Bringing Together Data to provide insights into Earnings & Employment

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The Wage and Employment Dynamics (WED) project aims to bring together data to provide insights into the dynamics of earnings and employment. The aim is to do this by integrating data across individuals across years, jobs, income sources and employers. This is a large project, with significant potential to improve our understanding of wage and employment issues from labour market entry, through job mobility and career progression to retirement decisions.

A team of researchers from UWE, University of London (CUL), UCL, and the National Institute of Economic and Social Research (NIESR) will create a wage and employment spine to do just this. We will train users on the spine and generate research findings of direct interest to policy makers. 

This will entail analysis of:

  • Employment: focusing on the drivers of hourly wages, part- and full-time employment, self-employment, underemployment, and retirement decisions
  • Households: focusing on the structure of households and household resources and the way they affect participation in the labour market, including child care, retirement decisions, and the impact of Universal Credit (UC)
  • Employer perspectives: focusing on how workers achieve wage growth both within and across firms, and how employers react to changing labour market conditions, such as shifts in skills, demand, technology and minimum wages.

At the heart of the project is the Annual Survey of Hours and Earnings (ASHE) and New Earnings Survey (NES). These survey datasets derive from a 1% sample of all employees in employment and will be developed to provide a truly longitudinal research resource. An ASHE/NES dataset which has longitudinal integrity across workers and jobs, with consistent referencing across data sets and time, will constitute a valuable research asset in its own right.

In addition, we aim to undertake six major linkage projects, in which we will create robust, documented linkages between the employee records contained in ASHE and data on:

  • enterprises and establishments – contained in the Interdepartmental Business Register, (IDBR);
  • personal and household characteristics – contained in the 2011 Census;
  • educational attainment – contained in HESA (Higher Education Student data);
  • benefit history – contained in DWP benefit records;
  • pay records – contained in HMRC PAYE data;
  • self-employment income – contained in HMRC Self Assessment (SA) records.

Through these various linking projects, we aim to create a core data set which allows integrated analysis of all forms of income across working lives, with the capacity to address a wide range of future analytical requirements. The end goal of the project is to turn this fully-linked dataset into a sustainable ‘wage and employment spine’ (WES), so that researchers no longer need to create new linkages each time. The linked data will be used for research purposes within the project itself, but the spine will form an ongoing resource for researchers. The intention is for the WES to form the basis for linked-data projects beyond 2022, both for academics and government agencies.

To find out more visit the WED website or sign up to their newsletter.

Online Event: Rules vs. Principles-based Regulation: What can we learn from different professions?

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Bristol Centre for Economics and Finance is hosting an online event on 28th May 2020: Rules vs. Principles-based Regulation: What can we learn from different professions?

There is an active debate in many disciplines about the most appropriate approach to regulation and enforcement. The workshop intends to bring together participants from different disciplines to provide an overview of the predominant approaches, along with the respective debates, experiences, and challenges. Common experiences and core issues can be identified.

The workshop aims to spark debate about regulation and whether we, across disciplines, could respond differently to the challenges we face and find novel ways to more efficient regulation.

Obtaining insight into other disciplines’ experiences shall enable us to rethink the predominant approaches. By learning from each other we can ask: Can we do better, both in our own disciplines and the common regulatory landscape? Might there be a better way?

The event is of interest to both public and private sector participants: Policy-makers, government enforcement agencies, academics, and industry professionals in the area of, and affected by, regulation, in various disciplines.

Sign up for this free event here

Workshop programme

13:00 – 13:05 Welcome Professor Felix Ritchie
  Cluster 1 Presentations: Data regulation in the public and private sector
13:05 – 13:15 Data in the public/private sector Design of incentive systems/evidence base Professor Felix Ritchie/Elizabeth Green
13:15 – 13:25 Data in the public sector Organisational trust Andrew Engeli – Office for National Statistics
13:25 – 13:35 Data in the private sector (I) Data Protection & Privacy Martin Hickley – Director Martin Hickley Data Solutions Limited
13:35 – 13:45 Data in the private sector (II) Data Analytics & Privacy Luk Arbuckle – Chief Methodologist Privacy Analytics
13:45 – 14:15 Cluster 1 Discussion
14:15 – 14:25 Break
  Cluster 2 Presentations: Financial markets and accounting
14:25 – 14:35 Rules vs principles in financial markets Financial Regulation & Compliance Expert witness Paul Keenan – Visiting Practitioner Professor in Financial Regulation in the Business and Law Faculty of the University of the West of England (UWE)
14:35 – 14:45 Rules vs principles in accounting (I) Practical accounting & Regulator Perspective Bryan Foss – Digital Non-Executive Director, Risk & Audit Chair, Visiting Professor and Board Readiness Coach
14:45 – 14:55 Rules vs principles in accounting (II) Auditing & Corporate Governance Ismail Adelopo/Florian Meier
14:55 – 15:25 Cluster 2 Discussion
15:25 – 15:35 Break
  Cluster 3 Presentations: Legal perspective and non-financial regulation
15:35 – 15:45 Legal perspective Financial crime Nicholas Ryder – Professor in Financial Crime
15:45 – 15:55 Non-financial regulation Modern slavery and other required reporting Jaya Chakrabarti – CEO Semantrica Ltd (tiscreport)
15:55 – 16:25 Cluster 3 Discussion
16:25 – 16:55 Summary and Closing remarks Nicholas Ryder Professor in Financial Crime

Business Models for Sustainability – A Workshop Collaboration

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The Future Economy Network (FEN) is a Bristol-based organisation born out of a need for sustainable business and better future thinking in response to the climate emergency. And in one of the most creative and environmentally conscious cities in the UK, what better place to meet the growing demand? All over the South West, FEN are seeing more and more active individuals and engaged businesses joining the network to learn about sustainability, meet like-minded others, and increase their sustainable business strength.

In response to the clear need for sustainable business growth, FEN are teaming up with UWE to create an engaging workshop titled “Business Models for Sustainability: The Barriers & Solutions”. There has been a significant growth in purpose before profit; businesses are increasingly seeing their customers demand social responsibility as an integrated part of the offer, not an afterthought or addition. With fantastic initiatives like B-Corp or Science Based Targets, businesses recognise that profit is no longer king, but the future of their growth (and survival) relies on the triple bottom line.

On 28th January, FEN and UWE will co-host a three-hour interactive workshop to better understand your business model. The session will start with two informative, introductory talks and then lead into personalised break out workshops.

What To Expect:

– Tools to develop business models for better understanding;

– Sustainable development and business models;

– Current and future business models.

One of the keynote speakers includes Peter Bradley, a leader in sustainable development at UWE. He is the principal investigator of the ‘Understanding and assessing business models for sustainability’ project, which researches the environmental and economic viability of business models that are intended for sustainable development. Alongside Peter, Ruth Smith from Sustainable Results Lab will be speaking on how Purpose beyond profit is the biggest movement in business right now. Ruth founded the Sustainable Results Lab to bring world class digital marketing to the environmental sector. Both speakers are members of FEN’s sustainability network.

The event will also include the usual elements of FEN’s weekly sustainable events programme that many have come to know and love, such as valuable networking, a friendly and motivational team, exciting 60 second pitches, and professional event delivery.

Grab your ticket here or pop into FEN’s new sustainability hub, Future Leap, to find out more about the diverse range of services available to those wishing to grow on their sustainability journey.

Update from Annie Tubadji, Senior Lecturer in Economics

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Senior Lecturer in Economics, Annie Tubadji is currently a Specially Appointed Lecturer at Hokkaido University in Sapporo, Japan.

As part of her visiting scholar activities, Annie will deliver an undergraduate and postgraduate course on the “Economics of Happiness” at Center for Regional Economic and Business Networks (REBN) Summer School Institute.

As part of my Visiting Scholar activities, I will deliver here two courses (undergraduate and graduate ones) on economics of happiness at their Center for Regional Economic and Business Networks (REBN) Summer School Institute.

As part of her visit, Annie will also be delivering two specially invited lectures.

More on the Summer School Institute can be found here

Pro-environmental employee and consumer behaviour conference 2019

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The Bristol Centre for Economics and Finance’s first conference on Pro-environmental employee and consumer behaviour was held on the 29th of April 2019.

The day was a major success with around 80 registered participants and 14 presenters with many attending organisations and academics.   The event was highly energised, with many thought provoking questions for speakers and an atmosphere full of interest. 

Bristol Green Capital introduced the day,  the afternoon session was opened by the Future Economy Group and the closing of the conference was led by Dr Peter Bradley. We would like to thank again everyone who participated.  The event will run again next year.  The slides from the day, for those who are further interested in the conference and would like to find out more, can be found here.

Australia’s bold proposals for government data sharing

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By Felix Ritchie.

In August I spent a week in Australia working with the new Office of the National Data Commissioner (ONDC). The ONDC, set up at the beginning of July, is barely two months old but has been charged with the objective of getting a whole-of-government approach to data sharing ready for legislation early in 2019.

This is a mammoth undertaking, not least because the approach set out in the ONDC’s Issues Paper proposes a new way of regulating data management. Rather than the traditional approach of trying to specify in legislation exactly what may or may not be allowed, the ONDC is proposing a principles-based approach: this focuses on setting out the objectives of any data sharing and the appropriate mechanisms by which access is governed and regulated.

In this model, the function of legislation is to provide the ground rules for data sharing and management within which operational decisions can be made efficiently. This places the onus on data managers and those wanting to share data to ensure that their solutions are demonstrably ethical, fair, appropriate and sensible. On the other hand, it also frees up planners to respond to changing circumstances: new technologies, new demands, shifts in attitudes, the unexpected…

The broad idea of this is not completely novel. In recent years, the principles-based approach to data management in government has increasingly come to be seen as operational best practice, allowing as it does for flexibility and efficiency in response to local conditions. It has even been brought into some legislation, including the UK’s Digital Economy Act 2017 and the European General Data Protection Regulation. Finally, the monumental Australian Productivity Commission report of 2017  laid out much of the groundwork, by providing an authoritative evidence base and a detailed analysis of core concepts and options.

In pulling these strands together, the ONDC proposals move well beyond current legislation but into territory which is well supported by evidence. Because of the unfamiliarity with some of the concepts, the ONDC has been carrying out an extensive consultation, some of which I was able to observe and participate in.

A key proposal is to develop five ‘Data Sharing Principles’, based on the Five Safes framework (why, who, how, with what detail, with what outcomes) as the overarching structure. The Five Safes is the most widely used model for government data access but has only been used twice before to frame legislation, in the South Australia Public Sector (Data Sharing) Act 2016 and the  UK Digital Economy Act 2017.

The most difficult issues facing the ONDC arise from the ‘why’ domain: what is the public benefit in sharing data and the concomitant risk to an individual’s privacy? How will ‘need-to-know’ for data detail be assessed? What are the mechanisms to prevent unauthorised on-sharing of data? How will shared data be managed over its lifecycle, including disposal? To what uses can shared data be put? Can data be shared for compliance purposes? How can proposals be challenged?

These are all good questions, but they are not new: any ethics or approvals board worth its salt asks similar questions, and would expect good answers before it allows data collection, sharing or analysis to proceed. A good ethics board also knows that this is not a checklist: ethical approval should be a constructive conversation to ensure a rock-solid understanding of what you’re trying to achieve and the risks you’re accepting to do so.

This is the also the crux of the principles-based approach being taken by the ONDC: it is not for the law to specify how things should be done, nor to specify what data sources can be shared. But the law does provide the mechanisms to ensure that any proposals put forward can be assessed against a clear purpose test around when data may and may not be shared and that appropriate safeguards are in place…

Finally, the law will require transparency; this has to be done in sunlight. A public body, using public money and resources for the public benefit, should be able to answer the hard questions in the public arena; otherwise, where is the accountability? The ONDC will require data sharing agreements to be publicly available, so people can see for what purpose (and with what associated protections) their data are being used.

To some, this need to justify activities on a case-by-case basis, rather than having a black-and-white yes/no rule, might seem like an extra burden. The aim of the consultation is to ensure that this isn’t the case. In fact, a transparent, multi-dimensional assessment is any project’s best friend: it provides critical input at the design stage and helps to spot gaps in planning or potential problems, as well as giving opponents a clear opportunity to raise objections.

Of course, even if the legislation is put in place, there is still no guarantee that it will turn out as planned. As I have written many times (for example in 2016), attitudes are what matter. The best legislation or regulation in the world can be derailed by individuals unwilling to accept the process. This is why the consultation process is so important. This is also why the ONDC has been charged with the broader role of changing the Australian public sector culture around data sharing, which tends to be risk-averse. The ONDC also has a role to build and maintain trust with the public through better engagement to hear their concerns.

From my perspective, this is a fascinating time. The ONDC’s proposals are bold but built on a solid foundation of evidence. In theory, they propose a ground-breaking way to offer a holy trinity of flexibility, accountability, and responsibility. If the legislation ultimately reflects the initial proposals, then I suspect many other governments will be beating a path to Australia’s door.

All opinions expressed are those of the author.

First speaker announced for 2018/19 BCEF Economic Research Seminar Series

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On Thursday, 27th September, we will have the pleasure to hear the presentation by our dear guest Steven Bosworth (University of Reading).

Steven will present his joint paper with Dennis J. Snower (Kiel) on the topic: “Organizational Ethics, Narratives and Social Dysfunctions”.

Paper Abstract:

All organisations are characterised by some degree of conflict between its members’ private interests and the organisation’s mission. This may manifest in corruption, fraud, or more banally, shirking. In response leaders can try to mould the identities of workers to make them more sensitive to the social costs of their actions.

We explicitly model the social interactions and constraints giving rise to this process, deriving an endogenous profile of wages, monitoring, and organisational culture. In this way we provide a theory of organisational dysfunction, and show how such dysfunctions might be mitigated through changes in government policies or social norms. These changes become particularly effective if they encourage both managers and workers to adopt more ethical narratives – organisational culture change is in this case self-reinforcing. Ineffective narratives on the other hand can cause pushback from employees when managers adopt a more ethically ‘strict’ stance. We derive the conditions under which beneficial or countervailing feedback effects can occur.

Dr Steven Bosworth is a behavioural economist working as a Lecturer at the University of Reading. His research uses microeconomic theory and controlled laboratory experiments to investigate how context, motivation and the social environment influence human cooperation. He has published on the topics of uncertainty and coordinated decisions, the distribution of prosocial dispositions in the society and competition, and the consequences of social fragmentation on wellbeing.

Before joining the University of Reading in 2017, Steven was a postdoctoral researcher at the Institute for the World Economy in Kiel, Germany, where he maintains an affiliation.

More information about Steve and his list of publications can be found here.

 

 

The Role of Social Norms in Incentivising Energy Reduction in Organisations

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By Peter Bradley

UWE Economics researcher Peter Bradley, has just published a chapter on “The Role of Social Norms in Incentivising Energy Reduction in Organisations” in collaboration with Matthew Leach and Shane Fudge. This is part of a collaboration by leading international academics to develop a research handbook on employee pro-environmental behaviour. The work stems from the UWE Economics groups sustainability related research.

The Research Handbook on Employee Pro-Environmental Behaviour brings contributions that consolidate existing research in the field as well as adding new insights from organisational psychology, human resource management and social marketing.

The whole book is available to download from Edward Elgar Publishing:

Research Handbook on Employee Pro-Environmental Behaviour edited by Victoria K. Wells, Diana Gregory-Smith and Danae Manika.

 

 

Using the Indices of Multiple Deprivation – it is (so much) more than just a top-line indicator.

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By Ian Smith

There has been a lot of interest in measuring disadvantage over the past 20 years in the UK even if this has not always been matched by government responses. The fifth iteration of the English IMD is to be reviewed over the next 12 months.  Clearly disadvantage is a complex thing and can be represented in many different ways.  As a geographer (or someone who periodically claims to be a geographer hiding in an Economics Department) I am particularly interested in area-based assessments of disadvantage.  I know such measures are problematic but what indicators are not?  I recently have had the opportunity with colleagues to review how the English Indicator of Multiple Deprivation works on behalf of Power to Change (see https://www.powertochange.org.uk/) and this is a short blog that captures some of the thinking that came out of that work (any errors or misinterpretations are all my/our fault and not necessarily shared by anyone at Power to Change).

So, the English IMD is a second-generation indicator of area-based deprivation that represents 7 ‘dimensions’ (or 10 sub-dimensions if you like) of disadvantage from worklessness to housing affordability, from health (mental and physical) to distance from your nearest post office. It is ‘second generation’ because it is not solely dependent on small area census data (as ‘first generation’ indices are/were) but is based on a range of small area administrative and census data from different sources within English government.

I am a fan. It is lovely.  My colleagues in other European countries are jealous of it (the basic model is oft copied) – both because of its breadth of content but also because of our lovely regular statistically ordered lower super output areas (LSOAs) that sometimes get conflated for neighbourhoods.  However, an indicator is a conceptual model of a real concept.  As George Box pointed out – all models are wrong, but some [of the better ones] are useful.  We and Power to Change were interested in posing the question of how useful is the IMD to Power to Change?

In particular, we were interested in how the IMD is used within a particular organisational context (Power to Change). We set up a set of dimensions to help us think about how an indicator (a statistical instrument ‘designed’ to perform a task) is constructed and deployed.  We asked people in Power to Change how they used the IMD and what was their assessment of the strengths and weaknesses for what they needed to do: investing in community businesses that alleviate disadvantage in England.  What struck us in these conversations was that the IMD was only being used in its top-line indicator format – what was being missed was the opportunity to use the IMD as an indicator system that can be moulded to the specific objectives of an organization.

We explored how to use the IMD as a system of indicators to shine a light on a specific objective: investing in community businesses. We compared spatial targeting at LSOA level for the top-line IMD indicator (the full 7 dimensional one) with the spatial targeting from a bespoke indictor bringing together the health and disability, education and qualifications and the geographic access to services dimensions.  Power to Change has hypothesised that community businesses some of which provide local services may impact on employability (skills) and on the health of residents in the communities that community business serve.  So, we constructed a focussed indicator from components of the topline IMD that focused only on geographic access to services, education and health (for details see Smith et al 2018).  We compared how the focussed IMD indicator would spatially target the attention of Power to Change in comparison to the top-line IMD indicator with a particular focus on the city-region of Liverpool and the County of Suffolk as examples of areas of interest for Power to Change.  We then mapped out the differences (using data and shapefiles obtained under a public licence) showing firstly the map of the top-line IMD indicator, secondly showing our ‘new’ indicator focusing on Power to Change’s priorities and thirdly what difference it makes in targeting.  These maps are shown in Figures 1 (for Liverpool) and Figure 2 (for Suffolk).  We have used the somewhat arbitrary threshold of 30% to indicate disadvantage (the most disadvantaged areas to be targeted) and compared the indicators.

Figure 1

The left-hand side map in both Figure 1 and Figure 2 shows neighbourhoods marked relative to the top-line IMD indicator where the deepest green areas are the most disadvantaged. In the middle map the same rule applies.  The right-hand map in these Figures shows what difference it makes for these areas.  In this right-hand map, the red areas are those that are marked as the most disadvantaged 30% under both indicators.  The blue areas are ‘advantaged’ under both measures.  However, the orange areas are marked as disadvantaged under the ‘better places’ indicator but not under the top-line IMD.

Figure 2

Given the greater importance given to access to services (albeit direct distance accessibility based on 2012 data), it is not surprising that Suffolk LSOAs become more disadvantaged under this measure. Thus, nearly half of Suffolk becomes ‘disadvantaged’ on this measure (30% most disadvantaged in England on this measure) than under the top-line IMD (more of Suffolk’s third map is coloured orange).  Perhaps it is of greater surprise that the prioritisation of Liverpool changes little under the new formulation.  Most of Liverpool’s neighbourhoods remain identified as ‘disadvantaged’ (marked as red in the third map along).

This is however, just a schema for moving resources around. It is an inevitable result of re-calculating the target IMD measure that some areas gain whilst others lose out (where resources are fixed). However, if areas in Suffolk gain whilst neighbourhoods in Liverpool do not lose out, then how would such a change modify the geography of disadvantage [under this measure] across England?  Using the 30% figure as the threshold of disadvantage just under half a million fewer people would be designated as living in a ‘disadvantaged’ area.  We did some cluster analysis of the ranking on the top-line IMD indicator and our suggested Power to Change indicator considering both how LSOAs clustered together (using forms of hot spot analysis) to capture how patterns of disadvantage form broad regions and secondly, we looked at the identification of outlier neighbourhoods (using the analysis of Anselin Local Moran’s I) to capture differences within these wider clusters.

Figure 3

On Figures 3 and 4 the LSOAs that are marked as red are ones than appear as advantaged (close to other advantaged areas). In these Figures we have a left-hand map that shows the clustering of indicator ranking in relation to Suffolk.  The middle map shows the Getis-Ord clustering for England as a whole whilst the right-hand map shows the Local Moran’s I maps which show where areas are located as outliers in wider regions.  Where there is red there is advantage and where there is blue there is disadvantage (from an area-based perspective).  Yellow areas are mixed (any area’s ranking is not easily predicted from the ranking of its neighbours).  It is also worth noting that the red and the blue areas are not necessarily all of the most disadvantaged areas – just areas that are close to others that are similarly ranked (whether high or low).

Figure 4

It is not surprising to see clusters or disadvantaged (blue) areas in England’s northern metropolitan areas, in the West Midland and in the extreme South West in Figure 3 that maps out the top-line IMD indicator. It is also not surprising to see the East and

North of London marked as deep blue although it is worth noting that the former Kent Coalfield areas remain marked as disadvantaged in blue. So, it is England to the south of the Wash to Severn axis as well as North Yorkshire that are marked as ‘advantaged’ regions under the top-line IMD indicator.  The Anselin outlier mapping (right hand map) in Figure 3 points out the presence of disadvantaged LSOAs in advantaged clusters and of the presence of advantaged LSOAs in disadvantaged clusters.

Moving to the Power to Change indicator in Figure 4 we see a change in the geography that might be targeted (in this case by investment in community businesses). More rural areas in the East and South West of England become identified as ‘disadvantaged’.  Areas in the East and North of London no longer become identified as disadvantaged in terms of the clustering on this measure’s ranking.  There is a different dynamic – to be disadvantaged area in London is to be surrounded by advantaged areas.  The East of England (including Suffolk) becomes identified with the cluster of disadvantage although there are clearly still advantaged area outliers in the sea of blue disadvantaged areas.  Although there are disadvantaged areas in the advantaged region of London.  It has to be stressed that this applies only to forms of disadvantage that flow from combinations of problematic educational, health and accessibility outcomes.  There would be a case for an organisation like Power to Change to use a form of IMD that relates specifically to their core mission as a spatial guide to targeting rather than just using the top-line IMD indicator.

The aim of the exercise is not to rubbish the general top-line IMD. I am still a fan – it is still offers useful insight into the patterns of generalised area-based disadvantage across England.  The English IMD is still useful to Power to Change in a general sense.  However, the aim of this has been to draw to attention the fact that deploying the indicator system in the light of what is trying to be achieved makes better use of the IMD system.  The East and North of London is clearly a region with many disadvantaged areas but if the aim of the exercise is to invest in community businesses that improve access to services, health and educational outcomes, there might be better areas on which to focus this specific form of investment.  Whatever form of analysis we come up with to capture disadvantage there is always a set of political choices about how to share out public spending.  However, the English IMD is more than just the top-line indicator and the top-line IMD was never intended to be the only way in which area-based disadvantaged was represented.

Although in this delicate dance of spatial targeting, the real answer is to invest more in welfare services. Perhaps that is one normative step too far?

If you want to read more about our work with Power to Change, please download the report we wrote for them (available from September).

Smith, I, Green, E, Whittard, D. and Ritchie, F. (2018) Re-thinking the indices of multiple deprivation (for England): a review and exploration of alternative/complementary area-based indicator systems. Final Report. Bristol Centre for Economics and Finance (BCEF) in the Bristol Business School at the University of the West of England (UWE).

Measuring non-compliance with minimum wages

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By Professor Felix Ritchie

When a minimum wage is set, ensuring that employees do get at least that minimum is a basic requirement of regulators. Compliance with the minimum wage can vary wildly: amongst richer countries, around 1%-3% of wages appear to fall below the minimum but in developing countries non-compliance rates can be well over 50%.

As might be expected, much non-compliance exists in the ‘informal’ economy: family businesses using relatives on an ad hoc basis, cash-only payments for casual work, agricultural labouring, or simply the use of illegal workers. However, there is also non-compliance in the formal economy. This is analysed by regulators using large surveys of employers and employees which collect detailed information on hours and earnings. This analysis allows them to identify broad characteristics and the overall scale of non-compliance in the economy.

In the UK, enforcement of the minimum wage is carried out by HM Revenue and Customs, supported by the Low Pay Commission. With 30 million jobs in the UK, and 99% of them paying at or above the minimum wage, effective enforcement means knowing where to look for infringements (for example, retail and hospitality businesses tend to pay low, but compliant, wages; personal services are more likely to pay low wages below the minimum; small firms are more likely to be non-compliant than large ones, and so on). Ironically, the high rate of compliance in the UK can bring problems, as measurement becomes sensitive to the way it is calculated.

A new paper by researchers at UWE and the University of Southampton looks at how non-compliance with minimum wages can be accurately measured, particularly in high-income countries. It shows how the quantitative measurement of non-compliance can be affected by definitions, data quality, data collection methods, processing and the choice of non-compliance measure.

The paper shows that small variations in these can have disproportionate effects on estimates of the amount of non-compliance. As a case study, it analyses the earnings of UK apprentices to show, for example, that even something as simple as the number of decimal places allowed on a survey form can have a significant effect on the non-compliance rates.

The study also throws light on the wider topic of data quality. Much research is focused on marginal analyses: looking at the relative relationships between different factors. These don’t tend to be obviously sensitive to very small variations in data quality, but that is partly because it is can be harder to identify sensitive values.

In contrast, non-compliance with the minimum wage is a binary outcome: a wage is either compliant or it is not. This makes tiny variations (just above or just below the line) easier to spot, compared to marginal analysis. Whilst this study focuses on compliance with the minimum wage, it highlights how an understanding of all aspects of the data collection process, including operational factors such as limiting the number of significant digits, can help to improve confidence in results.

Ritchie F., Veliziotis M., Drew H., and Whittard D. (2018) “Measuring compliance with minimum wages”. Journal of Economic and Social Measurement, vol. 42, no. 3-4, pp. 249-270. https://content.iospress.com/articles/journal-of-economic-and-social-measurement/jem448