As of November 24, 2024 there were over 274 million confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in France in January 2020. France has been the worst affected country in Europe with 39,028,437 confirmed cases, followed by Germany with 38,437,756 cases. Italy and the UK have approximately 26.8 million and 25 million cases respectively. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

The crime rate in the United Kingdom was highest in England and Wales in 2023/24, at 89.7 crimes per 1,000 people, compared with Scotland which had 55 crimes per 1,000 population and Northern Ireland, at 52.3 crimes per 1,000 people. During this time period, the crime rate of England and Wales has usually been the highest in the UK, while Scotland's crime rate has declined the most, falling from 93.4 crimes per 1,000 people in 2002/03, to just 52.3 by 2021/22. Overall crime on the rise In 2022/23 there were approximately 6.74 million crimes recorded by the police in England and Wales, with this falling to 6.66 million in 2023/24. Although crime declined quite significantly between 2002/03 and 2013/14, this trend has been reversed in subsequent years. While there are no easy explanations for the recent uptick in crime, it is possible that reduced government spending on the police service was at least partly to blame. In 2009/10 for example, government spending on the police stood at around 19.3 billion pounds, with this cut to between 17.58 billion and 16.35 billion between 2012/13 and 2017/18. One of the most visible consequences of these cuts was a sharp reduction in the number of police officers in the UK. As recently as 2019, there were just 150,000 police officers in the UK, with this increasing to 171,000 by 2023. A creaking justice system During the period of austerity, the Ministry of Justice as a whole saw its budget sharply decline, from 9.1 billion pounds in 2009/10, to just 7.35 billion by 2015/16. Although there has been a reversal of the cuts to budgets and personnel in the justice system, the COVID-19 pandemic hit the depleted service hard in 2020. A backlog of cases grew rapidly, putting a strain on the ability of the justice system to process cases quickly. As of the first quarter of 2023, for example, it took on average 676 days for a crown court case to go from offence to conclusion, compared with 412 days in 2014. There is also the issue of overcrowding in prisons, with the number of prisoners in England and Wales dangerously close to operational capacity in recent months.

Abstract

To support the urban poor during the COVID-19 crisis, the UK government provided a monthly Cash Transfer (CT) of 4,000 Kenyan Shillings (KSH) (or £27) to approximately 52,000 vulnerable people living in informal settlements in Nairobi and Mombasa over a period of three months. The COVID-19 CT was implemented by a consortium led by GiveDirectly, and the monthly stipend was paid using mobile money transfers, with the first transfers taking place from October 2020. The CT was designed to support beneficiaries to buy food or meet other high-priority needs-such as purchasing water, paying for medical care, or making rent payments as well as to reduce the use of negative coping strategies (e.g., selling assets, borrowing money).

OPM was contracted to conduct the monitoring and evaluation of the COVID-19 CT. The main objective of this evaluation was to determine whether, and to what extent, the emergency COVID-19 CT had a positive effect on its target population in informal urban settlements in Kenya. The evaluation also provided an assessment of the implementation parameters and mechanisms adopted as part of the design and delivery of the COVID-19 CT.

To fulfil these aims, the evaluation was structured around two separate components-an impact evaluation and a process review-and drew on multiple research methods through a mixed methods research framework. The objective of the quantitative impact evaluation was to assess whether the COVID-19 CT has had an impact on its beneficiaries, and to quantify the scale of any effect detected. This estimation of impact was based on a longitudinal non-experimental design, focusing on a panel of beneficiaries interviewed at three points in time (baseline - prior to the intervention, midline, and endline - post-intervention) over the course of the implementation period. All quantitative data collection took place remotely using Computer-Assisted Telephone Interview (CATI) software.

Geographic coverage

Nairobi and Mombasa in Kenya

Analysis unit

Individuals Households

Universe

The study population consists of individuals included in the lists of enrolled beneficiaries covered by Give Directly for the COVID-19 CT.

Sampling procedure

The evaluation team implemented a stratified one-stage probability sampling strategy for the selection of survey respondents from the individuals included in the lists covered by Give Directly for the COVID-19 CT. The goal was to select at baseline a sample of 1,000 eligible individuals who would receive the COVID-19 CT, which would then be interviewed by the evaluation team at baseline, midline, and endline.

The sampling strategy considered the following process:

1) The sample was drawn once the COVID-19 CT beneficiaries were considered as enrolled into the intervention. After discussions with Give Directly, it was decided that an individual was considered a future COVID-19 CT recipient when he/she had responded to the short SMS-based survey delivered by Give Directly.

2) The sample was drawn in two separate batches. The first batch of recipients comprised 6,838 vulnerable individuals from informal settlements in Nairobi, while the second batch contained 1,596 vulnerable individuals from Mombasa. We sampled the same number of beneficiaries from the first and second batches (500 individuals from each batch).

3) Explicit stratification was first applied based on the geographical location of the COVID-19 CT recipient. This entailed that we sample 500 individuals from Nairobi from the first batch, and 500 from Mombasa from the second batch. This allowed us to disaggregate our quantitative findings between Nairobi and Mombasa, and produce informative descriptive and regression analyses for each of the two cities included in the intervention.

4) Implicit stratification was then applied based on the following categorical variables: i) local partner from which the eligible beneficiary was selected, and ii) gender of the COVID-19 CT recipient. The goal of this stratification process was to enhance the representativeness of our sample in terms of these variables, so that our evaluation sample resembled as much as possible the distribution of these characteristics in the target population (i.e. the list of beneficiaries of the COVID-19 CT used as sampling frame for our sample).

5) We did not cluster our survey respondents. Apart from spill-over effect issues, which were not a concern due to the lack of a counterfactual in our methodological approach, this is normally a logistical necessity for in-person surveys. This was not an issue either, given the remote nature of the data collection process.

6) Extensive replacement lists were created to maximise efficiency during survey implementation without sacrificing representativeness of the sample. A detailed replacement protocol was elaborated, which took into account the stratification process described above.
Given the longitudinal nature of the evaluation, the same baseline respondents were tracked and re-interviewed at midline and endline so as to create a panel of survey respondents.The final baseline quantitative survey sample achievement is shown below, including the distribution by county

Sample achievement Baseline Survey Nairobi 500 Mombasa 500 Total 1,000

Midline Survey Nairobi 483 Mombasa 489 Total 972

Endline Survey Nairobi 463 Mombasa 478 Total 941

Mode of data collection

Computer Assisted Telephone Interviewing (CATI)