Financial Sector Development and Economic Growth: The Case of Ghana (1980-2009)

This study examines the relationship between Financial Sector Development and Economic Growth in Ghana using time series data from 1980-2009. The study investigates empirically the impact of financial sector development on economic growth in Ghana using the Granger Causality Test, the Johansen Cointegration and the Error Correction Modeling (ECM) techniques. The intent of the framework used is to find out whether there exists a long-run relationship between growth and finance. The study concludes that there exist a positive long run relationship between economic growth and financial sector development with financial sector developments Granger causing economic growth in Ghana. An enabling environment and financial sector interventions such as low interest rate that will enhance transfer of credit to the private sector must be pursued to enhance the economic development of Ghana. Government should put in place appropriate fiscal and monetary policies to encourage the increase of credits to the private sector of the economy. This will boost economic growth immensely as shown by the results from our analysis. Government should encourage domestic producers with favourable tax incentives to enable them produce more for export which will intend increase the country’s GDP to a great extent. Government policy should focus on ensuring that capital stock is allocated efficiently to the productive sectors of the economy such as industry and agriculture. Policies should be put in place to increase and improve upon the human capital accumulation of skills in all areas, both financial and real sectors of the economy, to have a positive effect on the Ghanaian economy.

The finance services industry encompasses a broad range of organizations that deal with the management of money. In Ghana, the financial services industry is categorized into three main sectors: • Banking and Finance (including Non-Bank Financial Services and Forex Bureau) • Insurance and

• Financial market/capital markets
The operating institutions include both foreign and local major banks, Rural and Community Banks (RCBs), Savings and Loans Companies (SLCs) and other finance and leasing companies.
Through the implementation of the Financial Sector Strategic Plan (FINSSP) the Government of Ghana intends to promote the evolution of a financial sector which is appropriate for the needs of a country moving towards middle income status. The vision is one of a financial sector which is responsive to the needs of the 21 st Century, particularly given the prospect of greater international and regional competition and opportunity for Ghanaian financial market participants.

Financial Development
The costs of acquiring information, enforcing contracts, and making transactions create incentives for the emergence of particular types of financial contracts, markets and intermediaries. Different types and combinations of information, enforcement, and transaction costs in conjunction with different legal, regulatory, and tax systems have motivated distinct financial contracts, markets, and intermediaries across countries and throughout history.
In arising to ameliorate market frictions, financial systems naturally influence the allocation of resources across space and time (Merton and Bodie, 1995, p. 12). To organize a discussion of how financial systems influence savings and investment decisions and hence economic growth, Ross Levine focused on five functions provided by the financial system. That is, in easing information, enforcement, and transactions costs, financial systems provide five broad categories of services to the economy. While there are other ways to classify the functions of the financial system (Merton, 1992;Merton and Bodie, 1995).
Financial development occurs when financial instruments, markets, and intermediaries amelioratethough do not necessarily eliminatethe effects of information, enforcement, and transactions costs. Thus, financial development involves improvements in the (i) production of ex-ante information about possible investments, (ii) monitoring of investments and implementation of corporate governance, (iii) trading, diversification, and management of risk, (iv) mobilization and pooling of savings, and (v) exchange of goods and services. Each of these financial functions may influence savings and investment decisions and hence economic growth.
Since many market frictions exist and since laws, regulations, and policies differ markedly across economies, improvements along any single dimension may have different implications for resource allocation depending on other frictions.
In terms of integrating the links between finance and growth theory, two general points are worth stressing from the onset. First, a large growth accounting literature suggests that physical capital accumulation per se does not account for much of economic growth.Thus, if finance is to explain economic growth; we need theories that describe how financial development influences resource allocation decisions in ways that foster productivity growth.
Second, there are two general ambiguities between economic growth and the emergence of financial arrangements that improve resource allocation and reduce risk. Specifically, higher returns ambiguously affect saving rates due to well-known income and substitutions effects.
Thus, financial arrangements that improve resource allocation and lower risk may lower saving rates. In a growth model with physical capital externalities, therefore, financial development could retard economic growth and lower welfare if the drop in savings and the externality combine to produce a sufficiently large effect.
There will be a look at how market frictions motivate the emergence of financial systems that provide these five financial functions and also describe how the provision of these functions influences resource allocation and economic growth 1 .
An effective and efficient financial sector that is well performing is expected to promote economic growth by positively influencing the rate of accumulation and efficiency of capital.
Financial intermediaries play an important role in lowering the costs of potential investments, exerting corporate control, managing risk and mobilizing savings and allocative investment in ways that may alter long-run growth paths 2 .
Until lately, the role of financial markets was not given much attention by economic theory as directly important for development while technological progress and population growth were considered as the main driving forces behind growth. Orthodox thinking changed with the advent of development growth models that state that investment in research and development in physical and human capitals are major determinants of economic growth. Against this backdrop, mounting concerns as to how to finance these investments and how financial intermediaries allocate funds naturally became important questions not just for growth but also in terms of distributional effects of growth at the macroeconomic level (Gross, 2001).
One of the debates in growth theory is the extent to which financial development leads to economic growth. It is not implausible to posit a positive correlation between growth in the financial and real sectors. However, the causal relationship is not clear. Which is the cause and which is the effect? Is finance the leading engine of economic development or does it simply follow growth from elsewhere?
Empirical studies have been conducted to establish this causality. Cross country regressions support the supply leading finance thesis wherein financial intermediation supports and sustains the growth process. Goldsmith (1969) motivated his path breaking study of finance and growth as follows: "One of the most important problems in the field of finance, if not the single most important one… is the effect that financial structure and development have on economic growth.
(p. 390)".Thus, he sought to assess whether finance exerts a causal influence on growth and whether the mixture of markets and intermediaries operating in an economy influences economic growth. Toward this end, Goldsmith (1969) carefully compiled data on 35 countries over the period 1860 to 1963 on the value of financial intermediary assets as a share of economic output.
A time series study will be performed on Ghana.
In view of the above, a motivation for this study was envisaged. Hence the topic "Financial Sector Development and economic Growth: The case of Ghana" where the study models relationship between economic growth and financial sector development for Ghana from the period 1980 to 2009 with the aim to studying the links between these two sectors.

Background and Economic Overview of Ghana
Ghana, once called the Gold Coast is situated on the west coast of Africa. It was the first country south of the Sahara to attain independence and undoubtedly one of the countries in Africa with unique characteristics with regard to its strategic geographic position, rich political history and immense natural resources. It occupies a total land area of 238,539 square kilometers (92,100 square miles) and lies between latitudes 4 and 11.5 degrees north and longitudes 3.11 degrees west and 1.11 degrees east of the Equator. Ghana shares boundaries with Burkina Faso to the North, Ivory Coast to the West, Togo to the East and the Atlantic Ocean to the south. One remarkable feature about this country is its closeness to the centre of the world as the Greenwich Meridian passes through Tema; the industrial city of the country. Ghana has an estimated population of 23 million (2009 UN estimates) with English as its official language.There are two major seasons in the country, namely the rain season, (between April and October) and the dry season (November to March).
Ghana is one of the most naturally endowed countries in Africa based on its agricultural, mineral and water resources. 57% of the total land area of Ghana is suitable for agriculture and out of this In the immediate post-independence era, the government of Kwame Nkrumah adopted socialist development strategy under which the state was to be predominant in all aspects of economic policy making and implementation. This period was characterized by: 1. Import Licensing: A comprehensive system of import licensing was instituted in November 1961.

Exchange Controls: The Exchange Control Act of 1961 imposed all-embracing exchange
Controls over the entire range of international transactions.
3. Quantitative restrictions on interest rates.
4. Forced lending programs, including requirements for banks to lend to sectors of the economies that were considered priority sectors by the government.
The implementation of the provisions of the Exchange Control Act together with the system of import licensing moved Ghana significantly towards a closed economy.
Within the banking sector, the following developments were taking place: 1. The Bank of Ghana became the pivot of all international banking activities, whether these related to remittances, letters of credit, collections, allocation of foreign exchange, travel or tourism.
2. In response to the changing macroeconomic environment, the Bank of Ghana Act (1963) was passed. The Bank was required to submit a report to the government anytime the money supply growth exceeded 15% for any year, stating the reasons for such a rise and recommending measures to contain the associated inflationary pressures.
3. The Bank of Ghana was empowered to set ceilings on advances or investments by commercial banks and given powers to control the banking system.

The Ghanaian Financial System
The financial sector includes a broad range of institutions, with the banking system accounting for most of the sector's assets ( The Ghanaian financial sector is also confronted by a large number of development challenges.
Competition in the banking sector appears to be weak, and banks need to overcome several major obstacles before they can expand lending to small and medium-sized borrowers and provide more housing finance.
Moreover, the government's planned divestiture of Ghana Commercial Bank (GCB) has been suspended due to public concern about the potential foreign domination of Ghana's banking sector, and the attendant risk that the interests of ordinary Ghanaians could be overlooked. As for other sectors, the insurance industry and financial markets are undeveloped and have yet to play a meaningful role in intermediating medium-to long-term funds for the economy. To develop the financial sector, and given the importance of financing for SMEs, the authorities could: (i) improve corporate governance and financial reporting for Ghanaian firms; (ii) introduce a suitable legal framework to facilitate the operation of credit reference bureaus; (iii) address the inadequacies identified in property titles and weak judicial enforcement of foreclosure processes; and (iv) ensure timely payment of the government's obligations.

The Ghanaian Banking Industry
In the early 1960s, the Bank of Ghana provided capital for the establishment of development banks, which were new banking institutions, created with clearly specified roles. This was a response to the feeling that commercial banks -with their policies of "borrowing short and lending short" were not suited to the task of mobilizing funds to finance medium and long-term investments. The following banks were incorporated to undertake the financing of specific projects in industry, agriculture, housing and merchant banking, respectively:

Non-Bank Financial Sector
The non-bank financial sector was relatively undeveloped. n.a n.a Source: FSDI As can be seen in Table above

Statement of the Problem
In so far as economic growth performance and financial sector development are concerned in Ghana, there has been little empirical research done to investigate the direction of causality or to determine whether a relationship exists between economic growth and financial sector developments.
In the face of this research insufficiency in Ghana about the contributions of the financial sector to economic growth, this research work seeks to empirically establish whether financial development in Ghana for the period under consideration positively impacted economic growth and whether a long-run relationship exists between the two variables.

Objectives of the Study
In view of how important financial development is to economic growth, the general objective of this work is to examine the impact of financial development on economic growth in Ghana through the harnessing of financial savings for investment ventures.
The specific objectives of the study are as follows: i) Find out empirically the impact of financial development on economic growth with the view to establishing whether or not financial development causes growth or growth causes financial development in Ghana; and ii) Investigate whether there is a long-run relationship between financial development and economic growth in Ghana.

Significance of the Study
The study is very significant in the sense that it is one of the researches to be conducted on the subject matter and shall provide greater incentive that could raise the confidence level for policy-makers in addressing reform needs, especially in the financial sector that is very much underdeveloped and now facing numerous issues of confidence crisis, adequate resource pooling and transferring in terms of loan availability investment projects whose purpose will be to enhance the growth process of an emerging nation.

Organization of the Study
The study is structured into five (5) chapters with chapter 1 covering the introduction. Chapter 2 deals with the literature review followed by Chapter 3 that focuses on the theoretical frame work, methodology and model specification that capture the empirical relationship between financial sector development and economic growth, hypotheses to be tested and data sources. Chapter 4 hinges on estimation and interpretation of empirical results while Chapter 5 concludes the study.

THEORETICAL AND EMPIRICAL LITERATURE REVIEW
Nobel Prize Laureates and other influential economists disagree sharply about the role of the financial sector in economic growth. Finance is not even discussed in a collection of essays by the "pioneers of development economics," which includes three winners of the Nobel Prize (Meier and Seers, 1984). Nobel Laureate Robert Lucas (1988) dismisses finance as a major determinant of economic growth calling its role "over-stressed." Joan Robinson (1952, p. 86) famously argued that "where enterprise leads finance follows." From this perspective, finance does not cause growth; finance responds automatically to changing demands from the "real sector." At the other extreme, Nobel Laureate Merton Miller (1988, p.14) argues that, "[the idea] that financial markets contribute to economic growth is a proposition too obvious for serious discussion." Similarly, Bagehot (1873), Schumpeter (1912), Gurley and Shaw (1955), Goldsmith (1969), and McKinnon (1973 have all rejected the idea that the finance-growth nexus can be safely ignored without substantially impeding our understanding of economic growth.
Resolving the debate and advancing our understanding about the role of financial factors in economic growth will help distinguish among competing theories of the process of economic growth. Furthermore, information on the importance of finance in the growth process will affect the intensity with which researchers study the determinants, consequences, and evolution of financial systems. Finally, a better understanding of the finance-growth nexus may influence public policy choices since legal, regulatory, tax, and macroeconomic policies all shape the operation of financial systems.Given that the principal interest of this study is to examine whether financial developments in Ghana have brought about economic growth, it is prudent to look at reviewed literatures on financial development and economic growth, both theoretical and empirical. This is indeed the central focus of this chapter.

Financial Development and Economic Growth
In reviewing theoretical literature on financial sector development and economic growth, the importance of financial systems for economic growth, and the connections between the operation of the financial sector and economic growth will be looked at. Theoretical models show that financial instruments, markets, and institutions arise to mitigate the effects of information and transaction costs.
Furthermore, theory demonstrates that differences in how well financial systems reduce information and transaction costs may influence saving rates, investment decisions, technological innovation, and steady-state growth rates. A comparatively less-developed theoretical literature examines how changes in economic activity can also influence financial systems with dynamic implications for economic growth. In all of these models, therefore, the financial sector provides a real service: it ameliorates information and transactions costs. Thus, these models eliminate the veil that sometimes rises between the so-called real and financial sectors.
Financial Development refers to the costs of acquiring information, enforcing contracts, and making transactions create incentives for the emergence of particular types of financial contracts, markets and intermediaries. Different types and combinations of information, enforcement, and transaction costs in conjunction with different legal, regulatory, and tax systems have motivated distinct financial contracts, markets, and intermediaries across countries and throughout history.
In arising to ameliorate market frictions, financial systems naturally influence the allocation of resources across space and time (Merton and Bodie, 1995, p. 12). To organize a discussion of how financial systems influence savings and investment decisions and hence economic growth, the focus will be on five functions provided by the financial system. That is, in easing information, enforcement, and transactions costs, financial systems provide five broad categories of services to the economy. While there are other ways to classify the functions of the financial system (Merton, 1992;Merton and Bodie, 1995), the following five categories are helpful in organizing a review of the theoretical literature and tying this literature to the history of economic thought on finance and growth.
In particular, financial systems: Since many market frictions exist and since laws, regulations, and policies differ markedly across economies, improvements along any single dimension may have different implications for resource allocation depending on other frictions.
In terms of integrating the links between finance and growth theory, two general points are worth stressing from the onset. First, a large growth accounting literature suggests that physical capital accumulation per se does not account for much of economic growth. [ King andLevine(1994),Jorgensen(1995),and Easterly and Levine (2001)]. Thus, if finance is to explain economic growth; we need theories that describe how financial development influences resource allocation decisions in ways that foster productivity growth.
Second, there are two general ambiguities between economic growth and the emergence of financial arrangements that improve resource allocation and reduce risk. Specifically, higher returns ambiguously affect saving rates due to well-known income and substitution effects.
Thus, financial arrangements that improve resource allocation and lower risk may lower saving rates. In a growth model with physical capital externalities, therefore, financial development could retard economic growth and lower welfare if the drop in savings and the externality combine to produce a sufficiently large effect.
Producing information and allocating capital is critical in financial development. There are large costs associated with evaluating firms, managers, and market conditions. Individual savers may not have the ability to collect, process, and produce information on possible investments. Since savers will be reluctant to invest in activities about which there is little reliable information, high information costs may keep capital from flowing to its highest value use. Thus, while many models assume that capital flows toward the most profitable firms, this presupposes that investors have good information about firms, managers, and market conditions. (Bagehot, 1873, p. 53) Financial intermediaries may reduce the costs of acquiring and processing information and thereby improve resource allocation (Boyd and Prescott, 1986). Without intermediaries, each investor would face the large fixed cost associated with evaluating firms, managers, and economic conditions. Consequently, groups of individuals may form financial intermediaries that undertake the costly process of researching investment possibilities for others. In Boyd and Prescott (1986), financial intermediaries look like banks in that they accept deposits and make loans (Kashyap, Stein, and Rajan, 1998). Allen (1990), Bhattacharya and Pfeiderer (1985), and Ramakrishnan and Thakor (1984) also develop models where financial intermediaries arise to produce information on firms and sell this information to savers. Unlike in Boyd and Prescott (1986), however, the intermediary does not necessarily both mobilize savings and invest those funds in firms using debt contracts. For our purposes, the critical issue is that financial intermediaries -by economizing on information acquisition costs--improve the ex-ante assessment of investment opportunities with positive ramifications on resource allocation.
By improving information on firms, managers, and economic conditions, financial intermediaries can accelerate economic growth. Assuming that many entrepreneurs solicit capital and that capital is scarce, financial intermediaries that produce better information on firms will thereby fund more promising firms and induce a more efficient allocation of capital (Greenwood and Jovanovic, 1990).
Besides identifying the best production technologies, financial intermediaries may also boost the rate of technological innovation by identifying those entrepreneurs with the best chances of successfully initiating new goods and production processes (King and Levine, 1993b). This lies at the core of Joseph Schumpeter's (1912, p. 74) view of finance in the process of economic development: The banker, therefore, is not so much primarily a middleman … He authorizes people in the name of society … (to innovate).
Stock markets may also stimulate the production of information about firms. As markets become larger and more liquid, agents may have greater incentives to expend resources in researching firms because it is easier to profit from this information by trading in big and liquid markets (Grossman and Stiglitz, 1980) and more liquid (Kyle, 1984;and Holmstrom and Tirole, 1993).
Intuitively, with larger and more liquid markets, it is easier for an agent who has acquired information to disguise this private information and make money by trading in the market. Thus, larger more liquid markets will boost incentives to produce this valuable information with positive implications for capital allocation (Merton, 1987). While some models hint at the links between efficient markets, information, and steady-state growth (Aghion and Howitt, 1999), existing theories do not draw the connection between market liquidity, information production,and economic growth very tightly.
Monitoring firms and exerting corporate governance. Standard agency theory defines the corporate governance problem in terms of how equity and debt holders influence managers to act in the best interests of the providers of capital (e.g., Coase, 1937;Jensen and Meckling, 1976;Fama and Jensen, 1983a,b;Myers and Majluf, 1984). The absence of financial arrangements that enhance corporate governance may impede the mobilization of savings from disparate agents and thereby keep capital from flowing to profitable investments (Stiglitz and Weiss, 1983). To the extent that shareholders and creditors induce managers to maximize firm value, this will improve the efficiency with which firms allocate resources and make savers more willing to finance production and innovation.
Others, however, hold that large informational asymmetries between firm managers and potential investors, however, may (a) keep diffuse shareholders from effectively exerting corporate governance, (b) allow managers to use their effective control rights to pursue projects that benefit themselves rather than the firm (for citations, see Shleifer and Vishny, 1997), and therefore (c) hurt resource allocation unless alternative financial arrangements arise to improve corporate governance. Small shareholders frequently lack the expertise and incentives to monitor managers. General voting rights frequently do not work effectively because managers have enormous discretion over the flow of information. Furthermore, the elected representatives of shareholders, the boards of directors, often do not represent the interests of the minority shareholders because they are "captured by management." Also, in many countries, legal codes do not protect the rights of minority shareholders and legal systems frequently do not enforce the legal codes that are actually on the books concerning minority shareholder rights. Thus, the large costs associated with verifying managerial performance may impede diffuse equity holders from overseeing firm behavior effectively, with adverse effects on resource allocation and potentially economic growth.
Aghion, Dewatripont, and Rey (1999) link the use of debt contracts to growth. Using Jensen's "free cash flow argument," Aghion, Dewatripont, and Rey (1999) show that debt instruments reduce the amount of free cash available to firms. This in turn reduces managerial slack and accelerates the rate at which managers adopt new technologies.
In an extraordinarily influential paper, Diamond (1984) develops a model in which a financial intermediary improves corporate governance. The intermediary mobilizes the savings of many individuals and lends these resources to firms. This "delegated monitor" economizes on aggregate monitoring costs and eliminates the free-rider problem since the intermediary does the monitoring for all the investors. Furthermore, as financial intermediaries and firms develop longrun relationships; this can further lower information acquisition costs.
Financial intermediaries that reduce informational asymmetries may ease external financing constraints and facilitate better resource allocation. Boyd and Smith (1992) show that capital may flow from capital scarce regions to capital abundant regions if the capital abundant regions have financial intermediaries that are sufficiently more effective at reducing the costs of monitoring than the capital scarce regions. Thus, even though the physical product of capital is higher in the capital scarce areas, investors recognize that their actual returns depend crucially on the monitoring performed by intermediaries. Poor financial intermediation will lead to suboptimal allocation of capital.
In terms of economic growth, Bencivenga and Smith (1993) show that financial intermediaries that improve corporate governance by economizing on monitoring costs will reduce credit rationing and thereby boost productivity, capital accumulation, and growth. Sussman (1993) and Harrison, Sussman, and Zeira (1999) , 1982;and Jensen and Murphy, 1990). Similarly, if takeovers are easier in well-developed stock markets and if managers of under-performing firms are fired following a takeover, then better stock markets can promote better corporate control by easing takeovers of poorly managed firms. The threat of a takeover will help align managerial incentives with those of the owners (Scharfstein, 1988;and Stein, 1988). Many, however, argue that well-functioning stock markets actually hurt corporate governance.
Risk amelioration. With information and transactions costs, financial contracts, markets and intermediaries may arise to ease the trading, hedging, and pooling of risk with implications for resource allocation and growth. This discussion is divided into three categories: cross-sectional risk diversification, inter-temporal risk sharing, and liquidity risk.
Traditional finance theory focuses on cross-sectional diversification of risk. Financial systems may mitigate the risks associated with individual projects, firms, industries, regions, countries, etc. Banks, mutual funds, and securities markets all provide vehicles for trading, pooling, and diversifying risk. The financial system's ability to provide risk diversification services can affect long-run economic growth by altering resource allocation and the saving rates. The basic intuition is straightforward. While savers generally do not like risk, high-return projects tend to be riskier than low return projects. Thus, financial markets that ease risk diversification tend to induce a portfolio shift toward projects with higher expected returns (Gurley and Shaw, 1955;Patrick, 1966;Greenwood and Jovanovic, 1990;Saint-Paul 1992;Devereux and Smith, 1994;and Obstfeld, 1994).
Acemoglu and Zilibotti (1997) carefully model the links between cross-sectional risk, diversification, and growth. They note that (i) high-return, risky projects are frequently indivisible and require a large initial investment, (ii) people dislike risk, (iii) there are lowerreturn, safe projects, and (iv) capital is scare. In the absence of financial arrangements that allow agents to hold diversified portfolios, agents will avoid the high return, risky projects because they require agents to invest disproportionately in a risky endeavor. Acemoglu and Zilibotti (1997) endogenize the degree of diversification and examine the impact of diversification choices on economic growth. Financial systems that allow agents to hold a diversified portfolio of risky projects will permit society to invest more in high-return projects with positive implications for growth.
In terms of technological change, King and Levine (1993b) show that cross-sectional risk diversification can stimulate innovative activity. Agents are continuously trying to make technological advances to gain a profitable market niche. Engaging in innovation is risky, however. The ability to hold a diversified portfolio of innovative projects reduces risk and promotes investment in growth-enhancing innovative activities (with sufficiently risk averse agents). Thus, financial systems that ease risk diversification can accelerate technological change and economic growth.
Besides cross-sectional risk diversification, financial systems may improve intertemporal risk sharing. In examining the connection between cross-sectional risk sharing and growth, theory has tended to focus on the role of markets, rather than intermediaries. However, in examining intertemporal risk sharing, theory has focused on the advantageous role of intermediaries in easing intertemporal risk smoothing (Allen and Gale, 1997,). Risks that cannot be diversified at a particular point in time, such as macroeconomic shocks, can be diversified across generations.
Long-lived intermediaries can facilitate intergenerational risk sharing by investing with a longrun perspective and offering returns that are relatively low in boom times and relatively high in slack times. While this type of risk sharing is theoretically possible with markets, intermediaries may increase the feasibility of intertemporal risk sharing by lowering contracting and transactions costs.
A third type of risk is liquidity risk. Liquidity is the ease and speed with which agents can convert financial instruments into purchasing power at agreed prices. Liquidity risk arises due to the uncertainties associated with converting assets into a medium of exchange. Informational asymmetries and transaction costs may inhibit liquidity and intensify liquidity risk. These frictions create incentives for the emergence of financial markets and institutions that augment liquidity.
In Diamond and Dybvig's (1983) seminal model of liquidity, a fraction of savers receive shocks after choosing between two investments: an illiquid, high-return project and a liquid, low-return project. Those receiving shocks want access to their savings before the illiquid project produces.
This risk creates incentives for investing in the liquid, low-return projects. The model assumes that it is prohibitively costly to verify whether another individual has received a shock or not.
This information cost assumption rules out state-contingent insurance contracts and creates an incentive for financial markets --markets where individuals issue and trade securities --to emerge.
Another form of liquidity involves firm access to credit. Holmstrom and Tirole (1998)  In light of the transaction and information costs associated with mobilizing savings from many agents, numerous financial arrangements may arise to mitigate these frictions and facilitate pooling. Specifically, mobilization may involve multiple bilateral contracts between productive units raising capital and agents with surplus resources. The joint stock company in which many individuals invest in a new legal entity, the firm, represents a prime example of multiple bilateral mobilizations.
To economize on the costs associated with multiple bilateral contracts, pooling may also occur through intermediaries, where thousands of investors entrust their wealth to intermediaries that invest in hundreds of firms (Sirri and Tufano 1995, p. 83). For this to occur, "mobilizers" have to convince savers of the soundness of the investments (Boyd and Smith, 1992). Toward this end, intermediaries worry about establishing stellar reputations, so that savers feel comfortable about entrusting their savings to the intermediary (DeLong, 1991;and Lamoreaux, 1995).
Financial systems that are more effective at pooling the savings of individuals can profoundly affect economic development. Besides the direct effect of better savings 20 mobilization on capital accumulation, better savings mobilization can improve resource allocation and boost technological innovation. Without access to multiple investors, many production processes would be constrained to economically inefficient scales (Sirri and Tufano, 1995). (Bagehot 1873, p. 3-4) argued that a major difference between England and poorer countries was that in England the financial system could mobilize resources for "immense works." Bagehot was very explicit in noting that it was not the national savings rate per se, it was the ability to pool society's resources and allocate those savings toward the most productive ends. Furthermore, mobilization frequently involves the creation of small denomination instruments. These instruments provide opportunities for households to hold diversified portfolios (Sirri and Tufano, 1995). Acemoglu and Zilibotti (1997) show that with large, indivisible projects, financial arrangements that mobilize savings from many diverse individuals and invest in a diversified portfolio of risky projects facilitate a reallocation of investment toward higher return activities with positive ramifications on economic growth.
Easing Exchange. Financial arrangements that lower transaction costs can promote specialization, technological innovation and growth. The links between facilitating transactions, specialization, innovation, and economic growth were core elements of Adam Smith's (1776) Wealth of Nations. He argued that division of labor --specialization -is the principal factor underlying productivity improvements. With greater specialization, workers are more likely to invent better machines or production processes (Smith, 1776, p. 3).
Men are much more likely to discover easier and readier methods of attaining any object, when the whole attention of their minds is directed towards that single object, than when it is dissipated among a great variety of things.
Smith (1776) focused on the role of money in lowering transaction costs, permitting greater specialization, and fostering technological innovation. Information costs, however, may also motivate the emergence of money. Since it is costly to evaluate the attributes of goods, barter exchange is very costly. Thus, an easily recognizable medium of exchange may arise to facilitate exchange (King and Plosser, 1986;and Williamson and Wright, 1994). The drop in transaction and information costs is not necessarily a one-time fall when economies move to money, however. Transaction and information costs may continue to fall through financial innovation. Greenwood and Smith (1996) have modeled the connections between exchange, specialization, and innovation. More specialization requires more transactions. Since each transaction is costly, financial arrangements that lower transaction costs will facilitate greater specialization. In this way, markets that promote exchange encourage productivity gains. There may also be feedback from these productivity gains to financial market development. If there are fixed costs associated with establishing markets, then higher income per capita implies that these fixed costs are less burdensome as a share of per capita income. Thus, economic development can spur the development of financial markets.

Endogenous Growth Literature
In the Solow model growth can arise only through continuous changes in technology and therefore is purely exogenous. Savings by itself does not generate growth. One recent avenue of research has been to question the relevance of the exogeneity assumption is the Solow model. 3 Two broad approaches have been developed, one that sees all inputs reproducible and the other that is based on externalities. In one particular case the externalities take the form of human capital building. In both approaches, the savings rate plays a key role in the growth of a capital and output per worker.
The first approach is the so-called AK-model (Rebelo, 1991). It is based on the hypothesis that all inputs are reproducible and in particular the state of knowledge through research and development. Therefore, the diminishing marginal productivity of capital, which in the neoclassical model leads to constant steady state values of capital and output per worker, is here compensated by an increasing quality of machinery. It can then be shown that using the same investment and saving hypotheses as in the neo-classical model, the steady state rate of growth of capital per worker in the AK-model is.
G y = g k = s A-n (2.10) Which implies that, for constant savings rate and population growth, s A>n, capital per worker can grow without bound. Moreover, an increase in the savings rate permanently raises the rate of growth of capital and output per worker.
The second approach introduces the externalities in the production process such that an increase in the output level of one firm affects positively factor productivity in another firm. Not all types of externalities, however, necessary linked to the production process and one type of externalities which is of particular interest concerns labor (Lucas, 1998 andMankiw et al. 1992 where y and k are the same as in the Solow model, say output and capital per worker and h is human capital per worker. In Solow's model, the quantity of labor available to the economy is determined by population growth and that there is no quality of skill effect, this model, output is consumed and saved as before except that there are two ways to save. 4 A fraction is saved for capital accumulation (k=sy) as before and another fraction q is saved to increase human capital quality (h=qy). In that case, the steady state, y, k and h grow at the same rate which is determined by the two savings rates such that, 4 Population is assumed to be constant here to clearly isolate the effect of endogenous labor g k = s a q 1-a =g y The major implication of this equation is that both savings rates have growth rate effect and not just level effects. As a consequence, growth is no longer determined by arbitrary physical or human capital. Clearly, this conclusion leaves room for policies that stimulate savings in either factor of production to affect the growth of the economy. As an illustration, Robertson (2000) for example evaluates the trade off between polices stimulate human capital building and polices that improve the productivity of physical capital in developing economies.
The major conclusions that can be draw from growth models with no explicit modeling of the financial sector can be summarized as follows: 1) The neo-classical theory of growth (i.e. the Solow growth model) shows that the savings rate has an effect on the level of capital per worker but not on its growth rate.an increase in savings generates accumulation of capital temporarily until a new steady state level of capital per capital per worker is reached. Then savings reverts to its role of providing capital for the new workers in every period on the basis of existing capital/labor ratio.
2) The AK-model of endogenous growth states that if improvement in the quality of capital through research and development can compensate for decreasing marginal returns of capital, the savings rate will affect the growth rate also in steady state; and 3) The growth model with endogenous labour and skill determination postulates that two types of savings matter for growth: saving for investment in physical capital and savings for investment in human capital. An increase in any of the two savings rates will increase the growth rate of per capital output.
Hence, without introducing financial markets explicitly, there are ground to believe that incentives of the population to save and more efficient channeling of saving can affect growth.
The latest developments in endogenous growth literature have shown how financial intermediaries affect the growth process directly.

Development Hypothesis
The development hypothesis ascribes considerable importance to the financial system in economic development. Lack of a developed financial system restricts economic growth and therefore government policy should be directed towards encouraging the growth of the financial system. This hypothesis views the financial systems as a necessary input into the development process (Kitchen, 1986).
Financial development has a dual effect on economic growth. First, the development of the domestic financial market may enhance the efficiency of capital accumulation and on the other hand, financial intermediation can contribute to raising the savings rate and thus investment rate.
In the literature, the proxy of financial development on economic growth is the level of real interest rate and the financial intermediation ratio (measure of the money supply as a ratio of total GDP). The money supply selected must reflect to a greater extent the development of the financial system and the potential for intermediation. Measurement of financial development seems more controversial because countries differ in the institutional environment and have drastically different financial structures according to their development stages. Two alternative proxies of financial development are employed. The first proxy is the currency ratio that is defined as the ratio of currency to the narrow definition of money, M1 that consists of currency outside banks plus demand deposits. A decrease in the currency ratio reflects real growth in the economy, especially at the economy's early stages, as there exists more diversification of financial assets and liabilities and more transactions will be carried out in the form of nonfinancial assets and liabilities and more transactions will be carried out in the form of noncurrency. The second proxy is the ratio of M2 (which consists of M1 plus call, savings, notice and time deposits) to nominal GDP or GNP that is widely regarded as a monetization variable.
The monetization variable is designed to show the real size of financial sector of a growing economy. We would expect the ratio to increase over time if the financial sector develops faster than the real sector. Roubini and Sala-i-Martin (1992) analyze the relationship between financial intermediation and growth by emphasizing the role of government policy. They develop a model in which financial repression becomes a tool that governments may use to broaden the abuse of the inflation tax. Thus financial repression yields higher seigniorage to finance government expenditures.
In the context of Fry (1988) a positive real interest rate stimulates financial savings and financial intermediation, thereby increasing the supply of credit to the private sector thus stimulating investment and growth.

Neo-Classical Production Function Hypothesis
The basis of this theoretical framework is a two-sector production function, with the two sector being developed by Feder (1983) in his study on exports relative to the framework employed, the two sectors consist of the financial and non-financial sector or the real sector as outlined by Odedukun (1998). The model shows how growth of the financial sector affects economic growth.
Equation 2.14 below descries the production function where output of the financial sector (F) depends of the quantity of labor (L F ) and capital (K F ) employed: F = F (L F, K F ) engaged there. In addition, because of the possibility of externalities or positive external effects which the output of the financial sector (i.e. financial intermediation) might have on the real sector, the real sector's output can be described as a function of the financial sectors' output so that the production function in the real sector is specified as equation 2.14 seen below: Because only two sectors are recognized to exist, we have the relationships described below, where Y, L and K denote the GDP, total labour force, and total capital stock respectively: In view of the above, Odedokun (1998)  Where GY is real GDP growth rate; GL, labour force growth rates ;GK ,the growth rate of capital stock; GX ,the growth rate of exports; GF being the growth rate of financial sector's output and λ,β, and θ are coefficients.

Evidence on the Direction of Causation
There are two possible causal relationships between financial development and economic growth. Patrick (1966) identified the two relationships as the demand following' and the 'supply leading'. The demand following views demand for financial services as dependent upon the growth of real output and upon the commercialization and financial institutions, their financial assets and liabilities and related financial services are a response to the demand for these services by investors and savers in the economy. As real national income grows, there will be more demand by enterprise for external funds, and hence a need in the increase in the level of financial intermediation so as to transfer saving to fast growing industries from the slow-growing ones. By so doing the expansion of the financial system is indeed a consequence of real economic growth.
The supply leading causal relationship has two functions. These are, to transfer resources form traditional low-growth sector to the modern high-growth sectors and to promote and stimulate a entrepreneurial response in the modern sector (Patrick, 1966). This implies that the creation of financial institutions and their services occurs in advance of the demand for them. Montiel (1997) used AK production to formalize the supply-leading hypothesis. This is a production were aggregate output is a linear function of the aggregate physical capital stock. Where Y is the growth rate of real GDP.
Consequently innovations in financial development can affect economic growth through three main channels. These are, through improved efficiency of intermediation (increased Ф), improved efficiency of the capital stock, measured by increases in the parameter A and an increase in the saving rate s. As a result the supply-leading hypothesis proposes that financial development causes economic growth.

Empirical Literature
Early empirical studies simply used a case study approach of relating cross-country growth rate with the level of financial development, e.g. IMF (1983) and McKinnon (1973). Others consist of just the examination of the direction of causation between economic growth and the level or growth of financial intermediation as in the case of studies reported by Jung (1986) and Odedokun (1992a), among others. Some others like Fritz (1984); Jao (1976); and Lanyi and Saracoglu (1983); adopted the approach of testing for the effects of financial intermediation variables (e.g. financial depth and the growth of real money balances) in the economic growth equations. Other recent empirical studies that were based on a similar approach include Gertlerand Rose (1991); Ghani (1992); King and Levine (1993a;1993  connection between theory and measurement in much of the finance and growth literature.
While fully recognizing this problem, many of the biggest advances in empirical studies of finance and growth have been methodological. The discussion is organized around econometric approaches. While serious improvements have been made in measuring financial development, future research that more concretely links the concepts from theory with data will substantively improve our understanding of the finance and growth link.
Empirical work done by some economists (Goldsmith, 1969) and McKinnon (1973) illustrates close ties between financial and economic development for a few countries. Other studies (Khan and Lintel, 1999), been M'rad (2000)  Demirgue- Kunt and Vojislav (1996) used micro data to develop a test of the influence of financial development on economic growth. Using firm level data, they estimated the proportion of firms whose rate of growth exceeds the growth that could have been supported only by internal resources; they run a cross-country regression and find that this proportion is positively related to the stock market turnover and to a measure of law enforcement. In other worlds, access 5 Occurs when a policyholder can take care to reduce his/her probability of a loss, but the insurance company cannot distinguish between loss due to careless and loss due to a random event that is the policyholder could have prevented.
to resources through financial markets leads to higher economic growth. Jung (1986) selected 56 countries having at least 15 annual observations on all variables of which 19 countries were developed countries. For each country they ran four (4) regressions two (2) using the case of currency ratio and income and the other two(2) of the monetization and income. In order to avoid the possible serial correlation in the residuals autocorrelation of the residuals was used in all regressions. Where → symbolizes "causes", C denoting the currency ratio and M representing monetization variable. We have regressions for the following: It was found out that the number of cases for C → Y and M→ Y was 22 and 34 respectively, slightly exceeding that of Y→ C and Y→ M, which was 20 and 28. A statistical test (binomial) indicates that the hypothesis of equal probability for the two causal relations, regardless of which measure of financial development is used, is not rejected even with more than 40% type one error. Jung (1986) found out that there exists some evidence indicating that LDCs have a supplyleading causality pattern more frequently than a demand-following pattern.
In the context of Jung (1986), LDCs are characterized by the causal direction running from financial to economic development and the developed countries (DCs) by the reverse causal direction, regardless of which causality concept is employed. On the other hand, monetization variable does not appear to distinguish the DCs from the LDCs.
In addition to cross-country studies, Khan and Senhaji (2000) carried out a study on 84 countries of the period  using four measures of financial development. These include liquid liabilities of banks and non-bank institutions as a share of GDP, the ratio of domestic credit and the private credit as a ratio of GDP. The regression analysis revealed that financial development is an important determinant in the cross-country growth differences. Furthermore they found out that even though the level of financial development explains the level of growth, it is precisely in the financial structures that are related to changes in economic growth for a given country.
In line with Odedokun (1998), most existing empirical studies on the role of finance on economic growth have no framework with standard theoretical underpinnings. They generally estimate regression equations of the following form: Economic Growth = f (Financial Development) (2.25) He later identified a framework that would accommodate some other repressors in Equation   2.25, based on the conventional neo-classical one sector aggregate production function in which financial development constitutes an input as seen below: Where Y is aggregate output, L is labor force, K is capital stock, F is the measure of the level of financial development, Z is a vector of the other factor that can be regarded as inputs in the aggregate production process and t is time period.
He carried out a study involving 71 LDCs using the model to test the effect of financial development on economic growth. In summary the study showed several findings. Firstly, Odedokun found out that financial intermediation promotes economic growth and growth promoting effects of financial intermediation are more predominant in the low-income than in the high-income developing countries. Furthermore, he found out that the effect on growth of the financial development variable is positive and significant and occurs only in over 45% of the cases. The negative effects of financial development on growth are generally insignificant and occur in only 15% of the cases. The results are completed by panel data estimates, which show that the coefficients of financial variables are statistically significant in all the equations.
Demetrades and Khaled (1996) employed times series vector autoregression (VAR) on a sample of 16 developing countries and examined the relationship between financial developments and economic growth. They used two proxies to measure financial development, that is, ratio of bank deposit liabilities to nominal GDP and the ratio of bank claims on the private sector to nominal GDP. Their findings provide little support that finance is a leading sector in economic growth.

Matsheka (1997) carried a study on the financing of economic growth and development in
Botswana. It is devoted to the investigation of the financial aspects of economic growth and development in the economy, that is, to investigate the process by which domestic resources have been mobilized to promote capital formation in the economy of Botswana. Under the same study, the role of the state, through taxation, in the financing of economic development was also considered.
From the study, he found that both real deposit rate and export growth affect economic growth positively but insignificantly whereas private sector credit and government saving affect it positively and insignificantly. Overall, the study found that increases in interest rates lead to higher savings, investment and hence economic growth, that is, through financial liberalization an economy can achieve positive economic growth rates. In addition, he did not use recent developments in econometric techniques such as cointegration and unit root tests; whereas this study makes use of them. Odedokun (1998) conducted another study that regards monetary asset as a vital input in the production process. He applied the equation that he adopted form Feder (1983) and added exports as an input in the production function. The function is given below as:

GY = λGK +ßGL +ϴGF +ɣGX (2.28)
Where GY is the growth rate of real GDP, GL is the growth rate of labor force, and GF is the growth rate of financial sector's output, GK is the growth rate of real capital stock, GX is the growth rate of export, λ,ß,ϴ,and ɣ are coefficients of capital stock, labor force, financial sector's output and exports, respectively.
He applied the model to cross section data for the period 1970-1980 and 1980-1990 covering developing countries. Two discoveries were made, that; growth of financial aggregates in real terms has positive impacts on economic growth of developing countries irrespective of the economic development attained and that financial deepening promotes economic growth in the low-income developing countries but has no perceptible effect in the high-income developing countries. Khan and Luintel (1999) examined the long run causality relationship between financial development and economic growth in a multivariate autoregression (VAR) setting using data from 10 countries. They used annual data, which had a time span between 36 and41 years. The VAR consisted of 4 variables; financial depth (FD) measured as a ratio of total populations (LYP), the logarithm of real per capital stock (LKP) and real interest rate (R). They found bidirectional causality between financial development and economic growth in all the sample countries they analyzed.
Kar and Pentecost (2000) examined the causal relation between financial development and economic growth in Turkey using five alternative proxies for financial development. They used the Granger Causality test to estimate the causality relationship. Their study was based on annual data that covered the period 1963-1995. Proxies that were used included ratio of broad money to gross national product (GNP), ratio of bank deposit liabilities to GNP, share of private sector credits in the domestic credit and the ratio of domestic to GNP.
In running the regressions, their statistical tests failed to reveal which side the causality was running in Turkey. They failed to determine whether the causality was a 'supply-leading' or a 'demand following' relationship. Their conclusion was that the direction of causality between financial development and economic growth is sensitive to the choice of measurement for financial development.
Kalima (2001)  previous studies. According to the study, the size of financial sector was negatively related to economic growth. Inflation was found to have a positive effect on growth. This was contradictory to a priori expectations where it was expected to have a negative effect. Even though the results are mixed one cannot clearly ascertain the cause but in her comment on the limitations of the study was the unavailability of some data. Amusa (2001) investigated the effects of financial development on South Africa's economic growth. In this study, he looked at the financial depth-economic growth link in South Africa using an endogenous growth model. He adopted the model that was used by King and Levine (1993). The model included financial development indicators covering both banking and securities market. The following indicators were used to proxy financial for financial depth in the banking sector; ratio of commercial bank assets to the Reserve bank plus commercials bank assets, the ratio of commercial liabilities to GDP and the ratio of private sector to GDP. For the securities market he used stock market capitalization to GDP and the ratio of stock plus bond market capitalization to GDP. In his conclusion, he found out that financial development exerts a positive and statistically significant effect on economic growth in South Africa. He further noted that the effect on economic growth varies with each of the different measures of financial development.
Waqabaca (2004)  However, findings from other studies are unclear and ambiguous due, in part, to limitation of data and at times to statistical bias. The other problem has been the in conclusion of data and at times to statistical bias. The other problem has been the inconclusiveness of the direction of causality between economic growth and financial development relative to uni-directional causality or bi-directional causality. In the context of Khan and Luintel (1999), this is dependent on the proxies used. In their study the results showed strong support for the supply-leading hypothesis when monetary aggregates and monetization variables were used as proxies for financing growth.

THEORETICAL FRAMEWORK AND METHODOLOGY
The chapter under consideration discussions the theoretical framework as well as methodology involved in this study on financial sector development and economic growth in Ghana. The first aspect of this chapter deals with a theoretical framework that links the theory behind financial development and its impact on economic growth. The other aspects deal with the model adopted, definitions of variables, estimation technique or methodology, scope and limitations of the study, data used and sources.

Theoretical Framework
The theoretical framework employed in this study is patterned after an adaptation of the model used by Odedokun, which was developed by Feder (1983) for evaluating the impact of export expansion on economic growth. Basically, the basis of Odedokun's theoretical frame work is a two-sector production, with the two sectors being the financial and non-financial (or real) ones.
The output of the financial sector (F) depends on the quantity of labour (L F ) employed in this sector so that the production function in the sector can be seen below as F= F (L F , K F ) (3.10) The output of the non-financial sector also depends on the quantities of labour (L R ) and capital (K R ) engaged there. In addition, because of the possibility of externalities or positive external effects that the output of the financial sector (i.e. financial intermediation) might have on the real sector, the real sectors' output can be described as a function of the financial sector's output so that the production function in the real sector is specified as equation 3.11 below: Because only two sectors are recognized to exist, we have the relationships described below, where total output (Y) is made of output from the financial sector and non-financial sector and non-financial sector total as well as labour force (L) and total capital stock (K): It can be easily observed that if δ> 0, it is the financial sector where the marginal productivities of both factor inputs are higher, with the reverse being the case if δ<0. A manipulation of the equations 3.12a, 3.12b, and 3.12c as done by Feder (1983), yields the aggregate output (or real GDP) growth equation thus: Where the letter G before a variable indicates its growth rate so that GY, GL, GF, and GK are the growth rates of real GDP, labour force, financial sector's output and the real capital stock respectively while 1 is the investment or change in capital stock respectively while I is the The two equations above are equivalent. The only exception being that while investment /GDP (I/Y) ratio features in the second equation so that I signifies the marginal productivity of capital stock, it is the growth rate of capital stock (dK/K or GK) which features in the first equation instead so that the λ represents the elasticity of real GDP with respect to the capital stock. By assuming that θ is constant across the small observations, equation 3.13a can be rotationally simplified to the following (with the same being equally applicable to equation 3.13b which we henceforth refrain form writing to any longer, for the sake of brevity): .14a is amenable to estimation, with estimates being separately provided for δ (or inter-sectoral factor productivity differential) and θ (a measure of the external effects of increase in financial intermediation on the real sector).if there inter-sectoralproductivity differential so that δ=0, it can be seen that the coefficient of GF would be equal to that of GF (F/Y).
By setting value of π in equation 3.14a to zero, we shall have a special case of this equation that can be written as: .14b is the form that can easily be arrived at simply introducing financial intermediation as an input in the aggregate production function, which would then take the form: Y=Y (L,K,F.). One advantage of this equation over equation 3.14a is that while collinearity is likely to exist between GF(F/Y) and GF, equation 3.14a may reduce the precision of the However, equations 3.14a and 3.14a and 3.14b should be regarded as being complementary to one another. The particular form of equation 3.14b actually estimated in Odedokun's study was arrived at regarding export as an input in the production process to obtain the following economic growth equation: GY = λGK +βGL + θGF + γGX (3.14c) Where GX is the export growth and γ is its coefficient.
Instead of considering growth of financial intermediation (G/F) to be the determinant of economic growth as in equation 3.14c, it is the level of financial depth (F/Y) that is sometimes considered as the appropriate proxy of financial intermediation that affects economic growth equations accordingly, another variant of economic growth, equation 3.14c was estimated but with the financial intermediation growth regressor (GF) now replaced by the level of financial depth (F/Y) thus: Where øis the coefficient of the level of financial depth F/Y.
Finally, it can be postulated that it is the marginal productivity of financial services employed as an input in the real sector (i.e. ∂R/∂F from equation 3.11) which is constant across the sample of observations, instead of the elasticity of real sector's output with respect to these financial services (i.e. instead of θ or ∂R/∂F multiplied by F/R ratio) that was postulated in deriving equation 3.14a. In this case, equation 3.13 a can be written as: The derivation of equation 3.15 from 3.13a in provided in Feder (1983). As was done in the case of equation of equation 3.13a, Odedokun presented 3.15 in notationally simplifies equation as: Where ø = δ / (1+δ) +∂R/∂F, which is constant across the sample observations since δ is defined to be constant in equation 3.12d and ∂R/∂F also is now being postulated to be a constant, instead of θ.

Methodology
The study adopts both descriptive and empirical analytical approaches. A test for unit root or stationarity will be conducted so as to establish the order to integration of the variables with the view to finding out whether there exists evidence of cointegration amongst the variables using the Johnansen and Juselius (1990) cointegration procedure where a determination of the existence of a long-run relationship between economic growth and financial sector development will be established. Moreover, the causal relationship (be it uni-directional or bi-directional) will be examined with the help of the Grander-causality approach. Estimation of the model will be done using cointegration and Error Correction Modeling (ECM) to check the availability of long run relationship among the variable that will be found to be most important determinant of economic growth.

Model Specification
The study adopts a model (equation 3.17 below) based on a modification of the model used by Odedokun (1998) where he used two alternative measures of financial intermediation, the stock of domestic credit to the private sector and the stock of liquid liabilities while this study uses the stock of domestic credit to the private sector as a proxy to investigate the role of financial sector development on economic growth in Ghana (country specific).
The GDP growth equation to be estimated in this study is seen below as: LnGDP= α o +α 1 Ln CPS + α 2 LnK + α 3 LnX+ α 4 LnL + ɛ (3.17) Where α 0 , α 1, α 2, α 3, α 4, α 5 are coefficients, ɛ is the error term assumed to be Gaussian white noise, GDP stands for gross domestic product (GDP), CPS is domestic credit to the private sector, K is capital stock, L being labor force, X represents exports and Ln stands for natural logarithm.

Expected Impact of Model Variables on Growth
LnX, exports affects economic growth through investment. An increase in export earnings can boost a country's import capacity where capital goods are bought with less difficulty. Capital investment goods are important because they are used in domestic investment. The expected sign between export and economic growth is positive. LnCPS, the level of domestic credit to the private sector excludes credit to the public sector and it represents more accurately the role of financial intermediaries in channeling funds to private market participants. In developing countries, most financial developments have occurred within the banking system and the use of CPS as a proxy for financial sector development seems appropriate (De Gregorio and Giudotti, 1995). CPS is expected to show a positive relationship on economic growth.
LnK, capital stock accumulation plays a major role in economic growth. In this study, gross capital formation (GCF) is used as a proxy for capital stock. It comprises of the government and the private sector. The coefficient of capital stock is expected to have a positive sign demonstrating that there is a positive relationship between capital stock and economic growth.
LnL, labour force is expected to affect economic growth in two ways. When there is full employment, a positive relationship is obviously expected. However, when there is high unemployment level, labour force is expected to have a negative impact on economic growth.

Hypotheses
The following specific hypotheses will be tested: (1a) the null that there is no positive relationship between financial sector development and economic growth, (1b) the alternate that there is a positive between financial sector development and economic growth; and (2a) the null that there is no long-run relationship between financial sector development and economic growth, (2b) the alternate that there is a long-run relationship between financial sector development and economic growth.

Data Exploration Techniques
Time series data for the period 1980-2009 are employed in this study and OLS applied for estimation purposes. Both Stationarity and Cointegration tests, recent developments in time series econometrics, would be applied during the estimation process.

Analysis of Stationarity
A stationary stochastic process is a process whose characteristics and structure are invariant with respect to time. Defined in terms of stochastic process, stationary implied that the mean and variance are constant over time and the value of covariance between two time periods depend only on the distance or lag between the two time periods and not on the actual time at which the covariance is computed (Gujurati, 1995). If the series is non-stationary, the series has to be made stationary to stationary can be done through differencing the non-stationary series.
A non-stationary series is referred to as homogenous. The number of times the original series must be differenced before stationarity is called the order of homogeneity. Empirical work based on time series data assumes that the underlying time series is stationary. But in the real world, many economic time series are non-stationary, meaning that the mean and the variance depend on time. In this case, regressing one time series variable on another may result not only in a very high t ratios and high R 2 although there might be no meaningful relationship between the two but also it may lead to a very low Durbin-Watson statistic. This problem results in spurious regression, which can lead to meaningless (nonsense) economic interpretation as well as forecast.
That is, we can show the models containing non-stationary lead to a problem of spurious regression, wherein the results obtained suggest that there are statistically significant relationships between the variables in the regression model when in fact all that is obtained is evidence of contemporaneous correlations rather than meaningful casual relations. Under such circumstance, the standard statistical tests based on t, F and other statistics are invalid even though they may show promising diagnostic test statistics.

Unit Root Test for stationarity
The issue of non-stationary series is of major concern to any econometric investigation being conducted by a researcher. Stationary ties series is one whose basic properties do not change over time while a non-stationary variable has some sort of upward or downward trend (Studenmund, 1997). For example, time series variable X 1 is said to be stationary if: 1) the mean of X t is constant overtime 2) variance of X t is constant overtime 3) yhe simple correlation coefficient between Xt and X t-k depends on the length of the lag (k) and on no other variables (for all k).
Consider equation 3.18, the popularly used test for testing stationarity in time series models below: Whereu t is the stochastic error term/white noise error term. Equation 3.18 is a first order or AR (1) regression in that we regress the value of Y at time t or its value at time t-1,if the coefficient of Y t-1 is equal to 1,we face what is known as the unit root problem(non-stationary situation). Assume: If equation 3.19 is run and we find that ρ=1, then the stochastic variables Y t has a unit root and thus the time series data will be known as a random walk. A time series data that has a unit root is known as a random walk, which is an example of non-stationary time series.
If a time series data is differenced once and the differenced series is stationary, we say that the original random walk series is integrated of order 1, I(1), and if it has to be differenced twice before becoming stationary, the original series is integrated of order 2, I(2). Generally, if a time series is to be differenced d times before it becomes stationary, it is integrated o order d, I(d). The presence of an integrated time series of order 1 or greater, results in a non-stationary time series, which often results in spurious correlation amongst economic variables and makes the t and F statistics statistically unreliable for significance of parameters.
There are several ways of detecting whether time series data is stationary. First, is to visually examine the data, where a quick glance for many times series data will tell you that the mean of a variable is increasing dramatically over time, implying that the series is non-stationary. Second, use the t test and the F test to find out whether the autocorrelation function (ACFs) for a variable tend to zero as the length of the lag increases. If the ACFs tend to zero fairly quickly, the variable tend to zero fairly quickly, the variable is stationary, but if not, then the variable is nonstationary. Last, the most popular method used for testing non-stationarity is the Dickey Fuller (DF) test that was designed by Dickey and Fuller (1979). The test normally examines the hypothesis that the variable in question has unit root and as a result is likely to benefit from being expressed in first difference form.
The following equations are estimated in order to run a DF test: ∆Y t = δY t -1 + u t (3.20) ∆Y t = β o + δY t-1 + u t (3.21) Where t is the time variable and in each case the null hypotheses is that δ = 0 signifying a unit root. In the case where all the error term u t is autocorrelated, the equation can be modifies as ∆Y t-1 = β o + β 1 t+δY t-1 + α i Σ∆Y t-1 + ɛ t (3.23) Under the situation where the DF test is applied to equation 3.23 it is called the Augmented Dickey-Fuller (ADF) test. The ADF test involves adding an unknown number of lagged first differences of the dependent variable to capture autocoreelated-ommitted variables that would otherwise enter the error term, u t.

Granger Causality
The detection of causal relationship among a set of variables is one of the objectives of empirical research. Granger (1969) formulated a procedure for detecting a causal relationship among the variables. The concept of causality in the Granger sense is mainly based on the following two assumptions: That future cannot cause past, it is the past and the present which cause future and that detection of causality is only possible between two stochastic process that should be linear and covariance stationary.
In the Granger sense then, a series X t said to cause Y t , if Y t is better predicted by model using the past values of X t and Y t than by a model using Y t alone (Pindyck and Rubinfeld, 1991). Inclusion of variable X t enhances the predictive power to the model in a statistical sense. To conclude that X t causes Y t ,we must reject the hypothesis that "X t does not causeY t ", and accept the hypothesis "Y t does not cause X t ". In the current study the dependent variable is the domestic credit to the private sector (LnCPS) that is lagged on present values of itself and regressed on past values of gross domestic product (LnY). The process is then reversed. F-statistic and final prediction FPE) criterion developed by Akaike (1969) were used to determine whether or not the inclusion of lags is any important. To test whether LnCPS causes LnY, we thus proceed as follows: First, test the null hypothesis "LnY t does not cause LnCPS t " by running two regressions.
Unrestricted regression: LnCPS t = Σα i LnCPS t-i + Σβ i LnY t-i + ɛ t (3.24) Restricted regression: LnCPS t = Σα i LnCPS t-i + ɛ t (3.25) And use the sum of squared residuals from each regression to calculate an F statistic and test whether the group of coefficients β 1, β 2, … β m is significantly different form zero. If they are, we can reject the hypothesis that "LnY t does not cause LnCPS t ".
Secondly, test the null hypothesis "LnCPS t does not cause LnY t " by running the same regression as above, but switching LnY t and LnCPS t and testing whether lagged values of LnCPS t are significantly different form zero. The concept of causality in the Granger sense is based on the assumption of stochastic process that are stationary that makes it advisable to undertake unit root tests before performing the granger causality test.

Cointegraton and Error Correction Modeling
If time series data reveal non-stationarity, cointegration can be used to remedy the situation.
Cointegration is a situation in which two non-stationary series integrated of the same order have long run relationship (Engle and Granger, 1987). The fact that two series are of the same order of integration, say I (1), does not necessarily mean that the series are cointegrated. For the variables to be cointegrated, they must be of the same order as well as having common stochastic trends.
The necessary condition for cointegration is that the residual obtained from the regression of the two series should be stationary. Cointegration can also be interpreted as follows: if two or more series are linked to form an equilibrium relationship spanning the long run and then even though the series themselves may contain stochastic trends they will nevertheless move closely together overtime and the difference between them will be stable.
The error correction model (ECM) formulation starts from the recognition that the variables are non-stationary (perhaps integrated of order one) but move together in the long-run, such that there exists a stationary linear combination of these variables (integrated of order zero). If such linear combination exists, then the variables are said to be cointegrated and that stationary linear combination is the ECM. ECM captures the long-run relationship. It reflects attempt to correct deviations from the long-run equilibrium path and its coefficient can be interpreted as the speed of adjustment or the amount of disequilibrium transmitted each period to economic growth. Thus cointegraion is the statistical equivalence of the existence of a long-run equilibrium relationship.
When there are two or more I (1) variables under consideration, the residual-based cointegration tests may be inefficient and may lead to contradictory results (Perseran and Perseran, 1997). A more satisfactory approach would then be used, and this involves methods like Johansen Maximum Likelihood procedure.

Johansen Cointegraiton Procedure
The Johansen's approach begins by setting vector autoregressive (VAR) system of the variables of interest. The standard vector autoregressive, VAR, model takes the following form: Where X t is defined as an Nx1 vector of the variables of interest,µ is a vector of constants which can be entered as restricted or unrestricted, t is a vector of trend, D is a vector of centered seasonal dummies while ɛ t is a vector of identically and independently distributed, iid (0, Ω) error terms. Having a trend in the cointegrating vectors can be understood as a type of growth in target problem, sometimes motivated by things like productivity growth due to technological development. In other situations we conclude that there is some growth due to technological development. In other situations we conclude that there is some growth in the data, which the model cannot account.
b) Rank (Π) = 0 = r; this implies that the Π matrix is null and hence equation 3.30 corresponds to the traditional differenced vector of the time series variables, hence the variables are not cointegrated. c) Rank (Π) = N> r but not zero: this is interesting case where the Π matrix is less than full rank. In this case the rank r is equal to the number of indistinct cointegrating vectors linking variables in X t, as such r is known as the cointegrating rank.
In the case when rank Π = r < N, the Π matrix can be decomposed into two P x r matrices, Π and β such that Π = αβ΄ ,where β represents the matrix containing coefficients of the cointegration vectors. rows of form (r ) distinct cointegration vector enters each equation. the actual mechanics latter has a reduced rank. The solution starts from conditioning out the short run dynamics,as well as the effects of the dummy variables on ∆X t and X t-k respectively. According to (Sjöö, 1997), we first regress first difference of X t on its lagged values and a column of ones (dummy) and save the residuals as R ot . Then we regress X t-k also on lagged values and a column of ones (dummy) and save the residuals as R kt . For example, the residuals R 1t and R 2t can be obtained as follows: ∆X t = Σρ 1t X t-i +γ 1 D t +R 1t (3.31a) X t-k = Σ ρ 2i ∆X t-i +γ 2 D t +R 2t (3.31b) Assuming our reparameterized error correction model is: ∆Xt= ΣΓ i ∆X t-i + ΠX t-k + θD t +ɛ t (3.32) Then ,we can re-write it in terms of residuals above as: R 1t =αβ΄R 2t +ɛ t (3.33) In general, the fitted residuals are used to construct the following product moment matrices: Sij = (1/t) ΣȒ 1t Ȓ΄jt (I, j = 0, k) (3.34) These product moment matrices are then used in order to find the maximum likelihood estimate (MLE) of β, the cointegrating vectors. This is done by solving the determinant: |λS kk -S ko S -1 oo S ok | = 0 (3.35) which yield p estimated eigenvalues ( λ 1 ,…, λ p ) and also the p estimated eigenvectors (V 1 ,…, Vr), which are normalized such that V΄S kk V =1 where V is the matrix of estimated eigenvectors, that is: The problem is that of determining how many of the eigenvectors represent significant cointegrating relationships. The Johansen-Juselius (1990) procedure employs two test statistics, the λ -max (or maximum eigenvalue test) and the trace test. The maximum eigenvalue test, whose test statistics is denoted by λ-max, is constructed as: λ -max = -T log (1-λ r+1 ) for r=0, 1, 2,… (3.37) Where T is the number of observations λ is the eigenvalue and r represents the cointegrating vectors. The null is that, there exist (r) cointegrating vectors agianst the alternative r+1 cointegrating vectors. The trace test is: This is a likelihood ratio test statistics denoted by λ -trace. The null hypothesis is that there exists r≤ p cointegrating vectors against the alternative r> p cointegrating vectors. It has been found that trace test is a better test, since it appears to be more robust to skewness and excess kurtosis (Sjöö, 1997). Furthermore, the trace test can be adjusted for degrees of freedom, which is important in small samples. In this study, the decision will depend on both the trace test and the maximum eigenvalue test.

Scope and Limitations of the study
The period of coverage for this study spans 1980-2009. Published data on all of the variables selected in this study were not all available in one whole but where gathered bit by bit from various sources.

Data Used and Sources
As stated before the study uses time series (

Introduction
The chapter under deals with the estimation of the economic growth model under consideration and the interpretation of the results. It begins with an assessment of the stationarity of the variables of the model. This set pace for the further examination of long run relationship between the dependent variables and the explanatory variables. Validation of said existence necessitated the estimation of the error-correction model (ECM) based on the cointegrating vector, Stata 10 was used for analysis of the data.

Stationarity Test
In order to assess the stationarity of the variable used in the models, all the variables were transformed into natural logarithm and Augmented Dicky-Fuller test was performed on the variables. The test was performed under the assumption: 1) that the times series variables follow a give trendthat is Augmented Dicky-Fuller test with trend and 2) the vice versa -Augmented Dicky-Fuller without trend. The importance of this is to determine whether trend variable must be included in the final model for estimation or not. The results are shown in table 4.1 below. With Trend: -4.343 (1%), -3.584 (5%), and -3.23 (10%) As shown in Table 4.1, the test statics and the p-values indicates that all the variables, except the natural log of labour force, were not stationary at level-that is they were not integrated at order zero [I (0)]. This means that there exist unit root among the variables. In order to use such variable to generate regression coefficient that are unbiased and efficient they must be made stationary. Consequently, the first difference of the real gross domestic product (GDP), credit to the private sector (CPS), export of good and services (Export) and physical capital formation (K) were taken and Augmented Dicky-Fuller test was performed on the variables. The results are shown in Table 4.2. With Trend: -4.352 (1%), -3.588 (5%), and -3.233 (10%) As shown in table 4.2 the first different of GDP, CPS, Export and K are all stationary. That is they are all integrated in order one [I(1)]. Theory posit that when two or more variables are integrated of other one then there might be a long run relationship between the variable which can be captured using error correction model (Engle and Granger, 1987). Granger causality test was performed to detect a causal relationship among between economic growth and the explanatory variables (Granger, 1969). Cointegration test was performed to assess the possibility of a long run relationship between the variables. The results indicated one cointegrating relationship as indicated in Table 4.3. Table 4.3 shows the results of the cointegration test. The coefficient is negative and statistically significant at one (1) percent. This indicates that there exist a long run relationship between economic growth and explanatory variable. It also gives credence to the use of error correction model in order to capture both long run and short run variations in economic growth and the explanatory variable under consideration. The result of the final economic growth model estimated in this study is presented in Table 4.4. Credit to the private sector (CPS) had a positive sign as expected and statistically significant at one(1) percent significant level. The result shows that a percentage increase of credit to the private sector will lead to 0.32 percentage increase in economic growth. This result is in line with a study conducted by Kamara (2007) on financial sector development and economic growth in Liberia.

Regression Results
Exports of good and service also had a positive sign as expected. The results indicate that a percentage increase in export leads to 0.2 percentage increase in economic growth. However, this is statistically insignificant at even 10 percent level.
Capital formation (Investment) also had a positive sign as expected. It shows that a percentage increase in capital formation (K) will leads to 0.14 percentage increase in economic growth (GDP) but this is not statistically significant.
The results also indicate that labour force contribute positively to economic growth. A percentage increase in labour force leads to 0.18 percent increase in economic growth, although this is not statistically significant.
The intercept has a negative sign which indicates that all other variables excluded in the model contribute negatively to economic growth; however, this is not statistically significant. In all the results reveal a positive relationship between financial development and economic growth in Ghana with the direction of causality predominantly running form financial development to economic growth.

Introduction
This study sort to examine the impact of financial development on economic growth in Ghana through the harnessing of financial savings for investment ventures in order to offer policy recommendation to enhance accelerated development in Ghana. More specifically the study sort to find out empirically the impact of financial development on economic growth with the view to establishing whether or not financial development causes growth or growth causes financial development in Ghana; and Investigate whether there is a long-run relationship between financial development and economic growth in Ghana. Time series data macroeconomic variables from 1980-2009 was used to run the error correction model in order to answer the hypothesis of this study.

Summary of Findings
The study found out that financial sector development ,that is, credit to the private sector, and increases in exports, capital formation and use of labour force have positive effect on economic growth in Ghana. More specifically developments that increases credit to the private sector turn out to increased economic growth by 0.32 percent. This is statistically significant and represents the main thrust to enhance economic growth. The Granger causality test revealed a one-way causation between economic growth and financial sector developments with financial sector developments Granger causing economic growth. This results was affirmed by the coefficient of the lagged dependent variable which is positive, though, but statistically insignificant. The results also show that there exist a long run relationship between economic growth and financial sector development with a fast rate of adjustment to the equilibrium of about 0.28 percent.

Conclusions
In line with the above findings the study concludes that there exist a positive long run relationship between economic growth and financial sector development with financial sector developments Granger causing economic growth in Ghana. The enabling environment and financial sector interventions such as low interest rate that will enhance transfer of credit to the private sector must be pursued to enhance the economic development of Ghana.

Policy Recommendations
Government should put in place appropriate fiscal and monetary policies to encourage the increase of credits to the private sector of the economy. This will boost economic growth immensely as shown by the results from our analysis.
Government should encourage domestic producers with favourable tax incentives to enable them produce more for export which will intend increase the country's GDP to a great extend as supported by the data analysis.
Government policy should focus on ensuring that capital stock is allocated efficiently to the productive sectors of the economy such as industry and agriculture. This should be done by factoring in the appropriate technology.
Policies should be put in place to increase and improve upon the human capital accumulation of skills in all areas, both financial and real sectors of the economy, to have a positive effect on the Ghanaian economy. Quality labour force adds to savings by investing in human capital.
Lack of a developed financial system restricts economic growth and therefore government policies should be directed towards encouraging the growth of the financial sector of the Ghanaian economy.