Name: Blessing or Curse? The Effect of aid on Corruption in Sub-Saharan Africa: A Quantitative Longitudinal Analysis (en Inglés)
SKU: 63018422
Price: 540.62 MXN
Availability: InStock
Author: Kelvin Okundaye
ISBN: 9783668673106

portada Blessing or Curse? The Effect of aid on Corruption in Sub-Saharan Africa: A Quantitative Longitudinal Analysis (en Inglés)

Blessing or Curse? The Effect of aid on Corruption in Sub-Saharan Africa: A Quantitative Longitudinal Analysis (en Inglés)

Formato

Libro Físico

Editorial

Grin Verlag

Autor

Kelvin Okundaye

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783668673106

Blessing or Curse? The Effect of aid on Corruption in Sub-Saharan Africa: A Quantitative Longitudinal Analysis (en Inglés)

Kelvin Okundaye (Autor) · Grin Verlag · Tapa Blanda

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Reseña del libro "Blessing or Curse? The Effect of aid on Corruption in Sub-Saharan Africa: A Quantitative Longitudinal Analysis (en Inglés)"

Seminar paper from the year 2018 in the subject Politics - International Politics - Region: Africa, grade: 1,7, University of Duisburg-Essen (Internationale Beziehungen und Entwicklungspolitik), language: English, abstract: This paper will explore the question of whether foreign development aid has an enhancive or reductive effect on corruption levels, and then conclude which of the theoretical camps is correct in the specific context of Sub-Saharan Africa. First, the relationship between aid and corruption will be discussed by reviewing the most relevant studies in literature, both on a general level and in African context. In addition to empirical findings, theoretical mechanisms for each strand shall be revealed. Next, our own quantitative breakdown, based on the newest available data will be established. After the definition of important terms and the presentation of our created indices, the paper shows a Pearson correlation analysis using 48 Sub-Saharan African countries with data from 2002 to 2014. Furthermore, the findings for robustness will be tested, using a multivariate ordinary least squares (OLS) regression. Results will be interpreted and applied to our discussion