“The Hidden Cost of Mass Deportations: Early-Life Mortality and the 1930s Mexican Repatriation in Texas” joint with Jesse McDevitt-Irwin (Draft available upon request)
From 1928 to 1935, the US government engaged in a campaign to push millions of Mexicans to leave the country. This ‘repatriation’ campaign worked by creating a deliberate climate of fear in the Mexican and Mexican-American community. Somewhere between 300,000 and over a million people fled the country. This repatriation campaign has been the subject of extensive research, but lack of data has led the effect of the campaign on the health of the Mexican immigrant population to remain largely unknown. We use childhood sex ratios from the US census as an indicator of maternal health and fetal-infant loss. We use a triple difference approach, leveraging the fact that the campaign began in Texas in 1928, whereas it began in 1931 in the rest of the country. We find that the repatriation campaign was associated with a substantial deterioration of fetal-infant health. In Texas, under-3 sex ratios in 1930 shifted heavily towards females for Mexican immigrant households. The change in sex ratios suggests an increase in combined stillbirths and infant mortality of about 14.5 percentage points. Maternal stress was a likely driver of this effect, but is not measurable in our setting. We explore withdrawal from public services and healthcare as another mechanism, proxied by a novel data set of birth registration completeness in Texas. In the medium run, we find that children of Mexican immigrants who were exposed to the campaign in Texas had worse educational outcomes.
"Crossed by the Border: Economic Assimilation of Mexican Involuntary Migrants" (Draft available upon request)
Inter-country conflicts usually lead to annexations and occupations, yet we don’t know much about how the populations that get absorbed assimilate both socially and in the labor market. This paper studies the economic and social outcomes of Mexicans that were absorbed by the US annexation of the American Southwest in the mid 19th century. I find achievement gaps in the labor market that get passed along to their children, as well as low intermarriage rates. These gaps are much bigger than the gaps observed for Latin American migrants and free Black Americans. One of the main drivers of these differences seems to be geography, since adding state X year fixed effects greatly reduces them.
“Acquisitions and Relational Management Practices”, joint with Ameet Morjaria, Michael Powell, and Rocco Macchiavello.
“US Border Wall and Organized Crime in Mexico”
“Gender Stereotypes in Job Advertisements: What Do They Imply for the Gender Salary Gap?” with Raymundo Campos, Eva Arceo, and Raquel Badillo. (Journal of Labor Research, 2022, vol. 43, issue 1, No 3, 65-102)
Gender stereotypes, the assumptions concerning appropriate social roles for men and women, permeate the labor market. Analyzing information from over 2.5 million job advertisements on three different employment search websites in Mexico, exploiting approximately 235,00 that are explicitly gender-targeted, we find evidence that advertisements seeking “communal” characteristics, stereotypically associated with women, specify lower salaries than those seeking “agentic” characteristics, stereotypically associated with men. Given the use of gender-targeted advertisements in Mexico, we use a random forest algorithm to predict whether non-targeted ads are in fact directed toward men or women, based on the language they use. We find that the non-targeted ads for which we predict gender show larger salary gaps (8–35 percent) than explicitly gender-targeted ads (0–13 percent). If women are segregated into occupations deemed appropriate for their gender, this pay gap between jobs requiring communal versus agentic characteristics translates into a gender pay gap in the labor market.
“Big Data, Google, and Unemployment” (Spanish) with Raymundo Campos Vázquez. (Estudios Económicos 9-vol. 35, no. 1, January-June, 2020)
We use Google Trends data for employment opportunities related queries in order to nowcast the unemployment rate in Mexico. We begin by discussing the literature related to big data and nowcasting in which user generated data is used to forecast unemployment. Afterwards, we explain the basics of several machine learning algorithms. Finally, we implement such algorithms in order to find the best model to predict unemployment using both Google Trends queries and unemployment lags.
“The Status of Mexico’s Economic Science” (Spanish). Coauthored with Raymundo Campos Vázquez. (El Trimestre Económico vol. 85-340)
We study both the authors and subjects discussed in Mexico’s six main economic journals from 2000 to 2017. We study how the presence of female authors has evolved in Mexican economic journals and, also, the interactions among scholars from different institutions using network analysis. On the other hand, we use text analysis on the abstracts to determine if certain keywords related to Mexico’s main economic problems or vulnerable groups were mentioned.