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This chapter uses the Ghana Living Standards Survey (GLSS) 7 datasets to investigate and examine the effect of rural non-farm diversification and its implications on agricultural (tree-crop) farming sector inequalities and sustainable development in Ghana. Applying a Gini-decomposition method and/or technique within a quantitative approach, the study outcome indicates the average non-farm income thus, increased income inequality among tree-crop smallholder rural livelihoods and households. Income diversification by farm households has gained the attention of governments, policy makers, and researchers because of its commonness and contribution to socio-economic development especially in developing countries. Aggregationally, non-farm self-employment reduced income inequality, and non-farm wage employment income led to an increase in income inequality. Increased rate of educational enrollment and achievement is the most important variable of non-farm income inequality. Government effort at expanding tree-crop acreages and improve yields have to degree achieved its intended policy implementation, increased rate of educational achievement could undermine the socio-economic policy cohesion and sustainable development of rural livelihood, communities, and national economy. Tree crop policies should take account of the spatial distribution of tree-crop commodity production and in particular, the implication and effect of rural non-farm diversification on agricultural sector inequalities.

The chapter examines the current trade war between the United States and China and its impact on the two countries’ military industrial complex in relation to economic growth and development. The trade war has both positive and negative impacts on the country’s economic growth and development. Both countries depend heavily on each other for trade and account for an incredibly significant portion of the global trade. The trade war also impacts on the military industrial complex with respect to the defense budgetary allocation and trade in arms. One of the rationales for the trade war by the Trump Administration is to boost up the domestic economy, which would benefit the military industrial complex. Likewise, the retaliatory tariffs by China are designed to protect the domestic economy and showcase its ability to withstand and challenge the United States. The military industrial complex is important to both the countries. The chapter applied a quantitative design with canonical correlation method to multiple regression, where there are multiple intercorrelated outcome variables relative to military industrial complex and the processes of economic growth and development. It shows that trade wars in the form of increased imposed tariffs on each other by the United States and China has had no significant adverse effect on the Military Industrial Complexes of the two biggest economies of the world.

In recent years, the global economy has undergone major transformations involving the liberalization of markets for traditional goods, services, and capital flows. This has led to the emergence of a world financial market underpinned by digital platforms, innovative and the rapid growth of integrated digital platforms, integration, investment, economic growth, development, and the potential for poverty reduction, especially, in the Global South and, in particular in sub-Saharan Africa. The goal of this chapter is to investigate the increasing accessibility and relationship between digital (e-economy) financial integration and poverty alleviation since the era of structural adjustment programs in sub-Sahara Africa with Ghana as a case study. The emphasis is on the New Digital Economy (NDE) relative to new sources of data from mobile and ubiquitous Internet connectivity. The processes of digitalization and financial sector integration and inclusion become increasingly contestable, decomposable, and reconfigurable, and the capacity to innovate will be a key success factor in policies geared toward poverty alleviation. The multiple linear regression model and its estimation using ordinary least squares (OLS) is doubtless the most widely used tool in econometrics. It helps to estimate the relation between a dependent variable and a set of explanatory variables. An OLS model for macro data set relative to a regression model is applied to provide the empirical estimations of the increasing accessibility and the relationship between digital financial integration, investment, economic growth, development, and poverty alleviation.

This chapter presents an investigation into the theory of labour market segmentation and income inequality in the Ghanaian mining sector. Mining activity especially gold mining has been a significant component of exports as well as employment and income earning in the three major mining regions of Ghana. While income growth is an economic benefit, the high incomes associated with the mining sector may lead to greater income inequality. This chapter provides an analysis of mining activity and income inequality in the Western, Eastern, and Ashanti regions of Ghana. The application of labour market segmentation and the Gini coefficient (a measure of inequality) for personal income are found to be significantly associated with the type and levels of mining employment. However, this observation is not linear as income inequality initially increases with mining activity before decreasing at medium to high levels of mining employment, thus following a Kuznets curve pattern. Segregating datasets for indigenous and expatriate staff reveals very different patterns of income inequality. It poignantly increases with indigenous and/or local community personnel relative to expatriate technical personnel at high levels of mining employment; income inequality is lower among the local community residents relative to nationals from other regions and/or from neighbouring countries. This means segmented labour markets (SLM) within the mining industry are likely to be a problem as they result in increased income inequality among locales relative to foreign expatriates.

Globalization and the rapid development of Information and Communications Technology (ICT) have advantaged economies which have invested in (re)skilling their human capital in technical knowledge. Similarly, ICT have spearheaded the growth and development of industrial and service sectors in emerging economies. This accounts for the progress of some Asian economies and the lagging behind of many sub-Saharan African countries, including Ghana. Primarily, reorientation of economic development strategy and human development policy in tandem with the demands of knowledge-based economy (KBE) and related development in ICT explain the differences among the world's economies. Our chapter discusses the extent to which ICT has been incorporated into the educational system to transform it from traditional education in order to reskill human capital in postprimary schools to support the creation and growth of KBE in Ghana. Moreover, the chapter assesses whether ICT infrastructure and syllabuses at postprimary schools meet the challenges of a KBE and an enhanced growth development in Ghana. Using the theories of evolutionary economic change, new growth, technology and knowledge gap, we intend to analyze the progress and challenges faced by Ghana as it strives to build a KBE that thrives on innovation and creativity, which in turn drive economic growth and development. In this respect, we examine postprimary schools in Ghana.