Nina Heyden, a US student on a Fulbright fellowship, has won this year’s Essex Secondary Data Analysis Award for an MSc investigating the effects of immigration on the labour market.
The prize recognises and rewards Essex students, alumni and staff who have recently submitted their dissertations and who have demonstrated flair and originality in using quantitative and qualitative data available through the UK Data Service.
Nina completed her dissertation ‘The Effect of Immigration on Task Specialisation in Great Britain’ in September, and said that when she was choosing somewhere to study for a Masters, “it was a huge draw that the UK Data Archive was here.”
Having studied maths and economics at undergraduate level (at Fordham University in New York), Nina said she “was looking to further my training in data analysis in economics, and searching for Masters courses with an interdisciplinary approach. Essex offered Applied Economics and Data Analysis, and it was very rare to find that combination of two of my interests. Plus, it was very exciting to think I could tap into that data collection for my dissertation and classes.”
Nina wanted to examine a theory put forward by economists Giovanni Peri and Chad Sparber. Their paper, ‘Task Specialization, Immigration, and Wages’ (American Economic Journal: Applied Economics, July 2009), suggests that while, in theory, increasing the supply of labour through immigration might reduce wages for native-born workers, in practice, “foreign-born workers specialise in … manual-physical labor … while natives pursue jobs more intensive in communication-language tasks.” In other words, native-born workers can adapt, thanks to their language skills, to take on different roles.
As Nina says, “The consensus in the literature is that wages don’t change all that much, on average, if at all.” She wanted to look at UK data and test the theory here. “It’s crucial to our work as economists to be able to make decisions based on data. It’s important to know what’s actually going on and make informed decisions – especially nowadays when truth is questioned so much.”
The obvious dataset to use was the UK Labour Force Survey – “most of the past literature on immigration in the UK draws on it” – and Nina wanted to look specifically at the period 2001-10, “to capture the trends around the accession of the new EU member states in 2004”. She brought the UK data together with the US Department of Labor’s Occupational Information Network (O*NET) dataset which ranks skills for each occupation.
She, too, found that native-born workers adapt. “The empirical data here supported the idea that there’s room for transition for native-born workers to move into different occupations, based on the fact that they have a comparative advantage in speaking the language. Communication-intensive tasks are rewarded with higher wages, so that helps shield native-born workers from wage loss.”
Naturally, Nina says “The UK Labour Force Survey data was instrumental for the study,” and adds that, “It was crucial to have the support of the Access/Helpdesk team to get access to the data, and the User Support and Training team, too. I took part in a safe data training programme [Secure Lab] which they provided.”
Her next steps are to get some work experience, and consider a PhD. “I’m very interested in the future of work and studying immigration from the perspective of the sending and receiving countries, and looking at vulnerable populations, and human capital accumulation, so I’ll definitely work on those trends, and this research will play into future research.”