Just a look back at what some of the official statistics and opinion polls told us about life in Britain in 2017…selected so they’re relevant to families and households, education and crime and deviance…
The proportion of women aged 18 who started university in 2017 was nearly 1/3rd greater than men – 37.1% compared to 27.3%.
Family size is declining: about 45% of children today have no siblings.
The ageing population: the proportion of people aged 65 and over in work has almost doubled since 1992 – 5.5% to 10.4% – there are now nearly 1.2 million over 65s in work.
The downsides of immigration: Of the 8008 people registered homeless in London (2015-16) only 3271 were British, nearly 3000 were from central or eastern Europe and fully 1,546 were Romanian
Crime and racial injustice: Young black youths are nine times as likely to be in England and Wales.
Class inequality: there are 59 theaters in London’s private schools, but only 42 in the West End.
I had intended to make this an all bells and whistles posts, but time, much like the year, has just about run out!
Firstly, simply because so much data is collected on individuals – not only via state surveillance but also via Amazon, Google, Facebook and Twitter,it means that protecting privacy is more difficult -especially when so much of that data is sold on to be analysed for other purposes.
Secondly, there is the possibility of penalties based on propensities – the possibility of punishing people even before they have done anything wrong..
Finally, we have the possibility of a dictatorship of data – whereby information becomes an instrument of the powerful and a tool of repression.
The value of big data lies in its reuse, quite possibly in ways that are have not been imagined at the time of collecting it. In terms of personal information, if we are to re-purpose people’s personal data than they cannot give informed consent in any meaningful sense of the phrase – because in order to so you need to know what data a company is collecting and what use they are going to put it to.
The only way big data can work is for companies to ask customers to agree to have their data collected ‘for any purpose’, which undermines the concept of informed consent.
There are still possible ways to protect privacy – for example opting out and anonymisation.
Opting out is simply where some individuals choose not to have their data collected – however, opting out can itself identify certain things about the users – for example, when certain people opted out of Google’s street view and their houses were blurred – they were still noticeable as people who had ‘opted out’ (and thus maybe had more valuable stuff to steal!)/
Anonymisation is where all personal identifiers are stripped from data – such as national insurance number, date of birth and so on, but here people can still be identified – when AOL released its data set of 20 million search queries from over 650K users in 2006, researchers were able to pick individual people out – simply by looking at the content of searches they could deduce that someone was single, female, lived in a certain areas, purchased certain things – then it’s just a matter of cross referencing to find the particular individual.
In 2006 Netflix released over 100 million rental records of half a million users – again anonymised, and again researchers managed to identify one specific Lesbian living in a conversative area by comparing the dates of movies rented with her entries onto the IMD.
Big data, it appears, aids de-anonymisation because we collect more data and we combine more data.
Of course it’s not just private companies collecting data… it’s the government too, The U.S. collects an enormous amount of data – amounts that are unthinkably large – and today it is possible to tell a lot about people by looking at how they are connected to others.
Probability and Punishment
This section starts with a summary of the introductory scene of minority report…
We already see the seeds of this type of pre-crime control through big data:
Parole boards in more than half the states of the US use big data predictions to inform their parole decisions.
A growing number of precincts use ‘Predictive Policing’ – using big data analysis to select which streets to parole and which individuals to harass..
A research project called FAST – Future Attribute Screening Technology – tries to identify potential terrorists by monitoring people’s vital signs.
Cukier now outlines the argument for big-data profiling – mainly pointing out that we’ve taken steps to prevent future risks for years (e.g. seat-belts) and we’ve profiled for years with small data (insurance!) – the argument for big data profiling is that it allows us to be more granular than previously – we can make our profiling more individualised – thus there’s no reason to stop every Arab man under 30 with a one way ticket from boarding a plane, but if that man has done a-e also, then there is a reason.
However, there is a fundamental problem of punishing people based on big data – that is, it undermines the very foundations of justice – that of individual choice and responsibility – by disallowing people choice – big data predictions about parole re offending are accurate 75% of the time – which means that if we use the profiling 100% of the time we are wrongly punishing 1 in 4 people.
Dictatorship of Data
The problem with relying on data to inform policy decisions is that the underlying quality of data can be poor – it can be biased, mis-analysed or used misleadingly. It can also fail to capture what is actually supposed to measure!
Education is a good example of a sector which is governed by endless testing – which only measure a slither of intelligence – the ability to demonstrate knowledge (predetermined by a curriculum) and show analytical and evaluative skills as an individual, in written form, all under timed conditions.
Google, believe it or not, is an example of a company that in the past has been paralysed by data – in 2009 its top designer, Douglas Bowman, resigned because he had to prove whether a border should be 3,4, or 5 pixels wide, using data to back up his view. He argued that such a dictatorship by data stifled any sense of creativity.
The problem with the above, in Steve Jobs’ words: it isn’t the consumers’ job to know what they want’.
In his book Seeing Like a State, the anthropologist James Scott documents the way in which governments make people’s lives a misery by fetishizing quantitative data:they use maps to reorganise communities rather than asking people on the ground for example.
The problem we face in the future is how to harness the utility of big data without becoming overly relying on its predictions.
Thought I’d start bashing out the occasional Friday post on good sociology movies… starting with Minority Report – which is a great intro to the ‘surveillance and crime control‘ aspect of the AQA’s 7192 sociology syllabus, crime and deviance topic,
It’s the opening scene in Minority Report which is really the relevant bit here: the arrest of Howard Marks:
In the above scene, John Anderton (played by Tom Cruise) is the chief of police of a special Washington D.C. ‘pre-crime’ unit – in which predictions are so accurate that people are arrested before they have committed a crime.
In the movie, predictions are actually made on the basis of some ‘psychic’ beings who are biogenically networked into the police’s systems, but this aside, the above movie acts as a great starting point for the topic of surveillance and pre-crime.
You can simply show the clip, and then get students to think about where in society authorities restrain or restrict people based on ‘big data’ which is a form of surveillance.
Within sociology, one might even say that there’s a more ‘fundamental’ layer of concepts that lie behind the above – such as ‘society’, ‘culture’ and ‘socialization‘, even ‘sociology’ itself is a concept, as are ‘research’ and ‘knowledge’.
Concepts also include some really ‘obvious’ aspects of social life such as ‘family’, ‘childhood’, ‘religious belief’, ‘educational achievement’ and ‘crime’. Basically, anything that can be said to be ‘socially constructed’ is a concept.
Each concept basically represents a label that researchers give to elements of the social world that strikes them as significant. Bulmer (1984) suggests that concepts are ‘categories for the organisation of ideas and observations’.
Concepts and their measurement in quantitative research
If a concept is to be employed in quantitative research, a measure will have to be developed for it so it can be quantified.
Once they have been converted into measures, concepts can then take the form of independent or dependent variables. In other words, concepts may provide an explanation of a certain aspect of the social world, or they may stand for things we want to explain. A concept such as educational achievement may be used in either capacity – we may explore it as a dependent variable (why some achieve fewer GCSE results than others?) Or: as an independent variable (how do GCSE results affect future earnings?).
Measures also make it easier to compare educational achievement over time and across countries.
As we start to investigate such issues we are likely to formulate theories to help us understand why, for example, educational achievement varies between countries or over time.
This will in turn generate new concepts, as we try to refine our understanding of variations in poverty rates.
Why Measure Concepts?
It allows us to find small differences between individuals – it is usually obvious to spot large differences, for example between the richest 0.1% and the poorest 10%, but smaller once can often only be seen by measuring more precisely – so if we want to see the differences within the poorest 10%, we need precise measurements of income (for example).
Measurement gives us a consistent device, or yardstick for making such distinctions – a measurement device allows us to achieve consistency over time, and thus make historical comparisons, and with other researchers, who can replicate our research using the same measures. This relates to reliability.
Measurement allows for more precise estimates to be made about the correlation between independent and dependent variables.
Indicators in Quantitative Social Research
Because most concepts are not directly observable in quantitative form (i.e. they do not already appear in society in numerical form), sociologists need to devise ‘indicators’ to measure most sociological concepts. An indicator is something that stands for a concept and enables (in quantitative research at least) a sociologist to measure that concept.
We might use ‘Average GCSE score’ as an indicator to measure ‘educational achievement’.
We might use the number of social connections an individual has to society to measure ‘social integration’, much like Hirschi did in his ‘bonds of attachment theory‘.
We might use the number of barriers women face compared to men in politics and education to measure ‘Patriarchy’ in society.
NB – there is often disagreement within sociology as to the correct indicators to use to measure concepts – before doing research you should be clear about which indicators you are using to measure your concepts, why you are choosing these particular indicators , and be prepared for others to criticize your choice of indicators.
Direct and Indirect indicators
Direct indicators are ones which are closely related to the concept being measured. In the example above, it’s probably fair to say that average GCSE score is more directly related to ‘educational achievement’ than ‘bonds of attachment’ are to ‘social integration’, mainly because the later is more abstract.
How sociologists devise indicators:
There are a number of ways indicators can be devised:
through a questionnaire
through recording behaviour
through official statistics
through content analysis of documents.
Using multiple-indicator measures
It is often useful to use multiple indicators to measure concepts. The advantages of doing so are three fold:
there are often many dimensions to a concept – for example to accurately tap ‘religious belief’ questionnaires often include questions on attitudes and beliefs about ‘God’, ‘the afterlife’, ‘the spirit’, ‘as well as practices – such as church attendance. Generally speaking, the more complex the concept, the more indicators are required to measure it accurately.
Some people may not understand some of the questions in a questionnaire, so using multiple questions makes misunderstanding less likely.
It enables us to make more nuanced distinctions between respondents.
Measuring the effectiveness of measures in quantitative social research
It is crucial that indicators provide both a valid and reliable measurement of the concepts under investigation.
In mid December 2017, The U.S. Senate voted through a tax-bill which will deliver a dramatic reduction in America’s corporate tax rate – from 35% to 20% – along with a reduction in inheritance tax which will allow the America’s wealthiest individuals to pass more tax-free money to their children (or other heirs). This Guardian article provides further details.
For A-level sociology students studying global development, this represents yet another example of a neoliberal policy – cutting taxes is a key aspect of the economic doctrine of neoliberalism.
The supposed rational behind the bill is to stimulate economic growth, but it is also likely to widen inequality and the bill is also predicted to add $1 trillion to the national debt
It’s also interesting to note that Donald Trump ran for president as an outsider who would stand up for the working people, but now it seems that it’s the wealthy, share-holding corporate class that’s going to benefit most from this policy.
Has there been an increase in hate crime since Brexit?
The Number of hate crimes reported to the police in England and Wales between 2012-13 and 2016-17 jumped by 90%, but is this because of an actual, underlying increase in hate crime incidents (or the seriousness of incidents that would warrant reporting), or is it just because people are now more likely to report ‘hate crimes’, maybe having interpreted something as a hate crime when, in fact, it wasn’t.
There is some evidence that suggests ‘misinterpreting’ or ‘over-reporting might be the case – court convictions in 2016 were lower than in 2010.
Possible increases for the increase in reporting are as follows:
The authorities actively encourage it
incidents can be registered anonymously
The victim or witness to a crime only has to interpret a crime as being racially motivated (for example) for it to be classed as a hate crime.
No evidence is needed to back up the reporting of the hate crime to get it recorded.
So it might just be, that there has not been an increase in hate crime at all, this could just be a complete social construction.
In fact, this ‘increase’ might be harmful – in that it suggests that we are more divided than we actually are!
There are three types of company in the big-data value chain: the companies who collect the data, data-analytics companies, and data-ideas companies. This new ‘organisational landscape’ will change the power-relations between businesses enormously, at least according to Viktor Mayer-Schonberger and Kenneth Cukier (2017) in ‘Big Data’: The Essential Guide to Life and Learning in the Age of Insight;.
‘Pure’ data companies are those which have the data, or at least access to it, but not necessarily have the right skills to extract the value from the data. A good example of such a company is Twitter, which has masses of data but licences it out to independent firms to use.
Data analytics companies are those with the statistical, programming, and communication skills necessary to mining insights from data – Teradata is a good exmaple of such a company.
Finally there are those companies with the ‘big-data mindset’ whose founders and employees have unique ideas about how to unlock and combine data to find new forms of value – for example, Pete Warden, the co-founder of Jetpac, which makes travel recommendations based on the photos users upload to the site.
Data analytics has recently been touted as being in the ‘prime position’ in the big-data value chain: there has been a lot of recent talk of the shortage of ‘data scientists’ in the age of ever increasing amount of data…. The McKinsey Global Institute has talked about this for example, and Google’s chief economist Hal Varian famously called statistician the ‘sexist job around’.
We have been given the impression that we are wallowing in data, but lack sufficient people with the skills to mine this data.
Cukier, however, thinks such claims are exaggerated because it is likely that this skills gap will close. Interestingly, in a recent talk on big data science, this view also seemed to be the consensus.
He predicts that what is more likely to happen is that firms controlling access to the data will start to charge more for it, and big data innovators will be be where the real money is…
Hyrbid Data Companies
Companies such as Google and Amazon stretch across all three links in the data value chain. Google collects data like search-query typos, uses it to create a spell-checker and employs people in-house to do the analytics. Such vertical integration is no doubt precisely why Google is today one of the world’s largest companies.
The New Data Intermediaries
Cukier also predicts that there are certain business sectors which will benefit from giving their data to third parties, because keeping it in-house will not be as beneficial to them as sharing their data and combining it with others – third parties are needed to facilitate trust – for example, travel firms will benefit from such an arrangement, not to mention the banking and finance sectors – where more data is better.
The Demise of the Expert
Cukier also predicts that big data analytics will see specialists in different fields being replaced with those with data-science skills able to manage whatever field based on data. He argues that ‘mathematics, statistics, perhaps with a sprinkling of programming and network science, will be as foundational to the modern workplace as numeracy was a century ago and literacy before that’.
Big Winners, Medium Sized Losers..
Large data companies such as Google and Amazon will continue to soar, but big data presents a challenge to the victors of small-world data such as Walmart, Nestle, Boeing…. How these will adapt remains to be seen.
There are, of course, opportunities for ‘smart and nimble start-ups’, but also individuals might start to sell their own data, possibly through new third party firms.
Reverend Billy and The church of stop shopping are critical of our addiction to shopping – especially at Christmas. They suggest we are facing a ‘Shopocalypse’ – arguing that over consumption fuels the debt crisis, global warming and destroys local economies and communities if products are purchased from TNCs.
Instead, they suggest that we should use Christmas as a time to develop positive new low-consumption habits – learning to be happy with less! The video below – ‘What Would Jesus Buy’ is an excellent documentary outlining their ‘activist performance art’ and their general critique of consumption at Christmas.
The Church uses its performance art to protest more widely than just at Christmas – they target unethical companies, such as banks who fund logging in the Rain Forest, and target their lobbies to protest their involvement, and get arrested a lot in the process!
There’s all sorts of links with the A level Sociology syllabus:
Linking to sociological theories… the Church is coming from a broadly leftist, Marxist perspective in its criticisms of our consumption habits.
Linking to Crime and Deviance – obviously what they are doing is deviant! More interestingly, it’s interested to note how their dealt with by the state – during many of their protests, they get arrested, spend a night in jail, then they’re back out again… while the far more harmful practices of the Corporations they protest against just carry on.
These activists are protesting what they see as ‘Green Crimes’ – companies which harm the planet, but of course these acts are not defined as such by the state.
Linking to Methods – you could argue that what they are doing is a form of ‘ethnomethodology?’ (look it up, it’s not a core part of the A-level syllabus!)
Linking to the Family – Personal Life Perspective maybe?
Linking to Education – well it’s educational!
And linking to religion – I dunno, I’m a bit confused about this! Possibly nothing at all?
And don’t forget to slow down your consumption, Ahmen! Although it’s possibly too late for that…?
Lord Bassam, Labour’s soon to be chief whip in the House of Lords has just agreed to repay £41 000 in expenses. The peer claimed a remarkable £260 000 over seven years to cover the cost of his accommodation in London, despite the fact that during that time, he commuted daily to his accommodation in London, a trip for which he claimed an additional £41 000 in travel expenses.
It might appear that logic would dictate that one of these must be a Fraudulent claim, a fraud committed against the UK taxpayer who pays these expenses, given that you can’t do both at the same time!
However, no sanctions or prosecutions were brought against Lord Bassam, because peers of Lord Bassam do not define his actions as a Fraud, just a ‘breach of the rules’.
Imagine if this had been someone in work claiming benefits – it’s pretty much the same thing! It’s a fraud against the UK taxpayer – the state would have taken action action against such a person.
This is a great illustration not only of how crime is socially constructed, but also yet more supporting evidence for the Marxist view of crime – in this case that ‘fraud’ is only ‘fraud’ when it’s being ‘committed’ by the poor.
Lord Bassam is far from the only only criminal peer: a recent study identified 16 ‘silent’ peers who had collectively claimed about £400 000 in expenses and daily allowances, over a year in which they had made no contribution whatsoever to debate.
A 2016 poll by Nationwide found that the average Brit spends £645 on Christmas. On average, people in the UK spend…
£117 on Christmas presents for their partner,
£145 on presents for their children,
£20 on their pet (lucky pets!).
This broadly corresponds with the Bank of England’s findings on Christmas spending which found that our spending in December increasing by around £500 per month. OK they’re not exactly the same, but in the same sort of ‘region’, and not crazily different (to use the technical term).
Looked at by household – A Survey by Go Compare (1) found that the average British household expects to spend £753 on Christmas festivities this year. Collectively that’s a staggering £21 billion splashed out on presents, food and drink, parties and decorations.
Regional Variations in Spending
Unsurprisingly, households on lower incomes spend a higher proportion of their monthly income on Christmas than – According to this BBC article, people in the North East spend around 26%, while in London the figure falls to around 16% of monthly household income.
The article also cites anecdotal evidence that people in poorer areas spend more on presents than people in richer areas.
Debt and Christmas
Again, according to the above BBC article, The Money Advice Trust, a charity which runs the National Debtline, polled 2,000 people and found 37% are putting Christmas presents on credit. NB As far as I can tell these are 2015 figures (it’s not that clear from the article!)
34 percent borrowed money to cover the cost of Christmas presents – figure equating to an estimated 16.9 million people.
More than one in five (21 percent) borrowed to put food on the Christmas table – equating to an estimated 10.4 million people.
All in all, it seems like there’s a lot of evidence that for the poorest third of households, it’s not so much Christmas, but more like Debtmass, which offers broad support for the validity of a Marxist theory of Christmas.