Trutz Haase & Jonathan Pratschke THE 2011 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA) Conceptual...
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Transcript of Trutz Haase & Jonathan Pratschke THE 2011 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA) Conceptual...
Trutz Haase & Jonathan Pratschke
THE 2011 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA)
Conceptual Underpinnings
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
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
THE PURPOSE OF DEPRIVATION INDICES
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 timeMay 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
REQUIREMENTS
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
MEASUREMENT CONSIDERATIONS
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)
A COMPREHENSIVE DEFINITION OF POVERTY
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
V1
V2
V3
V4
V5
V6
F1
F2
Ordinary Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors
TRADITIONAL APPROACH: EXPLORATORY FACTOR ANALYSIS (EFA)
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
V1
V2
V3
V4
V5
V6
L1
L2
Confirmatory Factor Analysis also reduces observations to the underlying Factors, however
1
2
3
4
5
6
NEW APPROACH: CONFIRMATORY FACTOR ANALYSIS (CFA)
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
STRENGTHS OF CFA-BASED DEPRIVATION INDICES
OVERVIEW OF SUCCESSIVE DEPRIVATION INDICES, HAASE & PRATSCHKE 1996 - 2012
91 96 02 06 11
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
06
06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
91 96 02 06 06
86 91 96
86 91 96
86 91 96
86 91 96
86 91 96
96
96
96
96
96
91
91
91
91
91
SA n=18,488
ED n = 3,409
NUTS 4 n = 34
NUTS 3 n = 8
NUTS 2 n = 2
NUTS 1 n = 1
Haase et al., 1996
Haase, 1999
Pratschke & Haase, 2004Haase & Pratschke, 2005 Haase & Pratschke, 2008
Haase & Pratschke, 2010Haase & Pratschke, 2012
91 96 02
91 96 02
91 96 02
91 96 02
91 96 02
Pratschke & Haase, 2001
01NI
01NI
01NI
01NI
01NI
01NI
Haase & Pratschke, 2011Level at which model is estimated
Level to which data is aggregated
06 11
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 UNDERLYING DIMENSIONS OF SOCIAL DISADVANTAGE
Age Dependency Rate1
Population Change2
Primary Education only3
Third Level Education4
Professional Classes
5Persons per Room
6
Lone Parents
7 Semi- and Unskilled Classes
8
Male Unemployment Rate9
Female Unemployment Rate 10
DemographicGrowth
Social ClassComposition
Labour MarketSituation
THE BASIC MODEL OF THE POBAL HP DEPRIVATION INDEX
SOLUTION 2:A LONGITUDINAL SEM MODEL
Professional Classes 200612
Semi- and Unskilled Classes 200613
Social ClassComposition
Lone Parents 20069
Male Unemployment Rate 200614
Female Unemployment Rate 200615
Labour MarketSituation
Age Dependency Rate 20067
Population Change 2002-068
Primary Education only 200610
Third Level Education 200611
DemographicGrowth
Persons per Room 200616
Professional Classes 2011 22
Semi- and Unskilled Classes 2011 23
Social ClassComposition
Lone Parents 2011 19
Male Unemployment Rate 2011 24
Female Unemployment Rate 2011 25
Labour MarketSituation
Age Dependency Rate 2011 17
Population Change 2006-11 18
Primary Education only 2011 20
Third Level Education 2011 21
DemographicGrowth
Persons per Room 2011 26
2006
2006
2006
2011
2011
2011
1
3
2
-0.61
0.46
-0.63
-0.51
0.53
0.69
-0.57
0.24
0.95
-0.860.20
-0.65
-0.76
-0.68
0.17
0.820.14
-0.54
0.36
-0.59
0.46
0.49
-0.58
0.73
-0.51
0.97
-0.89
-0.64
-0.86
-0.74
0.89
0.92
0.61
-0.06
0.10
0.03
0.04
0.03
0.35
-0.17
0.63
0.01
0.18
2006 2011
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
The Figure shows the distribution of the 2006 and 2011 Absolute HP Index Scores in 5-point ranges (one half of a standard deviation)
most disadvantaged most affluent
SMOOTHED DISTRIBUTION OF ABSOLUTE HP INDEX SCORES, 2006 AND 2011
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)
most disadvantaged most affluent
SMOOTHED DISTRIBUTION OF RELATIVE HP INDEX SCORES, 2006 AND 2011
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
most disadvantaged most affluent
most disadvantagedmost disadvantaged most affluentmost affluent
marginally below the average marginally above the average
disadvantaged affluent
very disadvantaged very affluent
extremely disadvantaged extremely affluent
MAPPING DEPRIVATION
COMPARISON OF 2006 AND 2011 ABSOLUTE INDEX SCORES
COMPARISON OF 2006 AND 2011 RELATIVE INDEX SCORES
ABSOLUTE INDEX SCORES2006
Absolute Index Score 2006Haase & Pratschke 2012
30 to 50 (22)20 to 30 (293)10 to 20 (2513)0 to 10 (6857)
-10 to 0 (5925)-20 to -10 (2294)-30 to -20 (564)-60 to -30 (20)
ABSOLUTE INDEX SCORES2011
Absolute Index Scores 2011Haase & Pratschke 2012
30 to 50 (2)20 to 30 (70)10 to 20 (838)0 to 10 (3397)
-10 to 0 (7181)-20 to -10 (5132)-30 to -20 (1719)-60 to -30 (149)
Shows the massive increase in disadvantage in wake of the recession after the 2006 Census, affecting literally every part of the country.
COMPARISON OF ABSOLUTE DEPRIVATION SCORES, 1991 AND 2006
RELATIVE INDEX SCORES2006
Relative Index Score 2006Haase & Pratschke 2012
30 to 50 (22)20 to 30 (293)10 to 20 (2513)0 to 10 (6857)
-10 to 0 (5925)-20 to -10 (2294)-30 to -20 (564)-60 to -30 (20)
RELATIVE INDEX SCORES2011
Relative Index Score 2011Haase & Pratschke 2012
30 to 50 (30)20 to 30 (474)10 to 20 (2412)0 to 10 (6232)
-10 to 0 (6483)-20 to -10 (2408)-30 to -20 (447)-60 to -30 (2)
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.
COMPARISON OF RELATIVE DEPRIVATION SCORES, 1991 AND 2006
CHANGE IN RELATIVE INDEX SCORES2006-2011
Change in Relative HP Index Scores, 2006-2011Haase 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)