Trutz Haase RESOURCE ALLOCATION MODEL FOR LOCAL DEVELOPMENT COMPANIES 7 th February 2013 LCDP National Event, F2 Rialto, Dublin v04

OVERVIEW 1.There are three factors which lie at the heart of a rational resource allocation for Local Development Companies: i.the relative size of the target population, ii.the relative affluence or deprivation of the respective areas, and iii.historical allocations 2.In Ireland, a robust measure for social disadvantage is provided by the Pobal HP Deprivation Index 3.Designing a Resource Allocation Model is not rocket science 4.Even a rudimentary Resource Allocation Model will facilitate a superior allocation of resources. It is based on objective criteria, results in a fairer distribution according to set criteria, is needs-focused and transparent in its application

WHY TO ADJUST FOR DEPRIVATION?  At least since the early 1990s it has been a widely shared understanding amongst policy makers that structural policies aimed at the individual alone do not suffice to overcome the disadvantage inherent in communities that comprise of a high level of concentration of people with exceptional needs.  The core of successive Local Development Programmes has been to provide additional resources to those communities which can objectively been identified to be amongst the most disadvantaged, and to assist them in breaking the spiral of decline.

THE POBAL HP DEPRIVATION INDEX - CONCEPTUAL UNDERPINNINGS  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  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 Exploratory Factor Analysis (EFA)Confirmatory Factor Analysis (CFA)

 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 POBAL HP DEPRIVATION INDEX - UNDERLYING DIMENSIONS

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 POBAL HP DEPRIVATION INDEX - MODEL SPECIFICATION For a detailed discussion on the fitting of a model using Confirmatory Factor Analysis (CFA) see Haase & Pratschke, 2005, 2008

most disadvantaged most affluent marginally below the averagemarginally above the average disadvantagedaffluent very disadvantagedvery affluent extremely disadvantagedextremely affluent THE POBAL HP DEPRIVATION INDEX - MAPPING DEPRIVATION

THE POBAL HP DEPRIVATION INDEX - DUBLIN INNER CITY (ED LEVEL) Look at North Dock C and Mansion House A, which are defined as “marginally below average deprivation” in an ED-level deprivation analysis

THE POBAL HP DEPRIVATION INDEX - DUBLIN INNER CITY (SA LEVEL) The SA-level analysis shows the detail of the distribution of affluence and deprivation within North Dock C and Mansion House A.

 true multidimensionality, based on theoretical considerations  provides for a balanced approach between urban and rural deprivation  is sensitive to demographic groups with higher services needs  no double-counting  rational choice 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) THE POBAL HP DEPRIVATION INDEX - SUMMARY

MODELLING POPULATION SHARES ACCORDING TO RELATIVE DEPRIVATION T – TOTAL POPULATION L – LOW (48.3%) M – MEDIUM (22.4%) H – HIGH ( 7.4%) T : >5 STD (Total Population) L: 0 STD 48.3% Population M: -1 STD 22.4% H: -2 STD 7.4%

THE RESOURCE ALLOCATION MODEL 2011 Census of Population 2011 Pobal HP Deprivation Index Reference Database for 18,488 Small Areas Total Population 100% Low Deprivation 48.2% Medium Deprivation 22.4% High Deprivation 7.4% 0% 100%0% Data aggregation to spatial area of interest (Local Development Company, Local Authority etc.) Administrative data on current allocations Combined Target Allocation Data Sources Reference Models Model Choices

The LDCs and LCDP …  represent the locus of “democratic experimentalism” (Charles Sabel, 1996)  embody society’s knowledge about (spatial) deprivation  demonstrate at local level how effective policies to ameliorate deprivation can be devised  act as advisors in the wider policy arena  aim at influencing resource distributions in the wider policy arena, such as to acknowledge the social gradient in health, education, housing and other outcomes The work of the LDCs and LCDP has to be exemplary in nature BACK TO THE ESSENCE OF THE LCDP

A Resource Allocation Model which …  takes into account both total population and relative deprivation  operationalizes the choices made with regards to stated objectives and criteria  allows for combination of alternative models according to varying SES gradients  can be applied to partial and total budgets or human resource allocations  allows resources to be scaled according to available budgets  allows for the stepwise implementation over multiple years SUMMARY