Globalisation and Female Economic Participation in Sub-Saharan Africa

Abstract

This study assesses the relationship between globalisation and the economic participation of women (EPW) in 47 Sub-Saharan African countries for the period 1990-2013. EPW is measured with the female labour force participation and employment rates. The empirical evidence is based on Panel-corrected Standard Errors and Fixed Effects regressions. The findings show that the positive effect of the overall globalisation index on EPW is dampened by its political component and driven by its economic and social components, with a higher positive magnitude from the former or economic globalisation. For the most part, the findings are robust to the control for several structural and institutional characteristics. An extended analysis by unbundling globalisation shows that the positive incidence of social globalisation is driven by information flow (compared to personal contact and cultural proximity) while the positive effect of economic globalisation is driven by actual flows (relative to restrictions).Policy implications are discussed with some emphasis on how to elevate women's social status and potentially reduce their victimisation to male dominance.

Globalization and the proportion of women in vulnerable employment in sub‐Saharan Africa: The role of economic, social, and political conditions

“…A 1% increase in de facto and de jure conditions respectively produced 0.057% and 0.376% increases in the proportion of women in vulnerable employment. These results are in tandem with the findings of Asongu et al (2017) and Bachetta et al (2009) on the dampening impacts of globalization on economic participation of women and increased informality, respectively.…”

“…A variety of factors, including economic, institutional, and regulatory conditions, could determine the proportion of persons in vulnerable employment. Using the theoretical and empirical formulations in Asongu et al (2017) and Bachetta et al (2009) as a framework to guide our empirical model specification, we consider the following functional relationship: $$${Y}_{it}={\alpha }_{it}+\beta {X}_{it}+\varphi {Z}_{it}\,+\,{\delta }_{it}+{\epsilon }_{it}$$$where i and t signify countries and years, respectively. Y is the proportion of women in vulnerable employment; X is our variable of interest and Z represents control variables; δ and ϵ it are country fixed effect and noise effect respectively, and α , β and φ are parameters to be estimated.…”

International comparative evidence of e-government success and economic growth: technology adoption as an anti-corruption tool

“…To examine how technology adoption influences economic growth, we are consistent with the previous papers in merging different sources of data (Tchamyou, 2018a, 2018b, Tchamyou et al, 2019Asongu et al, 2019). Data are collected through various sources (Neibel, 2018;Loan et al, 2019;Asongu et al, 2020). The dynamic generalized method of moments (GMM) [1] was conducted on 149 countries for the 2012-2016 period.…”

“…Following recent empirical literature focusing on the GMM estimation, this paper adopts the GMM estimation strategy for at least four main reasons (Bhattacherjee and Utkarsh, 2018;Baklouti and Boujelbene, 2019;Tchamyou et al, 2019;Asongu et al, 2020). In the first criterion, the number of cross-sections is higher than the corresponding number of periods in each country.…”

“…On the other, the instrumentation process allows for the use of internal instruments to control for simultaneity or reverse causality. Among the possible GMM alternatives available in the empirical literature, the approach adopted in this paper is the difference GMM approach that has been demonstrated by Roodman (2009aRoodman ( , 2009b and established to limit the proliferation of instruments and restrict over-identification [2], with an option that collapses instruments to mitigate instrument proliferation (Asongu et al, 2019;Tchamyou et al, 2019;Asongu et al, 2020).…”