Strategy Survival Guide

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Scenarios used in the Strategy Unit study Non-GM-Specific Regulations Tangled threads Part of the fabric UK Public Rejects GM UK Public Accepts GM Not made in the UK Bare minimum Separate weave GM-Specific Regulations The role played by the scenarios in the final report The five different scenarios played a crucial role in the final report. After we had defined the characteristics and conditions of each scenario, we considered the costs and benefits that would arise in each case. This enabled the study to highlight the importance of trade-offs and weighting of different costs and benefits. An example is attached at Annex A, for just one of the scenarios. As a final step, we also considered possible disrupters in each of the scenarios – how these might arise, how they would be dealt with and what the implications might be. Lessons learned Developing new scenarios is time-consuming - but definitely achievable. In some cases it will be possible to use "off the shelf" scenarios developed by other people. But in other cases, no existing scenarios will fit the bill. If so, it may well be worthwhile developing new scenarios, providing that sufficient time and resources are devoted to this exercise. Scenarios are a tool, not an end in themselves. Scenarios should be defined and used in the way that is most helpful to the study - there are no right or wrong answers. The importance of involving stakeholders and experts in scenario development. If scenarios are to be used, it is essential that they have widespread buy-in. The best way to achieve this is by giving the relevant people a "sense of ownership" of the scenarios. This scenarios were crucially important in the GM study, where opinion was so polarised and we faced criticism that it was there to provide evidence for a predetermined Government decision to allow the commercialisation of GM. The scenarios illustrated that we were studying all possibilities, including a "no- GM" future. The scenarios analysis enabled the SU to investigate whether government policy objectives could be supported across a range of possible outcomes. Scenarios can be powerful tools - but they cannot do everything! Scenarios should be seen as a complement to other techniques, such as risk assessment or cost-benefit analysis - not as a substitute. Scenarios need to have names! This may be one of the most difficult tasks in scenario development - but well-chosen scenario names are much more informative than 1, 2, 3 ... or A, B, C etc. Strategy Survival Guide – Strategy Skills Page 164
Strategy Survival Guide Version 2.1 Prime Minister’s Strategy Unit home | strategy development | strategy skills | site index Strategy Skills > Building an Evidence Base Looking forward - Counterfactual analysis > in practice Counterfactual analysis has theee broad aims: • To establish evidence of a causal relationship between a new policy, or change in policy, and outcomes the policy seeks to influence • To account for confounding factors, additional to the influence of policy, that might lead to measured change in outcomes • To provide estimates of the impact of policy What is it? The counterfactual is an estimate of the circumstances that would have prevailed had a new policy or policy change not been introduced. By comparing counterfactual outcomes (often referred to as either control or comparison group outcomes) with outcomes measured for those units subject to the new policy or policy change, causality or attribution can be established. A counterfactual analysis tool used by government to identify causality or attribution is the use of pilots. These enable the government to test new policies, or changes in existing policy, in a limited number of geographical areas prior to introducing them more widely. The objective is to determine whether the new policy gives rise to changes in the outcomes that policy seeks to alter. For example, counterfactual analysis might answer the question – is there a direct relationship between a new initiative to cut car crime and subsequent change in the number of reported car thefts, independent of other factor influencing car theft? Counterfactual analysis explicitly acknowledges the fact that the outcomes government attempts to influence are subject to a range of factors beyond the immediate scope of the policy being studied. For example, it can’t necessarily be assumed that measures to cut worklessness are entirely responsible for an observed fall in aggregate unemployment. Units exposed to the new policy or policy change are alternatively referred to as the programme, treatment or action group. In theory, causality can be attributed to the new policy because there are no systematic differences between the programme group and a 'true’ counterfactual group, except for the fact that the programme group has been exposed to the new policy. Differences in average outcomes between the programme group and the 'true’ counterfactual group therefore represents an unbiased measure of the programme’s impact. In reality, measuring the counterfactual is a difficult task. Evaluators use a variety of methods, depending on circumstances and opportunities open to them, to estimate the 'true’ counterfactual. The following approaches can be used: • Single group pre and post-test designs • Two group pre and post-test designs • Model-based econometric methods (simple regression adjustment, instrumental variables (IV), the Heckman selection estimator) • Statistical matching designs (e.g., propensity score or cell matching) • Interrupted time series analysis • Regression discontinuity designs • Randomised control trial (RCT) designs (alternatively referred to as random assignment, random allocation, experimental or randomised field trial designs). It is the latter of these that is considered to be the most powerful method of establishing a net effect over and above the counterfactual. This is because programme evaluators explicitly construct control and programme Strategy Survival Guide – Strategy Skills Page 165
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Building an Evidence Base 113 Data
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A Framework for Strategic Direction
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Recruiting a team In Practice: SU G
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Reinforcing Loops A + Balancing Loo
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Opportunities • Where are the goo
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