Conception of Local Budgeting Performance Indicators Storage
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Abstract
The paper devoted to the collecting, analyzing and publishing performance information in public budgeting with new information technologies applying importance. Its implementation proposed through the concept of using an interactive data warehouse on a countrywide scale with the local budgeting in Ukraine as example case. The model of data storage and using concept has relied on implementation of the local budget programs performance indicators formulated. The scientific and technical conditions of data utilizing described. Proposed model shows the scheme of performance information flows on an example of particular types of the performance indicators, the conditions for their directly beneficial uses are determined; the levels of qualitative and quantitative data composition for the information system were specified. In support of the data storage model conception implementation authors systematized the benefits of budget entity’s performance information publicity and its exploit for the further enhance performance information analysis in the multicriteria-based decision-making.
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References
Chadwick, A., May, C. Interaction between States and Citizens in the Age of the Inter-net:“e‐Government” in the United States, Britain, and the European Union. Governance, 16(2), 271-300 (2003).
Bevan, G., Hood, C. What’s measured is what matters: targets and gaming in the English public health care system. Public administration, 84(3), 517-538 (2006).
Lee, R. D., Johnson, R. W., Joyce, P. G. Public budgeting systems. Jones & Bartlett Publish-ers, (2012).
Cowper, J., Samuels, M. Performance benchmarking in the public sector: The United King-dom experience. Benchmarking, Evaluation and Strategic Management in the Public Sector, 11-32 (1997).
Moynihan, D. P., Pandey, S. K. The big question for performance management: Why do managers use performance information? Journal of public administration research and theory, 20(4), 849-866 (2010).
Ammons, D. N., Rivenbark, W. C. Factors influencing the use of performance data to im-prove municipal services: Evidence from the North Carolina benchmarking project.
Public Administration Review, 68(2), 304-318 (2008).
lo Storto, C. Benchmarking website performance in the public sector: a non parametric approach. Journal of Computers, 9(3), 636-643 (2014).
Shah, A. (Ed.). Public Expenditure Analysis. World Bank Publications (2005).
Shah, A. (Ed.). Budgeting and budgetary institutions. World Bank Publications (2007).
Bouckaert, G., Halligan, J. Comparing performance across public sectors. In Perfor-mance Information in the Public Sector. Palgrave Macmillan, London, 72-93 (2008).
Lægreid, P., Roness, P. G., Rubecksen, K. Performance information and performance steer-ing: integrated system or loose coupling? In Performance information in the public sector. Palgrave Macmillan, London, 42-57 (2008).
Wooldridge, Jeffrey M. “Difference-in-differences estimation,” Quantile, No.6, pp.25–47 (2009).
Ludwig, W. at al. ARB: a software environment for sequence data. Nucleic acids research, 32(4), 1363-1371 (2004).
Desouza, K. C., Jacob, B. Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043-1064 (2017).
Ministry of Education and Science of Ukraine Order dated 07.10.2017 № 992 "On Approval of the Typical List of Budget Programs and Performance Indicators for their Implemen-tation for Local Budgets in the" Education" http://zakon0.rada.gov.ua/laws/show/z0956-17
Løken, E. Use of multicriteria decision analysis methods for energy planning problems. Re-newable and Sustainable Energy Reviews, 11(7), 1584-1595 (2007)