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Empirical Economics

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04-04-2019

The economic cost of the Arab Spring: the case of the Egyptian revolution

Authors: Cruz A. Echevarría, Javier García-Enríquez

Published in: Empirical Economics | Issue 3/2020

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Abstract

This paper analyzes the effects that the Arab Spring and the subsequent revolution had on per capita real Gross Domestic Product in Egypt. The estimation procedure that we follow is the synthetic control method. After comparing the observed evolution of Egyptian real output in the period 2011–2017 with that of synthetic Egypt, our estimates show (i) an accumulated loss in the growth rate of per capita real Gross Domestic Product of 12.04% (a yearly average of 1.56%); (ii) an accumulated loss in the per capita real Gross Domestic Product of 6279.7 dollars (a yearly average of 897.1 dollars); and (iii) an accumulated loss in the aggregate real Gross Domestic Product of 582.5 billion dollars (a yearly average of 83.2 billion dollars).

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One exception is Groizard et al. (2016) which studies foreign tourists’ demand for travel to countries experiencing Arab Spring episodes by using a gravity model of bilateral tourism flows for Egypt, Syria, Tunisia and Yemen.

Matta et al. (2016) follow a similar approach where Tunisia is the case study.

The data come from the World Travel & Tourism Council (https://www.wttc.org) [last accessed December 2018].

The data source is the World Development Indicators Databank (http://databank.worldbank.org) [last accessed December 2018].

The data come from different sources: Quality of Government Standard Dataset (http://qog.pol.gu.se) for education, unemployment and inflation data [last accessed December 2018]; Transparency International’s 2010 report (http://www.transparency.org) for corruption data [last accessed December 2018]; and World Development Indicators Databank (http://databank.worldbank.org) for poverty [last accessed December 2018].

This heavily draws on Abadie et al. (2010, 2015).

If the number of treated units was more than one, the method could be applied independently to each of the treated units or to an aggregate of all treated units.

Note that the non-negative elements of matrix V can always be normalized to add up to, say, 1 as this does not affect the optimal choice for the vector of weights \(\mathbf {w}\).

This is defined as \((T_{0}-T^{*})^{-1}\sum _{t=T^{*}+1}^{T_{0}}\left( Y_{1t}-\sum _{j=2}^{J+1}Y_{jt}w_{j}^{*}\right) ^{2}\). Intuitively, the cross-validation technique selects the matrix V that minimizes out-of-sample prediction errors.

In particular, we use the R MSCMT package, version 1.3.3, which has proven to outperform other computational procedures implemented in R, Stata\(^{\copyright }\) and Matlab\(^{\copyright }\) in terms of computational time and accuracy (see Becker and Klößner 2017, 2018).

Acemoglu et al. (2016) adopt a slightly more restrictive criterion as only control units with a Pre-MSPE less than or equal to \(\sqrt{3}\) times that of the treated unit are considered to test the statistical significance of the treatment effect.

The suitability of introducing this discussion was kindly suggested to us by an anonymous referee.

For the sake of space saving the corresponding counterpart to Table 2 has not been included in this paper, but it is available upon request from the authors.

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Title: The economic cost of the Arab Spring: the case of the Egyptian revolution
Authors: Cruz A. Echevarría
Javier García-Enríquez
Publication date: 04-04-2019
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2020
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01684-7

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