Eduardo Fé is a statistician and econometrician studying how human traits—especially health and cognition—develop over a lifetime. His work combines secondary data analysis with experiments, and he has a particular focus on causal inference and nonparametric methods, including Regression Discontinuity and Partial Identification.

Eduardo takes a flexible approach to statistical and econometric modeling, favoring assumptions that are credible, interpretable, and allow the data to "speak for themselves." However, he has also contributed to parametric methods, including co-developing the Poisson Log-Half Normal distribution for modeling misreported count data. His research spans a wide range of topics, from the underreporting of crime statistics and healthcare in rural China to the impact of early-life experiences on adult outcomes. A significant portion of his recent work examines how retirement affects health and cognition.

Eduardo has published in leading journals such as the Journal of Political Economy, Journal of the Royal Statistical Society, Health Affairs, and the Journal of Quantitative Criminology. Before joining the University of Manchester, he was a Senior Researcher at the University of Oxford and a Lecturer at the University of Strathclyde. He earned his Ph.D. in Econometrics at the University of Manchester.

You can find his publications on Google Scholar profile. 

Before joining Manchester, Eduardo was a Senior Researcher at the University of Oxford (Blavatnik School of Government and Health Economics Research Centre) and a Lecturer in Economics at the University of Strathclyde. Eduardo completed a Ph.D. in Econometrics at the University of Manchester thanks to the support of Fundación Ramón Areces. Born in Santander (Spain) he completed his undergraduate education in Business Administration at the University of Cantabria, spending one year as Erasmus student at University of Ulster (Coleraine). 

Current projects.

For some code and other materials, I'm in GitHub! 

Education and partisan alignment.

This paper studies the effect of education on political alignment and interest in politics. We leverage quasi-experimental variation induced by the raising of the school leaving age in the UK in 1972 to obtain preliminary causal estimates via a randomisation-based Regression Discontinuity Design. These estimates, however, might confound the effect of education with political preferences of the specific population targeted by school-leaving age policies. We thus propose a Partial Identification framework to obtain more robust conclusions. Whereas our initial estimates suggested that education causes left-wing alignment, the more robust Partial Identification regions suggest education leads to more conservative preferences.

Partial Identification of the effect on couples of unemployment. 

This article explores the effect of unemployement on couples. We focus on consumption, house production and mental health. Critically, unlike in previous literature, we dealing with the pervasive problem of interference within the household. The key to our identification strategy is the combination of panel data with partial identification. 

Bounding the intergenerational transmission of cognitive
skills.

This paper employs a nonparametric bounds analysis to investigate the causal impact of parental cognitive skills on their children's cognitive development. Utilizing data from the U.K. Household Longitudinal Study, we explore how the cognitive abilities of parents in adulthood influence the cognitive skills of their children during late infancy and early adolescence, with distinct analyses for fathers and mothers. We adopt a partial identification framework to circumvent the need for instrumental variables or strong structural assumptions, providing credible bounds on the causal effects. Our approach acknowledges the complexity of intergenerational transmission by considering biological, sociocultural, and interaction effects between parental skills. 

Inference in medical cost effectiveness analysis with heavy tailed data

The bootstrap appears to be well suited for performing accurate inference in medical cost-effectiveness analysis, where data are generally drawn from a randomized experiment. However, the literature to date has given priority to asymptotic approximations. Cost data, though, often exhibit heavy tails and this presents challenges to asymptotic methods. In this paper, we study the performance of these methods for cost effectiveness analysis in medical research and compare them with inference based on the standard bootstrap. We find both methods fail to deliver reliable inference in the presence of heavy tails. We therefore consider two alternative resampling schemes: the wild bootstrap and randomization inference. Both methods provide accurate inference, providing the underlying statistic has finite expected value. In general, these methods provide inference with little size distortion and power above that achievable with asymptotic methods or the standard bootstrap. 

Exposure and contemporaneousness: What can we learn about the effect of drought on children's cognitive development?

With Y-Ling Chi (Imperial College)

In this paper, we combine a comprehensive Indonesian household survey with detailed meteorological data to explore what can be learned about the effect of drought on the long term cognitive development of Indonesian children living in rural areas. We face a common problem of latent exposure with observable contemporaneousness. The problem is compounded by plausible endogeneity and likely confounding. To estimate the effects of drought on Indonesian children's scores in a fluid intelligence test, we consider a battery of different identification assumptions which vary in credibility and power. Our most powerful assumptions point identify the effect of contemporaneousness, however they have debatable credibility. Our most credible assumptions, on the other hand, convey little information about the effect of contemporaneousness. In between these two extreme, we consider a range of   middle-of-the-way assumptions  which partially identify the effect of contemporaneousness. Specifically, we characterise some of the assumptions required to establish the sign of the effect of contemporaneousness. Our results reveal differential effects of drought across sexes, however we find at least two competing explanations which would explain these difference: natural selection, on the one hand, and family dis-investing on girls in the fact of hardship, on the other.

I will be happy to supervise Ph.D. students in any of the following areas:

• Applied Econometrics and Statistics.
• Experimental Economics and Experimental Pyschology (strategic thinking; social norms)