The Geographical Spread and the Economic Impact of Food Harvest 2020 – A Regional Perspective.

working paper JEL Codes; Q12 R12 R58Recently the agri-food sector has received increased attention in Ireland. The agri-food sector has been the traditional backbone of Irish exports, and despite the economic downturn Irish exports in this sector grew by an impressive 12 percent in 2011 (CSO 2012). The agri-food sector is regarded as Ireland’s largest indigenous industry, the potential of the sector in terms of exports, and its heavy dependence on domestic inputs are the key reasons for the increased attention. The real economic value of the agri-food sector in Ireland is analysed at national, and most importantly for this paper, at regional level. This paper examines the impact of the agri-food sector in addressing regional disparities in Ireland. The estimation of the true value of the agri-food sector is evaluated at regional level by analysing Gross Value Added, employment levels and productivity rates for the sector expressed in percentage of regional values. Gross-Value-Added in absolute terms and as a percentage of regional Gross-Value- Added provides us with a more thorough understanding of the regional importance of certain industries within the sector. In terms of employment, the rural context of the agri-food sector is discussed, including the geographical spread of the sector. A comparison of regional productivity levels is analysed at national and regional level. In addition, this paper geographically distributes the change in output and employment if the four main sector specific Food Harvest 2020 targets are achieved. As a preliminary contour of the agri-food sector in Ireland this research will be useful to all the key players in the sector

The effect of decoupling on farming in Ireland: A regional analysis

peer-reviewedData from the Irish National Farm Survey and Census of Agriculture were used to analyse the regional implications of the decoupling of direct payments for farmers in Ireland. A mathematical programming model was used to estimate the regional effects of decoupling while a micro-simulation model was exploited to map the geographic distribution of decoupled payments. The results show that under the historical decoupling scheme, milk quota will shift from less efficient to larger more efficient farms in all regions. Beef cattle numbers are projected to decrease on all farms, with the exception of the Mideast and Southeast regions where numbers are projected to increase. The regional effect of decoupling on sheep farming was marginal with all regions projected to benefit from the policy change. The analysis also shows, using a static micro-simulation model that a shift to a flat rate national calculation of the decoupled payment would result in a significant movement of revenues from the southern regions to the northwestern regions of the country. In particular, large beef and dairy farmers in the southern regions would lose out while small dairy and sheep farmers in the western and northern regions would be most likely to gain

Building a Static Farm Level Spatial Microsimulation Model: Statistically Matching the Irish National Farm Survey to the Irish Census of Agriculture

This paper looks at the statistical matching technique used to match the Irish Census of Agriculture to the Irish National Farm Survey (NFS) to produce a farm level static spatial microsimulation model of Irish agriculture. The match produces a spatially disaggregated population microdata set of farm households for all of Ireland. Using statistical matching techniques, economists can now create more attribute rich datasets by matching across the common variables in two or more datasets. Static spatial microsimulation then uses these synthetic datasets to analyse the relationships among regions and localities and to project the spatial implications of economic development and policy changes in rural areas. The Irish agriculture microsimulation model uses one of many combinational optimatisation techniques - simulated annealing - to match the Census of Agriculture and the NFS. The static model uses this matched NFS and Census information to produce small area (District Electric Divisions (DED)) population microdata estimates for a particular year. Using the matched NFS/Census microdata, this paper will then analysis the regional farm income distribution for Ireland.

How am I bringing an educationally entrepreneurial spirit into higher education?

The originality of my research lies in clarifying and explaining what it means for me to have an educational entrepreneurial spirit and the values I hold that demonstrate this spirit in an explanation of educational influence in learning. This explanation includes a responsibility for students and acknowledging my values of passion and care (‘love’ of what I do), safety, creativity and excellence within my practice. The unit of appraisal in a living theory methodology is the explanation of the influence in my own learning, the learning of others and in the learning of social formations. The methodological inventiveness, particular to the Living Educational Theory methodology, has afforded me an opportunity to express who I really am; body, mind and spirit. I use multimodal forms to communicate and express of the nature of the knowledge that I am generating. I can now claim that my values have become living standards of judgement. Music plays an integral part of my life and has been a source of enjoyment and inspiration for me over the years. I have shown its importance by embedding it within my doctoral research to express and represent the meaning of emotion. I explain the importance of addressing emotion in education and the merits of reflecting on our experiences in order to become more educationally entrepreneurial, by taking risks, awakening our creativity and bringing ideas into action. Within these safe educational spaces I connect the head with the heart, marry the ‘sense and soul’ (Wilber, 1988) to combine a constructivist, behaviourist, cognitive pedagogical approach that avoids a fragmented learning experience as I inspire others to bring their ideas to fruition

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 for Regional Analysis of Marine Related Employment

This paper presents a modelling framework that facilitates marine policy impact assessment at a scale that is below the national level. The spatial microsimulation approach provides a regional level of analysis not generally possible when dealing with ocean economy statistics that are often even difficult to compile at a national level and offers a powerful modelling tool for maritime spatial planning. The spatial microsimulation model is used to profile the spatial distribution of marine related employment in Ireland. It is then used to carry out a micro-level regional assessment of the impact of the Covid-19 pandemic restrictions on the distribution of employment in the Irish ocean economy. The results demonstrate that many of those made unemployed in the ocean economy during the first lock down were outside the main urban centers, particularly in the case of marine tourism and leisure and the marine natural resource based industries. The paper argues that the use of such spatial microsimulation approaches can facilitate a more evidence based policy response to an economic shock, such as the Covid-19 pandemic, in terms of industry and regional specific supports and can also inform more effective marine spatial planning

Spatial Modelling for Rural Policy Analysis

End of Project ReportThe objective of the project was to provide the diverse group of interest groups associated with the agri-food sector (farmers, policy makers etc.) with a microsimulation tool for the analysis of the relationships among regions and localities. This tool would also be able to project the spatial implications of economic development and policy change in rural areas. To this end the SMILE (Simulation Model for the Irish Local Economy) model was developed. SMILE is a static and dynamic spatial microsimulation model designed to analyse the impact of policy change and economic development on rural areas in Ireland. The model developed provides projection for population growth, spatial information on incomes and models farm activity at the electoral division (ED) level. The sub-projects funded under this project were concerned with the simulation, development and enhancement of a spatial econometric model of the Irish rural economy which would compliment the existing econometric models used in Teagasc; focusing on the agriculture and food sectors, previously constructed under the auspices of the FAPRI-Ireland Partnership by staff at Teagasc and NUI Maynooth. That partnership has produced an econometric model of the entire agri-food sector that has been simulated to produce estimates of the impact of policy changes on commodity prices, agricultural sector variables, food industry production, consumption of food both in Ireland and the EU and trade in food products, as well as costs, revenue and income of the agricultural sector. The SMILE model was built to compliment these other econometric models by using an holistic modeling approach that takes into account the spatial difference of rural populations, rural labour force and rural income

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