public policy innovation at the interface between central ... · between central steering and local...

PhD Training School, Paris, 1st October 2013

Public policy innovation at the interface

between central steering and local

autonomy

Jostein Askim, Department of Political Science, University of Oslo

Outline of the argument

• Policy innovation under multi-level governance– at the

interface between central steering and local autonomy

• Problem: trade-off between fostering innovative

practices at the local level and producing generalizable

knowledge

• Two ways of thinking about knowledge production and

policy innovation – relative strengths and weaknesses

• Mixed model – empirical manifestations – lessons

Policy innovation and MLG

• Policy innovation = generating new policies (interventions),

testing and verifying their results, and diffusing effective ones

• Policy innovation is always challenging but some challenges

are especially present in MLG settings

• MLG: central and local governments share responsibility for the

development, implementation and improvement of policies

• Central governments have primary responsibility for policies,

but have

– ideational limitations due to lack of proximity to practice and

– restricted authority and

– legitimacy to install change locally

Policy innovation and MLG (cont.)

• Local governments can generate new interventions but have

– restricted overview and

– their informal horizontal learning is error prone (de- and re-coding

experiences)

• “The laboratory of local governments” works in the sense that

new solutions are developed and implemented, and cross-polity

learning is active

• … but lessons have poor internal and external validity, and

lesson-drawing is therefore risky

Challenge for constructive research

• Most definitions of innovation equate innovation and change -

play down the newness and effectiveness of what is diffused

• Challenge: How to encourage innovative practices at local level

AND extract generalizable, sticky knowledge from innovative

practices

• Aid to thought: Two models of knowledge production

contrasting design templates for policy innovation programs

mixed model with empirical manifestations we can learn from

• Synthesis of literatures: innovation, evaluation, local

government, sociological neo-institutionalism

www.akf.dk

Experimental model Embedded model

Knowledge

objective

Better knowledge of the effects of

interventions

Better knowledge of the development of

interventions and their applications

Process of

discovery

Randomized, controlled experiment, ex

post to the development of intervention

Heuristic. Experience based problem

solving, learning and discovery

Relation to

generation of

interventions

Intervention is designed ex ante to the

experiment. The source of the

innovation is not important.

Intervention is designed in the

experiment. The source of the innovation

is the participants’ praxis and experience.

Criteria for valid

knowledge

Solid knowledge of effects and

causality

True and inspiring depiction of process,

outcome and context

Functional

requirements

Control the experiment.

Participants selected in a randomized

way; distinction between effect and

control group.

Measurement validity, internal validity

(causality versus correlation), external

validity and replicability

Nurture the innovative and

entrepreneurial spirits of participants

Participants self-select/volunteer based

on interest in and ability to conduct the

experiment.

Models of experience-based knowledge production

Trade-offs


Main

strength

Improve field

Production of knowledge with

high internal and external validity

Improve entity

Production of local knowledge

Legitimacy emanating from praxis

Main

weakness

Distance to practice Low internal and external validity

Risk of learning the wrong lesson

Key success

factor Control the experiment Nurture the spirits of inventors and

entrepreneurs

Models of

knowledge

production

Templates for designing

innovation programs in

MLG settings

Design templates for MLG policy innovation

Experimental model Embedded

If necessary:

Definition of

exemptions

(problem)

Coordinating agent selects regulations

from which exemptions can be granted.

Coordinating agent agrees exemptions

with line ministries prior to local level’s

involvement

Local level selects regulations from which

exemptions are necessary to develop policy

solutions.

Exemptions agreed case-by-case in dialogue

between applicants and line ministries,

brokered by coordinating agent.

Definition of

intervention

(solution)

Interventions (policy solutions) are

defined ex ante by coordinating agent

and applied at the local level

Interventions are created as part of local

experiments. Some but not all necessitate

exemptions from regulations

Selection of

participants

Selection based on criteria that facilitate

generalization of results (median or

critical case, etc.). Limited number of

participants is necessary to facilitate tight

monitoring of experiments.

Self-selection facilitates inclusion of

enthusiastic and able participants. Such field

leaders enable ex post diffusion of

innovations. Large number of participants

unproblematic.

Verification of

results

Evaluation as a scientific study of the

causal effects of interventions.

Experiments – de-contextualization to

distinguish “signal from noise”.

Evaluation as context-sensitive

documentation and description of the

experimental process and outcome.

Design templates for MLG policy innovation


If necessary:

Definition of

exemptions

(problem)





involvement



solutions.




Definition of

intervention

(solution)







Selection of

participants










unproblematic.

Verification of

results








Respective foci and strengths

Discovery Testing Diffusion

Embedded model

Experimental model

Self-fuelled

High-risk

Low-risk

Needs fuel


Definition of

exemptions

(problem)





involvement



solutions.




Definition of

intervention

(solution)







Selection of

participants










unproblematic.

Verification of

results








Mixed

model

Mixed model

• Square the circle?

• Combine strengths of embedded and experimental models of

knowledge production?

Empirical manifestations of mixed-model

knowledge production

• Benchmarking programs

• Piloting schemes

• «Right to challenge» schemes (DK)

• Free commune experiments (FCEs) – most

• More on FCEs

– What is it?

– Does it work?

– Lessons for policy innovation in MLG settings

FCE = a recipe for policy innovation at the

central steering/local autonomy interface

Program theory:

• A limited number of LGs are granted temporal exemptions from

selected national regulations, in order to try out ”illegal” policy

interventions. Successful innovations diffused.

• LGs invited to apply to be included in the program CG

selects participants based on criteria.

• Then LGs apply for waivers to conduct local experiments. CG

assesses these based on criteria. Approvals made valid for all

program participants

• Five cases: Sweden 84-92, Denmark I 85-93, Norway 86-92,

Finland 88-96, Denmark II 2012-

FCE experiences and lessons

• Borrowed legitimacy from both science and praxis

• Varieties in pre-definition of scope for local experiments,

selection and evaluation practices, predominantly embedded

approaches

• Some diffusion vertically though national rulemaking and local

implementation, both on the back of- and absent systematic

evaluation

• A lot of horizontal, voluntary diffusion, LGs readily learn from

context-sensitive (success) stories

• Highly effective as generators of new policy interventions, less

effective as test-beds for their effects

• Potential for generalization – limited by political context

Lessons: Getting the mix right (1/2)

Discovery and verification

• How to design policy innovation programs that can both

encourage innovation and extract generalizable results and

thereby encourage less risky diffusion of sticky innovations?

• Sequential combination strategies

• Two-step or interlinked programs

• Cf. Finnish FCE experience

• Simultaneous combination strategies

• Layered programs

• Cf. Swedish FCE experience

• Avoid selection bias in favor of field-leadning municipalites –

representativeness vs. legitimacy of results

• More pre-defining and down-scoping of problems and solutions

for local experiments – cf. signal/noise and ”evaluability”

• Evaluation practice that combines effects analysis and thick

description – legitimcacy

Lessons: Getting the mix right (2/2)

Safer diffusion of sticky innovations

Summary

• Policy innovation under multi-level governance involves

trade-off between fostering innovative practices at the

local level and producing generalizable knowledge

• Two ways of thinking about knowledge production and

policy innovation – relative strengths and weaknesses

• Mixed model – empirical manifestations – lessons

• Necessary to have critical perspective on both purely

embedded and purely experimental approaches to

policy innovation

BACKUP SLIDES

FCEs and templates for policy innovation (1/2)

Experimental Embedded Definition of

exemptions

(problem)

Coordinating agent selects

regulations from which exemptions

can be granted.

Coordinating agent agrees

exemptions with line ministries prior

to local level’s involvement

Local level selects regulations from

which exemptions are necessary to

develop policy solutions.

Exemptions agreed case-by-case in

dialogue between applicants and line

ministries, brokered by coordinating

agent.

Definition of

intervention

(solution)


defined ex ante by coordinating

agent and applied at the local level

Interventions are created as part

of local experiments. Some but

not all necessitate exemptions from

regulations

FCEs and templates for policy innovation (2/2)

Experimental Embedded Selection of

participants

Selection based on criteria that

facilitate generalization of results

(median or critical case, etc.).

Limited number of participants is

necessary to facilitate tight



enthusiastic and able participants. Such

field leaders enable ex post diffusion of

innovations. Large number of

participants unproblematic.

Verification of

results

Evaluation as a scientific study of

the causal effects of interventions.

Experimental to distinguish “signal

from noise”.




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