My research interests lie at the intersection of public, labor and normative economics. Under the overarching theme of equality of opportunity my research agenda is driven by two main objectives.
First, I aim to strengthen the methodological toolkit that is used to quantify the extent of inequality of opportunity in current societies. Thereby, my work connects to the literature branches on intergenerational mobility and inequality measurement.
Second, I aim to contribute to our understanding of which circumstantial life factors cause the unequal distribution of life chances. Thereby, my work connects to the literature branches on early childhood development and human capital formation.
Rising income inequalities are widely debated in public and academic discourse. In this paper, we contribute to this debate by proposing a new family of measures of unfair inequality. To do so, we acknowledge that inequality is not bad per se, but that its underlying sources need to be taken into account. Thereby, this paper is the first to reconcile two prominent fairness principles, namely equality of opportunity and freedom from poverty, into a joint measure of unfair inequality. Two empirical applications provide important new insights on the development of unfair inequality both over time (in the US) and across countries (in Europe). First, unfair inequality shows different time trends and country rankings compared to total inequality. Second, average unfair inequality doubles when complementing the ideal of an equal opportunity society with poverty aversion. Furthermore, we show that an exclusive focus on top incomes may misguide fairness judgments.
In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods - conditional inference regression trees and forests - represent a substantial improvement over existing estimation approaches. First, they reduce the risk of ad-hoc model selection. Second, they establish estimation models by trading off upward and downward bias in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This makes the measurement of inequality of opportunity more easily comprehensible to a large audience. The advantages of regreession trees and forests are illustrated by an empirical application for a cross-section of 31 European countries. We show that arbitrary model selection may lead researchers to overestimate (underestimate) inequality of opportunity by up to 300% (40%) in comparison to our preferred method. This illustrates the practical importance of leveraging machine learning algorithms to avoid misleading recommendations with respect to the need for opportunity equalizing policy interventions in different societies.
Equality of opportunity is an important normative ideal of distributive justice that co-determines macro-economic outcomes of societies. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest little scope for upward bias but strongly downward biased estimates of inequality of opportunity in the examined set of emerging countries. By bounding inequality of opportunity from above, we furthermore address recent critiques that worry about the prevalence of downward biased estimates and the ensuing scope for downplaying the normative significance of inequality
Work in Progress
Genetic Endowments, Educational Outcomes and the Mediating Influence of School Investments
With Benjamin Arold and Marc Stöckli
Draft in Preparation
Human capital is the product of natural endowments and a variety of environmental factors that interact in a dynamic manner. In this paper we investigate to what extent investments into schooling environments are able to alter the productivity of genetic endowments. The characterization of this process is of considerable importance for policymakers willing to address equity and efficiency concerns in the production of educational outcomes. We find that better teachers act as substitutes to genetic endowments. To be precise, a one standard deviation in our teacher quality index reduces the positive impact of a one standard deviation increase in the polygenic score for educational attainment by 25%. The positive impact on aggregate educational attainment works through an increase of the probability that genetically disadvantaged students graduate from college. However, we find no such positive impacts on standard measures of (non-)cognitive skills, personality and preferences.
The Closing Gender Gap and the Development of Socio-emotional Skills in Children
Draft in Preparation
Abstract and paper coming soon!
School Spending and Equality of Opportunity in Education: Evidence from School Finance Reforms
With Eric Hanushek, Marc Piopiunik and Marc Stöckli
Draft in Preparation
Abstract and paper coming soon!
While it is well documented that political participation is stratified by socio-economic characteristics, it is an open question how this finding bears on the evaluation of the democratic process with respect to its fairness. In this paper we draw on the analytical tools developed in the equality of opportunity literature to answer this question. We investigate to what extent differential political participation is determined by factors that lie beyond individual control (circumstances) rather than being the result of individual effort. Using rich panel data from the US, we indeed find a lack of political opportunity for the most disadvantaged circumstance types. Opportunity shortages tend to complement each other across different forms of participation and persist over time. Family characteristics and psychological conditions during childhood emanate as the strongest determinants of political opportunities.
Many studies have estimated the effect of circumstances on income acquisition. Perhaps surprisingly, the fraction of inequality attributable to circumstances is usually quite small—in the advanced democracies, approximately 20%. One reason for this is the lack of data on circumstance variables in empirical research. Here, we argue that all behaviors and accomplishments of children should be considered the consequence of circumstances: that is, an individual should not be considered to be responsible for her choices before an age of consent is reached. Using two data sets that contain data on childhood accomplishments, other environmental circumstances and the income as an adult, we calculate that the fraction of income inequality due to circumstances in the US rises from 27 to 43% when accounting for childhood circumstances. In the UK it rises from 18 to 27%.
The Local Impacts of Large-Scale Land Acquisitions: A Review of Case Study Evidence from Sub-Saharan Africa (with Daniel F. Heuermann). Journal of Contemporary African Studies, 2017, 35 (2), pp. 168-189.