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The Refugee Crisis

Professor Christian Dustmann comments on the current European debate on the refugee crisis and migration quotas on BBC World Service 

 

Immigrant and disadvantaged children benefit most from early childcare

Attending universal childcare from age three significantly improves the school readiness of children from immigrant and disadvantaged family backgrounds.

Press Release

Discussion Paper

iNews

UCL News

FAZ

VoxEU

 

The Criminal Behaviour of Young Fathers

CReAM Research by Christian Dustmann and  Rasmus Landersø, finds that  very young fathers who have their first child while they are still teenagers subsequently commit less crime if the child is a boy than if it is a girl. This  then has a spill over effect on other young men of a similar age living in the same neighbourhoods as the young father. The research was covered on the British press.

Press Release

Discussion Paper

VoxEU

The Telegraph

The Times

 

BBC 2

"I was quite prepared... to use the cover of the statistician's analysis": Former home secretary David Blunkett and Prof Dustmann on the 2003 report on EU accession

 

British Academy

Professor Christian Dustmann has been elected Fellow of the British Academy in recognition for his academic career and public engagement.

 

Handelsblatt

Professor Christian Dustmann ranked within the top 3 German speaking economists on the 2017 Handelsblatt ranking.

 

Brexit

BBC News

Professor Christian Dustmann discussing recent trends in foreign-born worker flows in and out of the UK on the BBC News at One.

 

CReAM seminar

CReAM - Seminar in Applied Economics Series
Will Dobbie (Princeton University)

'Measuring Bias in Consumer Lending'

Event date: Monday 8th October 2018
Time: 4:00-5:30 Place: Ricardo LT Speaker Room: 113

This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal loan applicants by exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against both immigrant and older loan applicants when using the firm’s preferred measure of long-run profits. In contrast, there is no evidence of bias when using a short-run measure used to evaluate examiner performance, suggesting that the bias in our setting is due to the misalignment of firm and examiner incentives. We conclude by showing that a decision rule based on machine learning predictions of long-run profitability can simultaneously increase profits and eliminate bias.