So#far:#We#have#awhole#lotof#classical#(deliverology)#to#do#this# # # Structure#your#approach#...
Transcript of So#far:#We#have#awhole#lotof#classical#(deliverology)#to#do#this# # # Structure#your#approach#...
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So far: We have a whole lot of classical administra5on tools
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• We found out that this was not always a solu5on: – It requires many pieces (what if some are missing) – Different combina5ons of pieces yield different results… – Context maDers a lot…
• poli5cs, informality, chaos, etc. make this a very difficult model
– And more
• So the search is on for new tools to get things done
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So NPM added (or refined exis5ng) to/in the toolbox
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And NPM added more
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And more…(and more than this)
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But do these tools work beDer than the classical management tools? Especially in development…
• Today we will ask the ques5on in respect of performance informa5on
• Monday we will look at contrac5ng out and concessions
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• Late 90s/early 00s • Targets plus funding plus hiring – ‘terror targets’ meant to focus in key sectors
• Including health • Imposed on public orgs and on contractors
– Also 5ed to real s5cks (some5mes also carrots) • Extrinsic incen5ves, for mo5va5on
– Was it successful?
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The official results: what do you think? • Life expectancy: progress • Health inequali7es: deteriora7on • Smoking: met • Mortality rates from heart disease: met • Inequali7es gap in rates of death from heart disease: progress • Mortality rates from cancer: progress • Inequali7es gap in rates of death from cancer: met • Suicide: progress • Four-‐hour wait in A&E: met • Access to GP services: progress • Under-‐18 concep7on rate: limited progress • 18 weeks from referral to treatment: met • Pa7ent experience: limited progress • Mental health services: met • MRSA rates: met • Clostridium difficile rates: met
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• In teams, discuss the first three readings: – Deliverology, Natsios, ‘Gaming in Targetworld’
• Lessons, precondi5ons to make this work, ways to make it work
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ARTICLE KEY LESSONS PRECONDITIONS STEPS TO MAKE WORK/LIMIT DANGERS?
McKinsey Devarajan Natsios Hood (Gaming in Targetworld) Mo5va5on revamped
Strong agency outside govt.; well structured targets; disciplined process; monitoring…makes goals useful The focus should not just be on ge]ng managers to manage: what about end users/ci5zens and poli5cians? ‘Results results results’ (the Bush version) emerged over decades, as central compliance based control…now it’s about performance… but it can choke development orgs. ‘what gets measured doesn’t always maDer…what maDers doesn’t always get measured… but what gets measured gets done’ These measures impact behavior in interes5ng ways; expect gaming (ratchet, threshold effects lead to underperformance; distor5onary tac5cs; under-‐aDen5on to good news) When the challenge is a puzzle…we want to have ‘third way’ mo5va5on (intrinsic) which targets can dampen
Strong top down leadership, measurable targets, more Is there a civic/poli5cal appe5te for this approach? How closely 5ed is performance management to compliance and control? Are metrics seen as comprehensive/ absolute, etc.? Assump5on is that these are always going to be a problem
They offer a method (deliverology) to do this Structure your approach to focus on ci5zens/poli5cians as well Be careful how concrete you make your incen5ves; do you allow for some flexibility, and more Some mi5ga5ng mechanisms (5ghtening rules, data defini5ons, refining targets, conduc5ng audit inves5ga5ons, threatening to discipline errant admirals)
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• In teams, discuss the next three readings: – Castro, Van Dooren and Thijs, Hood and Kamensky (in that order, please)
• Lessons, precondi5ons to make this work, ways to make it work
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ARTICLE KEY LESSONS PRECONDITIONS STEPS TO MAKE WORK/LIMIT DANGERS?
Castro Van Dooren and Thijs Hood and Kamensky
Performance evalua5on/budge5ng etc. can work Performance management full of paradoxes, because of complexi5es and ambigui5es of the world… Different types of performance informa5on generate different results: targets, rankings and intelligence
Why is the approach being adopted? What is the ins5tu5onal context (can you generate data, foster conversa5ons about data, etc.)
Various ‘success factors’: get real about context; be clear about purpose, ambi5ons; don’t go too complex; use informa5on with other systems; develop quality controls, etc. ‘Single loop’ solu5ons (beDer data etc.) are not enough; You need a strategy that deals with ambiguity of world: use performance informa5on to learn more than to control (make it agile); bring it close to the people (decentralize it); bring it closer to poli5cs (more actors, more messy conversa5on) Use the type of performance informa5on you need, and structure your system accordingly; Targets: if you can control gaming and really know what you want and just need a big top own push; Rankings: if you are aeer something more nuanced, where ratchet and threshold effects are lowered, but distor5ons s5ll high; Intelligence: we don’t really know where we are, and need PI to work it out (oeen con5nuous improvement)
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• But there is no ‘one best way’ • We need to be able to ‘find and fit’ the approach that works best
where we are – Learning lessons from the past; ensuring we learn lessons where we are too
• On Monday: Is this also true with concessions/priva5za5on as a tool – Going to Cancun twenty odd years ago
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See you Monday