![Inequality in Sub-Saharan Africa: New Data and New Insights from Anthropometric Estimates [An article from: World Development]](https://www.ebooknetworking.net/books/noimage.jpg)
Inequality in Sub-Saharan Africa: New Data and New Insights from Anthropometric Estimates [An article from: World Development]
Book Details
Author(s)A. Moradi, J. Baten
PublisherElsevier
ISBN / ASINB000RR7T8M
ISBN-13978B000RR7T87
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from World Development, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Reliable information on inequality within countries is highly scarce, especially for Less Developed Countries (LDCs). Using anthropometric measures, we extend the inequality database for Sub-Saharan Africa to no less than 28 countries over six five-year periods from 1950 to 1980, and to some 200 regions within those countries. In this process, we also test in depth the validity of objections against the derivation of inequality measures from height data. In a second step, we test the determinants of inequality within and between the 200 regions under study. Our set of explanatory variables includes protein supply, cash cropping, industrial structure, mineral resources, distance to the countries' capital, urbanization, education, population density, and ethnic fractionalization. We find that monoculture cash cropping increases inequality, whereas diversified cash cropping has the opposite effect.
Description:
Reliable information on inequality within countries is highly scarce, especially for Less Developed Countries (LDCs). Using anthropometric measures, we extend the inequality database for Sub-Saharan Africa to no less than 28 countries over six five-year periods from 1950 to 1980, and to some 200 regions within those countries. In this process, we also test in depth the validity of objections against the derivation of inequality measures from height data. In a second step, we test the determinants of inequality within and between the 200 regions under study. Our set of explanatory variables includes protein supply, cash cropping, industrial structure, mineral resources, distance to the countries' capital, urbanization, education, population density, and ethnic fractionalization. We find that monoculture cash cropping increases inequality, whereas diversified cash cropping has the opposite effect.
