MIP network member Áureo de Paula is a Professor in the Economics Department at University College London. He is also a Research Fellow at the Institute for Fiscal Studies and at CEPR, Research Staff at CeMMAP, and a Research Affiliate at the Population Studies Center. An applied economentrician, de Paula is interested in both methodological questions and empirical applications. He received a B.A. and M.Sc. in Economics from Pontificia Universidade Catolica and an M.A. and Ph.D. in Economics from Princeton University.
Describe your area of study and how it relates to current policy discussions surrounding inequality.
I am an applied econometrician and my interests range from methodological to more substantive topics. In short, my research revolves around understanding interaction behaviour of individuals, households or firms and their drivers. Inequality refers to how different individuals are within a neighbourhood, cluster or geography, more generally. Insofar as the decisions one takes may depend on the group, those interactions will be informed by and may also affect inequality and its determinants. More concretely, among the several substantive projects I have been recently engaged in, I am currently examining take up of social programmes and potential spillovers across eligible individuals using administrative data from Chile, for example. There is a large literature on the topic and such programmes are important levers in potentially alleviating inequality. Some of my current research projects focus on firms, using again administrative data on inter-firm transactions linked to employer-employee data in Ecuador and Brazil. It is notoriously difficult to confidently retrieve firm features, how they combine inputs as well as firm productivity from conventional data. Those elements are of course important in understanding not only firm behaviour but also how it articulates with inequality within and across firms on input factor remuneration, like worker compensation. The inter-firm transaction dimension nonetheless offers interesting avenues on how to measure the relevant ingredients appropriately to better understand those aspects. One final example is a recent project on the intra- and inter-generational transmission of socio-emotional skills in childhood using multi-generational data from the United Kingdom with adequate measurements at relevant ages. Several members of the HCEO have provided abundant and fundamental evidence on the economic importance of those. Examining how such skills correlate across generations may thus be relevant in understanding the conduits for the intergenerational correlations and inequalities in more conventionally measured aspects such as education, income and wealth.
What areas in the study of inequality are most in need of new research?
While inequality has been a topic of interest for quite a long time in economics and has recently sparked renewed interest, a better understanding of mechanisms driving such descriptive measurements is still needed. This ranges from interactions within and across households to the interplay between firms and employees and possibly across firms themselves and between those agents and the state. I agree with my colleague Richard Blundell’s remarks in this very series that this would entail examining how inequality across several domains (skills, health, consumption, wealth, etc.) are related; how individual association into households and firms contributes and responds to inequality; and how government policy influences those through taxation and welfare programmes. While good research exists on all of these, much more is needed. A deeper comprehension will require careful measurement, appropriate data but also an attention to relevant theories and how empirical facts can be leveraged in discriminating among hypothesis through adequate econometrics. In that respect, I feel that an integrated approach bringing in labour economics, industrial organisation, human capital theory and public economics is called for.
What advice do you have for emerging scholars in your field?
In the spirit of my answer above, it is important to keep an open mind to collaborative work within fields in economics (theory, econometrics, empirics). Other social and quantitative sciences also offer interesting complementary perspectives and opportunities. Innovative uses of data employing new tools in data science, for example, may provide novel insights into particular aspects of inequality and guide new theories and measurement resources to understand the problem. Thinking outside the box may require looking outside the literature for different tools and analyses. One should not be afraid to make such journeys!