The Spatial Distribution of Labour Force Participation and Market Earnings at the Sub-National Level in Ireland

The main aim of this paper is to provide a spatial modelling framework for labour force participation and income estimation. The development of a household income distribution for Ireland had previously been hampered by the lack of disaggregated data on individual earnings. Spatial microsimulation through a process of calibration provides a method which allows one to recreate the spatial distribution LFP and household market income at the small area level. Further analysis examines the relationship between LFP, occupational type and market income at the small area level in Co. Galway Ireland.Household Market Income Distribution, Employment, Spatial Microsimulation, Calibration, Mapping

The Spatial Distribution of Labour Force Participation and Market Earnings at the Sub-National Level in Ireland

The main aim of this paper is to provide a spatial modelling framework for labour force participation and income estimation. The development of a household income distribution for Ireland had previously been hampered by the lack of disaggregated data on individual earnings. Spatial microsimulation through a process of calibration provides a method which allows one to recreate the spatial distribution LFP and household market income at the small area level. Further analysis examines the relationship between LFP, occupational type and market income at the small area level in Co. Galway Ireland

Titling, Credit Constraints and Rental Markets in Rural Peru: Exploring Channels and Conditioned Impacts

This paper constructs a baseline and pursues an overall impact evaluation of the PETT (Programa Especial de Titulación de Tierras), an ambitious rural titling program created in Peru in 1992. The general evaluation of impacts on farmers shows a picture of not many positive effects, at least in the short period of the evaluation (2004-2006) and for a limited sample of farmers located in the Coast and Sierra regions. On average, most income variables (and income composition) do not seem to be impacted by titling, and there are no detectable effects on investments (except for permanent pasture in the Sierra) or other outcome variables, such as credit, land markets, or land conflicts. However, this general picture hides important impacts that may occur for some groups of farmers, or for farmers facing different constraints in the pre-intervention stage. Given the limitations, we investigated in more detail two important channels that are behind the potential impacts of rural titling programs: credit access and use of land rental markets.

The Spatial Distribution of Welfare in Ireland

In this thesis welfare is examined in a spatial context. A broader definition of welfare is taken so that it includes more than just income. In-kind benefits, indirect costs, life-satisfaction, locational effects are all examined in a spatial context. The impact of these welfare drivers on the spatial distribution is examined with each chapter focusing on a different welfare driver. Differences between areas may be psychical (e.g. climate) or structural (e.g. high education attainment) using a spatial approach can account for some of this variation. An interaction exists between space and the economy which results in agglomeration economies and clustering based on social class. However, there are market failures (e.g. congestion) which can reduce welfare. A broader measure of welfare which includes additional components and not just monetary income acknowledges the spatial heterogeneity that exists across space. A small area examination allows for pockets of deprivation and poverty to be identified. Some of the reasons behind the inequality that exist between and within areas is explored and described. Taking each component in isolation has the power to show the effects of that driver on welfare. International studies are often limited by a lack of income data at a small area level. This thesis uses the output from a spatial microsimulation model to overcome the lack of income data at a spatial scale. This income data is enhanced through a data fusion process to create and include additional spatially rich welfare data. Spatial methods such as interpolation and network analysis tools are utilised to calculate and create new small area datasets. Mapping tools such as GIS provide the added benefit of displaying results in an effective way. This newly created data can be used to calculate how welfare varies spatially depending upon the definition of welfare used. The broader definition of welfare adopted is based on conceptual underpinnings that any benefits/costs which increase/decrease individual potential to consume should be included in a measure of welfare. Drivers of welfare examined include intertemporal effects, housing, commuting, labour markets, spatial attributes and exposure to flooding. The sensitivity and impact of each component on individual welfare is examined. By using a spatial approach differences in the impact of each driver across space can be measured. Due to the heterogeneous nature of welfare, some drivers can have positive benefits in some areas but negative in others. By adopting a spatial approach these differences can be identified. Measuring welfare at a disaggregated spatial scale is required before we attempt to understand why the spatial distribution of welfare looks the way it does. Research such as this is crucial to evaluate and recommend policies that improve welfare and reduce spatial inequalities. Due to their limited nature, identifying areas with greater “need” allows resources to be targeted more efficiently. This thesis makes a number of recommendations in this regard as to why policy should adopt a more holistic approach to welfare. It highlights particular challenges in the area of data collection and the need for greater focus on spatial impacts of various policy measures at a small area level

The Spatial Distribution of Welfare in Ireland

In this thesis welfare is examined in a spatial context. A broader definition of welfare is taken so that it includes more than just income. In-kind benefits, indirect costs, life-satisfaction, locational effects are all examined in a spatial context. The impact of these welfare drivers on the spatial distribution is examined with each chapter focusing on a different welfare driver. Differences between areas may be psychical (e.g. climate) or structural (e.g. high education attainment) using a spatial approach can account for some of this variation. An interaction exists between space and the economy which results in agglomeration economies and clustering based on social class. However, there are market failures (e.g. congestion) which can reduce welfare. A broader measure of welfare which includes additional components and not just monetary income acknowledges the spatial heterogeneity that exists across space. A small area examination allows for pockets of deprivation and poverty to be identified. Some of the reasons behind the inequality that exist between and within areas is explored and described. Taking each component in isolation has the power to show the effects of that driver on welfare. International studies are often limited by a lack of income data at a small area level. This thesis uses the output from a spatial microsimulation model to overcome the lack of income data at a spatial scale. This income data is enhanced through a data fusion process to create and include additional spatially rich welfare data. Spatial methods such as interpolation and network analysis tools are utilised to calculate and create new small area datasets. Mapping tools such as GIS provide the added benefit of displaying results in an effective way. This newly created data can be used to calculate how welfare varies spatially depending upon the definition of welfare used. The broader definition of welfare adopted is based on conceptual underpinnings that any benefits/costs which increase/decrease individual potential to consume should be included in a measure of welfare. Drivers of welfare examined include intertemporal effects, housing, commuting, labour markets, spatial attributes and exposure to flooding. The sensitivity and impact of each component on individual welfare is examined. By using a spatial approach differences in the impact of each driver across space can be measured. Due to the heterogeneous nature of welfare, some drivers can have positive benefits in some areas but negative in others. By adopting a spatial approach these differences can be identified. Measuring welfare at a disaggregated spatial scale is required before we attempt to understand why the spatial distribution of welfare looks the way it does. Research such as this is crucial to evaluate and recommend policies that improve welfare and reduce spatial inequalities. Due to their limited nature, identifying areas with greater “need” allows resources to be targeted more efficiently. This thesis makes a number of recommendations in this regard as to why policy should adopt a more holistic approach to welfare. It highlights particular challenges in the area of data collection and the need for greater focus on spatial impacts of various policy measures at a small area level

Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices

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

Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

The impact of flooding disruption on the spatial distribution of commuter's income

peer-reviewedFlooding already imposes substantial costs to the economy. Costs are expected to rise in future, both as a result of changing weather patterns due to climate change, but also because of changes in exposure to flood risk resulting from socio-economic trends such as economic growth and urbanisation. Existing cost estimates tend to focus on direct damages, excluding potentially important indirect effects such as disruptions to transport and other essential services. This paper estimates the costs to commuters as a result of travel disruptions caused by a flooding event. Using Galway, Ireland as a case study, the commuting travel times under the status quo and during the period of the floods and estimated additional costs imposed, are simulated for every commuter. Results show those already facing large commuting costs are burdened with extra costs with those in rural areas particularly vulnerable. In areas badly affected, extra costs amount to 39% of earnings (during the period of disruption), while those on lower incomes suffer proportionately greater losses. Commuting is found to have a regressive impact on the income distribution, increasing the Gini coefficient from 0.32 to 0.38