Determinants of Global Literacy Rates

Alan Adelman, Scott Callahan, Omer Cem Kutlubay
Fall 2013

Set Up

  • World Bank data from 95 countries
  • most recent, complete data from 2010
  • Look for independant variables that contribute to a country's literacy rate

Variables

#Dependent Variable

LTR - Literacy rate, adult total (% of people ages 15 and above)

Independent Variables

GDP - GDP per Capita, UNEMP - Total Unemployment (%labor force), URBGR - Urban population growth (annual %), MOBILE - Mobile cellular subscriptions (per 100 people) INTERNET - Internet users (per 100 people) LEXP - Life Expectancy years BTOG - Ratio of Girls to Boys at School (Primary and Secondary) WMPOL - Proportion of seats held by women in national parliaments (%) SPGDP - Public spending on education, total (% of GDP) HTEXD - High-technology exports (current US$ Thousands) HTEXM - High-technology exports (% of manufactured exports) GINI - GINI Index

First we load the data.

Then assign Country column to row.names and set Country Name to NULL

ltrdata10 <- read.csv("~/Group_project-figure/ltrdata10.csv")

row.names(ltrdata10) <- ltrdata10$Country.Name

ltrdata10$Country.Name <- NULL
View(ltrdata10)

Finally we create a factor variable for "Region"

ltrdata10$Region <- factor(ltrdata10$Region)
#str(ltrdata10)
levels(ltrdata10$Region) <- c("Africa", "Asia", "Oceanic", "C. America", "Europe","Mid. East", "N. America", "S. America")

Regions

    Africa       Asia    Oceanic C. America     Europe  Mid. East 
        17         15          4         12         29          9 
N. America S. America 
         3          6

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