Geographic Variation in College Proximity to Estimate the Return to Schooling

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Using Geographic Variation in College Proximity to Estimate the Return to Schooling. Extension:examine the impact of changes in college proximity on non-monetary outcomes, such as health and well-being.

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Changes in college proximity can have a significant impact on non-monetary outcomes, such as health and well-being. Studies show that living close to a college campus increases access to support services and resources, which are especially helpful for students who may be struggling with mental health concerns or other personal issues (Roth et al., 2017). Additionally, having easy access to recreational outlets such as museums or cultural events can also boost overall physical and psychological health because it provides individuals with an avenue for positive engagement outside of academics (Goebel & Denny, 2018). Furthermore, the presence of a college within a community has been found to contribute positively to economic development through increased employment opportunities and higher property values due its potential for attracting new businesses. This can improve quality of life by providing additional income sources while raising standards across local communities (Giuliano & Hunt., 2011). Thus, changes in college proximity not only potentially affects monetary outcomes but also those related directly individual’s health wellbeing which should taken into consideration during any assessments this kind situation.

Excessive competition from deregulation and universal banking practices can engender insolvency and instability of the industry as banks fall prey to moral hazard, information asymmetry and pursue riskier strategies to mobilise more deposits. World Bank (2014) indicated that with GDP of USD 38.62 billion and population of 26.79 million, Ghana could boast of 29 universal banks whereas Nigeria had GDP of USD 568.5 billion, population of 177.5 million and 22 universal banks. Beck (2008) and Claessens (2009) opined the necessity of restraining competition in the banking system in order to sustain stability since undue competition could result in vulnerability to systemic risk (Allen & Gale, 2004; Carletti & Hartmann, 2003).

The energy crisis that plagues Ghana adversely affects economic growth culminating in increasing operational costs, declining business income and profitability (Adom, 2011; Anane, 2015; Andersen & Dalgaard, 2012; CEPA, 2007). The 2012-2016 energy crisis contributed to decline in real GDP growth rate from 8.8% in 2012, 7.3% in 2013, 4% in 2014 to 3.9% in 2015. Banking industry operating assets dropped to 19% in 2015 from 38% in 2014 (Anane, 2015, PwC, 2016). Rising average inflation rates from 9.1% in 2012, 11.5% in 2013, 15.5% in 2014 to 17.1% in 2015 coupled with depreciation of the cedi and imbalances in other macroeconomic variables impede development of the banking sector. Athanasoglou, Brissimis and Delis (2005), Kosmidou, Pariouras and Tannz (2005), Kutsienyo (2011) and Sibindi and Bimba (2014) documented empirical evidence of GDP growth impacting positively on the banking sector and rising inflation adversely affecting banking sector growth. IMF (2011) reported the possibility of poor asset quality of Ghanaian banks should the macroeconomic imbalanced linger on.

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