Proposal Report: Economic Growth and Financial Development
Economic growth, specifically long-term economic growth, and development relies on the capability of human capital to accumulate value. This means the ability of team managers to be more efficient with asset production, but also making sure efficient fund allocation is implemented and invested in the most useful places. Traditionally, banks, alternative financial institutions, stock markets, pensions funds have been utilize to evolve individual savings from income into additional capital monitored and invested by enterprises. The benefits and adverse affects of this type of model can be cyclical. The risk creates an environment where in order to ensure stable financial development and economic growth in any organization where an enterprise in the private or public sector or a specific government, are reliant on financial intermediaries.
A common model used in assessing the relationship between financial development and economic growth is the McKinnon-Shaw model. As AK and Kara (2011) say, “according to McKinnon- Shaw, Levine and King, several restrictions (financial repression) imposed on the banking system by the government can slow down the development of the financial system and therefore, can cause negative results on economic growth.” The authors identify these negative results as things like compulsory high-level reserve applications, higher interest rates.
In a study covering 109 industrialized and developing countries the relationship between financial development and economic growth was assessed, and it was found that economies that have financial markets have all developed on a more advanced levels than those countries that do not have financial markets.
Acharya (2011) McKinnon notes that financial markets create an environment or financial deepening, predominantly because they are exposed to more aggressive growth. The impact financial institutions have on economic growth is critical. This is known as financial intermediation and it’s usually implemented by investors and savers, but the result is monetization of the economy, which more efficient at transferring funds from a less traded sector to a more profitable sector.
In statistical redefinitions in the mid-1980s, pertaining rebuilding infrastructure the authors note that “once these structural shifts are taken into account, we find evidence of two co-integrating vectors. They are normalized on stock market capitalization and banking development AK and Kara (2011).” Data identifying restrictions are exclusion of real GDP from the first co-integrating vector, removal of LMC and linear homogeneity between LBY and LY in the second vector. Market volatility exerts a significant negative influence. Khan and Senhadji (2000) “A growing body of empirical analyses, including firm-level studies, and broad cross country comparisons, demonstrate a strong positive link between the functioning of the financial systme and long-run economic growth.”
Whether financial development has a causal influence on economic development has been a long lasting debate among economists. Beck (2008) notes that contemporary fiscal knowledge follows that the most effective and successful financial institutions in markets that enable markets to overcome friction, do so by “asymmetries and transaction costs” and they are able to create economic growth through numerous channels. As Beck (2008) notes these institutions.
An effective econometric model used by Goldsmith (1969) was to show the empirical positive correlation between GDP per capita and financial development. To effectively make this connection, Goldsmith (1969) needed to take data on the assets of financial exchange intermediaries as they relate to GNP and data plus changes in loan GNP between 1860 and 1963 across 35 countries on the sum of net issues of securities and bonds. The econometric model used in this:
g(i) = y(i,t) –y(i,t-1)= α + βf (i)+C(i) γ+ δy(i,t-1)+ ε (i)
Here in the regression econometric model, to control for convergence, initial income per capita is logged and included in the equation. In this model y represents log of real GDP per capita or a some form of welfare metric, g represents y’s growth rate, while f represents the measure of financial development. The error term is ε, with C being a set of conditioning information with I as the observational unit. All explanatory variables are measured as an initial value, or an average over the sample time range. β represents the possibility of reverse causation or an omitted variable bias. As this metric measures GDP directly some theorist pose household incomes and outcomes as a source for measuring economic growth fluctuations.
Coleman (1999) takes a different approach to measuring the connection between financial development and economic growth. In his econometric model he measures how household and village characteristic can influence demand for micro-credit and household outcomes. He holds governmental policy accountable. He uses survey data of over 1,800 households and treat landowners as exogenous to outcomes derived from welfare. For example, cases where farmers who own a certain set limit of land are eligible to borrow from financial institutions. After accounting for all of these variables he came up with the following econometric model identifying the influence of economic development on financial growth from a household and home ownership standpoint:
γ(i, j) = C(1) (i, j) α + βp(i, j) + C(2) (j)γ + δM (i, j) + ε(i , j)
In this model, there are two variables, M and p. Each one of these variables covers a respective changeable dummy value. M represents current and future borrowers and p represents those who already have access to credit. M is viewed as a household characteristic that is most unobservable the choice of a particular household to choose whether or not they want to access credit. Β in this model measured the impact of the particular credit program compared to the prospective borrowers.
Ross Levine (1997) notes that “economists hold startlingly different opinions regarding the importance of the financial system for economic growth. Walter Bagehot (1873) and John Hicks (1969) argue that it played a critical role in igniting industrialization in England by facilitating the mobilization of capital for “immense works.” There are many explanations that define where growth in GDP could stem from as a result of financial markets. Khan and Senhadji (2000) mention that John Schumpeter argues that successful banks empower technological innovations by anticipating the new wave in technologies and then investing in those projects. The act of funding these entrepreneurs would not be as possible without a decline in interest rates, and a willingness for banks to lend. It is also important that these financial institutions have a good eye for the companies that have the best chances of successfully implementing innovative products and production processes.
The Priori Expectations
In sum, causality data analysis shows a long term relationship between financial development and growth rates. The data specifically shows growth rates increase over a long durations of positive financial development. As Acharya (2011) notes, “A growing body of empirical analyses, including firm-level studies, and broad cross country comparisons, demonstrate a strong positive link between the functioning of the financial system and long-run economic growth (2011).”The same research shows no correlation between financial development and growth when short term relationship or duration are assessed. Data shows that it’s essential for financial industry to be present in markets looking for financial growth. While firm and household-level data provide a closer analysis of the mechanisms through which financial development positively influences economic growth in firms and households, measuring this growth through GDP can also be very telling of fundamental fluctuations. It must also not be overlooked that each respective econometric model is also subject to bias.
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