In the aftermath of the Manchester bombing, many questions were raised regarding the effectiveness of the government’s counter-extremism programmes, with particular focus on the Prevent arm of that strategy. For example, the FT reported that Salman Abedi was referred to Prevent, but that report was not followed up (although note that the police state that they cannot find any record of Abedi’s referral to Prevent). More generally, the Prevent programme has been called “toxic” by the now mayor of Manchester Andy Burnham and the Home Affairs Select Committee.
However, there are a number of examples of the Prevent programme also having done a lot of good. For example, two teenagers were stopped from travelling to Syria after being referred to Prevent by their parents in 2015, and it is credited with helping to stop 150 people (including 50 children) from going to fight in Syria in 2016.
Importantly, much of the discussion regarding Prevent’s effectiveness has focused on anecdotal evidence, the odd stylised fact here and there, a couple of case stories. Most criticisms or praise of Prevent focus on a few examples where it has not worked, been implemented badly, or succeeded. There are even calls to expand or to shut down Prevent without any evidence of whether or not it is actually an effective programme.
Indeed, as far as I’m aware, there has been no (publicly available) rigorous or systematic assessment of Prevent’s effectiveness. (Note that although the recently-launched book “De-Radicalisation in the UK Prevent Strategy: Security, Identity and Religion” by M. S. Elshimi claims to constitute such an assessment, its results are based on an absurdly small sample of only 27 people and therefore cannot be considered a systematic analysis.) However, conducting a systematic assessment could be a relatively simple procedure.
In particular, if data are available on the number of extremist / terror convictions and/or number of people successfully and unsuccessfully “treated” by Prevent at the level of individual local authorities, then it would be possible to use variations across those local authorities to assess Prevent’s effectiveness.
Put simply, the “outcome” variable (i.e. metric that assesses Prevent’s success) could be the number of extremist / terror convictions or the proportion of people referred to Prevent that are successfully treated. (Obviously if the number of convictions is used, it would be important to allocate those convictions to the local authority in which the extremist grew up and/or resided rather than where the extremist activity was carried out.) Of course, there would also be technical considerations regarding whether the outcome variable is a “count” variable, is bounded due to being expressed in percentage terms etc., but those can be dealt with relatively easily.
The explanatory “variable of interest” that would then measure the actual effectiveness of spending on the Prevent strategy would be each local authority’s annual budget for Prevent. If Prevent was effective, one would expect this variable to be negatively related to the number of convictions (since a successful Prevent would stop people before they committed a crime) and positively related to the proportion of successfully treated people. Alternative variables of interest could include the number of Prevent-dedicated personnel in each local authority or the amount of Prevent training that is provided to practitioners – each of these could be investigated to try to identify the most effective/important aspect of the Prevent strategy.
Note that it is unlikely that there would be any simultaneity between the Prevent budget (or other variable of interest) and the outcome variable – although it is plausible that current Prevent spending would be based on past extremist activity in a local area (i.e. local areas with higher extremist activity get more money for Prevent), it is unlikely to be the case that current Prevent spending reacts quickly enough to be affected by current extremist activity. Nonetheless, this could be investigated by using lags of the Prevent budget variable as instruments or as the variables of interest themselves (since it could well be the case that Prevent takes time to have an impact).
As Prevent has been running since 2003, and there are roughly 400 local authorities in the UK, that should give a sizeable panel of data on which to conduct some relatively simple regression analyses. Of course, a number of other factors would need to be taken into account – for example, the population of each local authority, the average income within it, any changes to Prevent guidelines and/or the introduction or suspension of other counter-extremism strategies. The “identification” of the impact of Prevent would therefore come through variation in Prevent spending (or other Prevent-related variables of interest) and outcomes across local authorities and across time.
Now, I don’t have the data to conduct this analysis. However, I suspect that the data are out there – I understand some organisations have created databases containing details of all extremist-related convictions over a reasonably lengthy period of time (for example, The Henry Jackson Society has a dataset on all Islamist-related convictions from 1998-2015, but this would also need to be supplemented with data on other forms of extremism covered by Prevent). Moreover, local authorities / the Home Office / the relevant government authority no doubt have records of the amounts that were spent on Prevent by local authorities (as well as the number of Prevent-related personnel etc.) on an annual basis . As such, combining the two together (along with the various controls) should provide a useable dataset fairly easily.
Hence, if the government and/or organisations with an interest in Prevent really do want to assess how effective is the Prevent strategy, then it actually isn’t very difficult to do so.