Martes 8 de abril

Andrés Espitia | University of Bonn

"When to request evidence?"

Abstract

Appropriate decisions depend on information gathered beforehand. We study the problemfaced by a decision-maker who can only access verifiable information through an agent whoprefers a particular decision. In a dynamic framework with exogenous decision timing, we analyze when to request evidence optimally and how it affects allocational efficiency. The optimalmechanism without transfers implements the efficient allocations if and only if the informationacquisition costs are low for both parties. Otherwise, the decision-maker commits to distortions(from the static optimal choice) to incentivize information acquisition. Optimal evidence acquisition exhibits a bias: it tends to request additional evidence when the current one favorsthe agent’s preferred choice. Applications include optimal testing policies for patients awaitingtransplants and the optimal promotion of employees subject to moral hazard.  

Andrés Espitia is a Postdoctoral Researcher at the Institute for Microeconomics at the University of Bonn. He earned his Ph.D. in Managerial Economics and Strategy from Northwestern University and holds master’s degrees from both Northwestern and the London School of Economics. His research lies at the intersection of microeconomic theory, organizational economics, and behavioral economics, focusing on information design, incentives, and decision-making within organizations. His job market paper, “When to Request Evidence?”, analyzes optimal evidence acquisition in strategic settings with information asymmetries. His other projects examine topics such as overconfidence in organizational contexts, robust contracting under social preferences, and the dynamics of research careers. Andrés has taught graduate and undergraduate courses in economics and has served as referee for journals such as the Journal of Law, Economics, and Organization and the Journal of Mathematical Economics.

Lunes 7 de abril

César Zambrano | New York University

"The Expected Wage Premium and Models of Random Job Search"

Abstract

Models of random search on the job make clear predictions about the expected wage premium: the premium or discount in pay that workers should anticipate in their next job offer relative to their current wage. Using survey data, I document empirical facts about the expected wage premium and I show that, as predicted by classic models of random job search: (a) the average expected wage premium is negative (a discount), and (b) it decreases with job tenure. However, these models cannot reconcile two observed empirical patterns: the substantial dispersion in expected wage premium, which suggests a sizable ladder of job opportunities workers can climb, and the small magnitude of the average expected discount, which indicates that wage gains from climbing this ladder are modest. I propose a model that can reconcile these facts, featuring: (i) productivity gains that are not immediately incorporated into the wages of employed workers and (ii) reallocation events in which workers move jobs for reasons other than wage gains. When calibrated to match these new empirical facts, the proposed model predicts less wage growth and lower wage inequality from job search and search frictions than classic random search models.

Cesar Zambrano is a Ph.D. candidate in Economics at New York University. His research focuses on macroeconomics, with secondary interests in labor markets, economic development, and microeconomic foundations of search models. His job market paper, “The Expected Wage Premium and Models of Random Job Search,” examines how workers' expectations about future wages shape labor market dynamics and proposes a new model that reconciles empirical discrepancies in wage progression. He holds an M.Sc. in Economics from PUC-Rio and a B.Sc. in Business Administration from the University of São Paulo. Prior to his doctoral studies, Cesar worked in the renewable energy sector as a financial analyst, contributing to major investment and financing operations in Brazil. His research has been supported by the MacCracken Fellowship at NYU.

Lunes 31 de marzo

Maite Deambrosi | University of Zurich

"Learning about Learning"

Abstract

How do people use their past learning experiences when deciding whether to invest in new learning opportunities? These experiences likely provide relevant information about learning abilities, which could be used to improve forecasts about future learning trajectories, leading to better choices in skill acquisition. However, we know little about how people actually use information from prior learning to guide their future skill investment decisions. To answer this question, I conduct a controlled experiment where participants complete a real-effort task that requires learning new information. By exogenously manipulating participants’ learning curves, I study how beliefs about ffuture learning trajectories and willingness to invest in future skill acquisition react to previous experiences. This design lets me compare participants’ belief updating against Bayesian predictions and identify how cognitive biases might prevent accurate learning from experience. The results reveal systematic mispredictions about learning trajectories that persist and often worsen after gaining experience, affecting skill investment decisions. This research contributes to the literature on human capital formation and decision-making under uncertainty, with important implications for educational practices and workforce development strategies.

Maite Deambrosi is a Ph.D. candidate in Economics at the University of Zurich, specializing in behavioral economics, empirical microeconomics, and experimental economics. She holds an M.A. in Economics from the University of Zurich (summa cum laude) and a B.A. in Economics from Università della Svizzera Italiana. During her doctoral studies, she was a visiting student at Harvard University’s Department of Economics. Her job market paper, “Learning about Learning,” explores how individuals update beliefs in dynamic decision environments. Maite’s research combines field experiments and applied microeconomic methods, with ongoing projects in areas such as health behavior in developing countries and misperceptions in the marriage market. Her work has been supported by several competitive grants, including the UBS Center for Economics in Society and the URPP at UZH. She has presented her research at leading academic conferences and has received awards for her teaching excellence.

Viernes 21 de marzo

Denise Barros | Federal Institute of São Paulo

"The Elephant in the Room: The Silence of Menopause in the Workplace"

Abstract

Menopause is a natural biological transition that affects a growing number of women in the workforce, yet it remains largely unaddressed in Management. This broad research project, The Elephant in the Room: The Silence of Menopause in the Workplace, examines the silence surrounding menopause and its professional implications through three interrelated studies.

The first study delves into social representations of menopause in a progressive women's magazine, providing a historical and cultural analysis. A content analysis of Revista Claudia (1961–2022) — a magazine known for addressing progressive issues such as women's integration into the workforce — found fewer than 20 articles on menopause, highlighting its persistent invisibility and strong age discrimination in media representations.

The second and third ongoing studies adopt empirical approaches to investigate how menopause is perceived and addressed in the workplace, identifying challenges, impacts, and possible organizational support strategies. The research relies on (1) an extensive systematic literature review in health research on menopause and work and (2) the sociologist Eviatar Zerubavel's perspective on the social dimensions of silence, exploring how silence shapes and reflects power structures and cultural norms. A survey (n=177) revealed that most respondents feel unsupported by their organizations and perceive menopause as a stigmatized topic. Exploratory Factor Analysis identified three key dimensions: (1) lack of organizational policies and leadership support, (2) impact of physical and emotional symptoms on performance, and (3) limited workplace openness to discussing menopause. The qualitative study, relying on in-depth interviews with women over 40 in leadership roles, reinforced these findings, exposing silence, stigma, and age-related prejudice as dominant themes.

This study contributes by historically contextualizing menopause in media and connecting its organizational silence to broader gender biases, demonstrating how hegemonic masculinity renders aging women invisible and undesirable in professional environments (Whiley et al., 2022). Addressing this silence is critical to fostering inclusive workplaces that acknowledge and support women throughout life. The findings call for policy interventions and cultural shifts to break the stigma surrounding menopause in professional settings.

Denise Barros is a Visiting Professor at the Federal Institute of São Paulo – Jacareí Campus and accredited professor at the PPGAd of UFF. She holds a Ph.D. in Administration and a Master's in Public and Business Administration from EBAPE/FGV, as well as an MBA in Marketing from COPPEAD/UFRJ. She has been a professor at FAF/UERJ, PPGA/Unigranrio, and the Professional Master's Program at EBAPE/FGV. She is an Associate Editor of the journal Gender, Work, and Organization and a track and working group leader at the ASAC (Administrative Science Association of Canada), Semead/USP, ANPAD, the Macromarketing Society, among others. She also served as a Representative-at-Large at the Academy of Management.

Lunes 17 de marzo

Catalina Eneström | IESE Business School

"Rethinking role clarity: How daily interactions shape employee understanding of work in hybrid work environments"

Abstract

The shift to hybrid work has disrupted employees' ability to understand their roles, tasks, and expectations, thereby negatively impacting their well-being and productivity. Drawing on Role Theory and Social Information Processing Theory, we propose that this shift uniquely inhibits employees’ role clarity (i.e., the extent to which employees understand their job responsibilities and performance expectations) by disrupting interpersonal sensemaking processes. We use experience sampling methodology to examine how working remotely versus in person reduces interpersonal sensemaking, which then leads to greater fluctuations in role clarity and consequently poorer work outcomes, such as diminished goal progress, job satisfaction, and productivity. Yet, we find that having flexibility in choosing which days to work remotely serves as a buffer against the potential downsides of hybrid work. All in all, this research underscores the importance of examining daily disruptions to role clarity. It also highlights potential downsides of remote work while simultaneously suggesting a practical solution to maintain employee productivity and well-being in hybrid work environments.

Dr. M. Catalina Eneström is a Postdoctoral Research Scholar in the department of Managing People at IESE Business School. She received her PhD in Experimental Psychology from McGill University. Prior to her PhD, Catalina received her Bachelor of Commerce at McGill University, half of which was completed at the Universidad Torcuato Di Tella. In addition, she has worked in consulting, sales, and marketing at international companies in various cities across Canada and in Argentina. Catalina’s research examines how people rely on their interpersonal relationships to make sense of their experiences at work and in life. She has published her work in journals such as the Journal of Personality and Social Psychology and the Journal of Social and Personal Relationships. Catalina is an active member of the organizational behavior and social psychology research communities. She runs the IESE work meaningfulness group and the podcast Just Your Average Employee.

Viernes 14 de marzo

Ezequiel Álvarez | UNSAM

"Bayesian Machine Learning para Ciencias Políticas, Economía y Negocios"

Abstract

El Bayesian Machine Learning es un aprendizaje automático que, en contraste con las redes neuronales, permite maximizar el aprovechamiento de la información previa del sistema, así como también entender con claridad los pasos que da la máquina al obtener sus conclusiones.  Estas cualidades lo hacen especialmente útil para las –no escasas– situaciones en las que la data no es abundante (menos de ~10mil datapoints, digamos), y en las que el entendimiento de los procesos internos del sistema aporta una herramienta crucial para potenciar la utilidad de la solución del problema, o para justificar con claridad las políticas públicas.  Las herramientas y técnicas del Bayesian ML son recientes y aún no tan conocidas, pero el poder de sus resultados ya es sorprendente.  Explicaré en forma sencilla en qué consiste el Bayesian ML, mostraré cómo el poder de sus resultados crece cuando se combina con conocimientos profesionales del problema en cuestión.  Detallaré cómo se puede modelar un sistema con un modelo probabilístico, y así desplegarlo para acceder a sus variables internas, donde inyectar información previa potencia aún más los resultados.  Daré dos ejemplos de trabajos realizados con esta herramienta.  En uno se estudia una mezcla de poblaciones dentro de una comunidad según sus decisiones colectivas electorales, lo que permitió al cliente optimizar sus correspondientes tácticas.  En el otro se busca estimar tempranamente las compras en un sistema de comercios.  En éste último fuimos convocados luego de que con las redes neuronales no se llegase a métricas satisfactorias.  Mostraré cómo con Bayesian ML entendimos el por qué, dimos una solución y proveimos al cliente con una herramienta nueva e inesperada.

Ezequiel Álvarez es Investigador de CONICET y profesor de la UNSAM, donde dirige el International Center for Advanced Studies.  Graduado del Instituto Balseiro, doctorado en Valencia (España) y con un postdoc en Stanford University (EEUU), su especialidad original es física teórica y hace varios años que también se especializa en Machine Learning, con particular interés desde la pandemia en las técnicas Bayesianas.  En particular, por Bayesian Machine Learning ha sido invitado en los últimos años a dar cursos y charlas sobre sus tema de investigación en una decena de prestigiosas Universidades de Europa y Estados Unidos. Actualmente realiza investigaciones en física de colisionadores y también en diversos trabajos para Gobiernos, Instituciones y empresas, como por ejemplo un detector temprano de brotes de dengue para la PBA que ya funciona exitosamente.

Miércoles 26 de febrero

Pablo Balán | Tel Aviv University

"Family Ties as Corporate Power"

Abstract

Policies interact with underlying social organizations, which may deflect their goal. Oneexample is legislation seeking to curtail business power. Can campaign finance regula-tion curb the political influence of economic actors? We identify a factor that may hin-der its effectiveness—the social structure of organizations. We argue that such regulationcreates cooperation dilemmas in firms’ leadership and propose that a specific feature oforganizations—family ties—help solve them. We evaluate this argument by studying a banon corporate contributions in Brazil, using granular data on family ties in Brazilian publiccompanies. We show that, following the ban, members of firms’ controlling families substi-tute individual for corporate contributions. Furthermore, we document the presence of peereffects in the contributions of family members, suggesting that family ties transmit influence.These bifurcated effects illustrate how organizational structure can be a source of de factopower and contain a cautionary tale for policymakers.

Pablo E. Balán is a Senior Lecturer (Assistant Professor) at the School of Political Science at Tel Aviv University. He holds a Ph.D. in Government from Harvard University, an M.A. in Political Science from Universidad Torcuato Di Tella, and a B.A. in Political Science from Universidad de San Andrés. His research lies at the intersection of comparative politics, political economy, and development, with a focus on state capacity, taxation, and the political behavior of firms. His work has been published in leading journals such as the American Economic Review, American Political Science Review, and World Development, and has been supported by institutions including the Stigler Center at the University of Chicago and the Lincoln Institute of Land Policy. Pablo is also a Research Affiliate at the Stigler Center and has conducted fieldwork in the Democratic Republic of Congo. He teaches courses on the political economy of development, governance, and electoral representation.

Miércoles 19 de febrero

Marcos Lissauer | Pennsylvania State University

"The Refinancing Channel of Mortgage Choice"

Abstract

This paper studies how endogenous mortgage choice and refinancing affects the transmission of monetary policy to consumption in the U.S. First, I document heterogeneous mortgage choices and refinancing behavior along the wealth distribution. Second, to explain these facts, I introduce endogenous refinancing and mortgage choice between fixed-rate mortgages (FRMs) and adjustable-rate mortgages (ARMs) into a standard heterogeneous-agent consumption-savings model. The model reveals new channels through which the mortgage market structure affects monetary policy transmission. I find that (i) expansionary monetary policy diminishes the effectiveness of future monetary  policy by reducing the share of ARMs in the economy, and (ii) the refinancing option shapes mortgage choice, reducing the aggregate transmission of monetary policy to consumption by decreasing the market share of ARMs from about 30% to 11%, while increasing its transmission to low-wealth households. These findings have implications for policies targeting refinancing costs.

Marcos Lissauer is a Ph.D. candidate in Economics at Pennsylvania State University. His research interests include asset pricing, household finance, macro-finance, and macroeconomics. He holds a B.A. in Economics and completed graduate studies at Universidad Torcuato Di Tella. His job market paper, “The Refinancing Channel of Mortgage Choice,” explores how mortgage refinancing decisions shape household financial behavior and macroeconomic dynamics. Marcos has held research assistant positions at Penn State, the World Bank, and Universidad Torcuato Di Tella, and has taught undergraduate and graduate-level courses in both the United States and Argentina. He has participated in international training programs such as the ECB Training School, the Princeton Initiative, and the Macro-Finance Society Summer School. His work combines empirical and theoretical methods to understand financial decision-making and policy transmission.

Jueves 13 de febrero

Emilio Colombi | University of Michigan

"The Distributional Consequences of Commodity Booms"

Abstract

This paper examines the response of income inequality to commodity price shocks and identifies the underlying mechanisms at play. I provide novel cross-country evidence from commodity-exporting nations, showing that positive commodity price shocks reduce labor income inequality between skilled and unskilled workers. This reduction in inequality is primarily driven by a fall in the skill premium, arising from the expansion of unskilled-labor-intensive sectors, such as construction, which grow more rapidly than skilled-labor-intensive ones, such as services. To interpret these results, I develop a quantitative multisector small-open-economy model that incorporates sectoral heterogeneity in both skilled-labor intensity and sensitivity to commodity price shocks. I use the model as a laboratory to explore the distributional impacts of various policy responses to commodity windfalls, demonstrating how saving or spending these gains can either reinforce or moderate the reduction in inequality. This analysis provides new insights into the broader economic and social impacts of commodity price shocks in emerging markets.

Emilio Colombi is a Ph.D. candidate in Economics at the University of Michigan. His research focuses on macroeconomics, international economics, and international finance, with an emphasis on the distributional consequences of macroeconomic shocks in emerging markets. He holds a B.A. in Economics from Universidad Nacional de La Plata (graduating first in his class), an M.A. in Economics from Universidad Torcuato Di Tella, and an M.A. in Economics from the University of Michigan. Prior to his doctoral studies, he worked as a research economist at the Central Bank of Argentina. His job market paper examines how commodity price shocks affect income inequality across skill groups, combining cross-country empirical evidence with a multisector small open economy model. Emilio has presented his work at several international conferences and has received multiple academic awards, including the Best Third Year Paper Prize at the University of Michigan.

Miércoles 12 de febrero

José Alvarado | Northwestern University

"Tax Avoidance and Wealth Inequality"

Abstract

The hypothesis that wealth inequality is driven by the higher returns earned by the rich—thereby offsetting the progressivity of the tax system—overlooks a key dimension: tax avoid-ance. This paper shows that tax avoidance not only undermines the progressivity of the tax system but is also one of the reasons why the wealthy earn higher returns. Using micro-data from Chilean tax records, I quantify tax avoidance and find that the top 0.01% of taxpayers reduce their tax payments by 80% through corporate investments. To measure the impact of tax avoidance on wealth inequality, I calibrate a Bewley-Huggett-Aiyagari heterogeneous agent model, incorporating two departures from standard approaches: (i) endogenous port-folio choices between safe and corporate risky assets, and (ii) tax functions that account for tax avoidance. The model successfully replicates the 50% wealth share held by the top 1%in Chile. The main intuition is that, given the presence of tax avoidance, the after-tax rate of return on risky assets increases, leading agents to reallocate their portfolios towards these assets, ultimately resulting in an even higher rate of return on wealth. The main quantitative result is that, without tax avoidance, the top 1% wealth share decreases from 50% to 11%. These findings suggest that tax avoidance is a key driver of wealth inequality.

José Alvarado is a Ph.D. candidate in Economics at Northwestern University. His research interests lie in macroeconomics, public finance, tax policy, and wealth inequality. He holds a B.Sc. in Industrial Engineering and an M.A. in Economics from the Universidad de Chile, as well as an M.A. in Economics from Northwestern University. His research combines administrative microdata with heterogeneous-agent macroeconomic models to study the role of tax avoidance in shaping wealth concentration. His work has been recognized with the Susan Schmidt Bies Prize for the best third-year research paper. Prior to his doctoral studies, he worked as a Junior Economist at the Central Bank of Chile and most recently served as Senior Economic Adviser at the Chilean Ministry of Finance.