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,%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

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.


[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.

Training Researchers to Work with Confidential Data: A New Approach

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Prof Felix Ritchie of UWE’s Business School has recently spent time with the Northern Ireland Statistics and Research Agency and makes the following analysis.

I’ve just spent two days at the Northern Ireland Statistics and Research Agency (NISRA), working with them to develop training for researchers who need access to the confidential data held by NISRA for research. This training is jointly being developed by the statistical agencies of the UK (NISRA, the General Register Office for Scotland, and the Office for National Statistics in England and Wales), as well as HMRC, the UK Data Archive and academic partners. The project is being led by ONS as part of its role to accredit researchers under the new Digital Economy Act, with UWE providing key input; other statistical agencies, such as INSEE
in France and the Australian Bureau of Statistics, are being consulted and are trialling
some of the material.

Training researchers in the use of confidential data is common across statistical agencies around the world, particularly when those researchers need access to the most sensitive data only available through Controlled Access Facilities (CAFs). The growth in CAFs in recent years has mostly come from virtual desktops which allow researchers to run unlimited analyses while still operating in an environment controlled by the data holder. There are now six of these in the UK, and many countries in continental Europe, North America and Oceania operate at least one. The existence of CAFs has led to an explosion in social science research as many things that were not previously allowed because it was too risky to send out data (such as use of non-public business data, or detailed personal data) have now become feasible and cost-effective.

All agencies running CAFs provide some training for researchers; around half of these use ‘passive’ training such as handouts or web pages, but the other half require face-to-face training. Much of this training has evolved from a programme developed at ONS in the UK in the 2000s and this training was recommended as an example of ‘best practice’ for face-to-face training by a Eurostat expert group.

However, this style of training is showing its age. Such training typically has two components: firstly how to behave in the CAFs and secondly how to prevent confidential data from mistakenly showing up in research outputs (‘statistical disclosure control’, or SDC). Both are typically taught mechanistically, in the form of dos and don’ts, explanations of laws and penalties and lots of SDC exercises. Overall the aim of the courses is to impart information to the researcher.

The new training is radically different from the old training. It starts from the premise that researchers are both the biggest risk and the biggest advantage to any CAF: the biggest risk because a poorly-trained or malcontented researcher can negate any security mechanism put in place; the biggest advantage because highly-motivated researchers means cheaper system design, better and more robust security and the chance for the data holder to exploit the goodwill of researchers in methodological research, for example.

In this world the main aim of the training is to encourage the researcher to see himself or herself as part of the data community. If this can be established then the rest of the training follows as a consequence. For example, knowledge of the legal environment or SDC is shared not because it keeps you out of jail but because everyone needs to understand this so the community as a whole works. This gives the course quite a different feel to more traditional courses: much of the day is spent in open-ended facilitated discussions exploring concepts of data access.

The training was designed from the ground up in order to take advantage of recent developments in thinking about data access and SDC. This was also done to avoid being restricted by having to ‘fit’ preconceived ideas about what worked or not; material was included on its own merits, not whether “this was what we used to do…”. For example, the previous SDC component had a large number of numerical examples, developed over many years, leading to attendees remarking on afternoons spent “doing Sudoku”. We reviewed every example to identify the minimum set of principles needing to be explored and then wrote a small number of new examples based on this minimum set. On the other hand, the previous training had relatively little to say about the context for checking outputs for confidentiality breaches; this has now been expanded as it fits with the ethos of understanding why things are done.

Of course, this was not all plain sailing. The original structure, trialled in June 2017, had just one presentation before being comprehensively abandoned. Modules have dropped in and out and been moved around. The initial test for the course has been completely rewritten (a topic for a later blog). Various sections have been inserted as ‘options’ to take account of regional variations in operating practices. Throughout this, multiple organisations have been able to feed into the process so that the final product itself has a sense of community ownership.

We are now at the stage of training-the-trainers to enable independent delivery around the UK. This is already generating much feedback for the future development of the course: for example, a need has arisen for ‘crib sheets’ to help in the facilitation of certain exercises. Overall, however, we are confident that we have a well-structured, informative, course that meets the needs of 21st century data training.

Further reading: for more information on the evidential and conceptual basis for the course, see Ritchie F., Green E., Newman J. and Parker T. (2017) “Lessons Learned in Training ‘Safe Users’ of Confidential Data“. UNECE work session on Statistical Data Confidentiality 2017. Eurostat.