Published on July 1, 2016
1. Performance-indicator based policy- making in Austria Policy Making in the Digital Era Johann Höchtl Department for E-Governance Danube University Krems, Austria Track on Data Driven Government June 29, EDF 2016, Eindhoven
2. Agenda 1. Governance with Complexity at Speed 2. Action Taking on Evidence 3. Wirkungsorientierte Steuerung – The Case of Austria 4. The Big Data powered Policy Cycle 5. Measures towards ePolicy Making 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 2
3. © SFGate, 7. Oct. 2013, http://www.sfgate.com/crime/article/Absorbed-device-users-oblivious-to-danger-4876709.php Speeding … CC0 ed_davad https://pixabay.com/photo-388253/ … on 19th century infrastructure
4. Digitization Connectivity Intelligence The Digital Virtuous Forces P. Parycek,G. Simonitsch, M. Fandler, P. Müller, 2014
5. MobilityBig Data Analytics Cloud Computing Digitization
6. Numbers at speed 1920: National Bureau of Economic Research (NBER): one employee 1929: The US in a Great Depression 1932: President Hoover supposed to take decisions on three year old numbers 1932: Russian immigrant professor Simon Kuznets invents what would become the GDP 1945: NBER +5.000 employees 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 6 “Migrant Mother” (1936), Public Domain
7. The Complexity of Numbers Component Amount (trillions) Percent Personal Consumption $11.21 69% Goods $3.87 24% Durable Goods $1.47 9% Non-durable Goods $2.43 15% Services $7.34 45% Business Investment $2.85 17% Fixed $2.74 17% Non-Residential $2.21 14% Commercial $.46 3% Capital Goods $1.06 6% Intellectual (Software) $.70 4% Residential $.53 3% Change in Inventories $.10 1% Net Exports ($.54) (3%) Exports $2.11 13% Imports $2.65 16% Government $2.86 17% Federal $1.11 7% Defense $.68 4% State and Local $1.74 11% TOTAL GDP $16.35 100% 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 7 http://useconomy.about.com/od/grossdomesticproduct/f/GDP_Components.htm Valentino Piana, A Graph Representation Of A Basic Macroeconomic Scheme: The Is-Lm Model: Economics Web Institute, 2001
8. Action Taking on Evidence 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 8 Decision Model Input Policy Discussion Policy Formation Policy Acceptance Provision of means Implementation Evaluation Agenda Setting Nachmias, David, und Claire Felbinger. 1982. „Utilization in the Policy Cycle: Directions for Research“. Review of Policy Research 2 (2): 300–308. doi:10.1111/j.1541-338.1982.tb00676.x.
9. Action Taking on Evidence 2.0 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 9 Decision Decision Making Model 2.0
10. The Case of Austria – Challenges • No long-term, legally binding budget management or long-term preview • Old budget: important management-related information missing • Only input and no output orientation: Who gets how much, instead of what has to be the outcome? • Lack of incentives for economic management of the budget • Small-sized, non flexible budget structure; lack of transparency • Missing Bigger Picture: What do we want to achieve with the budget? 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 10
11. The Case of Austria – Set up 2013: Implementation of the Principle of Outcome Orientation (Wirkungsorientierung), Global Budgets, Establishment of Federal Performance Management Office (FPMO) • Managing public administration based on its contributions towards achieving outcome in society (performance management) • Outcome statements, outputs and indicators per budgeting chapter • Performance management cycle: plan, implement, evaluate • Outcome oriented impact assessment 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 11
12. The Case of Austria – Outcome Orientation 1. Political objectives relating to a desired 2. societal outcome. It is the task of public administration to provide services = 3. Output. However, 4. external factors can play a role. Before services can be provided, the required resources = 5. Input must be ascertained. Finally, the 6. activities to generate output are carried out. 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 12 1. Model efficiency effectiveness Seiwald, Johann, Monika Geppl, and Andreas Thaller. 2016. Handbuch Wirkungsorientierte Steuerung - Unser Handeln erzeugt Wirkung. Wien: Bundeskanzleramt Österreich.
13. Implementation 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 13 5 headings 32 budget chapters global budgets detail budgets Budget structure Performance structure mission, strategy, outcome statement output statement performance contracts Annual Budget Supplements to Annual Budget MTEF, Strategy Report Performance Management: Integrating performance oriented budgeting and indicator systems in Austria. Ursula Rosenbichler & Alexander Grünwald, 2016
14. Implementation EDF2016 - Johann Höchtl Danube University Krems 14 | Outcome 1: Why this outcome? What is being done to achieve this outcome? What would success look like? Mission: Outcomes 1-5 29.06.2016
15. Outcome 1: Improving safety and security Why this outcome? • Safety and security in public and private life is a human right and essential to well-being. International comparisons show that Austria is one of the safest countries in the world. This high level of safety must be maintained and upgraded further. 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 15
16. Outcome 1: Improving safety and security What is being done to achieve this outcome? • Extending preventive work and awareness training • Combating crime effectively and efficiently with new methods and technologies • Special training programme on combating crime • Improving police response times (i.e. time between an emergency call and arrival at the scene) • Evidence-based human resource allocation • Analysing road accident patterns and identifying traffic hot spots29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 16
17. Outcome 1: Improving safety and security What would success look like? • Crime rate: desired outcome 2013: <x%; starting level 2011: y% [definition: total number of crime incidents per 100,000 inhabitants, source: Crime Statistics, Ministry of the Interior] • Percentage of crimes solved: desired outcome 2013: >x%; starting level 2011: y% [definition: ratio of cases solved to total number of crimes, source: Crime Statistics, Ministry of the Interior] • Number of road accidents with injuries: desired outcome 2013: <x; starting level 2011: y [definition: total number of persons killed in road accidents, source: Road Accidents Statistics, Statistik Austria] 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 17
18. The Case of Austria – Monitoring & Controlling 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 18 2. Governance, Controlling, Evaluation
19. The Case of Austria – Evaluation • Review of the achievement of objectives • Evaluation along outcome and output statements • Indicators: target/performance- comparison, automated assessment of target attainment and verbal explanation of development • Outcome targets: Overall assessment of outcome target and verbal explanation of its environment 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 19 https://www.wirkungsmonitoring.gv.at/
20. The Case of Austria - Annual Federal Performance Report 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 20 |
21. The Case of Austria – Legal Framework • Federal Constitution Act – §51(8): Federal Administration has to agree on Global Budgets according to Outcome Orientation – §51(9): Further provisions in respect to Evidence based Policy Making, Controlling and Transparency • Act on Federal Budgeting – Details on Global Budgets and Detailed Budgets – (Requirement to assess financial consequences of acts and regulations) • Further detailed in Bylaws of Ministry of Finance (What to Measure, How to Measure, Requirements for Indicators) and Austrian Chancellery (Controlling across ministries) 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 21
22. The Case of Austria – Room for improvements • Lacking Integration of ICT-Systems, therefore additional efforts to report plans and implementation performance metrics. • Subjective & qualitative indicators • Reports on effectiveness arrive comparatively late and leave little time for policy adjustments 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 22
23. The Big Data Powered Policy Cycle 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 23 Volume • Terrabytes • Petabytes Variety • structured • unstructured (Documents, Emails, Audio, Video) Velocity • Sensors • Social Networks • Stream Orientation • Realtime Processing
24. N + 1 N Period N - 1 • The cycle does not account for the possibilities of Big Data Analytics. • Evaluation at period N+1 happens to late. • Precious time to re-focus measures or drop measures is wasted. 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 24 Policy Discussion Policy Formation Policy Acceptance Provision of means Implementation Evaluation Agenda Setting
25. The traditional model of policy making is not apt for the 21st century 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 25
26. The ePolicy Cycle • Evaluation can happen at every stage of the cycle • Enables swift and justified adaptions to policy making 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 26 Policy Discussion Policy Formation Policy Acceptance Agenda Setting Implementation Provision of means Höchtl Johann, Peter Parycek, und Ralph Schöllhammer. 2015. „Big Data in the Policy Cycle: Policy Decision Making in the Digital Era“. Journal of Organizational Computing and Electronic Commerce, Dezember, http://dx.doi.org/10.1080/10919392.2015.1125187
27. Big Data Analytics is an Enabler • Access to evidence data through Integration of diverse data sources; • Efficiency gains of traditional action taking through stream processing and real time analytics; • Higher levels of effectiveness by identifying new fields of action taking through pattern mining. 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 27
28. Measures towards ePolicy Making • Integrate ICT-Systems using EU Interoperability Building Blocks – Core Components, EIF 3.0 (upcoming), eInvoicing, eProcurement • Operate ICT-Systems using Cloud Infrastructure – FIWARE, Hybrid Cloud Models • Evolve Systems using an agile implementation approach – Perpetual Beta, Design for Failure • Monitor usage • Listen to your stakeholders • Design for Co-creation 29.06.2016 EDF2016 - Johann Höchtl Danube University Krems 28
29. Johann Höchtl Department for E-Governance firstname.lastname@example.org @myprivate42 github.com/the42 myprivate42.wordpress.com/ at.linkedin.com/in/johannhoechtl 17-19 May 2017 Danube University Krems CfP: http://tinyurl.com/cedem17cfp
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Key Performance Indicator. EVA. EVA (or Economic Value Added) is a measure of a company's economic profit.