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

Willingness to Pay For Achieving Good Status Across Rivers in the Republic of Ireland

peer-reviewedThe Water Framework Directive mandates EU Member States to achieve good status across all surface waters. Derogations from this have to be proven based on infeasibility or disproportionate cost. This study explores public preference for water quality objectives and assesses willingness to pay (WTP) for achieving good status across all rivers in the Republic of Ireland using contingent valuation. Mean WTP for achieving full good status across rivers was estimated at €19 per respondent per annum. WTP was influenced by social class, subjective perceptions relating to household financial status, education, recreational use, environmental values and river basin district

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

Gibrat’s law and the change in artificial land use within and between European cities

editorial reviewedSeen from a satellite, observing land use in the daytime or at night, most cities have circular shapes, organised around a city centre. A radial analysis of artificial land use growth is conducted in order to understand what the recent changes in urbanisation are across Europe and how it relates to city size. We focus on the most fundamental differentiation regarding urban land use: has it been artificialised for human uses (residence or roads for instance) or is it natural, or at least undeveloped? Using spatially detailed data from the EU Copernicus Urban Atlas, profiles of artificial land use (ALU) are calculated and compared between two years, 2006 and 2012. Based on the homothety of urban forms found by Lemoy and Caruso (2018), a simple scaling law is used to compare the internal structure of cities after controlling for population size. We firstly show that when using the functional urban area (FUA) definition of cities, a kind of Gibrat’s law for land use appears to hold. However, when we examine cities internally, this is no longer clear as there are differences on average between city size categories. We also look at further city groupings using regions and topography to show that artificial land use growth across European cities is not homogeneous. Our findings have important implications relative to the sustainability of cities as this evidence is pointing towards increasing urban sprawl and stagnant growth in urban centres across cities of all sizes. It also has theoretical implications on the nature of sprawl and its scaling with city size

On the heterogeneity of urban expansion profiles in Europe

The difference of a city's artificial land use (ALU) radial profile to the average ALU profile is examined for 585 European cities. Using Urban Atlas 2012 data, a radial (or monocentric) approach is used to calculate a city's land use profile in relation to distance to the city centre. A scaling law is used which controls for city size and population. As a consequence, cities of varying degrees of size can be contrasted in a comparable way. Utilising the mean ALU profile for the entire sample of 585 cities, the difference to the mean profile is calculated for each city. Using these differences allows us to examine heterogeneity of the ALU across European cities but also examine these differences within cities. We utilise city groupings by city size and country to attempt to understand these differences. Combining Urban Atlas and Corine Land Cover data, the impact of water on the ALU profiles is examined. A city classification is also introduced which considers the difference to the average curve. Ordering methods are used to visualise cities within these classifications. Results highlight the level of heterogeneity between cities. Removing water, we can see that the cities with the highest levels of water have a higher level of ALU on average. Spain and France are found to have contrasting levels of ALU, Spanish cities having below average ALU and France above average. Using seriation techniques enables us to group and order cities into a typology which can be used to benchmark cities

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

On the heterogeneity of urban expansion profiles in Europe

33 pages, 11 figuresInternational audienceThe difference of a city's artificial land use (ALU) radial profile to the average ALU profile is examined for 585 European cities. Using Urban Atlas 2012 data, a radial (or monocentric) approach is used to calculate a city's land use profile in relation to distance to the city centre. A scaling law is used which controls for city size and population. As a consequence, cities of varying degrees of size can be contrasted in a comparable way. Utilising the mean ALU profile for the entire sample of 585 cities, the difference to the mean profile is calculated for each city. Using these differences allows us to examine heterogeneity of the ALU across European cities but also examine these differences within cities. We utilise city groupings by city size and country to attempt to understand these differences. Combining Urban Atlas and Corine Land Cover data, the impact of water on the ALU profiles is examined. A city classification is also introduced which considers the difference to the average curve. Ordering methods are used to visualise cities within these classifications. Results highlight the level of heterogeneity between cities. Removing water, we can see that the cities with the highest levels of water have a higher level of ALU on average. Spain and France are found to have contrasting levels of ALU, Spanish cities having below average ALU and France above average. Using seriation techniques enables us to group and order cities into a typology which can be used to benchmark cities