Index

Publication Date:
15 Jan 2020
Pages:
155–160
DOI:
https://doi.org/10.51952/9781447352013.bm001
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First published as a special issue of Policy & Politics, this updated volume explores policy failures and the valuable opportunities for learning that they offer. The book begins with an overview of policy learning and policy failure. The links between the two appear obvious, yet there are very few studies that address how one can learn from failure, learn to limit failure, and fail to learn. The book attempts to bring the two together. In doing so, it explores how dysfunctional forms of policy learning impact policy failure at the meso-level. The book expands on this by demonstrating how different learning processes generated by actors at the meso-level mediate the extent to which policy transfer is a success or failure. It re-assesses some of the literature on policy transfer and policy diffusion, in light of ideas as to what constitutes failure, partial failure, or limited success. This is followed by an examination of situations in which the incentives of partisanship can encourage a government to actively seek to exacerbate an existing policy failure rather than to repair it. The book studies the connections between repeated assessments of policy failure and subsequent opportunities for system-wide policy learning and reform. Finally, it introduces the idea of ‘policy myopia’ as a pressing source of failure in policy making and explores the possibility of developing policies that learn to help mitigate its impacts.

Note: page numbers in italic type refer to Figures; those in bold type refer to Tables.

A

absorptive capacity (ACAP) in epistemic learning 31, 31–32
BTB (bovine tuberculosis) policy making 32–33, 43
acquis communitaire 86
adaptation, in policy transfer 52, 63, 83
adaptive learning 10, 11, 51, 54, 56, 56, 63
adaptive policies 142–143, 144, 146
administrative capacity (ADCAP) in epistemic learning 31, 33–34
BTB (bovine tuberculosis) policy making 34–36
Advanced Institute of Science, Korea 63
agility 134
ALP (Australian Labor Party) 123–124, 125–126, 126–127
see also Labor Party, New South Wales
ambiguity 137, 138
analytical capacity (ANCAP) in epistemic learning 31, 36–38
BTB (bovine tuberculosis) policy making 24, 38–40, 43
Argyris, C 4
assemblage 72, 85, 87
AstraZeneca 61
Australian health insurance policy study 12, 113–114, 119, 122, 123–127, 128
Australian Labor Party (ALP) 123–124, 125–126, 126–127

B

badger culling 25–26, 32–33, 34, 35–36, 38–40, 41–42
see also BTB (bovine tuberculosis) policy making
BC Ferries 94, 99, 100, 101
see also British Columbia fast ferries study
behavioural psychology 6, 14
beliefs, and policy learning 26–27
Bell Labs 58, 59
Berry, FS 73–74
Berry, WD 73–74
Bhadwal, S 144, 146
Bird, Malcolm G. 11–12, 13, 93–111
‘borrowing scenarios’ 51
bounded rationality 12, 133
Bourne, John 32
Bovens, M 5, 15
Boxall, A. 115
Bresnahan, T 57
bricolage 72, 85, 87
British Columbia fast ferries study 11–12, 93–96, 98–102, 106, 107
Brown, L 143
BTB (bovine tuberculosis) policy making 10
absorptive capacity (ACAP) 32–33, 43
administrative capacity (ADCAP) 34–36
analytical capacity (ANCAP) 24, 38–40, 43
communicative capacity (COMCAP) 24–25, 41–42, 43
epistemic learning 10, 24–28, 27, 30–42, 31

C

Campbell, Gordon 102, 106
Canada:
‘sponsorship scandal’ 96
see also British Columbia fast ferries study
Carr, Bob 104
certification of actors, and policy learning 27, 27
China, and policy transfer 85
Churchman, CW 136
Civil Service Reform Act 1978, US 77
Clark, Glen 101
coalition government, UK 1, 42
Cohen, W 53
communicative capacity (COMCAP) in epistemic learning 40–41
BTB (bovine tuberculosis) policy making 24–25, 41–42, 43
competition, and policy diffusion 74
Conservative government, UK see coalition government, UK
contingent learning 6
‘contributor’ role in epistemic learning 28–29, 29, 31
Copenhagen, Denmark see Medicon Valley, SVM (Silicon Valley Model) of policy transfer
copying, in policy transfer 52, 62, 75
cultural factors, impact on policy-making 9

D

Davis, K 57
de Jong, M 85
decision-makers, relationships with epistemic communities 29–30, 36–37
deep uncertainty 135, 139, 140, 141, 143, 146
see also uncertainty
Defence Advanced Research Projects Agency, US 62
defence industry 57, 59
DEFRA (Department for Environment, Food and Rural Affairs) 25, 26, 32–33, 34, 35, 36, 38–39, 41–42
see also BTB (bovine tuberculosis) policy making
degenerate learning 13, 23–24
BTB (bovine tuberculosis) policy making 10, 24–28, 27, 30–42, 31, 43
deinstitutionalisation 114, 115, 119, 121–122, 123, 127, 128
democracy 95, 105, 116
Denmark see Medicon Valley, SVM (Silicon Valley Model) of policy transfer
Department for Environment, Food and Rural Affairs see DEFRA (Department for Environment, Food and Rural Affairs)
Dewey, J 116
diffusion see policy diffusion
divergence 83
Dolowitz, D 50, 52, 55, 76
‘double-loop’ learning 4, 144
Dror, Y 15
Dunlop, Claire A. 1–22, 23–47
dysfunctional learning 8, 10, 12, 15, 28, 42, 43, 78

E

East Asia, policy transfer 82–83
EBPM (evidence-based policy making) 23–24, 35, 41–42, 116
‘eclecticism’ 85
Egan, Michael 106–107
Elmore, RF 9, 114, 121
emulation, in policy transfer 52, 62, 75, 78
epistemic communities, and policy transfer 81
epistemic learning 27, 27, 43
absorptive capacity (ACAP) 31, 31–33, 43
administrative capacity (ADCAP) 31, 33–36
analytical capacity (ANCAP) 25, 31, 36–40, 43
BTB (bovine tuberculosis) policy making 10, 24–28, 27, 30–42, 31
communicative capacity (COMCAP) 24–25, 40–42, 43
unpacking of 28–30, 29
EU (European Union) 95
policy transfer and convergence 74, 82–83, 85–86
European Commission 86
Eurozone, policy failures 8, 9–10
evidence-based policy making (EBPM) 23–24, 35, 41–42, 116
expert inquiry in policy failure 116, 117, 118, 119–121
exploitative learning 53, 54
exploratory learning 53

F

‘facilitator’ role in epistemic learning 29, 29, 31
financial crisis, West’s response to 9
Flyvberg, B. 87
food banks, demand for 1–2
Fraser, Malcolm 124–125, 126, 127
Freedom of Information Act, US 79
Freeman, R 84

G

Gambardella, A 57
General Dynamics 59
Giest, Sarah 10–11, 13, 49–70
Godfray, Charles 33, 36, 39
governments:
avoidance of blame by 96, 97
exacerbation of policy failures 97, 106
‘gradualism’ 85
Graduate Research Centre, Texas 59, 63
Greiner, Nick 103
groupthink 7, 31

H

Hall, Peter 8
Hansson, S 136, 137
Harrison, K 79
Hassink, R 54
Hawke, Bob 125, 126
health policy reform 119
see also Australian health insurance policy study
Hirsch-Hadorn, G 136–137, 137
Hirschman, AO 31
Howard, John 126
Howlett, Michael 5, 12, 13, 133–154
hybridisation, in policy transfer 75, 83

I

ignorance 137, 138, 138
Illical, M 79
IMF (International Monetary Fund) 76–77
implementation analysis 23
inappropriate transfer 76, 77, 78
incomplete transfer 76, 77, 84
Independent Scientific Group see ISG (Independent Scientific Group)
information overload, and absorptive capacity 31–32
information sources, and absorptive capacity 32
infrastructure megaprojects 97–98, 106–107
see also British Columbia fast ferries study; Sydney Airport Rail Link study
innovation see SVM (Silicon Valley Model) of policy transfer
inspiration, in policy transfer 52, 62, 75
Institute of Science and Technology (IST), New Jersey 58, 59, 60
institutionalism 118–119, 128
definition of institutions 114
see also deinstitutionalisation; new institutionalism
International Monetary Fund (IMF) 76–77
IR (international relations) 74, 75
ISG (Independent Scientific Group) 25, 26, 32–33, 35–36, 38–39, 40–41, 42, 43
see also BTB (bovine tuberculosis) policy making
IST (Institute of Science and Technology), New Jersey 58, 59, 60
Italy, cultural factors in policy-making 9

J

Jacobs, A. 115
James, O 53
Janis, IL 7

K

KAIS (Korea Advanced Institute of Science) 60
Kargon, R 50, 57, 58, 60
Kay, Adrian 12, 13, 113–132
Keating, Paul 126
Kenney, M 57
King, David 26, 36, 39, 42, 43
KIST (Korea Institute of Science and Technology) 60, 61
Knight, FH 138
Korea Advanced Institute of Science (KAIS) 60
Korea Institute of Science and Technology (KIST) 60, 61
Krebs, John 35, 41
Kwakkel, JH 138, 140

L

Labor Party, New South Wales 102, 104–105, 106, 107
see also ALP (Australian Labor Party)
Lagendijk, A 54
Landwehr, C 55
leadership studies 23
learning see policy learning
learning clusters 54
learning in the shadow hierarchy 27, 28
learning regions 54
learning through bargaining 27, 28
Lendvai, N 83, 84
Leslie, S 50, 57, 58, 60
Levin, K 140
Levinthal, D 53
Lewis, J 120
Liberal Party, British Columbia 99, 102, 106, 107
Liberal Party-National Party coalition, New South Wales 102, 103, 104–105
Liberal-National Party Coalition, Australia 124–125, 126, 127
life sciences industry see Medicon Valley, SVM (Silicon Valley Model) of policy transfer
Lindblom, CE 134
Ling-Temco-Vought (LTV) Corporation 59
Lippman, W 116
listening, and absorptive capacity 31
localisation 72
‘norm localisation’ 84–85
Lockheed 59
Lodge, M 53
LTV (Ling-Temco-Vought) Corporation 59
Lundbeck 61

M

MAFF (Ministry of Agriculture, Fisheries and Food) 35
Major Projects Authority (MPA) 1
Malmberg, A 54
Marsh, D 50, 52, 55, 76
Martin, Paul 96
Martin, Thomas 59
Maskell, P 54
Maxim, L 136
May, PJ 117
McCann, E 84
McConnell, A 6, 23, 73, 87
McPhail, Joy 101
Medibank health insurance scheme, Australia 123, 124, 125, 126–127
Medicare health insurance scheme, Australia 123, 124, 125–126
Medicon Valley, SVM (Silicon Valley Model) of policy transfer 50, 61–62, 63, 64
Ministry of Agriculture, Fisheries and Food (MAFF) 35
Moore, G 57
‘moral hazards,’ and epistemic communities 37
MPA (Major Projects Authority) 1
Mukhtarov, F 74
mutation, in policy transfer 83, 85, 87

N

Nair, Sreeja 12, 13, 133–154
NASA, US 62
National Farmers Union (NFU) 39–40, 43
NDP (New Democratic Party), British Columbia 98–99, 100, 101, 102, 107
negative lesson-drawing 71, 75, 78–80
new institutionalism 114–115, 126–127
New Jersey, SVM (Silicon Valley Model) of policy transfer 50, 58–59, 62, 63, 64
New Labour government, UK, EBPM (evidence-based policy making) approach 35, 41
new public governance 120–121
Newig, J 55
Newman, Joshua 11–12, 13, 93–111
NFU (National Farmers Union) 39–40, 43
‘norm localisation’ 84–85
Novo Nordisk 61

O

OECD (Organisation for Economic Cooperation and Development) 123
Oliver, C. 115
organisational capacity 23–24, 30

P

Park Chung Hee 60
Patton, D 57
performance measures 115–116, 119–120
PFP (‘pay-for-performance’) 77–78
Pharmacia 61
policy diffusion 71, 72, 73–74, 86–87
‘dud’ 76–78
see also policy transfer
policy evaluation 2
policy failure 2, 13–15, 133
as catalyst for institutional change 121–123
definitions 3–4, 73
as a ‘degeneration’ of policy learning 23–24
expert inquiry 116, 117, 118, 119–121
failure to learn from 93, 95–98
future research 13–15
judgements and standards 115–117
learning from 24, 42–43, 113, 114, 117–121
macro level 9–10, 14, 15
McConnell’s typology of 6, 23
meso (group) level 7–8, 13, 14–15
micro (individual) level 6–7, 13–14
processes of 5
products 5–6
public political inquiry 116, 117–119
see also Australian health insurance policy study; British Columbia fast ferries study; BTB (bovine tuberculosis) policy making; Sydney Airport Rail Link study
policy learning 2, 13–15, 78–79, 95–96, 143
and beliefs 26–27
definitions 3–4, 26–27
future research 13–15
link to policy failure 24, 42–43
macro level 8–10, 14
meso (group) level 7–8, 13, 14–15
micro (individual) level 6–7, 13–14
and policy transfer 53–55
and political learning 117
processes of 4–5
products 5
typology of 27, 27–28
see also epistemic learning
policy myopia 12, 134, 145–146
learning to deal with 144–145
modelling of 134–138, 137, 138, 139, 140
and uncertainty 12, 13, 134, 135–138, 137, 138, 139, 140–143, 145–146
policy stages explanations of policy failure 23
policy tool analysis 23
policy transfer 11, 13, 50, 51–53, 71–73, 74–76, 86–87
intermediaries and interests 80–83
learning and adaptation in 53–55
negative lesson-drawing 71, 75, 78–80
and policy learning 53–55
processes in 75
soft forms of 53, 81, 83
success and failure in 55–56, 56
temporal transfers 85–86
truncated transfer and ‘dud’ diffusion 76–78
see also SVM (Silicon Valley Model) of policy transfer
policy transformation, and regime change 96
policy translation 83–86, 87
policy variation 83
political inquiry in policy failure 116, 117–119
political learning 117
politics type of policy failure 23
Pollitt, C 119
Princeton University 58
problem tractability, and policy learning 27, 27
process type of policy failure 23
‘producer of standards’ role in epistemic learning 29, 29, 31
programme type of policy failure 23
public political inquiry in policy failure 116, 117–119
public-private partnership schemes:
British Columbia fast ferries study 94
Sydney Airport Rail Link 103–104
Putnam, Robert 9

R

Radaelli, CM 8, 27
RBCT (Randomised Badger Culling Trial) 25, 34–35, 38
see also badger culling; BTB (bovine tuberculosis) policy making
RCTs (randomised control trials) 34–35, 41
reflexive learning 27, 28
regional learning 54
resilience 142–143, 145
Resolution Foundation 1
risk 138, 145
Rittel, HWJ 136, 140
robustness 134, 142, 143, 144, 145
Rose, R 52–53, 54
Rothberg, RI 9
Rudd, Amber 2

S

Sax, S. 124
Saxenian, A 57
Scania, Sweden see Medicon Valley, SVM (Silicon Valley Model) of policy transfer
Schön, D 4
selective borrowing 52
semiconductor industry 56–57
see also Silicon Valley
‘Silicon Somewheres’ see SVM (Silicon Valley Model) of policy transfer
Silicon Valley 49, 51, 52, 56–57, 61, 62
see also SVM (Silicon Valley Model) of policy transfer
Simon, HA 133, 136
‘single-loop’ learning 4, 144
Small Business Innovation Research Program, US 62
SMU (Southern Methodist University) 59
soft forms of policy transfer 53, 81, 83
South Korea, SVM (Silicon Valley Model) of policy transfer 50, 60–61, 62, 63, 64
‘sponsorship scandal,’ Canada 96
Stanford University 57, 58, 59
state corruption 9
Stirling, A 138
Stone, Diane 11, 13, 52, 53, 55, 71–92
structural adjustment policies 76–77
Stubbs, P 83, 84
‘super-wicked’ problems 140
SVM (Silicon Valley Model) of policy transfer 10–11, 49–50, 62–65
case selection 50–51
Medicon Valley case study 50, 61–62, 63, 64
New Jersey case study 50, 58–59, 62, 63, 64
South Korea case study 50, 60–61, 62, 63, 64
Texas case study 50, 59–60, 62, 63, 64
see also Silicon Valley
Swanson, D 144, 146
Sweden see Medicon Valley, SVM (Silicon Valley Model) of policy transfer
Sydney Airport Rail Link study 11–12, 93–96, 102–105, 106–107
synthesis, in policy transfer 52, 75

T

‘t Hart, P 5, 15
‘teacher’ role in epistemic learning 28, 29, 31
Teal, Gordon 59
Terman, Frederick 57, 58, 59, 60
Texas, SVM (Silicon Valley Model) of policy transfer 50, 59–60, 62, 63, 64
Thatcher, Margaret 96
TI (Texas Instruments) 59
Toens, K 55
transformative learning 53–54
trial and error, in policy transfer 84
trial-and-error learning 54, 55, 56, 63, 64–65
Triantafillou, P 120
Trussell Trust 1

U

UC (Universal Credit) 1–2
uncertainty 12, 13, 134, 135–138, 137, 138, 139, 140–143, 145–146
deep uncertainty 135, 139, 140, 141, 143, 146
Levels I–IV 138, 139, 140, 141–143
options, outcomes and values 137, 137
uninformed transfer 76, 84, 787
unintended consequences 84
un-learning 54, 63

V

van der Sluijs, JP 136

W

Walker, WE 138, 139, 140, 146
Ward, K 84
Washington Consensus 76–77
Weaver, K. 115
Webber, MM 136, 140
Whitlam, Gough 123–124, 125, 126–127
‘wicked’ problems 140
Wildavsky, Aaron 81
World Bank 76–77, 80

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