Données MRW 1992.
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@ -16,3 +16,6 @@ blog/representer-graphiquement-une-serie-temporelle-du-pib/gdppc-fr.py
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blog/representer-graphiquement-une-serie-temporelle-du-pib/img/*.svg
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blog/representer-graphiquement-une-serie-temporelle-du-pib/mpd2020.csv
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blog/representer-graphiquement-une-serie-temporelle-du-pib/mpd2020.xlsx
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blog/donnees-mankiw-romer-weil-1992/.ltx/*
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blog/donnees-mankiw-romer-weil-1992/mrw-1992.pdf
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3
Makefile
3
Makefile
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@ -24,6 +24,8 @@ publish:
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sed -i '' -e 's/<\/pre>/<\/code><\/pre>/g' ./output/posts/modele-de-solow/index.html
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sed -i '' -e 's/<pre class="src src-python">/<pre><code class="language-python">/g' ./output/posts/representer-graphiquement-une-serie-temporelle-du-pib/index.html
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sed -i '' -e 's/<\/pre>/<\/code><\/pre>/g' ./output/posts/representer-graphiquement-une-serie-temporelle-du-pib/index.html
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sed -i '' -e 's/<pre class="src src-python">/<pre><code class="language-python">/g' ./output/posts/donnees-mankiw-romer-weil-1992/index.html
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sed -i '' -e 's/<\/pre>/<\/code><\/pre>/g' ./output/posts/donnees-mankiw-romer-weil-1992/index.html
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assets:
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@rsync --recursive -avz assets/fonts output
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@ -43,6 +45,7 @@ clean:
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@rm -rf output/posts/simulation-du-modele-de-solow/.ltx
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@rm -rf output/posts/modele-de-solow/.ltx
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@rm -rf output/posts/fonction-de-production-ces/.ltx
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@rm -rf output/posts/donnees-mankiw-romer-weil-1992/.ltx
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push: publish assets clean
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@rsync --recursive -avz --progress output/* ulysses:/home/www/stephane-adjemian.fr
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@ -0,0 +1,23 @@
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import tabula
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import pandas as pd
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import requests
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response = requests.get('https://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf')
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with open('./mrw-1992.pdf', 'wb') as f:
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f.write(response.content)
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t1 = tabula.read_pdf('./mrw-1992.pdf', pages=28, silent=True)
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t1 = t1[0]
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t1.rename(columns={'0':'O', '1960':'GDP_1960', '1985':'GDP_1985', 'GDP':'g(GDP)', 'age pop':'g(POP)', 'Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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t2 = tabula.read_pdf('./mrw-1992.pdf', pages=29, silent=True)
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t2 = t2[0]
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t2.rename(columns={'0':'O', '1960':'GDP_1960', '1985':'GDP_1985', 'GDP':'g(GDP)', 'age pop':'g(POP)', 'Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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t3 = tabula.read_pdf('./mrw-1992.pdf',pages=30,silent=True, columns=[68, 150, 160, 170, 178, 205, 233, 251, 285, 306], guess=False)
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t3 = t3[0][6:43]
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t3.rename(columns={'436':'Number', 'QUARTERL':'Country', 'Y':'N', 'JO':'I', 'U':'O', 'RNAL':'GDP_1960', 'OF ECO':'GDP_1985', 'NOM':'g(GDP)', 'ICS':'g(POP)','Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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mrwdata = pd.concat([t1,t2,t3], ignore_index=True)
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@ -0,0 +1,216 @@
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#+OPTIONS: H:3 num:nil toc:nil \n:nil @:t ::t |:t ^:nil -:t f:t *:t TeX:t LaTeX:t skip:t d:t tags:not-in-toc creator:t timestamp:nil author:nil title:nil
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#+AUTO_TANGLE: t
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#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="../../css/stylesheet-blog.css">
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#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="../../css/theorems-fr.css">
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#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="../../fontawesome/css/all.css" />
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#+HTML_HEAD: <link rel="stylesheet" href="../../highlight/styles/base16/solarized-light.min.css">
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#+HTML_HEAD: <script src="../../highlight/highlight.min.js"></script>
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#+HTML_HEAD: <script>hljs.highlightAll();</script>
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#+LANGUAGE: fr
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#+STARTUP: latexpreview
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#+TITLE: Données pour reproduire l'estimation de MRW (QJE, 1992)
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#+DATE: Janvier 2023
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#+AUTHOR: Stéphane Adjemian
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#+EMAIL: stephane.adjemian@univ-lemans.fr
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#+BEGIN_QUOTE
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Je montre ici comment construire une base de données pour reproduire les
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estimations dans l'article de Mankiw, Romer et Weil (QJE, 1992) en extrayant les
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données reproduites dans le pdf de l'article.
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#+END_QUOTE
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\\
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\\
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\\
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Dans l'article « /A contribution to the Empirics of Economic Growth/ », Mankiw
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Romer et Weil (1992) proposent plusieurs estimations du modèle de Solow et d'une
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version augmentée avec du capital humain. Dans cette note, je montre comment
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récupérer les données utilisées par ces trois auteurs et reproduites dans un
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tableau à la fin de l'article (pages 434 à 436) à l'aide d'un petit code en Python.
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Nous utilisons deux librairies, qu'il faut éventuellement installer avec la
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commande =pip=, [[https://pypi.org/project/tabula-py/][tabula-py]] qui permet d'extraire des tables de documents PDF et [[https://pypi.org/project/pandas/][Pandas]].
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#+begin_src python :session :exports code :results none :mkdirp yes :tangle codes/mrw-data.py
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import tabula
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import pandas as pd
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#+end_src
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Pour télécharger la version PDF de l'article nous utilisons le module =requests= :
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#+begin_src python :session :exports code :results none :mkdirp yes :tangle codes/mrw-data.py
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import requests
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response = requests.get('https://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf')
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with open('./mrw-1992.pdf', 'wb') as f:
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f.write(response.content)
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#+end_src
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Sans même lire la documentation de la librairie =tabula-py= on récupère facilement
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le tableau sur les deux premières pages. Le seul problème est qu'il faut
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renommer les colonnes (de l'objet =DataFrame= retourné par la fonction
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=tabula.read_pdf()=).
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#+begin_src python :session :exports code :results none :mkdirp yes :tangle codes/mrw-data.py
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t1 = tabula.read_pdf('./mrw-1992.pdf', pages=28, silent=True)
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t1 = t1[0]
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t1.rename(columns={'0':'O', '1960':'GDP_1960', '1985':'GDP_1985', 'GDP':'g(GDP)', 'age pop':'g(POP)', 'Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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t2 = tabula.read_pdf('./mrw-1992.pdf', pages=29, silent=True)
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t2 = t2[0]
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t2.rename(columns={'0':'O', '1960':'GDP_1960', '1985':'GDP_1985', 'GDP':'g(GDP)', 'age pop':'g(POP)', 'Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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#+end_src
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Pour la dernière page, c'est un peu plus compliqué. La fonction
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=tabula.read_pdf()= a besoin d'aide pour identifier les colonnes (elle ne parvient
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pas à séparer les colonnes =O= et =1960=). On peut lui donner les coordonnées des
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séparateurs de colonnes (identifiées en éditant le fichier PDF sous [[https://www.gimp.org/][Gimp]] et en
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configurant les règles, en haut à gauche, avec des =points= comme unité de
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mesure). Même avec cette information supplémentaire, la fonction retourne des
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données incohérentes sur les premières et dernières lignes (à cause du format
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particulier de l'entête du tableau et des notes au bas du tableau). On élimine
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donc ces lignes.
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#+begin_src python :session :exports code :results none :mkdirp yes :tangle codes/mrw-data.py
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t3 = tabula.read_pdf('./mrw-1992.pdf',pages=30,silent=True, columns=[68, 150, 160, 170, 178, 205, 233, 251, 285, 306], guess=False)
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t3 = t3[0][6:43]
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t3.rename(columns={'436':'Number', 'QUARTERL':'Country', 'Y':'N', 'JO':'I', 'U':'O', 'RNAL':'GDP_1960', 'OF ECO':'GDP_1985', 'NOM':'g(GDP)', 'ICS':'g(POP)','Unnamed: 0':'I/Y', 'Unnamed: 1':'SCHOOL'}, inplace=True)
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#+end_src
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Enfin, il ne reste plus qu'à concaténer les trois objets =DataFrame= =t1=, =t2= et =t3= à l'aide de la fonction =concat= de Pandas :
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#+begin_src python :session :exports code :results none :mkdirp yes :tangle codes/mrw-data.py
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mrwdata = pd.concat([t1,t2,t3], ignore_index=True)
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#+end_src
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#+begin_src python :session :exports results :results value raw
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from tabulate import tabulate
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tabulate(mrwdata, headers="keys", tablefmt="orgtbl")
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#+end_src
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#+RESULTS:
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| | Number | Country | N | I | O | GDP_1960 | GDP_1985 | g(GDP) | g(POP) | I/Y | SCHOOL |
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|-----+--------+--------------------+---+---+---+----------+----------+--------+--------+------+--------|
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| 0 | 1 | Algeria | 1 | 1 | 0 | 2485 | 4371.0 | 4.8 | 2.6 | 24.1 | 4.5 |
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| 1 | 2 | Angola | 1 | 0 | 0 | 1588 | 1171.0 | 0.8 | 2.1 | 5.8 | 1.8 |
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| 2 | 3 | Benin | 1 | 0 | 0 | 1116 | 1071.0 | 2.2 | 2.4 | 10.8 | 1.8 |
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| 3 | 4 | Botswana | 1 | 1 | 0 | 959 | 3671.0 | 8.6 | 3.2 | 28.3 | 2.9 |
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| 4 | 5 | Burkina Faso | 1 | 0 | 0 | 529 | 857.0 | 2.9 | 0.9 | 12.7 | 0.4 |
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| 5 | 6 | Burundi | 1 | 0 | 0 | 755 | 663.0 | 1.2 | 1.7 | 5.1 | 0.4 |
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| 6 | 7 | Cameroon | 1 | 1 | 0 | 889 | 2190.0 | 5.7 | 2.1 | 12.8 | 3.4 |
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| 7 | 8 | CentralAfr. Rep. | 1 | 0 | 0 | 838 | 789.0 | 1.5 | 1.7 | 10.5 | 1.4 |
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| 8 | 9 | Chad | 1 | 0 | 0 | 908 | 462.0 | -0.9 | 1.9 | 6.9 | 0.4 |
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| 9 | 10 | Congo, Peop. Rep. | 1 | 0 | 0 | 1009 | 2624.0 | 6.2 | 2.4 | 28.8 | 3.8 |
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| 10 | 11 | Egypt | 1 | 0 | 0 | 907 | 2160.0 | 6 | 2.5 | 16.3 | 7 |
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| 11 | 12 | Ethiopia | 1 | 1 | 0 | 533 | 608.0 | 2.8 | 2.3 | 5.4 | 1.1 |
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| 12 | 13 | Gabon | 0 | 0 | 0 | 1307 | 5350.0 | 7 | 1.4 | 22.1 | 2.6 |
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| 13 | 14 | Gambia, The | 0 | 0 | 0 | 799 | nan | 3.6 | nan | 18.1 | 1.5 |
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| 14 | 15 | Ghana | 1 | 0 | 0 | 1009 | 727.0 | 1 | 2.3 | 9.1 | 4.7 |
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| 15 | 16 | Guinea | 0 | 0 | 0 | 746 | 869.0 | 2.2 | 1.6 | 10.9 | nan |
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| 16 | 17 | Ivory Coast | 1 | 1 | 0 | 1386 | 1704.0 | 5.1 | 4.3 | 12.4 | 2.3 |
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| 17 | 18 | Kenya | 1 | 1 | 0 | 944 | 1329.0 | 4.8 | 3.4 | 17.4 | 2.4 |
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| 18 | 19 | Lesotho | 0 | 0 | 0 | 431 | 1483.0 | 6.8 | 1.9 | 12.6 | 2 |
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| 19 | 20 | Liberia | 1 | 0 | 0 | 863 | 944.0 | 3.3 | 3 | 21.5 | 2.5 |
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| 20 | 21 | Madagascar | 1 | 1 | 0 | 1194 | 975.0 | 1.4 | 2.2 | 7.1 | 2.6 |
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| 21 | 22 | Malawi | 1 | 1 | 0 | 455 | 823.0 | 4.8 | 2.4 | 13.2 | 0.6 |
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| 22 | 23 | Mali | 1 | 1 | 0 | 737 | 710.0 | 2.1 | 2.2 | 7.3 | 1 |
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| 23 | 24 | Mauritania | 1 | 0 | 0 | 777 | 1038.0 | 3.3 | 2.2 | 25.6 | 1 |
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| 24 | 25 | Mauritius | 1 | 0 | 0 | 1973 | 2967.0 | 4.2 | 2.6 | 17.1 | 7.3 |
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| 25 | 26 | Morocco | 1 | 1 | 0 | 1030 | 2348.0 | 5.8 | 2.5 | 8.3 | 3.6 |
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| 26 | 27 | Mozambique | 1 | 0 | 0 | 1420 | 1035.0 | 1.4 | 2.7 | 6.1 | 0.7 |
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| 27 | 28 | Niger | 1 | 0 | 0 | 539 | 841.0 | 4.4 | 2.6 | 10.3 | 0.5 |
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| 28 | 29 | Nigeria | 1 | 1 | 0 | 1055 | 1186.0 | 2.8 | 2.4 | 12 | 2.3 |
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| 29 | 30 | Rwanda | 1 | 0 | 0 | 460 | 696.0 | 4.5 | 2.8 | 7.9 | 0.4 |
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| 30 | 31 | Senegal | 1 | 1 | 0 | 1392 | 1450.0 | 2.5 | 2.3 | 9.6 | 1.7 |
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| 31 | 32 | Sierra Leone | 1 | 0 | 0 | 511 | 805.0 | 3.4 | 1.6 | 10.9 | 1.7 |
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| 32 | 33 | Somalia | 1 | 0 | 0 | 901 | 657.0 | 1.8 | 3.1 | 13.8 | 1.1 |
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| 33 | 34 | S. Africa | 1 | 1 | 0 | 4768 | 7064.0 | 3.9 | 2.3 | 21.6 | 3 |
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| 34 | 35 | Sudan | 1 | 0 | 0 | 1254 | 1038.0 | 1.8 | 2.6 | 13.2 | 2 |
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| 35 | 36 | Swaziland | 0 | 0 | 0 | 817 | nan | 7.2 | nan | 17.7 | 3.7 |
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| 36 | 37 | Tanzania | 1 | 1 | 0 | 383 | 710.0 | 5.3 | 2.9 | 18 | 0.5 |
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| 37 | 38 | Togo | 1 | 0 | 0 | 777 | 978.0 | 3.4 | 2.5 | 15.5 | 2.9 |
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| 38 | 39 | Tunisia | 1 | 1 | 0 | 1623 | 3661.0 | 5.6 | 2.4 | 13.8 | 4.3 |
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| 39 | 40 | Uganda | 1 | 0 | 0 | 601 | 667.0 | 3.5 | 3.1 | 4.1 | 1.1 |
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| 40 | 41 | Zaire | 1 | 0 | 0 | 594 | 412.0 | 0.9 | 2.4 | 6.5 | 3.6 |
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| 41 | 42 | Zambia | 1 | 1 | 0 | 1410 | 1217.0 | 2.1 | 2.7 | 31.7 | 2.4 |
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| 42 | 43 | Zimbabwe | 1 | 1 | 0 | 1187 | 2107.0 | 5.1 | 2.8 | 21.1 | 4.4 |
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| 43 | 44 | Afghanistan | 0 | 0 | 0 | 1224 | nan | 1.6 | nan | 6.9 | 0.9 |
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| 44 | 45 | Bahrain | 0 | 0 | 0 | nan | nan | nan | nan | 30 | 12.1 |
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| 45 | 46 | Bangladesh | 1 | 1 | 0 | 846 | 1221 | 4 | 2.6 | 6.8 | 3.2 |
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| 46 | 47 | Burma | 1 | 1 | 0 | 517 | 1031 | 4.5 | 1.7 | 11.4 | 3.5 |
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| 47 | 48 | Hong Kong | 1 | 1 | 0 | 3085 | 13,372 | 8.9 | 3 | 19.9 | 7.2 |
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| 48 | 49 | India | 1 | 1 | 0 | 978 | 1339 | 3.6 | 2.4 | 16.8 | 5.1 |
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| 49 | 50 | Iran | 0 | 0 | 0 | 3606 | 7400 | 6.3 | 3.4 | 18.4 | 6.5 |
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| 50 | 51 | Iraq | 0 | 0 | 0 | 4916 | 5626 | 3.8 | 3.2 | 16.2 | 7.4 |
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| 51 | 52 | Israel | 1 | 1 | 0 | 4802 | 10,450 | 5.9 | 2.8 | 28.5 | 9.5 |
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| 52 | 53 | Japan | 1 | 1 | 1 | 3493 | 13,893 | 6.8 | 1.2 | 36 | 10.9 |
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| 53 | 54 | Jordan | 1 | 1 | 0 | 2183 | 4312 | 5.4 | 2.7 | 17.6 | 10.8 |
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| 54 | 55 | Korea, Rep. of | 1 | 1 | 0 | 1285 | 4775 | 7.9 | 2.7 | 22.3 | 10.2 |
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| 55 | 56 | Kuwait | 0 | 0 | 0 | 77,881 | 25,635 | 2.4 | 6.8 | 9.5 | 9.6 |
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| 56 | 57 | Malaysia | 1 | 1 | 0 | 2154 | 5788 | 7.1 | 3.2 | 23.2 | 7.3 |
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| 57 | 58 | Nepal | 1 | 0 | 0 | 833 | 974 | 2.6 | 2 | 5.9 | 2.3 |
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| 58 | 59 | Oman | 0 | 0 | 0 | nan | 15,584 | nan | 3.3 | 15.6 | 2.7 |
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| 59 | 60 | Pakistan | 1 | 1 | 0 | 1077 | 2175 | 5.8 | 3 | 12.2 | 3 |
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| 60 | 61 | Philippines | 1 | 1 | 0 | 1668 | 2430 | 4.5 | 3 | 14.9 | 10.6 |
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| 61 | 62 | Saudi Arabia | 0 | 0 | 0 | 6731 | 11,057 | 6.1 | 4.1 | 12.8 | 3.1 |
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| 62 | 63 | Singapore | 1 | 1 | 0 | 2793 | 14,678 | 9.2 | 2.6 | 32.2 | 9 |
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| 63 | 64 | Sri Lanka | 1 | 1 | 0 | 1794 | 2482 | 3.7 | 2.4 | 14.8 | 8.3 |
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| 64 | 65 | Syrian Arab Rep. | 1 | 1 | 0 | 2382 | 6042 | 6.7 | 3 | 15.9 | 8.8 |
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| 65 | 66 | Taiwan | 0 | 0 | 0 | nan | nan | 8 | nan | 20.7 | nan |
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| 66 | 67 | Thailand | 1 | 1 | 0 | 1308 | 3220 | 6.7 | 3.1 | 18 | 4.4 |
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| 67 | 68 | U. Arab Emirates | 0 | 0 | 0 | nan | 18,513 | nan | nan | 26.5 | nan |
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| 68 | 69 | Yemen | 0 | 0 | 0 | nan | 1918 | nan | 2.5 | 17.2 | 0.6 |
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| 69 | 70 | Austria | 1 | 1 | 1 | 5939 | 13,327 | 3.6 | 0.4 | 23.4 | 8 |
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| 70 | 71 | Belgium | 1 | 1 | 1 | 6789 | 14,290 | 3.5 | 0.5 | 23.4 | 9.3 |
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| 71 | 72 | Cyprus | 0 | 0 | 0 | 2948 | nan | 5.2 | nan | 31.2 | 8.2 |
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| 72 | 73 | Denmark | 1 | 1 | 1 | 8551 | 16,491 | 3.2 | 0.6 | 26.6 | 10.7 |
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| 73 | 74 | Finland | 1 | 1 | 1 | 6527 | 13,779 | 3.7 | 0.7 | 36.9 | 11.5 |
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| 74 | 75 | France | 1 | 1 | 1 | 7215 | 15,027 | 3.9 | 1 | 26.2 | 8.9 |
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| 75 | 76 | Germany, Fed. Rep. | 1 | 1 | 1 | 7695 | 15,297 | 3.3 | 0.5 | 28.5 | 8.4 |
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| 76 | 77 | Greece | 1 | 1 | 1 | 2257 | 6868 | 5.1 | 0.7 | 29.3 | 7.9 |
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| 77 | 78 | Iceland | 0 | 0 | 0 | 8091 | nan | 3.9 | nan | 29 | 10.2 |
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| 78 | 79 | Ireland | 1 | 1 | 1 | 4411 | 8675 | 3.8 | 1.1 | 25.9 | 11.4 |
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| 79 | 80 | Italy | 1 | 1 | 1 | 4913 | 11,082 | 3.8 | 0.6 | 24.9 | 7.1 |
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| 80 | 81 | Luxembourg | 0 | 0 | 0 | 9015 | nan | 2.8 | nan | 26.9 | 5 |
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| 81 | 82 | Malta | 0 | 0 | 0 | 2293 | nan | 6 | nan | 30.9 | 7.1 |
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| 82 | 83 | Netherlands | 1 | 1 | 1 | 7689 | 13,177 | 3.6 | 1.4 | 25.8 | 10.7 |
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| 83 | 84 | Norway | 1 | 1 | 1 | 7938 | 19,723 | 4.3 | 0.7 | 29.1 | 10 |
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| 84 | 85 | Portugal | 1 | 1 | 1 | 2272 | 5827 | 4.4 | 0.6 | 22.5 | 5.8 |
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| 85 | 86 | Spain | 1 | 1 | 1 | 3766 | 9903 | 4.9 | 1 | 17.7 | 8 |
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| 86 | 87 | Sweden | 1 | 1 | 1 | 7802 | 15,237 | 3.1 | 0.4 | 24.5 | 7.9 |
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| 87 | 88 | Switzerland | 1 | 1 | 1 | 10,308 | 15,881 | 2.5 | 0.8 | 29.7 | 4.8 |
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| 88 | 89 | Turkey | 1 | 1 | 1 | 2274 | 4444 | 5.2 | 2.5 | 20.2 | 5.5 |
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| 89 | 90 | United Kingdom | 1 | 1 | 1 | 7634 | 13,331 | 2.5 | 0.3 | 18.4 | 8.9 |
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| 90 | 91 | Barbados | 0 | 0 | 0 | 3165 | nan | 4.8 | nan | 19.5 | 12.1 |
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| 91 | 92 | Canada | 1 | 1 | 1 | 10,286 | 17,935 | 4.2 | 2 | 23.3 | 10.6 |
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| 92 | 93 | Costa Rica | 1 | 1 | 0 | 3360 | 4492 | 4.7 | 3.5 | 14.7 | 7 |
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| 93 | 94 | Dominican Rep. | 1 | 1 | 0 | 1939 | 3308 | 5.1 | 2.9 | 17.1 | 5.8 |
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| 94 | 95 | El Salvador | 1 | 1 | 0 | 2042 | 1997 | 3.3 | 3.3 | 8 | 3.9 |
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| 95 | 96 | Guatemala | 1 | 1 | 0 | 2481 | 3034 | 3.9 | 3.1 | 8.8 | 2.4 |
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| 96 | 97 | Haiti | 1 | 1 | 0 | 1096 | 1237 | 1.8 | 1.3 | 7.1 | 1.9 |
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| 97 | 98 | Honduras | 1 | 1 | 0 | 1430 | 1822 | 4 | 3.1 | 13.8 | 3.7 |
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| 98 | 99 | Jamaica | 1 | 1 | 0 | 2726 | 3080 | 2.1 | 1.6 | 20.6 | 11.2 |
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| 99 | 100 | Mexico | 1 | 1 | 0 | 4229 | 7380 | 5.5 | 3.3 | 19.5 | 6.6 |
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| 100 | 101 | Nicaragua | 1 | 1 | 0 | 3195 | 3978 | 4.1 | 3.3 | 14.5 | 5.8 |
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| 101 | 102 | Panama | 1 | 1 | 0 | 2423 | 5021 | 5.9 | 3 | 26.1 | 11.6 |
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| 102 | 103 | Trinidad & Tobago | 1 | 1 | 0 | 9253 | 11,285 | 2.7 | 1.9 | 20.4 | 8.8 |
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| 103 | 104 | United States | 1 | 1 | 1 | 12,362 | 18,988 | 3.2 | 1.5 | 21.1 | 11.9 |
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| 104 | 105 | Argentina | 1 | 1 | 0 | 4852 | 5533 | 2.1 | 1.5 | 25.3 | 5 |
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| 105 | 106 | Bolivia | 1 | 1 | 0 | 1618 | 2055 | 3.3 | 2.4 | 13.3 | 4.9 |
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| 106 | 107 | Brazil | 1 | 1 | 0 | 1842 | 5563 | 7.3 | 2.9 | 23.2 | 4.7 |
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| 107 | 108 | Chile | 1 | 1 | 0 | 5189 | 5533 | 2.6 | 2.3 | 29.7 | 7.7 |
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| 108 | 109 | Colombia | 1 | 1 | 0 | 2672 | 4405 | 5 | 3 | 18 | 6.1 |
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| 109 | 110 | Ecuador | 1 | 1 | 0 | 2198 | 4504 | 5.7 | 2.8 | 24.4 | 7.2 |
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| 110 | 111 | Guyana | 0 | 0 | 0 | 2761 | nan | 1.1 | nan | 32.4 | 11.7 |
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| 111 | 112 | Paraguay | 1 | 1 | 0 | 1951 | 3914 | 5.5 | 2.7 | 11.7 | 4.4 |
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| 112 | 113 | Peru | 1 | 1 | 0 | 3310 | 3775 | 3.5 | 2.9 | 12 | 8 |
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| 113 | 114 | Surinam | 0 | 0 | 0 | 3226 | nan | 4.5 | nan | 19.4 | 8.1 |
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| 114 | 115 | Uruguay | 1 | 1 | 0 | 5119 | 5495 | 0.9 | 0.6 | 11.8 | 7 |
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| 115 | 116 | Venezuela | 1 | 1 | 0 | 10,367 | 6336 | 1.9 | 3.8 | 11.4 | 7 |
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| 116 | 117 | Australia | 1 | 1 | 1 | 8440 | 13,409 | 3.8 | 2 | 31.5 | 9.8 |
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| 117 | 118 | Fiji | 0 | 0 | 0 | 3634 | nan | 4.2 | nan | 20.6 | 8.1 |
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| 118 | 119 | Indonesia | 1 | 1 | 0 | 879 | 2159 | 5.5 | 1.9 | 13.9 | 4.1 |
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| 119 | 120 | New Zealand | 1 | 1 | 1 | 9523 | 12,308 | 2.7 | 1.7 | 22.5 | 11.9 |
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| 120 | 121 | Papua New Guinea | 1 | 0 | 0 | 1781 | 2544 | 3.5 | 2.1 | 16.2 | 1.5 |
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@ -28,6 +28,8 @@
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- [[./fonction-de-production-ces][Propriétés de la fonction de production CES]]
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- [[./donnees-mankiw-romer-weil-1992][Données pour reproduire l'estimation de MRW (QJE, 1992)]]
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Reference in New Issue