Memoire Online - Financial development and economic growth: evidence from Niger

The relationship between financial development and economic growth is a controversial issue. For developing countries, empirical studies have provided mixed results. This study seeks to explore the relationship between financial development and economic growth in Niger from 1970 to 2010. We used two variables to proxy financial development namely; credit to the private sector to GDP denoted by CP and financial deepening denoted by FD. The economic growth is proxied by real GDP. In order to check this relationship a Vector Error Correction Model was carried out. Unit root test was conducted for stationarity, and all the series were found to be nonstationary at level. However their first differences were stationary at the same order. Additionally, Cointegration test was carried out, revealing that there was long run equilibrium relationship among the variables and economic growth. In case of the long run, financial deepening (FD) had positive impact on GDP however, credit to the private sector to GDP was found to hinder economic growth. Consequently, the authority in Niger should enact laws and policies to establish a central credit bureau linking all banks to limit defaults on loan payments. They should also build stronger and more diversified financial and banking sector by continuing with the liberalization policy so as to create competition among the banks. Finally, the populace should be sensitized and educated on the security of the banks against collapse in order to create confidence in citizens about the sustainability of the banking sector.

Keys Words: Financial Development; Economic Growth; Niger, Credit to private sector, Africa.

List of Tables

Chapter 1 Introduction

Niger's economic growth in the past decades has been relatively modest and was below the population growth rate, in the early 1960s through to the late 1970s economic growth was weakened by series of droughts that adversely affected the agricultural sector (accounting 40 percent of Niger GDP). From 1979 to 1982, economic growth was strengthened due to the world demand for uranium (Niger's main export product). This improved the terms of trade and raised export revenues base of the country. However, this strong growth was short lived due to the collapse of the global uranium price in the early 1980s, causing and accelerating prolonged recession. Although the 1994 devaluation of the CFA¹ franc improved the country's external competitiveness, real GDP growth was too low to boost per capita income; IMF (2007); Ministry of Finance and economy of Niger (2006).

Over the years, the study of economic growth has become one of the hottest research areas due to the strategic implications of economic growth to national development. Modern theories on economic growth offer several explanations to this; Katheline (2000). According to the classic definition of François Perroux (1981), economic growth is «an increase in the capacity of an economy to produce goods and services, compared from one period of time to another''. In practice, gross domestic product (GDP) is used to measure economic growth and the rate of economic growth is evaluated by the rate of change of GDP. The financial sector is an important contributor to GDP in every economy. Therefore, the level of growth and development of an economy is tied partly to profound reforms made in the financial and monetary structures as well as policy interventions. Changes in the financial sector are fundamental and vital for these reforms due to its role in spreading risk and mobilizing savings. It is therefore imperative to highlight the contributions of this sector to economic growth. Nonetheless, economic theories are divided on the importance of this sector in economic growth; Bagehot (1873); Hicks (1969); Goldsmith (1969); Schumpeter (1912); Robinson (1952); Lucas (1988).

Consequently, two main hypotheses are advanced whose central issue is whether or not the financial sector supports economic development. The first is led by Bagehot and others; Bagehot (1873); Mackinnon (1973); Shaw (1973); and Schumpeter (1912). They highlight the active role of the financial sector in promoting economic growth; Robinson (1952) and Lucas (1988) on the other hand believe there is no relationship between the financial sector and economic growth.

Crises in the banking sector in 1980s resulted in collapse of banks in developing countries especially in Africa; particularly, south of the Sahara. It forced the West African and Monetary Union (WAEMU) countries (among which Niger is a member) to engage in financial liberalization advocated by researchers as an intervention measure; McKinnon and Shaw (1973). This intervention, as envisaged at the time, would allow the recovery of the banking and the financial sectors and in fact, propel the growth of the economy. Unfortunately, the intervention has not been successful due to the fact that the financial sector is highly concentrated with higher intermediation margins; resulting in excess liquidity which has adverse effects on banking efficiency; Igué (2006).

A developed financial sector enhances economic growth, by promoting and mobilizing savings and providing information on investments opportunities so that resources can be channeled to productive ventures. It monitors the disbursement of funds, promotes trading, diversification and management of risk as well as facilitating the exchange of goods and services leading to economic growth; Levine (1997, 2004).

The objective of this study is to investigate whether the development of financial sector in Niger in the past 40 years has contributed to economic growth or not. Empirically, real GDP was used to indicate economic growth; two variables were used to indicate financial development. Financial deepening M2/GDP denoted by FD, and credit to the private sector to GDP by CP. Based on these, investigation to determine whether or not there was any relationship between financial development and economic growth in Niger was done.

1.1. Motivation

There have been some studies on the financial development and economic growth in some WAEMU countries, however to the best of my knowledge there has not been any report in the case of Niger. Its financial sector has not been given the necessary attention in research studies as an important indicator of economic growth. Hence, the need to fill this research and academic gap is of paramount importance. This study therefore, aims at analyzing the relationship between the financial development and economic growth of Niger and providing implicit and explicit policy suggestions that may be adopted by policy makers to promote financial development and economic growth.

1.2. Scope of the Study

Financial development is a broad term; it is a combination of the developments of financial institutions financial markets and financial assets []. The focus of this study is mainly on the development of financial institutions. Other aspects such as stock market will not be considered as the country has no effective and operational stock market. In fact, the whole of the West African Economic and Monetary Union (WAEMU)², has only one stock market located in Abidjan, Ivory Coast. Be it as it may, this regional stock exchange³ is not well developed to compete with and to break the monopoly of the banks.

1.3 Brief overview of economic growth and financial sector Development in Niger

1.3.1 Economic Growth

Since independence (in 1960), Niger has experienced several profound economic growth. This span of economic developments may be divided into five (5) different periods.

1963-78 The rural sector contribute more than half of total value added to GDP, with mining accounting for about 7 percent. Within this period, there were series of

2 an eight member organization comprising of former French colonies, Niger, Mali, Benin, Burkina Faso, Cote D'Ivoire, Togo, Senegal and Guinea Bissau.

droughts that weakened the agricultural sector which account for about 40 percent of the GDP, negatively impacting on growth. Per capita real GDP growth averaged about 0.8 percent per year; Ministry of Finance of Niger (2006).

1979#177;82: Higher uranium prices pushed per capita real GDP growth to an average of about 2.5 percent per year, in this period. The mining sector contributed an overall GDP of about 13 percent. This increased government revenue and facilitated greater public investment in infrastructure; Ministry of Finance of Niger (2006).

1983#177;93: International uranium prices and Niger's terms of trade declined sharply, significantly reducing export earnings, slowing investment, and weakening the financial sector. Limited policy adjustments to the terms of trade and political instability worsened the situation. Per capita real GDP declined, by an average of 3.4 percent a year; Ministry of Finance of Niger (2006).

1994#177;98: Along with devaluation of the CFA franc (in 1994), the Nigerien authorities initiated reforms that worked towards liberalizing the economy. These measures improved external competitiveness and strengthened the economy's overall supply response. Good weather conditions boosted the performance of the agricultural sector, although per capita real GDP growth still averaged just about 0.5 percent a year; IMF (2007).

1999-2010: The 1999 elections ushered in a democratic government and brought consensus on the need for prudent policies and reforms to strengthen growth and reduce poverty. Consequently state-owned companies were privatized and domestic and external trade liberalized. While these reforms strengthened the economy's supply response, droughts continued to hit the economy leading to limited progress in agricultural productivity resulting in modest per capita real GDP growth; IMF (2007).

1.3.2 Financial Sector Development

Niger is a member of the Economic and Monetary Union of the West Africa (WAEMU) which comprise of 8 former French colonies in West Africa. The Central Bank of the States of West Africa, known as Banque Centrale des Etats de l'Afrique de l'Ouest (BCEAO) in French which was established in 1962 is responsible for both the

management of the monetary policies, regulating and supervising the banking services of the member countries. The financial sector of Niger is relatively underdeveloped. The sector includes the central bank, ten commercial banks; the national fund of social security system; five insurance companies; three brokerage firms and about 270 microfinance institutions MFIs; Monograph of BCEAO (2004). Among the WAEMU member countries, Niger's ratio of broad money to GDP and deposit to GDP is the lowest. Based on this, it is safe to conclude that the financial intermediation in Niger is still very low. Total assets of the financial system at the end of 2005 stood at about 373 billion CFA francs, representing 21% of the GDP. The banking sector dominates the financial system with total assets accounting for about 63% where as the non financial sector accounted for about 29% with the insurance sector accounting for about 5.3% and the microfinance institutions accounting for about 2.7%; WAEMU (2005). The financial sector suffered serious difficulties in the late 80s and 90s. Banks, security funds and microfinance institutions went through severe financial crisis. Additionally, long period of political and economic instability and sluggish economic growth are factors that contributed to this financial crisis. Other factors that affected the sector include the inefficiency of the judiciary, poor financial sector policies, including supervision, lax banking, the rigidity of the structure of interest rates and sectoral allocation of credit; WAEMU (2003). Mismanagement, subsidized loans (especially in the late 70s and the 80s) and budget deficits also contributed to the failure of the financial institutions.

1.4 Disposition

The study is structured in four parts; the first chapter is an introduction focusing on the motivation, the scope and a brief history of Niger's economic growth and financial sectors. The second chapter is an overview of theories concerning financial development and economic growth. The third chapter is the description of data, variables, and results of empirical validation. The final chapter is the conclusion and policy implication of findings of the study.

Chapter 2 Literature Review

Economic growth is a goal and national development policy of every nation. Developed economies provide living evidence of the importance of the financial sector in economic growth. Financial development stimulates economic growth through investment which invariably contributes to increasing national productivity.

2.1 Financial development: a factor for economic growth

The positive correlation between financial development and economic growth has been recognized in the literature for over decades Bagehot and others Bagehot (1873), Schumpeter (1912), Goldsmith (1969) are among the pioneers of this topic. Financial structure quickly became one of the fundamental economic developments spurred by authors Gurley and Shaw (1967); McKinnon (1973), King and Levine (1993). In almost all studies, findings confirm that an efficient financial system contributes strongly to economic growth. The correlation between the two variables is widely accepted however the direction of causality is yet debatable. The concept of financial system generally includes banks and financial markets. Levine (2004) advanced five arguments that theoretically support the existence of strong and positive link between financial development and economic growth. He stated that financial system would:

Schumpeter (1911) argued that an efficient financial system greatly helps the growth of a nation's economy. For him well-functioning banks encouraged technological innovation by offering funding to entrepreneurs that have the best chances of successfully implementing production processes for innovative products. Goldsmith (1969) is another pioneer in studying the links between growth and financial development. His study focused on a sample of 35 countries over the period 1860 to

1963. He concluded that there is a positive link between the financial sector and economic growth. In an attempt to address the weakness in the work of Goldsmith, King and Levine (1993), focused their analysis on a sample of 80 developed countries over a period 1960 to 1989 by reviewing all financial factors likely to influence long-term economic growth and concluded that there was statistically significant and positive contribution of financial variables on economic growth.

Levine and Zervos (1998) assessed the impact of exchange stock market and development of the banking sector on economic growth with a sample of 49 countries over the period of 1976-1993 and using asset turnover, and market capitalization ratios, market volatility and bank development indicators as financial variables. They also considered as growth rate of real GDP, capital, productivity and savings as endogenous variables in line with earlier studies; King and Levine (1993). Their result highlighted the positive impact of financial variables on economic growth and penciled two mechanisms through which financial development is affected economic growth. The first is the increased efficiency of capital, through better resource allocation, and the second is the mobilization of savings which increases the volume of investment. They concluded that economies with high levels of financial development exhibited higher growth rates. Venet et al. (1998) in a study on the economies of sub-Saharan countries from 1970-1995 found out that financial deepening played a major role in the real growth of majority of countries within WAEMU, including Cameroon. They used economic growth measured by real GDP per capita as regressor and ratios of M2 to GDP, the nominal credit to the private sector and stock of real credit per capita as financial variables and concluded that there was a causal link between financial deepening and real economic growth of the countries except Niger. In its case, there was no causal significance; yet, according to them, the result does not necessarily imply the absence of economic ties between the two sectors of Niger.

2.2 Financial development: a less factor for economic growth

In fact, the issue of the relationship between financial development and economic growth is still debated. Some economists believe there is no significant relationship between financial system and economic growth. For instance, Lucas (1988) dismissed the finance-economic growth relationship by stating that economists «badly over-stress» the role financial factors play in economic growth. Mayer (1988) argues that a developed stock market is not important for financing of companies. Nonetheless, some authors such as Robinson (1952) assert that economic growth creates demand for financial instruments and that where enterprise leads, finance follows. Nguema (2000) studied financial intermediation and growth on Gabon, and concluded that despite the regular periods of excess liquidity in the banking system, banks did not finance growth. In other words, the development of the financial sector did not influence the economic growth of the country.

2.3 Financial liberalization and economic growth in the WAEMU countries

Generally, the term "financial repression" refers to the effects of strict regulation of financial systems and the arrays of restrictions imposed by States on the activity of financial institutions. Adopting financial liberalization policies was often considered a prerequisite for healthy and efficient financial sector; McKinnon and Shaw (1973). Theoretical and empirical studies conclude that financial development plays important role in economic growth, and that less developed financial systems may hinder economic growth, and that reforms involving the deployment of market mechanisms must be pursued. Evidence from these studies has been the foundation for the wave of financial liberalization of many developed and developing economies. For the WAEMU countries, the liberalization of financial systems to stop the collapse of the banks and propel investment began in the late 1980s; WAEMU annual report (2003). The reforms were primarily liberalization of interest rates, elimination of credit, operationalization of the minimum reserve system, renovation of the money market, creation of the Regional Stock Exchange known as Bourse Regionale de Valeures Mobilieres (BRVM) in French, and promotion of the microfinance sector. These

measures were implemented (as part of the liberalization policy in the monetary area) to improve the efficiency of banks for economic growth. However, some argue that financial repression has a reducing effect on growth.

McKinnon and Shaw (1973), King and Levine (1993) are the main advocates for financial liberalization. For them, a repressed financial system where the state controls the banking sectors is ineffective because government plays important role in credit allocation, through the maintenance of very low interest rates, subsidized interest rates for priority sectors (especially SOEs) and very high reserve requirements. This development is believed to disturb relative prices and resource allocation. Financial liberalization therefore, must first promote greater collection of savings, by increasing the supply of savings instruments and raising real interest rates. There is also the tendency to finance less productive investments in a financially repressed economy; McKinnon (1973). Furthermore, Shaw (1973) showed that the rate limits aggravate risk aversion and liquidity preference of financial intermediaries. According to Fry (1997), credit allocation is usually based on political affiliations rather than on the basis of efficiency in a repressed financial system. King and Levine (1993) also stated that financial repression reduces the services offered by the financial system for clients. It hinders innovation and weakens the growth rate of the economy. Therefore, theoretically, there is ample evidence that shows that financial repression adversely affects both the financial sphere and the real economy. Hence, the liberalization of financial systems advocated by economists as a measure to induce economic growth seems to be in the right direction. In contrast, a second approach argues that financial liberalization is harmful to financial development innovation and economic growth. Stiglitz (1981) opined that the function of capital markets, driven mainly by financial liberalization is affected by asymmetric information flow which undermines its effectiveness. For example, the head of a credit bank has more information on the terms and conditions on loans than the client who is taking risks to borrow. This results in adverse selection, and moral hazard.

Chapter 3 Empirical Analysis

Macroeconomic performance in Niger has been quite poor over the years and real GDP varied between 1970 and 2010. In this chapter variables of the study are described, data are analyzed and results validated empirically. Initially, the presence of unit root is enquired before determination of Cointegration. After Cointegration between the variables was established the existence of long run equilibrium relationship was confirmed. Finally, a Vector Error Correction technique was employed to examine the short and long run dynamics with the help of Eviews 7.1 Software.

3.1 Data and Description of Variables

3.1.1 Data

3.1.2 Economic Growth Indicator

Unlike Levine (2000) who used real GDP per capita in measuring economic growth, herein, real GDP was used as a proxy for economic growth because the population growth rate of Niger is higher than the GDP growth; such that dividing the real GDP by the population does not reflect the growth of the country. This variable would reflect the evolution of the economic development. Note that this indicator reflects the economic health of a country and its ability to finance domestic investment. Therefore the natural logarithm of real GDP is used as indicator of economic growth denoted by (GDP).

3.1.2 Financial Development Indicators

Based on Niger's situation and availability of data, two variables were successively selected to measure financial development, which are the ratio M2 divided by GDP denoted FD and credit to the private sector also divided by GDP denoted by CP. The ratio of credit to the private sector to GDP has been designed as

an indicator of financial intermediation. The higher this ratio is, the larger the volume of credit lending to the private sector. Additionally, credit to private sector to GDP as a proxy of financial development indicates not only a high level of domestic investments, but also a high development of the financial system. Furthermore, Financial deepening (FD) is designed as an indicator to capture the evolution of the liquidity of the economy. Moreover, Demetriades and Hussein (1996), King and Levine (1993) used this variable to measure the development of the financial sector. Increase in FD corresponds to increase in liquidity of the economy. The sign of FD was expected to be positive because the more liquid an economy is, the more opportunities exists for continued and sustainable growth. However, the sign for credit to the private sector to GDP (CP) may be ambiguous; allocation of non-performing credits may be a source of crises for banks as well as the economy and thus relate negatively to growth. On the other hand, it may be positively correlated with GDP if credits are deployed efficiently; []. Therefore, the logarithm of CP and FD were used as financial development indicators.

The Figures below illustrate each series in log levels of the variables as well as the first differences of the logs. As indicated in Figure 3.1, each series appears to be nonstationary. Whereas the first differences of the logs of the series in Figure 3.2 have stationary appearance.

The variables fluctuate over the years. The CP appears to decrease from 1970 up 1972, then increase abruptly in

1975. It then fell in 1978 and picked to a maximum in 1979. Thereafter, it decrease steadily in 1997 and finally
increase from 1997 to 2010. The FD increased from 1970 to 1980, then dropped to 1983 and increased steadily to

1994, then decreased to 1997 and finally increased in 2010. The GDP increased from 1970 to 1971, decreased in 1973, then increased steadily to 1979 and then dropped in 1984.Thereafter,it increased till 2010.

Figure 3.2: Log first differences of individual variables
From Figure DCP increased from 1970 to 1972, and then decreased sharply in 1976. It stabilizes a little in 1992,

and dropped 1995.Thereafter it fluctuates steadily till 2010. The trend of DGDP decreased from 1970 to 1972, and
then increased from 1972 to 1975. It then fell from 1978 to 1983. It increased so sharply from 1983 to 1985.

Finally, it fluctuates steadily from 1985 to 2010 even though it decreased in some years. The DFD decreased from year 1970 to 1972, and then increased litter in 1975.Thereafter decreased from 1980 to 1983 and picked up from 1983 to 1985. It then decreased slightly up 1999. From 1999 it increased sharply to 2004 then dropped till 2010.

Below are descriptive statistics at the level and the first difference of the variables.

3.2 Unit root Test

Most macroeconomics data are nonstationary; hence it was primordial to test for stationarity before the regression in order to avoid misleading results. Therefore, a formal test is applied in order to check the stationarity of the series. Series which are stationary at level is said to be integrated of order zero, I (0). The ones which attained

3.2.1 ADF test

Augmented Ducky Fuller (ADF) test was used to test for the stationarity. It consists of running a regression of the first differences of the series against the series lagged once, lagged difference terms and optionally, a constant and time trend. This can be expressed as follows:

The test for unit root was carried out on the coefficient of (). If the coefficient

is significant from zero, then the hypothesis that has a unit root is rejected. The

fact that the null hypothesis is rejected indicates stationarity. The null hypothesis is
that the variable is a non-stationary series (H0: ) and it is rejected when

If the computed value of the ADF statistic is more negative than the critical values, then the null hypothesis (H0) is rejected and the series considered to be stationary or integrated of order zero, I(0). Contrary, failure to reject the null hypothesis implied that the series is non-stationary leading to another test on the first difference of the series. If the series attained stationarity after the first difference, they are considered integrated of the order one, I (1). If not, further difference was conducted until stationarity was reached.

3.2.2 Test Results

Results of the unit root test showed that all the series were nonstationary. The ADF test statistics were lesser than the critical values indicating that the series were nonstationary at level (Table 3.3). Furthermore, all the variables attained stationarity at first difference at 10% significance level. The calculated values of the ADF statistics were more negative than the critical values implying that the series were integrated of order one I (1) (Table 3.4).

3.3 Empirical results

In this section, an optimal lag length is chosen and results of the Cointegration test as well as the Vector error correction model (VECM) estimates are presented.

3.3.1. Vector Autoregression (VAR) Lag Length

Since all the variables are integrated of order one, application of Johansen Cointegration test is more appropriate; Johansen (1991, 1995). Yet, Johansen Cointegration test is sensitive to the lag length. Therefore an optimal lag length (p) must be chosen. Also, before estimation of the VECM model with associated cointegrating vector, it is necessary to select optimal lag length of initial VAR. Different information criteria were computed for different time lags; each at 5% level of Likelihood Ratio (LR), Final Predict Error (FPE), Akaike Information Criteria (AIC), Schwarz Information Criteria (SC), and Hannan-Quinn information criteria (HQ). Result showed that the appropriate lag for all the criteria was one. Hence, the number of lags required in the Cointegration test was set to one (p=1).

* indicates lag order selected by the criterion , LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan-Quinn information criterion

3.3.2. Cointegration Test

As Engle and Granger (1987) pointed out, it is possible that a linear combination of nonstationary series may be stationary. If such stationary combination exists, the non-stationary time series are said to be co-integrated and it is then possible to interpret it as a long-run equilibrium relationship among the variables. Johansen (1995) suggested two test statistics based on Likelihood ratio (LR); the trace statistics and the Maximum Eigenvalue statistic. The first statistic tests the null hypothesis that the number of Cointegration vector is less than or equal to r against the alternative that

the number of Cointegration vector is equal to r. The second statistic tests the null hypotheses that the number of Cointegration vector is equal to r against the alternative that it is equal to r+1.

3.3.3. Cointegration results

The number of cointegrating vectors was tested based on the assumption that the series have linear deterministic trend and that there is an intercept. The null hypothesis that there is no cointegrating vector concerning the trace statistics could not be rejected since its value was greater than the 5% critical value. Since we failed to reject the null hypothesis with no Cointegrating equation this indicates that any cointegrating equation has not been found concerning the trace statistic. However, the maximum eigenvalue statistics test indicates one (1) cointegrating equation among the variables. Thus, there was one Cointegrating equation indicating long-run equilibrium relationship among the variables (Table 3.6).

3.3.4. Vector Error Correction Model (VECM)

The use of the vector error correction model was necessitated by the fact that the time series were nonstationary in their levels except in their differences, coupled with the fact that the variables were cointegrated. In case there was no Cointegration, VECM was not required. The VECM was applied in order to evaluate the short run properties of the cointegrated series and to find the real link between the variables. It enables the integration of the short-run fluctuations. The coefficient of the error correction term must be negative to report a force towards the long-run equilibrium. The regression equation of the VECM is express as:

Where Ä is the first difference operator, is the error correction term lagged one

Cointegrating Eq:	CointEq1
GDP(-1)	1.000000
CP(-1)	0.765 (0.144) [ 5.279]
FD(-1)	-1.311 (0.206) [-6.355]
C	-25.714
Error Correction:	D(GDP)	D(CP)	D(FD)
CointEq1	-0.061 (0.031) [-1.935]	0.183 (0.089) [ 2.059]	0.283 (0.058) [ 4.805]
C	0.018 (0.009) [ 1.858]	0.021 (0.027) [ 0.766]	0.035 (0.018) [ 1.922]
R-squared	0.089	0.100	0.377
Adj. R-squared	0.065	0.076	0.361
Sum sq. resids	0.145	1.141	0.496
S.E. equation	0.062	0.173	0.114
F-statistic	3.744	4.243	23.090
Log likelihood	55.560	14.374	31.019
Akaike AIC	-2.678	-0.619	-1.451
Schwarz SC	-2.593	-0.534	-1.366
Mean dependent	0.018	0.021	0.035
S.D. dependent	0.064	0.180	0.143
Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion		1.40E-06
		1.20E-06
		102.433
		-4.671
		-4.291

The coefficient should be negative and significant to show that the long run relationship exists among the variables and that deviation from equilibrium in the previous year is adjusted back to equilibrium in the current year. In other words, this indicates a long-run error correction among the variables. In particular, given that the

coefficient of is -0.061, this means that 6.1% of the disequilibrium in the previous

Based on the long run dynamic analysis the relationship between GDP, CP and FD can be expressed in terms of the coefficients as

We interpreted the coefficients in terms of elasticity. The GDP increased by 1.311 percent with an increase of one percent of FD. It had significant influence on the economic growth of Niger. However, an increase of one percentage of CP led to a decrease in GDP by 0.765 percent, which confirms the ambiguity of the sign of CP. With effective allocation of resources, it will be correlated positively with economic growth; otherwise not, especially in countries where the financial systems are not well developed. Generally, CP is expected to have positive effect on investment leading to economic growth; Demetriades and Hussein, (1996). Contrary, we found negative and significant impact of CP on economic growth in Niger. This result could be explained by the huge non performing loans on the private sector between 1970 and 1980. Higher CP means wider financial sector and higher

financial intermediation. Yet for Niger's case, the CP was lower; indicating restraint of the financial sector and lower financial intermediation. Additionally, the attitude of bankers to finance less risky projects lead to low capital intensity. This does little to improve investment and may create distortions in the economy. Furthermore, investment in Niger is weak and unstable leading to unexpected negative returns from projects with attendant negative impact on economic growth; De Gregorio and Guidotti (1995).

Chapter 4 Conclusion and Policy Implications

4.1. Conclusion

We investigated the existence of relationship between financial developments and economic growth in Niger with annual time series data from 1970 to 2010 using Vector Error Correction techniques. Results of ADF unit root test demonstrated that the series were non stationary at their levels but stationary at first difference. Additionally, Johansen Cointegration test was applied to study the long run equilibrium relationship among the variables and results indicate the existence of Cointegration among the variables. The VECM was estimated to improve the dynamism of the short and long run relationships. The error correction term is negative and statistically significant indicating that after a shock in a previous year, the long run disequilibrium will converge towards equilibrium at about 6.1% percent in a current year. In the long run, we found that an increase in financial deepening (FD) leads to an increase in GDP however; there was a negative and significant effect between credit to the private sector to GDP and economic growth.

4.2. Policy Implications

Based on the findings of the empirical analysis, suggestions are advanced for policy interventions. The negative relationship between credit to private sector and economic growth may be due to inefficient allocation of funds to productive sectors. This is mainly due to the fact that most borrowers default on loan payments. As a result, banks are reluctant to give out credit to many customers; they pursue selective lending to few sectors, especially the mining and telecommunication industries. This creates a situation where there is no readily available credit to legitimate entrepreneurs, leading to less economic growth. Hence, to boost economic growth and development, the authority in Niger should enact laws and policies to establish a central credit bureau linking all banks. This will collate the names and history of all borrowers such that previous loan defaulters as well as the ability of borrowers to honor loan payments can easily be determined by credit officers. On the other hand,

the positive correlation of financial deepening to economic growth indicated that financial deepening had much influence on the economic growth and developments of Niger. Therefore, to induce economic growth and developments, the government has to work towards building a stronger and more diversified financial and banking sector by continuing to liberalize the financial sector so as to create competition among the banks. Competition will drive banks to institute innovative policies and programs that are customer friendly. For instance banks may be forced to embark on vigorous advertisement to educate customers on the advantages of banking with them or allow creation of zero-balanced deposit accounts. This will encourage more clients to save resulting in more liquidity to promote economic growth. Another intervention is to formulate directives for banks to conduct periodic and scheduled sensitization and education programs for the populace about the security of the banks against collapse. There is the general belief that the banks in Niger may collapse at any time, resulting in many people not wanting to save. This perception is borne out of the collapse of the banking sector in the 1980s. Therefore, creating confidence among the citizens is vital for the sustainability of the banks and the economic growth of Niger.

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Appendix

VAR Lag Order Selection Criteria Endogenous variables: CP GDP M2 Exogenous variables: C Date: 07/26/12 Time: 03:52 Sample: 1970 2010 Included observations: 36
Lag	LogL	LR	FPE	AIC	SC	HQ
0	-16.79238	NA	0.000603	1.099577	1.231537	1.145634
1	99.05879	205.9576*	1.60e-06*	-4.836600*	-4.308760*	-4.652370*
2	105.2066	9.904788	1.89e-06	-4.678144	-3.754425	-4.355741
3	110.4105	7.516778	2.41e-06	-4.467251	-3.147652	-4.006676
4	119.3113	11.37318	2.57e-06	-4.461737	-2.746258	-3.862989
5	128.4008	10.09944	2.82e-06	-4.466709	-2.355351	-3.729789
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion


Sample (adjusted): 1971 2010
Included observations: 40 after adjustments
Trend assumption: Linear deterministic trend
Series: CP GDP FD
Lags interval (in first differences): No lags
Unrestricted Cointegration Rank Test (Trace)
Hypothesized			Trace		0.05
No. of CE(s)	Eigenvalue		Statistic		Critical Value	Prob.**
None	0.441065		26.03211		29.79707	0.1277
At most 1	0.061416		2.763230		15.49471	0.9766
At most 2	0.005682		0.227925		3.841466	0.6331
Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue)**
Hypothesized			Max-Eigen		0.05
No. of CE(s)	Eigenvalue		Statistic		Critical Value	Prob.**
None *	0.441065		23.26888		21.13162	0.0246
At most 1	0.061416		2.535305		14.26460	0.9728
At most 2	0.005682		0.227925		3.841466	0.6331
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level *MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegrating Coefficients (normalized by b'S11*b=I):
CP	GDP		FD
-2.494180	-3.258155		4.272959
-1.553644	1.676929		-0.177757

0.893488	4.323155		0.391353
Unrestricted Adjustment Coefficients (alpha):
D(CP)	-0.056446		0.038557		-0.001286
D(GDP)	0.018937		0.006920		0.003697
D(FD)	-0.086857		-0.003577		0.003878
1 Cointegrating Equation(s): Log likelihood 102.4326
Normalized cointegrating coefficients (standard error in parentheses)
CP	GDP		M2
1.000000	1.306303		-1.713172
	(0.34947)		(0.20415)
Adjustment coefficients (standard error in parentheses)
D(CP)	0.140787
	(0.06835)
D(GDP)		-0.047233
		(0.02441)
D(FD)		0.216637
		(0.04508)
2 Cointegrating Equation(s): Log likelihood 103.7002
Normalized cointegrating coefficients (standard error in parentheses)
CP		GDP		FD
1.000000		0.000000		-0.712449
				(0.61835)
0.000000		1.000000		-0.766072
				(0.47572)
Adjustment coefficients (standard error in parentheses)
D(CP)		0.080882		0.248568
		(0.07840)		(0.09776)
D(GDP)		-0.057983		-0.050097
		(0.02857)		(0.03563)
D(FD)		0.222195		0.276995
		(0.05309)		(0.06620)

Vector Autoregression Estimates
Date: 09/06/12 Time: 11:16
Sample (adjusted): 1971 2010
Included observations: 40 after adjustments
Standard errors in ( ) & t-statistics in [ ]
	GDP	CP	FD
GDP(-1)	0.965886 (0.05650) [ 17.0957]	0.243011 (0.15534) [ 1.56440]	0.293759 (0.10513) [ 2.79437]
CP(-1)	-0.054680 (0.03062) [-1.78580]	1.079734 (0.08418) [ 12.8258]	0.225660 (0.05697) [ 3.96086]
FD(-1)	0.081135 (0.04281) [ 1.89506]	-0.248549 (0.11771) [-2.11150]	0.631017 (0.07966) [ 7.92114]
C	0.865941 (1.52758) [ 0.56687]	-6.172351 (4.19993) [-1.46963]	-7.562115 (2.84233) [-2.66054]
R-squared	0.931321	0.899022	0.897710
Adj. R-squared	0.925598	0.890607	0.889186
Sum sq. resids	0.143120	1.081871	0.495495
S.E. equation	0.063052	0.173355	0.117319
F-statistic	162.7258	106.8380	105.3140
Log likelihood	55.90150	15.44621	31.06402
Akaike AIC	-2.595075	-0.572311	-1.353201
Schwarz SC	-2.426187	-0.403423	-1.184313
Mean dependent	27.37380	2.215064	2.566628
S.D. dependent	0.231156	0.524134	0.352429
Determinant resid covariance (dof adj.)		1.53E-06
Determinant resid covariance		1.12E-06
Log likelihood		103.8142
Akaike information criterion		-4.590710
Schwarz criterion		-4.084046

Vector Error Correction Estimates Date: 08/16/12 Time: 22:31 Sample (adjusted): 1971 2010 Included observations: 40 after adjustments Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq:	CointEq1
GDP(-1)	1.000000
CP(-1)	0.765519 (0.14499) [ 5.27997]
FD(-1)	-1.311466 (0.20636) [-6.35514]
C	-25.71474
Error Correction:	D(GDP)	D(CP)	D(FD)
CointEq1	-0.061700 (0.03189) [-1.93501]	0.183910 (0.08928) [ 2.05985]	0.282994 (0.05889) [ 4.80530]
C	0.018184 (0.00979) [ 1.85806]	0.020999 (0.02740) [ 0.76631]	0.034749 (0.01808) [ 1.92244]
R-squared	0.089695	0.100443	0.377976
Adj. R-squared	0.065740	0.076770	0.361607
Sum sq. resids	0.145582	1.141404	0.496608
S.E. equation	0.061896	0.173312	0.114318
F-statistic	3.744264	4.242996	23.09087
Log likelihood	55.56039	14.37487	31.01913
Akaike AIC	-2.678019	-0.618744	-1.450957
Schwarz SC	-2.593575	-0.534300	-1.366513
Mean dependent	0.018184	0.020999	0.034749
S.D. dependent	0.064037	0.180374	0.143077
Determinant resid covariance (dof adj.)		1.40E-06
Determinant resid covariance		1.20E-06
Log likelihood		102.4326
Akaike information criterion		-4.671629
Schwarz criterion		-4.291632

Financial development and economic growth: evidence from Niger

Abstract

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