For decades, not to say centuries, access to information and financial flows related to public finances was restricted to small groups of experts and decision makers. Gradually, public budgets become openly available (mostly in hard copies and pdf) and online data about the tender procedure is offered in some cases (e.g. http://ted.europa.eu/). Lately, a growing number of local and central governments are publishing their spending decisions as open data (e.g. http://data.gov.uk/data/openspending-report/index). That is a prerequisite step for data-driven transparency, accountability and innovation but there is a series of important issues to be anticipated. The quality of data is considered to be low compared to the questions that can be answered through the data. It is impossible to draw reliable conclusions with respect the source, the destination and the effectiveness of public money. Budget and spending classifications are incompatible even within a single organization and country and there is not a standard method for representing company names and activities.
Thus, a minimal and compact common ground should be established in order to use more efficiently the existing open datasets and to make targeted requests on opening the missing information.
1. Insights in Global Public Spending
Michalis Vafopoulos, National Tech. Univ. Athens, publicspending.net
(joint effort with M. Meimaris, J. M. Alvarez Rodriguez, G. Xidias, G. Vafeiadis, M. Klonaras & P. Kranidiotis)
2. The era of Open
budgets, spending, registries, contracting…
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3. Open but Effective?
oWho really gets the public money?
oFor what? From whom?
oCan we compare them?
oIs public spending effective?
o<your question goes here>
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4. Useful economic open data
1. The full cycle of public money
2. Uniform Company names
3. Compatible Payment categories
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6. Follow Public Money all the Way
Vocabulary (fpm)
oA compact and minimal way to
model the flows of public money
oFrom budget to spending
including business information
and prices
oWork in progress (ask inside)
http://www.publicspending.net/vocab-fpm
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7. Useful economic open data
1. The full cycle of public money
2. Uniform Company names
3. Compatible Payment categories
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8. 2. Not uniform Company names
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The problem:
different names
for the same
company
“Oracle” in the Australian
public spending
9. Reconciling Company names: the
CORFU technique (work in progress)
Rodríguez, Jose María Álvarez, Ordoñez de Pablos, Patricia, Vafopoulos, Michalis N. and Labra, José Emilio
10. 3. Compatible Payment categories
The problem:
Spending decisions are using
different (or not any!) classification
schemes (e.g. CPV, UNSSC, NAICS)
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11. Compatible Payment categories
Transforming classification
schemes or literal descriptions to
CPV, expanding:
The MOLDEAS project
Methods On Linked Data for E-procurement Applying Semantics
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15. Work in progress
o New data (more countries and cities)
o New links (e.g. registries, business info)
o New uses (e.g. open public economics)
you are invited:
to follow together public money all the
way through
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16. Utopia?
o Still inconsistent & not enough Open data?
Yes, but to persuade people to open the
data we need real cases
- If we fail may go back to the closed world
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18. References
o Vafopoulos, Michalis N., Rodríguez, Jose María
Álvarez, Meimaris, Marios, Xidias, Ioannis, Klonaras, Michailis and
Vafeiadis, Giorgos, Insights in Global Public Spending (May 12, 2013). Available at
SSRN: http://ssrn.com/abstract=2264958 or http://dx.doi.org/10.2139/ssrn.2264958
o Vafopoulos, Michalis N., The Web Economy: Goods, Users, Models, and Policies
(July 26, 2012). Michalis Vafopoulos (2012) "The Web Economy:
Goods, Users, Models, and Policies", Foundations and Trends® in Web Science: Vol.
3: No 1-2, pp 1-136. http://dx.doi.org/10.1561/1800000015. Available at SSRN:
http://ssrn.com/abstract=2117855
o ALVAREZ, J. and LABRA, J. 2012. Towards a pan-european e-procurement platform
to aggregate, publish and search public procurement notices powered by Linked
Open Data: the MOLDEAS approach. International Journal of Software Engineering
and Knowledge Engineering. 22, 3 (2012), 365–383.
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