Research

Working Papers

Abstract: Procrastination, or the irrational delay of an intended course of action, is typically believed to be an undesirable trait which is associated with sub-optimal outcomes. In higher education this is mostly displayed in last minute work on coursework and dissertations resulting in a demand for deadline extensions. University policy only allows students to apply for extensions in ``exceptional circumstances outside of the student’s control, that may have a negative effect upon performance or ability to meet a deadline or to sit an examination". These typically include bereavement, serious short-term illness or significant adverse personal circumstances. The COVID pandemic resulted in a relaxation of conditions under which students were able to obtain an extension on their dissertation deadline, with a no-questions-asked policy for short-term extensions (up to 14 days). It removed the need to provide supporting evidence and included technical issues as a reason to request an extension. As a result of this relaxation, extension requests tripled in both years following the start of the pandemic.

Students whose reasons for requesting extensions comply with pre-COVID rules are likely to raise these reasons as justification for the request. The no-question asked policy presented an opportunity for procrastinators to also obtain extensions. This study looks at the impact of granting extensions on the performance of students on the dissertation module and on other, concurrently assessed, modules. We separate the analysis based on the length of the extension and analyse the impact of this relaxation in this policy.  

From our analysis of the data, we find that students who receive longer extensions are associated with a lower performance in the dissertation itself and also in some of the other modules that are taking place concurrently. Controlling for programme of study, supervisor, dissertation topic and previous academic performance, the results persist.

Procrastination has been steadily linked to poorer academic performance (Han et al. (2019), Steel (2007), Kim & Seo (2015)). Ferrari & Scher (2000) identify that procrastination is more likely to occur with tasks that are more effortful and those that generate more anxiety. This effect is stronger, the larger the distance between the present date and the deadline. This is the reason that ECON3036 dissertation is a very good candidate for research, since it is a long project that starts at the beginning of the semester and ends very close to semester 1 exams. Van Eerde (2003) make an argument that a better measure for observed delays in submission may actually be self-discipline rather than procrastination. Both these traits are strongly linked with intertemporal preferences and discounting behaviour (Shamosh & Gray (2008)). 

Given the literature on this topic, the frequency of extensions provides some insight on students’ tendency to procrastinate. Some students have a propensity to delay working on coursework, despite potential negative consequences. This can be measured by eliciting students’ time and risk preference. 

From an educational point of view, the design of a forward-feedback mechanism aimed at modifying the tendency of students to request extensions on coursework deadlines, provides a valuable project worth pursuing. An improvement in time management is, on its own, an important transferable skill worth developing in students. However, the main rationale for this project is to improve student engagement in long duration assessment, with the aim of improving their academic performance. The introduction of some stepping-stones in the assessment journey using a progress tracking system is one mechanism that we would like to explore and evaluate.

Abstract: Engagement in online materials available on Blackboard has been a critical issue especially during the pandemic, where face to face teaching flipped to synchronous or asynchronous teaching and blended learning. The aim of educators during the COVID period was to find innovative ways to engage students via blended learning. One tool for engaging students with the online materials is continuous e-assessment. Continuous e-assessment has many positive externalities on students’ performance and engagement. Holmes (2015 & 2018), among others found that introduction of e-assessments led to a significant increase in virtual learning environment activity. The question that arises is how successful continuous e-assessment on engaging students with the online materials during the pandemic was and whether this had any impact on students’ performance.

In this research I investigate the impact of continuous e-assessment on students’ engagement and performance. I focus on first year Maths for Economics and third year Applied Economics modules. In the first year of the pandemic, online tests were introduced in both modules only as a method of engagement, where full marks were given upon completion of the tests. After the pandemic, online tests in these modules were used as a method of engagement and assessment. This natural experiment allows me to test the impact of formative and summative e-assessments on students’ engagement with online materials and on their performance in their final exams. More specifically, I test whether weekly formative or summative e-assessments lead students to engage more with the online materials of each topic on Blackboard. I also test whether formative or summative e-assessments are associated with better performance in students’ final assessment. Preliminary results show that summative e-assessments plays a significant higher role on students’ engagement and performance than formative e-assessments. 

Abstract: Human capital has been found to be important for aggregate productivity, and large individual human capital losses are associated with job displacements. I investigate the role of involuntary job separations (since displacements have increased during the 2008 financial crisis) on the UK's productivity puzzle. By linking the "British Household Panel Survey'' with the "Understanding Society'' dataset I extract a unique dataset of worker's employment histories for the UK from 1990 to 2011, and observe the following results: displacements can explain on about the 24 percent of the post-crisis gap, if aggregate labour productivity had followed the path of past recessions. Furthermore, almost the 78 percent of this effect can be explained by the drop in wages of high educated workers and the rest 22 percent by the drop in wages of low educated workers (JEL Classification: J24, J31, J63, E24).

Presented in: the "Royal Economic Society Conference 2016 (University of Sussex)'', the "40 Symposium of the Spanish Economic Association (SAEe 2015)'' winning the "Grant: Fundacion Ramon Areces for SAEe Symposium 2015 Girona",  the "14th Conference on Research on Economic Theory and Econometrics (C.R.E.T.E. 2015)'', and the "2015 Understanding Society Scientific Conference''.

Abstract: I exploit household data to estimate the magnitude and the temporal pattern of displaced workers earnings for the UK economy. By using the "British Household Panel Survey" from 1990 to 2011, I observe the effect of wage cuts and hours of working decreases on earnings losses after a job separation. I introduce a decomposition of earnings in aggregate level and I find that earning losses after a job displacement are mainly driven by cuts in wages (80%-90%) and not by decrease in hours of working. From the empirical estimations I get the following results: Earnings losses for workers who were employed after a displacement, are 8 percent in the short run and 5 percent in the long run. If I consider also unemployed and inactive, the drop is larger to 30 percent in the short run and 20 percent in the long run (JEL Classification: J31, J32, J63).

Abstract: This paper empirically examines the worker's choice of using different search channels on finding a job for the UK economy. We focus on the UK's labour market where the use of referrals as a search channel is by 50% lower than that in the US. We estimate matching functions for 6 different channels and also introduce a new method in the literature which handles better possible endogeneity issues. By using the "Quarterly Labour Force Survey" and the "Vacancy Survey" datasets the results show that the most efficient channel are referrals and the second most efficient one are job advertisements. The channel with the lower efficiency is jobcenter, jobmarket or training and employment agency office (JEL Classification: J0).