70)
(838)
(3397)
(7181)
(5132)
(1719)
(149)
COMPARISON OF ABSOLUTE DEPRIVATION SCORES,
1991 AND 2006
 Shows the massive increase in disadvantage in wake of the
recession after the 2006 Census, affecting literally every
part of the country.
RELATIVE
INDEX SCORES
2006
Relative Index Score 2006
Haase & Pratschke 2012
30 to 50
20 to 30
10 to 20
0 to 10
-10 to 0
-20 to -10
-30 to -20
-60 to -30
(22)
(293)
(2513)
(6857)
(5925)
(2294)
(564)
(20)
RELATIVE
INDEX SCORES
2011
Relative Index Score 2011
Haase & Pratschke 2012
30 to 50
20 to 30
10 to 20
0 to 10
-10 to 0
-20 to -10
-30 to -20
-60 to -30
(30)
(474)
(2412)
(6232)
(6483)
(2408)
(447)
(2)
COMPARISON OF RELATIVE DEPRIVATION SCORES,
1991 AND 2006
 The pattern between affluence and disadvantage, whereby
affluence is greatest in the urban peripheries and gradually
declining towards more rural locations, remains broadly
intact.
 There is some indication that the reach of the affluent
commuter belts has somewhat diminished.
 Within the Greater Dublin Area, there is a marked shift in
the location of the most affluent areas. Whereas in 2006
the Western part of the Region scored high in affluence, in
2011 this is again primarily concentrated in Dun Laoghaire /
Rathdown.
CHANGE IN
RELATIVE
INDEX SCORES
2006-2011
Change in Relative HP Index Scores, 2006-2011
Haase and Pratschke 2012
improvement by more than 30 points
(15)
improvement by 20 to 30 points
(45)
improvement by 10 to 20 points
(405)
improvement by less than 10 points (8195)
no data in 2006
(252)
deterioration by less than 10 points (9210)
deterioration by 10 to 20 points
(350)
deterioration by 20 to 30 points
(14)
deterioration by more than 30 points
(2)