Tobias Weise

The Rise of Democratic Legitimation - Why International Organizations Speak the Language of Democracy

Mon, 16 Dec 2019 00:00:00 +0100

After an exciting journey with my dear colleagues Klaus Dingwerth and Henning Schmidtke, our joint article The rise of democratic legitimation: why international organizations speak the language of democracy has now been published in the European Journal of International Relations.

I am grateful for the great experience and inspiring discussions !

Reviews and Mentions

SNIS IG Awards 2019: Our paper was awarded the International Geneva Award 2019 by the Swiss Network for International Studies

Calculating the Democratic Membership of IOs illustrates code and date for calculating various measures of the democratic quality of international organizations
Calculating the Economic Inequality of IOs demonstrates how we calulated various measures for economic inequality of IO member states.

International Organizations Under Pressure published

Thu, 21 Mar 2019 00:00:00 +0100

After long years of research, discussion and review, the book International Organizations under Pressure. Legitimating Global Governance in Challenging Times by Klaus, Antonia Witt, Ina Lehmann, Ellen Reichel, and myself has been published by OUP. Here, I will collect links and ressources that are connected to the book and its related research.

Thanks to the great team of the Global Norms project!

Reviews and Mentions

What makes the EU, the UN , and their peers legitimate? by Klaus Dingwerth and Antonia Witt, discussing the main argument of the book.
Brexit is not a singular event and shows that internationale organizations are under pressure by Klaus Dingwerth and Antonia Witt, appeared in HSG Focus.
Conference report: The Legitimation and Delegitimation of Global Governance Organizations, summarizing the conference organized by the Global Norms team in September 2013.
The Global History of the IAEA Workshop, summarizes the Vienna workshop, where parts of the IAEA chapter were discussed.
Quick Data: NSA participation in IAEA GC, presents data on the number and kind of non-state actors participating in IAEA General Conferences
Quick Data: Topics and Themes in IAEA Discourses, a quantitative look at various topics in the IAEA discourse over time.
Impressions of the 56th IAEA GC, a quick field report of the 56th IAEA General Conference.

Analyse von Karriereseiten von Handelsunternehmen - Text und Bild

Mon, 23 Jul 2018 00:00:00 +0200

Für den aktuellen Sammelband Moderne Personalpolitik in Handel und Vertrieb habe ich mir die Frage gestellt, wie sich Karriere-Seiten von Handelsunternehmen hinsichtlich ihres Employer Brandings semi-automatisiert untersuchen lassen. Zielfragen sind hier auf der textuellen und auf der bildlichen Ebene angesiedelt: Welche Aussagen werden getroffen? Wie unterscheiden sich die Texte der einzelnen Seiten? Welche Bilder werden auf den Seiten genutzt? Wie unterscheiden sich die Bilder zwischen den Seiten? Im folgenden beschreibe ich kurz einen ersten Ansatz, mit dem sich diese Fragen beantworten lassen. Einige Passagen im R-Code sind sicherlich noch optimierungsfähig. Falls jemand Vorschläge hat, freue ich mich auf Kommentare.

Auswahl von Daten

Für diesen Post und für das Sammelbandkapitel möchte ich mich auf die Karriere-Seiten der größten Handelsunternehmen in Deutschland fokussieren. Zur Hilfe dient der Deloitte Report “Global Powers of Retailing 2018”. Dieser enthält die TOP 250 Handelsunternehmen der Welt.

Ausgewählt werden Unternehmen mit Sitz in Deutschland.

Auswahl:

Schwarz Gruppe
Aldi Gruppe
Metro AG
Edeka Gruppe
Rewe Gruppe
Otto
Tengelmann Warenhandelsgesellschaft
dm-drogerie markt GmbH
Dirk Rossmann GmbH
Globus Holding GmbH
Bauhaus GmbH
Deichmann SE
Müller Holding
McKesson Europe AG
HORNBACH Baumarkt
Zalando SE
Intersport Deutschland SE

In einem nächsten Schritt werden die zugehörigen Handelunternehmen (Tochtermarken) identifiziert. Ausgewählt wurden nur solche Unternehmen, die auch im niedergelassenen Filialhandel tätig sind.

Dann werden die Webseiten der identifizierten Unternehmen besucht und Karriere-Seiten identifiziert, die ein Employer-Branding enthalten. In der Regel sind dies die allgemeinen Karriere-Seiten, die Informationen zu “Das Unternehmen als Arbeitgeber”, “Arbeiten beim Unternehmen”, “Warum hier Arbeiten?” oder “Unser Angebot” enthalten. Links zu archivierten Seiten werden für die Reproduzierbarkeit identifiziert (falls in der Wayback Machine archiviert). Karriereseiten von Verbundunternehmen wurden nur aufgenommen, wenn sie sich vom Hauptunternehmen unterschieden.

Hier der daraus resultierende Datensatz, der hier runtergeladen werden kann:

library(tidyverse)
company_list <- read.csv2("employer_branding_company_list.csv")
company_list$Archiv.URL <- as.character(company_list$Archiv.URL)
company_list$URL.Branding.Seite <- as.character(company_list$URL.Branding.Seite)
company_list$ID_Einzelunternehmen <- as.character(company_list$ID_Einzelunternehmen)
company_list$Einzelunternehmen <- as.character(company_list$Einzelunternehmen)
knitr::kable(company_list, format = "markdown")

ID_Hauptunternehmen	Hauptunternehmen	ID_Einzelunternehmen	Einzelunternehmen	URL.Branding.Seite	Archiv.URL
1	Schwarz Gruppe	1.1	Lidl	https://jobs.lidl.de/de/das-sind-wir-2429.htm	https://web.archive.org/web/20171029214432/https://jobs.lidl.de/de/das-sind-wir-2429.htm
1	Schwarz Gruppe	1.2	Kaufland	https://karriere.kaufland.de/kaufland-entdecken/ueber-uns/wer-wir-sind.html	https://karriere.kaufland.de/kaufland-entdecken/ueber-uns/wer-wir-sind.html
2	Aldi Gruppe	2.1	Aldi Nord	https://www.aldi-nord.de/karriere/	https://web.archive.org/web/20180301025907/https://www.aldi-nord.de/karriere/
2	Aldi Gruppe	2.2	Aldi Süd	https://karriere.aldi-sued.de/de/Wir-als-Arbeitgeber/Warum-zu-ALDI-S%C3%9CD	https://karriere.aldi-sued.de/de/Wir-als-Arbeitgeber/Warum-zu-ALDI-S%C3%9CD
3	Metro AG	3.1	METRO Cash & Carry	https://www.metro-cc.com/de-DE/karriere/warum-metro-cash-and-carry	https://www.metro-cc.com/de-DE/karriere/warum-metro-cash-and-carry
3	Metro AG	3.2	Real	https://www.real.de/unternehmen/jobs-karriere/arbeiten-bei-real/	https://www.real.de/unternehmen/jobs-karriere/arbeiten-bei-real/
4	Edeka Gruppe	4.1	Edeka-Verbund	http://www.edeka-verbund.de/Unternehmen/de/karriere/studierendeundabsolventen/was_wir_ihnen_bieten_2/was_wir_ihnen_bieten_3.jsp	https://web.archive.org/web/20171009164658/http://www.edeka-verbund.de/Unternehmen/de/karriere/studierendeundabsolventen/was_wir_ihnen_bieten_2/was_wir_ihnen_bieten_3.jsp
4	Edeka Gruppe	4.2	Diska	http://www.diska.de/karriere/	https://web.archive.org/web/20171009161045/http://www.diska.de/karriere/
4	Edeka Gruppe	4.3	Treff3000	https://www.treff3000.de/html/liste/ausbildung_2018.html	https://www.treff3000.de/html/liste/ausbildung_2018.html
4	Edeka Gruppe	4.4	NP.Discount	https://www.np.de/unternehmen/	https://web.archive.org/web/20180127083854/https://www.np.de/unternehmen/
4	Edeka Gruppe	4.5	Netto Marken-Discount	https://www.netto-online.de/karriere/Warum-zu-Netto.chtm	https://www.netto-online.de/karriere/Warum-zu-Netto.chtm
5	Rewe Gruppe	5.1	Rewe	https://karriere.rewe.de/arbeiten-bei-rewe/unsere-arbeitswelt/leistungen.html	https://karriere.rewe.de/arbeiten-bei-rewe/unsere-arbeitswelt/leistungen.html
5	Rewe Gruppe	5.2	Penny	http://karriere.penny.de/das-ist-jetzt-dein-job/vorteile/	https://web.archive.org/web/20171031150735/http://karriere.penny.de/das-ist-jetzt-dein-job/vorteile/
5	Rewe Gruppe	5.3	Nahkauf	https://www.nahkauf.de/kaufleute-gesucht/	https://web.archive.org/web/20170928055928/http://www2.nahkauf.de:80/kaufleute-gesucht/
5	Rewe Gruppe	5.4	Toom	https://karriere.toom.de/komm-in-unser-team	https://karriere.toom.de/komm-in-unser-team
6	Otto Gruppe	6.1	Lascana
6	Otto Gruppe	6.2	Manufactum	https://www.manufactum.de/manufactum-stellenangebote-c-37/	https://www.manufactum.de/manufactum-stellenangebote-c-37/
6	Otto Gruppe	6.3	myToys	https://mytoysgroup.jobs/	https://web.archive.org/web/20171016113744/https://mytoysgroup.jobs/
6	Otto Gruppe	6.4	BonPrix	https://www.bonprix.de/corporate/karriere/arbeitswelt/	https://web.archive.org/web/20170706033530/https://www.bonprix.de/corporate/karriere/arbeitswelt/
6	Otto Gruppe	6.5	Frankonia	https://www.frankonia.de/service/karriere-bei-frankonia.html	https://web.archive.org/web/20160817164139/http://www.frankonia.de/service/karriere-bei-frankonia.html
6	Otto Gruppe	6.6	SportScheck	https://www.sportscheck.com/unternehmen/jobs/ausbildung-bei-sportscheck/	https://www.sportscheck.com/unternehmen/jobs/ausbildung-bei-sportscheck/
7	Tengelmann Warenhandelsgesellschaft	7.1	KiK	http://www.kik-textilien.com/unternehmen/karriere/	http://www.kik-textilien.com/unternehmen/karriere/
7	Tengelmann Warenhandelsgesellschaft	7.2	OBI	https://www.obi.de/karriere/warum-obi/	https://web.archive.org/web/20170916230628/https://www.obi.de/karriere/warum-obi/
8	dm-drogerie markt GmbH	8.1	dm	https://www.dm.de/arbeiten-und-lernen/arbeiten-bei-dm/	https://www.dm.de/arbeiten-und-lernen/arbeiten-bei-dm/
9	Dirk Rossmann GmbH	9.1	Rossmann	https://www.rossmann.de/unternehmen/karriere/arbeiten-bei-rossmann.html	https://web.archive.org/web/20170611153136/https://www.rossmann.de/unternehmen/karriere/arbeiten-bei-rossmann.html
10	Globus Holding GmbH	10.1	Globus	http://www.globus.de/de/unternehmen/arbeitenbeiglobus/arbeitenbeiglobus.html	https://web.archive.org/web/20171019154754/http://www.globus.de:80/de/unternehmen/arbeitenbeiglobus/arbeitenbeiglobus.html
11	BAUHAUS GmbH	11.1	BAUHAUS	https://www.bauhaus.info/arbeitgeber-bauhaus	https://www.bauhaus.info/arbeitgeber-bauhaus
12	Deichmann SE	12.1	Deichmann	http://www.deichmann-karriere.de/arbeitgeber/	http://www.deichmann-karriere.de/arbeitgeber/
13	Müller Holding	13.1	Müller	https://www.mueller.de/unternehmen/karriere/arbeiten-bei-mueller/	https://www.mueller.de/unternehmen/karriere/arbeiten-bei-mueller/
14	McKesson Europe AG	14.1	gesundleben Apotheken
15	HORNBACH Baumarkt	15.1	Hornbach	https://jobs.hornbach.com/Germany/content/Arbeiten-bei-HORNBACH/?locale=de_DE	https://web.archive.org/web/20170709011417/https://jobs.hornbach.com/Germany/content/Arbeiten-bei-HORNBACH/?locale=de_DE
16	Zalando SE	16.1	Zalando	https://jobs.zalando.com/de/culture/	https://web.archive.org/web/20170730015257/https://jobs.zalando.com/de/culture/
17	Intersport Deutschland SE	17.1	INTERSPORT	https://www.intersport.de/unternehmen/karriere/	https://www.intersport.de/unternehmen/karriere/

Erstellen des Corpus

Die identifizierten Webseiten wurden heruntergeladen und in Text-Dokumente umgewandelt. Der komplette Korpus im Text-Format kann hier heruntergeladen werden. Anschließend wurden sie in einen Text-Korpus überführt und bearbeitet. Zur Standard-Bearbeitung gehörte das Entfernen von überflüssigen Leerzeichen, die Transformation in Kleinbuchstaben und das Entfernen von Stopwörtern.

library(tm)
# Korpus laden
web_corpus <- VCorpus(DirSource(directory = "webpages/", encoding = "UTF-8",
mode = "text"))

# Korpus bereinigen
web_corpus <- tm_map(web_corpus, stripWhitespace)
web_corpus <- tm_map(web_corpus, content_transformer(tolower))
web_corpus <- tm_map(web_corpus, removeWords, c(stopwords("german"), "(m/w)", "(w/m)",
"(b.a.)", "mehr…", "..", "../",  "(m/f)", "()," , "aldi", "rewe", "obi", "süd",
 "bauhaus", "netto", "group", "mytoys", "metro", "gernsheim", "cash", "carry", "toom",
 "the", "and", "for", "our", "bonprix", "nord", "real", "müller", "globus", "diska",
 "markt", "zalando", "2018", "2017/2018", "sucht", "edeka", "hornbach", "dass", "otto",
 "company", "fachcentren", "berlin", "intersport", "germany", "0221", "elias", "gabi",
 "angela", "kik", "barry", "daniela", "waltrop", "gmbh", "manufactum", "köln",
 "ascdsc", "johannes", "rossmann", "försterling,", "isabelle", "warenhaus",
 "gmbhregion"))

# Korpus auswerten
dtm_texts <- DocumentTermMatrix(web_corpus, control = list(stemming = F))

# Korpus-Wortlisten in tidytext-Format umwandeln
library(tidytext)
dtm_texts_tidy <- tidy(dtm_texts)

# Informationen über die Unternehmen hinzufügen
library(stringi)
dtm_texts_tidy$ID_Hauptunternehmen <- as.numeric(
  stri_extract(dtm_texts_tidy$document, regex = "[^.]*"))
dtm_texts_tidy$ID_Einzelunternehmen <- (stri_extract(
  dtm_texts_tidy$document, regex = "[^_]*"))
dtm_texts_tidy <- dtm_texts_tidy %>% inner_join(company_list %>%
  select(ID_Hauptunternehmen, Hauptunternehmen) %>%
  distinct(ID_Hauptunternehmen, Hauptunternehmen))

dtm_texts_tidy <- dtm_texts_tidy %>% inner_join(company_list %>%
  select(ID_Einzelunternehmen, Einzelunternehmen) %>%
  distinct(ID_Einzelunternehmen, Einzelunternehmen))

Text-Analyse

Zunächst wird eine Wordcloud zur Übersicht über die Worte erstellt, die im gesamten Korpus mehr als 2 mal vorkommen.

library(wordcloud2)
set.seed(7712)
dtm_texts_tidy %>% select(term, count) %>% filter(count > 2) %>%
 wordcloud2(., fontFamily = "Times", color = "black")

In einem nächsten Schritt werden die Texte auf ihre Emotionalität hin untersucht. Dies geschieht mit Hilfe des SentiWS Wortschatzes, der Schlüsselworten eine Emotionalität von -1 für negative bis +1 für positive Worte zuordnet.

## Download SentiWS hier: http://wortschatz.uni-leipzig.de/de/download
## Quelle Vorbereitung Wortliste https://www.inwt-statistics.de/blog-artikel-lesen/text-mining-part-3-sentiment-analyse.html
# Vorbereiten
sent <- c(
  # positive Wörter
  readLines("SentiWS_v1.8c/SentiWS_v1.8c_Positive.txt",
            encoding = "UTF-8"),
  # negative Wörter
  readLines("SentiWS_v1.8c/SentiWS_v1.8c_Negative.txt",
            encoding = "UTF-8")
) %>% lapply(function(x) {
  # Extrahieren der einzelnen Spalten
  res <- strsplit(x, "\t", fixed = TRUE)[[1]]
  return(data.frame(words = res[1], value = res[2],
                    stringsAsFactors = FALSE))
}) %>%
  bind_rows %>%
  mutate(words = gsub("\\|.*", "", words) %>% tolower,
         value = as.numeric(value)) %>%
  # manche Wörter kommen doppelt vor, hier nehmen wir den mittleren Wert
  group_by(words) %>% summarise(value = mean(value)) %>% ungroup

# Mit unserem Wortkorpus vereinen
dtm_texts_tidy <- dplyr::left_join(x = dtm_texts_tidy, y = sent,
  by = c("term" = "words"))

# Ergebnisse darstellen
sentiment_texts <- dtm_texts_tidy %>%
group_by(document, Einzelunternehmen, Hauptunternehmen) %>%
summarise(sentiment = mean(value, na.rm = T)) %>%
arrange(sentiment)
knitr::kable(sentiment_texts, format = "markdown")

document	Einzelunternehmen	Hauptunternehmen	sentiment
7.1_KiK.txt	KiK	Tengelmann Warenhandelsgesellschaft	0.0040000
1.2_Kaufland.txt	Kaufland	Schwarz Gruppe	0.0217400
5.4_Toom.txt	Toom	Rewe Gruppe	0.0303045
5.1_Rewe.txt	Rewe	Rewe Gruppe	0.0338438
6.5_Frankonia.txt	Frankonia	Otto Gruppe	0.0371200
4.1_Edeka-Verbund.txt	Edeka-Verbund	Edeka Gruppe	0.0393625
3.1_Metro Cash and Carry.txt	METRO Cash & Carry	Metro AG	0.0438091
4.5_Netto Marken-Discount.txt	Netto Marken-Discount	Edeka Gruppe	0.0547536
4.3_Treff3000.txt	Treff3000	Edeka Gruppe	0.0579500
4.4_NP.Discount.txt	NP.Discount	Edeka Gruppe	0.0617000
5.2_Penny.txt	Penny	Rewe Gruppe	0.0673750
2.2_Aldi Süd.txt	Aldi Süd	Aldi Gruppe	0.0691900
2.1_Aldi Nord.txt	Aldi Nord	Aldi Gruppe	0.0698000
7.2_OBI.txt	OBI	Tengelmann Warenhandelsgesellschaft	0.0759565
16.1_Zalando.txt	Zalando	Zalando SE	0.0783435
4.2_Diska.txt	Diska	Edeka Gruppe	0.0794250
17.1_Intersport.txt	INTERSPORT	Intersport Deutschland SE	0.0845786
1.1_Lidl.txt	Lidl	Schwarz Gruppe	0.0859882
9.1_Rossmann.txt	Rossmann	Dirk Rossmann GmbH	0.0945375
15.1_Hornbach.txt	Hornbach	HORNBACH Baumarkt	0.0952947
8.1_dm.txt	dm	dm-drogerie markt GmbH	0.0960385
5.3_Nahkauf.txt	Nahkauf	Rewe Gruppe	0.0977500
11.1_BAUHAUS.txt	BAUHAUS	BAUHAUS GmbH	0.1059600
10.1_Globus.txt	Globus	Globus Holding GmbH	0.1146913
12.1_Deichmann.txt	Deichmann	Deichmann SE	0.1164455
6.6_SportScheck.txt	SportScheck	Otto Gruppe	0.1193833
3.2_Real.txt	Real	Metro AG	0.1201692
13.1_Müller.txt	Müller	Müller Holding	0.1202969
6.3_myToys.txt	myToys	Otto Gruppe	0.1261353
6.2_Manufactum.txt	Manufactum	Otto Gruppe	0.1339591
6.4_BonPrix.txt	BonPrix	Otto Gruppe	0.1770605

Dies lässt sich auch graphisch, geordnet nach Haupt- und Einzelunternehmen darstellen.

library(ggrepel)
ggplot(sentiment_texts, aes(x = reorder(Hauptunternehmen, -sentiment), y = sentiment)) +
  theme_minimal(base_size = 28) + xlab("Unternehmensgruppe") + ylab("mean sentiment") +
   coord_flip() +
  geom_point(aes(x = Hauptunternehmen, y = sentiment), size = 8) +
  geom_label_repel(aes(label = Einzelunternehmen), box.padding = unit(0.35, "lines"),
    point.padding = unit(0.5, "lines"), size = 8)

Und noch eine kleine Untersuchung, was die Top 5 positiven und negativen Worte in den einzelnen Texten nach Hauptunternehmen sind.

dtm_texts_tidy %>% group_by(Hauptunternehmen) %>%
  top_n(5, value) %>%
  ggplot(aes(x = reorder(term, value), y = value, group = Hauptunternehmen)) +
  geom_col() + coord_flip() + theme_minimal(base_size = 22) + xlab("") +
   ylab("sentiment") +
  facet_wrap( ~ Hauptunternehmen, ncol = 4, scales = "free")

dtm_texts_tidy %>% group_by(Hauptunternehmen) %>%
  filter(value < 0.1) %>%
  top_n(-3, value) %>%
  ggplot(aes(x = reorder(term, value), y = value, group = Hauptunternehmen)) +
  geom_col() + coord_flip() + theme_minimal() + xlab("") + ylab("sentiment") +
  facet_wrap( ~ Hauptunternehmen, ncol = 4, scales = "free")

Bild-Analyse

Wie sieht es mit der Bildsprache der Karriereseiten aus? Um die vorhandenen Bilder zu sammeln, werden die Karriere-Webseiten komplett heruntergeladen und die Bild-URLs extrahiert, um sie später analysieren lassen zu können. Etwas Nachbearbeitung ist leider manuell noch nötig.

library(rvest)
library(urltools)

# Leere Zeilen entfernen
company_list <- company_list %>% filter(Archiv.URL != "")

# Loop zum Suchen und Sammeln der Bild-URLs
image_urls_raw <- list()

for(i in 1:nrow(company_list)) {
  name <- company_list[i, 4]
  html_content <- read_html(company_list[i, 5])
  img_urls <- html_content %>% html_nodes("img") %>% html_attr("src")
  image_urls_raw[[name]] <- img_urls
}

# Manuelles Bearbeiten der Bild-URLs, wo nötig
image_urls_raw[["Lidl"]] <- paste0("https://", domain(company_list[1, 5]), image_urls_raw[["Lidl"]])
image_urls_raw[["Aldi Nord"]] <- paste0("https://", domain(company_list[3, 5]), "/karriere/", image_urls_raw[["Aldi Nord"]])
image_urls_raw[["Aldi Süd"]] <- paste0("https://", domain(company_list[4, 5]), image_urls_raw[["Aldi Süd"]])
image_urls_raw[["METRO Cash & Carry"]] <- paste0("https://", domain(company_list[5, 5]), image_urls_raw[["METRO Cash & Carry"]])
image_urls_raw[["Real"]] <- paste0("https://", domain(company_list[6, 5]), "/",  image_urls_raw[["Real"]])
image_urls_raw[["Edeka-Verbund"]] <- paste0("https://", domain(company_list[7, 5]),  image_urls_raw[["Edeka-Verbund"]])
image_urls_raw[["Diska"]] <- gsub("..", "", paste0("https://", domain(company_list[8, 5]), image_urls_raw[["Diska"]]), fixed = T)
image_urls_raw[["NP.Discount"]] <- paste0("https://", domain(company_list[10, 5]), "/",  image_urls_raw[["NP.Discount"]])
image_urls_raw[["Rewe"]] <- paste0("https://", domain(company_list[12, 5]),  image_urls_raw[["Rewe"]])
image_urls_raw[["Penny"]] <- paste0("https://", domain(company_list[13, 5]),  image_urls_raw[["Penny"]])
image_urls_raw[["Nahkauf"]] <- paste0("https://", domain(company_list[14, 5]),  image_urls_raw[["Nahkauf"]])
image_urls_raw[["Toom"]] <- paste0("https://", domain(company_list[15, 5]),  image_urls_raw[["Toom"]])
image_urls_raw[["Frankonia"]] <- paste0("https://", domain(company_list[19, 5]),  image_urls_raw[["Frankonia"]])
image_urls_raw[["OBI"]] <- gsub("../", "", paste0("https://", domain(company_list[22, 5]), "/karriere/",  image_urls_raw[["OBI"]]), fixed = T)
image_urls_raw[["Globus"]] <- paste0("https://", domain(company_list[25, 5]),  image_urls_raw[["Globus"]])

Im nächsten Schritt werden die gesammelten Bilder durch den Dienst Microsoft Azure Face API untersucht. Die KI liefert dann Ergebnisse zu den erkannten Gesichtern zurück, z.B. das Alter und Geschlecht.

# in Anlehnung an https://bigdataenthusiast.wordpress.com/2016/10/17/face-api-microsoft-cognitive-services/
# Unternehmensnamen und URLs zusammenführen
urls_df <- data.frame(company = NA, url = NA)
for (i in 1:length(image_urls_raw)) {
  for(j in 1:length(image_urls_raw[[i]])){
    df <- data.frame(company = names(image_urls_raw[i]), url = image_urls_raw[[i]][j])
    urls_df <- dplyr::bind_rows(urls_df, df)
  }
}
urls_df <- na.omit(urls_df)

# Azure Face API einbinden
library(httr)
face_api_url = "https://westeurope.api.cognitive.microsoft.com/face/v1.0/detect?returnFaceId=true&returnFaceLandmarks=false&returnFaceAttributes=age,gender,smile,emotion,makeup"
api_results_list <- list()

# Achtung!
# Hier muss der eigene "Ocp-Apim-Subscription-Key" Wert eingetragen werden.
# Dazu ist ein Azure-Konto anzulegen :-)

for (i in 1:nrow(urls_df)) {
  url <- urls_df[i,2]
  api_url_generator <- paste0("{'url':'", url, "'}")
  result = POST(face_api_url,
              body = api_url_generator,
              add_headers(.headers = c("Content-Type" = "application/json",
                                       "Ocp-Apim-Subscription-Key" = "DEIN_SCHLÜSSEL")))
  nameing <- paste0(urls_df[i,1], "##", i)
  res <- content(result)
  api_results_list[[nameing]] <- res
  Sys.sleep(0.3)
}

## In Datensatz konvertieren
api_results_df <- data.frame()

for (i in 1:length(api_results_list)) {
  for (j in length(api_results_list[[i]])) {
    df <- (as.data.frame(api_results_list[[i]][j]))
    if (length(df) > 0) {
      df$name <- names(api_results_list[i])
      api_results_df <- dplyr::bind_rows(api_results_df, df)
    }
  }
}

## In einem neuen Datensatz zusammenführen

face_data <- api_results_df %>% filter(!is.na(faceId))
face_data$company_name <- sub("##.*", "", face_data$name)

Die Daten lassen sich nun auch graphisch auswerten.

face_data$faceAttributes.gender_num[face_data$faceAttributes.gender == "male"] <- 0
face_data$faceAttributes.gender_num[face_data$faceAttributes.gender == "female"] <- 1


labels <- c(faceAttributes.age = "Alter",
            faceAttributes.smile = "Lächeln",
            faceAttributes.gender_num = "Geschlecht",
            faceAttributes.emotion.anger = "Wut",
            faceAttributes.emotion.contempt = "Verachtung",
            faceAttributes.emotion.disgust = "Ekel",
            faceAttributes.emotion.fear = "Angst",
            faceAttributes.emotion.happiness = "Zufriedenheit",
            faceAttributes.emotion.neutral = "neutral",
            faceAttributes.emotion.sadness = "Traurigkeit",
            faceAttributes.emotion.sadness = "Traurigkeit",
            faceAttributes.emotion.surprise = "Überraschung"
            )

face_data <- left_join(x = face_data, y = company_list,
  by = c("company_name" = "Einzelunternehmen"))

face_data %>%
  keep(is.numeric) %>%
  select(-faceRectangle.height, -faceRectangle.left, -faceRectangle.top,
     -faceRectangle.width, -starts_with("ID_Haupt")) %>% na.omit() %>%
  gather() %>%
  ggplot(aes(value)) +
    facet_wrap(~ key, scales = "free", nrow = 4, labeller = labeller(key = labels)) +
    geom_histogram() + theme_minimal(base_size = 18) + ylab("Anzahl") + xlab("Werte")

ggplot(data = face_data, aes(x = faceAttributes.gender, faceAttributes.age)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(size = faceAttributes.emotion.happiness)) +
  theme_minimal(base_size = 28) +
  xlab("Geschlecht") + ylab("Alter") +
  scale_size_continuous(name="Wert Happiness", range = c(4, 8))

face_data %>% group_by(Hauptunternehmen) %>%
  ggplot(aes(x = Hauptunternehmen, y = faceAttributes.age,
     shape = as.factor(faceAttributes.gender_num))) +
  geom_jitter(height = 0.01, width = 0.3, size = 7) + coord_flip() +
   theme_minimal(base_size = 28) +
  xlab("") + ylab("Alter") + scale_shape_manual(name="Geschlecht", values = c(2, 6)) +
   theme(legend.position="bottom")

Kommentare oder Fragen?

Ich freue mich auf Kommentare und Fragen. Eine detailliertere Auswertung und Interpretation findet sich dann im bald erscheinenden Sammelband.

Book on Transparency and Participation in the IAEA and OPCW published online

Thu, 21 Jul 2016 00:00:00 +0200

After successfully defending my dissertation thesis “Participation and Transparency in Intergovernmental Security Organizations. Resource and Norm Driven Opening in the IAEA and OPCW” in late June 2016, the book is now available online in various formats. Check out https://book.tobiasweise.de to read the book online, download it as pdf or for your e-reader.

Thanks to everyone who has supported me during the five-year voyage !

Tagesspiegel e-Paper - Automatischer Download und Mail-Versand zum Kindle

Wed, 04 Nov 2015 00:00:00 +0100

Achtung! Im August hat der Tagesspiegel sein Login-System umgestellt und die aktuelle Lösung funktioniert leider nicht mehr!

Hier ein kurzes Python-Skript zum automatischen Download des aktuellen E-Papers und Versand per e-Mail an den Kindle-Reader. Leider gibt es keine Einstellung beim Tagesspiegel, die es erlaubt, das aktuelle E-Paper per e-Mail zu beziehen. Das Skript unten läuft auf jedem Linux-Server, auf dem ein Mailserver läuft. Einfach einen cron-job für 6 Uhr morgens anlegen, und der neue Tagesspiegel landet auf eurem Gerät. Alternativ kann man so aber auch ein persönliches Archiv anlegen, dann einfach den letzten Teil des Skriptes löschen.

import requests
from time import *

# setting variables
lt = localtime()
today = strftime("%d.%m.%Y", lt)
url_today = "http://e-paper.tagesspiegel.de/epaper/econtent.php?rel=a4%s" % today
file_today = "%s.mobi" % today

# download the e-paper after authentication
r = requests.get("http://abo.tagesspiegel.de/aboangebote/e-paper")
q = requests.post("http://abo.tagesspiegel.de/aboangebote/e-paper", data = {"form_id":"epaper", "customer_number":"hier die Kundennummer eintragen", "zip_code":"hier die PLZ eintragen"})

s = requests.get(url_today)

f = open(file_today, "w")
f.write(s.content)
f.close()

# and email this to the kindle device
import smtplib
from email.MIMEMultipart import MIMEMultipart
from email.MIMEBase import MIMEBase
from email import Encoders

sender = 'hier Absender-Adresse eintragen'
receivers = 'hier e-Mail Adresse des Kindle Geräts eintragen'
SUBJECT = "Tagesspiegel"

msg = MIMEMultipart()
msg['Subject'] = SUBJECT
msg['From'] = sender
msg['To'] = receivers

part = MIMEBase('application', "x-mobipocket-ebook")
part.set_payload(open(file_today, "rb").read())
Encoders.encode_base64(part)

part.add_header('Content-Disposition', 'attachment; filename="new.mobi"')

msg.attach(part)

smtpObj = smtplib.SMTP('localhost')
smtpObj.sendmail(sender, receivers, msg.as_string())

Viel Spaß.

Participation and Transparency at the IAEA and OPCW: dissertation submitted

Thu, 08 Oct 2015 00:00:00 +0200

Some days ago, I handed in my dissertation “Participation and Transparency in Intergovernmental Security Organizations. Resource and Norm Driven Opening in the IAEA and OPCW” at the University of Bremen. In the study, I show how the IAEA and OPCW have become more open to non-state participation and how they have become more transparent since their founding years. Both is remarkable given the high sovereignty costs that the organizations’ inspection activities have for their member states. Further, participation and transparency compete with strong norms of confidentiality in both organizations. In addition, I show that participation and transparency increase in a wide range of the organizations’ output. Both in their talk, decisions and actions, the IAEA and OPCW become more open.

Looking at explanations, I find that increasing participation is best explained by a resource based logic. Both organizations allow more non-state access for functional reasons, especially to gain expertise and advice for their inspections and verification activities. Increasing transparency, however, is best told as a norm based story. Rhetoric references to transparency and related changes in the organizations’ rules and action are strongest since a global norm of appropriate, transparent global governance has spread. In a short time, both the IAEA and OPCW have not only acknowledged that transparency should be an important principle for governance in their institutions, but also that new media strategies and investments in transparency towards the general public are required.

I will prepare a shorter version of the dissertation in the coming months. If you are already interested in the data I have collected on the organizational openness of the IAEA and OPCW, it is already available here: https://github.com/tobiasweise/igo_openness_iaea_opcw.

Calculating the Democratic Membership of International Organizations in R

Wed, 10 Jun 2015 00:00:00 +0200

In our collaborative research on the democratic rhetoric of IOs, we are developing explanatory models that explain why IOs use democracy as a normative point of reference when they describe who they are and what they do in their annual reports. In one of our models, we check if the democratic quality of its member states helps to explain this phenomenon. In this blog post, I illustrate one way how we calculate democratic membership. This may be of interest to some of you because I

first show how to download the COW-IO dataset and the DD-Index by Cheibub, Gandhi, and Vreeland, 2010 data in R.
Second, I show how to merge the two data-sets.
Third, I show how to calculate the share of members for the IOs.

If there are any comments, please feel free to send me a mail or please leave a comment, below.

Importing IGO membership data

First, I import the COW data-set and subset it for our sample and then bring it into shape for the next steps.

# download the COW data
temp <- tempfile()
download.file("http://www.correlatesofwar.org/data-sets/IGOs/IGO_igounit_v2.3.zip", temp)
data.cow <- read.csv(unz(temp, "igounit_v2.3.csv"))


# filter IOs and only years after 1979
library(dplyr)
data.cow.sub <-   data.cow %>% filter(ioname == "UNIDO" | ioname == "UNESCO" | ioname == "IBRD" | ioname == "IMF" | ioname == "ILO" | ioname == "ICC" | ioname == "IAEA" | ioname == "BIS" | ioname == "WTO" | ioname == "GATT" | ioname == "IWhale" | ioname == "ISA" | ioname == "OECD" | ioname == "GEF" | ioname == "ICAO" | ioname == "PCA" | ioname == "UN" | ioname == "OPEC" | ioname == "OSCE" | ioname == "ComSec" | ioname == "WMO") %>% filter(year > 1979)

# transform into long data format
library(reshape2)
data.cow.sub <- melt(data.cow.sub[, c(1, 3:217)], id.vars=c("ioname", "year"), variable.name="country",	value.name = "membership")

#and remove states that are not full members from the data-set
data.cow.sub <- filter(data.cow.sub, membership == 1)

Data in the COW data-set is only available until 2005. We need data until 2012 for our analysis. Therefore, we need to add information for the missing years. As a simplifying assumption, we assume that no countries have left an IO since

So, first we copy the 2005 data for the 2006-2011 years.

copy1 <- filter(data.cow.sub, year == 2005)
copy1[which(copy1$year == 2005), 2] <- 2006
copy2 <- filter(data.cow.sub, year == 2005)
copy2[which(copy2$year == 2005), 2] <- 2007
copy3 <- filter(data.cow.sub, year == 2005)
copy3[which(copy3$year == 2005), 2] <- 2008
copy4 <- filter(data.cow.sub, year == 2005)
copy4[which(copy4$year == 2005), 2] <- 2009
copy5 <- filter(data.cow.sub, year == 2005)
copy5[which(copy5$year == 2005), 2] <- 2010
copy6 <- filter(data.cow.sub, year == 2005)
copy6[which(copy6$year == 2005), 2] <- 2011


data.cow.sub <- rbind_all(list(data.cow.sub, copy1, copy2, copy3, copy4, copy5, copy6))
rm(copy1, copy2, copy3, copy4, copy5, copy6)

Next, we need to add data for the IOs that have joined the IOs since 2005. We have created a separate file (download), containing the new member states that is now added to the existing data.

add.members <- read.csv("io-members-since-2006.csv")
data.cow.sub <- rbind_all(list(data.cow.sub, add.members))

Importing DD-Index data

Next, I import the DD-index data.

# download pwt8.1 in excel format and import relevant sheet
library(RCurl)
temp2 <- tempfile()
download.file("https://uofi.box.com/shared/static/d27425539c9d662a7041.xls", temp2, method = "wget", mode = "wb")

library(xlsx)
data.dd <- read.xlsx2(file = temp2, sheetIndex = 1, colIndex = c(2, 3, 69), colClasses = c("character", "numeric", "numeric"), startRow = 1, endRow = 9160)

# filter out years before 1980
data.dd <- filter(data.dd, year > 1979)

Next, we have to match both data source. This can be achieved with the help of the countrycodes package.

# add country code to cow data with countrycodes-package
library(countrycode)
data.cow.sub$isoc <-  countrycode(data.cow.sub$country, origin="country.name", destination="iso3c", warn=T)

## Warning in countrycode(data.cow.sub$country, origin = "country.name", destination = "iso3c", : Some values were not matched: domrepublic, etimor, kosovo, nokorea, sokorea, stlucia

# add non-matching values manually
data.cow.sub$isoc[which(data.cow.sub$country == "domrepublic")] <- "DOM"
data.cow.sub$isoc[which(data.cow.sub$country == "etimor")] <- "TLS"
data.cow.sub$isoc[which(data.cow.sub$country == "kosovo")] <- "UNK"
data.cow.sub$isoc[which(data.cow.sub$country == "nokorea")] <- "PRK"
data.cow.sub$isoc[which(data.cow.sub$country == "sokorea")] <- "KOR"
data.cow.sub$isoc[which(data.cow.sub$country == "stlucia")] <- "LCA"

# next, do the same thing for the dd-index data
data.dd$isoc <-  countrycode(data.dd$ctryname, origin="country.name", destination="iso3c", warn=T)

## Warning in countrycode(data.dd$ctryname, origin = "country.name", destination = "iso3c", : Some values were not matched: Serbia and Montenegro, Yemen Arab Republic

# add non-matching values
data.dd$isoc[which(data.dd$ctryname == "Serbia and Montenegro")] <- "SCG"
data.dd$isoc[which(data.dd$ctryname == "Yemen Arab Republic")] <- "YEM"

# merge, keeping all entries in data.cow.sub
data.complete <- merge(x = data.cow.sub, y = data.dd, by.x = c("isoc", "year"), by.y = c("isoc", "year"), all.x = T)

# rename GATT to WTO
library(car)
data.complete$ioname <- recode(data.complete$ioname, recodes = "'GATT' = 'WTO'")

In the next step, I need to summarize the variables to calculate the share of democratic members in each organization in each year.

# calculate number of membes, number of democracies and share of democracies
dem.mem.io.year <- data.complete %>% group_by(ioname, year) %>% summarise(n_members = sum(membership, na.rm = T), n_dem = sum(democracy, na.rm = T), dem_share_dd = mean(democracy, na.rm = T))

# add dummy for all IOs with dem_share_dd > 0.5
dem.mem.io.year$dummy_dem_share_dd <-  ifelse(dem.mem.io.year$dem_share_dd > 0.5, 1, 0)

# export inequality measures in IO-year format
write.csv2(dem.mem.io.year, file = "dd-members-io-year.csv")

Calculate democratic and non-democratic major powers

Are there IOs with unchallenged democratic major powers among their members? We would assume that in this case, democratic rhetoric is more likely. I create variables that first capture if there are only major democracies (maj_pow_dem) in an IO, second, if there are only autocratic major powers in IOs (maj_pow_auth) and third if both democratic and authoritarian major powers are members of an IO (maj_pow_both).

# filter major powers
data.sub <- data.complete %>% filter(country == "china" | country == "france" | country == "germany" | country == "japan" | country == "russia" | country == "uk" | country == "usa")
data.sub$ioname <- as.factor(data.sub$ioname)
data.sub$country <- as.factor(data.sub$country)

# create variables for presence of democracies and authocracies
data.sub$auth <- ifelse(data.sub$country == "china" | data.sub$country == "russia", 1, 0)
data.sub$dem <- ifelse(data.sub$country == "france" | data.sub$country == "uk" | data.sub$country == "usa" | data.sub$country == "germany" & data.sub$year > 1990 | data.sub$country == "japan" & data.sub$year > 1990, 1, 0)

# summarize to IO years
summary <- data.sub %>% group_by(ioname, year) %>% summarise(authocracies = mean(auth), democracies = mean(dem))

# create variables for status of major powers maj_pow_dem, maj_pow_auth, maj_pow_both
summary$maj_pow_dem <- ifelse(summary$authocracies == 0, 1, 0)
summary$maj_pow_auth <- ifelse(summary$democracies == 0, 1, 0)
summary$maj_pow_both <- ifelse(summary$authocracies != 0 & summary$democracies != 0, 1, 0)

# export
write.csv(summary, file = "major-powers-calculated.csv")

Calculate democratic and non-democratic regional powers

Are there IOs with unchallenged democratic regional powers among their members? The assumed mechanism is the same like for major world powers.

# filter major powers
data.sub <- data.complete %>% filter(country == "india" | country == "china" | country == "japan" | country == "brazil" | country == "nigeria" | country == "soafrica" | country == "russia" | country == "france" | country == "germany" | country == "uk" | country == "usa")
data.sub$ioname <- as.factor(data.sub$ioname)
data.sub$country <- as.factor(data.sub$country)

# regional powers data is only available for year > 1990
data.sub <- data.sub %>% filter(year > 1989)

# create variables for presence of democracies and authocracies
data.sub$auth <- ifelse(data.sub$country == "china" | data.sub$country == "russia" | data.sub$country == "soafrica" | data.sub$country == "nigeria" & data.sub$year < 1999, 1, 0)
data.sub$dem <- ifelse(data.sub$country == "india" | data.sub$country == "japan" | data.sub$country == "brazil" | data.sub$country == "nigeria" & data.sub$year > 1998 | data.sub$country == "france" | data.sub$country == "germany" | data.sub$country == "uk" | data.sub$country == "us" , 1, 0)

# summarize to IO years
summary.2 <- data.sub %>% group_by(ioname, year) %>% summarise(authocracies = mean(auth), democracies = mean(dem))

# create variables for status of major powers maj_pow_dem, maj_pow_auth, maj_pow_both
summary.2$maj_pow_reg_dem <- ifelse(summary.2$authocracies == 0, 1, 0)
summary.2$maj_pow_reg_auth <- ifelse(summary.2$democracies == 0, 1, 0)
summary.2$maj_pow_reg_both <- ifelse(summary.2$authocracies != 0 & summary.2$democracies != 0, 1, 0)

# export
write.csv(summary.2, file = "major-regional-powers-calculated.csv")

Regime change and the effect of young democracies

We assume that there may be an effect of young democracies on the democratic rhetoric of IOs. Thus, we first need to identify young democracies and then calculate the share of those in each IO-year.

# first, detect regime changes
data.dd$reg.change <- ifelse(data.dd$democracy != lag(data.dd$democracy) & data.dd$year != 1980, 1, 0)
data.dd$reg.change.dem <- ifelse(data.dd$reg.change == 1 & data.dd$democracy == 1, 1, 0)


data.complete <- merge(x = data.cow.sub, y = data.dd, by.x = c("isoc", "year"), by.y = c("isoc", "year"), all.x = T)

regime.change <- data.complete %>% group_by(ioname, year) %>% summarise(n_members = n(), young.dem = sum(reg.change.dem, na.rm = T), young.dem.share = sum(reg.change.dem, na.rm = T) / n())

library(zoo)

regime.change <- regime.change %>% group_by(ioname) %>% mutate(young.dem.share.4 = rollmax(young.dem.share, 5, fill = c(NA, "extend"), align = "right" ))

# export
write.csv(regime.change, file = "young-democracies-calculated.csv")

Plot

As the plots show, there is some variation across IOs when it comes to democratic membership. The general trend over time shows that IOs have become more democratic over the years.

library(googleVis)

# plot IO summaries
plot.df <- dem.mem.io.year %>% group_by(ioname) %>% summarise(dem_share = mean(dem_share_dd, na.rm = T))
pl1 <-  gvisBarChart(plot.df, xvar = "ioname", yvar = "dem_share", options = list(height = 450, width = 640))

# plot annual summary
plot.df <- dem.mem.io.year %>% group_by(year) %>% summarise(dem_share = mean(dem_share_dd, na.rm = T))
plot.df$year <- as.Date(as.character(plot.df$year), "%Y")
pl2 <- gvisLineChart(plot.df, xvar = "year", yvar = "dem_share", options = list(height = 450, width = 640))

Calculating the Economic Inequality in International Organizations in R

Wed, 29 Apr 2015 00:00:00 +0200

In our collaborative research on the democratic rhetoric of IOs, we are developing explanatory models that explain why IOs use democracy as a normative point of reference when they describe who they are and what they do in their annual reports. In one of our models, we check if the economic inequality of an IO’s membership helps to explain this phenomenon. In this blog post, I illustrate how we calculate economic inequality. This may be of interest to some of you because I

first show how to download the COW-IO dataset and the Penn World Tables data in R.
Second, I show how to merge the two data-sets.
Third, I show how to calculate the Gini-Coefficient of inequality for the IOs.

If there are any comments, please feel free to send me a mail or please leave a comment, below.

Importing IGO membership data

First, I import the COW data-set and subset it for our research project’s sample of 20 IOs and then bring it into shape for the next steps.

# download the COW data
temp <- tempfile()
download.file("http://www.correlatesofwar.org/data-sets/IGOs/IGO_igounit_v2.3.zip",temp)
data.cow <- read.csv(unz(temp, "igounit_v2.3.csv"))
unlink(temp)

# filter IOs and only years after 1979
library(dplyr)
data.cow.sub <-   data.cow %>% filter(ioname == "UNIDO" | ioname == "UNESCO" | ioname == "IBRD" | ioname == "IMF" | ioname == "ILO" | ioname == "ICC" | ioname == "IAEA" | ioname == "BIS" | ioname == "WTO" | ioname == "GATT" | ioname == "IWhale" | ioname == "ISA" | ioname == "OECD" | ioname == "GEF" | ioname == "ICAO" | ioname == "PCA" | ioname == "UN" | ioname == "OPEC" | ioname == "OSCE" | ioname == "ComSec" | ioname == "WMO") %>% filter(year > 1979)

# transform into long data format
library(reshape2)
data.cow.sub <- melt(data.cow.sub[, c(1, 3:217)], id.vars=c("ioname", "year"), variable.name="country",	value.name = "membership")

#and remove states that are not full members from the data-set
data.cow.sub <- filter(data.cow.sub, membership == 1)

Data in the COW data-set is only available until 2005. We need data until 2011 for our analysis. Therefore, we need to add information for the missing years. As a simplifying assumption, we assume that no countries have left an IO since

So, first we copy the 2005 data for the 2006-2011 years.

copy1 <- filter(data.cow.sub, year == 2005)
copy1[which(copy1$year == 2005), 2] <- 2006
copy2 <- filter(data.cow.sub, year == 2005)
copy2[which(copy2$year == 2005), 2] <- 2007
copy3 <- filter(data.cow.sub, year == 2005)
copy3[which(copy3$year == 2005), 2] <- 2008
copy4 <- filter(data.cow.sub, year == 2005)
copy4[which(copy4$year == 2005), 2] <- 2009
copy5 <- filter(data.cow.sub, year == 2005)
copy5[which(copy5$year == 2005), 2] <- 2010
copy6 <- filter(data.cow.sub, year == 2005)
copy6[which(copy6$year == 2005), 2] <- 2011

data.cow.sub <- rbind_all(list(data.cow.sub, copy1, copy2, copy3, copy4, copy5, copy6))
rm(copy1, copy2, copy3, copy4, copy5, copy6)

Next, we need to add data for the states that have joined the IOs since 2005. We have created a separate file (download), containing the new member states that is now added to the existing data.

add.members <- read.csv("io-members-since-2006.csv")
data.cow.sub <- rbind_all(list(data.cow.sub, add.members))

The data now includes complete membership data for the IOs from 1980-2011.

Importing Penn World Tables

Next, I import the Penn World Tables data. It includes annual GDP data for most of the states that are in the COW-IGO data-set. For the analysis, we use the rgdpna measure, which is “RealGDP at constant 2005 national prices (in mil. 2005US$).”

# download pwt8.1 in excel format and import relevant sheet
temp2 <- tempfile()
download.file("http://www.rug.nl/research/ggdc/data/pwt/v81/pwt81.xlsx",temp2)

library(xlsx)
data.penn <- read.xlsx2(file = temp2, sheetIndex = 3, colIndex = c(1, 2, 4, 18), colClasses = c("character", "character", "numeric", "numeric"), startRow = 1, endRow = 10358)

# filter out years before 1980
data.penn <- filter(data.penn, year > 1979)

unlink(temp2)

Next, we have to match both data sources. This can be achieved with the help of the countrycodes package.

# add country code to cow data with countrycodes-package
library(countrycode)
data.cow.sub$isoc <-  countrycode(data.cow.sub$country, origin="country.name", destination="iso3c", warn=T)

## Warning in countrycode(data.cow.sub$country, origin = "country.name", destination = "iso3c", : Some values were not matched: domrepublic, etimor, kosovo, nokorea, sokorea, stlucia

# add non-matching values manually
data.cow.sub$isoc[which(data.cow.sub$country == "domrepublic")] <- "DOM"
data.cow.sub$isoc[which(data.cow.sub$country == "etimor")] <- "TLS"
data.cow.sub$isoc[which(data.cow.sub$country == "kosovo")] <- "UNK"
data.cow.sub$isoc[which(data.cow.sub$country == "nokorea")] <- "PRK"
data.cow.sub$isoc[which(data.cow.sub$country == "sokorea")] <- "KOR"
data.cow.sub$isoc[which(data.cow.sub$country == "stlucia")] <- "LCA"

# merge, keeping all entries in data.cow.sub
data.complete <- merge(x = data.cow.sub, y = data.penn, by.x = c("isoc", "year"), by.y = c("countrycode", "year"), all.x = T)

# rename GATT to WTO
library(car)
data.complete$ioname <- recode(data.complete$ioname, recodes = "'GATT' = 'WTO'")

Calculate inequality measures

Finally, we can calculate measures of inequality. Here, I limit myself to the Gini-coefficient. Other measures of inequality (e.g. the spread of the data, variances, etc) can be calculated in a similar way.

library(ineq)

inequality.io.year <- data.complete %>% group_by(ioname, year) %>% summarise(n_members = n(), gini = ineq(rgdpna), mean = mean(rgdpna, na.rm = T))

# export inequality measures in IO-year format
write.csv2(inequality.io.year, file = "inequality-io-year.csv")

Calculate share of LDCs in IO membership

As an additional measure of inequality, we look at the share of LDCs in the IOs’ membership. LDC data is taken from the United Nations (and summarized by us here.

## import LDC data
LDCs <- read.xlsx2("LDCs.xlsx", sheetIndex = 1, colIndex = c(1:5), colClasses = c("character", "numeric", "character", "numeric", "numeric"))

# filter LDCs
LDC.sub <- LDCs %>% filter(LDC == 1)

# merge with COW
LDCs.merged <- merge(x = data.cow.sub, y = LDC.sub, by.x = c("isoc", "year"), by.y = c("stateabb", "year"), all.x = T)

#summarize to IO-year
LDC.shares <- LDCs.merged %>% group_by(ioname, year) %>% summarise(n_members = n(), n_LDC = sum(LDC, na.rm = T), LDC_share = sum(LDC, na.rm = T) / n())

# export
write.csv2(LDC.shares, file = "share.ldcs.csv")

Plot

As the plots show, there is some variation across IOs when it comes to inequality. The general trend over time shows that IOs have become more unequal over the years.

library(googleVis)

# plot IO summaries
plot.df <- data.complete %>% group_by(ioname) %>% summarise(n_members = n(), gini = ineq(rgdpna))
pl1 <-  gvisBarChart(plot.df, xvar = "ioname", yvar = "gini", options = list(height = 450, width = 650))

# plot annual summary
plot.df <- data.complete %>% group_by(year) %>% summarise(n_members = n(), gini = ineq(rgdpna))
plot.df$year <- as.Date(as.character(plot.df$year), "%Y")
pl2 <- gvisLineChart(plot.df, xvar = "year", yvar = "gini", options = list(height = 450, width = 650))

And here’s a plot for the annual share of LDCs in our IO population

# plot annual summary
plot.df <- LDC.shares %>% group_by(year) %>% summarise(LDC_share = mean(LDC_share))
plot.df$year <- as.Date(as.character(plot.df$year), "%Y")
pl3 <- gvisLineChart(plot.df, xvar = "year", yvar = "LDC_share", options = list(height = 450, width = 650))

New Article: Between Functionality and Legitimacy: German Diplomatic Talk About the Opening of Intergovernmental Organizations

Mon, 16 Feb 2015 00:00:00 +0100

Have you ever wondered how diplomats, i.e. arguably still the most powerful actors in international organizations, think and talk about the idea of non-state participation? In my most recent article in Global Governance, I argue that German diplomacy so far only rarely conceptualizes non-state participation in intergovernmental organizations as a valuable step improving IO legitimacy. Rather, a functional understanding of IO participation, i.e. highlighting the material benefits of NGOs and others, persists. The notion that NGOs may also contribute to a larger legitimacy of IOs, e.g. by establishing participative and transparent procedures, is rarely mentioned and even actively challenged.

Below’s the abstract. Please feel free to comment!

Who should be allowed to participate in intergovernmental organizations? There is a growing debate about the increasing opening of IGOs for nonstate actors. Explanations of this phenomenon either highlight the functional benefits of opening, or the need of opening to maintain or increase organizational legitimacy. This article analyzes how German diplomatic talk frames nonstate participation and refers to functionality or legitimacy when justifying the opening of IGOs. The perspective of diplomats, the main gatekeepers of change in IGOs, has rarely been considered for analysis. This article argues that German diplomatic discourse about opening is mainly functional. There is only limited reference to nonstate participation as an element of IGO legitimacy. Further, there are elements <in German diplomatic talk that challenge the legitimacy of nonstate actors.

OPCW’s 19th Conference of the States Parties | Some impressions

Mon, 08 Dec 2014 00:00:00 +0100

This is a cross-posting from the Global Norms Blog

From 2 to 5 December 2014, I had the pleasure to take part in the 19th Conference of the States Parties (CSP) of the Organisation for the Prohibition of Chemical Weapons (OPCW). The OPCW implements the Chemical Weapons Convention, the international treaty banning the use, storage and production of all chemicals used for warfare. The OPCW became active in 1997, it is thus quite a young international organization. The organization has nearly reached universality. A great achievement given its demanding obligations that states agree to when signing the CWC.

During my participative observation of the proceedings of the CSP, I learned a lot about the organization. First, as other IGOs, most notably the IAEA, the OPCW struggles with its self-understanding as a technical organization. There are some changes in recent years: the award of the Nobel Peace Price and the destruction of the Syrian CW program. Those caused some public attention for the organization as it stepped into a more political role. Some member states observe and criticize that tendency. Often, they made their concerns heard during the plenary debate. Second, I had the impression that the self-organization of the represented NGOs, especially through the Chemical Weapons Coalition, was quite strong. There was thus a high quality of civil society participation. The Coalition organized some fine side events and discussed critical issues with relation to the work of the OPCW.

Getting Access, Who participates?

The OPCW has no formalized process of accrediting NGOs. Yet, the application for invitation is a simple web form with information on the NGO and the names of the delegates. Getting approved, however, requires a formal decision by the member states. This CSP has shown that the approval process is not free of political considerations. Individual member states vetoed a few NGOs or individuals, banning them from the CSP (see e.g. the German statement, condemning this practice).

The list of participants comprises 63 NGOs. Those include research institutions, some industry and science associations, and many humanitarian organizations. During the CSP, NGOs have access to all public sessions and side events. The CSP can also close sessions to the public. During this CSP, states discussed a special agenda item on the implementation of the Convention and CW destruction in a classified session. Even so, since the last CWC Review Conference, NGOs have the opportunity to address the CSP. This was amply used during this year’s CSP. The impact of those speeches may be debated, but the general perception, at least from the NGOs and some state delegations, was quite positive. At least, the statements are also made available to the public on the CSP website.

Compared to this large number of NGOs, only 7 other IOs participated. Further, the participation, or permanent occupation of seating space, of state delegations was mixed. However, ambassadors in their function as permanent representatives to the OPCW headed most delegations. This is different in the IAEA, where delegation heads are often officials from ministries.

Atmosphere of the CSP

The CSP seemed to be driven by overarching consensus on most agenda items. The state delegates were relaxed and the general proceedings appeared to be well planned. Take as an indicator that the CSP already closed in the morning of its final day. The year before even a day early. At this year, most notable public debate evolved around the final report of the CSP. The report is decided upon in the plenary, paragraph by paragraph. Discussion evolved around a statement by Italy and 56 other states on CW use in Syria. This was the most visible moment of politics, when states like Syria, Iran, Cuba, China and Russia debated the position, wording and connotations of the text. After consultations, the states fond a consensus draft, preserving the OPCW’s consensus culture. Of course, I had no insights into the private negotiations and committee work, which is more prone to be filled with political conflicts.

In summary, I enjoyed getting new insights into the working of the OPCW. Also, I liked getting to know people that are passionately “working together for a world free of chemical weapons” (The OPCW’s recent motto, see image video, below). </p>

Comments, thoughts, criticism? Please share in the comments or via e-mail.

Tobias Weise

The Rise of Democratic Legitimation - Why International Organizations Speak the Language of Democracy

Reviews and Mentions

Related Blog Posts

International Organizations Under Pressure published

Reviews and Mentions

Related Blog Posts

Analyse von Karriereseiten von Handelsunternehmen - Text und Bild

Auswahl von Daten

Erstellen des Corpus

Text-Analyse

Bild-Analyse

Kommentare oder Fragen?

Book on Transparency and Participation in the IAEA and OPCW published online

Tagesspiegel e-Paper - Automatischer Download und Mail-Versand zum Kindle

Participation and Transparency at the IAEA and OPCW: dissertation submitted

Calculating the Democratic Membership of International Organizations in R

Importing IGO membership data

Importing DD-Index data

Calculate share of democratic members

Calculate democratic and non-democratic major powers

Calculate democratic and non-democratic regional powers

Regime change and the effect of young democracies

Plot

Calculating the Economic Inequality in International Organizations in R

Importing IGO membership data

Importing Penn World Tables

Calculate inequality measures

Calculate share of LDCs in IO membership

Plot

New Article: Between Functionality and Legitimacy: German Diplomatic Talk About the Opening of Intergovernmental Organizations

OPCW’s 19th Conference of the States Parties | Some impressions