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The 2011 Pobal HP Deprivation Index for Small Areas (SA) Conceptual Underpinnings

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Dublin, August 2012

The 2011 Pobal HP Deprivation Index for Small Areas (SA)Conceptual Underpinnings

It is difficult to simultaneously comprehend the spatial distribution of multiple indicators at multiple points in time

For practical purposes, there is a need for a single indicator which draws a variety of observations together

Such indices can provide the basis for the effective targeting of the most disadvantaged areas

Such indices can provide a means by which to assess changes over time, and facilitate monitoring and evaluation

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

Requirements

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)

Traditional Approach: Exploratory Factor Analysis (EFA)

- Ordinary Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors

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- 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

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- 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

Haase et al., 1996

Haase, 1999

Pratschke & Haase, 2001

Pratschke & Haase, 2004

Haase & Pratschke, 2005

Haase & Pratschke, 2008

Haase & Pratschke, 2010

Haase & Pratschke, 2012

Haase & Pratschke, 2011

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

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Age Dependency Rate

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Demographic

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Population Change

Growth

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Primary Education only

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Third Level Education

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Persons per Room

Social Class

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Composition

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Professional Classes

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Semi- and Unskilled Classes

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Lone Parents

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Labour Market

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Male Unemployment Rate

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Female Unemployment Rate

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Solution 2:

A Longitudinal SEM Model

2006

2011

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

most disadvantaged

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)

Smoothed Distribution of AbsoluteHP Index Scores, 2006 and 2011

most disadvantaged

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

most disadvantaged

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

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.

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.