Month: November 2016

Ethnicity and Crime: The Role of Cultural Factors

Last Updated on November 13, 2016 by Karl Thompson

Some Sociologists have suggested that cultural differences, especially differences in family life, may be responsible for underlying differences in offending between ethnic groups.

Single Parent Families are more common among African-Caribbean Families, which may be related to higher rates of crime

In 2007 Almost half the black children in Britain were being raised by single parents. Forty-eight per cent of black Caribbean families had one parent, as did 36 per cent of black African households.

Rates of teenage motherhood are also significantly higher among young black women and despite constituting only 3 per cent of the population aged 15 – 17, they accounted for 9 per cent of all abortions given to women under the age of 18.

The higher rates of single parenthood in Black-Caribbean families may lead to boys from this group being more likely to offend because of the lack of a male role-model to provide guidance and keep them in check.

However, there is a lot of evidence to suggest that British Caribbean single parents are far from isolated, and not even really ‘single’ at all. Research by Geoffrey Driver in the 1980s revealed that Caribbean single mothers are often well-connected to other people in their communities, so not necessarily isolated. Networks existed within neighbours to provide informal help with childcare and school runs. Other research has also found that family connections to brothers and sisters (uncles and aunts) are strong in British Caribbean communities, while Tracey Reynolds (2002) points out that many single Caribbean mothers are in a long term relationship with a man who doesn’t live with them, but visits every day and plays an active role in childcare.

In contrast, Asian families tend to be more stable, which might explain the lower rates of offending within Asian communities.  Marriage is still seen as a key milestone in Brit-Asian life. A UK National Statistics report says the highest proportions of married couples under pension age, with or without children, are in Asian households. Over half of Bangladeshi (54%), Indian (53%) and Pakistani (51%) households contained a married couple, compared with 37% of those headed by a White British person.

However,  there is a dark-side to Asian family life, and that comes in the number of Forced Marriages associated with Asian communities. One report from 2008 suggests that there are up to 3000 third and fourth generation  Asian women who are subjected to forced marriages in the UK. This crime will of course be practically invisible in the official statistics.

The culture of anti-school black masculinity may also be related to higher rates of black criminality

Tony Sewell (1997) observed that Black Caribbean boys may experience considerable pressure by their peers to adopt the norms of an ‘urban’ or ‘street’ subculture. More importance is given to unruly behaviour with teachers and antagonistic behaviour with other students than to high achievement or effort to succeed, particularly at secondary school.

Sewell (2003) argued that “black boys today have real opportunities but they are failing to grasp them. I talk to middle class, black parents who tell me they literally have to fight to keep their boys on task. These are boys from well-resourced homes, they go to the better state schools and yet they are performing below their potential. A black male today faces anti-school peer pressure that dominates our schools. Ask your son about it if you need some enlightenment. A head teacher told me how one student was jumped outside of his school: he was beaten and his attackers took his mobile phone, his trainers, his jacket and his cap. In our inner cities, black male youth culture has moved from a community of safety and brotherhood to one of fear of each other.”

Evaluating the Role of Cultural Factors

There are limitations with cultural explanations of differences in offending

Firstly, these theories might be accused of explaining crimes by drawing on crude stereotypes – there are significant cultural variations within Black and Asian ethnic groups, and official statics only collect very basic stats on ethnicity (literally just recording whether people are Black or Asian) thus there is no real way to evaluate the above theories.

Secondly, it is difficult to separate out cultural from material factors such as unemployment and poverty, which are emphasised by Left Realists.

Thirdly, these theories don’t take into account the fact that underlying differences in crime rates may be a response to blocked opportunities which are in turn caused by structural racism in wider society.

Fourthly, these theories do not consider the fact that that the statistics might be a social construction and exaggerate the true extent of Black and Asian criminality. Critical criminologists, for example, argue that the over-representation of minority groups in the criminal justice system is because they are more likely to be criminalised by agents of social control.

 

Dambisa Moyo’s Dead Aid – A Summary and Criticism

Dambisa MoyoIn this blog post I summarise Dambisa Moyo’s views on the problems with Aid as a strategy for development – she is talking about Official Development Aid rather than Emergency relief aid.

I’m mainly drawing from her writing at the end of chapter 3 and the whole of chapter 4 – and I offer up a few criticisms all the way through – before you read this through – please note my main criticism of Moyo’s work –

The main criticism I have of Moyo is that she uses statistics that show correlations between a high level of aid receipts and poor economic growth and then attempts to imply causality (aid causing poor growth) by using emotive, highly selective, anecdotal and even hypothetical (she invents a country – Dongo) ‘evidence’ to back up her case.

I say ‘imply causality’ because she never actually uses the word ‘cause’ – but the reader is left with the impression that this is what she is driving at. The end result for the less well informed reader is that they are stuck with a number of ‘easy to understand memorable case studies’ which imply that aid causes poverty – even though Moyo never actually says as much.

Anyway, here is my interpretation of the criticisms Moyo makes about the role of aid in development and a few criticisms that some people might make of Moyo’s work.

Criticism 1 – Aid does not bring about economic growth

At the end of chapter 3 – Aid is not working, Moyo starts to outline her basic criticism of Aid – This basic criticism being that aid has not effectively promote economic growth in Africa – Over 1 trillion dollars has been pumped into Africa over the past 60 years and there is little to show for it. In fact, according to Moyo, aid is malignant, it is the problem!

Moyo explains this through the following hypothetical example

 ‘There’s a mosquito net maker in Africa. He manufactures around 500 nets a week. He employs 10 people, who each have to support upwards of 15 relatives. However hard they work, they cannot make enough nets to combat the malaria-carrying mosquito.

Enter vociferous Hollywood movie star who rallies the masses, and goads Western governments to collect and send 100, 000 mosquito nets to the affected region, at a cost of $1 million, the nets arrive, the nets are distributed and a good deed is done.

With the market flooded with foreign nets, however, our mosquito net maker is promptly out of business. His ten workers can no longer support their dependents. 

Now think of what happens 5 years down the line when the mosquito nets are torn and beyond repair, we have now mosquito nets, and no local industry to build any more. The long term effect of the ‘aid injection’ has been to decimate the local economy and make the local population dependent on foreign aid from abroad.’

Backing this up with some stats, Moyo goes on to point out that ‘even the most cursory look at the data suggests that as aid has increased over time, Africa’s growth has decreased with an accompanying higher incidence of poverty. Over the past thirty years, the most aid-dependent countries have exhibited growth rates averaging minus 0.2 % per annum.

Moyo also argues that a direct consequence of aid-driven interventions has been a dramatic descent into poverty – citing Zambia as an example, and the fact that when aid flows were at their peak between 1970 and 1998 – poverty in Africa rose to a staggering 66%.

The problem Moyo has here is that she fails to present sufficient evidence to make her case – it’s well known that the later part of the period above was a time of global economic slowdown compared to the previous 20 years, which itself could play a major role in Africa’s poverty, as could be the case with the debt crisis. One could also simply cite Botswana and Ghana as case studies of aid-recipient countries that have grown to counter her one example of Zambia.

Criticism 2 – Aid Encourages Corruption, which in turn retards growth

Unlike the previous section, Moyo does use a reasonable amount of statistical (drawn mainly from Transparency International) and case study evidence in this section…

According to Moyo – If the world has one image of African statesmen, it is one of rank corruption on a stupendous scale. One of the best examples of this is Mobutu, who is estimated to have looted Zaire to the tune of $5 billion. He is also famous for leasing Concorde to fly his daughter to her wedding in the Ivory Coast shortly after negotiating a lucrative aid deal with Ronald Reagan in the 1980s.

Having provided a couple more examples of ‘classic African Dictators’, Moyo then cites that classic statement made in n 2004 by the British envoy to Kenya, Sir Edward Clay,  who complained about rampant corruption in the country, commenting that Kenya’s corrupt ministers were ‘eating like gluttons’ and vomiting on the shoes of foreign donors. In February 2005 (prodded to make a public apology), he apologised, saying he was sorry for the ‘moderation’ of his language, for underestimating the scale of the looting and for failing to speak out earlier.

Moyo further argues that at least 25% of World Bank Aid is misused. One of the worst examples is in Uganda in the 1990s – where it is estimated that only 20% of government spending on education actually made it to local primary schools.

According to Moyo, while it is not the only cause ‘aid is one of the greatest aides to corruption’ – arguing (Actually it might be more accurate to say ‘asserting’ given the lack of evidence in this section of her book) that ‘with aid’s help, corruption fosters corruption, nations quickly descend into a vicious cycle of aid’.

However, Moyo now drifts from the data and starts implying causality by asserting that growth cannot occur in an environment where corruption is rife, citing the following (un-evidenced) reasons (among others).

  • Corruption leads to worse development projects – corrupt government officials award contracts to those who collude in corruption rather than the best people for the job. This results in lower-quality infrastructure projects.
  • Foreign companies will not invest in countries where corrupt officials might siphon off investment money for themselves rather than actually investing that money in the country’s future.
  • Aid is corrosive in that it encourages exceptionally talented people to become unprincipled – putting their efforts into attracting and siphoning off aid rather than focusing on being good politicians or entrepreneurs.

Criticism 3 – Aid Corrupts Civil Society

Dambisa Moyo: Spreader of Neoliberal Hegemony?

OFFERING NO CONCRETE EXAMPLES OR EVIDENCE TO SUPPORT HER POINT, in this section Moyo asserts that Africa needs a middle class which trusts each other in order for development to occur. The problems is that in an aid environment, governments are more interested in lining their own pockets rather than encouraging entrepreneurs, meaning that the middle class cannot expand until it reaches that ‘critical mass’ which leads to sustained growth.

Criticism 4 – Aid undermines social capital

ONCE AGAIN OFFERING NO CONCRETE EXAMPLES OR EVIDENCE TO SUPPORT HER POINT, here Moyo argues that… In an aid dependent environment, there is no need for you to trust your neighbour and no need for your neighbour to trust you… Foreign aid weakens social capital by thwarting accountability mechanisms, encouraging rent-seeking behaviour, siphoning off scarce talent from employment positions and removing pressures to reform inefficient policies and institutions.

On the above two points it is also worth noting that these criticisms are really just fusions of the previous two criticisms of aid – that it prevents economic growth and breeds corruption.

Criticism 5 – Aid and Civil War

Moyo points out that there are three fundamental truths about conflicts today: they are mostly born out of competition for control of resources; they are predominantly a feature of poorer economies; and they are increasingly internal conflicts.

She then goes on to say that ‘this is why foreign aid foments conflict. The prospect of seizing power and gaining access to unlimited aid wealth is irresistible’. Unlike in the previous two sections, here she offers up one example to support her argument (Sierra Leone) before reminding us that aid also causes conflict more indirectly by reducing the prospects for economic growth.

The Economic Limitations of Aid

Having outlined five downsides of aid, Moyo then outlines its economic limitations – suggesting that there are four – once again lacking examples

  • Aid reduces savings and investment – assertion, no examples
  • Aid can be inflationary – assertion no examples
  • Aid chokes off the export sector (Dutch Disease) – cites unreferenced IMF studies
  • Aid causes bottlenecks due to low absorption capacity – Uses Uganda as an example

Aid and Aid Dependency

The end result of all the above is that aid leads to Aid Dependency – to the extent that aid makes up 13% of the average African country’s GDP. According to Moyo, this throws up the following problems

  • It makes Africans lazy
  • It leads to low tax revenues (no need to tax the citizenry if money is flooding in from outside!)
  • Citing Boone (1996) – it leads to bloated inefficient public sectors.
  • Finally, it leads to Western donors being able to call the shots.

In the final section of the chapter, Moyo pays homage to Peter Bauer, and briefly mentions that both William Easterly and Paul Collier disagree with the ‘one size fits all’ aid approach to development – before introducing the next sections of the book which are devoted to explaining why Africa should adopt free market (encouraging FDI/ Issuing bonds etc.)  rather than aid driven solutions to underdevelopment.

Criticisms of Moyo

Really, I’d just like to go back to what I said at the beginning and say that…

The main criticism I have of Moyo is that she uses statistics that show correlations between a high level of aid receipts and poor economic growth and then attempts to imply causality (aid causing poor growth) by using emotive, highly selective, anecdotal and even hypothetical (she invents a country – Dongo) ‘evidence’ to back up her assertions.

I say ‘imply causality’ because she never actually uses the word ‘cause’ – but the reader is left with the impression that this is what she is driving at. The end result for the less well informed reader is that they are stuck with a number of ‘easy to understand memorable case studies’ that imply aid causes poverty – even though Moyo never actually says as much – possibly because she might think that, really, there is insufficient evidence to make the case which she alludes to.

One has to reflect on why Moyo is so selective – I think it unlikely that an Oxford and Harvard Graduate has failed to read widely enough for this to be innocent – Especially when the author has 8 years at Goldman Sachs under her belt….so could it be that this is simply an overt attempt to promote a neoliberal anti aid agenda?

Educational Technology – Increasing Inequality and Other Potential Problems

Does the increasing use of educational technology enhance the ‘learning experience’ for learners, or does it just reinforce existing social problems such as inequality of educational outcomes? Personally I’m sceptical about the benefits of educational technology. 

In its recent report, OFCOM describe young people as prolific users of digital media, with the vast majority of young people perceiving digital technologies in highly positive ways, and approximately 25% reporting that they see ICT as the key to their future career. (OFCOM 2013, see also Logicalis 2013).

This widespread enthusiastic adoption of digital technologies is met by equally enthusiastic encouragement by business leaders, many of whom voice optimism that such technologies can help maintain UK economic competitiveness in the global knowledge economy. Gantz and Reinsel (2012) for example note that CIOs, data scientists and digital entrepreneurs already know that there is huge, untapped potential in the rapidly expanding collection of digital bits, although this will require the tagging and analysing of big data if this is to be realised, while Lent (2102) suggests the long established blurring between consumption and production is accelerated by the web which opens up new capacities for self-generated value, pointing to a new entrepreneurial spirit amongst today’s younger generation, which should be embraced.

This optimism seems to be mirrored by the DFES1 which has an overwhelmingly positive view of the future role of ICT in schools and colleges, noting that it has transformed other sectors, and that pupils need ICT to equip them with future-work skills. In DFES literature, ICT seems to be presented as a neutral set of technologies through which individual students can be empowered, with emphasis on the benefits such technology can bring to schools, such as more personalised learning, better feedback, a richer resource base and the possibility of extending the learning day.

Following Ball (2013) this optimistic tone surrounding ICT fits with the neoliberal reorientation to economic global competitiveness as part of a global flow of policy based around a shift towards a knowledge based high skills economy, and in terms of broader (‘classic’) sociological theory these optimistic voices correspond to the largely optimistic theories of disembedded individualisation (following Dawson 2012) originally advanced by Giddens and Beck in early 1990s, in that digital technology is constructed as something which can enhance the capacity for young people to employ agency and craft innovative transitional choice-biographies (Giddens, 1991, p5, Beck 1992, p135-6). If there is any truth in this, we should, over the next few years, see several hundreds of thousands of young digital entrepreneurs engaging in cyber-reflexivity and creating innovative online solutions to the systemic problem of decreasing youth employment opportunities, irrespective of their class-location (Beck and Beck-Gernsheim, 2002, p39).

There are, however, several factors which suggest that this vision of the (dismebedded) individualised cyber-reflexive entrepreneurial future is either naive or ideological. Firstly, the extent to which today’s so called ‘digital natives’2 are genuinely innovative digital entrepreneurs rather than simply being ambivalent-consumers of digital products remains unclear3; secondly, cyberspace is far from a neutral arena, in reality I think it is more accurate to view it as a field of action in which the type of agency employed (e.g. whether productive/ entrepreneurial or banal/ consumptive) will be influenced by factors such as cultural capital and social networks; thirdly, this vision overplays the actual opportunities available for using digital media as a route to career success or self-employment – for example little mention is given to the problematic fact that millions of young people in Asia will be entering the ‘flat’ digital-labour market in the coming decade, able to survive off much lower returns than their UK competitors; fourthly, there seems to little interest in operationalising what kind of opportunities will be opening up for digital entrepreneurs in the future – there may well be hundreds of thousands more 20-somethings with their own digital-companies by 2020, but it is uncertain what side of the high skills low skills informational economy (referred to by Apple 2012) the majority of tomorrow’s digital workforce will find themselves; and finally there is the possibility that this is the latest discourse innovation in the denigration of teachers and state education through constructing technologically reticent staff as a barrier to progress, as well as paving the way for further privatisation with the forthcoming renewal of the ICT curriculum being fully endorsed and part-authored by Google, Microsoft, and IBM4.

It is also the case that I see little evidence of digital innovation in my mundane workaday reality – instead what I mainly see is digital-addiction, banal banter, and browsing for shoes, with today’s digital youth seeming largely content to construct themselves through digital-consumption and self-expression. Many of today’s students attach huge significance to such aspects of their lives (browsing for clothes and shoes is a favoured activity in tutorial, as are discussions about the post-exam trip to Malia, photos from the previous year’s trips being standard as social networking profile pictures). It is also apparent that the mobile devices through which many young people access online culture are themselves fetish-objects, central to young people’s experience of being themselves (as researched by Jotham 2012), that young people generally remain uninterested or unable to engage with the more technical aspects of these technologies5 which might actually equip them with the skills to be digitally-entrepreneurial, and that mobile devices link young people to heavily commodified space (Bolin 2012) which connects users directly to corporate (read neoliberal) protocols (Snickars and Vonderau, 2012).

It follows that youth engagement with digital media seems much more likely to centre around what Kenway and Bullen (2008) call the corporate curriculum (2008) which normalises the libidinal economy, a hyperreal realm of carnivalesque jouissance fuelled by desires based on values associated with lifestyle commodity aesthetics rather than the work ethic or responsibility, with any sense of ‘digital entrepreneurship’ being limited to the self-conscious commodification of the self through personal branding via social networking sites (Marwick 2011).

I also think that many students struggle as a result of what Bauman (2013a, 2013b) refers to as the pointillist experience of time online… ‘marked as much by the profusion of ruptures and discontinuities…. more prominent for its inconsistency and lack of cohesion than for its elements of continuity and consistency…. broken up, or even pulverized, into a multitude of ‘eternal instants’. This concept has been developed by Niehaus (2012), exploring what he calls ‘iTime’, describing this experience as being structured by an addictive hunt for frissons, short instants of excitement and pleasure; with each moment ever-more packed with contents, references, and tasks which taken together are likely to take precedence over the linear, single-minded time of one activity.’ This process is likely to be accelerated through multitasking, through which 16-24 year olds manage to squeeze in the equivalent of 9 hours and 30 minutes of data consumption per day (as noted by Davis 2013).

According to Bauman (2013b), those young people who are distracted by pointillism and the jouissance of the corporate curriculum, engaged in what he would call ‘banal’ cyber-reflexivity, are afflicted with a ‘fatal coincidence of the compulsion/ addiction of choosing with the inability to choose’, and if Bauman is correct, those who are more engaged with such aspects of digital media are probably less-likely to have thought about their long-term futures, and be less able to construct the kind of entrepreneurial ‘choice’-biographies that DFES champion (Bauman, 2012).

While there is a lack of critical research available on the use of digital media in an educational context (as Selwyn 2014 notes), there is some evidence that higher levels of ‘social’ use of digital technologies could be correlated with lower levels of engagement  with educational opportunities. Fisher’s (2009) personal experience of teaching in an FE college was that FE students who were heavy users of communications technologies were more likely to get bored of standard, offline lessons, Junco (2011) has theorised that the negative correlation between the frequency of posting updates on Facebook and final GPA could have been due to due to cognitive overload, given that the former variable was not negatively correlated with time spent engaged in college work, while Hall and Baym’s (2012) analysis of mobile maintenance expectations uncovered that once established mobile technologies can encourage high levels of ‘mundane maintenance’ to meet communicative obligations within a friendship group.

Possible avenues for research….

There’s definitely scope for further research to examine the extent to which student use of digital technology6 encourages the production neoliberal subjectivties, and the scope for and meaning of resistance to such subjectivities. One possible avenue might be to look at the extent of ‘digital entrepreneurship’ (for example, ability to code and create software or use software to generate innovative products) compared to other more common uses of digital media (such as information-seeking, maintaining social networks and game-playing).

My own feeling is that it would be useful to employ Bauman’s theoretical framework7 to explore the extent to which different forms of (socially embedded) digital-reflexivities stratify young people into (different types of) digital-producers and digital-consumers, although there is potential for this to be a ‘sociology of education’ type study, which might usefully draw on the theoretical work of Bordieu, exploring how digital reflexivities are embedded in social networks and influenced by cultural capital, and how these reflexivities influence students’ ability to meet the performative demands of further education.

Works cited

Apple,M (2010) Global crises, social justice and education, Routledge: New York.

Ball, S (2013) The education debate, Kindle Edition.

Bauman, Z (2013a) Dividing time, or Love’s Labour’s Lost, Thesis Eleven 2013 118: 3

Bauman, Z (2013b)  The art of life, Kindle Edition (originally published 2008).

Bauman, Z (2012) On education: Conversations with Riccardo Mazzeo, Polity Press: Cambridge.

Beck, U (1992) Risk society: towards a new modernity, Sage: London.

Beck, U and Beck-Gernsheim, E (2002) Individualisation, Sage: London.

Bolin, G (2012) Personal media in the digital economy, in Snickars, P and Vonderau, P (2012) Moving data: The iphone and the future of media, Columbia University Press: New York.

Davis, M (2013) Hurried lives: Dialectics of time and technology in liquid modernity. Thesis Eleven 118:7.

Dawson, M (2012) Reviewing the critique of individualization: The disembedded and embedded theses. Acta Sociologica 55: 305.

Fischer, M (2009) Capitalist realism: Is there no alternative? Kindle Edition.

Gantz, J and Reinsel, D (2012) The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east, IDC. (Accessed online January 25/ 2014 – http://www.emc.com/leadership/digital-universe/iview/index.htm).

Giddens, A (1991) Modernity and self identity: Self and society in the late modern age, Polity: Cambridge.

Hall, J and Baym, N (2012) Calling and texting (too much): Mobile maintenance expectations, (over)dependence, entrapment, and friendship satisfaction. New Media and Society 2012 14: 316.

Jotham, V (2012) iSpace? Identitiy and space – A visual ethnography with young people and mobile phone technologies. PhD Thesis, University of Manchester, Faculty of Humanities.

Junco, R (2011) Too much face and not enough books: The relationship between multiple indicies of Facebook use and academic performance. Computers in Human Behaviour, 28: 1 (http://www.sciencedirect.com/science/article/pii/S0747563211001932, accessed 24/01/ 2014).

Kenway, J & Bullen, E (2008) ‘The global corporate curriculum and the young cyberflaneur as global citizen’ in Dolby, N & Rizvi, F (eds.) Youth moves – Identities and education in global perspectives, Routledge, New York.

Lent, A (2012) Generation enterprise: The hope for a brighter economic future, the RSA. (http://www.thersa.org/action-research-centre/enterprise-and-design/enterprise/enterprise/generation-enterprise, accessed 25/ 01/2014.)

Livingstone, S (2008) Taking risky opportunities in youthful content creation: teenagers’ use of social networking sites for intimacy, privacy and self-expression, New Media and Society, 10: 293.

Logicalis (2013) Realtime generation (http://www.uk.logicalis.com/knowledge-share/reports/real-time-generation-2013/, accessed 22/01/ 2014).

Marwick, A (2011) I tweet honestly, I tweet passionately: twitter users, context collapse, and the imagined audience, New Media and Society, 13: 114.

Niehaus, N (2012) Whenever you are, be sometime else’. A philosophical analysis of smartphone time (https://www.academia.edu/3664754/Whenever_you_are_be_sometime_else._A_philosophical_analysis_of_smartphone_time, accessed 22/ 01/ 2014).

Selwyn (2014) Making sense of young people, Education and digitial technology: The role of sociological theory. Oxford Review of Education 38:1.

1http://www.education.gov.uk/schools/teachingandlearning/curriculum/a00201823/digital-technology-in-schools accessed 16/01/2104, updated 18 October 2013

2Despite the fact that recent research by the Open University suggests the concept bears no relation to empirical reality, the DFES and business analysts still seem all too willing to use it.

3 In my own college, reporting of 60+ hours a week use of digital-media is not uncommon, but the majority seem to simply use digital media for communication with significant-peers, entertainment or consumer-related information-seeking purposes, and thus it seems likely that most 16-19 year olds are currently more accurately characterised as digital-consumers rather than genuinely innovative digital-producers/ or a range of diverse prosumer hybrids.

4https://www.gov.uk/government/news/harmful-ict-curriculum-set-to-be-dropped-to-make-way-for-rigorous-computer-science DFES 11/01/2012, accessed 16/01/2013

5for example, Livingstone (2008) reported that teenage users of a variety of social networking sites were unsure of what aspects of their profiles were private, which requires a ‘deeper’ level of technical awareness than that required to maintain a profile, but in itself is hardly a ‘deep’ level of technical knowledge.

6I use the term broadly at this stage, although I realise I may need to limit the study to certain types of digital-engagement.

7If that’s even possible given his love of ambivalence?

Official Statistics on Ethnicity and Crime

A summary of how ethnic minorities are over-represented at different stages of the criminal ‘justice’ process in England and Wales

Last Updated on August 11, 2021 by Karl Thompson

Official government statistics suggest that both black and asian people are more likely to be stopped by the police and go to jail than white people in England today.

The Home Office records statistics on the ethnic backgrounds of people as they ‘progress’ through the criminal justice system, such as:

  1. Stop and search
  2. Arrest statistics
  3. Prosecutions
  4. Convictions
  5. Custodial remands
  6. Custodial Sentences
  7. Prison Population

The main publication documenting this data is ‘Statistics on Race and the Criminal Justice System‘, the latest publication date being November 2018 (next release November 2021).

The latest report notes that ethnic minorities, especially black people are over-represented at many stages of the criminal justice process – but especially in the stop and search practice.

The figures below show the percentages of different ethnic groups represented through stop and search to the prison population:

NB the percentages above do not show us the percentages proportionate to the numbers of White, Black and Asian in the population so on their own they are misleading. 22% of the population isn’t Black, for example, so black people are hugely over-represented in the stop and search statistics (something the England and Wales Police Force is well aware of as something of a ‘problem’!)

Official Statistics on Ethnicity and Crime: The Main Differences…

Proportionate to the overall numbers in the adult population as a whole…

  • Black people are approximately SIX times more likely to be stopped and searched and SIX times more likely to be sent to jail;
  • Asian people are THREE times more likely to be stopped and searched than White people, but have a similar chance of being sent to jail.

The rest of this post provides a little more detail on how the stats vary at different stages of the criminalisation process. 

Stop and Search Statistics by Ethnicity

Stop and search has long been an issue of concern by Human Rights campaigners in England and Wales

According to this BBC summary (2013) The Equality and Human Rights Commission (EHRC) said in some areas black people were 29 times more likely to be stopped and searched. The commission said the disproportion between different ethnic groups remained “stubbornly high”.

The highest “disproportionality” ratios were found in the following places:

  • In Dorset black people were 11.7 times more likely than white people to be stopped
  • In West Mercia, Asian people were 3.4 times more likely than white people to be stopped
  • In Warwickshire, people of mixed race were 4.4 times more likely than white people to be stopped and searched.

The report also looked at the use of Section 60 of the Criminal Justice and Public Order Act under which police can stop and search someone for weapons, without suspicion that the individual is involved in wrongdoing, providing that a senior officer has a reasonable belief that violence had or is about to occur.

stop and search.jpg

Under section 60, In the West Midlands, black people were 29 times more likely than white people to be targeted and Asian people were six times more likely than white people to be targeted, which is what the above spoof advert mush be drawing on.

EHRC chief executive Mark Hammond said “the overall disproportionality in the use of the powers against black, Asian and mixed race people remains stubbornly high.”

And the latest figures figures (from the 2018 report above) note that things have got worse:

“The proportion of stop and searches conducted on White suspects decreased from 75% in 2014/15 to 59% in 2018/19 and increased for all minority ethnic groups.

The largest increases were from 13% to 22% for Black suspects and from 8% to 13% for Asian suspects.”

As the table below shows the overall number of people being stopped and searched by the police has declined in the last five years, but the proportions of Black and Asian people stopped and searched compared to whites has increased.

It seems that when the police are asked to use Stop and Search more selectively, they select to stop and search less white people and more ethnic minorities.

Arrest Rates following Stop and Search

The rates are converging, which I guess suggests the police are ‘getting it right’ in equal amounts across ethnic groups:

Arrest Statistics by Ethnicity

The total number of arrests have gone down over the last five years, in line with the declining crime rates. The arrest statistics have remained stable over time, with 77% of arrests being made of white people, 10% black and 7% Asian in 2018.

One stand-out trend for reasons for arrest is that Black people are less likely to be arrested for ‘violence against the person’ and more likely to be arrested for drugs than other ethnic groups – drugs is also the main reason for stop and search, so the two could be correlated.

Penalty Notices and Ethnicity

The main reason white people get given a penalty notice is for being ‘drunk and disorderly’, while for Black and Asian people the main reason is ‘cannabis possession’.

It’s interesting to note here that white people are getting notices for actually being offensive, while for black and asian people it’s merely possessing a drug the system has chosen to make illegal. There’s a significant link to interactionism here!

Prosecution and trial statistics 

The Crown Prosecution service (CPS) is responsible for deciding whether a crime or arrest should be prosecuted in court. They base it on whether there is any real chance of the prosecution succeeding and whether it is better for the public that they are prosecuted.

Ethnic minority cases are more likely to be dropped than whites, and blacks and Asians are less likely to be found guilty than whites. Bowling and Phillips (2002) argue that this is because there is never enough evidence to prosecute as it is mainly based on racist stereotyping. In 2006/7 60% of whites were found guilty, against only 52% of blacks, and 44% of Asians.

When cases go ahead members of ethnic minorities are more likely to elect for Crown Court trail rather than magistrates (even through Crown Courts can hand out more severe punishments), potentially because of a mistrust of magistrates.

Convictions

The conviction ratios are very similar for all ethnic groups, suggesting little racial bias at this stage of the criminal justice system:

Black people receive by far the longest sentences, but this seems related to much higher rates of repeat offending, while a much higher proportion of white people being prosecuted are first time offenders….

The 2018 report produced the impressive flow chart below, make of it what you will!

Personally my takeaway is that there seems to be broad equality in the way different ethnicities are treated, and a lot more repeat offending by Black offenders, hence their longer prison sentences.

Prosecutions and Convictions by Type of Offence and Ethnicity

To summarise to the extreme, White people mainly get convicted for theft, Black and Asian people for Drugs.

It’s also worth noting that Black people have significantly lower rates for violent crime than White or Asian people.

Prison Population by Ethnicity

The younger the age group, the fewer white people there are in jail:

And for the under 25s, the number of ethnic minorities in jail has increased proportionate to White people over the last five years:

More than half of children in jail are ethnic minorities

The latest report also has stats on children moving through the criminal justice system.

The figures are even more skewed against ethnic minorities compared to the adult statistics.

It’s more than a little disturbing to note that 51% of children in prison are from ethnic minority backgrounds.

Victim surveys

The British Crime Survey indicated that 44 per cent of victims were able to say something about the offender who was involved in offences against them. Among these, 85 per cent of offenders were said by victims to be ‘white’, 5 per cent ‘black’, 3 per cent ‘Asian’ and 4 per cent ‘mixed’. However, these stats are only for the minority of ‘contact’ offences and very few people have any idea who was involved in the most common offences such as vehicle crime and burglary. Therefore, in the vast majority of offences no reliable information is available from victims about the ethnicity of the criminal.

Self-report studies

Though not ‘official statistics’ because they’re not done by the government routinely, it’s interesting to contrast the above stats to this alternative way of measuring crime. Self-report studies ask people to disclose details of crimes they committed but not necessarily been caught doing or convicted of. Graham and Bowling (1995) Found that blacks (43%) and whites (44%) had similar and almost identical rates of crime, but Asians actually had lower rates (Indians- 30%, Pakistanis-28% and Bangladeshi-13%).

Sharp and Budd (2005) noted that the 2003 offending, crime and justice survey of 12,000 people found that whites and mixed ethnicity were more likely to say they had committed a crime, followed by blacks (28%) and Asians (21%).

Related Posts

You might also like these two further posts on official statistics, ethnicity and crime….

Posts which explain the differences in crime statistics by ethnicity: