Publilc sector productivity - Patrick Dunleavy, United Kingdom
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The presentation by Professor Patrick Dunleavy outlines key steps to measure public sector productivity, emphasizing the importance of accurate output and input metrics, as well as quality control strategies. It discusses the distinct challenges faced by different types of agencies, including citizen-facing departments and those delivering complex outputs, while also highlighting the need for comparative analyses and improved data collection methods. Recommendations prioritize improvements in measuring productivity across various types of agencies, from large central transaction agencies to decentralized service providers.
Publilc sector productivity - Patrick Dunleavy, United Kingdom
- 1. Presentation to Annual Meeting of the OECD Senior Budget Officers’ Network OECD, Paris, 24-25 November 2016 Public Sector Productivity – Measurement Challenges, Performance Information and Prospects for Improvement Professor Patrick Dunleavy Department of Government, London School of Economics and Political Science, and Institute for Governance and Policy Analysis, University of Canberra © P. Dunleavy 2016 [email protected] @PJDunleavy
- 2. Six essential steps in measuring productivity in the public sector • Identify and count the agency’s ‘core’ outputs or activities • Develop unit costs or activity accounting for core outputs • Develop a ‘cost-weighted total output’ metric for each agency • Develop an accurate total inputs cost number for the agency • Divide total outputs by total inputs to give a total factor productivity (TFP) number for the organization – labour productivity measures less useful given outsourcing • Decide on a strategy for controlling quality issues
- 3. Two ways of controlling quality issues when measuring agencies’ productivity • For most central departments and agencies – treat services’ core outputs as being of uniform quality over time or across agencies, unless strong evidence suggests quality lapses or variations – in which case ‘conditionalize’ the data point(s) involved • For most decentralized agencies, delivering personal services – apply an additional quality-weighting to the ‘total outputs weighted by unit costs’ metric – e.g. using data on quality from sector auditors, complaints, public opinion surveys – choose an appropriate reweighting level
- 4. Citizen-facing ministries/ agencies with large transactional loads • Usually includes social security department/ agencies, taxation department, national regulatory bodies – account for a high % of central state staffing • Individual services transactions are easily aggregated into total outputs using cost-weighting • Quality control is pretty standardized in these ‘machine bureaucracies’, but allow for lapses etc. – first strategy in Quality slide above • Large gains to be made from developing productivity paths for these agencies, and comparing paths across countries (even in agencies do partly different things) – easy for leaders/ managers/ stakeholders to learn lessons
- 5. Large central departments delivering complex outputs • Examples include defence, foreign affairs • Some progress made on getting to more final outputs e.g. ‘unit availability days’ in defence, or end-activity data (like flying hours) • But complex outputs mean no single metric (e.g. ‘teeth to tail ratio in defence) works. We need baskets of metrics on outputs • Perhaps next stage would be further developing disaggregated measures and activity costing • But in principle productivity paths over time and across countries should be do-able and would be invaluable for policymakers and managers
- 6. Measuring productivity in decentralized public sector organizations • Hospitals, schools, social care organizations, police forces, fire services etc. all deliver services where quality is an important factor • Ideally choose the fuller quality-weighting approach set out above, reweighting total outputs to reflect quality at some appropriate level • Large N of delivering agencies allows for multivariate regression analysis (fitting a regression line and explaining outliers) • Or a ‘data envelopment analysis’ (DEA) comparing agencies with the same service mix on costs and efficiency • Cross-national analyses of other large N settings can be instructive, • And ultimately perhaps productivity paths
- 7. Improving productivity measures in national statistics for large public service sectors • Very aggregate numbers are produced at this level, e.g. for entire public healthcare or public education systems. Strong focus only on individually delivered services so far by NSAs (National Statistical Authorities) • Useful for macro-economic understanding of growth • A lot of focus on fixing output levels (using some inputs and sometimes outcome components also) • Quality weighting of outputs is feasible for NSAs, but not common yet • At this level of aggregation not much potential for lesson-drawing that is directly helpful for leaders, managers or stakeholders
- 8. Conclusions and recommendations • Some substantial progress has been made on activity costing, but productivity as total outputs/total inputs is rarer • Comparing TO/TI performance over time is valuable (and avoids a lot of measurement difficulties). Comparative productivity paths for similar central department/agency types could be very instructive in improving performance • Suggested priority order for seeking improvements: 1 Large central government transaction agencies; 2 Large central agencies producing complex outputs; 3 Analysis of large N networks of decentralized agencies delivering individual services, then collective services 4 Further developing sectoral-level data in national accounts
- 9. Thank you for listening