Using SimBritain to Model the Geographical Impact of National Government Policies

Ballas, Dimitris; Clarke, Graham; Dorling, Danny; Rossiter, David

doi:10.1111/j.1538-4632.2006.00695.x

Cited by 62 publications

(55 citation statements)

References 23 publications

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“…The general method is outlined in more detail in Chin et al (2005), , , Cassells et al (2010) and Lymer et al (2008). The use of microsimulation methodology to create synthetic small area estimates of population characteristics was pioneered in the late 1980s and early 1990s (see work by Birkin and Clarke 1988, 1989and Williamson 1992 and has been used subsequently to derive small area estimates of income and income poverty (Anderson 2007;Ballas 2004;Lymer et al 2008;) as well as other population characteristics and policy impacts (see also work by (Ballas et al 2005(Ballas et al , 2007Williamson et al 1998)). The model used here is described below.…”

Section: Spatial Methodologymentioning

confidence: 97%

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

Miranti

McNamara

Tanton

et al. 2010

Appl. Spatial Analysis

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There is a substantial literature within Australia examining poverty rates for different family types at the national level. This study presents the first Australian estimates of poverty rates for different types of families at a local level. This paper builds upon the SpatialMSM/08B model, which fuses together data from the 2006 Australian Bureau of Statistics Census of Population and Housing and the 2002-03 and 2003-04 Surveys of Income and Housing. We examine differences in rates of income poverty for lone persons, sole parents, couples and couples with children. The results show that people living by themselves and sole parents have the highest poverty rates. In addition, there are pronounced spatial differences in the poverty rates of people living in different family situations, although the highest poverty rates for all family types tend to be in Australia's rural areas, with poverty clusters in most of the capital cities.

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Section: Spatial Methodologymentioning

confidence: 97%

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

Miranti

McNamara

Tanton

et al. 2010

Appl. Spatial Analysis

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“…), to inform current and future policy‐making it is necessary to have data that allows the spatial impact of policy decisions to be examined (Ballas et al. ). This in turn allows policy‐makers and researchers to understand, estimate, or predict areas are most likely to benefit from a change in policy.…”

Section: Datamentioning

confidence: 99%

A location quotient approach to producing regional production multipliers for the Irish economy

Morrissey

2016

Papers in Regional Science

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Regional issues in Ireland, although a relatively small and geographically homogenous country, attract considerable attention, much of which centres on the size of Dublin and the South East and its perceived dominant share of the national economy. Using location quotients (LQs) analysis and linking the production multipliers to their two digit NACE codes, this paper presents the first exploration of possible regional industrial specialization in related sectors in Ireland at the NUTS 2 level. Linking the production multipliers with NACE codes by region allows one to explore whether potential region specialization is associated with Jacobian externalities in Ireland. Using a relatively novel LQ approach, and a cut-off value of 1.25 or above as an indicator of industrial specialization and clustering, this paper found that in the Border, Midlands West (BMW) region and the South East region, 29 and 32 sectors respectively had LQ greater than the cut-off value of 1.25. In terms of Jacobian externalities, this paper found businesses in related sectors; particularly finance and insurance and transport and telecommunications are more likely to cluster in the BMW region. From a policy perspective this would indicate that there may be a role for the Regional Assemblies, particularly the Assembly for the BMW region to help develop and co-ordinate regional industrial policy in Ireland. From a regional science perspective, this paper adds empirical evidence to the literature on Jacobian and related variety in regional economies.JEL classification: R11, R12, R15

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“…For depiction simplicity, percentage counts have been used where possible. One of the most important variables to map is the simulated mean income, which is not typically available from public sources and which can be extremely useful for the analysis of the geographical implications of government policies and for estimating poverty and wealth at the local level (Ballas, Clarke, Dorling, and Rossiter 2007;Campbell and Ballas 2013;Miranti, McNamara, Tanton, and Harding 2011). This information was not available before data-fitting at this geographical level.…”

Section: Geographical Analysis Of Microdatamentioning

confidence: 99%

sms: AnRPackage for the Construction of Microdata for Geographical Analysis

Kavroudakis¹

2015

J. Stat. Soft.

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Spatial microsimulation is a methodology aiming to simulate entities such as households, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed.

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Using SimBritain to Model the Geographical Impact of National Government Policies

Cited by 62 publications

References 23 publications

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

A location quotient approach to producing regional production multipliers for the Irish economy

sms: AnRPackage for the Construction of Microdata for Geographical Analysis

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