Foreign Aid and Governance in Africa

2019

SSRN Journal

This study investigates how increasing economic development affects the green economy in terms of CO2 emissions, using data from 44 countries in the SSA for the period 2000-2012. The Generalised Method of Moments (GMM) is used for the empirical analysis. The following main findings are established. First, relative to CO2 emissions, enhancing economic growth and population growth engenders a U-shaped pattern whereas increasing inclusive human development shows a Kuznets curve. Second, increasing GDP growth beyond 25% of annual growth is unfavorable for a green economy. Third, a population growth rate of above 3.089% (i.e. annual %) has a positive effect of CO2 emissions. Fourth, an inequality-adjusted human development index (IHDI) of above 0.4969 is beneficial for a green economy because it is associated with a reduction in CO2 emissions. The established critical masses have policy relevance because they are situated within the policy ranges of adopted economic development dynamics.

Section: 22identification and Exclusion Restrictionssupporting

confidence: 85%

Economic Development Thresholds for a Green Economy in Sub-Saharan Africa

2019

SSRN Journal

“…categorisation of endogenous explaining and strictly exogenous variables) is assessed with the Difference in Hansen Test (DHT) for instrument exogeneity. This criterion for assessing the validity of the exclusion restriction is consistent with the standard instrumental variable (IV)approach in which, failure to reject the null hypothesis of the over-identifying restrictions test is an indication that the strictly exogenous variables do not explain the outcome variable beyond the proposed mechanisms (Beck et al, 2003;Asongu & Nwachukwu, 2016d). Hence, in order for the exclusion restriction assumption to hold, the null hypothesis of the DHT should not be rejected in the results that are disclosed in the next section.…”

Section: Generalised Methods Of Moments: Specification Identificationmentioning

confidence: 78%

Insurance Policy Thresholds for Economic Growth in Africa

2019

Eur J Dev Res

Self Cite

This study investigates the role of insurance in economic growth on a panel of forty-eight countries in Africa for the period 2004-2014. The research question the study seeks to answer is the following: what thresholds of insurance penetration positively affect economic growth in Africa? The empirical evidence is based on Generalized Method of Moments. Life insurance increases economic growth while the effect of non-life insurance is not significant. Increasing both life insurance and non-life insurance has negative net effects on economic growth. From an extended analytical exercise, 4.149 of life insurance premium (% of GDP) is the minimum critical mass required for life insurance to positively affect economic prosperity while 1.805 of non-life insurance premium (% of GDP) is the minimum threshold required for non-life insurance to positively affect economic prosperity. Thresholds are also provided from the Hansen (1999) Panel Threshold Regression technique using a balanced sample of 28 countries.

“…Hence, in the findings that are reported in the empirical results section, the exclusion restriction assumption is confirmed if the null hypothesis of the DHT connected to instrumental variables (IV) (year, eq (diff)) is not rejected. This process of assessing the validity of exclusion restriction is similar to the standard IV procedure, whereby the failure to reject the null hypothesis of the Sargan Overidentifying Restrictions (OIR) test is an indication that strictly exogenous variables affect inclusive development exclusively via the suspected endogenous variable channels (Asongu & Nwachukwu, ; Beck, Demirgüç‐Kunt, & Levine, ).…”

Section: Methodsmentioning

confidence: 99%

Environmental degradation and inclusive human development in sub‐Saharan Africa

Sustainable Development

2018

Self Cite

127

In the light of challenges to sustainable development in the post-2015 development agenda, this paper assesses how increasing carbon dioxide (CO 2 ) emissions affect inclusive human development in 44 countries in sub-Saharan Africa for the period 2000-2012. The following findings are established from Fixed Effects and Tobit regressions: first, unconditional effects and conditional impacts are respectively positive and negative from CO 2 emissions per capita, CO 2 emissions from liquid fuel consumption, and CO 2 intensity. This implies a Kuznets-shaped curve because of consistent decreasing returns; and second, the corresponding net effects are consistently positive. The following findings are apparent from Generalized Method of Moments regressions: first, unconditional effects and conditional impacts are respectively negative and positive from CO 2 emissions per capita, CO 2 emissions from liquid fuel consumption, and CO 2 intensity. This implies a U-shaped curve because of consistent increasing returns; and second, the corresponding net effects are overwhelmingly negative. Based on the robust findings and choice of best estimator, the net effect of increasing CO 2 emissions on inclusive human development is negative. Policy implications are discussed.