IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

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

Listed author(s):
  • Stephen Hynes


  • Karyn Morrissey


  • Cathal O'donoghue


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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa06p431.

in new window

Date of creation: Aug 2006
Handle: RePEc:wiw:wiwrsa:ersa06p431
Contact details of provider: Postal:
Welthandelsplatz 1, 1020 Vienna, Austria

Web page:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. S Openshaw & L Rao, 1995. "Algorithms for Reengineering 1991 Census Geography," Environment and Planning A, , vol. 27(3), pages 425-446, March.
  2. Stephen Hynes & Cathal O'Donoghue, 2004. "Farm Income Mobility and Inequality in Ireland 1994-2001," Working Papers 0078, National University of Ireland Galway, Department of Economics, revised 2004.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa06p431. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.