This blog post summaries a presentation I gave at our recent event: Transforming Rehabilitation: Learning from the PbR results.
In this blog, I will discuss the data I presented at our recent event, ‘Transforming Rehabilitation: Learning from the PbR Results’. The event was held shortly after the publication of the first reoffending figures which showed that reoffending in the first cohort had been reduced by 1.9%. My colleague Jack Cattell has already discussed these figures in an excellent blog, so this blog is about the how external drivers across the criminal justice system might be affecting these results.
By CRC? – PbR Figures, who is in control of reoffending?
In order to make conclusions from these results, one must be aware that there are many factors that can influence reoffending. When faced with (improved or reduced) performance results – it’s too easy to fall into the trap of thinking that the reasons for this were outside of your control, or that the results were dominated by one particular driver.
In understanding the reoffending rates by Community Rehabilitation Companies (CRCs), there are many possible drivers across the CJS, and they can be complex to understand. So, could the CRCs have achieved the difference in performance we have seen under TR? Internal factors such as CRC procedures and workflows, and offender profiles will have an effect. But so too will a myriad of external influences from other agencies of the CJS, for example police positive outcome rates, court conviction rates and timeliness of police & court procedures. Since these factors are outside a CRC’s control, I wanted to investigate and determine whether any might have had an effecting change in the reported reoffending rates.
First off, it is important to bear in mind the national context for the period from baseline to first cohort results, which was a time when the total estimated crime fell and police recorded crime remained constant.
Further, positive outcome rates, specifically charges and cautions had been reduced – in general police arrest rates were down and police had been using other disposals (such as community resolutions) to reduce the number of people – particularly young people – in custody.
And individuals were being processed more quickly during the baseline: on average court processes are now taking 10-15 days longer.
Give this context, the national reoffending rate was reduced by 1.8%. That’s a surprisingly resistant figure, given the changes in context nationally. Large changes in this binary rate will not be seen regardless of policy or systematic change in CRCs.
(Of course, the full picture of the reoffending stats has the rate down, but the frequency up – suggesting a shrinking group of more prolific reoffenders. But that is for another post).
Differences in CRCs?
I will now look at these potential drivers of reoffending rates at the CRC level and determine if contexts really were different or helped to drive different performance. To highlight any of these potential differences, I will look at two CRCs who were at opposite ends of the reoffending rate changes.
What is surprising, is that there are no large, obvious differences between those two CRCs when comparing police outcome rates, court effectiveness and court timeliness measures. For example, the police positive outcome rates were reasonably different at the start of the period, but by the end of 2015 had converged to be broadly similar – the best performing CRC had reduced reoffending while police positive outcomes fell by a significant amount.
Similarly, the lowest performing CRC had raised rates against the baseline despite court timeliness both increasing, and being higher than other CRCs.
We’ve seen that reoffending rates are complex measures dependent on many factors, from individual, to regional to national level, and the interaction between them. The rates don’t change very much, even though the contexts can differ wildly.
Even comparing the CRCs at either end of performance spectrum there were not huge differences in the police and court factors. This can be seen in different results for CRCs operating in widely similar contexts.
So, my advice is this: don’t fall into the trap of feeling the results are not within your ability to affect. Understanding the wider picture is helpful to contextualise local results. In the following blogs, my colleagues will be focusing on what is in your control, and my colleagues will be identifying what works and using that to inform good practice.
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