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Trutz Haase & Jonathan Pratschke THE 2011 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA) Conceptual Underpinnings Dublin, August 2012

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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

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Deprivation IndexSmall 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 comparisonTo 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

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Deprivation IndexSmall 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 REQUIREMENTS

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Deprivation IndexSmall 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 variablesOverall less restrictive May require transformationsn/a Requires dimensional analysis to avoid double counting n/a Requires methods and weights for combining into single index scores n/a MEASUREMENT CONSIDERATIONS

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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

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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 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)

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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 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)

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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

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OVERVIEW OF SUCCESSIVE DEPRIVATION INDICES, HAASE & PRATSCHKE 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, 2004 Haase & Pratschke, 2005 Haase & Pratschke, 2008 Haase & Pratschke, 2010 Haase & Pratschke, Pratschke & Haase, NI Haase & Pratschke, 2011 Level at which model is estimated Level to which data is aggregated 0611

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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

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Age Dependency Rate 1 Population Change 2 Primary Education only 3 Third Level Education 4 Professional Classes 5 Persons per Room 6 Lone Parents 7 Semi- and Unskilled Classes 8 Male Unemployment Rate 9 Female Unemployment Rate 10 Demographic Growth Social Class Composition Labour Market Situation THE BASIC MODEL OF THE POBAL HP DEPRIVATION INDEX

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SOLUTION 2: A LONGITUDINAL SEM MODEL

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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

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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 disadvantagedmost affluent

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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 disadvantagedmost affluent

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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 disadvantagedmost affluent

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most disadvantaged most affluent marginally below the averagemarginally above the average disadvantagedaffluent very disadvantagedvery affluent extremely disadvantagedextremely affluent MAPPING DEPRIVATION

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COMPARISON OF 2006 AND 2011 ABSOLUTE INDEX SCORES

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COMPARISON OF 2006 AND 2011 RELATIVE INDEX SCORES

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ABSOLUTE INDEX SCORES 2006

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ABSOLUTE INDEX SCORES 2011

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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

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RELATIVE INDEX SCORES 2006

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RELATIVE INDEX SCORES 2011

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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

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CHANGE IN RELATIVE INDEX SCORES

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