Scottish Prison Population Projections: June 2024 Edition

This report presents short-term Scottish prison population projections for the six month period from May to October 2024. They have been produced using ‘microsimulation’ scenario modelling which simulates prison arrivals and departures, and estimates the number of individuals in prison.

7 Technical annex

Terminology definitions

This section includes explanations of modelling practice and terms to describe the level and range for the projections of the total, sentenced and remand populations.

Microsimulation Scenario definitions

Each new edition of this publication since November 2023 has featured nine different scenario variants. These scenarios use common underpinning data on court case progression and the prison population, from SCTS and SPS respectively. The scenarios are distinct from each other in that they rely on differing assumptions about how future levels of court conclusions and remand arrivals might change.

The microsimulation prison population modelling methodology requires each scenario to be simulated repeatedly dozens of times so that the combined output yields confidence intervals for the total, sentenced and remand population for each scenario. The full projection includes the 95% confidence intervals for each of the nine scenarios. These are overlapping in figures 13 and 15, such that it is not possible to easily identify individual scenarios. The values denoting the upper and lower estimates of the populations in the scenarios in table 2 represent the corresponding outer bounds of the 95% confidence interval for widest ranging scenarios in the projection.

Court throughput rate scenario definitions

The central court throughput rate scenario variants assume that the conclusions per courtroom will be similar over the next few months to the average level and range over the past 12 months leading up to the 1st May 2024 data cut off date (i.e. May 2023 to April 2024 for the projections in this edition of the publication). The "high" scenario assumes the average and range of case conclusions per court per day will be around 10% greater than it has been over the same period, and the "low" scenario assumes that the average case throughput per court will be 10% smaller. A rolling sampling period means that for each edition of the projections the model uses data on case conclusion rates from right up to the data cut off date, thereby accounting for the most recent trends in each successive update to the modelling. As an aside, previous iterations of the modelling included additional scenarios where assumptions about the level of conclusions in future at a national level was informed by changes to the number of High Court, Sheriff Solemn and Sheriff Summary trial courtrooms in accordance with revisions to the Court Recovery Programme. These are no longer necessary but will be re-introduced if it is appropriate to do so in order to represent potential further changes to the Court Recovery Programme.

Remand arrival rate scenario definitions

The distribution of remand arrival rates for scenarios which use central remand arrivals are based upon the daily levels for the entire 12 month period leading up to the data cut off date (1st May 2024). This is effectively the baseline for remand arrivals and the start and end dates progress from one edition of the projections to the next so that the sample period always covers the 12 months leading up to the data cut off date. The “low” and “high” remand arrival scenarios use assumptions in line with the most extreme remand inflow levels in recent history. The “low” remand arrivals scenario in the latest edition of the projections uses a range of remand arrivals sampled from the contiguous three month period with the lowest remand arrivals seen in recent history (excluding seasonal periods). The “high” remand arrivals scenarios use remand arrivals sampled from the contiguous 3-month period with the highest remand arrivals in recent history: February to April 2024. Sampling ranges from a longer period of time can give a more representative sample of what could happen in future. When developing scenarios to replicate rates of arrivals/conclusions in more extreme months it may be appropriate to sample from a single month, but tends to support sensitivity analysis.



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