ACSS-PSL Institute Research Seminar

Exploring Nigerian Auxiliary Generator Use with Variable Length Markov Chains with Covariates

Coauthor: Hugo Le Picard

Abstract:

While Nigeria is the first Sub-Sahara economy, its centralised electricity network is very unreliable and is down approximately 45 % of the time. Business and wealthy households use auxiliary power generators to compensate for these blackouts. The aggregated capacity of these auxiliary generators is estimated between 10 and 15 GW, roughly three times more than the actual available capacity of the centralised grid. Generators are a large economic burden and induce health and environmental issues, hence their use should be discouraged. However, usage patterns of these generators remain unstudied which prevents estimating the effects on their use that could be induced by different types of improvements of the centralised power grid. We discuss in this presentation preliminary results obtained on a unique data set of 68 generators installed in Nigeria companies and recorded for several days together with the state of the centralised power grid. Each generator is represented by a binary time series (on and off) recorded with a time resolution of ten minutes. The state of the portion of the power grid to which each company is connected is represented in a similar way. To analyse such binary time series, we rely on Variable Length Markov Chains (VLMC). These are parsimonious Markov chains that can combine short and long memory components. This is particularly adapted to model generator use patterns that are independent from the status of the centralised network, owing to its unreliability. We also consider a recent extension of VLMC to situations where a time series can be influenced by its own past but also by external covariates. This extension is well adapted to capture generator use patterns that depends only on external factors (the state of the centralised network) or on both external factors and the past status of the generator itself. Using a BIC based automatic model selection, we show that all types of dependencies are present in our generator data set. Using simulations generated from the obtained models we assess to what extend they can be used to study the potential effects on usage patterns of improvements of the quality of the centralised grid.

Electrification and Deforestation in Côte d'Ivoire: a spatial econometric analysis

Coauthors: Raja Chakir and Anna Creti

Abstract:

This study analyses the links between electrification and deforestation in Côte d'Ivoire. First, we assess the alignment of night lights intensity data with the official electricity coverage statistics, which are available only at the regional level. Then, using panel data on night lights intensity, we investigate the relationship between electrification and deforestation in greater detail, focusing on a fine resolution at departmental level. In this analysis, we take into account both spatial autocorrelation and individual heterogeneity. Our findings reveal that electrification has an overall positive impact on deforestation with a direct positive impact in electrified localities and a negative indirect impact on neighboring ones. This empirical evidence, contrasting with prior findings on developing countries, carries significant implications for environmental policy and sustainable development efforts.

Trust in male and female advisers: An experimental investigation

Abstract:

We use an online experiment to investigate advisers‘ recommendation follow-up and the choice of an adviser to receive a recommendation from. We are interested in the role of the adviser’s gender and of whether the adviser is portrayed as an expert or not. Participants play the Monty Hall game, a decision game for which most participants have a false intuition about the expected payoff maximizing action. Before making the final decision determining whether they win the prize or not, each participant receives a recommendation from an adviser. For some participants, it is randomly determined whether they receive the recommendation of a male or a female adviser. In other conditions, participants must choose an adviser between two males and two females before receiving the recommendation from the chosen adviser. Advisers are either portrayed as experts or not. Presenting advisers as experts shifts the recommendation follow-up gender gap in favor of female advisers. In conditions where participants have to choose an adviser among several ones, female participants choose more often a female adviser. Men who chose a female adviser follow more often the recommendation than the ones who chose a male adviser

Marketing and gender stereotypes: building a corpus of gendered products

Abstract:

In the field of gender studies in marketing, we explore how the market reinforces gender stereotypes through its offer. We manually collected a corpus of products from gender-segmented markets (children's publishing and clothing) and analyzed it with two softwares: Tropes (semantic analysis) and Iramuteq (textual data analysis). We now want to take our research a step further and would like to systematize data collection and analysis in order to strengthen the validity of our conclusions.

Sentometrics: An Overview of Methodology and Applications

Abstract:

The advent of massive amounts of textual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. This seminar will present this new research field and illustrate possible applications in economics and finance.

Social Media as a Bank Run Catalyst

Coauthors: Tony Cookson, Corbin Fox, Javier Gil-Bazo and Christoph Schiller

Abstract:

Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects were felt broadly in the U.S. banking industry. We employ comprehensive Twitter data to show that preexisting exposure to social media predicts bank stock market losses in the run period even after controlling for bank characteristics related to run risk (i.e., mark-to-market losses and uninsured deposits). Moreover, we show that social media amplifies these bank run risk factors. During the run period, we find the intensity of Twitter conversation about a bank predicts stock market losses at the hourly frequency. This effect is stronger for banks with bank run risk factors. At even higher frequency, tweets in the run period with negative sentiment translate into immediate stock market losses. These high frequency effects are stronger when tweets are authored by members of the Twitter startup community (who are likely depositors) and contain keywords related to contagion. These results are consistent with depositors using Twitter to communicate in real time during the bank run.

Interpretable Machine Learning for genomic, metagenomic and other Omics data

Prison, Mental Health and Family Spillovers

Coauthors: M. Bhuller and K. Løken

Abstract:

Does prison cause mental health problems among inmates and their family members? Correlational evidence shows that the prevalence of mental health problems is much higher in the inmate population than in the general population, but it remains silent on causality. We exploit the strengths of the Norwegian setting and the richness of the data to accurately measure the impacts of incarceration on the health of the defendants and their family members. First, we use an event-study design around the case decision event. The event study is complemented with an instrumental variable (IV) strategy that takes advantage of the random assignment of criminal cases to judges who differ in their stringency. Both methods consistently show that the positive correlation is misleading: incarceration lowers the prevalence of mental health disorders among defendants as measured by mental-health related visits to health care professionals. We further demonstrate that this effect lasts long after release and is unlikely to be driven by a shift in health care demand. Family members also experience positive spillovers on their mental health, especially spouses.

When are Google data useful to nowcast GDP: An approach via preselection and shrinkage

Coauthor: Anna Simoni; CNRS - CREST

Abstract:

Alternative data sets are widely used for macroeconomic nowcasting together with machine learning-based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, a theoretically grounded nowcasting methodology is proposed that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them with Monte-Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tends to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.

Explainable Performance

Coauthors: Sullivan Hué, Aix-Marseille University - Aix-Marseille School of Economics, Christophe Hurlin, University of Orléans, Christophe Pérignon, HEC Paris

Abstract:

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features, XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4280563

The Covid-19 Crisis: an NLP Exploration of the French Twitter Feed (February-May 2020)

Abstract:

The Covid-19 pandemic offers a spectacular case of disaster management. In this literature, the paradigm of participation is fundamental: the mitigation of the impact of the disaster, the quality of the preparation and the resilience of the society, which facilitate the reconstruction, depend on the participation of the populations. Being able to observe and measure the state of mental health of the population (anxiety, confidence, expectations, ...) and to identify the points of controversy and the content of the discourse, are necessary to support measures designed to encourage this participation. Social media, and in particular Twitter, offer valuable resources for researching this discourse. The objective of this empirical study is to reconstruct a micro history of users’ reactions to the pandemic as they share them on social networks. The general method used comes from new processing techniques derived from Natural Language Processing (NLP). Three analysis methods are used to process the corpus: analysis of the temporal evolution of term occurrences; creation of dynamic semantic maps to identify co-occurrences; analysis of topics using the SVM method. The main empirical result is that the mask emerges as a central figure of discourse, at least in the discourse produced by certain social media. The retrospective analysis of the phenomenon allows us to explain what made the mask a focal point not only in conversation, but also in behaviors. Its value resides less in its functional qualities than in its ability to fix attention and organize living conditions under the threat of pandemic.

Measuring Crime Reporting and Incidence: Method and Application to #MeToo

Abstract:

This paper studies the Me Too movement’s effects on sex criminality. As many victims do not report to the police, a long-standing empirical challenge with reported crime statistics is that they reflect variations in victim reporting and crime incidence. To separate both margins, I develop a duration model that studies the delay between the incident’s occurrence and its report to the police. The model accounts for unobserved heterogeneity, never-reporters, and double-truncation in the data. I apply it to the police records of large US cities. Contrary to the widespread view that #MeToo was a watershed moment, I find that sex crime reporting had already been increasing for years before its sudden mediatization in October 2017. Nonetheless, the movement had a positive, persistent impact on victim reporting, particularly for juveniles, racial minorities, and victims of misdemeanors and old crime incidents. The increase in reporting translates into drastically higher probabilities of arrest for sex offenders. Using reported non-sexual crimes as a control group, difference-in-differences estimates suggest the movement also had a sizable deterrent effect.

Bet on a bubble asset ? An optimal portfolio allocation strategy

Coauthors: Gilles de Truchis, Elena Dumitrescu, Sebastien Fries

Abstract:

We discuss portfolio allocation when one asset exhibits phases of locally explosive behavior. We model the conditional distribution of such an asset through mixed causal-non-causal models which mimic well the speculative bubble behaviour. Relying on a Taylor-series-expansion of a CRRA utility function approach, the optimal portfolio(s) is(are) located on the mean-variance-skewness-kurtosis efficient surface. We analytically derive these four conditional moments and show in a Monte-Carlo simulations exercise that incorporating them into a two-assets portfolio optimization problem leads to substantial improvement in the asset allocation strategy. All performance evaluation metrics support the higher out-of-sample performance of our investment strategies over standard benchmarks such as the mean-variance and equally-weighted portfolio. An empirical illustration using the Brent oil price as the speculative asset confirms these findings.

The Origins of Commodity Price Fluctuations

Coauthors: Sarah Mouabbi (Banque de France) et Adrien Rousset Planat (London Business School)

Abstract:

We build novel indexes of commodity price developments by simulating news-reading. Our proposed computer-based, narrative approach is flexible, unified and spans the global commodity market, including energy, industrial and precious metals, and agricultural commodities. Empirical evidence and human readings of news articles indicate that our indexes capture commodity-price supply and demand components. Index-peaks track the post-crisis collapse of commodity markets, other market-specific developments, as well as the recent COVID-19 crisis. The richness of news content allows us to decompose the supply and demand indexes into a number of key determinants that shaped commodity markets since the beginning of the 21st century, including business cycle effects, geopolitical risk, natural disasters, and climate change considerations. Preliminary results reveal that the nature of commodity price movements matters for macroeconomic outcomes, firms’ decisions, and asset prices.