The Exploring Happiness Index

A new tool created by Elliot Jones ’18 (Macro Program)

Elliot Jones ’18 (Macroeconomic Policy and Financial Markets) is a Sovereign Credit Risk Analyst at the Bank of England. Together with co-creator Jessica Golding, he has established Exploring Happiness, a research organization that looks to bring produce evidence-based research in order to generate policy recommendations focused towards sustainably increasing wellbeing across all areas of society.

The latest project to come out of the initiative is the Exploring Happiness Index. This is an online tool that anyone can use to help them achieve their goals and to support their mental health.

Learn more about the index in the video and read on to find out how to start using it:

Explaining the concept of the index

Everyone is different and what makes each of us happy is also different. Some people are career-driven, others live for the social scene and some are highly family-orientated. Despite this, we all also have a lot in common – we all value our health, both mental and physical, the quality of our personal relationships matters a lot and we all like to have something to do that makes us feel worthwhile. It is these basic fundamentals that we use to build our index. We take evidence-based research on the main determinants of life satisfaction (which is taken as being synonymous with happiness) to bring together a group of components that we all have in common and these become the building blocks of the index.


The differences of users are captured in two main ways – by identifying the circumstances and preferences of each user. When creating an account each user can choose from 13 different individual types such as an employed worker, a student or a retired person. As a result of this choice, the components that make up the index will change to reflect that individual types circumstances (e.g. an employed worker has a ‘Work’ component, a student has an ‘Education’ component and a retired person has neither of these, but greater weight is applied to their ‘Leisure Time’ component). 

Next, users are then able to choose how important each of the components are to them and the weights in the index will shift to reflect these choices. These two steps make the index unique for each user.

Three benefits of using the index

  1. Being informed:  Our decisions, big or small, will play an important role in determining our happiness and we are more likely to make the right decision when we have better information available to draw upon. The usage of this index provides users with this information, perhaps meaning just knowing how happy you are could make you happier.
  2. Mental health tool: Using this index allows users time to reflect, to think about what has been going well and what has been more challenging. There is good evidence available which points to the benefits of self-reflection on mental health. This has been shown for self-reflection in a number of forms (e.g. from expressive writing to gratitude journaling), across various life stages and as an effective treatment for those with diagnosed mental illnesses. Our view, which we intend to robustly test in the future, is that the method of self-reflection that this index requires will boost users resilience and mental wellbeing.
  3. The ultimate tracker: Nowadays, it is not uncommon to track several parts of our lives, from steps to sleep to calories. But what’s the point in tracking these things? For most people, it’s because they believe if they do more steps or sleep better, they will end up feeling better. This index allows you to check whether that’s true in practice.

Get starting with the Exploring Happiness Index

You can create an account here or find out a little more first by heading to the index homepage.

If you have any feedback, please email or use the feedback form on our website. 

Elliot Jones ’18 is a Sovereign Credit Risk Analyst at the Bank of England. He is an alum of the Barcelona GSE’s Master’s in Macroeconomic Policy and Financial Markets.

LinkedIn | Exploring Happiness

Does Air Pollution Exacerbate Covid-19 Symptoms? Evidence from France

Economics master project by Mattia Laudi, Hubert Massoni, and James Newland ’20

The Eiffel Tower under a dark red sky
Image by Free-Photos from Pixabay

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


For patients infected by Covid-19, underlying health conditions are often cited as a source of increased vulnerability, of which exposure to high levels of air pollution has proven to be an exacerbating cause. We investigate the effect of long-term pollution exposure on Covid-19 mortality, admissions to hospitals and admissions to intensive care units in France. Using cross-sectional count data at the local level, we fit mixed effect negative binomial models with the three Covid-19 measures as dependent variables and atmospheric PM2.5 concentration (µg/m3) as an explanatory variable, while adjusting for a large set of potential confounders. We find that a one-unit increase in PM2.5 concentration raised on average the mortality rate by 22%, the admission to ICU rate by 11% and the admission to hospital rate by 14% (rates with respect to population). These results are robust to a large set of sensitivity analyses. As a novel contribution, we estimate tangible marginal costs of pollution, and suggest that a marginal increase in pollution resulted on average in 61 deaths and created a 1 million euro surcharge in intensive care treatments over the investigated period (March 19th – May 25th).

A map of air pollution and a map of Covid deaths in France


The study is a strong indication that air pollution is a crucial environmental factor in mortality risks and vulnerability to Covid-19. The health risks associated with air pollution are well documented, but with Covid-19 in the spotlight we hope to increase awareness of the threat caused by pollution, not only through direct increased health risks, but also through external factors, such as pandemics.

We show the aggravating effect of long-term pollution exposure to three levels of severity of Covid-19 symptoms in France: admission to hospitals for acute Covid-19 cases, admission to intensive care units for the most severe vital organ failures, and fatalities (all expressed per 100,000 inhabitants). Using cross-sectional data at the départemental (sub-regional) level, we fit mixed effect negative binomial models with the three Covid-19 measures as dependent variables and the average level of atmospheric concentration of PM2.5 (µg/m3) as an explanatory variable. We adjust for a set of 18 potential confounders to isolate the role of pollution in the spread of the Covid-19 disease across départements. We find that a one-unit increase in average PM2.5 levels increases on average the mortality rate by 22%, the admission to ICU rate by 11% and the admission to hospital rate by 14%. These results are robust to a set of 24 secondary and sensitivity analyses per dependent variable, confirming the consistency of the findings across a wide range of specifications.

We further provide numerical – and hence more tangible – estimates of the marginal costs of pollution since March 19th. Adjusting for under-reporting of Covid-19 deaths, we estimate that long-term exposure to pollution marginally resulted in an average 61 deaths across French départements. Moreover, based on average daily costs of intensive care treatments, we estimate that pollution induced an average 1 million euros in costs borne by hospitals treating severe symptoms of Covid-19. These figures strongly suggest that areas with greater air pollution faced substantially higher casualties and costs in hospital services, and raise concerns about misallocation of resources to the healthcare system in more polluted areas.

Our paper provides precise estimates and a reproducible model for future work, but is limited by the novelty of the phenomenon at the centre of the study. Our empirical investigation is restricted to the scope of France alone due to cross-border inconsistencies in Covid-19 data collection and reporting. Once Covid-19 data reporting is complete and consistent, we hope future studies will examine the effects of air pollution at a greater scale, or in greater detail. On the other hand, more disaggregated data – at the individual or hospital level – would allow more precise estimates and a better understanding of key factors of Covid-19 health risks and would also allow the use of surface-measured air pollution. Measured pollution data is available for France, but is inherently biased when aggregated at the départemental level, due to lack of territorial coverage. If precise data tracking periodic Covid-19 deaths becomes available for a wider geographic region, we specifically recommend a MENB panel regression incorporating a PCFE for spatially correlated errors. This will produce the most accurate estimates.

Going forward, more accurate and granular data should motivate future research to uncover the exact financial costs attributable to air pollution during the pandemic. Precise estimation of costs of Covid-19 treatments and equipment (e.g. basic protective equipment for personnel or resuscitation equipment), should feature in a more accurate cost analysis. Hospital responses should be thoroughly analysed to understand the true cost of treatments across all units.

It is crucial that the healthcare costs of pollution are globally recognised so that future policy decisions take them into account. Ultimately, this paper stresses that failure to manage and improve ambient air quality in the long run only magnifies future burdens on healthcare resources, and cause more damage to human life. During a global pandemic, the costs of permitting further air pollution appears ever more salient.

Connect with the authors

About the Barcelona GSE Master’s Program in Economics

Demand Estimation in a Two-Sided Market: Viewers and Advertisers in the Spanish Free-to-Air TV Market

Competition and Market Regulation master project by Sully Calderón and Aida Moreu ’20

Photo by Glenn Carstens-Peters on Unsplash

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


Our research arises in a context where “free” services in one market cannot be understood without taking into consideration the other side of the market. The Spanish free-to-air TV industry is a two-sided market in which viewers demand TV programs (for free) and advertisers demand advertising spots for which they pay a price that depends mainly on audience.  Our main contribution to the two-sided market literature is estimating both viewers and advertisers demand to be able to understand the interactions of both sides of the free-to-air TV market.

This analysis is carried out by developing an econometric analysis of the free to air TV market in Spain that captures the reaction of viewers to a change in advertising quantity and the effect on price of ads that this would bring. We specified Viewers Demand in the Spanish free-to-air TV through a logit model to analyse the impact of advertising minutes on the audience share and, we specified Advertisers Demand by an adaptation of the model of Wilbur (2008) to understand the effect of audience share and advertising quantity on prices of ads.  


The results of the Viewers Demand model show an elastic demand and that viewers are averse to advertising regardless of the day but during prime time they are a bit more ad tolerant, especially from 10pm to 11 pm. 

Logit estimation of Viewers Demand. Download the paper to read text version.

On the other side of the market, the Advertising Demand model shows that advertisers are relatively inelastic to both an increase of adds and an increase in audience share. This may be due to the fact that the data available for this project is precisely coming from the most viewed channels, for which advertisers would have more inelastic demand.

Logit estimation of Advertising Demand. Download the paper to read text version.

As expected, the results show that advertisers are more elastic with regards to audience share than to quantity of advertising.

Connect with the authors

  • Sully Calderón, Economic Advisor at Comisión Federal de Competencia Económica (México)
  • Aida Moreu, Research Analyst at Compass Lexecon (Madrid)

About the Barcelona GSE Master’s Program in Competition and Market Regulation

Tracking the Economy Using FOMC Speech Transcripts

Data Science master project by Laura Battaglia and Maria Salunina ’20

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


In this study, we propose an approach for the extraction of a low-dimensional signal from a collection of text documents ordered over time. The proposed framework foresees the application of Latent Dirichlet Allocation (LDA) for obtaining a meaningful representation of documents as a mixture over a set of topics. Such representations can then be modeled via a Dynamic Linear Model (DLM) as noisy realisations of a limited number of latent factors that evolve with time. We apply this approach to Federal Open Market Committee (FOMC) speech transcripts for the period of Greenspan presidency. This study serves as exploratory research for the investigation into how unstructured text data can be incorporated into economic modeling. In particular, our findings point at the fact that a meaningful state-of-the-world signal can be extracted from expert’s language, and pave the way for further exploration into the building of macroeconomic forecasting models, and in general into the usage of variation in language for learning about latent economic conditions.

Key findings

In our paper, we develop a sequential approach for the extraction of a low-dimensional signal from a collection of documents ordered over time. We apply this framework to the US Fed’s FOMC speech transcripts for the period 08-1986 to 01-2006. We retrieve estimates for a single latent factor, that seem to track fairly well a specific set of topics connected with risk, uncertainty, and expectations. Finally, we find a remarkable correspondence between this factor and the Economic Policy Uncertainty Indices for United States.


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About the Barcelona GSE Master’s Program in Data Science

Institutional real estate investors, leverage, and macroprudential regulation

VoxEU article by Manuel A. Muñoz ’13 (Macroeconomic Policy and Financial Markets)

I am honoured to share my new VoxEU article (with you), which I believe it’s relevant for the ongoing debate on how to strengthen the macroprudential regulatory framework for nonbanks:

Ensuring that institutional real estate investors are subject to countercyclical leverage limits would be particularly effective in smoothing the housing price and the credit cycle.

In addition, the associated ECB working paper suggests that this type of regulation would allow for rental housing prices to increase less abruptly during the boom, an issue that policymakers in several countries of the euro area have attempted to handle via price regulation (an alternative that could generate price distortions).

Also on VoxEU by Manuel A. Muñoz

Macroprudential policy and COVID-19: Restrict dividend distributions to significantly improve the effectiveness of the countercyclical capital buffer release (July 2020)

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Manuel A. Muñoz ’13 is Senior Lead Expert at the European Central Bank. He is an alum of the Barcelona GSE Master’s in Macroeconomic Policy and Financial Markets.

Are you a Barcelona GSE alum with a new paper or project to share? Learn how to submit your work to the Voice!

Future paths for monetary policy in a world of lower inflation

Recap of Professor Jordi Galí’s ECB Forum presentation by Maximilian Magnacca Sancho ’21 (ITFD)

The stage for ECB Forum 2020
Photo by ECB on Flickr

On November 11, 2020 the first day of the ECB Forum on Central Banking began with three exciting talks on how shifts in the global economy have changed the game for central banks worldwide. Among the distinguished guest speakers at the Forum was UPF, CREi, and Barcelona Graduate School of Economics Research Professor, Jordi Galí. While the ECB had him slated to speak on the “Inflation Objective, Structural Forces, and Central Bank Communication,” Professor Galí spent his presentation focusing predominantly on inflation targeting at central banks and whether it should be revised going forward. 

Watch the ECB Forum panel discussion with Jordi Galí, Volker Wieland, and Annette Vissing-Jørgensen, chaired by Philip Lane on YouTube

His presentation spoke directly to an ongoing debate amongst academics and financial-market watchers, as there have been structural changes in the economy since the last update of the ECB’s policy in 2003. Structural changes are a normal function of an economy as it progresses along with society, though it has some troublesome side effects. Most concerningly, they reduce the effectiveness of policy in alleviating economic downturns by influencing the transmission mechanisms of monetary policy. This raises the challenge for policymakers in picking the best policy.

These structural changes ultimately centre around one crucial variable that is influenceable by central banks. The Steady-State Real Interest Rate, colloquially known as R*.  Central banks will be constrained by this rate and hit the zero lower bound sooner if they withhold from changing the inflation target – as argued by Jordi Galí in his presentation. A lower R*, as is being observed, necessitates a higher inflation target so that monetary policy (done through a changing of the interest rate) will have less incidence of the zero lower bound and thus reducing the chance of ineffectiveness.

This is obviously an urgent issue faced by policymakers worldwide during the ongoing COVID-19 crisis from the forefront of monetary economics research, yet what Professor Galí was able to do was bring it back to the basics of economics. He emphasised how the models are built on assumptions, and if assumptions change – as the data indicate they have – then the framework for thinking about these issues need to be updated.

In this spirit, Professor Galí proposed three potential policy changes for central bankers to consider in light of the ECB strategic review: he proposed more countercyclical fiscal support; changing to average inflation targeting; or a higher inflation target from its current position of “below or at 2%”. He acknowledged the challenges and potential pitfalls of all these policies, while also speaking to their potential improvement upon the current policy. 

It was great to see a Barcelona GSE professor invited to speak at such a prominent and interesting event held annually and frequented by policymakers and academics working on some of the most challenging issues in central banking. It highlights the quality of research that is being done at Barcelona GSE and the quality of the professors conducting that research being sought after by policymakers. 

Maximilian Magnacca Sancho ’21 is a student in the Barcelona GSE Master’s in International Trade, Finance, and Development.

This post was edited by Ashok Manandhar ’21 (Economics).

Structure and power dynamics in labour flow and company control networks in the UK

Data Science master project by Áron Pap ’20

Droplets of dew collect on a spider web
Photo by Nathan Dumlao on Unsplash

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


In this thesis project I analyse labour flow networks, considering both undirected and directed configurations, and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree distribution, strong, naturally emerging communities with geo-industrial clustering and high assortativity. I also document that distinguishing between the type of investors of firms can help to better understand their degree centrality in the company control network and that large institutional entities having significant and exclusive control in a firm seem to be responsible for emerging hubs in this network. I also devise a simple network formation model to study the underlying causal processes in this company control network.


Conclusion and future research

Intriguing empirical patterns and a new stylized fact are documented during the study of the company control network, since there is suggestive evidence that the types and number of investors are strongly associated with how “interconnected” a firm is in the company control network. Based on the empirical data it also seems that the largest institutional investors mainly seek opportunities where they can have significant control without sharing it with other dominant players. Thus the most “interconnected”/central firms in the company control network are the ones who can maintain this power balance in their owner structure. 

The devised network formation model helps to better understand the potential underlying mechanisms for the empirically observed stylized facts about the company control network. I carry out numerical simulations, sensitivity analysis and also calibrate parameters of the model using Bayesian optimization techniques to match the empirical results. However, these results could be “fine-tuned” at different stages further, in order to have a better empirical fit. First, the network formation model could be enhanced to represent more complex agent interactions and decisions. But also, the model calibration method could be extended to include more parameters and a larger valid search space for each of those parameters.

This project could also benefit from improvements to the utilised data. For example more granular data on the geographical regions could help to understand the different parts of London more and to have a more detailed view of economic hubs in the UK. Moreover, the current data source provides a static snapshot of the ownership and control structure of firms. Panel data on this front could enhance the analysis of the company control network, numerous experiments related to temporal dynamics could be carried out, for example link prediction or testing whether investors follow some kind of “preferential attachment” rules when acquiring significant control in firms.

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Áron Pap, Visiting Student at The Alan Turing Institute

About the Barcelona GSE Master’s Program in Data Science

Competition and Price Asymmetries in the Retail Gasoline Market: an Application to a Spanish Cartel Case

Competition and Market Regulation master project by Silvia Brumana and Roger Medina ’20

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


Asymmetric pricing in the retail fuel market describes the situation in which retail prices are adjusted quickly in case of positive shocks in the wholesale costs, whereas the adjustment is much slower in case of negative ones. This asymmetry in the reaction of retail operators is captured by the well-known expression rockets and feathers. By relying on a reduced version of a database used in Moral and Gonzalez (2019) containing the series of diesel prices in Spain and Brent prices for the period 08/2014-06/2015, we address the following research question: how did the fuel prices of the retail gas stations in a subset of Spanish provinces react in this period to changes in the price of Brent? In order to answer this question, we construct a pricing equation that takes into account all the possible factors that can affect pricing with the aim of isolating the effect of negative shocks and the effect of positive shocks in the price of Brent on the price of gasoline of the various Spanish operators. Then, we check whether the difference between these two effects is statistically significant to see whether there were asymmetric reactions. Furthermore, since the common rocket and feathers phenomenon could be considered as a collusive device, and since in the period covered by the database a cartel was active among the main fuel companies in Spain, we also investigate the relation between asymmetric pricing and competition.

Figure 1. Spatial distribution of gas stations in Barcelona


The aim of our project was to capture the presence of asymmetric pricing in nine Spanish provinces between 2014 and 2015 by taking into account the fact that during the period considered the CNMC fined five of the largest oil companies in Spain for collusion. To do this, we estimated an Error Correction Model for each province by accounting for both panel heterogeneity and cross-sectional dependence to capture both the short-run and the long-run relation between the retail diesel prices and the Brent price. 

Our results show that there exists a correction mechanism that pushes the retail prices back to the long-run equilibrium relation with the Brent in case of shocks, even if the magnitude of the correction may be overestimated potentially because of the pattern of the retail fuel price in the period before the fine of the CNMC became public.

Figure 2. Evolution of the Brent price and the average retail fuel price in Alicante in the period considered. Note that the CNMC fined the major oil companies in Spain in February 2015.

As far as the short-term adjustments are concerned, the results are less clear because of the lack of significance of most of the coefficients; this is potentially due both to the presence of imperfect collinearity arising from issues with the lag selection in each province as well as to the already mentioned patterns of the diesel prices. Furthermore, as to what concerns the effect of the fine of the CNMC, the results show that in many provinces there is some variability in the pricing of the gas stations after February 2015 that is not accounted for in the rest of the model. We may not confidently state that this is entirely due to the fine of the CNMC: in fact, the related coefficient may capture some variability due to the attempts of firms of recovering the losses incurred before the fine was issued. Consequently, we suggested that we may need to first solve the issue of the lag selection and then, if the effect of the fine is still significant, we may eventually add some control for the profitability of the firms, even if this would imply expanding the database because of the absence of variables capturing the evolution of profits of the firms observed.

To conclude, although our analysis may still need some refinement especially in the lag selection procedure, we suggest that the relation between asymmetric pricing and competition is worth exploring. In addition, it would be also interesting to understand whether aggregating the data in the database we used at a weekly level, for instance, would lead to different estimates and insights or would simply lead to biased findings as suggested in the literature.

Connect with the authors

  • Silvia Brumana, Economics Intern at the UK Competition & Markets Authority (CMA)
  • Roger Medina, Senior Research Fellow at the Ostrom Institute

About the Barcelona GSE Master’s Program in Competition and Market Regulation

The Impact of Quantitative Easing on Sectoral Stock Prices in the Euro Area

Macroeconomic Policy and Financial Markets master project by Annalisa Goglione, Rahel Krauskopff, and Aditi Rai ’20

Photo by NICHOLAS CAPPELLO on Unsplash

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


The Global Financial Crisis prompted central banks to adopt unconventional monetary policies such as the asset purchase programs. In our thesis, we analyze whether securities purchases carried out by the ECB have had an impact on stock prices and whether these effects vary across sectors. We decompose the central bank announcement surprises into two opposing effects, a pure monetary policy shock and an information shock. We find that the pure monetary policy shock has indeed significant effects on stock prices across all sectors, suggesting that controlling for the information shock is important when analyzing the effects of central bank announcements.


In our paper, we analyzed the effects of Quantitative Easing on stock prices in the Euro Area. Adding to previous literature, we not only analyzed the effect on the benchmark index but considered sectoral indices as well in order to allow for heterogeneous responses across different industries. Using the methodology of Altavilla et al. (2019), we first extracted the QE factor from each ECB press conference, starting from 2002. In a first exercise, we used the QE factor as a regressor to explain stock price changes on the press conference days. In line with asset pricing theory, we find negative effects of the QE shock on all sectoral stock price indices, yet, significance differs.

Overall QE Shock

In a second step, we decomposed the shock into two components, using sign restrictions. The two shocks are referred to as information shock and pure monetary policy shock and are characterized by positive co-movement and negative co-movement of interest rates and stock prices, respectively.

Information Shock
Pure Policy Shock

We looked at these two components separately in order to assess whether insignificant results obtained in the overall regression may be due to the two components off-setting each other, a presumption which was confirmed. Furthermore, we find that the pure monetary policy shock has significant effects on stock prices across all sectors, while the information shock appears to be more important on specific sectors, which are more related to financial markets. A more in-depth analysis of the reasons for this heterogeneity could provide further insight into the effects of QE on stock prices. Moreover, repeating this exercise with a larger sample and intraday stock price data might yield improved results. Other extensions could be the analysis of specific sectors for various countries.

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

Economic Forces: Pondering price theory

A newsletter about supply and demand by Brian Albrecht ’14 (Economics of Public Policy)

Economic Forces is a new weekly newsletter by Brian Albrecht ’14 (Assistant Professor of Economics, Kennesaw State University) and Josh Hendrickson (Associate Professor of Economics, University of Mississippi).

“We are both professors of economics with a passion for what used to be called price theory. This newsletter is our attempt to work through and clarify points in price theory,” the authors explain in the newsletter’s introduction.

“You’ll have to pry supply and demand from my cold, dead hands.”

That’s the title of Brian’s first post to the newsletter. In it, he gives an overview of the Economic Forces project and and “a simple defense of (the increasingly scoffed at by the loudest voices online) supply and demand. “It seems silly to need to defend supply and demand within economics circles,” Brian says, “But it is 2020…”

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Brian Albrecht ’14 (PhD, University of Minnesota) is Assistant Professor of Economics at Kennesaw State University. He is an alum of the Barcelona GSE Master’s in Economics of Public Policy.