Microsimulation for Local Impact Analysis: an Application to Plant Shutdown*

Rephann, Terance J.; Mäkilä, Kalle; Holm, Einar

doi:10.1111/j.0022-4146.2005.00368.x

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…O'Donoghue, Ballas, Clarke, Hynes, and Morrissey () outline how an SM model may be integrated with spatial models of energy and agricultural output to optimize biomass production. Ballas, Clarke, and Dewhurst () and Rephann, Mäkilä, and Holm () have linked an SM model with a hypothetical factory closure to elicit the effect on the spatial distribution of employment and population dynamics. Lindgren, Strömgren, Holm, and Häggström Lundevaller () consider the first‐round employment effects as a result of a hypothetical investment.…”

Section: Literature Reviewmentioning

confidence: 99%

Regional income and wave energy deployment in Ireland

Farrell

O’Donoghue

Morrissey

2020

Papers in Regional Science

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Alongside environmental benefits, renewable energy deployment is often evaluated on grounds of regional development. Focusing on wave energy deployment in Ireland, this paper quantifies employment-related welfare change net of associated subsidy costs. Although the added employment reduces inter-regional inequality, certain subsidies increase total income inequality by a greater extent.Total inequality increases by 0.25% in the preferred scenario. This pattern of incidence persists under an optimistic scenario where all manufacturing activity is carried out locally. This finding highlights that policies of regional development should consider the spatial distribution of associated subsidy costs. KEYWORDSinequality, spatial microsimulation, regional development, renewable energy JEL CLASSIFICATION D31; H23; Q42; Q43; Q48; R12

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Section: Literature Reviewmentioning

confidence: 99%

Regional income and wave energy deployment in Ireland

Farrell

O’Donoghue

Morrissey

2020

Papers in Regional Science

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“…Ageing the population within spatial microsimulation models have employed a variety of different methods. These can fully dynamically age a spatial base dataset as in the case of the SVERIGE model (Rephann et al, 2005) or the original SMILE model (Ballas et al, 2005a). The database employed in SVERIGE to estimate the equations and simulate the population is called TOPSWING (Total Population of Sweden Individual and Geographical database), a longitudinal database, which contains information on all Swedish residents including the location of residents in co-ordinates accurate to 100 meters (Vencatasawmy et al, 1999).…”

Section: Dynamic Versus Staticmentioning

confidence: 99%

“…The time horizon is the inter-temporal scope such as 10 years, 20 years, 24 hours etc, while the period of analysis refers to the time unit over which the model increments such as year, month, day or hour. In the models that focus on household welfare (such as Rephann et al, 2005;Ballas, et al, 2005a;Anderson et al, 2007), the time horizon is typically multi-annual and the period of analysis is typically the year. This may also be the case with land use models as with Alberti and Waddell (2000).…”

Section: Dynamic Versus Staticmentioning

confidence: 99%

Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices

O’Donoghue¹,

Morrissey²,

Lennon³

2013

IJM

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Spatial microsimulation modelling has developed for over a half century and is now a mainstream analytical tool within the microsimulation community accounting for a very significant proportion of papers at conferences and within the journal. There have been a number of recent surveys of "mainstream" spatial microsimulation models and associated methodologies. The contribution of this paper relates mainly in extending these surveys by considering other micro based simulation models that incorporate a spatial or geographic dimension. We feel this is important as in many areas of microsimulation modelling, there are parallel literatures that have developed that apply simulation techniques to micro units that are not labelled microsimulation or in the case of the papers reviewed in this paper not labelled spatial microsimulation. The paper reviews a number of different application areas of spatially focused microsimulation models, including demography, welfare, health, regional development, transport planning, agrienvironmental analysis, crisis planning, land use and planning. We also review a number of the methodological choices made by modellers including scope, and spatial disaggregation, data sources, data creation methodology, validation and calibration and simulating change.

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“…There has been some relevant work aimed at the micro‐analysis of the socio‐economic impacts of major area‐based investment or disinvestment (e.g., see Rephann et al. ; Ballas et al. ), but there is a need and potential for more research in this field.…”

Section: Further Analysis Of the Outputs: Area Analysis And Microsimumentioning

confidence: 99%

A spatial microsimulation approach to economic policy analysis in Scotland

Campbell

Ballas

2013

Regional Science Policy & Practice

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Abstract. Regional scientists have increasingly been playing a very important role in the development and application of spatial microsimulation models for policy analysis. It has long been argued that spatial microsimulation modelling has enormous potential for the evaluation of the socio-economic and spatial effects of major developments in the regional or local economy. This paper aims to add to this rapidly expanding work, by presenting a new spatial microsimulation model (SIMALBA) for Scotland (the development of which was co-funded by the Scottish Government) and by demonstrating how it can be used to perform what-if policy analysis in Scotland. The focus of the paper is on economic aspects of social and spatial inequality in the capital of Scotland, Edinburgh. The paper shows how spatial microsimulation modelling can address previously unanswered research questions in Scotland, particularly those relating to fiscal policy. The SIMALBA model has estimated income data for Scotland at output area level geography and this is the focus of the various 'what-if' policy scenarios. Simulated data has been created using a deterministic reweighing algorithm to build a spatial microsimulation model by combining UK Census data for 2001 and Scottish Health Survey (SHS) data for 2003. The analysis demonstrates the importance of geography by examining trends at OA level in Scotland. The paper concludes with a discussion of the simulated data and resulting policy scenarios as well as the impact of this analysis for policy formation in Scotland.JEL classification: C5, I3, R1, R28

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Microsimulation for Local Impact Analysis: an Application to Plant Shutdown*

Cited by 8 publications

References 29 publications

Regional income and wave energy deployment in Ireland

Regional income and wave energy deployment in Ireland

Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices

A spatial microsimulation approach to economic policy analysis in Scotland

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