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THE 2016 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA) CONCEPTUAL BASIS Trutz Haase THE 2016 POBAL HP DEPRIVATION INDEX FOR SMALL AREAS (SA) CONCEPTUAL BASIS Trutz Haase & Jonathan Pratschke Dublin, August 2017

A NEW INTEREST IN HEALTH AND EDUCATION INEQUALITIES 1. There is a growing interest A NEW INTEREST IN HEALTH AND EDUCATION INEQUALITIES 1. There is a growing interest in the effects of social class on key outcomes in relation to health and education 2. Social gradients in these arenas are important from an equality perspective and must be taken into account when planning services 3. Measuring the effects of social class on health and education permits the development of evidence-based resource allocation models and facilitates the monitoring of social gradients 4. The precise measurement of social gradients plays an important part in the design of performance measurement frameworks and in the evaluation of interventions to improve social equality

HEALTH OUTCOMES WITH NORMALLY DISTRIBUTED MEASURE OF RELATIVE AFFLUENCE / DEPRIVATION Deprived Health Risks HEALTH OUTCOMES WITH NORMALLY DISTRIBUTED MEASURE OF RELATIVE AFFLUENCE / DEPRIVATION Deprived Health Risks Above Average Risks Below Average Risks SD 0. 1% Affluent (Average Health Risks) -3 -2 2. 1% High -1 13. 6% Moderate 0 34. 1% 1 34. 1% 2 13. 6% 3 2. 1% 0. 1% Low Source: Haase, Pratschke (2017)

SOME CONSIDERATIONS WHEN THINKING ABOUT HEALTH INEQUALITIES q Health Outcomes: Mortality, Life Expectancy, Self-rated SOME CONSIDERATIONS WHEN THINKING ABOUT HEALTH INEQUALITIES q Health Outcomes: Mortality, Life Expectancy, Self-rated Health, Long-term Limiting Conditions, Cancer, Respiratory Diseases, Coronary Heart and Cardio-vascular Diseases q Socio-economic Contrasts: Education, Social Class, Deprivation Indices q Geographic Scale: specific spatial contrasts - e. g. individual towns, counties or regions, Electoral Divisions (ED), Small Areas (SA) q Display: Emphasis on Relative Risks, using Bar and Line Charts, Boxplots, and Maps q Category Scales: Quintiles, Deciles, Standard Deviations q Y-Scale: raw percentages, percentage deviation (Mean = 0), standardised mean deviation (e. g. Mean = 0, STD = 10)

WHY DO WE USE DEPRIVATION INDICES? q The Census and many sample surveys (TILDA, WHY DO WE USE DEPRIVATION INDICES? q The Census and many sample surveys (TILDA, GUI, Healthy Ireland the Irish Health Survey) include variables which relate to social class. q Social class is often measured by educational attainments, occupation, employment status or income. q Whilst each of these indicators allow us to identify social gradients, it is arguably more effective to measure social class using a multivariate measurement model based on clearly-defined and theoretically-supported dimensions. q The resulting composite index is likely to perform better than any individual indicator when predicting health-related and educational outcomes. It also has the advantage that it may be used even where individual-level measures of social class are not available (e. g. in administrative datasets such as POD, P-POD, HIPE, NDTRS, NDRDI, CTL, etc. ).

ADVANTAGES OF COMPOSITE DEPRIVATION INDICES 1. It is difficult to simultaneously comprehend the spatial ADVANTAGES OF COMPOSITE DEPRIVATION INDICES 1. It is difficult to simultaneously comprehend the spatial distribution of multiple socioeconomic indicators at a high level of spatial disaggregation at multiple points in time. 2. For practical purposes, therefore, it is helpful to have a composite measure of social class which draws together a variety of related observations. This is a more reliable predictor of health and educational outcomes than any individual indicator. 3. Deprivation indices such as the Pobal HP Deprivation Index provide a reliable means of assessing the effect of social class on health and education, facilitating monitoring and evaluation. 4. The Pobal HP Deprivation Index also provides a means to support the targeting of disadvantaged areas and communities through the application of evidence-based Resource Allocation Models.

THE STARTING POINT FOR DESIGNING A DEPRIVATION INDEX: A COMPREHENSIVE DEFINITION OF POVERTY q THE STARTING POINT FOR DESIGNING A DEPRIVATION INDEX: A COMPREHENSIVE DEFINITION OF POVERTY q 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) q 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. , Do. E – UK, 1995)

TRADITIONAL APPROACH TO COMBINING MULTIPLE INDICATORS: EXPLORATORY FACTOR ANALYSIS (EFA) q Exploratory Factor Analysis TRADITIONAL APPROACH TO COMBINING MULTIPLE INDICATORS: EXPLORATORY FACTOR ANALYSIS (EFA) q Exploratory Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors V 1 V 2 F 1 V 3 V 4 V 5 F 2 V 6 q EFA is an exploratory technique; . i. e. essentially data-driven q all variables load on all factors q the structure matrix is the (accidental) outcome of the variables available q EFA cannot be used to compare outcomes over time

APPROACH UNDERLYING THE HP DEPRIVATION INDICES: CONFIRMATORY FACTOR ANALYSIS (CFA) q Confirmatory Factor Analysis APPROACH UNDERLYING THE HP DEPRIVATION INDICES: CONFIRMATORY FACTOR ANALYSIS (CFA) q Confirmatory Factor Analysis also reduces observations to the underlying Factors, however d 1 V 1 d 2 V 2 d 3 V 3 d 4 V 4 d 5 V 5 d 6 V 6 L 1 L 2 q CFA requires a strong theoretical justification before the model is specified q Researchers determine which observed variables are associated with which latent construct q variables are treated as imperfect manifestations of the latent concepts q CFA models can allow the comparison of outcomes over time q CFA facilitates the objective evaluation of the quality of the model through fit statistics

OVERVIEW OF SUCCESSIVE DEPRIVATION INDICES, HAASE & PRATSCHKE 1996 - 2012 06 SA n=18, OVERVIEW OF SUCCESSIVE DEPRIVATION INDICES, HAASE & PRATSCHKE 1996 - 2012 06 SA n=18, 488 ED n = 3, 409 91 96 86 91 96 02 06 06 NUTS 4 n = 34 91 96 86 91 96 02 06 06 NUTS 3 n = 8 91 96 86 91 96 02 06 06 NUTS 2 n = 2 91 96 86 91 96 02 06 06 NUTS 1 n = 1 91 96 86 91 96 02 06 06 06 01 NI 01 NI 11 06 11 91 96 02 06 11 06 11 91 96 02 06 11 Haase et al. , 1996 Haase, 1999 Pratschke & Haase, 2001 Level at which model is estimated Level to which data is aggregated Pratschke & Haase, 2004 Haase & Pratschke, 2005 Haase & Pratschke, 2008 Haase & Pratschke, 2010 Haase & Pratschke, 2011 Haase & Pratschke, 2012

HP DEPRIVATION INDICES 2011 -2016 2011 Pobal HP Deprivation Index SA 06 11 All-Island HP DEPRIVATION INDICES 2011 -2016 2011 Pobal HP Deprivation Index SA 06 11 All-Island HP Deprivation Index 11 RI 11 NI 2016 Pobal HP Deprivation Index 06 11 16 Longitudinal HP Deprivation Index ED 06 11 11 RI 11 NI 91 96 02 06 11 16 NUTS 4 06 11 11 RI 11 NI 91 96 02 06 11 16 NUTS 3 06 11 11 RI 11 NI 91 96 02 06 11 16 NUTS 2 06 11 11 RI 11 NI 91 96 02 06 11 16 NUTS 1 06 11 11 RI 11 NI 91 96 02 06 11 16 Haase & Pratschke, August 2017 Haase & Pratschke, 2012 Pratschke & Haase, 2014 Haase, Pratschke & Gleeson, 2014 Level at which model is estimated Level to which data is aggregated Haase & Pratschke, October 2017

THE UNDERLYING DIMENSIONS OF SOCIAL DISADVANTAGE q Demographic Decline (predominantly rural) § population loss THE UNDERLYING DIMENSIONS OF SOCIAL DISADVANTAGE q Demographic Decline (predominantly rural) § population loss and the social and demographic effects of emigration (age dependency, low education of adult population) q Social Class Deprivation (applying in rural and urban areas) § social class composition, education, housing quality q Labour Market Deprivation (predominantly urban) § unemployment, lone parents, low skills base

THE MEASUREMENT MODEL OF THE POBAL HP DEPRIVATION INDEX d 1 Age Dependency Rate THE MEASUREMENT MODEL OF THE POBAL HP DEPRIVATION INDEX d 1 Age Dependency Rate d 2 Population Change d 3 Primary Education only d 4 Third Level Education d 5 Persons per Room d 6 Professional Classes d 7 Semi- and Unskilled Classes d 8 Lone Parents d 9 Male Unemployment Rate d 10 Female Unemployment Rate Demographic Growth Social Class Composition Labour Market Situation • • The Pobal HP Deprivation Index is estimated using a multiple group means and covariance structure model This makes it possible to measure the change that occurred in the mean of the latent variables between 2006, 2011 and 2016

STRENGTHS OF THE HP DEPRIVATION INDICES q true multidimensionality, based on theoretical considerations q STRENGTHS OF THE HP DEPRIVATION INDICES q true multidimensionality, based on theoretical considerations q provides for an appropriate treatment of both urban and rural deprivation q no double-counting q rational approach to indicator selection q uses variety of alternative fit indices to test model adequacy q identical structure matrix across multiple waves q identical measurement scale across multiple waves q true distances to means are maintained (i. e. measurement, not ranking) q distinguishes between measurement of absolute and relative deprivation q allows for true inter-temporal comparisons

MAPPING DEPRIVATION most disadvantaged most affluent marginally below the average disadvantaged very disadvantaged extremely MAPPING DEPRIVATION most disadvantaged most affluent marginally below the average disadvantaged very disadvantaged extremely disadvantaged marginally above the average affluent very affluent extremely affluent

DISTRIBUTION OF ABSOLUTE AND RELATIVE HP INDEX SCORES, 2006, 2011 AND 2016 HP Deprivation DISTRIBUTION OF ABSOLUTE AND RELATIVE HP INDEX SCORES, 2006, 2011 AND 2016 HP Deprivation Index Score Min 2006 absolute HP Deprivation Score -41. 52 2011 absolute HP Deprivation Score Max Mean Standard Deviation 38. 34 0. 00 10. 00 -37. 77 32. 96 -6. 87 9. 60 2016 absolute HP Deprivation Score -43. 47 36. 10 -4. 24 10. 08 2006 relative HP Deprivation Score -41. 52 38. 34 0. 00 10. 00 2011 relative HP Deprivation Score -30. 76 39. 97 0. 00 10. 00 2016 relative HP Deprivation Score -39. 25 40. 31 0. 00 10. 00

DISTRIBUTION OF ABSOLUTE HP INDEX SCORES, 2006, 2011 AND 2016 most disadvantaged most affluent DISTRIBUTION OF ABSOLUTE HP INDEX SCORES, 2006, 2011 AND 2016 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) and the subsequent recovery by 2. 8 Index Scores (or 0. 28 of a standard deviation) between 2011 and 2016.

ABSOLUTE INDEX SCORES 2006 ABSOLUTE INDEX SCORES 2006

ABSOLUTE INDEX SCORES 2011 ABSOLUTE INDEX SCORES 2011

ABSOLUTE INDEX SCORES 2016 ABSOLUTE INDEX SCORES 2016

RELATIVE INDEX SCORES 2006 RELATIVE INDEX SCORES 2006

RELATIVE INDEX SCORES 2011 RELATIVE INDEX SCORES 2011

RELATIVE INDEX SCORES 2016 RELATIVE INDEX SCORES 2016

COMPARISON OF RELATIVE DEPRIVATION SCORES BETWEEN 2006 AND 2016 q The most basic pattern COMPARISON OF RELATIVE DEPRIVATION SCORES BETWEEN 2006 AND 2016 q The most basic pattern of affluence and disadvantage has remained broadly intact over this 15 -year period: affluence is highest in the urban peripheries and gradually declines as one moves towards more rural locations. q There is some indication that the reach of affluent commuter belts has diminished, particularly in the years following the recession. q Within the Greater Dublin Area, there has been a shift in the location of the most affluent areas. Whereas in 2006 the Western part of this area scored highly, in 2011 and 2016 affluence is once again concentrated in its traditional heartlands (e. g. Dun Laoghaire/Rathdown.

CHANGE IN RELATIVE INDEX SCORES 2006 -2011 CHANGE IN RELATIVE INDEX SCORES 2006 -2011