Research

This paper investigates colorism, racial discrimination based on skin color, in men’s football. Firstly, using machine learning algorithms, we extract players’ skin tones from online headshots to examine their impact on fan-based ratings and valuations. We find evidence of a skin tone penalty, where darker-skinned players face lower fan-driven market values and ratings. Secondly, using algorithm-based ratings and employing a Difference-in-Discontinuities design with geolocated penalty kicks data, we show that lighter-skinned players enjoy a premium higher by 1.25 standard deviation than their darker-skinned peers, conditional on scoring a penalty. Additionally, we find evidence that non-native players with dark skin face a double penalty. Leveraging the COVID-19 pandemic as a natural experiment, we highlight the role of fans’ stadium attendance in algorithm-based results. The findings underscore direct skin tone discrimination in football and highlight fans’ role in perpetuating algorithmic bias. Working Paper

Iron Curtain and Big Data are two words usually used to denote completely two different eras. Yet, the context the former offers and the rich data source the latter provides, enable the causal identification of the effect of networks on migration. Academics in countries behind the Iron Curtain were strongly isolated from the rest of the world. This context poses the question of the importance of academic networks for migration post the fall of the Berlin Wall and Iron Curtain. Using Microsoft Academic Knowledge Graph, a scholarly big data source, mapping of academics’ networks is possible and information about the size and quality of their co-authorships, by location is achieved. Focusing on academics from Eastern Europe (henceforth EE) from 1980-1988 and their academic networks (1980-1988), We investigate the effect of academic network characteristics, by location, on the probability to migrate post the fall of the Berlin Wall in 1989 and up to 2003, marking the year many EE countries held referendums or signed treaties to join the EU. The unique context ensures that there was no anticipation of the fall of the Eastern Bloc and together with the data that offers unique rich information, identification is achieved. Approximately 30k academics from EE were identified, of which 3% were migrants. The results could be explained by two channels, the cost and signaling channel. The cost channel is how the network characteristic reduces or increases the cost of migration and thus acting as a facilitator or a de-facilitator of migration. The signal channel on the other hand in which the network characteristic serves as a signal for the academic himself and his quality and his potential contribution and addition to the new host institution, thus also serving as a facilitator or a de-facilitator of migration. We find that mostly network size and quality results could be explained by the cost channel and signalling channel, respectively. Size of the network tends to be more important than the quality, which is a context-specific result. We find heterogeneous effects by fields of study that align with previous lines of research. Heterogeneous effects are explained by two things: threat of attention and arrest by KGB and the role of reputation, language, and network barriers. 

CESifo Working Paper

This paper presents a content analysis of gender stereotypes in popular song lyrics using word embeddings. We begin by explaining how we curated a novel data set comprising lyrics from popular songs in the US over the past 70 years. We then explain word embeddings, detailing both their nature and their application to our lyric corpus. Subsequently, we present a case study that examines the prevalence of gender stereotypes across various music genres. Our findings showed that while all genres exhibited stereotyping of men and women, the specific content of these stereotypes varied significantly by genre, often in surprising ways, such as that gender stereotypes in hip-hop, often perceived as being distinctly sexist, were rarely stronger in hip-hop than in other genres. Finally, we reflect on the strengths and limitations of using word embeddings to study music lyrics and provide suggestions for their best application to sociological questions.

Submitted to the Bulletin of Sociological Methodology

The categorization of individuals into in-groups and out-groups, displays of in-group favoritism, and out-group prejudice, are all features humans engage in and display (Allport et al., 1954). The classification of individuals into different groups is context dependent, influenced by factors such as immigration waves, group salience, and perceived distance (Fouka and Tabellini, 2022). This project builds on social identity theory (Turner,1987; Turner et al., 1994) and identity formation (Bisin et al., 2016). More specifically, I ask: What happens when social ground boundaries change? Particularly, if a previously considered out-group is now part of the in-group. For example, when a group is considered “legally white” and thus eligible for naturalization for US citizenship (pre-1952). How do the newcomer in-group identity and assimilation efforts change in response to this? Do they choose to culturally conform or culturally distinguish themselves from the incumbent in-group now that the cost of assimilation is lower? How does the incumbent in-group respond to this? How are other existing out-groups perceived by the newcomer and incumbent in-group? This case was demonstrated by the Arabs who successfully “claimed their whiteness” in 1915, and consequently, were eligible for naturalization, due to the racial prerequisites in naturalization laws since 1790 and up until 1952. The above questions can be answered not only due to the specific legal context but also due to the extremely unique data I have collected on the Arab diaspora, especially their Arab-American newspapers circulated in the US. 

More updates coming soon!

How do the cultural preferences of parents shape the labor market outcomes of their children? The literature has mainly focused on proving the existence of this intergenerational transmission of cultural preferences and its influence on children’s long-run outcomes. However, there is a lack of understanding of the role of these inherited cultural preferences on children’s educational and occupational choices that ultimately lead to these outcomes. We leverage rich Swedish administrative data on university applications and labor market outcomes combined with structural modeling to address this research gap. Exploiting the cross-country variation in culturally linked risk preferences provided by the diverse pool of immigrants to Sweden, we study the importance of the risk profile of university-major choices among second-generation migrants and how the risk preferences of their parents’ birth countries affect migrant children’s sensitivity to these risk profiles. As a next step, we will study the long-term labor market consequences of these choices, both at the individual level and for aggregate welfare.

We have obtained ethical approval from the Swedish Ethical Review Authority. Using successful grant applications from ANR-17-EURE-00 and support from Rothschild Migration Chair, we have acquired registry data from Statistics Sweden. 

Draft coming soon. 

This project aims to study the "sociology of innovation"; an adaptation of the sociology of industry by Granovetter (1998) by focusing on Germans who arrived in the US post the failed German revolution of 1848. The German failed revolution of 1848 marks the dividing line between early industrialization and the industrial revolution. In the Second Industrial Revolution, Germany was a pioneer in chemistry, steel, and machinery. Thus, observing Germans arriving from 1848 onwards, we can study the role of know-how, its transmission mechanisms, what matters for inventors, what happens to occupations of immigrants, what's the role of different skill composition, and quantify the impact on US innovation. 

We are using the full sample US historical census, ship lists containing 4 million Germans that arrived in the US from 1850 to 1897 (containing information on occupations at home), yearbooks of R&D labs, and historical patent data (1790-2010). 

More updates soon!