Presentation at the IRCHSS Summer School on Poverty Measurement , UCC, Cork, June 20 12

1. Presentation at the IRCHSS Summer School on Poverty Measurement, UCC, Cork, June 2012 the Multidimensional Measurement of PovertyConceptual Underpinnings of Individual, Family and SpatialMeasurements of Poverty and Deprivation

2. Format of Presentation Measuring Poverty and Social Inclusion at the Level of Individuals or Households Measuring Child and Family Well-being Deprivation and its Spatial Articulation

3. Measuring Poverty and Social Inclusion at the Level of Individuals or Households

4. Poverty - a Multidimensional concept While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved difficult to develop measures that adequately capture this multidimensionality account for the “ecological” and multilevel context of well-being facilitate comparisons over time This presentation explores the role of Structural Equation Modelling (SEM) as a technique for constructing improved measurements of poverty, child and family well-being and spatial deprivation.

5. DEFINING POVERTY • Townsend - the conceptual bedrock for the European concept of poverty “People are in poverty when “their resources are so seriously below those commanded by the average individual or family that they are, in effect, excluded from ordinary living patterns, customs and activities.” (Townsend, P., 1979) • 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, reflecting the definition adopted in the mid-1980s by the European Council)

6. From At-Risk-of-Poverty to Material Deprivation The purpose of EU deprivation indicators is to capture a situation of exclusion from a minimum acceptable way of life due to a lack of resources. (EU Council of Ministers, 1985) Originally, this focused solely on the relative at-risk-of-poverty rate, essentially a unidimensional measure related to income, which takes as its sole reference point the individual nation state. In an attempt to develop a wider European perspective on the measurement and interpretation of poverty, this has been accompanied in recent years by measures of “material deprivation”, which assess relative poverty in terms of the affordability of a basket of consumption items, defined in a unified manner across EU Member States. This partially reflects a shift in focus from social cohesion within each country to social cohesion within the enlarged community.

7. Material Deprivation (EU-SILC)

8. SERIOUS QUESTIONS REMAIN WITH RESPECT TO THE MATERIAL DEPRIVATION INDICATORS reliability: Because the cost of most of these items varies dramatically, it is difficult to interpret individual responses. This is compounded by the fact that the material deprivation index does not lend itself to longitudinal analysis, as the cost of individual items (and their diffusion within the population) varies considerably over time. policy relevance: The poor correlation (r=0.3) between the at-risk-of-poverty rates and material deprivation across the EU countries generates confusion: e.g. the risk of poverty is much lower in a number of former Eastern European countries, but so is the median income, which correlates highly (r=0.83) with material deprivation. aggregation into a household deprivation score: The simple additive approach based on a 0/1 scoring assigns equal value to each item, effectively giving the same importance to not affording a PC to not having an indoor toilet. (Gilbert, N., 2011)

9. Questions Also remain as to what constitutes a truly Multidimensional concept of poverty Some argue that the shift from the relative at-risk-of-poverty rate to the concept of material deprivation represents at least a partial move from a simple to a more complex concept of poverty (Fahey, 2010). Others questions the extent to which material deprivation provides a more valid, reliable and transparent depiction of poverty than a direct measure of income (Gilbert, 2011). Researchers have sought to broaden the financial measures to include wealth, imputed rent and the psychological consequences of financial strain. There is a widespread realisation that comprehensive measurement of social exclusion must include many other dimensions. Few studies have managed to successfully operationalise the measurement of these additional dimensions when developing poverty indices.

10. EU Commonly Agreed Primary Social Inclusion Indicators

11. THE CONVENTIONAL APPROACH TO ANALYSING DIMENSIONALITY: EXPLORATORY FACTOR ANALYSIS (EFA) • Exploratory Factor Analysis (EFA) reduces variables to a smaller number of underlying Dimensions or Factors V1 F1 V2 V3 V4 V5 F2 V6 • EFA is an exploratory technique (i.e. data-driven) • all variables load on all factors • the structure matrix is determined by the nature of the variables • Principal Components Analysis (a commonly-used EFA technique) does not account for measurement error (v1-v6 are assumed to be perfect indicators) • EFA can not be used to compare outcomes over time

12. The Dimensionality of Material Deprivation (EU-SILC) Using EFA, Nolan (2010) identifies three dimensions in the EU-SILC indicators: Consumption deprivation – items relating to food, heat, a holiday, a car or a PC, and avoiding arrears on rent or utilities. Household facilities – such as bath or shower and indoor toilet, a telephone, a colour TV and a washing machine. Neighbourhood environment - noise, pollution, crime and violence. But note, that this structure already differs from that postulated.

13. A Superior Approach: Confirmatory Factor Analysis (CFA) • variables are conceptualised as the (imperfect) manifestations of underlying, latent concepts d V1 1 L1 d V2 2 d V3 3 d V4 4 d V5 L2 5 d V6 6 • 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 variables • variables are assumed to only partially reflect the latent variable • CFA model (with factorial invariance) allows for the comparison of outcomes over time • CFA facilitates the objective evaluation of model “fit” using statistical tests and indices

14. APPLYING CFA TO THE BRITISH HOUSEHOLD PANEL STUDY (BHPS) Walker, R., Tomlinson, M, Williams, G. Multi-dimensional Measurement of Poverty and Well-being: A UK Case-Study

15. A Multidimensional Model of Poverty(BHPS 1991-2003) Walker, R., Tomlinson, M, Williams, G. Multi-dimensional Measurement of Poverty and Well-being: A UK Case-Study

16. Conventional and SEM-based Poverty Measures in Comparison The relative income measures show a pattern of stable or slightly increasing poverty during the John Major period 1992–97 and then a decline when New Labour took office from 1997 onwards while the absolute income measure, with the poverty threshold held constant in real terms, shows a continuous decline. The analogous PI-based measures, on the other hand, both show steady declines throughout the whole period, which is to be expected since a portion of the index is designed to capture aspects of poverty theorised to be more stable and less sensitive to short-term fluctuations in the national economic situation.

17. Measuring Child and Family Well-being

18. MEASURING CHILD AND FAMILY WELL-BEING - GROWING UP IN IRELAND Bronfenbrenner’s Ecological Perspective on Child Development

19. TAKING THE THEORETICAL FRAMEWORK SERIOUSLY There is a need to take the theoretical framework seriously in the study design The nested structure of potential influences on child well-being needs to find explicit expression This requires latent variable modelling and testing influences along the direct and indirect pathways postulated by the theoretical framework Classical statistical models, such as ANOVA and multiple regression, can only test the influence of a set of risk and protective factors on a single outcome variable and do not account for mediated effects Structural Equation Modelling offers a feasible solution to this problem

20. An Ecological Model of child Well-being Measurement Model for PCG Well-being Measurement Model for SCG Well-being Risk and Protective Factors Risk and Protective Factors SCG Well-being PCG Well-being Child Well-being Measurement Model for Child Well-being

21. Risk and Protective Factors Financial Difficulties Non-Irish Ethnicity SCG Well-being Local Problem Scale Low Social Class Local Services Scale Equivalised Household Income Decile Haase-Pratschke Deprivation Score ESRI Basic Deprivation PCG Well-being Health Status (Child) Low Education (PCG) Life Events (Child) Health Status (PCG) Child Well-being Gender (Child) Age (PCG) Pratschke, J., Haase, T., McKeown, K. 2011 Well-being and the family system: A structural equation model of relational and contextual influences. GUI Conference, December.

22. A structural equation Model of Child and Family Well-being Child well-being SCG well-being PCG well-being d d d d d d d d d d d d DCON_P DCOH_P DCON_S DCOH_S P2_PCG P3_PCG P1_SCG P2_SCG P3_SCG DSAT_P DSAT_S P1_PCG ParentingPCG ParentingSCG DyadicSCG DyadicPCG DepressionSCG DepressionPCG d d d d d Financialdifficulties Localproblems Non-Irishethnicity Localservices Low socialclass Haase-Pratschke HHincome Health ofChild ESRIDeprivation Life Eventsof child Low educ.of PCG Genderof child Healthof PCG ChildDifficulties Self-Concept ScholasticAchievement Age ofPCG Note 1: covariances between disturbance terms for Child Well-being and Parenting (PCG and SCG) not included in figure. Note 2: all covariances between independent variables omitted from figure D_MATH D_READ EMO_P CON_P PH_POP PH_HAP HYP_P PH_APP T_EVAL REL_P PH_INT d d d d d d d

23. Influences on Child and family Well-being Goodness of Fit: N: 4,881 CFI: .951 RMSEA: .023 All effects significant at p < .05 Financial Difficulties - . 08 Non-Irish Ethnicity SCG Well-being - . 10 Local Problem Scale - . 10 Low Social Class R²=.04 - . 06 Local Services Scale Equivalised Household Income Decile Haase-Pratschke Deprivation Score - . 15 ESRI Basic Deprivation - . 11 PCG Well-being . 09 R²=.17 - . 10 Health Status (Child) - . 11 - . 06 - . 28 . 41 Low Education (PCG) . 04 - . 10 . 08 Life Events (Child) - . 07 - . 04 Health Status (PCG) - . 04 Child Well-being Gender (Child) . 07 . 12 Age (PCG) R²=.31 Pratschke, J., Haase, T., McKeown, K. 2011 Well-being and the family system: A structural equation model of relational and contextual influences. GUI Conference, December.

24. Key Findings on the Determinants of Child Well-being The analysis confirms the importance of the mother’s well-being as a mediating factor on the child. A one unit improvement in the mother’s well-being is associated with a 0.4 unit direct improvement in child well-being. In stark contrast, the direct effect of the father’s well-being on the child (.04) is almost negligible once we control for other factors. A striking result is the strongly mediated effect of many contextual influences, in harmony with the ecological model of child well-being. With the exception of the mother’s health and the Haase-Pratschke Deprivation Index, which have a significant direct effect on child well-being, all other socio-economic factors, including financial variables and local area problems, have a distaleffect on child well-being that is mediated by the mother’s well-being.

25. Reflection on Methodological Issues in the Measurement of Child Well-being The conceptualisation of well-being as a higher-order latent concept reveals itself to be a powerful and well-supported hypothesis. The assumption that the well-being of children cannot be understood without simultaneously analysing the well-being of their parents is reinforced. All of the key influences identified in this analysis are in line with our previous research on child and family well-being using independent data – including the finding that a unit change in maternal well-being is associated with almost half a unit change in child well-being. Parents act as a buffer between economic risk factors and child well-being. Socio-economic risks doinfluence parental well-being, and thus have a mediated effect on children.

26. Deprivation and its Spatial Articulation

27. RECONSIDERING THE DEFINITION OF POVERTY AND DEPRIVATION • 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)

28. The Importance of the concept of Opportunity Deprivation Attributes can be measured using Census or administrative data on the individual (e.g. ethnicity, education, social class etc.) Possessions can also be measured through individual-level Census or administrative data (e.g. income, wealth, consumption etc.) However, Opportunities cannot be measured in the individual. Opportunities reflect interactions, and require specific measures Any measure of poverty or deprivation that rests on individual-level data alone will inevitably be biased towards urban forms of deprivation

29. A Hypothesis of 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

30. The Basic Model of Affluence and Deprivation d Age Dependency Rate 1 Demographic d Population Change Growth 2 d Primary Education only 3 d Third Level Education 4 d Professional Classes Social Class 5 Composition d Persons per Room 6 d Lone Parents 7 d Semi- and Unskilled Classes 8 Labour Market d Male Unemployment Rate Situation 9 d Female Unemployment Rate 10

31. Dynamic Path Diagram for Irish Deprivation Measures, 1991 - 2006 Initial Growth Rapid Growth Slow-Down Haase, T. & Pratschke, J. 2008 The New Measures of Deprivation in the Republic of Ireland. Dublin: ADM.

32. Change in Absolute Deprivation Scores, 1991-2006 The figure shows the unprecedented growth in Ireland over the 1991 to 2006 period, with greatest changes occurring between 1996 and 2002.

33. Change in Relative Deprivation Scores, 1991-2006 The figure shows the final distribution of Relative Deprivation Scores, after controlling for the underlying trend and standardising its spread. The scores thus look at deprivation at each point in time; i.e. as it might be perceived in relative terms.

34. ABSOLUTE AFFLUENCE AND DEPRIVATION 1991 - 2006 1991 1996 2006 2002

35. Comparison of Absolute Deprivation Scores, 1991-2006 • Shows how affluence has grown throughout the whole country. • Greatest change between 1996 and 2002. • Shows how affluence has grown in concentric rings around the main urban centres, effectively demarcating the urban commuter belts. • Shows that, with the exception of Dublin Inner City, cities in general have not improved in their affluence as much as the rest of the country.

36. RELATIVE AFFLUENCE AND DEPRIVATION 1991 - 2006 1991 1996 2006 2002

37. Comparison of Relative Deprivation Scores, 1991-2006 • Apart from Dublin Inner City, where there is evidence of substantial gentrification, there are only small differences in Relative Deprivation Scores between 1991 and 2006.

38. Strengths of CFA-based Deprivation Indices • true multidimensionality, based on theoretical considerations • no double-counting • rational approach to the selection/construction of indicators • statistical tests and alternative fit indices to test model adequacy • identical structure matrix can be imposed across multiple waves • identical measurement scale can be obtained across multiple waves • true distances to means are maintained (i.e. measurement, not ranking) • distinguishes between absolute and relative deprivation • allows for true inter-temporal comparisons

39. WHAT ARE THE ATTRIBUTES OF A SATISFACTORY MEASURE OF POVERTY OR DEPRIVATION? • facilitates the comprehension of multiple indicators. • provides a basis for the effective targeting of the most disadvantaged individuals, families or areas. • improves our understanding of the pathways through which poverty and well-being are mediated. • provides a means by which to compare relative need, assess change over time, and facilitate monitoring and evaluation. • enjoys broad support amongst key stakeholders, including government departments, state agencies and community representatives.

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