Data
Country-Socioeconomic-Status-Scores-Part-II

Country-Socioeconomic-Status-Scores-Part-II

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Dustin Carrion
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Computer Systems Machine Learning
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each countrys income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Banks World Development Indicators Database and the United Nations Common Database. See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries. VARIABLE DESCRIPTIONS: unid: ISO numeric country code (used by the United Nations) wbid: ISO alpha country code (used by the World Bank) SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population region5: Five category regional coding schema regionUN: United Nations regional coding schema DATA SOURCES: The dataset was compiled by Shawn Dorius (sdoriusiastate.edu) from a large number of data sources, listed below. GDP per Capita: Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13. United Nations Population Division. 2009.

10 features

unidnumeric74 unique values
0 missing
wbidstring74 unique values
0 missing
countrystring74 unique values
0 missing
yearnumeric14 unique values
0 missing
sesnumeric281 unique values
0 missing
classstring3 unique values
0 missing
gdppcnumeric863 unique values
0 missing
yrseducnumeric782 unique values
0 missing
region5string5 unique values
0 missing
regionUNstring17 unique values
0 missing

19 properties

1036
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
5
Number of numeric attributes.
0
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
50
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

0 tasks

Define a new task