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Cutting refugees’ benefits results in more crime and less education

Reducing welfare benefits for refugees and immigrants is largely ineffective for increasing employment and promoting integration, and instead leads to poverty, ‘survival crime’ and less schooling, according to a new study from CReAM's Christian Dustmann and co-authors from the Rockwool Foundation.

Press Release

Discussion Paper

UCL News

Disadvantaged boys benefit most from early school years

Research by Christian Dustmann and Thomas Cornelissen finds that boys from disadvantaged backgrounds benefit most from early schooling, helping to narrow the skills gap (60-80%) with boys from high socio-economic backgrounds.

Press Release

Discussion Paper

UCL News

The Times

The Indepedent

Tes

Housing costs have exacerbated income equality in Germany

CReAM Research by Christian Dustmann and co-authors finds that changes in housing expenditures dramatically exacerbated the rise in income inequality in Germany since the mid-1990s. The research was covered on the German press.

Press Release

Discussion Paper

VoxEU

FAZ

UCL News

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

 

Brexit

BBC Three Counties

Christian Dustmann discussing Theresa May's comments on EU workers 'jumping the queue' on BBC Three Counties.

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.