Author: BSE Communications

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.

Read this paper in the online repository

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.

Read this paper in the online repository

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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.

Read this paper in the online repository

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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.

Read this paper in the online repository

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.”

Read Sam’s blog post on Flint’s website

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).

Connect with BSE authors

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.

Read this paper in Nature Medicine (Open Access)

(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

Connect with BSE authors

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.

Application of bagging in day-ahead electricity price forecasting and factor augmentation

Publication in Energy Economics by Kadir Özen ’21 (PhD Track) and Dilem Yıldırım

Illustration of an energy plant and city lights with market graph

This paper has been published in the November 2021 issue of the journal Energy Economics.

Abstract

The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. In such an environment, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating the traceability of the predictor selection procedure, we propose the method of Bootstrap Aggregation (bagging). To forecast day-ahead electricity prices in a multivariate context for six major power markets, we construct a large-scale pure price model and apply the bagging approach in comparison with the popular Least Absolute Shrinkage and Selection Operator (LASSO) estimation method. Our forecasting study reveals that bagging provides substantial forecast improvements on daily and hourly scales in almost all markets over the popular LASSO estimation method. The differentiation in the forecast performances of the two approaches appears to arise, inter alia, from their structural differences in the explanatory variables selection process. Moreover, to account for the intraday hourly dependencies of day-ahead electricity prices, all our models are augmented with latent factors, and a substantial improvement is observed only in the forecasts from models covering a relatively limited number of predictors.

Highlights

  • We forecast day-ahead electricity prices for major markets with a large-scale model.
  • The method of Bootstrap Aggregation (bagging) is applied to generate forecasts.
  • Bagging appears to be very competitive and promising compared to the popular LASSO.
  • Factor augmentation is proposed to capture intraday hourly dependencies of prices.
  • Augmentation improves forecasts only for models with limited number of predictors.
View on Science Direct

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portrait

Kadir Özen ’21 is a student in the PhD Program at UPF and BSE. He is an alum of the BSE Master’s in Economics and Finance (PhD Track).

Marta Morazzoni and Andrea Sy win EEA Young Economist Award

Their paper, “Female entrepreneurship, financial frictions and capital misallocation in the US,” has also been published in the Journal of Monetary Economics.

A photo of the two BSE alumni who wrote the award-winning paper, Marta Morazzoni and Andrea Sy, posing together at UPF Ciudatella Campus where they are both PhD candidates.
EEA Young Economist Awardees Marta Morazzoni ’18 and Andrea Sy ’18

BSE alumni Marta Morazzoni and Andrea Sy (both Economics Class of 2018) received the 2021 Young Economist Award from the European Economic Association and Unicredit Foundation for their paper, “Female entrepreneurship, financial frictions and capital misallocation in the US.”

The paper has also just been published in the Journal of Monetary Economics. (It originally appeared as a Barcelona School of Economics Working Paper.)

“New empirical evidence, highly relevant policy implications”

The EEA Young Economics award committee consisted of Philipp Kircher, Giacomo Ponzetto and Antonella Trigari. They noted that “the paper addresses an extremely important topic, offers new empirical evidence from micro-level data cleverly identifying informative moments, and builds a state-of-the-art general equilibrium model to rationalize the evidence and to provide highly relevant policy implications.”

Read the award committee’s report on the EEA website

Paper abstract

We document and quantify the effect of a gender gap in credit access on both entrepreneurship and input misallocation in the US. Female entrepreneurs are found to be more likely to face a rejection on their loan applications and to have a higher average product of capital, a sign of gender-driven capital misallocation that decreases in female-led firms’ access to finance. These results are not driven by differences in observable individual or businesses characteristics. Calibrating a heterogeneous agents model of entrepreneurship to the US economy, we show that the observed gap in credit access explains the bulk of the gender differences in capital allocation across firms. Eliminating such credit imbalance is estimated to potentially increase output by 4%, and to reduce capital misallocation by 12%.

Key findings

  • In the US, female entrepreneurs receive less business funding compared to male entrepreneurs.
  • Female-owned firms operate with lower levels of assets, resulting in gender-driven capital misallocation.
  • Female-led businesses are nonetheless relatively more profitable and have better credit risk scores.
  • Removing the gender gap in business financing is estimated to potentially increase output by 4%.
Read the published paper in JME

Connect with BSE authors

portrait

Marta Morazzoni ’18 is a PhD candidate at Universitat Pompeu Fabra and BSE. She is an alum of the BSE Master’s in Economics.

portrait

Andrea Sy ’18 is a PhD candidate at Universitat Pompeu Fabra and BSE. She is an alum of the BSE Master’s in Economics.

The Wisdom of the Crowd: Using ensemble machine learning techniques as an early warning indicator for systemic banking crises

Finance master project by Gabriela Lavagna, Helena Patterson, and Robizon Razmadze ’21

Blurred chart on a computer screen
Image by Pexels from Pixabay

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

We develop early warning models for systemic crisis prediction using machine learning techniques on macrofinancial data for 36 countries for quarterly data spanning 1970-2013. Machine learning models outperform logistic regression in out-of-sample predictions under the recursive window forecasting mechanism. In particular, using the ensemble random forest algorithm for both feature selection and prediction substantially outperforms the logit models. We identify the key economic and financial drivers of our models using the random forest framework by extracting each feature’s Gini impurity and corresponding information gain. Throughout the time period, the most important predictors are credit, foreign liabilities, asset prices and foreign currency reserves. 

Conclusions

  • The aim of the study was to construct a machine learning methodology to improve the predictive ability of systemic crises models. We applied these algorithms on macrofinancial data for 36 countries for quarterly data spanning 1970-2013. The results of the paper show that predictions of financial crises are more accurately obtained via machine learning algorithms as opposed to logit regression models in out-of-sample predictions (obtaining an AUC score of 0.77-0.81).
  • During the analysis, our goal was not only to be able to improve the predictive power of the models, but also to be able to select the most relevant and concise predictor variables. We applied the random forest ensemble algorithm to undertake feature selection and concluded that, over the years, the variables credit, foreign liabilities, asset prices and currency reserves were most important in predicting systemic crises.
  • The value-add derived from the models developed by us can be viewed in two main directions: Firstly, the need to use time series data as a means of predicting crises has meant many authors in the past have been unable to avoid the ‘looking to the future’ issue. We managed to alleviate this risk by using a recursive window estimation mechanism. The main benefit of this methodology is that it would allow policymakers to observe the predictors in real-time. Second, by being able to rank variables in order of importance, we were able to reveal the key economic and financial drivers which should be used by policymakers in evaluating any pressing risk of systemic crises. 
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About the BSE Master’s Program in Finance

Gender Gap and Retirement Decisions: the Maternity Pension Supplement in Spain

Economics master project by Jorge Casanova, Horia Guias, Carlos Javier López, Andrea Salvanti, and Patrick Sewell ’21

Family graphic spray painted on pavement
Photo by Sandy Millar 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

Embedded in the growing governmental efforts to reduce the gender income gap, in 2016 a retirement pension supplement for mothers of at least two children was introduced in Spain. Through an Oaxaca-Blinder decomposition analysis, we find that the policy had a smaller-than-expected shrinking effect in the gender gap in retirement pensions. Using a difference-in-differences approach, we identify that the trade-off between the supplement incentivizing early retirement and the penalty this retirement modality entails in Spain is the mechanism driving this result. Finally, we developed a dynamic choice model to simulate women’s behavior under alternative versions of the policy.

Summary

Our main motivation was to analyse whether the maternity supplement proposed by the Spanish government in 2016 fostered gender equality through a reduction of the gender gap in retirement income. We decompose the average monthly retirement income for both men and women into its determinants and estimate how the gender difference in returns on pension of having two or more children changes after the policy is introduced. Our result is that the policy had a smaller-than-expected shrinking effect in the gender gap in retirement pensions, as the gender gap for regular retirement closes but the gap conditional on early retirement (i.e. below 65 years) remains unaffected.

chart

Figure 1: Time Series of Average Monthly Retirement Income: negative trend reverted for women retiring at 65 after the introduction of the policy but not for women retiring at an earlier age.

Using a difference-in-differences approach, we observe that the policy has a positive effect on all retirement hazard rates – i.e. the probability of retiring at a certain age, conditional on not having done so before.

One reason for the lower income effect is due to the trade-off that women face when they consider retiring before reaching the age at which they would start receiving their full retirement pension. On the one hand, early retirement increases the value of leisure, which could be especially beneficial for women with difficult working biographies. On the other hand, early retirement entails a penalty on the pension. For this group of women, early retirement reduces this penalty and hence, changes the trade-off in favour of early retirement, making the substitution of retirement income for more leisure more appealing.

Finally, we develop a dynamic choice model that depicts the trade-off between income and leisure that women face in retirement decisions, which can be used in a next step to simulate different retirement policies and compare their outcomes.

Future research on the maternity benefit ought to shed light on the different effects it produced between women who were and were not in a couple, and how much agents value leisure relative to money.

Download the full paper

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About the BSE Master’s Program in Economics