Download CRP #015 Financial Constraints and PSD

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CREPOL RESEARCH PAPERS: # CRP015 Title: Financial Constraints and Private Sector Development within the ECOWAS Author: Amie Gaye Introduction
In the aftermath of the recent global financial crisis which resulted in widespread and persisting negative socioeconomic repercussions, the region of Sub-Saharan Africa (SSA) emerged as the least affected. Being one of
the least developed regions globally, one may anticipate that it may have been gravely affected especially with
its strong reliance on foreign aid and investment originating from western countries which declined markedly.
The impact of the crisis was however delayed due to SSA’s limited integration with the global financial system,
weakening the transmission of the crisis and thereby cushioning it from its adverse effects witnessed by other
continents in particularly Europe. After years of initial stagnation, SSA is presently witnessing steady growth
with seven of the ten fastest growing economies in the world found in SSA (The Economist, 2012). The IMF
Economic Outlook 2013 forecasts continued robust growth of 5.5% into 2014, driven by increased investment
in exporting sectors and a rebound in agriculture.
However, increased economic growth has not translated into increased economic welfare in SSA and challenges
of chronic unemployment still persist. Women and youth are victims of the large wave of unemployment which
translates into poverty, worsening human development performance and discontent. As a result, a thriving
private sector appears to be a solution to these challenges faced by policy makers, in addition to bolstering
growth. To date, no economy has enjoyed prosperity without a vibrant private sector, the engine of growth and
development. Its success results in positive welfare effects of employment creation, poverty alleviation,
encouraging consumption and boosting aggregate demand in the economy. It maximizes citizens’ contribution,
making optimal use of available talent, which emboldens entrepreneurial spirit and innovation, boosting
knowledge and skill accumulation which cumulatively stirs growth.
Policy makers have a role to play in the creation of a conducive business environment to enable the existence of
a vibrant private sector, engaging economic actors, improving output and efficiency on a path to sustainable
development. Sound macroeconomic management, stable exchange rate regimes and low inflation are important
ingredients for growth, whilst political instability discourages investment and private actor involvement
particularly in long-term undertakings, with heightened uncertainty.
The significance and need for enhanced private sector development (PSD) in Sub-Saharan Africa cannot be
stressed enough at this moment in time. It is essential for growth stimulation and business innovation to ward
off past decades of erratic growth and external pressures from troubled advanced economies abroad battling
recession and debt crisis, following a period of global economic turmoil. Such a strategy has triumphed in
emerging countries particularly in China where liberalization and business-friendly policies have fuelled
innovation and entrepreneurial flair, resulting in a sharp increase in private ownership and poverty reduction. A
vibrant private sector will seize the present growth opportunities presented by the SSA continent, which other
foreign countries are attempting to do.
1 After years of large public sector dominance, the private sector in SSA is slowly emerging. SSA’s limited
integration into the global financial system translates into limited availability of finance which the empirical
literature has confirmed is essential for growth. Consequently, capital constraints have curbed the regions
ability to reach its full growth potential, with negative implications for PSD. Thus with Africa’s recent
impressive growth trends, now is a good time to reflect on means of unleashing the private sector, whose
potentials are multifaceted aside from driving growth. The first would be in engaging the millions of
unemployed youth, who upon completion of education (both formal and informal) are faced with the
challenging task of seeking employment in their field. The second aspect is the prospect of attracting the
proportion of the labour force engaged in informal employment, who often constitute a large proportion in
many African countries.
The purpose of this paper is to (1) assess the significance of finance for growth, (2) evaluate the determinants of
the supply of private sector credit within ECOWAS countries and propose a means of breathing life into the
predominantly underdeveloped sector in these countries, characterized by financial constraints and informal
enterprises. The paper will be in two parts. Part A will address the first aforementioned research objective with
the use of an augmented-Solow growth model, employing panel data methodology. Part B utilises a standard
credit supply model, incorporating an economic diversification index. The inclusion of the latter variable is
significant in revealing the capacity of the sampled countries’ ability to resist external and internal shocks, in
addition to indicating its appeal factor as an investment haven for highly liquid countries looking to minimise
risk and in turn ease the credit constraints faced by local firms. These approaches cumulatively provide a
holistic treatment of the issue, by highlighting the impact of capital constraints to the private sector and isolating
the determinants of the availability of credit. Results obtained from these empirical investigations will be
utilised to postulate recommendations to enhance the workings of the private sector within ECOWAS countries.
Review of the Literature
The Solow growth model (1965) is a neoclassical theory which presents a Cobb-Douglas production function
on the assumption of exogenous technology. This traditional model implies that growth is driven by population
growth and physical capital deepening which is enabled via savings and investment and subsequently
converging towards a steady state level of growth. The model thus implies physical capital deepening
determines the speed to a steady state level of growth which also suggests growth is bounded. Furthermore, the
model postulates diminishing returns to physical capital and constant technology.
2 However, the absence of accounting for technical progress results in the need for the adoption of endogenous
growth models which identify and encapsulate a significant driver of growth omitted from the Solow model;
human capital accumulation which enables unbounded growth with no steady states. The significance of human
capital accumulation has been endorsed by Lucas (1988) and Romer (1986) who advocated education, training,
research and development as a means of driving growth. Income disparities across countries can subsequently
be narrowed via augmented human capital. Barro and Sala-i-martin (1999) contend that the existence of human
capital lessens the issue of diminishing returns to capital thereby spur growth even in the absence of technical
advancement.
Nevertheless, it is notable that the aforementioned models ignore the existence of capital market frictions and
imply that savings translates directly into investment. Thus finance affects growth in two ways; by enhancing
both physical and human capital accumulation. In an assessment of the role of income distribution and
investment in education, Galor and Zeira (1993) found financial intermediation to spur human capital and in
turn, growth. The impact of finance on development has dominated research agendas and continues to spur
interest, renewed in the wake of the recent global financial crisis. Supporters of the relevance of finance for
growth stems from Schumpeter (1912), with empirical and theoretical affirmations from Goldsmith (1969),
McKinnon (1973), Shaw (1973), Fry (1978), King and Levine (1993), Levine and Zervos (1998) and Berkhart
et al (2001).
Evidence establishing a positive impact of finance on growth has been found at cross-country level (Macro) and
across industries; at the micro-level (Rajan and Zingales, 1998; Guiso et al 2005). As a result, there has been a
call for the development of financial institutions in order to reap the gains of growth originating from the
functions of finance; resource allocation, risk management facilitation, savings mobilization, ease of trade,
corporate control and management monitoring, (Levine, 1997).
Via these functions, finance additionally
mitigates information, enforcement and transaction functions (market frictions). Underdevelopment of financial
markets has the negative impact of yielding capital constraints, a key issue to address in developing regions
such as Sub-Saharan Africa, where there exists a high concentration of small and informal enterprises in
primary industries hindered by limited finance. Beck et al (2004) confirms that financial development enhances
the growth of small firms’ more than large firms.
Another growing phenomenon is interest in the relationship between growth and the interaction of human
capital and finance. Using a translog production function with a growth equation in a panel of 82 countries over
21 years, Evans et al (2002) interact money and human capital to obtain a positive relationship with growth. The
authors propose that their results indicate that financial development is as important as human capital and are
complementary in the growth process. Whereas research in this area is currently in its infancy, authors such as
3 Outreville (1999), De Gregorio and Guidotti (1995), Mishkin (2007), Ang (2008) and Hakeem (2010) found
positive relationships. Mishkin (2007) maintains that globalisation stimulates financial development in
developing countries leading to growth but only when these countries enjoy high levels of human capital along
with the right institutions. Low human capital may be a contributor to the underdeveloped financial systems
prevalent in SSA, which worsens capital constraints, impeding growth.
Like a harness, capital constraints chain the region preventing its gallop towards higher growth rates. Hakeem
(2010) conducts a growth accounting exercise including four financial development indicators in a panel data
study of 24 African countries, including an interactive term between human capital and finance. Financial
development indicators do not emerge significant in explaining growth, however the interactive term was found
positive throughout, signalling complementarity between finance and human capital. The author attributes the
poor performance of financial development proxies to long periods of financial repression and crowding-out of
private sector by the public sector. Demetriades and Law (2006) argue that low performance of financial
development indicators in developing countries is as a consequence of weak institutional frameworks. They
propose that for finance to yield long-term growth, sound institutions must be present.
The Economic and Business Environment within West African Countries
This section analyses the business environment within ECOWAS countries, which gives an insight as to why
PSD is impeded, highlighting weaknesses and areas for improvement. PSD has a key role to play in reviving the
economies of post-conflict countries such as Sierra Leone and Liberia, thus in order to implement the right
policies, a good place to start is in considering how one’s economy compares with others.
Table 1 below gives an overview of the economic standings of the ECOWAS countries in 2012. Real GDP
growth averaged at 6% within the region, with Sierra Leone obtaining the highest growth rates driven by natural
resource prices resurgence after the global financial crisis. Cote d’Ivoire exports as a percentage of GDP is the
highest of 15 ECOWAS countries at 55%. With its exports composed primarily on commodities, this leaves
Cote d’Ivoire vulnerable to negative external shocks (poor harvest, drought) which will adversely affect growth
and balance of trade since exports constitute half of GDP. The Liberian economy is similarly heavily dependent
on exports; strengthened agriculture which has accelerated its growth over recent years in the aftermath of
political instability, attracting foreign direct investment.
4 Cape Verde and Gambia have the highest proportion of external debt well above the average of 27%. Both
countries are devoid of natural resources endowed by their fellow ECOWAS counterparts, with a small export
base and reliant on tourism. A weak international position coupled with a wide disequilibrium in their trade
balances has hindered economic development in these small ECOWAS countries. Chronic trade deficits weaken
the capacity of a country to service its external debts and its ability to implement growth enhancing policies.
Still suffering from the ails of past political instability, Guinea observes the highest rate of inflation 15.2%
followed by Sierra Leone and Nigeria at 13.8% and 12.2% respectively. The CFA countries on average have the
lowest rates of inflation, since a primary requirement of being a member of the WAEMU (West African
Economic and Monetary Union) is to adhere to a maximum annual inflation rate of 3%. All countries observed
a negative trade balance with the exception of Nigeria who benefited from sharp resurgences in oil prices being
an oil dependent economy.
Table 1: Selected economic statistics of ECOWAS countries in 2012
Country
Cape Verde
Gambia
Ghana
Guinea
Liberia
Nigeria
Sierra Leone
Benin
Burkina Faso
Cote d'Ivoire
Guinea Bissau
Mali
Niger
Senegal
Togo
Average
Real GDP
Growth
4.3
3.9
7.0
3.9
8.3
6.3
19.8
3.8
8.0
9.8
-1.5
-1.2
11.2
3.5
5.0
6.14
Exports
% of
GDP
42.5
28.0
43.1
29.7
45.8
38.3
25.4
14.9
26.9
55.0
16.4
30.7
25.4
26.4
40.0
32.57
External
Debt % of
GDP
77.9
43.6
21.7
30.8
12.3
2.4
33.3
17.6
25.1
24.5
18.0
28.6
20.0
31.9
17.7
27.03
Inflation
2.5
4.6
9.2
15.2
6.8
12.2
13.8
6.7
3.6
1.3
2.2
5.3
0.5
1.1
2.6
5.84
Trade
Balance of
Goods
-42.0
-28.8
-10.8
-18.4
-35.6
15.2
-15.3
-11.9
-2.0
10.2
-7.1
0.4
-9.2
-19.2
-14.8
-12.62
Fiscal
Balance
% GDP
(incl
grants)
-7.5
-4.4
-11.5
-3.3
-0.5
0.9
-2.8
-0.8
-3.1
-3.4
-1.8
-1.1
-3.5
-5.7
-6.8
-3.69
Source: IMF Regional Economic Outlook May 2013: Sub-Saharan Africa,
The World Bank’s Doing Business 2012 reported that 36 of the 46 surveyed Sub-Saharan African countries
improved regulations and business environment in 2011.
5 Chart 1: ECOWAS countries’ rankings on Ease of Doing Business Survey 2013
180 160 140 120 100 80 60 40 20 0 Source: World Bank’s Doing Business 2013 Chart 1 above exhibits ECOWAS countries’ aggregate rankings on all surveyed indicators of the ease of doing
business survey in 2013. 183 countries are surveyed globally, ranked from 1 to 183 across 10 major categories;
starting a business, dealing with construction permits, registering property, getting credit, protecting investors,
paying taxes, trading across borders, enforcing contracts, resolving insolvencies and getting electricity. (See
Table 2 in Appendix for country rankings in each category surveyed). Singapore is a benchmark obtaining an
aggregate ranking of 1, being the economy with the best performance globally, i.e. the best place to do business.
The SSA Regional average of 140 is also included in Chart 1, which is very telling, indicating that of the 15
countries only 3 are above this average, except for Sierra Leone which also obtained a ranking of 140.
The Doing Business 2013 Report identified Burkina Faso and Mali as one of the top ten improvers since 2005.
The reports also highlights that of the 50 economies implementing the most useful business regulations, 17 can
be found in SSA. Ghana has implemented business friendly policies to bolster its business environment and
private sector. These include redenominating the Ghana Cedi in 2007 for ease of transaction and accounting
purposes. Ghana’s Doing Business ranking has however fallen one place from 63 in 2012 to 64 in 2013, but still
remains the best performer among the ECOWAS countries. Conversely, Guinea and Guinea Bissau emerge as
the worst performing economies in the region with rankings of 176 and 179 respectively, plagued with years of
unrest and poor macroeconomic management. Chart 1 indicates that all eight members of the WAEMU are
below the SSA regional average, trailing all non-CFA countries (with the exception of Guinea). Heavy
bureaucracy and corruption have been contributing factors to the poor performance of WAEMU countries. A
6 striking observation in Table 2 in the Appendix is that all WAEMU countries obtain identical scores for Getting
Credit; 129 and 127 in 2013 and 2012 respectively. These imply that regardless of economic conditions within
the CFA space, access to credit remains unchanged and aside from Gambia and Guinea, WAEMU countries
were awarded the lowest rankings in that category.
While these rankings do not give a precise and holistic indication of a country’s competitiveness, the annual
rankings can be considered an indicator of policymakers’ performance on improving business environments for
investors and firms. Other considerations significant to business environments within these countries include:
human capital development, high transport costs, inadequate infrastructure, raw materials, water, regulatory
policies and land rights.
Credit Constraints and Development
A contributing factor to stunted PSD in West Africa has been capital constraints which this study aims to focus
on addressing. Underdevelopment of financial systems and relatively limited integration to the global financial
systems, limits their capacity to benefit from global booms. Additionally, UNCTAD (2013) reports that SSA is
globally the least recipient of foreign direct investment (FDI), despite a 5.5% increase in 2012 whilst inflows
contracted sharply in developed countries. A significant proportion of SSA’s FDI inflows are aimed at its
natural resources (Morisset, 2001). Thus there is a positive correlation between FDI inflows into an African
country and the size and nature of its natural resources. In recent years, Ghana and Nigeria have been the most
attractive countries for FDI in West Africa due to their oil reserves. Morrisset (2001) argues that an
improvement of business environments boosting their level of international competitiveness will attract FDI.
Start-ups especially small firms are faced with costly financing of projects as information asymmetries cause
banks and investors to attach high interest rates to mitigate potential risks (Eben 2008). Some have argued the
developing financial markets in SSA are key to unlocking growth by surmounting the challenge of capital
constraints. However, capital markets in SSA are observed to lack depth and suffer from illiquidity.
With the economic climate post-crisis, there is an even stronger need to compete for capital across the world, a
challenge faced by SSA. The risk profile of SSA makes it difficult to borrow from international private lenders,
leaving the option to borrow only from public authorities. SSA’s external debt issues have been found to result
as a consequence of proceeds of the borrowings to be utilised on non-economically productive use such as
consumption which doesn’t stir growth and long periods of maturity servicing of which is based on volatile
export proceeds (Seck, 2008).
Cumulatively, the aforementioned issues have contributed to the financial constraints faced by ECOWAS
countries. Despite challenges of credit faced by all ECOWAS countries, the impact is even stronger within nonCFA countries (Cape Verde, Gambia, Ghana, Guinea, Liberia, Nigeria and Sierra Leone). These countries
7 (aside from Cape Verde) are dotted along the coast of West Africa and free from growth impediments faced by
their landlocked counterparts, i.e. limited access to ports restricting international trade and mounting
transportation costs. Despite being endowed with such an advantage and subsequently presented with a better
opportunity, they remain capital constrained, unable to significantly develop, exhibiting modest growth rates.
A UN commissioned report in 2004 identified private sector impediments in developing countries to be; poor
access to finance, poor level playing field and lack of knowledge and skills. Consequently, recent studies have
attempted to scrutinise and highlight constraints to development and postulate possible means of improving
current conditions. Thus the current exercise attempts to contribute to existing knowledge, in addition to putting
forward recommendations to alleviate the impediments of limited finance. Over the years, there has been
repetitive implementation of unsuccessful policies despite resulting in limited positive outcomes, thus
consequential in weak improvement in this area. In addition, there appears to be a disconnect between practice
and scholarly development.
In order to drive private sector development, policy makers must be open to
considering and implementing research finding and recommendations.
Table 3: Growth of Domestic Credit to Private Sector of ECOWAS Countries
Country Cape Verde Gambia, The Ghana Guinea Liberia Nigeria 2006 0.15 0.20 -­‐0.29 0.17 0.32 0.00 2007 0.15 0.04 0.31 -­‐0.25 0.13 0.92 2008 0.16 0.17 0.10 0.01 0.24 0.34 2009 0.03 0.05 -­‐0.01 -­‐0.22 -­‐0.01 0.14 2010 0.00 0.04 -­‐0.02 0.47 0.21 -­‐0.35 2011 0.04 0.06 -­‐0.01 0.60 0.11 -­‐0.15 Average 0.09 0.09 0.01 0.13 0.17 0.15 Sierra Leone 0.01 0.01 0.35 0.31 0.07 -­‐0.02 0.12 0.04 0.08 0.03 0.88 0.09 0.24 -­‐0.02 -­‐0.03 0.17 -­‐0.05 0.14 0.51 -­‐0.04 0.11 0.00 0.24 0.07 0.05 0.01 0.56 -­‐0.05 0.17 0.06 -­‐0.19 0.07 -­‐0.04 0.07 0.14 0.02 0.15 0.03 0.15 0.04 -­‐0.01 0.04 0.11 0.03 0.03 0.05 0.15 0.05 0.15 0.00 0.90 0.14 0.09 0.12 0.31 0.07 0.03 0.05 0.52 0.03 0.13 0.04 0.10 0.12 0.16 0.14 0.06 0.06 0.16 0.12 Benin Burkina Faso Cote d'Ivoire Guinea-­‐Bissau Mali Niger Senegal Togo Average Source: World Bank’s World Development Indicators, 2013
Table 3 above shows the growth of private sector credit over a six year period which encapsulates both pre and
post-crisis periods. Across the ECOWAS region, private sector credit has been low and remained so in periods
8 of economic prosperity and worsening during crisis. These observations yield evidence to the financial
repression hypothesis. Stagnation of credit to the private sector occurred prior to the recent global and financial
crisis and persisted thereafter, remaining unaffected by any economic activity, seemingly a fixed neglected
component of the economy. Table 3 highlights the magnitude of effort that has been devoted towards alleviating
credit constraints faced by the private sector, despite several calls and promises to enhance PSD by African
governments. Indeed, few firms are perhaps established enough to take advantage of external finance via stocks
and bonds, underscoring the degree of capital constraints faced by the majority of firms. Firms within countries
with high growth rates and relatively sophisticated financial markets such as Ghana and Nigeria, face the same
predicament as their low income counterparts. This illustrates the degree of financial market underdevelopment.
Table 4 in the Appendix compares money and credit over a decadal period and reveals an increase in money
and quasi-money (M2 as % GDP) across all ECOWAS countries. This increase is however not reflected in the
claims on central government figures which contracted over the eleven year period, whilst private sector credit
increased. For WAEMU countries, crowding-in of the private sector is observed i.e. an expansion in money
leads to increased private sector credit as public sector credit contracts. This observation is due to a stringent
convergence criterion within the WAEMU that requires fiscal balance to GDP ratio of zero. Fiscal deficits
cannot be monetised and finance needs to be raised by issuing bonds. The WAEMU’s central bank is
independent and thus is strict in enforcing these rules. By contrast, non-CFA countries face no such constraints
or unified directives regarding fiscal deficits and are free to exercise discretion at the national level. Weak
central bank independence within non-CFA countries has manifested in frequent monetisation of deficit, a
culture which may be ingrained and prove difficult to discontinuity. A monetary expansion in these countries
results in the government absorbing a higher proportion than the private sector (see Table 4; Gambia and
Guinea). Financial repression is most severe in Guinea-Bissau, Niger and Sierra Leone. The proportion of
money creation within the two zones between 2000 and 2011 is comparable (see averages). However, with the
omission of Cape Verde which is an outlier driving results in the non-CFA zone, it can be observed that private
sector credit on average is higher within the WAEMU. Money creation; M2 as a % of GDP has been low across
all ECOWAS countries compared to other growing regions such as East Asia (China; 180%, Malaysia; 139%,
Singapore 136%) and Latin America (Brazil; 74%, Chile; 76%, Bolivia 68.7), (WDI, 2013).
In a recent study Stampini et al (2011) to estimate the size of the private sector in Africa between 1996 and
2008, the study found that the private sector makes up two-thirds of investment and credit. Cross-country
variability existed only for consumption; high within low income countries and low for oil exporters. Such
results coupled with the above analysis reveal the significant potential of the private sector and why it
improvements should be high on governments’ agendas, especially constraints faced by small and medium
9 sized enterprises. There are several possible government interventions which will later be considered based on
the results of this exercise.
Methodology and Data
The corner-stone of this study is the employment of the augmented-Solow model in assessing the significance
of private sector credit in contributing to growth within ECOWAS countries. The study will benefit from
Mankiw, Romer and Weil (1992) and Hakeem (2009) in addressing two key research questions:
•
Does private sector credit spur growth in West Africa?
•
Is increased credit coupled with human capital accumulation a necessary condition for credit to have a
positive impact on growth?
Addressing the first question will highlight the effect of current efforts to address credit constraints faced by
firms on growth. In accordance with theoretical literature, the coefficient on credit is expected to be positive and
significant, with its magnitude highlighting its importance for the growth process, for the hypothesis to hold.
The second question requires the construction of an interactive term between education and private sector
credit. A positive interactive term implies that for positive growth results, policies to increase private sector
credit must occur simultaneously with increased educational investment. These include investment on
vocational training. In such a scenario, a policy to increase one without the other will yield futile results and
will lead to a faulty conclusion that private sector development does not yield growth. Thus this hypothesis calls
for coordinated investment across these sectors. Increased finance to the private sector may also impact
education and training; as firms expand, employees both new and old are drawn to embrace the expansion and
incorporate the organisations’ strategies and goals. A flourishing private sector also increases the desire to
increase one’s human capital in a bid to seize market opportunities, especially for those unemployed or with
limited skills.
Part B of the study will decompose the determinants of the supply of private sector credit within the ECOWAS
utilising a standard credit supply model, enhanced with an export diversification variable. This export
concentration index is calculated by UNCTAD and determines the degree of market concentration of an
economy. The index ranges between 0 and 1, with 1 indicating maximum market concentration and low
diversification whereas 0 implies the contrary. The greater the degree of economic diversification (i.e. close to
0), the greater the degree of resilience an economy exhibits towards external shocks. Furthermore, the index
also implies the degree of sophistication of an economy, which also has implications for potential foreign direct
10 investment for countries seeking to minimise risk. Combining the results of both exercises, recommendations
will be made to address the credit constraints faced by ECOWAS’ private sector.
Part A: Empirical Model
The augmented Solow model presented by Mankiw et al (1992) deviates from the original model with the
incorporation of human capital and introducing constant returns to scale of the production function. The
assumptions eradicate the existence of a steady state level of output and imply income disparities between
countries may persist despite similar rates of capital accumulation and population growth (Favero, 2001). The
Cobb-Douglas production is thus:
!
𝑌! = 𝐾!! 𝐻! (𝐴! 𝐿! )!!!!! , 𝛼 + 𝛽 > 1 (𝑖)
Where Y denotes output at period t, K; capital, H; human capital, A; technology, L; labour, with α and β shares
of factors to output.
Differentiating (i), taking logs and substitution yields:
!
!
!
ln 𝑦! = 𝑙𝑛𝐴! + 𝑔! + !!! ln 𝑠! − !!! ln 𝑛 + 𝑔 + 𝛿 + !!! ln (ℎ)
(ii)
Where ln yt is the log of output per capita, lnA0; technology, gt captures resources, institutional factors and
climate and sk; the rate of savings. (n+g+δ) is as per augmented Solow model; population growth, knowledge
advancement and depreciation rate respectively. Ln h is the log of human capital, with α being the share of
capital and β the share of labour to output.
The equation implies that output per capita is positively dependent on technology, resource endowments,
climate and institutions, the savings rate and human capital but negatively related to population growth and the
rate of depreciation.
Contrary to the Mankiw et al’s method which involves a cross-country regression, this study constructs a panel
data framework of ECOWAS countries, using five-period data with 5 year intervals; from 1985 to 2010.
Differences in production functions across countries, makes the employment of panel data attractive which
allows for heterogeneity within countries. The common assumption of identical production functions across
countries, “gives rise to omitted variable bias as the country-specific aspect of the aggregate production
function that is ignored in these studies may correlate with the included explanatory variable, thus creating
variable bias”, Hakeem (2009). Whereas ECOWAS countries possess several similarities e.g. geographical
11 location, language and ethnicity, differences also exists which include resource endowment, GDP, development,
common currency membership (CFA countries) and population. Via its modelling specification and estimation
procedures, panel data accounts for these variations. Additionally, panel data will prove most informative for
the purpose of this study as it overcomes the shortcomings of cross-section studies by exploiting the time and
cross-sectional component and in so doing increases the degrees of freedom; improving efficiency of estimates
and circumventing collinearity issues. The multiple regression models to be estimated are as follows:
𝑳𝒏𝑹𝑮𝑫𝑷𝒊𝒕 = 𝜶𝒊 + 𝜷𝟏 𝑳𝒏𝑮𝑭𝑪𝑭𝒊𝒕 + 𝜷𝟐 𝑳𝒏 𝒏 + 𝒈 + 𝜹
𝒊𝒕
𝑳𝒏𝑹𝑮𝑫𝑷𝒊𝒕 = 𝜶𝒊 + 𝜷𝟏 𝑳𝒏𝑮𝑭𝑪𝑭𝒊𝒕 + 𝜷𝟐 𝑳𝒏 𝒏 + 𝒈 + 𝜹
𝑬𝒏𝒓𝒐𝒍
𝒊𝒕
+ 𝜷𝟑 𝑳𝒏𝑬𝒏𝒓𝒐𝒍𝒊𝒕 + 𝜷𝟒 𝑳𝒏𝑪𝑹𝑬𝑫𝑰𝑻𝒊𝒕 + 𝜺𝒊𝒕 (𝟏) 𝒊𝒕
+ 𝜷𝟑 𝒈𝑳𝒏𝑬𝒏𝒓𝒐𝒍 + 𝜷𝟒 𝑳𝒏𝑪𝑹𝑬𝑫𝑰𝑻𝒊𝒕 + 𝜷𝟓 𝑪𝑹𝑬𝑫𝑰𝑻 ∗
+ 𝜺𝒊𝒕 (𝟐) All variables are in logs, using data from 1990 to 2010, with five year intervals. RGDP denotes real GDP per
capita and GFCF (gross fixed capital formation) represents stock of physical capital. Similar to Mankiw et al
(1992), g+δ is assumed to be constant at 5% since data on these are not available. Enrol is a proxy for human
capital using secondary school enrolment rates and Credit is domestic credit to the private sector. For the
purpose of rigour, broad money as a percentage of GDP (M2) is also adopted as a measure of financial
development in the place of Credit in order to assess the significance of money creation for growth within the
ECOWAS. The choices of variables originate from the work of Mankiw et al (1992) but are also widely used in
the growth literature.
The second regression includes an interactive between private sector credit and human capital to address the
second research question of this study. All explanatory variables are expected to have positive coefficients with
the exception of β2 in both models since ceteris paribus a higher depreciation and population growth rate should
affect growth negatively.
In order to determine the optimal estimation technique, both fixed and random effects will be estimated for each
model and the Hausman test will subsequently be utilised to determine the best model.
Analysis of Results
Table 5 below displays the correlation matrix of variables used in the regression. This table is useful in the
alleviation of potential multicollinearity problems and gives a tentative insight of the relationship that exists
between growth (RGDP) and its contributors as identified in the literature. The strongest correlation coefficients
12 for growth emerge as Enrol, mirroring Mankiw et al’s results. Credit is positively correlated with growth,
capital deepening and human capital, observations which are in line with the predictions of the financial
development literature. M2 is positively associated with growth and capital deepening, with a large correlation
coefficient with Credit implying a potential case of multi-collinearity if both are included in a regression.
Table 6: Correlation matrix table of variables
Variables LRGDP LGFCF LNSD LRGDP 1.00 LGFCF 0.43 1.00 LNSD -­‐0.24 -­‐0.03 1.00 LENROL 0.69 0.26 -­‐0.12 LCREDIT 0.59 0.57 -­‐0.04 LM2 0.65 0.59 -­‐0.19 LENROL LCREDIT LM2 1.00 0.35 0.53 1.00 0.81 1.00 Since correlation merely highlights the linear association that exists between variables, the multiple regression
that follows will exhibit the true relatinship between variables along with the magnitude of impact on the
dependent variable growth.
Table 7 below reports the results of the growth regression models, using robust Driscoll-Kraay standard errors
with 75 observations. The Hausman statistic consistently indicates that the fixed effects estimator is the most
appropriate for the sample of countries under study, due to the degree of heterogeneity exhibited and thus only
these are reported. Time effects where considered when running these regressions which however did not
emerge significant and were consequently dropped. In Model 1, all variables are significant with expected signs
as per the endogenous growth and financial development theories, with the exception of LNSD. Both private
sector credit and education which is proxied by secondary school enrolment, exude equal impact on growth
exceeding that of capital deepening (GFCF). Population growth however appears consistently insignificant
throughout the exercise.
Model 2 assesses financial depth using M2, which emerges insignificant. Physical capital (GFCF) and human
capital are both statistically significant at the 1% with the inclusion of M2, whilst the R2 drops to 26%, implying
that model 1 possesses better explanatory power. Comparing models 1 and 2, it can be observed that for growth
within the ECOWAS, private sector credit is most important perhaps as a consequence of the crowding-out
effect identified in Table 4. The findings of positive coefficients for financial development indicators are
contrary to that observed by Hakeem (2010) in a panel of 24 SSA countries of which 8 were from the
ECOWAS. These findings are however consistent with King and Levine (1993), with coefficients for credit and
13 enrolment rates considerably higher. These suggest that implications for growth from education and increased
credit are considerably higher after years of financial repression.
Model 3 includes an interaction term between private sector credit and the education proxy, constructed by
multiplication of LCredit and LEnrol. Whilst GFCF is positive and significant, credit and enrolment are also
significant but negative. However, the interaction term LCredEnrol is positive and significant at 1%, a result
supporting the endogenous financial theory of Ang (2008) and empirically affirmed by Evans et al (2006),
Mishkin (2007) and Hakeem (2010). The result gives evidence of complementarity between finance and
education; increasing credit to a low-skilled private sector impacts growth negative as indicated by the negative
coefficient on LCredit, as resources are wasted. Thus the model postulates that a combination of increased
credit and education of the private sector is required to boost real GDP per capita by 0.12%.
Table 7: Panel data results with log of Real GDP per capita as the dependent variable
Variables
Cons
LGFCF
LNSD
LENROL
LCREDIT
Model 1
5.8763 (0.1465)***
.0608 (0.0305)*
0.0406 (0.0416)
0.1616 (0.0061)***
0.1586 (0.0208)***
Model 2
5.8708 (0.1193)*** 0 .1382 (0.0226)***
0.0233 (0.0586) 0.1926 (0.0165)*** Model 3
6.9124 (0.2687)***
0.0593 (0.0252)**
0.0437 (0.0432)
-­‐0.1594 (0.0598)**
-­‐0.2328 (0.0659)***
A
6.1447 (0.2177)*** 0.0797 (0.0323)**
0.0332 (0.0458)
0.1246 (0.0220)***
-­‐0.0939 (0.0512)*
B
6.7499 (0.3708)*** 0.0640 (0.0306)*
0.0476 (0.0439)
-­‐0.3982 (0.1436)**
0.1437 (0.0187)***
_
_
_
LM2
_
_
0.0131 (0.0126)
LCredEnrol
_
_
_
0.1205 (0.0202)***
LCredSqr
_
_
_
_
0.0615 (0.0151)***
_
0.4024
F(5,14)=108, (0.000)
_
0.3911
F(5,14)=507, (0.000)
LEnrolSqr
R2
F-­‐statistic
_
_
0.3605
0.2620
F(4,14)=1205, F(4,14)=329, (0.000)
(0.000)
_
0.0905 (0.0220)***
0.3861
F(5,14)=96, (0.000)
Note: * denotes significance at 10%, ** and *** at 5% and 1% respectively. Robust Driscoll-Kraay standard errors in parentheses
Furthermore, of the three models, model 3 possesses the greatest R2 at 40%. As the financial system increases in
sophistication, credit provision enhances the accumulation of human capital.
14 Columns A and B relate to Model 1, with the inclusion of quadratic terms for private sector credit and
enrolment for the purpose of sensitivity analysis. Model 1 is chosen over Model 2 as credit to the private sector
is considered superior to M2 as an indicator of financial development (Mckibbin, 2007), in addition to being the
focus of study and emerging statistically significant. Each quadratic term is constructed by squaring the logged
variable which appeared in previous regressions, i.e. LCredsqr obtained by squaring LCredit. A positive
coefficient on the quadratic terms imply increasing returns to scale consistent with endogenous and a negative
coefficient; diminishing returns. LCredsqr and LEnrolSqr both emerge significant and positive whilst LCred
and LEnrol are negative but significant in columns A and B, exhibiting increasing returns and supporting both
endogenous growth theories and that of financial development. Hakeem (2010) observed diminishing returns to
credit for SSA as a whole and attributes these findings to underdevelopment of financial systems, contrary to
the present sample of ECOWAS countries with more recent data. Consequently, ECOWAS countries are
relatively financially developed with evidence of significant room for improvement, efforts of which should be
aimed towards increased private sector credit along with human capital accumulation.
Part B: Determinants of Private Sector Credit in West Africa
As highlighted in the reviewed literature, the role of finance is critical for development and perhaps even more
so for a credit constrained continent. In the African context wherein financial development is the lowest in the
world with limited access to foreign borrowing, credit to the private sector is heavily relied upon by private
enterprises. A World Bank survey on the business environment consisting of 10,032 firms around the world;
80% of which were SMEs found obstacles to operation and growth varied widely across regions. The leading
constraints for Africa were financing, corruption, infrastructure and inflation. Interest rates in Africa were the
second highest in the world (Latin America with the highest rates) with the greatest collateral requirements.
The study found retained earnings to be the commonest source of firm financing for Africa, followed by equity
(WBES, 2003). The econometric investigation that followed found, “firms that are private, small, newer, devoid
of foreign direct investment (FDI) and cater to the domestic market generally tend to face more acute business
constraints than firms that are older, larger, exporting, have FDI and or are state (SOEs) owned… globally on
average small and medium firms report being more constrained than large firms”, (WBES, 2003). Large firms
were however found to be more impeded by infrastructure than SMEs.
Having established the significance of private sector credit for growth within the ECOWAS, this section of the
study will disaggregate the determinants of credit, benefiting from Roula et al (2011). The illustration below
15 puts into context the present issue of credit constraints faced by firms operating within the ECOWAS and
intervention required to ameliorate the situation.
Illustration 1: The demand and supply of private sector credit
Quantity Demanded (Q) Q4 C A Q1 Q3 D Q2 B X S2 E S1 Q2 D1 I2 I1 Lending Rate (i) The market for credit does not clear at point E and is in disequilibria. Firms face a downward sloping demand
curve, with an increase in credit associated with higher interest rates up to a point B (quantity demanded Q2).
Lending institutions have the affinity to lend up to a point E, at an interest rate of I1, after which risks may be
deemed too high and banks are unwilling to lend, thus the supply curve becomes perfectly elastic. This
unwillingness may be as a consequence of the risk profile of borrowers, imperfect information, difficulty and
costs of debt collection. Additionally, issues of economies of scale may lead to a preference for offering large
loans over smaller ones if processing costs are equal and bigger loans fetch higher interest rates. The model
implies credit rationing, indicated by the distance AB (Q1 to Q2). Credit is unavailable, despite firms desire to
obtain more loans. Such financial constraints are suffered by many firms in the private sector within the
ECOWAS and thus impede their development particularly due to inability to expand and benefit from
economies of scale.
16 In order to ease the financial constraints faced by firms, an intervention or a change of incentives for lenders
must occur to raise the supply of credit to the new supply curve S2. The resulting reduction in interest rates (I2)
will increase the availability of credit and demand (Q3), closer to the new equilibrium at point X. The size of
credit rationing shrinks to CD which is smaller than AB. Government intervention with the purpose of
encouraging the private sector especially SME’s can intervene by providing commercial lenders the means to
increase credit thereby meeting the demand at Q3 and at cheaper cost of capital at I2. This micro analysis
coupled with the results of the regression to follow will postulate ways in which incentives to increase credit
can be enabled in addition to ways in which policy intervention can bolster access to finance.
Data, Results and Discussion on the Determinants of Credit
The model of the determinants of credits captures all forces graphically illustrated above and is indicated below:
𝐿𝐶𝑟𝑒𝑑𝑖𝑡!" = 𝜋 + 𝛿! 𝑅𝐺𝐷𝑃!" + 𝛿! 𝑀2 + 𝛿! 𝐿𝑅!" + 𝛿! 𝐸𝐶𝑅!" + µμ!"
The dependent variable is the log of credit to the private sector which is determined by a firm’s wealth and
ability to borrow proxied by real GDP per capita, growth rate of money supply (M2), interest rate or cost of
borrowing; the lending rate (LR) and export concentration ratio (ECR) reflecting diversification and
sophistication of an economy’s exports. Ceteris paribus, an increase in money supply in the economy should
increase credit available to the private sector. The lending rate is the only variable that is expected to emerge
negative since the higher the cost of borrowing, the lower the demand for credit ceteris paribus. Lending
institution in a bid to reduce exposure to risk from payment defaults attach a higher interest rate, also based on a
firm’s profile. The ECR’s impact on credit may emerge negative, signalling low economic diversification
resulting in reduced credit, with limited efforts during this period exerted towards increased credit. Conversely,
a positive coefficient implies that the diversification process is under way and thus spurring increased private
sector credit. Data for this exercise is from 1995 to 2010 due to a shorter data range for the ECR variable.
In the sampled region, the most economically diversified country for the period is Senegal with an average
score of 0.24, whilst Nigeria is the least diversified; 0.86. The ECOWAS average score is 0.48 for the period
with the WAEMU receiving an average score of 0.46, just over the ECOWAS average. The non-CFA countries
obtain a score of 0.56 but an exclusion of Nigeria brings the non-CFA average in equality with that of the
WAEMU. Thus one can infer that in terms of economic diversification, ECOWAS countries are relatively
similar and moderately diversified on average within the observed period.
17 Lending rates were constant across WAEMU countries during the sampled period, with only a few yearly
variations and thus omitted from the regression due to limited variations. Table 6 below exhibits the fixed
effects regression results of the credit model for non-CFA countries, with the correlation matrix and descriptive
statistics of variables in the Appendix.
Table 8: Panel data results with the log of Private sector credit as the dependent variable
Variables Coeff. And Standard Errors Constant -­‐4.083 (1.312)*** LRGDP 1.003 (0.193)*** M2 0.003 (0.002)** LR -­‐0.018 (0.009)* ECR -­‐0.810 (0.331)** R2 Observations F-­‐statistic 0.679 106 F(4, 93)= 16.23, (0.000) Note: * denotes significance at 10%, ** and *** at 5% and 1% respectively. Standard errors in parentheses.
All variables emerge statistically significant, with the constant capturing all other factors that impede private
sector credit. LRGDP is the greatest contributor to credit with the expected sign along with M2, albeit a weak
positive influence. This latter result echo crowding-out effect of the public sector discussed earlier, which Table
4 indicated was worse within non-WAEMU countries. A 1% point increase in money supply growth practically
has no effect on private sector credit. Lending rates are observed to have a negative impact on credit as
hypothesised. Average lending rates for the sampled period is 22.4%, with a minimum of 9.9% and a maximum
of 43.8%. The findings on lending rates thus concur with the findings of World Bank’s 2003 survey.
The coefficient on ECR indicates that non-WAEMU countries are not economically diversified which possesses
negative implications for private sector credit. These countries are heavily dependent on export earnings of few
commodities and natural resources vulnerable to fluctuations in global prices and demand. These findings are
similar to those observed by Roula et al (2011) in their assessment of the Arab League and found evidence that
their sample of Arab countries had not commenced economic diversification. The authors note that oil revenue
receipts were not reinvested to diversify Arab economies. An environment to facilitate diversification must be
in place in order to boost economic resilience from external shocks and ease the financial constraints for the
private sector whilst fuelling growth and development. Limited access to finance weakens competitiveness and
18 makes it difficult for these countries to diversify and compete in the world economy by hindering human capital
development, innovation and investment in research and development.
This study focusses on financial constraints and in particular access to finance which is observed here via
lending rates to severely hinder credit. These results are very revealing and help explain why private sector
credit has remained sluggish over the past decades and thus results in a dormant private sector. Proceeds from
natural resource exports are not being channelled towards increasing credit which as established in Part A, is
significant for development. Being the most regionally integrated region in Africa, such depths of integration
should also reflect in policies to enhance the private sector’s operations. Part of the reason why lending
institutions are reluctant to lend is as a result of risk profiles or perceived risk of borrowers, faced with
asymmetric or inadequate information. The WAEMU’s central bank operates two surveillance bodies that
monitor the banking system within the zone responsible for credit control, admin and disciplinary measures in
order to ensure efficient banking within the space (www.bceao.int). Outside the WAEMU zone, other
ECOWAS debt control processes are lengthy and difficult. An integrated system where the credit history of a
borrower is easily accessible and shared across the ECOWAS will benefit lenders significantly.
Conclusion
In recent years, SSA has recovered from years of erratic growth and stagnation, presently harbouring two-thirds
of the world’s fastest growing economies. The paper however postulates that economic prosperity by definition
of positive growth rates should translate into enhanced welfare for Africans. Additionally, for the current
momentum to be accelerated, a thriving private sector is required which also brings with it welfare enhancement
for citizens by addressing the continents socio-economic issues. The private sector has been harnessed by
capital constraints and as observed in non-CFA countries studied here issues of crowding-out by the public
sector.
The study brings to the fore that finance matters for growth within ECOWAS countries, with emphasis on
increasing credit to the private sector being a more effective means than broad money, which results in more
crowding-out of the private sector. Human capital accumulation is also observed to be an important contributor
to growth as per Mankiw et al (1992). Results also indicate a combination of increased credit and education,
consistent with endogenous growth and theories of financial development are vital in driving growth. These
results lead to the consideration of the determinants of credit, in order to project a holistic perspective.
19 A model of credit determination is adopted, with the inclusion of a measure of economic diversification.
Evidence that ECOWAS countries are in the process of economic diversification is welcomed, with
implications of growing market sophistication and resilience against negative shocks. High lending rates are an
impediment to private sector development, similar to the findings of the 2003 World Bank survey. Exportorientation does not translate into increased private sector credit within the ECOWAS, with evidence of credit
substitution between local sources and foreign private credit sources.
Furthermore, the study highlights the positive correlation between central bank independence and fiscal
discipline which is observed to be strongest within the WAEMU. Non-CFA countries are observed to possess
lesser central bank independence and in turn suffer fiscal discipline, leading to crowding-out to the detriment of
the private sector. Central bank independence is however a political decision and the desire to exercise fiscal
discipline should be internalised. Thus all factors assessed in this paper which presents a holistic approach to
growth and financial development should be factored by policy makers in designing policies to encourage the
private sector.
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Appendix
Table 4: Money and Credit
22 M2 as % of
GDP
Claims on
Domestic credit
central
to private sector
government, etc.
(% of GDP)
(% GDP)
Country
Benin
Burkina Faso
Cote d'Ivoire
Guinea-Bissau
Mali
Niger
Senegal
Togo
Average CFA
2000
29.88
21.13
22.2
42.72
23.68
8.16
23.72
26.75
24.78
2011
40.01
29.57
40.46
40.23
29.72
21.45
40.22
48.52
36.27
2000
-3.7
2.66
7.38
10.23
-2.17
4.39
4.81
6.34
3.74
2011
-2.82
-1.81
7.24
1.73
-4.07
0.56
2.51
5.79
1.14
2000
12.09
11.72
15.5
7.9
16.5
4.8
18.68
16.04
12.9
2011
24.55
19.77
18.06
11.78
21.00
14.18
28.96
29.62
20.99
Cape Verde
Gambia, The
Ghana
Guinea
Liberia
Nigeria
Sierra Leone
64.37
19.8
28.17
11.68
11.64
22.16
16.36
76.98
54.95
30.85
36.41
38.21
33.58
28.66
23.99
0.55
19.28
4.24
170.43
-2.65
51.38
13.56
24.39
10.61
22.82
13.69
1.71
5.92
40.13
6.74
13.97
3.99
3.29
12.46
2.11
64.49
16.33
15.19
9.14
16.44
21.09
10.15
Average Non-CFA
Average Non-CFA
excl. Cape Verde
24.88
42.8
38.17
13.24
11.81
21.83
18.30
37.11
40.54
13.19
7.09
14.72
Source: World Development Indicators 2013
Variables and Data Sources
Name RGDP GFCF n Credit Variable definition Real GDP per capita constant 2005 USD Gross fixed capital formation (% of GDP) Population, total Domestic credit to private sector (% of GDP) Source Penn World PWT 7.1 WDI WDI WDI Enrol Secondary School enrollment rates WDI M2 Broad money as a % of GDP WDI ECR Measure of degree of market concentration UNCTAD LR Exports Lending Rates (%) Volume of Exports % change WDI & WAMI IMF 23 Imports Volume of Imports % change IMF Correlation Matrix
Variables LCredit LCredit 1 LRGDP 0.82 M2 -­‐0.13 LR -­‐0.41 ECR -­‐0.09 LRGDP 1 -­‐0.19 -­‐0.14 -­‐0.25 M2 1 0.28 0.07 LR ECR 1 -­‐0.24 1 Descriptive Statistics
Variable LCredit Obs 122 Mean 2.24 Std. Dev. 0.85 Min 0.49 Max 4.13 LRGDP M2 LR ECR 112 106 112 112 6.95 24.77 22.36 0.52 0.62 16.80 7.55 0.20 5.08 -­‐12.29 9.86 0.22 8.27 94.38 43.75 0.92 Table 2: The World Bank Doing Business Survey 2013: Rankings of Surveyed 183 Countries by Topic 24 The World Bank Doing Business Survey 2013: Rankings of Surveyed 183 Countries by Topic
Dealing with Overall Ranking Starting a Business Construction Permits
Country 2013 2012 2013 2012 2013 2012
Cape Verde 122 121 129 127 122 121
Gambia
147 143 123 121 90 91
Ghana
64 63 112 104 162 160
Guinea
178 181 158 184 152 172
Liberia
149 154 38 35 126 127
Nigeria
131 131 119 119 88 86
Sierra Leone 140 148 76 69 173 171
Benin
175 176 153 155 111 120
Burkina Faso 153 149 120 118 64 63
Cote d'Ivoire 177 177 176 173 169 168
Guinea-­‐Bissau 179 178 148 148 117 108
Mali
151 145 118 115 99 93
Niger
176 175 167 164 160 156
Senegal
166 162 102 98 133 131
Togo
156 161 164 175 137 139
Source: http://www.doingbusiness.org/data/exploreeconomies
Registering Getting Electricity Properties Getting Credit Protecting Investors
2013 2012 2013 2012 2013 2012 2013 2012
106 105 69 65 104 97 139 136
119 121 120 118 159 158 177 176
63 66 45 37 23 38 49 46
88 114 151 152 154 152 177 176
145 146 178 178 104 97 150 147
178 177 182 182 23 38 70 66
176 176 167 170 83 127 32 29
141 133 133 129 129 127 158 155
139 132 113 109 129 127 150 147
153 150 159 158 129 127 158 155
182 183 180 180 129 127 139 136
115 111 91 91 129 127 150 147
118 115 87 86 129 127 158 155
180 179 173 173 129 127 169 167
89 91 160 163 129 127 150 147
Paying Taxes
2013 2012
102 102
179 179
89 80
183 181
45 98
155 139
117 110
173 172
157 151
159 161
146 139
166 167
151 146
178 177
167 163
Trading Across Borders
2013 2012
63 61
87 87
99 98
133 131
137 138
154 153
131 132
130 130
173 175
163 163
116 118
152 150
176 176
67 67
101 103
Enforcing Contracts
2013 2012
38 36
65 71
48 47
131 131
163 168
98 97
147 145
178 178
109 109
127 126
142 142
133 133
140 140
148 148
157 157
Resolving Solency
2013 2012
185 185
108 108
114 115
141 133
159 161
105 104
154 157
132 132
113 112
76 73
185 185
120 118
130 129
90 90
96 97
Source: http://www.doingbusiness.org/data/exploreeconomies 25