RESOURCE ALLOCATION MODEL FOR LOCAL DEVELOPMENT COMPANIES Trutz

RESOURCE ALLOCATION MODEL FOR LOCAL DEVELOPMENT COMPANIES Trutz Haase v 04 7 th February 2013 LCDP National Event, F 2 Rialto, Dublin

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 1990 s 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 Exploratory Factor Analysis (EFA) Confirmatory Factor Analysis (CFA) q EFA is essentially an exploratory technique; . i. e. data-driven q CFA requires a strong theoretical justification before the model is specified q all variables load on all factors q the researcher decides which of the observed variables are to be associated with which of the latent constructs q the structure matrix is the (accidental) outcome of the variables available q EFA cannot be used to compare outcomes over time q variables are conceptualised as the imperfect manifestations of the latent concepts q CFA model allows the comparison of outcomes over time q CFA facilitates the objective evaluation of the quality of the model through fit statistics

THE POBAL HP DEPRIVATION INDEX UNDERLYING DIMENSIONS 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 POBAL HP DEPRIVATION INDEX MODEL SPECIFICATION d 1 Age Dependency Rate d 2 Population Change d 3 Primary Education only d 4 Third Level Education d 5 Professional Classes d 6 Persons per Room d 7 Lone Parents d 8 Semi- and Unskilled Classes d 9 Male Unemployment Rate d 10 Female Unemployment Rate Demographic Growth Social Class Composition Labour Market Situation For a detailed discussion on the fitting of a model using Confirmatory Factor Analysis (CFA) see Haase & Pratschke, 2005, 2008

THE POBAL HP DEPRIVATION INDEX MAPPING DEPRIVATION most disadvantaged most affluent marginally below the average disadvantaged very disadvantaged extremely disadvantaged marginally above the average affluent very affluent extremely affluent

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.

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

MODELLING POPULATION SHARES ACCORDING TO RELATIVE DEPRIVATION T – TOTAL POPULATION L – LOW (48. 3%) M – MEDIUM (22. 4%) H – HIGH ( 7. 4%) M: -1 STD 22. 4% H: -2 STD 7. 4% L: 0 STD 48. 3% Population T : >5 STD (Total Population)

THE RESOURCE ALLOCATION MODEL Data Sources Reference Models Model Choices 2011 Census of Population 2011 Pobal HP Deprivation Index Total Population 100% 0% Reference Database for 18, 488 Small Areas Administrative data on current allocations Low Medium High Deprivation 48. 2% 22. 4% 7. 4% 0% 100% 0% Combined Target Allocation Data aggregation to spatial area of interest (Local Development Company, Local Authority etc. )

BACK TO THE ESSENCE OF THE LCDP The LDCs and LCDP … q represent the locus of “democratic experimentalism” (Charles Sabel, 1996) q embody society’s knowledge about (spatial) deprivation q demonstrate at local level how effective policies to ameliorate deprivation can be devised q act as advisors in the wider policy arena q 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

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