financial stability and economic growth: a cross-country study
TRANSCRIPT
Int. J. Financial Services Management, Vol. 5, No. 2, 2011 121
Copyright © 2011 Inderscience Enterprises Ltd.
Financial stability and economic growth: a cross-country study
Lordina P. Manu Department of Finance, University of Ghana Business School, P.O. Box LG 78, Legon, Ghana Email: [email protected]
Charles K.D. Adjasi* Department of Finance, University of Stellenbosch Business School, Cape Town, South Africa Email: [email protected] *Corresponding author
Joshua Abor Department of Finance, University of Ghana Business School, P.O. Box LG 78, Legon, Ghana Email: [email protected]
Simon K. Harvey Department of Economics, College of Business Administration, University of Nebraska, Lincoln, NE 68588-0489, USA Email: [email protected]
Abstract: The study examines the relationship between financial stability and economic growth in Africa. Using a dynamic fixed-effect model, the results reveal that financial stability impacts positively on economic growth. Specifically, the results indicate that capital adequacy, liquidity and asset quality have significant effects on the GDP growth rate both in the long and the short run. It is recommended that the agencies concerned, mainly the central banks and the governments of African countries, should pursue policies that enhance the stability of their financial systems in order to spur economic growth in their respective countries.
Keywords: financial stability; economic growth; Africa.
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Reference to this paper should be made as follows: Manu, L.P., Adjasi, C.K.D., Abor, J. and Harvey, S.K. (2011) ‘Financial stability and economic growth: a cross-country study’, Int. J. Financial Services Management, Vol. 5, No. 2, pp.121–138.
Biographical notes: Lordina P. Manu is an Assistant Lecturer at the Department of Finance, University of Ghana Business School, Ghana.
Charles K.D. Adjasi is Associate Professor of Development Finance, University of Stellenbosch Business School, Cape Town, South Africa. He was also Senior Lecturer and Head of the Department of Finance, University of Ghana Business School, Ghana. He has been a Visiting Scholar at the IMF and is a researcher at the African Economic Research Consortium.
Joshua Abor is a Professor of Finance, Department of Finance, and currently the Vice Dean of University of Ghana Business School, Ghana. He has been a Visiting Scholar at the IMF and is a researcher at the African Economic Research Consortium.
Simon K. Harvey is currently a PhD student at the Department of Economics, College of Business Administration, University of Nebraska, USA.
1 Introduction
An unstable financial system normally results in financial crisis. Financial crisis has dire implications for economic growth. A typical example is the financial crisis in US which has not only impeded growth but has also affected some Asian and European economies. Balino and Sundarajan (1991) define financial crisis in general as ‘a situation in which a significant group of financial institutions have liabilities exceeding the market value of the assets, leading to runs and other portfolio shifts, collapse of some financial firms and government intervention’. Hence, the term ‘crisis’ could be said to refer to ‘a situation in which an increase in non-performing loans, an increase in losses (because of such problems as foreign exchange exposure, interest rate mismatches and contingent liabilities), and decrease in the value of investments cause generalised solvency problems in a financial system and lead to liquidation, mergers or restructuring’.
Cheang (2004) emphasised that the stability of the domestic financial system is of the first priority when foreign investors consider whether they should set up businesses in markets outside their origins. Thus, the inflow of foreign capital could stimulate domestic economic activities and labour demand.
Many countries in Africa have made notable efforts over the past one-and-a-half decades to reform their financial system. Considered as an integral part of macroeconomic policy, the financial reforms are expected to bring about significant economic benefits, particularly through a more effective mobilisation of domestic savings and a more efficient allocation of resources (Khan et al. 2005).
Even though studies have examined the effect between finance and growth in Africa, there is little evidence on financial stability or financial soundness and economic growth. Financial stability is a new concept and shows the financial system’s efficiency and solidity. This means key financial institutions need to operate without disruptions in order to increase public confidence in the financial system. Lack of confidence in the
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financial system can affect savings as well as the intermediation process and may consequently lead to inefficient allocation of resources. Therefore, the smooth running of an economy will depend on the existence of adequate financial infrastructure in terms of the entire set of legal and regulatory systems, accounting, payments systems, credit reporting and securities markets.
This paper contributes to the literature by examining the relative importance of financial stability in influencing economic growth in African countries. The study is based on a sample of 29 countries, covering the period 1996–2006. The analysis uses panel cointegration techniques to investigate the long-run and short-run dynamics between financial stability and economic growth. It estimates a dynamic fixed-effect model which includes lagged indicators of financial stability, including capital adequacy, liquidity and asset quality, and other control variables that affect economic growth.
The rest of the paper is structured as follows: Section 2 provides an overview of related literature and discusses the stylised facts, Section 3 deals with the empirical methods and the results, while Section 4 concludes the study.
2 Overview of literature
The importance of finance in economic growth and development follows the works of Schumpeter (1912), Goldsmith (1969) and McKinnon (1973). Financial sector growth helps mobilise savings necessary for the production process and for investment activities by firms, thereby providing the channel for supplying much-needed finance for economic growth. King and Levine (1993a, 1993b) and Levine and Zervos (1998) identify three main channels through which financial development influences economic growth. These are the level of intermediation, efficiency and composition. The level of intermediation is frequently measured by the size of bank credit to GDP ratio and to stock market capitalisation ratio. Efficiency is measured by private sector credit to GDP ratio, total value of shares traded on the stock market to GDP ratio, turnover ratio, legal and institutional development, and composition by maturity of bank credit as a ratio of fixed income securities.
Depending on the theoretical framework employed, the effects of financial markets on growth can be transient or lasting. In traditional growth theories, the effects are transient. That is, they are present only during the transition to an economy’s steady-state growth path. In new theories of endogenous growth, the effects of financial markets can be lasting. That is, they can elevate the economy to a higher growth path permanently (Deabes, 2004). The financial system may, therefore, influence the growth rate permanently in one of the following ways: (a) improving the average productivity of capital, (b) channelling investment funds to firms in the process of financial intermediation, and (c) savings.
On the empirical side, economists hold diverse views regarding the relationship between the financial sector and economic growth. For that matter, the importance of the financial sector to economic growth has been a matter of great debate. Growing bodies of studies have demonstrated that there is a strong link between the financial sector and economic growth. Writers such as Bekaert et al. (2004), Capasso (2004) and Senbet and Otchere (2005) believe that there is a strong correlation between financial market development and economic growth.
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In most of the studies that provide evidence in support of the relationship between finance and growth, there exists another debate concerning the causal relationship between the two. These have given rise to three conflicting hypotheses about the relationship between the financial sector and economic growth. The supply-leading hypothesis, i.e. whether the financial sector (through intermediation) causes economic growth; the demand-following hypothesis, i.e. whether economic growth enhances the financial sector’s intermediation; and bidirectional causality hypothesis, i.e. whether the financial systems may be both growth inducing and growth induced. Some studies showing evidence of supply-leading hypothesis include, e.g. Pagano (1993), Obstfeld (1994), Levine and Zervos (1998) and Fry (1997). Gelbard and Pereira Leite (1999) also find evidence for Sub-Saharan Africa. They find that some progress has been achieved in terms of modernising the financial sector since mid-1980s, but conclude that much remains to be done. Evidence for demand-following hypothesis is provided by Eschenbach (2004) and Boulila and Trabelsi (2004). These studies show that economic development establishes a demand for financial services, which is passively satisfied by a growing financial sector. Therefore, causality runs predominantly from growth to finance. Finally, studies that show bidirectional causality include Harrison et al. (1999), who found that there is a feedback effect between finance and growth: growth reduces the cost of financial intermediation by attracting new market entrants, by reducing monitoring costs and by promoting specialisation. Demetriades and Hussein (1996) found that for the majority of the countries they examined, causality is bidirectional. Luintel and Khan (1999) also used a sample of ten less-developed countries to conclude that the causality between financial development and output growth is bidirectional for all countries.
Other writers such as Lucas (1998), Neusser and Kugler (1998) and Chandavarkar (1992), however, do not believe in the importance of financial sector in economic growth. Lucas stated that ‘the importance of financial matters is very badly overstressed’, while according to Chandavarkar (1992), none of the pioneers of development economics even list finance as a factor of growth. Robinson (1952) also believes that the financial system does not spur economic growth; rather, it simply responds to development in the real sector. More recent writers such as Auerbach and Siddiki (2004) also hold the view that the financial sector is not important in determining growth.
Recent literature however shows that the stability of the financial system is crucial in augmenting economic growth. A stable financial system is one that enhances economic performance in many dimensions, whereas an unstable financial system is one that detracts from economic performance (Schinasi, 2004). A financial system that is robust is less susceptible to the risk that a financial crisis will erupt in the wake of real economic disturbances and is more resilient in the face of crises that do occur (Bank for International Settlements, 1997).
The theoretical link between financial stability and economic growth can be seen as hinging on the five distinct factors which intersect macroeconomics and finance (Christiano et al., 2010). These factors – (a) asymmetric information and agency problems in financial contracts, (b) the possibility of sudden and dramatic re-appreciations of market risk, (c) adjustments in credit supply as a critical channel by which market risk becomes systemic, (d) bank funding conditions – the creation of inside money – as major determinants of bank lending decisions, and (e) central bank liquidity as a substitute for market liquidity when private credit vanishes – have the capacity to influence banks’ liquidity preference over time. These changes in banks’ liquidity preferences can become a major cause of disruption for the broad economy.
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For instance, the ratio of non-performing loans to gross loans measures the asset quality in the loan portfolio of the banking system. A high ratio is indicative of deterioration in the financial sector’s credit portfolio and financial contracts. This in turn has adverse implications for the financial institutions’ payment flows, net revenue and solvency. Investment activity is therefore severely hampered due to the reduction in loanable funds for investment. A severely hampered investment retards economic activity and invariable slows down growth.
Similarly, if the banking system has a low capital adequacy ratio, this would give an indication of an increased likelihood of banks becoming insolvent. The probability of insolvency increases the risk associated with investment since investment financing is no longer guaranteed; this in turn slows down investment activity, productivity and growth. Again, liquidity in the banking system is crucial to economic activity. An increasingly illiquid banking system results in a slowdown in capital accumulation and subsequently in productivity gains.
A stable financial system is therefore one that enhances economic performance in many dimensions, whereas an unstable financial system is one that detracts from economic performance (Schinasi, 2004). Houben et al. (2004) believe that the increased importance of financial sector stability is related to four major trends in the financial economy of the past decades. First, financial systems have expanded at a significantly greater pace than the real economy. In advanced economies, total financial assets now represent a multiple of annual economic production. Second, the process of financial deepening has been accompanied by a changing composition of the financial system, with an increasing share of non-monetary assets and, by implication, greater leverage of the monetary base. Third, as a result of increasing cross-industry and cross-border integration, financial systems have become more interwoven, both nationally and internationally. Fourth, the financial system has become more complex, in terms of the intricacy of financial instruments, the diversity of activities and the concomitant mobility of risks.
A financial system that is robust is less susceptible to the risk that a financial crisis will erupt in the wake of real economic disturbances and is more resilient in the face of crises that do occur (Bank for International Settlements, 1997). The research further states that banking and financial crises can have serious repercussions for these economies in terms of heightened macroeconomic instability, reduced economic growth and a less efficient allocation of savings and investment. Ferguson (2002) further states that the most useful concept of financial instability involves some notion of market failure or externalities that can potentially impinge on real economic activity.
The world economy over the last two years has been affected by global financial crisis which has hit both developed and developing countries. This financial instability has caused significant economic and social consequences, evidenced by a drop in real activity, corporate bankruptcies, a rise in unemployment and increased poverty. Thus, there is no need to stress the importance of financial stability, both at the national and at the international levels, in a context marked by economic interdependence and increased financial liberalisation (BCEAO, 2005).
Cheang (2004) examined the condition of financial stability and its relationship with the Macao economy. He believes that the cost of financial crises is usually more easily observed than the contribution of financial stability to an economy. He found that the financial system in Macao is sound, yet he could not find a strong relationship between financial stability and growth. He also stated that although financial stability in Macao is not a direct contributor to the economic growth, it is undeniable that the stable financial system has played a significant role in underpinning the growth of the economy in good years and limiting the downside of business operations in difficult times.
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2.1 View of Africa’s financial sector
During the post-independence era, African governments intervened in the financial sector. The menu of intervention was quite extensive and included nationalisation of private banks, establishment of entirely new state banks and non-bank financial institutions, imposition of quantitative restrictions on the allocation of credit and restriction of external capital flows. Though the intentions of government intervention in the financial sector might have been benevolent in the sense of mobilising capital needed for investments and allocating capital to priority sectors, the actions were counterproductive and produced utterly dysfunctional financial systems. Moreover, the intended goals of capital mobilisation and allocation of capital to growth areas were not realised (Senbet and Otchere, 2005).
By the 1980s, the financial sector was experiencing the problems of mismanagement and inefficient allocation of resources that plagued the public sector in the majority of African countries. The financial system was characterised by pervasive default on loans, especially by state-owned enterprises, which accounted for a large share of the domestic credit supply (Ndikumana, 2001). In fact, the era produced a lost decade for Sub-Saharan Africa (1980s), whereby the region marched backwards, while the rest of the world, particularly the emerging countries in East Asia and Latin America, moved forward (Senbet and Otchere, 2005). The inefficiencies and distortions of the financial systems were exacerbated by the emergence of severe macroeconomic difficulties in the late 1970s and 1980s. Also, the illiquid nature of bank assets (loans) which are financed by liquid liabilities (deposits) threatens the stability of banks by exposing them to runs by depositors who cannot accurately assess the financial health of banks, because of the existence of asymmetric information between depositors and banks (Diamond, 1984).
The malfunctioning state of financial sectors impacted negatively on domestic savings mobilisation, intermediation, investments and, consequently, growth. Informal savings channels are prevalent in view of the grossly inadequate formal financial systems (Senbet and Otchere, 2005). On average, real per-capita GDP did not grow in Africa over the 1965–1990 period (Easterly and Levine, 1995). Specifically, in Sub-Saharan Africa, average real income, investment and savings have declined systematically in every decade since the 1970s (Ndikumana, 2001).
Most African countries have thus undertaken significant reforms. The objectives of these reforms were to create level-playing field for financial institutions and markets for instilling competition, strengthening their governance and supervision, and adopting a market-based indirect system of monetary, exchange and credit management for better allocation of financial resources. They were also to build more efficient, robust and deeper financial markets. Reforms covered seven areas: financial liberalisation, institutional strengthening, domestic debt, monetary management, banking law, foreign exchange and capital market (Kahn, 2005).
Among the major elements of financial sector reforms was the restructuring and recapitalisation of banks, which included privatisation of state-owned banks. These came with regulatory and supervisory schemes. This notwithstanding, essentially, the functions of savings mobilisation and financial intermediation have not fully recovered since reforms were initiated (Nissanke and Aryeetey, 1998). The deeper and politically sensitive issues of institutional development, such as contractual and legal systems, accounting and disclosure rules, and regulatory and supervisory mechanisms are still incomplete. Thus, despite the extensive financial sector reforms that have taken place, Sub-Saharan African financial systems face severe inefficiency, illiquidity and thinness (Senbet and Otchere, 2005).
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Ownership in the banking industry evolved significantly from a predominantly state-controlled system to a more privately owned system. The banking system is, however, still highly concentrated in the majority of Sub-Saharan African countries (Gelbard and Pereira Leite, 1999).
Financial sector reforms have been and continue to be among the key pillars of structural adjustment programmes (SAP) in Africa. The financial sector reforms were intended to reverse the adverse consequences of the repressive financial policies of the post-independence era, although the pace and the extent of policy reform varied across countries. Financial sector reforms and developments are a crucial channel for global integration and for keeping Africa at the cutting edge of best international practices (Senbet and Otchere, 2005).
It is primarily through the supply of credit to the private sector that financial intermediation stimulates real economic activity. However, the domestic credit to the private sector as a percentage of GDP has been low for most Sub-Saharan African countries, excluding South Africa (Table 1). Apart from Mauritius, where the domestic credit to private sector makes up 63% of GDP, credit to the private sector in all other countries makes up less than 50% of GDP. It has been noted that the decline in credit supply in Sub-Saharan Africa may be a factor for the poor economic performance of the subcontinent over the past three decades (Ndikumana, 2001). Table 1 Domestic credit to private sector as a percentage of GDP for 1996–2005
Country % Country % Angola 4.01 Mauritius 63.00 Botswana 14.85 Mozambique 12.28 Burundi 22.51 Namibia 48.63 Cameroon 7.72 Nigeria 13.71 Cape Verde 33.39 Rwanda 10.00 Ethiopia 21.41 Senegal 19.25 Gabon 9.73 Seychelles 26.28 Gambia, The 12.84 Sierra Leone 3.10 Ghana 11.45 Sudan 4.20 Guinea 3.69 Swaziland 15.30 Kenya 26.91 Tanzania 5.78 Lesotho 13.06 Uganda 6.17 Madagascar 9.38 Zambia 7.65 Malawi 7.57 Zimbabwe 30.03
Source: Computed from African Development Indicators (2007)
The performance in the banking system has somewhat improved in some countries, but it is still low in the majority of countries. High proportions of non-performing loans illustrate the inefficiencies in the credit allocation process and in loan repayment enforcement systems. Table 2 shows the average of bank non-performing loans to gross loans for some selected countries from 2000 to 2005. Average non-performing loans still make up more than 10% of gross loans in a number of countries. On a trend basis however, there is some improvement in the trend of non-performing loans over the years.
Average GDP growth rates show some significant economic growth in most African countries. Table 3 shows the average growth rates categorised on the basis of speed of growth and oil exporters. Expected growth is higher mostly within the oil-exporting countries; nonetheless, there is appreciable growth even within the slow-growth
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countries. More importantly, the growth rates being recorded are significant improvements from those of the 1970s and 1980s. Indeed, the reforms in most of these countries, primarily the financial sector reforms, appear to have improved the financial system and stabilised it, making it easy to raise capital for economic activity. Hence, the appreciable growth rates are also induced by an improved financial sector. Table 2 Average bank non-performing loans to gross loans from 2000 to 2005
Country % Country % Angola 5.45 Nigeria 21.17 Botswana 2.63 Rwanda 21.38 Gabon 9.37 Senegal 13.65 Ghana 17.08 Sierra Leone 18.13 Kenya 16.35 South Africa 1.93 Madagascar 12.78 Uganda 4.78 Mozambique 16.63 Zambia 9.78 Namibia 2.53 Zimbabwe 11.43
Source: Computed from African Development Indicators (2007)
Table 3 Africa GDP growth rates, by country type, 1996–2005
Slow-growth economies (GDP growth less than 4% a year)
Diversified, sustained-growth economies (GDP growth 4% a
year or more)s Oil exporters Country GDP growth Country GDP growth Country GDP growth Zambia 3.80 Mozambique 8.3 Equatorial
Guinea 30.8
Guinea 3.70 Rwanda 7.6 Chad 9.0 Niger 3.50 Sao Tome and
Principe 7.1 Angola 8.5
Malawi 3.30 Botswana 6.7 Sudan 6.3 Mauritania 3.30 Uganda 6.1 Nigeria 4.3 Togo 3.30 Cape Verde 5.8 Congo,
Democratic Republic of
3.4
Madagascar 3.20 Mali 5.8 Gabon 1.1 Lesotho 3.00 Tanzania 5.3 Kenya 2.90 Ethiopia 5.2 Eritrea 2.41 Sierra Leone 5.2 Seychelles 2.30 Burkina Faso 5.0 Comoros 2.13 Mauritius 4.8 Central African Republic
0.85 Ghana 4.7
Guinea-Bissau 0.47 Benin 4.6 Burundi 0.43 Senegal 4.5 Congo, Democratic Republic of
0.08 Cameroon 4.2
Zimbabwe –2.20 Gambia, The 4.2 Namibia 4.0
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3 Empirical analysis and results
To test the relationship between financial stability and growth, the theoretical underpinnings of growth follow the model used by Ndebbio (2004), modified to include financial stability. The model presumes that growth is determined by financial stability variables and other macroeconomic variables. Dyberg (2001) categorises various indicators of financial stability. Accordingly, these indicators include capital adequacy, asset quality, earnings and profitability, liquidity reserves, and market risk ratios.
The financial stability variables include capital adequacy, liquidity and asset quality. Capital adequacy is proxied by regulatory capital to risk-weighted assets, liquidity by liquid assets to total assets, and asset quality by non-performing loans to gross loans. The macroeconomic variables include financial depth, inflation, population growth and trade openness.
Our investment model is specified as:
, 0 1 , 2 , 3 , 4 , 5 , 6 , 7 , , ,i t i t i t i t i t i t i t i t i t i tY CA AQ LIQ FDEP INF POP TO V eα β β β β β β β= + + + + + + + + + (1)
where
Y = The dependent variable is GDP in constant US dollars
CA = Capital adequacy variable is the ratio of regulatory capital to risk-weighted assets
AQ = Asset quality variable is the ratio of non-performing loans to gross loans
LIQ = Liquidity variable is the ratio of liquid assets to total assets
FDEP = Financial depth variable is given as M2/GDP
INF = Consumer Price Index
POP = Population
TO = Trade openness is the ratio of exports plus imports to GDP
V =.The variable that captures the heterogeneity of the cross-sectional units
e.= Error term.
Subscript i and t represent country and time, respectively. In this case, i represents the cross-section dimension, and t represents the time-series component. Inflation, financial depth, population growth rate and trade openness are the control variables. They are included to avoid the possible error of omitting other relevant independent variables in the equation.
The motivation for financial stability variables are discussed briefly below:
• Capital adequacy: A high ratio gives an indication of a reduced likelihood of banks becoming insolvent. When banks become insolvent, it may lead to a loss of confidence in the financial system and a reduction in savings. This will go on to affect the intermediation process, investments and subsequently growth. In that respect, a positive and significant relationship is expected between the capital adequacy variable and the dependent variable, GDP growth rate.
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• Asset quality: NPL to gross loans measures the asset quality in the loan portfolio. An increase in this ratio signals deterioration in the financial sector’s credit portfolio and thereby in the financial institutions’ payment flows, net revenue and solvency (Dyberg, 2001). This could undermine the intermediation process, decrease investments and subsequently retard growth. As such, this ratio is expected to have a significant and negative relationship with the dependent variable.
• Liquidity: The liquidity variable is represented by the ratio of liquid assets to total assets. The linkage between liquidity and economic growth is supported by the existing empirical evidence in that countries with liquid markets experience faster rates of capital accumulation and subsequently greater productivity gains (Pollin et al., 2002). A positive, significant relationship is expected.
With respect to the macroeconomic variables, a priori, it is expected that financial depth is positively related to growth. Inflation is expected to be negatively related to growth; similarly, trade openness has a positive association with economic growth
3.1 Empirical results
The study covered a sample of 29 African countries over the 1996–2006 period. Table 4 Descriptive statistics
Variable Mean (%) Std. dev. (%) GDP growth 4.338 4.373 Capital adequacy 16.777 6.591 Asset quality 37.301 124.61 Liquidity 39.933 21.912 Financial depth 34.136 5.785 Inflation 15.558 34.238 Population 2.230 1.188 Trade openness 74.435 41.923
Table 4 presents some descriptive statistics on the panel of countries. Average GDP growth for the period (4.338%) shows appreciable economic activity in Africa. Indeed, though there is variation across countries in GDP growth, the magnitude of the standard deviation in relation to the mean value shows that the variation is not significantly large. Concentrating on financial sector stability indicators of interest, we find that capital adequacy ratio has a mean of 16.777%. To increase financial sector soundness, there is the need to increase this ratio further by improving on the capitalisation of financial institutions. The value of average asset quality is quite high and worrying – increasing non-performing loans could undermine financial intermediation. Similarly, further increases in the liquidity ratio would help to speed up financial intermediation and capital accumulation. What we find interesting in the descriptive statistics so far is a robust albeit marginal GDP growth occurring within a robust albeit marginally sound financial sector.
Prior to carrying out the empirical modelling, a correlation matrix of the variables is obtained and results are displayed in Table 5. The correlation matrix depicts associations among the variables and shows the absence of high correlation, and hence the absence of problems of multicollinearity.
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Table 5 Correlation matrix
GDP INF CA AQ LIQ POP FDEP Trade GDP 1.0000 INF –0.0751 1.0000 CA 0.2161 0.0147 1.0000 AQ –0.0932 0.0508 –0.0403 1.0000 LIQ –0.0453 0.2316 0.1573 –0.224 1.0000 POP 0.262 –0.0174 0.0961 0.1095 –0.1142 1.0000 FDEP 0.0017 0.3954 0.024 0.1514 0.2591 –0.1952 1.0000 Trade –0.1204 0.1042 0.0032 –0.1562 0.0557 –0.0416 –0.0314 1.0000
To explore the long-run characteristics of the panel model, all variables must be integrated of order I(1). Similarly, variables to be estimated in the short-run dynamic model must be stationary; hence, all the variables were tested for unit roots in a panel framework, and the panel stationarity tests applied are based on Im, Pesaran and Shin (hereafter IPS, 1997). The IPS test is based on a null hypothesis of the presence of unit roots in the data against an alternate hypothesis of stationary series. The test is derived from an estimation of panel versions of Dickey–Fuller types of equations, and the IPS test in particular increases the power of tests derived by Levin and Lin (1993) when individual ADF (augmented Dickey–Fuller) tests include a trend in the specification.
As shown in Table 6, the IPS results of the tests suggest the existence of unit roots for most of the variables at 5% significance level, except the non-performing loans to gross loans and the liquid assets to total assets variables. This notwithstanding, given the relatively small sample size and the possibility of a lower power of the tests, it is generally taken that there could be a common unit root process for the variables. Table 6 Panel unit root test on level variables
t-bar Cv10 Cv5 Cv1 W[t-bar] p-value GDP –1.470 –1.700 –1.750 –1.850 0.084 0.534 Capital adequacy –1.804 –1.700 –1.750 –1.850 –1.495 0.068* Asset quality –1.333 –1.700 –1.750 –1.850 0.732 0.768 Liquidity –2.160 –1.700 –1.750 –1.850 –3.176 0.001** Financial depth –1.554 –1.700 –1.750 –1.850 –0.310 0.378 Inflation –1.875 –1.700 –1.750 –1.850 –1.829 0.034** Population –0.093 –1.700 –1.750 –1.850 6.706 1.000 Trade openness –1.171 –1.700 –1.750 –1.850 1.524 0.936
Note: ** and * indicate significance at 5% and 10%, respectively
A test for the presence of unit root in the series at first difference is shown in Table 7. The result suggests that the series are stationary at 5% level of significance, except the asset quality and the population variables.
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Table 7 Panel unit root test on variables at first difference
Im–Pesaran–Shin test t-bar Cv10 Cv5 Cv1 W[t-bar] p-value
GDP –1.863 –1.700 –1.750 –1.850 –1.770 0.038 Capital adequacy –2.129 –1.700 –1.750 –1.850 –3.030 0.001 Asset quality –0.637 –1.700 –1.750 –1.850 4.019 1.000 Liquidity –2.647 –1.700 –1.750 –1.850 –5.476 0.000 Financial depth –2.383 –1.700 –1.750 –1.850 –4.226 0.000 Inflation –1.748 –1.700 –1.750 –1.850 –1.226 0.110 Population –2.010 –1.700 –1.750 –1.850 –2.508 0.006 Trade openness –2.113 –1.700 –1.750 –1.850 –3.004 0.001
Given the strong possibility of a common unit root process among the variables, the model was explored for long-run trends based on the panel cointegration technique. Generally, there are mainly two different approaches for the panel cointegration tests: residual-based procedure, pioneered by Engel and Granger in their two-step approach for pure time-series data, and maximum-likelihood-based approach, reminiscent of the Johansen and Julius Trace’s maximum likelihood procedure. Residual-based panel cointegration test statistics were first introduced by studies such as McCoskey and Kao (1998), Kao (1999) and Pedroni (1999); and maximum-likelihood-based panel cointegration test statistics were introduced by Groen and Kleibergen (2003), Larsson and Lyhagen (1999) and Larsson et al. (2001). For the purposes of this study, the Kao (1999) test is employed.
The ADF and PP (Phillips–Perron)-type tests are performed on the residuals of the panel OLS (ordinary least squares) model estimated. These were chosen taking into consideration the emerging argument that time-series cointegration tests, even in the presence of large sample sizes, often yield conflicting conclusions or ‘mixed signals’, meaning that there is often a low correlation of empirical p-values. Both tests by Fisher suggest that the variables are cointegrated at 1% significance level (Table 8). Table 8 Fisher version of the Kao residual-based test for co-integration
Test Assumption Statistics (chi-square) p-value Dickey–Fuller No cross-sectional correlation 387.32 0.0000 Phillips–Perron No cross-sectional correlation 508.17 0.0000
The residual-based test of Fisher is supported by Hadri’s (2000) residual-based test for unit root. This Lagrange multiplier (LM) test has a null of stationarity, and its test statistic is distributed as standard normal under the null. The series may be stationary around a deterministic level, specific to the unit (i.e. a fixed effect) or around a unit-specific deterministic trend. The error process may be assumed to be homoscedastic across the panel or heteroscedastic across units. Serial dependence in the disturbances can also be taken into account using a Newey–West estimator of the long-run variance. The residual-based test is based on the squared partial sum process of residuals from a demeaning (detrending) model of level (trend) stationarity.
The Hadri test for unit root suggests that irrespective of the assumption about the residual, the null hypothesis of non-stationary series is rejected, indicating the existence of a cointegrating relationship among the covariates (Table 9).
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Table 9 Hadri panel unit root test for e
Eps Z (mu) p-value Z (tau) p-value Homo 19.541 0.0000 5.577 0.0000 Hetero 15.285 0.0000 2.76 0.0029 SerDep 8.166 0.0000 7.43 0.0000
Notes: 0 = all 29 time series in the panel are stationary processes; Homo = homoscedastic disturbances across units; Hetero = heteroscedastic disturbances across units; SerDep = controlling for serial dependence in errors (log truncated error = 2)
Results from both classes of tests and the test for common trend of Nyblom and Harvey (2000) suggest that variables are cointegrated or that there exists a long-run relationship between the endogenous variables and the covariates. The next section performs the panel error correction model using dynamic fixed-effect estimator and the pooled mean group estimator.
We estimate the dynamic panel vector error correction model using the procedure of Pesaran et al. (1995). A pooled mean group model and a dynamic fixed-effect model are estimated to evaluate short- and long-run dynamics of the model. Table 10 reports the dynamic fixed-effect estimates. The table provides a long-run (top panel) and a short-run analysis (bottom panel) as well. It also provides estimates of the adjustment parameter. Our discussion of the results concentrates on financial sector stability variables. The results are discussed next.
In the long run, regulatory capital to risk-weighted assets, the proxy for capital adequacy, is positive and significantly related to GDP.1 This implies that an increase in regulated capital helps increase the quality and soundness of financial institutions in their financial intermediation activities. Thus, a relatively high capital adequacy ratio helps banks to absorb losses and reduce the likelihood of insolvency. This facilitates longer-term project financing by banks, which in turn induces rapid capital accumulation for increased economic activity, thereby resulting in increased GDP and economic growth.
The long-run results also show that the proxy for asset quality, non-performing loans to gross loans, is significant and negatively related to GDP. This implies that when the ratio of non-performing loans to gross loans increases, it impacts negatively on growth. Thus, growth deteriorates. This is because high proportions of non-performing loans illustrate the inefficiencies in the credit allocation process and in loan repayment enforcement mechanisms. This may be as a result of high incidence of moral hazards or information asymmetry. One way in which high default rate affects the real sector is that it discourages lending to the private sectors which act as the engine of growth to economies. By this means, it reduces the overall supply of credit, which depresses private investment. In this line, employment is also affected, as is income. This practice will eventually slow the growth in any economy. Also, when the ratio of non-performing loans to gross loans increases, it sends bad signals to the public.
The measure of liquidity is found to be positive and significantly related to GDP in the long run. This means that higher liquidity in the financial sector is likely to boost growth in an economy. When liquidity is high, investors are able to access funds rapidly when needed for productive purposes. Liquidity of the financial sector is therefore very important in speeding up capital accumulation for productive activities and economic growth. The traditional financial sector indicator – financial depth – is also positively and significantly related to GDP. Thus, a deeper intermediation of the financial sector within the economy is necessary for increased GDP activity and growth. Deeper intermediation helps mobilise necessary capital needed to economic activity.
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The control variables for GDP show results that are largely in line with a-priori expectations. However, inflation appears to be positively related to GDP. Indeed, this is plausible given the possibility of a non-linear effect of inflation on economic activity.
The short-run dynamic analysis shows results which largely mimic the long-run analysis. More importantly, the error correction term is significant and displays the right sign. The error correction term shows that any disequilibrium in the short run is adjusted and corrected to long-run equilibrium at a speed of 34.21%. The error correction term also confirms the existence that there is causality running from financial sector stability variables and economic growth. With the exception of asset quality, which appears to be insignificant in explaining GDP growth, all other variables are significant in explaining GDP growth. Table 10 Dynamic fixed-effect panel estimation
Long-run dynamics Coefficient Std. err. Z p > |z| GDP (–1) 0.9650 0.0132 73 0.0000 Capital adequacy 0.0106 0.0031 3.46 0.0010 Asset quality –0.0016 0.0005 –3.25 0.0010 Liquidity 0.0064 0.0028 2.27 0.0230 Financial depth 0.2057 0.0326 6.31 0.0000 Inflation 0.0912 0.0315 2.89 0.0040 Population –0.0964 0.0146 –6.58 0.0000 Trade 0.0000 0.0000 3.9 0.0000 Short-run dynamics Error correction term –0.3421 0.0284 –12.04 0.0000 Δ GDP 0.4330 0.0375 11.55 0.0000
Δ Capital adequacy –0.0143 0.0011 –12.49 0.0000
Δ Capital adequacy (–1) 0.0066 0.0009 7.03 0.0000
Δ Asset quality –0.0002 0.0002 –1.21 0.2280
Δ Asset quality (–1) 0.0001 0.0001 0.64 0.5210
Δ Liquidity –0.0108 0.0012 –8.74 0.0000
Δ Liquidity (–1) 0.0048 0.0009 5.59 0.0000
Δ Population 0.0703 0.0066 10.59 0.0000
Δ Population (–1) –0.0353 0.0050 –7.09 0.0000
Δ Trade 0.2344 0.0504 4.65 0.0000
Δ Trade (–1) –0.1007 0.0339 –2.97 0.0030
Δ Inflation –0.3419 0.0352 –9.71 0.0000
Δ Inflation (–1) 0.1403 0.0303 4.63 0.0000
Δ Financial depth –1.2681 0.0473 –26.81 0.0000
Δ Financial depth (–1) 0.5604 0.0495 11.31 0.0000 Constant –0.0280 0.1323 –0.21 0.8330
Financial stability and economic growth 135
4 Conclusion and implications
This paper examined the effect of financial stability on economic growth in emerging markets. The results from a dynamic panel data model showed that a stable financial sector is necessary for economic growth. Specifically, capital adequacy and liquidity in the financial sector are important elements for spurring economic growth. This is because high capital adequacy cushions banks from possible insolvency through the absorption of losses that are likely to occur as a result of moral hazard and high risk-taking. This has a way of facilitating business transactions and, for that matter, economic activity. Also, an increase in liquidity increases economic activity in the sense that investors can have easy access to funds as and when needed for productive purposes. Asset quality is also important in the financial sector. A low asset quality base negatively influences growth. This arises from the fact that high proportions of non-performing loans indicate ineffectiveness in the credit allocation process and in the system instituted for loan repayment. This is often due to the problem of information asymmetry, especially in the financial sector. This in essence may impede productive investments and inhibit economic activity and growth.
Financial stability clearly requires sufficient banking profitability, liquidity, capital and quality of assets. A prerequisite to formulating effective policies to enhance economic growth is thus to understand the factors that ensure financial stability. Based on the findings of the study, the following is recommended for policy makers: Generally, the oversight of banking and supervision departments in most central banks in Africa is overstretched if financial stability is to be monitored. Specifically, African countries should aim at establishing Financial Stability Authorities with the exclusive oversight to see to the stability of the various financial systems. Some specific policies which emanate from this paper as follows: There should be an improvement in the legal environment for financial transactions and proper supervision of all key financial institutions to ensure that sound practices and principles are adopted. In that respect, the following areas should be of interest: accounting, payments and settlements, banking supervision, securities market supervision, insurance supervision, and other financial conglomerates. This requires a well-developed and coherent legal environment that forces regulators to faithfully and obediently implement and enforce regulation. This also suggests that central banks should be independent of political interference in the daily execution of supervisory tasks but are accountable in the use of their powers and resources to pursue clearly defined objectives. These efforts will help governments achieve financial stability and high-quality intermediation. Also, increasing the capital requirement necessary for operation of major financial institutions is very important and will go a long way to cushion banks and other financial institution from any losses that are likely to derail the whole system. Especially, risk-based capital requirements serve as a buffer against depositor losses.
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Note 1 We proxy economic activity growth with GDP changes in the long-run estimates; in the short-
run analysis however, GDP growth is implied by construct since the log difference of GDP is equivalent to GDP growth.