Economics

Using recent data from voluntary mass testing, I provide credible bounds on prevalence of SARS-CoV-2 for Austrian counties in early December 2020. When estimating prevalence, a natural missing data problem arises: no test results are generated for non-tested people. In addition, tests are not perfectly predictive for the underlying infection. This is particularly relevant for mass SARS-CoV-2 testing as these are conducted with rapid Antigen tests, which are known to be somewhat imprecise. Using insights from the literature on partial identification, I propose a framework addressing both issues at once. I use the framework to study differing selection assumptions for the Austrian data. Whereas weak monotone selection assumptions provide limited identification power, reasonably stronger assumptions reduce the uncertainty on prevalence significantly.

This paper studies the impact of an university opening on incentives for human capital accumulation of prospective students in its neighborhood. The opening causes an exogenous fall on the cost to attend university, through the decrease in distance, leading to an incentive to increase effort - shown by the positive effect on students' grades. I use an event study approach with two-way fixed effects to retrieve a causal estimate, exploiting the variation across groups of students that receive treatment at different times - mitigating the bias created by the decision of governments on the location of new universities. Results show an average increase of 0.038 standard deviations in test grades, for the municipality where the university was established, and are robust to a series of potential problems, including some of the usual concerns in event study models.

The Massachusetts Attorney General issued an Enforcement Notice in 2016 to announce a new interpretation of a key phrase in the state's assault weapons ban. The Enforcement Notice increased sales of tagged assault rifles by 616% in the first 5 days, followed by a 9% decrease over the next three weeks. Sales of Handguns and Shotguns did not change significantly. Tagged assault rifle sales fell 28-30% in 2017 compared to previous years, suggesting that the Enforcement Notice reduced assault weapon sales but also that many banned weapons continued to be sold. Tagged assault rifles sold most in 2017 in zip codes with higher household incomes and proportions of white males. Overall, the results suggest that the firearm market reacts rapidly to policy changes and partially complies with firearm restrictions.

The COVID-19 pandemic has completely disrupted the operation of our societies. Its elusive transmission process, characterized by an unusually long incubation period, as well as a high contagion capacity, has forced many countries to take quarantine and social isolation measures that conspire against the performance of national economies. This situation confronts decision makers in different countries with the alternative of reopening the economies, thus facing the unpredictable cost of a rebound of the infection. This work tries to offer an initial theoretical framework to handle this alternative.

At the end of 2012 the International Monetary Fund (IMF) has suspended its financial assistance to the Democratic Republic of the Congo (DRC). Due to inflationary pressures which occurred in the last quarter of 2016, several decision-makers called for a reopening of a formal cooperation with the IMF. This process was formally completed in December 2019. The restart of IMF programs was greeted with satisfaction by politicians and widely commented in the media. However, recent history shows that the DRC managed to achieve exceptional economic performance, between 2012 and 2016, without being in a formal cooperation with the IMF. Some people wonder whether IMF assistance is a curse for recipient countries? We argue that the underlying problem has nothing to do with accepting or not the IMF assistance, but rather in the ability of policy makers to establish effective leadership and good governance for the development and implementation supporting structural reforms.

By applying network analysis techniques to large input-output system, we identify key sectors in the local/regional economy. We overcome the limitations of traditional measures of centrality by using random-walk based measures, as an extension of Blochl et al. (2011). These are more appropriate to analyze very dense networks, i.e. those in which most nodes are connected to all other nodes. These measures also allow for the presence of recursive ties (loops), since these are common in economic systems (depending to the level of aggregation, most firms buy from and sell to other firms in the same industrial sector). The centrality measures we present are well suited for capturing sectoral effects missing from the usual output and employment multipliers. We also develop an R package (xtranat) for the processing of data from IMPLAN(R) models and for computing the newly developed measures.

This paper identifies latent group structures in the effect of motherhood on employment by employing the C-Lasso, a recently developed, purely data-driven classification method. Moreover, I assess how the introduction of the generous German parental benefit reform in 2007 affects the different cluster groups by taking advantage of an identification strategy that combines the sharp regression discontinuity design and hypothesis testing of predicted employment probabilities. The C-Lasso approach enables heterogeneous employment effects across mothers, which are classified into an a priori unknown number of cluster groups, each with its own group-specific effect. Using novel German administrative data, the C-Lasso identifies three different cluster groups pre- and post-reform. My findings reveal marked unobserved heterogeneity in maternal employment and that the reform affects the identified cluster groups' employment patterns differently.

Immigration legal services providers (ISPs) are a principal source of support for low-income immigrants seeking immigration benefits. Yet there is scant quantitative evidence on the prevalence and geographic distribution of ISPs in the United States. To fill this gap, we construct a comprehensive, nationwide database of 2,138 geocoded ISP offices that offer low- or no-cost legal services to low-income immigrants. We use spatial optimization methods to analyze the geographic network of ISPs and measure ISPs' proximity to the low-income immigrant population. Because both ISPs and immigrants are highly concentrated in major urban areas, most low-income immigrants live close to an ISP. However, we also find a sizable fraction of low-income immigrants in underserved areas, which are primarily in midsize cities in the South. This reflects both a general skew in non-governmental organization service provision and the more recent arrival of immigrants in these largely Southern destinations. Finally, our optimization analysis suggests significant gains from placing new ISPs in underserved areas to maximize the number of low-income immigrants who live near an ISP. Overall, our results provide vital information to immigrants, funders, and policymakers about the current state of the ISP network and opportunities to improve it.

This paper investigates the heterogeneous impacts of either Global or Local Investor Sentiments on stock returns. We study 10 industry sectors through the lens of 6 (so called) emerging countries: China, Brazil, India, Mexico, Indonesia and Turkey, over the 2000 to 2014 period. Using a panel data framework, our study sheds light on a significant effect of Local Investor Sentiments on expected returns for basic materials, consumer goods, industrial, and financial industries. Moreover, our results suggest that from Global Investor Sentiments alone, one cannot predict expected stock returns in these markets.

Two distinct trends can prove the existence of technological unemployment in the US. First, there are more open jobs than the number of unemployed persons looking for a job, and second, the shift of the Beveridge curve. There have been many attempts to find the cause of technological unemployment. However, all of these approaches fail when it comes to evaluating the impact of modern technologies on employment future. This study hypothesizes that rather than looking into skill requirement or routine non-routine discrimination of tasks, a holistic approach is required to predict which occupations are going to be vulnerable with the advent of this 4th industrial revolution, i.e., widespread application of AI, ML algorithms, and Robotics. Three critical attributes are considered: bottleneck, hazardous, and routine. Forty-five relevant attributes are chosen from the O*NET database that can define these three types of tasks. Performing Principal Axis Factor Analysis, and K-medoid clustering, the study discovers a list of 367 vulnerable occupations. The study further analyzes the last nine years of national employment data and finds that over the previous four years, the growth of vulnerable occupations is only half than that of non-vulnerable ones despite the long rally of economic expansion.