Idea go

For brands to work as brands, it must be possible to rank them. Blackberry’s smart phones are out, Samsung’s smart phones are in. Burger King sells bigger burgers than McDonald’s. And so on. For academic disciplines, academic departments, and academics to work as brands, it must likewise be possible to rank them. Disciplines are thus ranked in terms of the measurable benefits, such as salaries, they produce for those who study them. Departments are ranked in terms of the measurable benefits they produce for universities (money from various sources, such as fees and research income) and students (student satisfaction ratios, employment ratios, staff-student ratios, etc.). Academics are ranked in terms of the measurable benefits they produce for academic departments (e.g. income generated through stellar publications in the REF, grants, consultancy, etc.). Such rankings create and sustain competition in a marketplace of ideas, people, and educational products that can be systematically compared and assessed for their excellence.

Competition has been a central feature of academic life for a very long time. Nonetheless, the current system of market-based competition built around brands and imagery is arguably quite new. Scholars like Daniel Boorstin and Jean Baudrillard wrote about social worlds dominated by images quite some time ago, but even they did not foresee what has recently happened to academic life.

Arguably, this new system is a result of sustained political will and effort and has been imposed onto universities from the outside. This point is important because the modus operandi of branded academia is based on an astonishing misunderstanding of scholarship. The recent and on-going reforms of higher education are enforcing an individualisation of academic labour. High-stakes audits such as the REF, for instance, treat scholarship as a matter of quantifiable ‘outputs’ (Do you have the four you need?) that can be neatly attributed to individual scholars and academic departments. From this perspective, the knowledge contained in such ‘outputs’ is a valuable commodity (literally – lots of money at stake in the REF) that is owned by its producer and should be shared only under very specific conditions (e.g. if it’s been published and protected by copyright). Academic labour therefore can be described in terms of the efforts of individuals who are fundamentally in competition with each other.

That academics would gamely play along with such a system is astonishing, as the collective nature of scholarship is nowadays so widely acknowledged in sociological and anthropological studies of processes of academic enquiry. The mad genius who scribbles revolutionary secrets on his office walls in the middle of the night might make for entertaining Hollywood movies, but it’s really very rare to meet such a savant on a real-life campus. (I do think I saw John Nash at the airport once, but I was probably wrong.) Academic labour is a collective process in many senses. For instance, one could think of it in terms of the cumulative development of academic knowledge that is systematically tested and refined. Or one could consider the construction of academic knowledge through historically and geographically research cultures, power structures, and institutional arrangements. And so forth. In one way or another, we all know about the collective nature of academic life, simply by virtue of our day-to-day work experience. That’s, for instance, why we make sure to include a page, or two, or three, of acknowledgements in our books and why failure to do so might be considered bad form indeed.

This new individualism in academic life has serious consequences. Notably, it promotes a winner-takes-all culture that greatly rewards the most highly achieving and leaves little room for the scholarly development of many junior academics. Universities and departments nowadays have internalised that they are brands that continuously need to demonstrate their excellence in teaching and research to get by. Making strong claims to labels such as “world-class”, “internationally leading”, or “internationally excellent” is eminently important in the academic marketplace – so important, indeed, that nothing else will do anymore. This trend has led, for instance, to inflationary entry requirements even for junior academic positions. If you don’t have those world-leading publications straight after your PhD, you might as well kiss that lectureship goodbye! In turn, this increases the advantages enjoyed by those who have been privileged or fortunate enough to receive special mentoring, opportunities, and support during their doctorates and who have studied at the most prestigious universities. Those who might need just a little bit more time to make good on their potential probably won’t get that extra time. Thus, the ‘culture of excellence’ is making an uneven playing field even more uneven. Moreover, it obscures this achievement by treating successful scholarship as the outcome of individual efforts, and not as a result of collective labour processes. Up there, among the stars of academic life, all this probably makes a lot of sense. Down here on the ground, one wonders…

READ RELATED ARTICLES

Accountability, Compliance and Bureaucratisation in Higher Education
So Much Noise: Are Academics being Over-Branded?
How Does Sociology Feel?
Job Search 2.0: Breaking the Silence

Sociologists are brands. To survive or even thrive in the academic marketplace, sociologists need to take care of their image. The more research grants, star-studded publications, and other esteem indicators they can show, the better they will do when competing with other sociologists for jobs, more research grants, and more publication opportunities. As a friend of mine frequently tells me, this is just the way things are, and resistance is futile!

A long time ago, in a world that now seems far, far away, Howard S. Becker raised the question whose side sociologists are on. Becker’s paper and the debate of which it formed part addressed one of sociology’s core concerns, namely its preoccupation with public issues that are politically controversial. Becker stated the problem thus:

“To have values or not to have values: the question is always with us. When sociologists undertake to study problems that have relevance to the world we live in, they find themselves caught in a crossfire. Some urge them not to take sides, to be neutral and do research that is technically correct and value free. Others tell them their work is shallow and useless if it does not express a deep commitment to a value position.” ¹

The issues of class, race, gender, sexuality and so forth that have motivated sociological enquiry since its inception are all entangled in broader struggles about politics and politically significant values. After Becker, many sociologists have convincingly argued that claims to the primacy “technically correct and value free” research are themselves deeply politically laden.

Today, the question whether to have values or not to have values (or which values to hold) does not seem to matter very much anymore. By default, the branded sociologist must not have values, or, to be more precise, the value of the brand must be protected at all costs, to the exclusion of other values. The value of a sociologist’s brand is coterminous with her or his image with employers, public and private organisations that might award prestigious and well-funded consultation projects, other sociologists, and so on. It can be measured in the total monetary value of research grants, the number of stars awarded to publications, and other indicators of professional achievement that directly determine a sociologist’s career prospects (as well as the extent to which sociologist will even have careers after the end of their PhDs). To take a stand on a politically controversial issue might mean to spoil one’s image, and a bad image might be fatal for one’s career. Opportunities might dry up, and employers might take exception at the unwanted attention created by a public controversy.

To be sure, there is still some space left for sociologists to be controversial, as, for example, recent debates about higher education policy in the UK have shown. However, this space seems to be narrowing considerably, and it might be accessible to fewer and fewer sociologists. As universities align themselves with the operational principles of the business world, they adopt its hierarchical structures and forms of communication. Top-down micro-management is increasingly the norm, and employees are more and more expected to be adhere to management’s views and opinions (or not to express an opinion at all). The Rod Thornton controversy at the University of Nottingham and the suspension of staff at Middlesex University for publicly voicing dissent at management decisions are high-profile examples of this trend.

Universities have for a long time been places in which ideas, opinions, and analyses of society’s state can be debated freely and in which scenarios for a different future can be envisioned. Today, these pursuits have taken a backseat to university’s role in training a skilled workforce, and many consider them unnecessary or undesirable altogether. Thus, sociologists who take sides may find themselves increasingly on the margins of academic life.

¹ Becker, Howard S. (1967) ‘Whose Side Are We On?’, Social Problems, vol. 14, no.3, p.239.

A Congressional Briefing on social surveys and statistics (American Academy of Political and Social Science)

Federally sponsored surveys such as the American Community Survey, the National Survey of Child Health and the Panel Study of Income Dynamics tell us who we are as a nation. They help policymakers and business owners make informed decisions every day. Unfortunately, response rates to such surveys have been declining, putting the validity of survey research into question. As Congress prepares to consider funding for vital Census Bureau programs in the next fiscal year, and in light of last year’s House vote to eliminate funding altogether for the American Community Survey, last month The American Academy of Political and Social Science (AAPSS) put together a Congressional Briefing on the impact of falling response rates to social surveys and what can be done about it.

This briefing was an opportunity to review the state of survey research and its role in the federal statistical system, assess the nonresponse challenge to major social surveys, discuss alternate approaches for providing more reliable data at less cost, and review ways Congress can help address this problem. AAPSS was pleased to host this important discussion around surveys and their recent report, The Nonresponse Challenge to Surveys and Statistics.

The briefing was sponsored by the American Academy of Political and Social Science, the Annie E. Casey Foundation, the National Academies’ Committee on National Statistics, SAGE and the Russell Sage Foundation.

Read more about the event Partners

Download the Agenda

Find further materials from the event here:

• Promising Approaches to Gathering Data For Policy Makers, Business and the Public
• Paul Emrath on Behalf of the National Association of Home Builders: Impact of Falling Response Rates to Social Surveys and What Can Be Done
• Where Do We Go from Here? Conclusions

We also hope to make a video of the briefing available soon.

Speakers

PAUL EMRATH is Vice President for Survey and Housing Policy Research at the National Association of Home Builders (NAHB) where he heads NAHB’s Survey and Housing Policy Departments. His Housing Policy Department conducts a number of regular, periodic surveys—such as the monthly survey that generates the widely cited NAHB/Wells Fargo Housing Market Index—as well as special surveys on topics like home buyer preferences. His Housing Policy Department conducts a broad range of policy-related research, such as estimating the economic impact of home building and analyzing proposed legislative and regulatory changes. He also participated in the development of the new Rental Housing Finance Survey and has advised the Census Bureau on tabulating housing data from the decennial Census and American Community Survey. Previously, Dr. Emrath worked for NAHB as Housing Policy Analyst (1992-1994), Director of Survey Analysis (1994-1995), Senior Economist (1995-1999), Regulatory Economist (1999-2001) and Assistant Vice President (2001-2009). Prior to joining NAHB, he taught economic theory and statistics at the University of Wisconsin-Oshkosh for four years. His Ph.D. is in Economics from the University of Wisconsin-Milwaukee.

DOUGLAS S. MASSEY is the Henry G. Bryant Professor of Sociology and Public Affairs at Princ­eton University, where he teaches in the Woodrow Wilson School of Public and International Affairs, the Department of Sociology, the Program in Latin American Studies, the Program in African Ameri­can Studies and the Urban Studies Program. He has served on the faculties of the University of Chicago and the University of Pennsylvania. His research fo­cuses on international migration, race and housing, discrimination, education, urban poverty and Latin America, especially Mexico. In addition to serving as President of the American Academy of Political and Social Science, he is Past-President of the American Sociological Association and the Population Association of America.

DAVID MCMILLEN served as a professional staff member for the Senate Governmental Affairs Committee and the House Oversight and Government Reform Committee. He worked on a broad range of information policy issues covering the collection, dissemination and preservation of government information. His legislative work included the Paperwork Reduction Act, the E-Government Act, the Electronic Freedom of Information Act and government organization issues including the consolidation of federal statistical agencies and the creation of the Homeland Security Agency. In the Senate, he authored the language that created the Bureau of Transportation Statistics. In the House, he authored several bills on the federal statistical system and the decennial census. Prior to working for Congress, Dr. McMillen worked at the Census Bureau where he helped develop a macro-economic/demographic model for population projection, and was part of the management team that initiated the Survey of Income and Program Participation-a longitudinal survey of households. Dr. McMillen received his Ph.D. in Applied Social Statistics from the University of Illinois-Urbana/Champaign.

CLARENCE PAGE, the 1989 Pulitzer Prize winner for Commentary, is a columnist syndicated nationally by Tribune Media Services and a member of the Chicago Tribune’s editorial board. Page is also a regular contributor of essays to The News Hour with Jim Lehrer and has been a regular on The McLaughlin Group, NBC’s The Chris Matthews Show, ABC’s Nightline and BET’s Lead Story news panel programs.

KENNETH PREWITT is the Carnegie Professor of Public Affairs and Vice-President for Global Centers, Columbia University. Previous positions he has held are: Director of the U.S. Census Bureau, President of the Social Science Research Council, Senior Vice President of the Rockefeller Foundation, Director of the National Opinion Research Center and Professor at the University of Chicago. His current professional Boards and Committees include the National Academies’ Standing Committee on Social Science Evidence for Use (chair) and The State of the USA (Vice-Chairman).

ROGER TOURANGEAU is a vice president at Westat. Before coming to Westat, he was Research Professor at the University of Michigan’s Survey Research Center and the Director of the Joint Program in Survey Methodology at the University of Maryland. He has been a survey researcher for more than 30 years.

Informed social policymaking relies on survey-based research. Understanding the effects of mass immigration, for example, or responding to the changing nature of the American workforce, or addressing the health and educational needs of contemporary American families requires good data that tell us about American society.

Most publicly reported statistics come from survey samples, not from tabulations of administrative data or actual population counts.  For example, almost all of what people commonly refer to as “census data” does not come from an actual enumeration of the U.S. population but from a sample survey that is administered along with the census—the 2010 U.S. Census contained just four questions about the household and seven questions about each household member—all other data now come from the American Community Survey (ACS), which samples about 2 million households. In addition to the census, other federal sample surveys include the Current Population Survey (which produces monthly labor force statistics), the National Survey of Family Growth (birth and childbearing statistics), and the National Health Interview Survey (health statistics). The federal government also supports other surveys indirectly through grants, including the New Immigrant Survey, the Panel Study of Income Dynamics, and the American National Election Study.

But survey researchers are increasingly unable to get people to respond to surveys.  This is a real worry because nonresponse can lead to biased research and because nonresponse poses a significant threat to the federal statistical system in its entirety.

This volume of The ANNALS abundantly highlights the critical importance of surveys to contemporary society, addresses the causes and consequences of rising survey nonresponse, and explores ways to address the challenges that nonresponse brings.

Key Observations and Findings

• High rates of nonresponse do not necessarily translate into bias.  Research in this volume shows how response rates by themselves are poor indicators of bias (in some cases, raising response rates can even increase bias).  But rising nonresponse increases the likelihood of bias, and survey nonresponse is increasing at an alarming rate.
• The survey nonresponse problem is likely going to continue to worsen, and survey costs are likely going to continue to rise. We show that nonresponse is related with the frequency of single-person households, the prevalence of households without children, and long commute times—all seemingly endemic to social life today. The increase in nonresponse is driven by powerful generational changes: younger cohorts are associated with low response and less comfortable with communication techniques favored by survey researchers, such as telephone surveys and face-to-face interviews.
• The simplest way to address nonresponse is to reward respondents financially for their participation. Evidence clearly indicates that financial incentives increase response rates, but this also increases costs.
• Reliance on nonsurvey data can assist in getting quality survey-based estimates.  Using administrative records and “paradata” to strengthen and extend survey research are critical ways to adjust survey estimates and reduce budget pressures on the survey organizations. It may also reduce the level of survey fatigue in the general public. Interestingly, the most important limitation on the potential use of administrative systems as data for nonresponse adjustment is legal: federal legislation prohibits the public release of the data collected by the federal government, so it can only be done in-house by the Census Bureau or some other authorized agency.
• Rising costs and likely federal budget cuts will probably mean serious efforts to overhaul one or more major federal surveys (instruments that have not undergone major redesigns in decades) or to consolidate federal data collection into fewer surveys (or both). The current model for many high-quality but costly surveys may soon become unsustainable. Research in this volume shows that response rates for the most difficult and expensive kinds of surveys—longitudinal studies that follow the same respondents and reinterview them over time—have not seen the same increases in nonresponse rates as have been observed for one-time surveys.
• The volume is replete with technical details of how to maximize the likelihood of response and compensate for nonresponse. One promising analytic technique highlighted in the volume is the multi-level integrated database approach, which employs multiple databases to collect as much information as possible about a target population to maximize the accuracy of survey estimates. Data systems integration is definitely a direction in which the field is moving.

Perhaps the greatest threat to the future of survey data is the lack of public recognition of the importance of statistical and scientific surveys in the world today.  It is probably time for an industry-wide effort to improve the image of survey research and, critically, to differentiate legitimate social scientific surveys from the onslaught of unwanted solicitations that most Americans are trying so hard to fend off.

Protecting the integrity of survey research also requires better efforts to educate our political leadership and society writ large. Must all become better educated to the critical importance of social surveys in contemporary society, not only because they provide basic information to inform citizen-voters in a democratic society but also because they generate key knowledge for American businesses that enable us to compete effectively in a global, knowledge-based economy.  We offer this volume as a first step in a broader and more concerted effort to educate policy-makers and the public about the key importance of social surveys to the healthy functioning of postindustrial society.

Issue Conclusion:

Where Do We Go from Here? Nonresponse and Social Measurement

Written Statement of Paul Emrath:

On Behalf of the National Association of Home Builders, Impact of Falling Response Rates to Social Surveys and What Can Be Done

Federal surveys have been getting more expensive to administer, in part because the number of people who actually respond to surveys has been progressively declining.  As a result, researchers are developing and implementing new methods for collecting the information that our policy makers, business leaders and the public officials depend on. Many of these new approaches are highlighted in volume 645 (January 2013) of the Annals of the American Academy of Political and Social Science.  These approaches use new technologies and combine strategies to collect needed data. Below is an explanation of some of the technical terms and new approaches presented in the Annals.

Multi-modal Surveys

Combining several different methods of approaching people for surveys is a promising approach to maximize response rates. For example, the American Community Survey now is conducted through a multi-modal survey. First, a statistically valid random sample of people to be surveyed is selected from an address database. These people are mailed a letter asking them to fill out the survey on a secure website. Response rates for the website are fairly high, especially considering that the cost of this first step is very low. If people don’t respond, they are mailed a survey they can submit by mail. Then, a sub-sample of those who don’t respond are called, and some of those who don’t respond by phone are visited in-person by survey-takers. While the last steps are more costly, since they are only used for a subset of the survey participants, the multi-modal approach increases the response rate while keeping costs down. The response rate for this survey has been consistently near 98 percent of all household units.

This approach can be further refined: one possibility for the decennial Census, for example, is a promotional campaign asking people to pre-register electronically so they can receive the questionnaire by email.

Other Improvements in Survey Design Can Improve Response Rates

• Response rates improve significantly when the survey targets are contacted before the survey, with a letter or email that explains why the survey is relevant to the person being asked to take the survey.
• Multiple calls or other approaches to the person also increase response rates.
• Reducing the burden of the survey, for example by limiting the number of questions, filling in information in advance where it can be obtained from administrative sources, using a “matrix” approach so that not every person surveyed has to answer every question and using proxy reports by others who are knowledgeable when the person doesn’t respond, can all help improve the response rate and reliability of survey data.
• Financial incentives can improve response rates.

Surveys Can be Adjusted for Non-Response

Survey reliability can be improved by weighting the answers of the responses from subgroups to ensure that the sample reflects as much as possible the population being surveyed. Similar statistical measures can be used to adjust where people choose not to respond to individual questions.

Data Can be Obtained by Linking to Administrative Records

Federal administrative agencies such as the Internal Revenue Service, the Social Security Administration, the Center for Disease Control (which houses vital statistics) and the Department of Housing and Urban Development, as well as state and local agencies such as school boards and Departments of Public Health all maintain important data. If cross-linked appropriately, with great care to protect individual privacy, administrative data can provide important information that bolsters the accuracy and reliability of surveys. However, it does have limitations; for example, health records will not capture those uninsured people who do not seek care and education records won’t include the homeschooled child.

Digital Data

Digital data refers to information that is created electronically by government, business and individuals. These data might be gathered when someone makes a phone call, purchases an item, conducts a web search, or drives on an HOV lane, to give just a few examples. Commercial firms collect digital data on everything from phone records to purchasing patterns to travel records. The government also collects digital data, especially for its security and anti-terrorism programs. Some of these data, again with proper privacy protections, could be used to provide the kind of information that business, in particular, relies on.

‘I keep saying the sexy job in the next ten years will be statisticians… The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades… Because now we really do have essentially free and ubiquitous data.’

It includes contributions from the CEO of Waterstones, the UK’s National Statistician, Chair of the All Party Parliamentary Group on Statistics, Director of YouGov and Editor of the Guardian’s DataBlog.

The booklet illustrates the concrete steps which these professionals have taken in order to become confident using highly prized quantitative skills in their chosen careers. You can read about the value of quantitative skills to business, journalism, academia, the public sector, politics and charities in this collection of blog posts below. For the uninitiated, let’s start by clarifying what quantitative skills are and why they matter.

What are Quantitative Skills?
Generating and analysing data requires you to be numerate and statistically savvy. Quantitative skills (QS) involve the ability to handle data and use numerical evidence systematically. QS can include anything from the ability to design surveys or experiments to assess and use quantitative evidence from surveys, digital media, archives and open data.

Why do they matter?
QS underpin effective, evidence-based planning and procedure in the public, private and other sectors, as well as ‘blue skies’ thinking. However there is a worrying QS deficit in the UK, with 55% of employers reporting widespread QS weaknesses amongst their employees.

Where can QS take me?
Broad numerical skills are highly prized in practically every sector, as these blog posts will attest. QS are not just transferable between different work places, they translate well overseas too. Global management consulting firm McKinsey has estimated that by 2018 there will be a shortage of 15 – 20,000 data scientists and up to 1.5m data savvy managers and professionals in the US alone.

What is the British Academy doing for QS?

In 2011, the British Academy launched a four-year programme to deepen awareness and demonstrate the importance of QS in the humanities and social sciences. The Academy believes that statistical literacy and numeracy are vital to a functioning modern democracy, add value to academic endeavour and empower individuals in all walks of life.

Using Quantitative Skills in Business

More to come in the following weeks!

READ RELATED ARTICLES

Five minutes with Andrew Herbert: former Chairman of Microsoft Research
Bridging Theory with Practice:Qualitative Research to Aid Fire & Rescue
Gathering Data for Policy Makers, Business and the Public
Property Crime, Violence and Recession

James Daunt
CEO, Waterstones

“QS is central to any retailer’s armoury.

Tell us about your studies and early work experiences.
I studied History as an undergraduate. My first job after university was as an investment banker where they had an exceptional emphasis on training and encouraged me to take an MBA.

When did you first learn some QS?
The QS skills I learnt on the MBA gave me an ability to assess performance and risk and to ground this empirically. Without these skills, such assessment is liable to be subjective and therefore more likely to be wrong.

How do you use QS in your work now?
I now run a chain of bookshops and my QS are key. Flair, talent, instinct and the like matter, but without a firm quantitative basis trouble will soon come calling.

What advice would you give current students about QS?
If your occupation has any quantitative aspect to it – and very few do not – to get a firm grounding will be of immense value. It will also help keep household expenses in line which makes most people sleep better and saves the odd domestic – in itself not to be sniffed at!

Joe Twyman
Director of Political and Social Research, YouGov

“As a political pollster I use my quant skills every day and literally could not do my job without them.”

Tell us about your studies and early experiences of QS.
I did some holiday work while at secondary school that first introduced me to the joys of data entry. I studied Politics at University.

How did you start learning about and using QS?
After graduating, I started on the graduate training scheme at Research Services Limited (now Ipsos-MORI) where I learnt more detailed methods and techniques whilst doing market research into tomatoes and wonder bras. I later became the founding director of YouGov. Highlights of the job have included co-ordinating election studies in many countries and providing expert analysis of opinion polls for the media.

What do you think QS give you that other skills don’t?
QS give you the power to interrogate primary data and draw your own conclusions, rather than relying on the interpretation of others. They also allow you to critically interpret other analysis in order to separate facts from opinions.

What advice would you give current humanities and social science students?
When you are applying for jobs in this economic climate, it is likely you will be up against a large number of applicants. Being able to demonstrate quant skills is one of the most positive and effective ways of differentiating yourself from the other candidates – both in my industry and many others.

Read more interviews from researchers  on how they use their quantitative skills.

These case studies represent a selection from the British Academy’s Stand Out and Be Counted booklet.

READ RELATED ARTICLES

Five minutes with Andrew Herbert: former Chairman of Microsoft Research
Bridging Theory with Practice:Qualitative Research to Aid Fire & Rescue
Gathering Data for Policy Makers, Business and the Public
Property Crime, Violence and Recession

Listen now

READ RELATED ARTICLES
Making Sense of Crime Trends
The Myths of Offender Profiling.
Property Crime, Violence and Recession
Undercover Pressures

Previously I posted an analysis of trends in police data over the past decade, as part of an argument that talk of ‘overall crime’ was best left to crime involving overalls. I concluded by arguing that we might see a rise in homicide in the coming years as a result of the current economic depression.
This piece takes the analysis in a slightly different direction, taking a look at data in the Crime Survey for England and Wales (CSEW) and the British Crime Survey (BCS) over a longer time-frame: 1981 to 2012. The underlying frame of reference of this piece is the early 1980s recession. As Danny Dorling pointed out in his important study of homicide trends, published here, the 1981 recession had a significant and lasting impact. Those young men in particular who entered the jobs market in the early 1980s, when jobs were few and far between, experienced an elevated risk of homicide victimisation that was still measurable in the late 1990s, nearly 20 years on.
To put the point differently, one way of looking at the BCS/CSEW dataset for the 1981 to 2011/12 period is as a chronicle of the effects and after effects of the early 1980s recession. The rise in ‘overall crime’ from 1981 to 1995 can then be seen as a long-wave social reaction to the economic shock of the 1980s. The decline since the mid 1990s would then be seen as the long-wave social recovery from this shock.
Property victimisation
So, what do the BCS/CSEW data show? This first graph shows indexed trends for some property offences: burglary, theft from the person, vehicle theft, robbery and bicycle theft, with 1981 as 100.The orange line with triangle data points shows the all BCS/CSEW trend. As can be seen, ‘overall’ BCS/CSEW victimisation episodes rose from 1981 through to 1995, before decreasing to their starting point. Burglary (in red) and vehicle crime (in green) followed a similar trajectory, though rising more quickly and declining more sharply. Theft from the person rose to 1995 before stabilising at this higher level. Robbery followed a similar, if more exaggerated, pattern. Bicycle theft had its own pattern. It rose dramatically to 1995. It then declined before rising again from the early 2000s.
To summarise what this graph shows,  it reflects the interaction of at least two factors. First there are the underlying political economic factors related to economic recession and growth. The start of the trend line coincided with the early 1980s recession, which had a major effect up and down the country right into the 1990s. During that period property crime rose as more resorted to various forms of theft to make ends meet. The improving economic position from the mid 1990s on offers some explanation for the generally declining levels of these property crimes.
The second factor is more contingent, related to improved security on the one hand and increased availability on the other. Some of the dramatic declines in burglary and vehicle theft are likely related to improvements in home and vehicle security. This might help to explain why we have not witnessed an increase in these offences in the current depression.
People and bicycles are less easy to secure than cars and homes of course. Moreover, people carrying expensive electronic gadgets make attractive targets. The growth in bicycle use means there are more to steal. These all help to explain why robbery, theft from the person and bicycle theft either stabilised or rose while burglary and vehicle crime were falling. Why go through the risk and hassle of breaking into a house when you can steal a smartphone or a laptop or pinch a bike with a cheap pair of bolt cutters?
As an aside, the ongoing and widespread anxiety about street crime and ‘mugging’ would, on the basis of this graph, be quite rational. Relatively speaking, theft and robbery is more common than 20 years ago. The fear of street crime trumps the decline in ‘overall crime’ precisely because the ‘overall’ trend masks what is happening. We don’t need elaborate theories about public fear being out of kilter with ‘real’ crime trends.
Violence victimisation
What of the other main victimisation category covered by the BCS/CSEW dataset: violence? The graph below gives indexed trends for three categories of interpersonal violence: domestic, acquaintance and stranger violence.This very striking graph tells us something very important I think. First there is the all BCS/CSEW trend, this time in turquoise. As before, it rises from 1981 to 1995 before declining back to its starting point. The green ‘stranger violence’ stays stable throughout, failing to rise at all. If the early 1980s recession had an impact on interpersonal violence, it wasn’t reflected in stranger violence.
Acquaintances and intimate partners, on the other hand, were a different matter. Domestic violence grew particularly sharply during this period, staying at a relatively higher level for longer. The growth in acquaintance violence was also notable, though it declined at an earlier point.
It is widely recognised that most people, and in particular women and children, are far more at risk of harm from those they know and love than from strangers. This graph indicates that recessions might significantly intensify this known risk.
Recessions and depressions are harmful in all sorts of ways. People lose their jobs; families struggle to get by; individual self-esteem takes a battering; physical and mental health problems increase. Some people kill themselves as a result. Suicide is again on the rise in the UK. Some take out their anger on others: their partners, children and families notably.
Against the backdrop of the current depression the BCS/CSEW dataset offers a sense of what happened in the last major recession and what may be round the corner. It shows how misguided smugness about the current fall in ‘overall crime’ might be.
There is no inevitability in the rise in homicide, domestic and acquaintance violence in the coming year. Sadly, though, it would be more surprising if they did not increase than if they did.
Methodological note
The methodology of the BCS and CSEW has changed over the years. Scotland was covered in the BCS until the late 1980s, but was dropped thereafter. In the early 2000s the BCS moved from an annual survey of around 5,000 people to a rolling survey of around 50,000. There are now plans to scale it back to around 30,000 people. In recent years the experiences of under 16s has started to be included, though they weren’t before. The name was also changed from BCS to CSEW.
The BCS/CSEW surveys also only cover a small number of victimisation experiences: mainly burglary, vehicle and other kinds of thefts and property crimes, and certain violence offences. Homicide is not included, nor are sexual, though data on the latter are published in a separate report.
In summary, there are all sorts of data irregularities and omissions with the BCS/CSEW dataset. It is not the ‘gold standard’ measure of crime that some claim. That said, it does have at least one distinct advantage over police recorded crime data. It is based on interviews with members of the general public about their victimisation experience. In that sense it gives a better guide to the certain crime victimisations than police data, which are reliant on the public reporting offences and/or the police recording them.

Read more articles by Richard Garside HERE.

READ RELATED ARTICLES

Making Sense of Crime Trends
Outlawed Research
The Formula
Danny Dorling on Inequality