Tag Archives: economics

Breizh Camp, Economics with Computers

I have been invited, as keynote speaker, at the 6th BreizhCamp, organized in Rennes, from March 23rd till March 25th, “la conférence des développeurs du grand ouest” as they call it. I am deeply honored, since it is a huge conference… The organizing committee asked me to give a (brief) talk on data, and big data. But data is just the visible tip of the iceberg, and I cannot talk about data without mentioning algorithms. So I will try to talk about algorithmics, econometrics, machine learning, and data (and big data, of course).

Slides are now online… More to come in the next days…

La Science Economique, et son maudit Nobel

Je voulais revenir rapidement sur le prix Nobel décerné il y a moins de 48 heures à Jean Tirole, suite à plusieurs billets que j’ai pu lire, ici et là sur internet. Il y a quelques années, dans un chapitre de le modèle et le récit (publié en 2001), Pierre André Chiaporri racontait la petite anecdote suivante

quand on lui demandait pourquoi il faisait de l’économie “mathématique” plutôt que “littéraire”, Paul Samuelson répondait qu’il aurait souhaité pouvoir faire de l’économie littéraire – mais que c’était trop difficile pour lui. Ce sentiment est, je crois, partagé par beaucoup d’économistes de nos jours : de nombreux problèmes en économie théorique sont trop complexes pour être étudiés sans l’aide d’un minimum de formalisation.

(je recommande d’ailleurs la lecture de l’intégralité de l’article, qui discute l’importance de la modélisation en science économique, avec en toile de fond la théorie du consommateur, reprenant ainsi des idées que l’on trouvait déjà dans la théorie du consommateur est-elle réfutable ? ). En lisant ce paragraphe, je n’ai pu m’empêcher de penser au petit livre Economic Fables (publié par openbookpublishers). J’avais déjà évoqué ce livre dans un précédant billet. Pour ceux qui ne connaisse pas son auteur, Ariel Rubinstein (אריאל רובינשטיין) est un économiste, professeur à Tel Aviv et à NYU. Il a publié des livres de référence – dont une très bonne introduction à la théorie des jeux . Ce qui est amusant, ce que ce livre est un livre de fables, ce qui peut surprendre pour quelqu’un qui m’avait habitué à

Continue reading La Science Economique, et son maudit Nobel

Modeling Dynamic Incentives: Application to Basketball

I will give a talk on “Modeling Dynamic Incentives: Application to Basketball” at the GERAD (Groupe d’études et de recherche en analyse des décisions) on June, 10th June, 6th. This is some joint work with Nathalie Colombier and Romuald Elie

An important aspect of the strategy of most organizations is the provision of incentives to the employees to meet the organization’s objectives. Typically this implies tying pay to performance (see Prendergast, 1999). In order to reward employees for their effort, firms spend considerable resources on performance evaluations. In many cases, evaluation consists of comparing actual performance to a pre-defined individual target. Another frequently used format is relative performance evaluation. Relative performance evaluation may motivate employees to work harder.But it may also be demoralizing and create an excessively competitive workplace, which may hinder overall performance; see Lazear (1989). Determining the overall impact of relative performance evaluation is crucial for companies. Economic research on relative performance evaluation has mainly focused on the comparison of final performances between competitors,like in tournament theory, and on quantitative and subjective performance ratings (Lazear and Gibbs, 2009). In contrast, what happens during a competition and the impact of feedback frequency on effort have so far received little attention. Following Berger and Pope (2011), we decided to use a basketball application to get a better understanding of the role of the feedback information. Sports datasets allow to observe score and team behavior continuously (during a game but also during the season) which can be use as a proxy of the effort. Berger an Pope (2010) asked ”can loosing lead to winning ?” looking at the impact of the halftime score difference on winning probability in NCAA (college) and NBA (pro) games. More precisely, they studied whether a team loosing at halftime is more likely to win than expected using a logit model. They find that usually the higher the score difference the more likely the are to win. But if the halftime score difference is around 0 they observe a discontinuity: loosing with a small difference (e.g. down by 1 point) can lead to increase the effort and win the game. In this paper we try answer the question ”when loosing lead to winning ?”.

Natural Catastrophe Insurance: How Should the Government Intervene?

An updated version of the joint paper with Benoit Le Maux is online on http://papers.ssrn.com/.

The present paper develops a new theoretical framework for analyzing the decision to provide or buy insurance against the risk of natural catastrophes. In contrast with conventional models of insurance, the insurer has a non-zero probability of insolvency that depends on the distribution of the risks, the premium rate, and the amount of capital in the company. Among several results, we show that risk-averse policyholders will accept to pay higher rates for a government-provided insurance with unlimited guarantee. However, depending on the correlation between and within the regional risks, a government program can be more attractive to high-correlation than to low correlation areas, which may lead to inefficiencies if the insurance ratings are not appropriately chosen.

Going further on journal clustering: looking within a cluster

Following my post on academic journals, Miss Lambda asked me about journals in a very specific area, namely agricultural, environmental and energy economics. I found it interesting since I do not have any idea about journal that can be in that domain of research. So I looked at some journals form the French CNRS list (online here). Hence, I have been looking for words in the title of 26,000 articles, published in 29 journals, in Agricultural, Environmental and Energy Economics. I considered American Journal of Agricultural Economics (AJEE), Ecological Economics (EE), Journal of Environmental and Economic Management (JEEM), Climate Policy (CP), Energy Economics (EE), Energy Journal (EJ), Energy Policy (EP), Environment and Planning A, B, C, D (EP-A, B, C, D), Environmental and Resource Economics (ERE), Environmental Modeling and Assessment (EMA), European Review of Agricultural Economics (ERAE), Resource and Energy Economics (REE), Agricultural Economics (AE), AMBIO: A Journal of the Human Environment (AMBIO), Australian Journal of Agricultural and Resource Economics (AJARE), Canadian Journal of Agricultural Economics (CJAE), Climatic Change (CC), Ecological Modeling (EM), Energy Studies Review (ESR), Environment and Development Economics (EDE), Environmental Science and Policy (ESP), Environmental Values (EV), Food Policy (FP),
Global Environmental Change (GEC), Journal of Agricultural Economics (JAE), Society and Natural Resources (SNR), Water Resources Research (WR).

Now if we look at the principal component analysis, and projection of the journals on the first two axis, we have

On that graph, it is hard to say anything… The only thing is see is that on the lower part, we have journals focusing on modeling issues.

The top of the most common words in those journals is the following,

> colnames(MATRICE[,1:32])
[1] "climate"       "analysis"      "environmental" "change"
[5] "energy"        "model"         "policy"        "case"
[9] "water"         "economic"      "management"    "food"
[13] "study"         "development"   "market"        "agricultural"
[17] "approach"      "effects"       "carbon"        "global"
[21] "impact"        "production"    "land"          "china"
[25] "assessment"    "forest"        "impacts"       "urban"
[29] "demand"        "spatial"       "emissions"     "modeling"

If we look at their projections on the first two axis, we have (it looks like the second axis has been inverted here)

i=or if we focus on the top 30

Now, if we look at clusters, and use a hierarchical model, we obtain

So here, a dozen journals are extremely close. They are more focusing on agricultural issues. Note that Energy Economics and Energy Policy are in the same cluster, but quite far away from Energy Journal (which looks strange). We can also observe that Climatic Change alone, far away from all the other journals (actually, it is a journal were I just got a paper accepted… and since my areas of research are quite far away from agricultural economics, I can understand that).