The Renaissance Economist – Research Revelations from a Rockstar of mine

Post by BSE Alum Nils Handler ’18 (International Trade, Finance, and Development Master’s Program)

Anxiously I was awaiting our meeting to begin, equally enthused by the prickling curiosity of what I was about to learn while simultaneously filled with an intimidating level of respect considering whom I was about to meet. Given how much I had read, heard and thought about his research while at the Barcelona School of Economics and his strong voice as a public intellectual, I was positively surprised when he confirmed our meeting. It was right after Claudia Goldin was awarded the Nobel Prize, so he surely was at his desk.

Daron Acemoglu is a towering figure in the economics profession as the breadth and depth of his research is virtually unparalleled. Rumors have it that he can churn out a paper on a weekend, many if not most of them land in the profession’s top 5 outlets, and that even close peers cannot keep up with his mathematical modelling. During his PhD at the London School of Economics, each chapter of his thesis is said to have merited a doctorate by itself. 

When doing my masters in economic history at LSE, I vividly remember how Acemoglu, Johnson and Robinson – AJR – turned the discipline upside down at the time with their work on why nations fail. Their instrumental variable approach leveraging settler mortality data allowed a novel perspective on the longue durée, including hypothesis testing, that attracted heavy criticism and universal attention among scholars of economic history and development alike. 

Ein Bild, das Menschliches Gesicht, Person, Lächeln, Wand enthält.

Automatisch generierte Beschreibung

With Prof. Daron Acemoglu in his office at MIT

Now as part of my PhD, I am working on the direction of technological change, that is what determines whether technological development is dirty, thus heavily polluting, or clean, in that it emits much less carbon, and thus paves the way for economic growth to be green. As a Master student at LSE, I applied Acemoglu’s framework to understand why Costa Rica had become the “Switzerland of Central America”, while Honduras, the country where I had worked with street kids during my year as civil servant, a Banana republic.

I think of the knowledge in economics as an ever-expanding circle, with an exponentially increasing surface and a diameter so large by now that virtually nobody can reach both ends at the same time. Similarly, philosophers after Immanuel Kant could no longer labor all subfields in the profession. It takes what I would call a Renaissance Economist – like Acemoglu – to reach on both ends of the circle, that is an economist with a rare ease of penetrating seemingly disparate topics. The concept is based on the belief that human potential is limitless and that people should seek to develop themselves in multiple areas of expertise.  

Personally, I have developed what I would call a “green growth mindset” over the course of my PhD. I founded the d\carb future economy forum as part of my PhD, because I was keen on engaging with inspiring speakers discussing the big questions that had drawn me to economics in the first place. Economists such as Philippe Aghion, Ufuk Akcigit and Cameron Hepburn, who state that economic growth can be reconciled with environmental protection, as a matter of fact this constitutes the growth story of the 21st century. This was one of the questions that continued to puzzle me after my ITFD course on economic growth, that is whether the limits to growth constitute a binding constraint. It helped me find my own position in this tricky question, for which I consider green growth more persuasive than degrowth, as I believe that behavioral changes such as the ones enforced during the pandemic are insufficient to induce the 7 percent annual emission reductions required for net zero by 2050.

Psychologically, a growth mindset signifies that you don’t limit yourself with your own belief, consider your own abilities as malleable, in essence your brain a muscle that can be trained. The most powerful boost for your own motivation is outgrowing your own personal boundaries, that is achieving something you did not think you are capable of. That’s how I felt after completing the ITFD-program, and I do have to say it’s slightly addictive. That is why I am so enormously grateful for Jaume Ventura’s guidance in pursuing a PhD, because nobody in my family has ever done one, and my own imposter syndrome was keeping from choosing that route. Further, as a result of my first blog-post here at BSE Voice, a friend put me in touch with an actual US Navy Seal, who helped me navigate the emotional rollercoaster of the first year of PhD courses – I thought it couldn’t get tougher than the ITFD, yet then it did. Further, I can recommend Laura Blattner’s 15 rules to help you make it through grad school when things get tough. 

In short, I believe we should green our growth policies, for example as part of a new growth model for my native Germany, and that you can all achieve more than you think you are capable of if you conquer your own fear. At times I feel like I resemble one of the scientists in Acemoglu’s models of directed technological change, as I used to work on extractive industries at the World Bank up until the Paris Agreement inspired me to consider decarbonizing my own cv. Now I am working on green innovation and green growth, that is searching effective ways to lower the carbon content of our economy. 

This motivated my various questions to Prof. Acemoglu: Has his research caused the renaissance of green industrial policies, that is large, government-led finance and support for green industries? Are the latter synonymous with green subsidies in his models? Can those provide a segue for effective carbon pricing to subsequently kick-in? Will they suffice to reach a green growth equilibrium to ensue by inducing low-carbon innovation, and can high energy prices substitute their role? And what are the origins of this research on the direction of technological change? 

There is a Netflix documentary stating that MIT is the place for those who don’t fit in elsewhere, whose ideas appear crazy anyplace else. For me, this belief is that you can and should apply randomized controlled trials not merely in development economics, but much rather apply them to decarbonize our economies. I am immensely grateful that my host Jacquelyn Pless reinforced that believe in welcoming me to MIT Sloan. While in Boston, I was fortunate to also meet Josh Lerner, the doctoral advisor of my BSE professor Albert Bravo-Biosca, who had first inspired this idea in me, and – while at a conference in Chicago – the wonderful John List, who encouraged me to keep seeking to change the world with this idea. 

Ein Bild, das Menschliches Gesicht, Person, Bücherregal, Lächeln enthält.

Automatisch generierte Beschreibung

With Prof. Josh Lerner in his office at the Harvard Business School

Thanks to technological progress, you can now ask AI whether superintelligence, i.e. the hypothetical scenario where Artificial Intelligence is surpassing human intelligence, is around the corner. It’s modest assessment of its own capabilities – compared to Daron Acemoglu – suggests that this scenario is still very far away, as you can read below:

Who is smarter? ChatGPT or Daron Acemoglu? 

Daron Acemoglu is an acclaimed economist and professor known for his expertise in the fields of political economy, economic development, and institutions. He has made significant contributions to these areas through his research, publications, and teaching. Acemoglu has received numerous awards and honors for his work, including the John Bates Clark Medal, which is awarded to the best economist under the age of 40 in the United States.

On the other hand, ChatGPT is an AI language model developed by OpenAI. It is designed to understand and generate human-like text based on the training it has received from a large corpus of data. While ChatGPT is a sophisticated AI model with impressive language capabilities, it is important to note that it operates based on statistical patterns in the data it has been trained on and does not possess the same level of domain-specific expertise and intelligence as human experts like Daron Acemoglu.

Therefore, when it comes to comparing intelligence and expertise, Daron Acemoglu, as a highly respected economist and scholar, would be considered more knowledgeable and smarter in his specific field of study. However, it is worth mentioning that ChatGPT can provide a wide range of general knowledge and information across various domains beyond economics, making it a valuable tool for accessing information and generating text.

If this amused you and sparked your curiosity further, Nick Bostrom’s “Superintelligence – Paths, Dangers, Strategies” elaborates these questions in more detail, and “Power and Progress” by Prof. Acemoglu describes how Power does not spread in an egalitarian fashion as a result of technological progress, as well as potential antidotes. 


  1. That is epistemology, metaphysics, logic, axiology and political philosophy.
  2. Curious for tried ‘n tested Navy Seal techniques? Headspace, plus breathing exercises when you’re panicking: breath in for four seconds, hold for eight, breath out for four. Repeat. This WILL calm your mind, and calm is contagious, as they say.
  3.  He wouldn’t go that far 
  4. yes
  5. absolutely
  6.  The million dollar question
  7. yes
  8.  Hicks (1932), Kennedy (1964)

Fake news harms the economy

Research by Stefanie J. Huber ’10 (Economics Program) and co-authors

several hands hold mobile devices displaying an article with the title "Fake News" and a big share button.

Fake news significantly impacts economic dynamics, leading to higher unemployment and lower production. Additionally, people tend to overestimate their ability to distinguish between accurate and false information. However, once they are made aware of this (through experience), their willingness to pay to protect themselves from fake news increases. These findings are derived from two discussion papers involving our alumni community.

In the digital age of the internet and social media, misleading information, often referred to as fake news, has gained momentum since the beginning of the new millennium. Whether political or economic in nature, fake news spreads rapidly. BSE Economics alum Stefanie Huber, now Associate Professor at the University of Bonn, along with Professors Tiziana Assenza, Fabrice Collard, and Patrick Fève from the Toulouse School of Economics, have examined how fake news influences economic dynamics and business cycle fluctuations.

Higher unemployment, lower production

To measure the impact of fake news shocks on the economy, the team compiled a novel dataset, called the “Fake News Atlas” database. It incorporates news fact-checked by PolitiFact, a non-profit and nonpartisan fact-checking organization founded in 2007.  PolitiFact adheres to the principles of the International Fact-Checking Network.

Since the effects of fake news shocks cannot be directly measured, the research team used a proxy VAR model. This approach allows to determine the dynamic causal impact of “fake news shocks” – sudden surges of misleading information. The study analyzes monthly US data from January 2007 to December 2022, including the unemployment rate, industrial production, a business cycle factor summarizing key information about the business cycle, and the one-month-ahead macroeconomic uncertainty index.

Through our model, we demonstrated that technology-based fake news shocks have a significant impact on economic development,” Stefanie Huber explains. As a result, unemployment rises while industrial production falls. Furthermore, fake news shocks contribute significantly to business cycle fluctuations. They also influence consumers to cut their spending. This downturn negatively affects the labor market, leading to reduced working hours and a decrease in job vacancies.

Underlying mechanism

What mechanism is at play here? In Economics, we would say that fake news shocks act as aggregate uncertainty shocks. Technology-related fake news shocks sow seeds of uncertainty that reverberate through the economy. “One can perhaps imagine it like this,” says Huber: “When market participants see these fake news, even if they can identify them as fake, they don’t know if other market participants will fall for them. This creates uncertainty and hence, hampers investment.”

Figure 1 shows a specific example of a fact-checked news item, as shown on PolitiFact website. A conspiracy theory that claims 5G towers are used to brainwash people has surfaced on social media. In a Facebook post sharing a TikTok video, a narrator refers to U.S. patent No. 5356368A and claims it’s proof that 5G towers are used to induce thoughts in people’s consciousness. Such fake news is liked, commented on, and shared by thousands and sometimes millions of media users. Some of the fake news is fact-checked and subsequently marked as “fake.” However, even then, the commentaries often continue to debate the potential “fakeness.”

A Facebook post claiming that 5G towers are used for brainwashing receivees a "pants on fire" rating from Politifact's Truth-o-meter"
Figure 1: Example of a fact-checked news item

For the readership of the BSE community, this particular fake news might sound like absolutely irrelevant noise. And yes, it is noise but not irrelevant—it creates uncertainty. Consider companies producing 5G towers or 5G-technology-based products, their investors, and shareholders. Observing this news creates uncertainty for all of them, even if they can identify it as fake. They do not know how many potential customers or shareholders might be misled by this fake news, potentially inducing a decrease in demand for 5G products and services in the future. Hence, fake news shocks can mimic the disruptive effects of classical aggregate uncertainty shocks.

People overestimate ability to recognize fake news

In another discussion paper, Stefanie Huber, along with Tiziana Assenza and Alberto Cardaci, investigated whether citizens are able to recognize fake news and are willing to pay to protect themselves from the harms of fake news.

In a survey-experiment with a r epresentative sample of 2,413 individuals over 18 years of age, covering various ages, genders, education levels, ethnicities, marital statuses, household sizes, residential regions, and party affiliations, participants from the US population evaluated the accuracy of a series of fact-checked headlines. These statements covered a heterogeneous spectrum of news topics and channels. The approach focused on citizens’ ability to discern the accuracy of information based on the content of the news.

Huber and her co-authors find that the vast majority of respondents have great confidence in their ability to assess the accuracy of news; 82.64% of participants indicate having a “good” or “very good” ability to identify news or information that distorts reality or is even false. “Interestingly, only 37.61% of our respondents believe that the average citizen is capable of distinguishing between correct and fake news,” says Huber.

The key insight: Participants significantly overestimated their own ability to distinguish between correct and fake news.

A silver lining: Once participants realized (through experience!) that they were more susceptible to falling for fake news than they thought, their willingness to pay for protective measures such as fact-checking services significantly increased.

Understanding and combating fake news are crucial for maintaining economic and political stability as well as informed decisions for each individual. “Our research shows how simple awareness campaigns can make a significant difference in the fight against misinformation,” says Huber.

Both studies were funded by the German Research Foundation (DFG) and the French National Research Agency (ANR).

Podcast:

Fabrice Collard gave an interview to CEPR host Tim Phillips about the team’s research. Listen here: https://cepr.org/multimedia/how-fake-news-shapes-business-cycle

Publications and non-technical summaries:

  1. Assenza, T., Collard, F., Fève, P. & Huber, S. (2024): From Buzz to Bust: How Fake News Shapes the Business Cycle. https://cepr.org/publications/dp18912

Non-technical summary: VoxEU column

  1. Assenza, T., Cardaci, A. & Huber, S. (2024): Fake News: Susceptibility, Awareness and Solutions. https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_290_2024.pdf 

Non-technical summary: VoxEU column

Connect with the authors

  • Stefanie J. Huber ’10 (Economics). Associate Professor at the University of Bonn and faculty member of the ECONtribute Cluster of Excellence, Germany. 
  • Tiziana Assenza. Associate Professor at the Toulouse School of Economics. 
  • Alberto Cardaci. Serves as Market Insights Senior Manager in the corporate sector. Previously, he was Assistant Professor at the Chair of Macroeconomics and Finance, Goethe University Frankfurt
  • Fabrice Collard. CNRS Senior Researcher at the Toulouse School of Economics and a Research Fellow at CEPR. 
  • Patrick Fève. Professor of Economics at the Toulouse School of Economics.

Author info is current as of June 2024.

Novel Avenues for Monetary Policy: Insights from Heterogeneous Agent Models

PhD Track master project by Simone Cigna, Isabel Figueiras, Antonio Giribaldi, and Franziska Schwingeler ’22

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.

Abstract

This literature review focuses on the contribution of the heterogeneous agents framework to the empirical robustness of macroeconomic models. 

First, we focus on the transmission of monetary policy in an economy characterized by heterogeneous agents. We do this by analyzing both the quantitative HANK model and an analytical representation (THANK). 

Secondly, we illustrate the greater suitability of heterogeneous agent models for economic and welfare analysis in a developing country context. 

Finally, we analyze agent-based models as a potential avenue to address a higher degree of heterogeneity and complexity in the data.

HANK and THANK models

We see that the HANK model delivers a more accurate representation of the wealth and consumption distribution of households, but it still lacks important dimensions of household heterogeneity. For instance, the distribution of capital gains is crucial to match the empirical evidence on movements of capital and equity prices. 

The THANK model attempts to give a tractable representation of the HANK model, keeping a certain degree of idiosyncratic uncertainty. Yet, in contrast with the latter, it is not able to address issues related to wealth distribution and welfare.

The challenges of working with macroeconomic simulations

Even with heterogeneous agents, it is unclear whether macroeconomic simulations of the scaling up of micro-evaluations can really do more than making us aware and cautious of forces in general equilibrium that might alter the effects found in RCTs. 

Given the data-challenges in developing countries, the dimension of the informal economy, and in general the immense complexity that remains beyond what is captured in the models, the model predictions might still be inaccurate. 

The introduction of agent-based models attempts to represent more complex and richer economic dynamics. However, this enhancement comes with some drawbacks. First, researchers are left with almost arbitrary freedom in choosing the inputs of the models (e.g., behavioral equations governing agents’ behavior). Second, causal mechanisms in the model are unclear (“black box” critique).

Better models for complex, real-world economies

Despite these challenges, the introduction of the heterogeneous agents’ framework makes macroeconomic models more suitable to capture the complex dynamics in real-world economies.

We conclude that such development in the macroeconomic field is key to enhance the relevance of the models’ policy prescriptions and to improve the ability of their empirical performance.

Connect with the authors

Simone Cigna, Isabel Figueiras, Antonio Giribaldi, and Franziska Schwingeler ’22 are PhD students in Economics in the doctoral program organized jointly by Universitat Pompeu Fabra and the Barcelona School of Economics.

Author info is current as of May 2023.

About the BSE PhD Track Master’s Program

Stacking Ensemble: a quick review

Maxim Fedotov ’22 (Data Science Methodology)

Stacking Ensemble diagram
Stacking Ensemble

Intro

Stacking is an ensemble method that is used widely in supervised learning. As always, we have some training data, and the goal is to predict target variable in new data. The basic structure of the method consists in two levels of learners: base and meta. The main idea is that the meta learner combines predictions of the base learners to provide final response. That is, predictions of the base learners are used to calibrate the meta learner. The beauty of ensemble techniques in general is that they allow to capture various aspects of patterns in data using heterogeneous machine learning models. It explains why ensembles often happen to exhibit strong predictive power. For peak performance, it is recommended to have some degree of diversity in the base learners and sufficiently small correlation among their predictions.

Of course, at first sight, this algorithm might seem to be prone to overfitting. However, stacking is constructed in a way that helps to avoid it. In fact, it is the most subtle part of the technique. In short, while calibrating the meta learner, one uses cross-validation type of procedure to combine predictions of the base learners.

Along the text, I will use some pseudo-code notation which I will be defining on the go. Also, the text was prepared having in mind the stacking ensemble implementations from scikit-learn and mlens Python packages.

The purpose of this text is to describe meta learner calibration and base learners training separately to avoid any misinterpretations that could arise when mixing these components together.

I start with a short description of a basic supervised learning setup to define some notation. Then, I proceed to how the meta learner is trained. Finally, I explain how one obtains predictions for new data. Take a coffee, and let’s jump in!

Setup

In this text, we consider a basic numerical data setup. That is, we have a target which we want to predict, and the feature matrix which carries information about some features of n observations.

Let’s quickly setup some notation:

  1. Data: X_train (feature matrix), y_train (target).
  2. Number of rows (observations) in the training data: n.
  3. Cross-validation folds (disjoint): cv_folds = [fold_1, …, fold_K], such that union(cv_folds) = {1, … , n}.
  4. Base learners: base_learners = [base_learner[1], ..., base_learner[B]].
  5. Meta learner: meta_learner.
  6. New data: X_new.

The learners here are generic objects that define a learning model, that can be fitted on data via applying a generic method .fit() and predict a target via method .predict(). Whenever the learners are trained, I mention it explicitly. With this setup, we proceed to describing how one trains the meta learner.

Stacking Ensemble Training

Now, we are ready to discuss how we can train a stacking ensemble and then obtain predictions for new data.

Meta learner calibration

One first fits the base learners on the training data using a cross-validation approach. Remember, our final goal here is to train the meta learner, which will be producing final target predictions. So, we do not want to propagate information about target realisation of the i-th observation into a base learner prediction for it since doing this will cause extreme overfitting of the meta learner. That is why we use a cross-validation type of procedure. Then, when we have predictions of the base learners obtained via the cross-validation approach, we can concatenate them with our initial feature matrix, i.e. considering them as new features for the meta learner. So, to train the meta learner one simply fits it to the target given the extended feature matrix. That is it!

Now, let’s summarise the procedure into a piece of pseudo-code:

Algorithm for meta learner training

Note that construction of X_meta may vary from one implementation to another. For example, one may choose a subset of features to use (propagate) in the meta-learner. Attention: do not confuse it with a concept of feature propagation that was introduced to cope with a problem of missing data in learning on graphs by Rossi, E., Kenlay, H., Gorinova, M.I., Chamberlain, B.P., Dong, X. and Bronstein, M.M. in the paper “On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features.” Learning on Graphs Conference; PMLR, 2022.

As we can see, the structure is not complex. Of course, there are some technicalities that do not appear in this demonstrative pseudo-code. For example, one might want to do the cross-validation and base learners training in parallel to speed up performance. For a reference on the state-of-the-art implementation in Python, check out scikit-learn and mlens packages.

Meta learner training

Base learners training

I describe this step after meta learner calibration on purpose so that we do not confuse the former with a part of the latter. So far, we have calibrated the meta learner. However, to predict the target for new data we also need to train the base learners so they are able to produce base predictors for the unseen dataset.

The procedure is straightforward. One can just fit each base learner on the whole training dataset X_train, y_train. Again, when training the meta learner we were using the cross-validation approach to avoid excessive overfitting of the meta learner. Here, we do not have to do this since we will use the base learners to obtain base predictions for newly occurred data that was never seen by the model. So, we can utilize all the data that we have.

Stacking Ensemble prediction

At this point, we have the meta learner calibrated and the base learners trained. So, to predict target values for newly occurred data, we first obtain the base target predictions, and then use the meta learner.

Algorithm for Stacking Ensemble prediction.

Conclusion

Ensemble methods are particularly known for their decent prediction performance in supervised-learning setups if used appropriately. Stacking ensembles exhibit hierarchical structure with two levels: base and meta. Meta learner combines responses of base learners to provide final prediction of a target variable. To avoid overfitting, meta learner is trained involving cross-validation type of procedure used to obtain base-learner predictions.

I discuss how stacking ensembles can be trained and used for prediction in supervised learning problems. I decided to maintain some level of generality in the method description while providing pseudo-code examples.

Hope it helps!

References

Connect with Maxim

Portrait of Maxim Fedotov

Maxim Fedotov ’22 is an MRes student in Statistics at Universitat Pompeu Fabra. He is an alum of the BSE Master’s in Data Science Methodology.

Burning Growth: Rising Temperatures in a Growth-at-Risk Approach

A case study of Colombia. Macroeconomic Policy and Financial Markets master project by Camila Camilo, Angie Rozada, and Carlos Sanz ’22

A hydroelectric plant in Colombia

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.

Abstract

The rise in global temperatures has been a growing source of concern for policy makers given its potential impact for sustainable economic activity. 

This paper explores the effects of a temperature shock on the distribution of economic growth in Colombia. Specifically, we focus on the impact of these shocks on economic risks. 

Our findings suggest the existence of asymmetries on the lower and upper tail of the distribution, indicating that higher temperatures leads to less optimistic outcomes for GDP growth. We also found negative and significant effects of temperature shocks on non-agricultural sectors. 

The results are obtained using quantile regressions under the growth-at-risk (GaR) approach and local projections.

Temperature shocks on climate-exposed sectors

The analysis of the effects of temperature shocks on climate-exposed sectors suggest that several components of GDP are affected by increased heating.

  • Manufacturing activity seems to be negatively affected after a temperature shock, specially on the upper tail.
  • The entire distribution of construction shows a strong response to temperature shocks, suggesting that labor productivity could be severely affected by heat stress in this sector.
  • The generation of electricity by hydro-powers in Colombia could be explaining the negative effects on the energy sector after an increase in temperatures.
  • Results for agriculture are not statistically significant, possibly capturing the national attempt to increase resilience of this sector in Colombia.

Protecting Colombia’s economy from the impacts of global warming

Colombia has taken some steps in the mitigation and adaptation of its economy to rising temperatures. Especially, it has adopted the National Adaptation Program, whose principal objective is to reduce the country’s vulnerability and increase its response capacity to the impacts of global warming on economic growth, especially for the agricultural sector.

The findings for this sector and the mitigating measures adopted suggest that these efforts could reduce the country’s vulnerability to rising temperatures. However, the country should take complementary strategies with a broader scope of sectors such as energy and construction, that appear to be more vulnerable to warming.

For example, in the energy sector some of 18 the adaptation measures that could be taken are optimization of the conventional energies and improvement of efficiency and diversification of energy sources and promotion of renewal energy.

Further research

Further analysis about the channels and causes of the effects of temperature shocks on GDP growth could be done for a regional level. The heterogeneity on temperature levels and on the reliance on climate-exposed sectors across different regions in Colombia could imply differential effects depending the geographical area analyzed.

Connect with the authors

Camila Camilo ’22 is a Division Chief at the Central Bank of the Dominican Republic in Santo Domingo, DR.

Angie Rozada ’22 is an Expert in the Financial Stability Department at Banco de la República (Central Bank of Colombia).

Carlos Sanz Pérez ’22 is a Trainee at the European Central Bank in Frankfurt, Germany.

Author info is current as of March 2023.

About the BSE Master’s Program in Macroeconomic Policy and Financial Markets

A Short Literature Review on Workplace Discrimination

PhD Track master project by Maddalena Grignani, Wei-Liang Hsu, Jiaxun Liu, and Georgii Zherebilov ’22

People icons in a variety of colors

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.

Abstract

In this literature review, we focus on workplace discrimination with particular attention paid to the hiring process. We classified the hiring process into two stages: CV-screening and interview. 

Following the theoretical discussion of stereotypes supporting the existence of discrimination, two empirical pieces of research are introduced: 

  • The first paper shows that race affects applicants’ callback rate during the screening process
  • The second study shows that the probability of a female musician being hired increases with the introduction of blind auditions. 

Lastly, from a sociological aspect, we claim that cultural similarities can affect employers’ hiring decisions, from which workplace discrimination might also appear.

Connect with the authors

All four of the authors are currently PhD students in Economics in the doctoral program organized jointly by Universitat Pompeu Fabra and the Barcelona School of Economics. (March 2023)

About the BSE Master’s Program in Economics

Migration Shocks and Occupational Downgrading: Evidence from Venezuelan Migrants in Chile

Economics master project by Sunidhi Agarwal, Ignacio Ariznavarreta, Nour Chamseddine, Ricardo Gonçalves, and Ignacio Ramón Oliva ’22

Overlapping flags of Venezuela and Chile

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.

Abstract

This paper examines the downgrading in job status that immigrant workers suffer when settling in a new country. We consider the massive Venezuelan exodus and the impacts this shock had on the job outcomes of migrants who settled in Chile.

Our approach is based on linear regression analysis and multinomial logistic regression models to estimate the penalty immigrants face. To this end, we use household-level data and employ two job-status indexes.

Results show that migrants who arrived before the 2015 Venezuelan crisis did not face significant downgrading. However, migrants who have arrived after 2015 do. Findings are relevant to understanding the impact of massive and sudden migratory shocks.

Connect with the authors

The authors pose for a group photo outside the graduation venue on a sunny day in Barcelona
The authors celebrate together at their graduation ceremony in Barcelona (July 2022).

Author info is current as of February 2023.

About the BSE Master’s Program in Economics

The Impact of the 2014-2016 Russian Financial Crisis on Remittances, Consumption, and Credit: The Case of Kyrgyzstan

International Trade, Finance, and Development master project by Rachel Breaks, Clément Durif, Peiyao Sun, and Joule Voelz ’22

Life in the Kyrgyz mountains. A family works and plays outside on a vast, green plain with rolling hills and mountains in the background under a blue sky dotted with fluffy white clouds. The family is in front of a white canopy tent and a beige dome hut, and a few horses graze around them.
Photo by Oziel Gómez on Unsplash

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.

Abstract

In this paper we consider the negative medium-term impact of the Russian Financial Crisis of 2014-2016 on remittances, consumption patterns, and credit in the Kyrgyz Republic.

Using panel data from the Life in Kyrgyzstan survey, we show a significant drop in migration to Russia and remittances from Russia on the extensive and intensive margins.

Households with a migrant abroad in Russia just prior to the crisis experience an average fall in real per capita income and an increase in poverty, while households both with and without a migrant rebalance their consumption basket to cope with the economic downturn.

Connect with the authors

Author info is current as of February 2023.

  • Rachel Breaks ‘22 is from Manchester, UK. She now works for the UK Government on international trade strategy with Latin America and the Caribbean.
  • Clément Durif ‘22 is from Nice, France. He now works at Asia Centre, a Paris-based think tank specializing in international relations.
  • Peiyao Sun ‘22 is from Hangzhou, ZJ (China). She is a pre-doctoral fellow at the ETH-Zurich in Zurich, Switzerland.
  • Joule Voelz ’22 is from San Francisco, CA (USA). She is a Data Science Methodology student at the Barcelona School of Economics.

About the BSE Master’s Program in International Trade, Finance, and Development

Sam Juthani ’13 on crypto, DLT, and digital regulation

Economics of Public Policy alum Sam Juthani ’13 recently wrote about the maturing digital assets market in a blog post for Flint Global, where he works in the Markets and Investor Advisory practice.

BSE Economics of Public Policy alum Sam Juthani ’13 recently wrote about the maturing digital assets market in a blog post for Flint Global, where he works in the Markets and Investor Advisory practice.

Sam shared an overview of his post on LinkedIn:

“I’ve blogged about the outlook for crypto and DLT (distributed ledger technology). While crypto assets might have hogged the headlines, the big story is the investment in the underlying infrastructure. And that’s where there’s a huge amount of commercial possibility – from payments, digital bonds, tokenisation, and safe, stable ways to access a wider digital economy.”

He pointed out that “2023 is a major year for digital regulation, and there’s a risk that innovation is stopped in its tracks by policymakers who are rightly trying to stop cases of abuse and fraud. This isn’t a question of balance so much as understanding – and businesses have an important role to play in helping policymakers understand that world.”

You might also like to check out Sam’s post on the collapse of FTX from November 2022: History repeats itself – first as banking, then as crypto (with Conor Sewell).

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Portrait of Sam Juthani

Sam Juthani ’13 is a Manager at Flint Global in the UK. He is an alum of the BSE Master’s in Economics of Public Policy.

Machine learning model to predict mental health crises from electronic health records

Publication in Nature Medicine by Roger Garriga ’17 and Javier Mas ’17 (Data Science) et al

Article cover in Nature Medicine

The use of machine learning in healthcare is still in its infancy. In this paper, we describe the project we did to predict psychotic episodes together with Birmingham’s psychiatric hospital. We hope to see these sorts of applications of ML in healthcare become the new standard in the future. The technology is ready, so it’s just a matter of getting it done!

Paper abstract

The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and to the mitigation of burdens and costs. However, the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm’s use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases. To our knowledge, this study is the first to continuously predict the risk of a wide range of mental health crises and to explore the added value of such predictions in clinical practice.

(You can also read about the project in more detail in this article from UPF)

Citation

Garriga, R., Mas, J., Abraha, S. et al. Machine learning model to predict mental health crises from electronic health records. Nat Med (2022). https://doi.org/10.1038/s41591-022-01811-5

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Roger Garriga ’17 is a Research Data Scientist at Koa Health. He is an alum of the BSE Master’s in Data Science.

Javier Mas ’17 is Lead Data Scientist at Kannact. He is an alum of the BSE Master’s in Data Science.