Research

_{Abstract: Empirical results in economics often stem from success in controlled experimental settings, but often fail when scaled up. This study presents a behavioral intervention and a scalable equivalent aimed at reducing teacher shortages by motivating high school students to pursue an education degree. The intervention was delivered through WhatsApp chats by trained human promoters (humans arm) and rule-based Chatbots programmed to closely replicate the humans program (bots arm). Results show that the humans arm successfully increased high-school students’ demand for and enrollment in education majors, particularly among high-performing students. The bots arm showed positive but smaller and statistically insignificant effects. These findings indicate that a relatively low-cost intervention can effectively reduce teacher shortages, but scaling up such interventions may have limitations. Therefore, testing scalable solutions during the design stage of experiments is crucial.}

Link: IZA WP

_{Abstract: Inequality in access to high-quality teachers is an important driver of student socioeconomic achievement gaps. We experimentally evaluate a nationwide zero-cost government program in Ecuador to reduce teacher sorting (that is, lower-income students are more likely to attend understaffed schools with less qualified teachers) based on an insight from behavioral economics: order effects. In the treatment arm, the teachers’ job application platform showed hard-to-staff schools first, while in the control group teaching vacancies were displayed in alphabetical order. In both arms, hard-to-staff schools were labeled with the same icon. Teachers in the treatment arm were more likely to apply to hard-to-staff schools, rank them as their highest priority, and be assigned to a job vacancy in one of them. The effects were not driven by inattentive, altruistic, or less-qualified teachers. Instead, choice overload and fatigue seem to have played a role. As teachers in the treatment group were more likely to apply to schools with poorer and lower-performing students that typically have the lowest numbers of applicants per vacancy, the intervention helped to reduce the unequal distribution of qualified teachers across schools of different socioeconomic backgrounds.}

Link: IZA WP

_Abstract:

_{This paper assesses the results of an experiment designed to identify discrimination in users’ following behavior on Twitter. Specifically, we created fictitious bot accounts that resembled humans and claimed to be PhD students in economics. The accounts differed in three characteristics: gender (male or female), race (Black or White), and university affiliation (top- or lower-ranked). The bot accounts randomly followed Twitter users who form part of the #EconTwitter academic community. We measured how many follow-backs each account obtained after a given period. Twitter users from this community were 12% more likely to follow accounts of White students compared to those of Black students; 21% more likely to follow accounts of students from top-ranked, prestigious universities compared to accounts of lower-ranked institutions; and 25% more likely to follow female compared to male students. The racial gap persisted even among students from top-ranked institutions, suggesting that Twitter users racially discriminate even in the presence of a signal that could be interpreted as indicative of high academic potential. Notably, we find that Black male students from top-ranked universities receive no more follow-backs than White male students from relatively lower-ranked institutions.}

Link: WP

_Abstract:

_{We experimentally study the interplay between political and other social identities in forming social ties in a context of intense political polarization. We created accounts on Twitter that signaled their political preference for one of the two leading candidates in the 2022 Brazilian Presidential election, their preference for a football club (interpreted as an affective dimension of identity, given football’s importance to socialization in Brazil), or both. The bots randomly followed Twitter accounts with congruent and incongruent identities across these dimensions. We computed the proportion of followbacks and blocks received. Both dimensions of identity are relevant to forming ties, but the effect of sharing political identity is larger. Moreover, affective identity becomes substantially less relevant when information about political identity is available, indicating that political identity can overshadow the affective dimension. Still, shared affective identity has a positive effect in fostering ties even among politically opposite individuals. This suggests that shared affective identities have the potential to reduce politically induced societal divides, despite this effect being lower in a polarized setting.}

Link: WP

_Abstract:

_{Recent literature shows that massive immigration can trigger crime-related concerns and preventive behavioral reactions, even when crime rates are unaffected. A plausible hypothesis to explain these misperceptions is that the media disproportionately amplifies crimes when they are allegedly perpetuated by immigrants vis-a-vis natives. This paper uses daily newspaper and TV data from Chile (a country that tripled its foreign-born population in less than ten years) combined with daily homicides data to show that the frequency of crime-related news during the days immediately following a homicide is significantly larger when a foreigner committed the homicide than when a native committed it. TV news is the main driver of the effect: relative to the variation in TV after a homicide committed by a native, the number of crime- related news on TV in the following two weeks increases by 12 percent when a foreigner was allegedly the perpetrator.}

Draft coming soon