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.

 

 

Beyond pay gaps: Inequality at work

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By the researchers of the “Earnings gaps and inequality at work” project, Bristol Business School.

On 25 May 2018, UWE Economics hosted an expert workshop on ‘Beyond Pay Gaps: Inequality at Work’. Six experts were invited to share their reflections, based on their own research, on two questions:

1) What is the nature of inequality at work?

2) Is the pay gap an adequate indicator? If not, how can we improve our assessments of inequality at work?

The key aim was to foster a discussion on how to conceptualise and study inequality at work. In an earlier blog entry the workshop organisers’ provided a response to UWE’s reporting on the gender pay gap, which highlighted the fact that some progression on the gender pay gap is not in itself a sign of overall success. There are aspects of inequality at work that are captured by pay indicators and nonetheless merit our attention.

The morning session of the workshop focused on conceptualisations of inequality at work and featured the presentations of three distinguished scholars of labour and inequality. Dr Alessandra Mezzadri (SOAS University of London) drew on her long-standing research on the garment industry in India to highlight patterns of inequality and gender exploitation. Professor Bridget O’Laughlin (Institute of Social Studies) reflected on the concepts of Marx’s political economy framework as well as its conceptual gaps to study inequality at work. Professor Harriet Bradley (UWE Bristol) illustrated how a three-part conceptual framework based on production, reproduction and consumption can be used to conceptualise gender inequality at work.

In the afternoon session, three distinguished academics on gender, organisation and inequality presented on methodological approaches to study inequality at work. Dr Hannah Bargawi (SOAS University of London) discussed how a pyramid-shaped understanding of inequality at work can guide us through moving our focus between different levels of inequality. Dr Olivier Ratle (UWE Bristol) presented the qualitative methods used to study early career academics’ experience of work. Dr Vanda Papafilippou (UWE Bristol) described a range of methods from the field of sociology of education to study the workplace.

The presentations generated rich discussions on the conceptualisations of social reproduction, the complexity of inequality and the relations between the material and the cultural. The participants agreed that research on these themes is both timely and needed. Furthermore, a podcast series on ‘Feminism, Gender and the Economy’ featuring two interviews with workshop speakers will be launched in 2018/2019 academic year. Watch this space for the upcoming podcast series!

This workshop was funded by UWE Bristol. The workshop’s organisers are grateful to all participants for their thoughtful contributions and productive discussions.

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).

Bringing the ‘political’ back into the economy: A report from I Workshop in Contemporary Political Economy (UWE Bristol-Paris 1 Sorbonne)

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By Danielle Guizzo and Bruno Tinel

 

The 1st Workshop in Contemporary Political Economy recently took place at the Bristol Business School on June 28th, 2018. It was the first event of a recently established partnership between the UWE Economics subject group (AEF) and the Paris 1 Department of Economics (Pantheon Sorbonne University) as a response to the increasing importance of pluralism in economics teaching and research in the post-crisis scenario. A large proportion of attendants were young scholars (early-career researchers or PhD students), who represent a promising generation for promoting the expansion and excellence of research in political economy as the future of the international PE community.

We are very pleased to inform the community that the workshop was a great success in terms of the quality of the presentations, the number of participants, and the pluralism of subjects. The presentations and subsequent discussions explored the recent frontiers in contemporary political economy, aiming at expanding three main areas: critical macroeconomics; financialisation; and ideology, power and the state.

The final session constituted of a roundtable about the future of pluralistic research in economics and the possibility of engagement with the mainstream of the discipline. Participants expressed the importance of institutional support and an active scholarly community to move beyond standardized metrics and diamond lists in research assessment exercises if we seek to achieve an open, equal dialogue in Economics that allows inclusivity.

Thanks to the great enthusiasm of the workshop’s participants, we will continue to organize a yearly workshop with the purpose of further promoting and disseminating teaching and research in political economy and pluralistic economics, expanding the partnership between UWE and Paris 1-Pantheon Sorbonne, and improving communication and academic exchange among scholars. Therefore, the Department of Economics at Paris 1-Pantheon Sorbonne will organize the second edition of the workshop in contemporary political economy.

We would like to thank the Bristol Centre for Economics & Finance (BCEF), as well as the Accounting, Economics and Finance (AEF) for the grants and support they provided, allowing for the organization of this workshop. We also express our gratitude to the presenters, who delivered excellent talks and provided a space for the exchange of ideas that significantly contributes to future partnerships and prospective research projects.

 

Mexico and Trump

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By Laura Povoledo

I had the good fortune of visiting Mexico last year on a research visit funded by the British Academy. Mexico is a beautiful country, full of rich history and diverse culture. But it is also a country with huge social problems.

Mexico is the 11th most populated country in the world with around 127 million people. It has been estimated that 42% of Mexico’s total population lives below the national poverty line. Getting millions out of poverty will require enormous effort, but in recent years a growing middle class has emerged, thanks to sustained economic development. The economy of Mexico is now the 11th largest in the world by purchasing power parity, and according to Goldman Sachs, by 2050 Mexico will be the 5th largest economy in the world.

However, recently Mexico has been through some very difficult years. In 2016 the GDP growth rate was below 2% and inflation was 4%. The peso steadily depreciated against the dollar, forcing the government to increase the price of gasoline (half of the fuel consumed by Mexico is imported from the US). Given the country’s poor public transport infrastructure, the cost of private transport is especially important in Mexico, so the increased price of petrol will severely affects households’ living standards. The recent increase in the minimum wages is unlikely to meet the needs of those on low incomes. This deteriorating economic environment has prompted Standard & Poor’s to change their perspective from “stable” to “negative”. And of course, in 2016 Trump was elected.

One of the risks posed by a failing economy is nationalism (and Trump himself is an example). However, Latin America nationalism is different from Trump’s right-wing nationalism. Nationalisms in Latin America have often been associated with left-wing political positions. This is explained by the colonial past and the struggles of national liberation. Mexico will hold its general election on July 1st, and the left’s presidential candidate, Andrés Manuel López Obrador, is as opposed to NAFTA as is Trump.

NAFTA has led to an enormous expansion of US companies in Mexican territory, and several Mexican economists I talked to were often keen to point out that protectionists policies will ultimately damage American interests. It has been estimated that in its 22 years NAFTA has generated 6 million jobs in the US. 40% of the components of Mexican exports are actually produced in the US, in other words, 40 cents of every dollar spent on Mexican exports support jobs in the US. There are now 35 millions of Mexicans living in the United States, of which about 11 million were born in Mexico. Mexican immigrants take the hardest and lowest paid occupations and they provide a source of manpower in many industries, such as agriculture, construction and food processing.

During my visit there I often found a strong rejection of Trump’s rhetoric and a determination to fight against all adversities. A resurgence of national pride may not be totally undesirable if it spurs Mexico to take anti-poverty measures, to support its growing middle class and to dismantle the other wall that is holding it back, that of corruption.

 

A response to UWE gender pay gap reporting: looking at Bristol Business School

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By the researchers of the ‘Earnings gaps and inequality at work’ project, BBS

In compliance with new UK legislation, UWE Bristol published its own gender pay gap report in March 2018. Whilst recognising the need to do more to close the pay gap between women and men, that UWE achieved a gender pay gap of 13.15% has been portrayed as a sign of progress. The UWE figure is lower than the national average of 18.1% (ONS 2016)[1] and has decreased by 4.85 percentage points since 2003.[2] But if the current rate of progress is anything to go by it will be another 40 years  before the gender pay gap at UWE is closed. In Bristol Business School[3] (BBS), 67% of academic staff at lecturer level and 53% at senior lecturer level are women compared with 44% and 43% at associate professor and professor levels respectively. That women are overrepresented at lower grades is not a sign of real progress, but rather that women struggle through career progression. It is then crucial to better understand what explains the gender pay gap and what actions are necessary to tackle gender discrimination at work.

 

 

 

 

 

 

 

Within academia, gender gaps manifest for various reasons including slower progression paths (Krefting 2003; Holiday et al. 2014; Winslow and Davis 2016). In BBS, women are well represented at the executive and management levels which might indicate specific barriers against academic promotion such as from senior lecturer to associate professor and above. A recent Times Higher Education article highlighted that one in three UK universities are going backwards on female professorships. Studies have also revealed discrimination in academic publishing. Women are less likely to push for first authorship when collaborating with men, and women authored articles face higher levels of scrutiny in the peer review processes (West et al. 2013; Hengel 2017).  Women tend to apply for fewer external grants, although their success rate tends to be higher. In addition, women face disproportionate burdens in relation to career progression once they have children.  Female academics face a motherhood citation penalty whilst having children appears to advantage men’s career progression. A recent study in the US showed that men with children were 35 percent more likely than women with young children to secure tenure-track positions. Men with children are also 2 percent more likely to secure these positions compared with women without children (Mason et al. 2013). There is quite some variation across disciplines in terms of gender discrimination with economics being particularly bad in terms of apparent gender imbalances in student enrolment and all the way through in academic careers (Ginter and Khan 2004; Goldin 2013; Tonin and Wahba 2014; Crawford et al. 2018).

The Gender Pay Gap is not about Equal Pay

The gender pay gap captures some of the outcomes of discrimination – for example, wage inequality, job segregation and differential progression – but it obscures others. More importantly, it tells almost nothing about the complex mechanisms via which societal norms, class, power relations, institutional structures, workplace practices, legislation, and individual attributes interact and reinforce gendered, as well as other types of discrimination. As explained in this Guardian’s video, the gender pay gap data does not reveal anything about equal pay, which is whether women and men are paid equally for the same type of job. Furthermore, research has shown that  the allocation of workload, autonomy over one’s time use, the burden of pastoral care, and  physical and emotional conditions of work are on average worse for women compared to male colleagues (Holliday et al. 2014; Stier and Yaish 2014). Even the ways in which women are assessed on the quality of their work are highly discriminatory. Women experience substantial negative bias in students’ assessments.  Women in academia operate under workplace conditions that are stacked against them.

Inequality and mental health

There is increased understanding that inequalities within societies worsen mental health outcomes for oppressed groups. In relation to women’s mental health, there has been a tendency to pathologise the ways in which women individually deal with abuse or oppression rather than recognising the structural or societal causes. For example, the diagnosis of Borderline Personality Disorder (BDP) is applied predominantly to women whilst many of the traits associated with the diagnosis fit closely with gender based abuse and trauma. Men with similar manifestations of mental health outcomes are more likely to be diagnosed with post-traumatic stress disorders that recognise the underlying cause (Shaw and Proctor 2005).

The unequal burden of domestic work that academic women shoulder also takes its toll on mental health. The publish or perish dictum for academic progression together with growing administrative and teaching loads mean that women struggle more to put in the ‘extra hours’ in the evening or at weekends for research expected in academia today. Fagan et al. (2011) reviewed international evidence on working time arrangements on work-life ‘balance’ and discovered that paid employment increases the well-being of women, but those who work long hours in paid employment while retaining primary responsibility for domestic tasks at home are at particular risk of poorer mental health. A study using data from the British Household Panel Survey (BHPS) found that working hours over 49 hours a week was associated with poorer mental health for women, but not men. We would expect the effects to be intensified for women with small children. UWE’s commitment to ‘mental wealth’ means that our institution has to take the issue of workloads seriously for all, but particularly for women.

What can be done?

What is to be done? Some think the women’s behaviour change is what is needed to close the gender pay gap. For example, women need to be more assertive in demanding pay rises and promotion. Within BBS, a number of women have received support in the form of coaching to help identify goals, strategies and behaviours that would most support their achievement. UWE has established programmes in women’s leadership and there is a “women in research” mentorship programme with a high take up. There are also numerous self-help groups online and many of us form small communities of care, nurture and peer support and collaboration. Whilst these initiatives are important and help women to identify coping mechanisms and navigate the system, they do nothing to challenge the institutional structures stacked against them.  What is needed is better institutional provisions to ameliorate the disproportionate burdens faced by women which could begin with making sure that women are pushed to apply for every opportunity for internal research support open to them; additional provision for early career women academics; support for women returning from maternity leave in order to catch-up on research; better maternity leave provision (with 6 weeks full pay followed by 12 weeks of half pay, UWE is amongst the least generous in UK HEIs); expanding the number of AP positions and reversing the balance of gender representation to ensure parity is reached at the professorial level in the next 5-10 years; and training on gender issues to all staff.

But more than this, we need to better understand how various forms of inequality – gender, race, class, disability, citizenship status, and religion – place workers in conditions of particular vulnerability in the workplace. This blog has focused on women academics, but many of our colleagues provide critical support for the functioning of the university in the form of admin, student support, cleaning and catering. UWE’s gender pay gap reporting does not paint a rosy picture for women and, in addition, a much deeper understanding of inequality at work is clearly needed. It is positive that BBS has taken the lead under the championing of Donna Whitehead, who is committed to the promotion of women and minorities. With the support of the faculty, a number of us are embarking on innovative research on this topic in order to strengthen UWE’s Inclusivity 2020 strategy and commitment to implementing necessary actions. We hope that in this way BBS and UWE could become the drivers of change in the sector.

A group of us in Economics have received internal funding to kick off a research project on Earning Gaps and Inequality at Work. We are interested in developing a multi-method approach to study quantitative and qualitative aspects of inequality at work. As part of this project, an expert workshop to discuss if and how we should look beyond the (gender) pay gap to understand inequality in the workplace will be held on 25th May 2018 at UWE. External participants with expertise on these themes will also be interviewed and a podcast series on inequality at work will be launched at the beginning of the new academic year.

 

Crawford, Claire; Neil M Davies, & Sarah Smith (2018). Why do so few women study economics? Evidence from England, available at http://www.res.org.uk/SpringboardWebApp/userfiles/res/file/Womens%20Committee/Publications/why%20do%20so%20few%20women%20study%20economics,%202018.pdf

Fagan, C., Lyonette, C., Smith, M. and Saldana-Tejeda, A. 2011. The influence of working time arrangements on work-life integration or ‘balance’: A review of the international evidence. Conditions of Work and Employment Series No. 32. Geneva: ILO.

Ginther, D. K., & Kahn, S. (2004). Women in economics: moving up or falling off the academic career ladder?. The Journal of Economic Perspectives18(3), 193-214.

Goldin C. (2013). Notes on Women and the Undergraduate Economics Major. CSWEP Newsletter. (Summer) :4-6, 15.

Hengel, E. (2017). Publishing while Female. Are women held to higher standards? Evidence from peer review. Retrieved from https://doi.org/10.17863/CAM.17548

Holliday, E. B., Jagsi, R., Wilson, L. D., Choi, M., Thomas Jr, C. R., & Fuller, C. D. (2014). Gender differences in publication productivity, academic position, career duration and funding among US academic radiation oncology faculty. Academic medicine: journal of the Association of American Medical Colleges89(5), 767.

Krefting, L. A. (2003). Intertwined discourses of merit and gender: Evidence from academic employment in the USA. Gender, Work & Organization10(2), 260-278.

Liff, S., & Ward, K. (2001). Distorted views through the glass ceiling: the construction of women’s understandings of promotion and senior management positions. Gender, Work & Organization8(1), 19-36.

Mason, M.A., Wolfinger, N.H. and Goulden, M., (2013). Do babies matter?: Gender and family in the ivory tower. Rutgers University Press.

Shaw, C. and Proctor, G. (2005). I. Women at the margins: A critique of the diagnosis of borderline personality disorder. Feminism & Psychology, 15(4), pp.483-490.

Stier, H., & Yaish, M. (2014). Occupational segregation and gender inequality in job quality: a multi-level approach. Work, employment and society28(2), 225-246.

Tonin, M., & Wahba, J. (2014). The sources of the gender gap in economics enrolment. CESifo Economic Studies, IZA DP No. 8414.West, J. D., Jacquet, J., King, M. M., Correll, S. J., & Bergstrom, C. T. (2013). The role of gender in scholarly authorship. PloS one8(7), e66212.

Winslow, S., & Davis, S. N. (2016). Gender inequality across the academic life course. Sociology Compass10(5), 404-416.

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[1] The average for higher education institutions is 15.9%, lower than the national average, as outlined in this Time Higher Education article.

[2] The rate of progress in closing the gender pay gap has been much more substantial in other higher education institutions, such as Sheffield University, where the gender pay gap reduced from32.2% in 2003 to 15.2% in 2017 – see Sheffield reporting here.

[3] Figures for gender distribution across academic grades have been put together from information available on the UWE website and may not be up to date. All personnel in management positions were counted as senior lecturers unless otherwise stated in their online staff profile.

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

Russia: A Mercantilist Economy

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Dr Nadia Vanteeva, Senior Lecturer in Economics at the Bristol Business School, gives this abstract from her latest research project.

The rapid Russian industrialization at the end of the 19th century took place behind high tariffs, protecting nascent firms against foreign competition; such firms also enjoyed protection against domestic competition through state-imposed entry restrictions. Furthermore, firms enjoyed state-subsidized capital loans, state-supported cartel pricing and wage controls. This led to the characterization that Tsarist industrialization policy was a classic example of List’s infant industry hypothesis. However, the new industries at the time were concentrated first in railroad construction, followed by the iron and steel, coal mining and machine tools.

All of the above industries were chosen by the state for development and were also under its close governance. Under a comparative advantage hypothesis, none of the above capital-intensive industries were likely candidates for success, given Russia’s then economic and technological backwardness. Gerschenkron hypothesized that the motivation for the Tsarist industrialization plan was to provide industrial support to develop a modern military. If Gerschenkron’s hypothesis is correct, then direct state involvement in industrialization is not a temporary phenomenon as the case in many countries, but a more permanent feature of Russian economic model. Thus Tsarist industrial revolution may be a better example of a mercantilist economy spanning Russia’s large contiguous empire area in much the way described by Heckscher’s continental system. It might explain not only the peculiar emphasis in Russia for the capital goods rather than consumer goods industry, but also where such industries may be located, and why some regions are more favoured for industrial development over others.

Degree Algorithms: Equity and Grade inflation

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In his recent working paper Dave Allen highlights the substantial differences in the way that university degree calculations can be made.

The algorithms UK universities use to calculate a student’s final degree outcome can be complex and sometimes counter-intuitive; some commentators suggest that they have contributed to ‘classification inflation’ across the UK higher education sector. A less well understood concern is that the variety in algorithms potentially means the same set of marks can be awarded a different classification depending on what university the student attended.

The 17 questions and answers below aim to clarify the issue.

1.         Is grade inflation happening?
The recent HESA data on degree qualifications confirms a continued increase (or inflation) in the proportion of ‘good honours’ (1st and 2:1s) being awarded – from 68% of all graduates in 2012/13 to 75% in 2016/17 Likewise, the proportion of first has increased from 18% to 26%.  While the numbers cannot be disputed, the cause is.

2.         Why is it occurring?
According to the Cambridge pro-vice chancellor for education, Professor Graham Virgo, grade inflation is not “a cause for concern” and is “down to tuition fees because students are more motivated and are working harder”. Alternatively, others like Nick Hillman, director of the Higher Education Policy Institute claim, “Universities are essentially massaging the figures, they are changing the algorithms and putting borderline candidates north of the border,” (The Telegraph)

3.         Have there been changes to university degree Algorithms?
There have been significant changes to degree algorithms in the last 10 years. The recent report by  UUK-GuildHE (Understanding Degree Algorithms, Oct 2017) found that many HEIs had changed their algorithms – primarily to ensure internal consistency between departments and faculties, but also to achieve “competitor or sector alignment” (page 18). (see also Higher Education Academy’s 2015)

4.         What is a degree algorithm?
Degree algorithms describe the process universities use to translate module outcomes into a final degree classification (1st, U2, L2, 3rd). The algorithm software calculates the weighted average of the ‘counting’ modules, this average mark then determines the classification.

5.         What is a weighted mark?
Degrees are made up of a number of modules and can have different credits e.g. 10, 15, 30, 20, 40 etc., students typically study 120 credits a year or 360 credits in total. The weighted average takes account of different module sizes (credit weightings). To calculate a weighted average the module marks are first multiplied by their credits, these weighted values are then added together; finally, this total is divided by the total value of credits.

6.         Are all algorithms the same?
While all UK universities adopt the same classifications, how universities arrive at these classifications is a very different matter. The variation comes in how the average of each ‘counting’ year is weighted and whether some module marks are ‘discounted’ or removed from  the calculation.

7.         What is a ‘counting’ year?
Simply those years of study included in the degree calculation, it is notable that most UK  universities do not include year 1 marks in their algorithms – the focus is on year 2 and 3.

8.         Why is there a greater weight on year 3 studies?
The higher weighting given to year 3 marks captures the notion of the student’s ‘exit velocity’ or  the standard that the student is performing at as they graduate from university. Alternatively,  the higher weightings on year 3 might reflect a university’s requirement that programmes must become more challenging as students progress through them.

9.         Is each counting year weighted equally?
There is wide variation in the weightings applied to year 2 and 3 marks. This can range from 50/50 [Oxford Brookes] to 20/80 [Derby].

10.       How does the year weighting affect the degree mark?
This is best illustrated using an example. Assume the year 2 and 3 average marks are 64.38% and 69.00% respectively. If weighted equally [50/50] the combined average would be 66.69%, if weighted 20/80, this combined mark increases to 68.08 – an increase of 1.39% – all because a greater weight has been placed on the year 3 average mark. It follows that had the year 2 and 3 average marks been switch around, the increase in the overall average using a 20/80 weighting would be smaller [i.e. 65.30%].

11.       How does discounting a module affect the weighted average?
Discounting or, removing the lowest marks can only improve the overall degree average. It follows also that “If only the worst, outlying marks are omitted, it is possible that this would lead to grade inflation” (UUK-GuildHE p.37).

Again, we can use a worked example to show the impact. From the previous example, if we exclude the lowest marks for 30 credits (in each year) the year 2 marks become 69.17% (up from 64.38%), the year 3 marks become 70.83% (up from 69.00%). Applying the same weightings the degree mark increases to 70.0% (50/50) and 70.5% (20/80) – the 2:1 is now a 1st. It follows also that the differences between those algorithms that discount and those that do not, will become greater as the discounted module marks get lower.

12.       How common is discounting?
Without some central ‘register of practice’, it is hard to say exactly. The UUK-GuildHE survey suggests that up to a third of those universities contacted use discounting. It follows also that a large proportion of those universities that discount also apply differential weightings. The gradual shift in the use of discounting has probably been the significant driver behind grade/classification inflation.

13.       What is a borderline candidate?
Most algorithms take the degree ‘average’ to either one or two decimal points e.g. 69.5% or  69.45%. This results in borderline marks where the exam board is a called upon to determine what classification is awarded. There are various methods, one includes using a simple rule whereby marks equal to or less than 0.5% below a classification boundary are awarded the higher classification ‘automatically’ and confirmed by the exam board (thus a 1st does not start at 70%, it starts at 69.5%). Alternatively, marks within a given band (e.g. 68.5% – 69.49%) might be granted an ‘uplift’ in classification (e.g. from a U2 to a 1st) using the preponderance principle: a 1st could be awarded if the student has 60 credits in the higher boundary in their final year. Not surprisingly, these borderline adjustments can have a significant impact on individual student’s classification and the overall profile for a given programme.

14.       How do the different weightings and discounting effect a
university’s overall results?
The distribution of the degree classifications can vary significantly depending on the algorithm   used. Figure 1 shows a simulation using the same set of marks for a number of students (211 in total) where 6 different algorithms are applied. The first four algorithms (UNI[1] to UNI[4]) have  different weights for each counting year (Y2 and Y3 only), these weights range from 50/50 to 25/75, the fifth algorithm (UNI[5]) ‘discounts’ 20 credits from each year and uses a 25/75 weighting. For comparison, the sixth algorithm (UNI[6]) uses all years of study, equally weighted (which would be the outcome if the Grade Point Average (GPA) was applied – see below).

The impact is quite dramatic. In terms of the different weightings alone (UNI[1] to UNI[4]) the proportion of 1st ranges between 16% to 23%. The difference increases significantly once discounting is applied (UNI[5]), form 16% up to 32%. In Figure 1 the proportion of students achieving a 2:2 (awarded where the average mark falls between 50-59%) also declines significantly from 28.9% (UNI [1]) to 18% (UNI[5]). This simulation suggests a student’s post university ‘life chances’ may be significantly dependant on how their chosen university determines their classification (all other things being equal).

15.       Do most students understand the degree algorithm that applies to them?
A good question. The simpler the algorithm the more likely the students will understand its implication, if not use it to set personal academic targets. However, the truth is that many algorithms are very complex, and many use more than one rule to determine the degree classification. Here the interested reader might like to see a YouTube video posted by Sheffield University, in particular the comments below this video. It is also very likely that students do not take into consideration the degree algorithm when choosing a university.

16.       What is the bigger problem Grade inflation or Equity?
While the national data shows significant increases in the proportion of 1st and U2 being awarded, we cannot definitively say there has been ‘grade inflation’ – to determine this we would need the actual module marks. The increase in 1st and U2 is likely to be a combination of students working harder and gradual changes in degree algorithms. What we can say – with some certainty: is it is a concern that under the current system the same set of marks can result in such a wide range of degree outcomes. If equity and rigor are to be the hallmarks of UK higher education provision, these differences cannot be ignored or defended.

17.       What can be done about it?
If valid comparisons between students’ achievements are to be made, it is follows that all universities should adopt the same algorithm when classifying degree outcomes. In this context the consistent use of the USA GPA classification system (or similar) has clear benefits. Jonathan Wolff (professor of philosophy at University College London) accepts that adopting the GPA is a “move in the right direction” but also takes the view that “we should simply issue students with transcripts to record their study, and leave it at that (Guardian).  This is a laudable idea but one which students (and employers) might find difficult to accommodate.

Allen, D. O. (2018) Degree algorithms, grade inflation and equity: the UK higher education sector, Bristol Centre for Economics and Finance, Working Paper 1803,