Karyn Morrissey

The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health

The Lancet Countdown tracks progress on health and climate change and provides an independent assessment of the health effects of climate change, the implementation of the Paris Agreement, 1 and th ...

Building a static farm level spatial microsimulation model for rural development and agricultural policy analysis in Ireland

Using statistical matching techniques, economists can now create attribute rich datasets by matching across the common variables in two or more datasets. The farm level spatial microsimulation model developed in this paper uses one of many combinational optimisation techniques – simulated annealing – to match the Irish Census of Agriculture to the 2005 Irish National Farm Survey (NFS). Using the new spatially disaggregated farm population microdata this paper then briefly analyses the impact of future Common Agricultural Policy (CAP) reform on the distribution of family farm income in rural Ireland.

Examining the factors associated with depression at the small area level in Ireland using spatial microsimulation techniques

Abstract The World Health Organisation (WHO) predicts that, world-wide, depression will be the second largest source of burden of disease by 2020. It is currently estimated that approximately 300,000 Irish people experience depression. To date no research has been carried out on the small area incidence of depression in Ireland. Although research has looked at access levels to acute psychiatric hospitals for each electoral division (ED) in Ireland, there is currently no research on the accessibility of mental health services to individuals with depression. This paper aims to address this gap in the literature. First, a simple logistic regression model is used to identify the determinants of depression at the national level. Secondly, the spatial distribution of individuals with depression is estimated at the small area, ED level using a spatial microsimulation model. Finally, a spatial interaction model is used to analyse access to acute inpatient facilities at the national level and community-based facil...

Spatial microsimulation for rural policy analysis

The aim of this book is to explore the challenges facing rural communities and economies and to demonstrate the potential of spatial microsimulation for policy and analysis in a rural context. This is done by providing a comprehensive overview of a particular spatial microsimulation model called SMILE (Simulation Model of the Irish Local Economy). The model has been developed over a ten year period for applied policy analyis in Ireland which is seen as an ideal study area given its large percentage of population living in rural areas. The book reviews the policy context and the state of the art in spatial microsimulation against which SMILE was developed, describes in detail its model design and calibration, and presents example of outputs showing what new information the model provides using a spatial matching process. The second part of the book explores a series of rural issues or problems, including the impacts of new or changing government or EU policies, and examines the contribution that spatial microsimulation can provide in each area.

The Local Impact of the Marine Sector in Ireland: A Spatial Microsimulation Analysis

Abstract The ocean is an important natural resource and offers considerable income and employment opportunity to the Irish economy. The last two decades have seen a realisation that whilst nationally economies may benefit from trade liberalisation, specific regions may not. This has led to the development of local level modelling frameworks. Using a spatial microsimulation model this paper estimates the employment and income contribution of the marine sector at the county, small area and household level. It was found that the marine sector offers higher than average income to employees in coastal locations and thus is an important source of income and employment Ireland.

The SMILE Model: Construction and Calibration

In the previous chapter we reviewed the use of spatial microsimulation models for policy analysis and reviewed the type of applications for which the methodology has been employed. In the absence of spatially representative micro-data in Ireland, we require a technique for generating this data and hence the microsimulation model. In this chapter we describe a number of methodologies for doing this and evaluate the performance of methods chosen for our ‘Simulation Model of the Irish Local Economy’ (SMILE). To recap, the primary focus of the SMILE framework is to assess the socio-economic impacts of policy or economic changes. The motivation for the model is to assess the impact of these changes in the context of agricultural, rural and environmental policy in addition to the more standard analysis of economic and social policy change.

A Review of Microsimulation for Policy Analysis

There are a wide range of methodological frameworks and techniques for policy evaluation and socio-economic impact assessment. A useful distinction is to divide the literature on such models by the level of resolution adopted. It is then possible to identify macro, meso and micro approaches. Macro models, dealing with whole countries or nations, are most common in economics and social policy. Meso-scale models, where countries or nations are split into regional zones, have a longer tradition in regional science, planning and geography (McCann 2001; Stimson et al. 2006). For example, many former macro-scale models such as input–output techniques are now increasingly appearing in the literature at the regional scale. Quantitative geographers have tended to build meso-scale models for smaller geographical regions, such as small-area census-based zoning systems within cities (Wilson 1974; Foot 1981; Stillwell and Clarke 2004; Fotheringham and Rogerson 2009). These types of model have a long history of applied success but the complex dynamics which underlie social and economic change, as emphasised in Chap. 2, can produce very different results within different small-area localities and even within individual households (or firms). In particular, it is useful to be able to understand, estimate or predict which localities, households or individuals (given their demographic and socio-economic characteristics) are most likely to benefit from a change in socio-economic or environmental policies. Thus, it could be argued that policy relevant modelling is a challenging research area which is well suited to a modelling framework which emphasises household or individual-level processes at the local or micro level rather than aggregated processes at the macro/meso-level.

Using the EU-SILC to model the impact of the economic crisis on inequality

In this paper we attempted to chart the impact of the early part of Ireland’s economic crisis from 2008–2009 on the distribution of income. In order to decompose the impact of changes in different income components, we utilised a microsimulation methodology and the EU-SILC User Database. This simulation based methodology involved the disaggregation of the 6 main benefit variables in the EU-SILC into 17 variables for our tax-benefit model. Validating, our results were positive, giving us confidence in our methodology. We utilised the framework to model changes in the level of income inequality from the period just before the crisis in 2004 to the depth of the worst year of the crisis in 2009. In terms of the impact of the economic crisis, we found that income inequality fell in the early part of the crisis modelled in this paper. Much of this change was due to rising inequality of market incomes, (even when discounting unemployment). This was due to the differential effect of the downturn on different sectors where some sectors such as the construction and public sectors were significantly hit, while the international traded sectors have been relatively immune from the downturn and have seen continued growth. The impact of the tax-benefit system has been to mitigate this upward pressure, with a gradual rise in the redistributive effect of the tax-benefit system driven by an increase in demand on the benefits side and increased progressivity on the tax side.Jel codesH22, H55, C15

Measuring relatedness in a multisectoral cluster: an input–output approach

ABSTRACT Studies in evolutionary economic geography have found that knowledge spillovers, crucial for the development and maintenance of clusters, tend to flow between sectors that are related via similar inputs and/or outputs. Thus, there is a growing body of literature stating that industrial variety within clusters is beneficial for economic growth, whereby local industrial diversity sparks creativity, new ideas and innovations. Within this context, the Irish Maritime and Energy Resource Cluster (IMERC) is a diverse, multisectoral cluster. Using an Input–Output table this paper examines the intra-cluster linkages, as well as the relatedness in terms of inputs and outputs across the four IMERC pillars: Marine Energy; Shipping, Logistics and Transport; Maritime Safety and Security; and Yachting Products and Services. This analysis found that although IMERC has weak intra-cluster linkages, the four pillars share a high number of related inputs and outputs. Based on this analysis, IMERC has the potential to develop into a strong maritime cluster.

Validation Issues and the Spatial Pattern of Household Income

Chapter 4 described a methodology for the creation of a dataset containing micro-units, their incomes and labour market characteristics within a spatial context using spatial microsimulation methods. As static spatial microsimulation is essentially a method to create spatially disaggregated microdata that previously did not exist, an important issue relates to the validation of the synthetic data generated (Voas and Williamson 2001a). Validation techniques examine model outputs in systematic ways to reveal deficiencies/errors in the model outputs. As such, model validation forms an integral part of the overall development and application of any model. Oketch and Carrick (2005) point out that it is only through validation that the credibility and reliability of a model can be assured.