capital mobility, financial development & growth: an ... · an empirical evidence from...

1

Capital Mobility, Financial Development & Growth:

An Empirical Evidence from Sub-Saharan Africa

Jeo Lee

CeFiMS, School of Finance and Management

SOAS, University of London,

London, WC1H 0XG, United Kingdom

October 2018

ABSTRACT

This study re-examines and contributes to the literature on the finance-growth linkage in sub-Saharan

Africa. The findings, from applying various econometric methods, showed a moderate to high capital

mobility over the period 1996-2014, measured by the Feldstein-Horioka investment-savings coefficient.

The results identified that financial factors such as money supply and ‘private credit provided by the

financial sector’ are positive drivers for long-run growth while the impact of bank-related factors and

‘foreign direct investment’ on growth were limited, showing a negative effect in the long-run. In the

short-run, more linkages in finance-growth were identified especially in the middle-income Island-

states. The results clearly show the existence of substantial cross-country heterogeneity and diversity

in the policies and financial-economic conditions of the sample. The presence of bi-directional finance-

growth causality indicates finance not only leads but also follows growth. The estimates imply that

traditional investment bank-lending for accumulating physical capital (e.g. power infrastructure

investment) and financing for a higher-value added-agro-industrialisation would promote growth

potential.

Key words: Panel Estimation, Factor Analysis, Heterogeneity, Feldstein-Horioka Coefficient, Capital

Mobility, Financial Development, Economic Growth, sub-Saharan Africa

JEL Classification: C22, C23, F21, F31, O16

* The author retains full responsibility for errors, views and interpretations expressed or discussed. E-mail: [email protected]

2

1. INTRODUCTION

Economic growth, usually measured by the growth of national output valued in prices, is a

necessity to remove poverty and to raise living standards for many developing countries. Sub-Saharan

Africa (SSA) has been one of the most rapidly growing regions in the world, with growth rates often

exceeding 5% per year, following economic stagnation between 1975 and 1995 (Word Bank 2012).

The identified drivers of SSA’s economic growth have been favourable external and local factors such

as high commodity prices, the availability of global investment capital, growing domestic demand

generated by consumption, government spending on public infrastructure investment, reduced external

debt, declining civil and military conflicts and corruption. Sustaining the growth trend in SSA, retaining

accountable macroeconomic policies and political stability, most African countries will have reached

middle-income status by 2025 although about 13 fragile states will remain traditional aid recipients

(Devarajan and Fengler 2012). Economic theory suggests long-run economic growth entails raising

total factor productivity through the determinants in development such as private investment in physical

capital, public investment in infrastructure and education for labour skills, technology, research,

innovation, and a favourable business environment (Sloman 2007). In Solow model 91956), the growth

rate is generally determined by initial income as well as by the savings rate and population growth that

this model well fits to most of the SSA sample that an increase in savings rate implies higher investment

and the growth rate.

Private and public investment for growth obviously needs domestic savings; if there appears a

gap between savings and investment, foreign capital should inflow to fill the gap if investment

opportunities exist. In particular, foreign direct investment (FDI) is a component of foreign capital

flows that promotes economic growth for SSA through technology transfer (Seetanah and Khadaroo

2007) although some argue that financial development and FDI are the by-products of economic growth

(Khan and Senhadji 2000; King and Levine 1993). Others argue the effects of external and public

capital investment on growth are heterogeneity among countries, regions and sectors (Gramlich 1994;

Straub 2008). For example, Kamara (2013) finds that financial development indirectly enhances

economic growth through the relationship between FDI and growth in SSA in the period 1981-2010.

Financial development in SSA, measured by private sector credit to GDP, has increased; banking sector

assets have also increased in the region; external financial inflows (e.g. private capital flows, remittance)

to SSA have significantly increased, from US $20 billion in 1990 to above US $120 billion in 2012

while official development assistance (ODA) increased less between 2001 and 2012 due to the growth

achieved (Mlachila et al 2013, 2016). In fact, several African countries have recently successfully

attracted foreign capital through issuing government bonds in international financial markets signalling

investors’ growing confidence in the future growth potential of African economies as well as improved

business climates in the region. In 2014, Africa was the second most attractive destination for FDI in

the world although its share still is only 5% of total world FDI. In addition, there has been a recent

decreasing trend of FDI partially owing to the allocated FDI in less productive investment such as

mining or oil extraction rather than accumulating critical social infrastructure such as power supply and

education for higher value-added production. Financial intermediation can affect economic growth

through capital formation such as project finance for infrastructure investment. Financial development

and FDI on growth often show an endogeneity in the framework of finance-growth and FDI-growth

relationships so that financial factors not only lead growth but also follow growth, and therefore, the

nature of the relationship between finance and economic growth has important growth policy

implications for finance sector development (Agbetsiafa 2004).

Studies generally view that financial development and investment have positive effects on

economic growth (Romer 1986; Lucas 1988; Dollar 1992; Collier and Gunning 1999; Levine et al 2000;

Sachs and Warner 2001; Levine 2005; Arslanalp et al 2010) through: supporting the efficient allocation

DP146 Centre for Financial and Management Studies | University of London

and intermediation of funds (Solow 1956; Swan 1956) that may reduce inequality by bringing benefits

to lower income people (Beck et al 2007); smoothing consumption-investment (Koopmans 1963; Cass

1965); enhancing private capital marginal productivity (Barro 1990); and boosting competitiveness and

demand for labour (World Bank, 2013). For example, a positive correlation between financial

development and the real per capita GDP growth rate was found in Southern African Development

Community (SADC) member countries (Allen and Ndikumana 1998), a positive impact of financial

intermediation (e.g. bank deposit, bank credit) on economic growth in Nigeria (Ibrahim 2012; Efayena

2014; Chinweoke et al 2014) and credit to private sector and total domestic credit induced economic

growth in Ghana (Adu et al 2013). There is also a directional causal relationship between finance and

growth in African countries showing bi-directional or unidirectional causality (Oluitan 2012; Adusei

2013). Umar et al (2015) show, however, bank credit has a negative effect on economic growth in

Nigeria; and the broad money in Ghana is not growth-inducing (Adu et al 2013). Most empirical

findings suggest that the impact differs across regions, income levels, institutional characteristics of the

economy, and the level of financial development. For example, Barajas et al (2013) find the beneficial

effect of financial deepening (measured by ‘private credit to GDP’ and ‘stock market turnover’) on

growth is heterogeneous among 150 countries for the period 1975–2005 such that the effect is generally

smaller in oil exporting countries, Middle East and North Africa (MENA), and in lower-income

countries. They analyse that these differences might be related to regulatory characteristics and

differences in the ability to provide access to financial services. Similarly, Favara (2003) explains that

financial development is generally growth-promoting with a medium-sized financial market and access

to finance while the least and best developed financial systems have negative impacts on economic

growth; therefore financial sector development may decrease per capita GDP when financial

intermediaries are poorly developed. Yusifzada and Mammadova (2015) also elucidate that financial

development indicators show clear differences across developed and developing countries with

developed countries ahead in financial depth, and access to finance and efficiency, emerging countries

standing between developed and developing countries. Developing countries have a negative

relationship between access to finance and economic development up to the threshold but a positive

relationship beyond; in contrast, access to finance is growth-promoting up to some threshold level in

developed and emerging countries. Visual evidence of the factor ranking linkages based on the dataset

obtained from the world development database shows clear linkages between growth (capita GDP),

financial market sophistication, macroeconomic stability, savings, current account, and deposit-lending

spread; but some inconsistency in investment factor behaviour – Botswana, Mauritius, and South Africa

are highly ranked showing more linkages between the financial-economic factors than those in other

SSA economies (Appendix, A2).

This study investigates whether capital mobility (measured by the Feldstein-Horioka investment-

savings coefficient) and finance-growth relationship differ across SSA regions and income levels. The

FH hypothesis indicates that the savings-retention coefficient seems low (high in capital mobility) for

developing countries and there is a low capital mobility in developed and less-developed countries

although the validity of FH coefficient as a measure of capital mobility has been debated due to the

homogeneity assumption across countries. Empirical literature based on SSA data supports the

presence of a moderate capital mobility (Mamingi 1997, Payne and Kumazawa 2005; De Wet and Van

Eyden 2005; Adedeji and Thornton 2006; Cooray and Sinha 2007; Esso and Keho, 2010; Bangake and

Eggoh 2010; Padawassou 2012). A positive impact of finance on growth is assumed so that a rise/fall

in the level of financial development and capital mobility, with economic factors unchanged, leads to a

rise/fall in the level of capita GDP in SSA regions. Alternatively, the magnitude of the impact may be

limited by a lack of financial depth, access, scale, competitiveness and efficiency in financial systems

and institutions.

Capital Mobility, Financial Development & Growth: An Empirical Evidence from Sub-Saharan Africa

4

Based on the findings, this study emphasises traditional investment bank lending for

infrastructure investment (e.g. power supply) and transforming to a higher-value-added-agro-

industrialisation for short-term growth (Songwe and Winkler 2012) that would lead to raise income

levels, savings, higher skill based job creation, technology transfer, financial depth, and ultimately a

substantial economic development (Zamfir 2016).

In estimating the FH coefficients and the finance-growth relationship, various estimators and

methods are applied and are briefly described in Section 2.1. Datasets used are for the period 1996-

2013 including consolidated SSA country annual datasets, five regional-SSA panels, forty individual

SSA countries and world-income group panels, together with sets of financial and economic data as

explanatory variables in the estimation to identify statistically significant determinants of economic

growth and a potential pathway and transmission effects between economic and financial factors. The

datasets are described in Section 2.2 and descriptions of the data are presented in Appendix A1. The

results are summarised in Section 3 and presented in Appendices followed by discussion in Section 4.

In the final section, conclusions are drawn.

2. METHODOLOGY AND DATA

2.1 Methods

Earlier empirical studies on growth and FDI used cross-section data analysis due to data

availability. However, cross-country regressions often rely on unrealistic assumptions, ignoring both

the country specific effects and the endogeneity in the explanatory variables, and it is thus difficult to

produce efficient and unbiased parameter estimates using the ordinary least square (OLS) method.

Therefore, more recent studies utilise panel approaches to estimate regression models, and these tend

to reduce the biases in the value of coefficients and non-stationary regressors and simultaneity-biases.

Popular panel methods used to estimate the FH coefficients are the fully modified ordinary least squares

estimator (FMOLS: Phillips and Hansen, 1990) and the dynamic OLS (DOLS, used in Mamingi, 1997;

Adedeji and Thornton, 2006; Bangake and Eggoh, 2010; Mark and Sul 2003, among many others).

Pedroni (2000) stressed that using a panel FMOLS estimator will satisfy the size of the observation for

even a small number of panels and the heterogeneity in individual data in the panel. This study utilises

the FMOLS and DOLS as well as other methods including the robust-LS (RLS-M estimator), the

Generalised Methods of Moment (GMM), the Generalised Linear Model (GLM), the Autoregressive

Distributed Lag Model (ARDL), and the pooled-LS (PLS) whenever the FMOLS and DOLS are

inappropriate to produce statistically significant estimates, owing, for instance, to the behaviour of

datasets, missing data, outliers, or insufficient data to satisfy the normality and the variance

homoscedasticity in the error terms among other residual diagnostics tests.

The RLS offers down-weighting outliers, in the case that discarding potential outliers is not a

good idea, even though the outliers can distort the sample mean and the variance, assuming the data are

not normally distributed, and the OLS is not a maximum likelihood estimator (MLE) so that the OLS

may produce false results from the data’s single outlier that can totally offset the results when the OLS

estimator is used. Most datasets in this study fit for RLS M-estimator that is not strongly affected by

outliers with a small sample size. Using the RLS M-estimator would reduce the bias in the estimates

that resulted from a severe heterogeneity, outliers in the datasets, omitted data and data distribution

issues by using the median rather than averaged values. The RLS M-estimator replaces the squared

residuals used in the OLS by another function of the residuals (Hampel et al. 1986). The weakness of

the RLS, however, is that the confidence intervals for robust estimates are wider than least squares

although the estimator’s median has 50% breakdown while the mean has 0%.


Another complementary method applied for this study is the generalised method of moments

(GMM). The GMM is an estimation procedure that does not require complete knowledge of the

distribution of the data but chooses an initial weight matrix to find a consistent and efficient estimator

until convergence using an iteration of the GMM. The GMM estimator (β) minimises the error term by

choosing optimally the weighting matrix (IV x IV: a multiplicative constant of the inverse of the

variance of the orthogonality conditions) for the smallest variance GMM estimator that can be

consistent (Hansen 1982; Hall 2005).

In addition to the RLS and GMM estimations, this study considered applying the generalised

linear model (GLM) for the estimation of small sample data to relax normality in the error structure.

The GLM estimates logs of multiplicative effects, and allows for non-uniform variance, although it uses

the same X the independent variables and a vector of parameter coefficients () in classical LS with a

vector of residuals (), the difference of the GLM estimator is the existence of the link function (g) and

the is an exponential family (used gamma and Gaussian if applicable) in the model specification: �̂� =

𝑔−1(�̂�) + 휀̂: 𝐸(�̂�) = 𝑔−1(�̂�𝑋). The 𝜃 = �̂�𝑋, is the n covariates combine to give the linear predictor,

where the link function (g) in the GLM is differentiable and monotonic. Besides, the PLS specification

of the fixed effects (FE) and random effects (RE) models accounts for the heterogeneity across countries,

and the variation across is random and uncorrelated with the regressors in the model (Greene 2008).

The Hausmann (1978) test is carried out to evaluate whether the FE or RE model fits better for the

variables.

The panel cointegration tests (Kao 1999; Kao and Chiang, 2000; Pedroni 2004) have been the

broadly used methods (Abbott and De Vita, 2003; De Wet and Van Eyden 2005; Payne and Kumazawa

2005; Fouqau et al 2009; Kumar et al 2014). The Pedroni panel cointegration test generally offers a

consistent and efficient estimation of cointegrating vectors given the existence of heterogeneity of

parameters across countries. The estimator uses four ‘within dimension’ panel tests for common time

factors, and three group ‘between-dimension’ tests for the group mean cointegration. The model

specification of the cointegration tests selected are no deterministic trend, a maximum time lag of 1 for

the Pedroni test, and a time lag of 2 for the Kao test based on the Akaike Information Criterion (AIC)

and the Schwartz Information Criterion (SIC). The null hypothesis for both tests, where there was no

cointegration in the variables, was considered.

All the above methods do not estimate a directional causality between variables, therefore the

Granger (1969, 1988) causality test is utilised with a time-lag=1 to reflect short-term causality. The

process for testing for Granger causality between two stationary variables 𝑦𝑡 and 𝑥𝑡 involves the

estimation of vector-autoregression model as in equations:

𝑦𝑡 = a1 + ∑ 𝛽i𝑥𝑡−i

𝑛

𝑖=1

+ ∑ 𝛾𝑖𝑦𝑡−i

𝑚

𝑗=1

+ 𝜇1𝑡; 𝑥𝑡 = a1 + ∑ 𝜃𝑖𝑥𝑡−i

𝑛

𝑖=1

+ ∑ 𝛿𝑖𝑦𝑡−i

𝑚

𝑗=1

+ 𝜇2𝑡.

Here both 𝜇1𝑡 and 𝜇2𝑡 are uncorrelated with white-noise error terms. Unidirectional causality

occurs when only 𝑥 Granger causes 𝑦 but not vice versa. Bidirectional causality results when causality

exists in both directions, i.e. 𝑥 to 𝑦 and 𝑦 to 𝑥.

Additionally, the principal-component analysis (PCA) is used to identify the interconnection

among the financial development indicators and the growth variables. The PCA is useful to capture

what regression and causality analysis cannot. It generates the eigenvalue and eigenvectors (loadings)

and the figures report the first and the second principal components to identify the diversification among

the financial indicators. The PCA produces bi-plot factor loadings for the visualisation of the directional

and spatial common factors for the variables in the system.


6

To test data stationarity, the augmented Dicky Fuller (1979) test for each data series and also the

panel unit root test of the Levin-Lin-Chu (LLC: 2002) and the Im-Pesaran-Shin (IPS: 1997) were carried

out. These established that both saving and investment variables and also other financial and economic

variables are integrated to I(1). The methods of unit root tests are described in detail in almost all

econometrics textbooks, and hence are not repeated in this section, and the results are not reported in

the Appendix due to space constraints.

All methods mentioned above are applied in line with the equations (1) and (2) below, can be

written as generic forms of the following:

𝑦𝑖𝑡 = 𝛼 + 𝛽𝑥𝑖𝑡

′ + 𝑢𝑖𝑡, (𝑖=1,…,𝑁; 𝑡=1,…,𝑇), 𝑢𝑖𝑡=𝜇𝑖+𝛾𝑖𝑡. (1)

∆𝑦𝑖𝑡 = 𝑐 + 𝜃∆𝑥𝑖𝑡′ + 휀𝑖𝑡, (𝑖=1,…,𝑁; 𝑡=2,…,𝑇), 𝜀𝑖𝑡=𝜗𝑖+𝜔𝑖𝑡. (2)

- 𝑦𝑖𝑡: The dependent variables including the log of the level of national income per capita or the GDP per

capita; or the growth rate of national income per capita or GDP per capita.

- 𝑥𝑖𝑡: The vector of explanatory variables or the determinants that affect the GDP or income per capita.

- 𝜇𝑖 and i: Country-specific effects.

- 𝑖𝑡 and it: Idiosyncratic error term, where 𝐸(𝜇𝑖)=𝐸(𝑖𝑡)=𝐸(𝜇𝑖, 𝑖𝑡)=0 ∀ 𝑖−1,...,𝑁; 𝑡=1,...,𝑇; and

𝐸(𝑖𝑡,𝑖,𝑡−1)=0, 𝐸(𝑦𝑖,1𝑖,𝑡)=0 ∀ 𝑖−1,...,𝑁; 𝑡=2,...,𝑇.

2.2 Data

Sets of available annual data are obtained from the database of World Development Indicators

(WDI: World Bank) and the IMF International Financial Statistics (IFS). Due to the availability,

accuracy and consistency of most of the financial development data, this study focuses on the sample

period between 1996 and 2013 although where financial data for a longer term is required and available,

the analysis extends back to 1980 and forward to 2014. Four sets of sample datasets are selected for

the estimation:

(I) Consolidated annually averaged data series between 1995 and 2014 of development indicators

(economic, financial and human development datasets). This is not panel data but an averaged series

from 46 developing African countries. The gross domestic product (GDP) and the growth national

income data are all per capita unit, and therefore, they represent the standard of living aspect of

economic growth. All of the data is logarithmic and divided by GDP in order to convert it into rates

unless it is in the form of ratios or growth (first differenced). The data has five categories: (i) Financial

factors (M2, ‘domestic credit to private sector’, ‘credit provided by banking sector (CB, % of GDP)’,

‘credit provided by financial sectors (CFS, % of GDP)’, savings (S, % of GDP), investment (fixed

capital formation: FCF % of GDP), FDI inflows/outflows (FDII; FDIO % of GDP), portfolio equity

inflows (PE, $), the total value of stocks traded (STV, % of GDP ), net official development assistance

(ODA, % of capital formation), remittance, and external balance on goods and services (EB, % of GDP);

(ii) infrastructure (energy consumption, electricity consumption, ‘airplane usage of fleets’, ‘airplane

usage by people’); (iii) industry and trade (agriculture as a % of GDP; manufacturing; services; import;

export; industry value added); (iv) economic growth (‘national income per capita’; ‘GDP per capita’);

(v) and human factors (‘school enrolment’; ‘private health expenditure’; ‘health expenditure by public

sector’).

(II) Another consolidated data-series represents the global income groups including high-income

countries (HI), middle-income countries (MI), low-income countries (LI), and sub-Saharan African

countries (SSA) for the period over 1996-2013.

(III) None of previous studies considered estimating the FH coefficient based on only a

geographical aspect although the geographical factor seems significant in the SSA economic


development, therefore, the regional SSA panel datasets which represent the geography (South, East,

West, Islands, and Landlocked) as well as the income level (low- and middle-income) from the 40 SSA

countries are considered for estimating the degree of capital mobility for the period 2000-2014. For

individual estimation, two sample periods 1980-1999 and 2000-2014 are used for the comparison.

(IV) Only a few countries in SSA region have reasonably well organised and functioning

financial markets (Yartey, Adjasi 2007). Selected panel dataset consists of the stock market

capitalisation, banking assets (as a % of GDP), and insurance sector assets (as a % of GDP) for a group

of nine countries over the period 2000-2013 to investigate the impact of finance on growth. The trends

and descriptive statistics are presented in Appendix (A3-A7).

2.3 Some stylised features in financial factors and summary statistics

The overall financial depth, access, and efficiency of financial institutions and financial markets

in SSA are generally less developed than for other developing regions of the world (Cihak et al 2012;

Yusifzada, Mammadova 2015, Appendix A7, see also the table below). Over the period 1996-2014,

the value of market capitalisation (bn$) is much lower for SSA (425) compared with the world average

(10,483), MENA (1115) and HI countries (34,118) although the percentage of GDP market

capitalisation in SSA (117.8) is higher than the world average (65) and high-income countries (98.7)

because the size of GDP in SSA is much smaller than other regions. The portfolio equity inflows (PE:

bn$) in SSA are lower (6) than that of the EU (300) and HI countries (511) although the PE is higher

than that of MENA (3.1) and LI (-0.3). The total value of stocks traded as a % of GDP is much lower

in SSA (18%) than in HI countries (118%), EAC (81%), and the EU (59%) in that order. The regional

securities markets in the South African Customs Union, the West African Economic and Monetary

Union and also in a small number of MI SSA countries such as South Africa, Nigeria, Kenya and

Mauritius have been attracting international capital inflows since 1999 (Beck et al 2011). However, the

scale and access of banking systems in SSA remains underperforming due to ‘low loan-to-deposit

ratios’, ‘short-term lendings’, and large portions of banks’ assets are held in the form of government

securities although many state-owned-banks have been privatised under reforms in the 1980s and 1990s.

Selected financial system characteristics 2008-2010

Financial Efficiency Access Depth

Lending-

deposit spread

(%)

Stock

market

turnover ratio (%)

Accounts

per thousand adults

from commercial banks

Private

credit to

GDP %

Stock market

capitalization +

outstanding private debt security to GDP %

World

Average

7.7 56.9 904.7 56.3 71.2

SSA 11.7 11.0 261.0 20.1 46.1

Developing

economies

8.8 37.2 580.2 34.5 42.5

East Asia &

Pacific

7.3 67.4 668.6 46.8 70.9

High-income OECD

2.6 98.9 2320.2 124.0 108.2

Source: Global Financial Development Database 2008-2010, Cihak et al (2012). Correlations among financial system characteristics

(Cihak et al 2012): (i) financial institutions: depth vs access (0.79); depth vs efficiency (0.46); access vs efficiency (0.46); (ii) financial markets: depth vs access (0.36); depth vs efficiency (0.44); access vs efficiency (0.48).

Other institutional characteristics including weak contractual frameworks for banking activities

such as weak creditor rights and judicial enforcement mechanisms may slow the development of

banking sector and financial markets operations as discussed in McDonald and Schumacher (2007),

Andrianaivo and Yartey (2009) and Beck et al (2011). Other features of SSA banking structures are

large informal sectors, highly concentrated branches in urban centre, high-cost operations and

constrained competition. A large share of the population is unbanked. For example, over 2002-2015,

the financial infrastructure statistics show that the numbers of borrowers and depositors using the

banking sector (per 1000 adults) in SSA are only 20 and 92 respectively compared to the world average


8

of 147 and 553. Commercial bank branches (per 100,000 adults) are also fewer in SSA (2.7) than the

world average (17) and the EU (31). Firms using banks to finance investment are 14.8% in SSA which

is lower than MI (20%) but higher than that of LI (12.3%), consequently, more than 85% of firms in

SSA use the non-banking sector for financing investment. The interest rate spread (lending rate –

deposit rate) which measures the efficiency of financial intermediation in SSA is much higher (11%)

than other regions in the world (4.4 - 5.4 %) in 2014 reflecting a low score (1.6) of depth of credit

information index (0=low, 8=high) for SSA compared to the world average (4.7), and the EU (5.9). A

high net interest margin and low profitability ratios, as measures of financial institutions’ efficiency,

indicate a low efficiency in the financial intermediaries in SSA.

Recently pan-African banking groups have rapidly expanded mobile-phone based money services and

payment system across borders with more than 30% of deposits in at least 13 SSA countries in 2011

(Devarajan and Fengler 2012). Since 2007, M-PESA and Mkesho in Kenya, enable most Kenyans to

transfer money and bill payments without access to a bank deposit account or through a partnership between

a telecom-company and a commercial bank. Three other pan-African banking groups based in Nigeria

(United Bank of Africa), South Africa (Standard Bank), Togo (Ecobank) and Mali (the Bank of Africa group)

have also expanded in the regions promoting competition with national banks, facilitating technologies, and

consolidating banking supervision (Beck et al. 2011). This cell-phone based individual-level banking brings

significant benefits to transaction activities. However, Beck et al (2012) discussed that traditional

intermediation activities within banking operations would promote economic growth in the long-run and

stabilise the economy in the medium-run especially in LI countries as investment banking incorporates a

large-scale infrastructure finance for development investment.

Summary statistics of financial variables are presented in Appendix2, Tables 5-7. Over the sample

period 1996-2014, the financial factors – the ratios of broad money (M2), national income, trade, air transport

passengers, and FDI outflows – show an increasing tendency (measured by the skewness). For the same

period, energy consumption, ‘credit provided by the private sector’, domestic savings, and ‘public sector

health expenditure’ show a decreasing tendency. The tendency of decreasing savings, increasing income

and capita GDP indicates that current account deficits (i.e. capital account surplus) prevailed in most SSA

countries to smooth the consumption and import. A relatively high variation (measured by the standard

deviation divided by the mean) is found in external balance (deficits -2.18), FDI outflows (1.45), and the

growth of national income per capita (+1.45). Over the period 1996-2014, the average FDI inflows to SSA

are relatively higher (2.7%) than those of EAP (2.2%) and SA (1.3%) although relatively lower than the EU

(4.3%), LAC (3.2%), and HI countries (2.8%). Net official development assistance (ODA: % of capital

formation) is high in LI countries (53%) and in SSA (22%), but ODA does not exist in the EU and HI

countries. Both FDI and ODA flows have decreased for the sample period. The causality tests show that

FDI in SSA has no impact on growth in the short-term, but ODA positively affects growth. Personal

remittance inflows (RE: % of GDP) are relatively high for LI (3.8%), SA (3.6%) and SSA (2.1%), and low

in HI (0.2%) countries. The RE in SSA positively affects growth in the short-term while in the long-term,

the effect differs depending on the regions in SSA (Appendix 3, Table 6).

3. EMPIRICAL RESULTS

3.1 Measuring the Degree of Capital Mobility

Feldstein-Horioka (1980) assumes that the savings-retention coefficient would be lower for

developing economies so that the degree of capital mobility would be higher. Overall results show that

the estimates of the averaged FH coefficient of the individual samples and regional panels indicate

moderate to high capital mobility. The averaged individual savings-investment coefficient from each

of the 40 SSA economies shows 0.54 for 1980-1999 and 0.59 for 2000-2014 suggesting a moderate

degree of capital mobility (Appendix 3, Table 2.4). The findings are similar to estimates (a range of


0.39-0.73) in previous studies - Adedeji and Thornton (2006) from six country samples (Cameroon,

Gabon, Ghana, Nigeria, South Africa and Zimbabwe) from1970-2000; the coefficients (0.52-0.62) in

Kumar et al (2014); for Benin, Cote d’Ivoire and Niger in West Africa, the estimate shows 0.78, 0.87

and 0.67 respectively for the latter period, which is in line with the findings in Esso and Keho (2010),

suggesting their capital mobility was not much changed over the period 1980-2014. Estimates for

Kenya, Mauritius, Namibia, Zambia, Central Africa, and Congo (D.R.) show lower FH coefficients for

the latter period, indicating their capital mobility has increased (2000-2014); in contrast, for Rwanda,

Angola, Lesotho, Madagascar, Niger, Sierra Leone, and Tanzania, the coefficients are relatively higher

for the latter period, indicating the capital mobility has decreased South Africa’s estimate shows 0.97

(1980-1999) which is consistent with the findings in Coorary and Sinha (2007), indicating a low capital

mobility while the coefficient is remarkably lower, 0.29, for the latter period suggesting that a highly

capital mobility , however, it is statistically insignificant.

The estimates from the panel of 7 regional samples (South-SSA; East-SSA; West-SSA;

Landlocked-SSA; Island-SSA; LI-SSA; MI-SSA) are summarised in Appendix 3 (Tables 1 and 2.1).

Pedroni cointegration tests show a long-run cointegrating relation between investment and savings in

all panel regions except South-region, and LI group panels that suggests the investment of these two

groups are financed from other sources of capital inflows than savings. The results from the Kao

cointegration tests show all seven panels have a long-run cointegrating relationship between investment

and savings, rejecting the null hypothesis of ‘no cointegration’. The panel estimates of the Islands-

group and the West-region show relatively higher FH coefficients than those of other regions due mainly

to their relatively higher savings and credit provided by banks over the sample period. The averaged

coefficients from the FMOLS and DOLS for the regional panels show a range between 0.26 and 0.33,

which may be interpreted as a moderate-to-high capital mobility over the period (1996-2013). The

estimated range (0.26-0.33) is similar to the range (0.208-0.243) in Cyrille (2010), slightly lower than

the ranges (0.38-0.58) in Bangake and Eggoh (2010), and (0.51-0.73) in Adedeji and Thornton (2006).

The estimates of the SSA MI group savings-retention coefficient (0.35) is higher than from the LI group

in SSA (0.31). The estimate of the West-SSA panel shows a relatively high savings-retention

coefficient (0.38) followed by the Islands-group (0.30), the South group (0.21), the East group (0.17)

and the Landlocked-group (0.08). The heterogeneity in the savings- retention coefficients among the

panel regions is examined using the Hausemann test (1978). Specifically, the random-effect (RE)

estimates range between 0.22 and 0.24 across models which is similar to the findings (0.24) in Payne

and Kamazawa (2005), and in De Wet and Van Eyden (2005: 0.28). The Hausmann test shows a

preference for the fixed effects model for the panel groups of West-SSA, South-SSA and Island States,

while the random effect model is preferred for the panel groups of LI, MI, East-SSA and Landlocked-

states. Using a different model, the Hausmann test shows a preference for the fixed effect model for all

panel groups. The PLS models show a relatively higher value of FH coefficient for the MI group (fixed-

random effect: 0.31-0.38) than those of the LI group (0.15-0.23); and the West-SSA group (0.31- 0.29),

the Landlocked group (0.33-0.18) and the South-SSA group (0.20-0.19). The estimates from the GLM

show a range 0.26-0.04: Islands-group (0.26), South-SSA group (0.18) followed by the MI group (0.04)

and the LI group (0.04). The estimates from the panel GMM with instrumental variables of growth

effects on savings reveal that the Islands-SSA group shows a higher coefficient (0.69) than the East-

SSA group (0.49) and the Landlocked group (0.25). The findings from varied methods show that the

difference in the coefficients in the regions consistently relies mainly on the difference in their saving

rates in the panel groups, for example, the estimate of the Islands-SSA panel in SSA shows a relatively

higher FH coefficient than the LI panel. Furthermore, a low value of FH confidents is attributed to the

net effects of the differences between savings and dis-savings as well the diversity of the level of

financial and economic development in the regions, yet the former effect prevails (Appendix 2, Figures

3, 5).


10

For comparison, the FH coefficients of world regional- and world income-group panels are

estimated using the RLS-M estimator (Appendix, Table 2.3). Results range between 1.27 and 0.21 in

the EU (1.27), the world-HI group (1.07), the world-MI group (MI: 0.92), the East Asia and Pacific

group (EAP: 0.84), the world-LI group (LI: 0.38), the Latin America and the Caribbean group (LAC:

0.32), and the Middle East and North African group (MENA: 0.21). Overall, the FH coefficient from

the HI group estimation shows higher FH coefficients than from the LI group in line with the findings

in the SSA regional panel estimation, so that the low savings-retention coefficient in the LI group is

mainly caused by low saving rates or current account deficits, thus, not necessarily reflecting the degree

of capital mobility. The long-run cointegration tests accept the hypothesis of no cointegrating

relationship between savings and investment in the EU and HI group panels suggesting investment

capital flows the panel groups regardless of the savings rate. In contrast, the cointegration tests reject

the null hypothesis so that there exists a cointegrating relationship between investment and savings in

the panel groups of MENA, EAP, MI, and LI. Besides, the gap between investment and saving in the

world MI group is relatively narrow while the gap between investment and savings in SSA regions is

wider, volatile and inconsistent, and in particular the investment-savings gap in SSA-LI group is

constantly large (Appendix, Figures 1 and 2).

The short-run directional relationship between savings and investment also reveals a strong

regional heterogeneity (Appendix 5, Figure 10). The savings in the South- and East-regions in SSA is

attributed to investment in the regions. In contrast, the investment in the Landlocked-states and Island-

states panels influences the savings in the regions. The current account surplus/deficit influences the

investment in the South–region. This result confirms that the coefficient of the FH savings retention in

South-region does not necessarily reflect a capital mobility from the FH context in order to fill the gap

between savings and investment; instead, the current account deficits/financial account surplus would

be related with the capital inflows in South-region. Bi-directional linkages between current account

(+, -) and financial account (-, +) are identified in South- and East-regions. There is no direct or indirect

causal relationship between investment and savings in the West-region in the short-run indicating that

investment would be financed by other capital sources (Appendix 5, Figure 10).

3.2 Does finance promote growth? A global income-level & the SSA regional comparison

The long-run cointegrating relationship between growth and financial factors is estimated using

the FMOLS and the GMM methods based on the datasets of three global income group samples ( HI,

MI, LI countries) as well as regional samples (the EU, LAC, EAP, South Asia (SA), MENA and the

SSA group). The estimates are summarised in Appendix, Table 5. The overall results clearly show the

impact of financial factors on growth differs depending on the income level.

In the FMOLS estimates of the world-LI panel, the impact of ‘credit provided by banking sector

(CB)’ shows negative. The estimates of the world-MI group, the FDI inflows positively affect the

growth; for the HI panel, the stock turnover, portfolio equity (PE), remittance, positively affect the

growth while the impact of ‘credit provided by financial sector’ (CFS) and ‘official-development-

assistance (ODA)’ is negative on the growth while the impact of CB on growth is negative.

Using the GMM method, the estimates of the HI panel identify only the PE which positively

affects growth while the impact of CB on growth is negative. The impact of savings (S) on growth in

both MI and LI panel groups is positive. The CB affects positively the growth of the MI panel. The

ODA and FDI inflows affect negatively the growth of the LI panel. The estimates show a positive

effect of FDII on growth for only the MI countries (+0.05), a marginal effect on the LI countries

(+0.005), and, for the SSA, a negative effect. The impact of PE on growth shows a positive and

significant effect for HI (+0.09) and MI (0.05), while a marginal effect shows for LI (-0.001) and SSA

(+0.017). The STV has a positive and statistically significant effect on growth for HI (0.38) and a


marginal effect on growth for MI (0.03). The effect of ODA is negative and significant for LI (-0.04)

while there is no statistically significant effect for MI and SSA countries. Finally, the effect of FCF on

growth is positive for MI (+1.23) while negative for HI (-4.06), LI (-0.13) and SSA (-0.22). For the

SSA group, the FMOLS and the GMM estimators identify that CFS and ‘personal remittance inflows’

have positive effects on the growth.

The results from both long-run and short-run estimates affirm that a significant regional

heterogeneity exists in the finance-growth relationship in the five geographical panels (West-; East-;

South-regions; Islands-states; Landlocked-states) and two income panels (MI and LI) in SSA.

The results of the finance-growth relationship from FMOLS, RLS-M and the PLS estimations

are summarised in Appendix, Tables 6 and 7. The statistically significant financial factors on the growth

in the regions are: ‘bank-credit to bank-deposits (CBD)’, ‘remittances (RE), ‘financial system deposits

(FSD)’, ‘bank-asset (BA)’, ‘credit to the private sector (CPS)’, foreign claims (FC), ‘private credit by

deposit banks and financial institutions (PCDBF)’, ‘loans from non-resident banks (LNRB)’, ‘external

loans, % of deposits of the non-banking sector (ELDNB)’ and ‘external loans, % of bank deposits

(ELDB)’. Generally, the factors of ‘domestic and external deposits’ play a positive role on growth

while bank assets (i.e. loans) – with an exception in West-region – and ‘external loans’ show a negative

effect on growth. CBD is the key common factor in growth to all regions except the South-region. The

impact of remittance on growth is positive in the South- and East- regions but negative in the Islands-

and Landlocked-panels. For the Landlocked-state panel, two variables, CBD and FSD have a positive

effect on growth while BA shows a negative effect. For the South region, LNRB, and CPS show a

positive sign while the RE variable shows a negative sign. For East region, the CBD, and BA show a

positive sign while the PCDBF and RE show a negative sign. West region and Islands-state panel show

more finance-growth linkages than other regions. For West region, RE, FSD, and BA show a positive

sign while FC and ELDB show a negative sign on growth. For the Islands-group, CBA, and FC show

a positive sign while RE, PCDBF, ‘liquid liabilities’, and ‘export loans and deposits’ show a negative

sign.

The impact of the ‘credit to government and state enterprises (CGSE)’ on growth is positive for

the South-region only from the FMOLS estimation. When the PLS estimator is used, the positive

impact of CGSE on growth exists in both South-region and the Island-states panels while a negative

effect of CGSE on growth is identified in the West-region. PCB has a positive effect on growth only

for the East-region. Further heterogeneity in regions is identified from the PLS estimation; the cross-

section fixed effect model of the PLS estimates identifies a regional heterogeneity showing that BA

have a negative effect on growth to all regions except the West-region where the factor positively affects

growth.

The results of the short-run directional relationship between the financial factors and growth in

the regions is presented in Appendix, Table 8. The factors that statistically significantly influence (+,

-) growth are FDI, bank credit, and financial sector credit. A bi-directional causality between the growth

and ‘deposit by bank and financial sector’ is identified in the South- and East-regions and Island-states

panels; and a bi-directional causality between the growth and CFS and ELDNB is identified in East-

region and the Island-states. A unidirectional causality from finance to growth is identified in the South-

region in that FSC and ELDNB influence the growth. On the other hand, a unidirectional causality

from growth to finance (PCB) is identified in the Landlocked-states panel. In addition, the West-region

and the Island-states panels show more linkages in the finance-growth relationship reflecting economic

and monetary policies on the development of financial intermediation, which in turn will promote

growth in West-region (Atindehou et al 2005) and the Island-states group.

Previous empirical studies provide evidence of bi-directional causality between finance and

growth in Chad, Kenya, Sierra Leone, South Africa and Swaziland (Oluitan, 2012; Adusei 2013); and

a unidirectional causality from finance to growth in Central African Republic, Congo Republic, Gabon,


12

Ghana, Nigeria, Senegal, South Africa, Togo, and Zambia, (Agbetsiafa 2003; Atindehour et al 2005;

Akinlo and Edbetunde 2010). The growth causes financial development in Ivory Coast, Kenya, Zambia

(Agbetsiafa 2003; Akinlo and Edbetunde 2010; Atindehou et al 2005) and results suggest a cross-

country heterogeneity (Padawassou 2012) in the level of economic and financial development obviously

indicating the heterogeneity in the regional panel estimation of the impact of finance on growth as well.

3.3 Long- and short-run determinants of growth in SSA

The estimates from GMM and FMOLS of the long-run determinants of growth are summarised

in Appendix, Table 3. The overall findings suggest the level of growth factors (income and GDP) are

mainly explained by increased money supply and imports over the period 1996-2014 while the growth

of GDP and income are explained by the increased manufacturing sector, export and CFS over the same

period. The FDI flows and CB on both the level and growth of GDP and income show a negative sign.

It is identified that the agricultural sector shows a negative sign on the growth of GDP in the long-run.

The manufacturing sector shows a negative sign on the level of income over the same period. Estimates

from the FMOLS on income growth show a positive impact on export and CFS while the ‘banking

sector’, FDI inflows and FDI outflows show a negative sign.

The estimates of the F-test-based causality tests identify the short-run determinants on growth in

SSA (Appendix, Figures 8-11). Two sets of data are used, regional panel datasets to estimate short-

term impact of the FH factors on growth, and categorised comprehensive datasets to identify statistically

significant pathway causal effects.

The short-run directional causality tests identify that the investment in South-, West-regions and

the Landlocked-group panels contributes to growth in the regions while savings in the East-SSA panel

lead growth in the region. For the Islands-states panel, investment and savings do not influence growth

in the region, rather growth and investment affect savings (Appendix 5, Figure 10).

In the short-run, a substantial directional pathway effect of the agricultural sector on various

economic and financial factors is identified. The agricultural sector positively affects the welfare-

growth variables (health expenditure, schooling, income and GDP per capita), industry factors

(manufacturing, services, air-transport for freight and import), money supply, investment, and CFS.

Therefore, linking short-term channels of the agricultural sector with the long-run growth factors such

as manufacturing sector and export would lead to a rapid and sustaining growth potential through raising

income and growth in SSA. For instance, capital allocation for technology in processing, packaging,

manufacturing raw agricultural products as well as for transport and power-supply infrastructure would

provide a leveraging effect for the comparative advantages to resource-producing SSA countries. The

SSA-manufacturing sector has been chronically weaker than other developing countries in the world

(Mlachila et al 2016, Table 1). The identified bi-directional relations are (i) education (a proxy by

school enrolment rate) and the level of income per capita, (ii) education and fixed capital formation (a

proxy of investment), (iii) M2 and savings, (iv) industry value added and CFS, (v) energy and capital

formation (infrastructure), (vi) air-transport passengers and industry, and (vii) air transport passengers

and CFS. Furthermore, the estimates indicate that enhancing the agricultural sector and education

influences the level of income and investment. Other identified causalities include: (i) export has a

positive influence on money supply, investment, FDI inflows, air transport of freights, and electricity

consumption while imports have an influence on only one variable, services; (ii) manufacturing and

service sectors influence savings, infrastructure-investment, and air-transport; (iii) energy consumption

influences investment, infrastructure, and government consumption; (iv) investment is influenced by

income, savings, growth, manufacturing and export sectors while investment influences infrastructure,

air transport of freights, education and industry; (v) investment and electricity usages lead the level of

income and GDP; (vi) increased savings affects attracting FDI, domestic investment, money supply,


investment, and FDI inflows; (vii) the level of GDP and national income influence investment,

education and electricity consumption (Borensztein et al 1998); (viii) financial factors including

‘corporate and public debt’, ‘bond % GDP’, remittances, and LNRB influence growth while growth

influences foreign capital flows, investment, ODA, CPS and PCB. CFS influences external balance,

electricity, industry, and export while the credit provided by the private sector influences industry.

External balance influences money, investment, air transport of freights, and education. Interest rate

spread is influenced by ODA inflows, remittances, foreign claims, savings and growth (Appendix 5,

Figures 8-10). There is bi-directional causality between growth and financial factors (bank assets, bank

deposits, interest rate spread and financial system deposits). Moreover, triangular-interaction-

causalities are identified between income, education, and investment which implies improving any of

these would influence the other two variables; and similarly between CFS, industry and the number of

air-transport passengers (Appendix 5).

3.4 Financial factor analysis: A global and regional comparison The bi-plot loadings from the PCA (Principal Component Analysis) show the financial factors

considered in the system are more correlated among themselves in MI and HI groups than those in LI

and SSA groups (Appendix: Figures 4-7) consistent with the findings in sub-sections 3.3 and 3.4. The

combined first and second principal components of the financial factors are highest for the MI group

(84.2%) followed by the HI group (77.4), the LI group (73.6) and the 46-SSA (71.6%). Adding

economic factors namely growth, savings, and investment, the combined first and second PC decreases

to 62% for the SSA group from 71.6%, the first and second PC of the financial factors. The factors of

savings, investment and growth are more closely correlated among themselves than with other financial

factors. The investment factor is more globally correlated (82.2% from the 1st and 2nd PC) among the

income and regional groups than the savings factor (76%). The savings behaviour in SSA data seems

similar to that of the LAC region while SSA investment closely relates to that of the EAP region.

Comparing the bi-plot loadings of financial factors in the SSA regions, the combined first and second

components show that the financial factors in the Islands-panel is highly integrated among themselves

(91.6%) followed by South-region (67.8%), East-region (66.8%), Landlocked states (59.1%), and West-

region (58.4%).

3.5 Case study of 6-SSA financial development on growth Lewer (2016) reports that private equity in SSA as a whole increased from 3.02 bn$ in 2010 to

3.89 bn$ in 2015 and foreign Africa-focused PE investment grew from 46% in 2010 to 92% in 2015.

However, only a few countries in the SSA region have reasonably well organised and functioning

financial markets (Yartey, Adjasi 2007) measured by the degree of depth, access, and efficiency of both

financial institutions and financial markets (Sahay et al 2015). For a 9-country panel group (Botswana,

Cote d'Ivoire, Ghana, Kenya, Mauritius, Namibia, Nigeria, South Africa, and Zambia), the independent

variables, the stock market capitalisation, bank assets (% of GDP) and insurance assets (% of GDP), on

growth were estimated. The results show that bank assets and insurance assets have positive effects,

0.52 and 0.18 respectively, on growth while the effect of stock market capitalisation (-0.01) has

negligible effect on growth (Appendix 3, Table 8).

4. DISCUSSION

Feldstein-Horioka (1980) find that the savings-investment coefficient would be lower for

developing economies so that the degree of capital mobility would be higher as economic theory

suggests capital should flow from richer to poorer countries for investment opportunities. In contrast,


14

Prasad et al (2007) find that capital has been flowing from poor to rich countries based on 59 developing

countries over the period 1970-2004 suggesting the FH coefficient of savings-retention as a measure of

capital mobility is uninformative, if not invalid, as originally estimated with 1970s data. Capital flows

from poor to rich countries due to financial and institutional impediments and political instability that

limit a poor country’s ability to absorb foreign capital (Durham 2004; Prasad et al 2007). Financial

systems and banking sectors in SSA have been relatively less developed in terms of lower returns on

bank-financed investment, considerably fewer borrowers from commercial banks, and fewer bank

branches compared with other developing countries for similar levels of financial depth, leading to a

weakness on the demand side of the credit market (Korner and Schnabel 2010; Barajas et al 2013,

Appendix 2, A2.5-A2.7) that may lead to low capital inflows regardless the value of the FH coefficient.

Sachs (1981, 1983) finds that about 65% of the change in investment was financed by capital inflows

rather than by savings so that investment was more correlated with current account flows. The estimates

of the long-run FH coefficient in this study show a moderate capital mobility (0.59 on average) for the

individual countries, however, in the short-run, the West-region, Island-states, and Landlocked-states

in SSA show that savings do not lead investment, instead investment leads savings and current account

in the Island-states, and investment leads current account in the East-region. The estimates based on

the data of Republic of Central Africa, Comoros, Eritrea, Ghana, Kenya, and South Africa are

statistically insignificant reflecting the fact that in the short-run, savings, investment, current account

and capital account are inter-linked although, in the long-run, the difference between savings and

investment is supposed to be equal to the current account balance (Obstfeld, 1986; Ghosh 1995; Coakley

et. al 1998; Hussein and Mello 1999). A majority of countries in SSA with large trade deficits, typically

borrowing money for importing goods and thus running a current account deficit, would have capital

account inflows in order to balance the international payments regardless of the value of the FH

coefficients. Agbetsiafa (2004) and Cooray, Sinha (2007) find no capital mobility or no long-run FH

correlation from their estimates for Ghana, Ivory Coast, Kenya, Nigeria, Rwanda, South Africa, and

Zambia. Esso and Keho (2010) found 3 of 7 member countries (Benin, Cote d’Ivoire and Niger) show

low capital mobility raising issues such as political risk, human capital, and infrastructure as the causes

of low capital mobility. Since 1985, many SSA countries have reduced trade restrictions and

encouraged capital flows, and gradually liberalised exchange rate regimes and financial systems which

may lower the FH coefficients, and thus a lower FH coefficient would be expected since 1995. The

findings, however, show that there have been many changes in FH coefficient values. Other factors can

explain why the FH high/low savings-retention coefficients do not necessarily reflect a low/high capital

mobility including the pro-cyclical nature of saving and investment (Obstfeld, 1986), the presence of

consumption of non-tradable goods (Murphy, 1984), the uncovered interest parity (Montiel 1994),

currency premium, structural factors (Ozmen, 2007, Kool and Keijzer 2009), and current account

targeting by government expenditures (Artis and Bayoumi, 1991). Some studies observed relatively

high capital mobility for OECD country groups and G7 countries with high FH coefficients (Coakley

et al 2004; Pelgrin and Schich 2008; Rao et al 2010) while some found no evidence of correlation

between savings and investment in G7 countries (Narayan and Narayan, 2010) although the extent of

empirical studies supporting the FH savings-retention hypothesis is such that the coefficients were

generally consistent with low capital mobility for the OECD data compared to those for developing and

less developed country samples (Dooley et al 1987; Wong 1990; Vamvakidis and Wacziarg 1998;

Kasuga 2004; Payne and Kumazawa 2005). Another important point is the net effect of the presence

of significant cross-country heterogeneity (Kasuga, 2004; Padawassou 2012, Figures 3.1-3.5). The

estimates of FH coefficients show a range of 0.21-0.35 for panel estimations (Payne and Kumazawa

2005; De Wet and Van Eyden 2005) and a range of 0.36-0.73 for individual country estimations

(Adedeji and Thornton 2006; Bangake and Eggoh 2010) such that the panel estimates show a higher

capital mobility (i.e. low savings-investment coefficients) than those of individual country estimates


reflecting the net offsetting effect between the gap between savings and dis-savings as well as the level

of financial and economic development and the offsetting effect of the former shows larger on the value

of the FH coefficients in the consolidated SSA dataset.

International capital – (FDI), portfolio investment (PI) and bank-loans – flows for risk-adjusted

returns on assets located in different countries through the financial intermediaries. The estimates

suggest a significant role of ‘private credit provided by financial sector’ and investment for long-run

growth in SSA and in the short-run, investment leads the growth in the South-region, West-region and

Landlocked-states in SSA. In contrast, the estimates show that the banking industry has not

substantially promoted the long-run growth in SSA. The investment (% of GDP) in SSA is 28.8 percent,

lower than in other world regions in 2014, partially resulting from low private investment and the high

cost of lending capital due to risk premiums (Haque et al., 1999). One reason is the interest rate spread

which is much higher (11%) than in other groups in the world (e.g. SA (5.4%), EAP (4.8), the EU (4.5)

and MENA (4.4). A high net interest margin and low profitability ratios indicate a low efficiency

partially due to limited competition in the SSA financial intermediaries which in turn increase the cost

of intermediation and undermine the positive impact of finance on economic growth. Challenges are

imminent in SSA banking intermediaries to narrow the interest rate spreads, lower service fees and

expand access to credit for small and medium enterprises. Private-investment, FDI inflows and national

income per capita in SSA have been disappointing and uneven in the regions. In fact, the data shows a

decreasing trend of FDI in SSA over the period 1996-2014 that partially reflects the levels of depth,

accessibility, and efficiency that still lag behind other developing regions (World Bank 2015; Sahay et

al 2015; Merchettini 2015; Mlachila et al 2016). In particular, FDI is a way of filling the gap between

available supplies of domestic savings and government revenue needed to achieve growth (Caves 1996;

Mlachila et al 2013, 2016), and it leads to increases in the manufacturing sector (Cyrille 2010), export

and growth (Choe 2003; Okonkwo et al 2015) or the growth leads FDI inflows (Onyeiwu 2004) in some

African countries. FDI alone does not necessarily lead to growth if it does not effectively promote

export or import substitution (Balasubramanyam et al. 1996). Most African countries have encouraged

FDI and therefore reformed their economic policies, investment laws and financial systems initiated

since the mid-1990s, consequently the previous policy-induced disadvantages have been considerably

reduced (Collier and Gunning 1999). Limiting the FDI outflows from Africa is due to several factors

including discouraging political instability, inconsistent and unsuitable national economic policies,

closed trade regimes (United Nations, 2005), inflation and currency instability (Alaba 2003; Rogoff,

Reinhart 2003). Identified causes of Africa’s slow growth and reducing private investment include a

higher ethno-linguistic and religious diversity, undemocratic dictatorships, high incidence of civil war

(Easterly and Levine 1997; Collier 2000), a low-productivity trap with low wages, unfavourable

business environment, low return-on-capital (Bigsten et al 1999), a low life expectancy and high

population growth (Bloom, Sachs, 1998), and a higher proportion of capital flight (Collier and Gunning

1999). Besides, the Cobb-Douglas production function indicates that technological progress

encourages output growth per worker but the stock of natural resources is fixed as an input factor that

can limit growth. Changes in technological progress have permanent growth effects while other factors

have a level effect as explained in the Solow model (1956). Lucas (1988) also pointed out that

technological progress, physical and human capital accumulation, and institutions are the sources of

growth potential. The estimates of a weak and limited effect of FDI on growth partially result from

inefficient transmission of FDI effects into more productive and technological transferable

manufacturing sector, export, and the level of human capital in order to boost the growth (Carkovic and

Levine, 1997) along with less developed financial intermediaries and unfavourable institutional

characteristics (Easterly and Levine 1997; Durham 2004). Prasad, et al. (2007) argue that foreign

capital has a greater impact on growth only in industrial countries while developing countries have

limited absorptive capacity for foreign capital inflows because of the existence of weak financial


16

markets. Besides, Bresser-Pereira and Gala (2008) point out that borrowing to finance a country’s

consumption rather than to invest means the country grows less, but that leads to an appreciation of the

exchange rate and a rise in wages. Enhancing the depth, access and efficiency in traditional investment

banking in SSA for a firm-level financing as well as large-scale project financing to increase private

credit toward investments will be an important challenge to sustain long-run growth in SSA. Easterly

and Levine (1997) estimated that a lack of financial depth reduced the annual growth rate by 0.3

percentage points. The results also show a clear finance-growth relationship in the short-run such that

financial factors, especially, ‘private and public international debt securities’, ‘foreign bank loans’, and

‘financial deposits’ influence the short-term growth in SSA while investment and CFS positively affect

long-run growth. Considering the presence of ‘finance-growth’ and ‘growth-finance’ directional

relationships in SSA regions and the insight from the results, the short-run and long-run growth

strategies should essentially link to sustain the growth further. For example, achieving a short-run

growth would lead to increasing savings, income-level and investors’ confidence in the capacity of debt

repayment in order to attract FDI flows; as a result of enhanced investment, if productively arranged,

the short-run growth will directly link to a long-run growth over time.

Another important growth strategy, linking short- and long-run growth determinants, is the

linkage between the short-term growth trigger of the agricultural sector and the long-term growth

triggers of the manufacturing sector and exports. As pointed out in sub-section 3.4, the agricultural

sector has a notable multi-dimensional trigger leading industry, welfare and growth factors in the short-

term; on the other hand, the factors of export, manufacturing industry, and education are identified as

long-run growth drivers from the results. As discussed in Gunning (1999), promoting an export-led

policy in SSA countries is critical in pursuing growth and national income as it will facilitate reducing

current account/trade deficits and attracting capital inflows for essential infrastructure investment. A

simplified growth diagram that entails a value-added function is illustrated in Appendix, Figure 12:

VA=f (capital/infrastructure + labour + technology export) together with timely and appropriate

policies (e.g. Appendix, A2) on the input factors in the VA function would accelerate and sustain the

growth in SSA. Africa’s traditional exports are predominantly concentrated in natural resources (e.g.

oil, minerals, and metals) and primary agricultural goods (e.g. crops, cattle, fisheries among others)

(IMF 2011). The fundamental vulnerability in the export structure results from exporting lower-value-

added to GDP at the same time as importing higher-value-added products which can lead to current

account deficits as well as exposing a severe volatility of commodity price. Furthermore, the export-

structure can cause seasonal and intertemporal unemployment and uncertainty in the government

revenues as well as national income. For these reasons, various UN and World Bank reports encourage

boosting the manufacturing sector and infrastructure investment in the SSA region. Songwe and

Winkler (2012) found positive effects of exports on growth and labour productivity using 30 SSA

countries over the period 1995-2008. Contrary to expectation, they found more specialisation, instead

of diversification in export products, due to a high comparative advantage (i.e. competitiveness) which

yields more benefits, the larger effects of exports on the value added and labour productivity. They

also confirmed the positive relationship between exports and labour productivity is stronger for

manufactured exports than raw commodity exports. Importantly, if SSA were to adopt more actively

innovative industrialisation policies to transform the export-structure from exporting raw resource-

based commodities to the manufacture of higher-value-added products it would lead to not only

accelerating growth (Wood and Mayer 1998) but also the accumulation of physical capital (i.e.

infrastructure) and skilled human capital through better education and health care (Mlachila et al 2013)

– to absorb the surging working-age population in the region can be comparatively advantageous. For

example, as suggested in Songwe, Winkler (2012), Nigeria and other oil-exporters could expand the

pharmaceuticals industry while Cote d’Ivoire and other cocoa-producing countries could move into

manufacturing chocolate-based products, and Chad and other agricultural producers could move into


hygienically/organically manufactured agricultural products for short-term growth leading to higher

income levels and savings rather than diversifying products over the medium term. Transforming

export-structure requires financing businesses’ physical capital investment as well as power/transport

infrastructure investment so promoting efficient investment banking and attracting FDI inflows are

essential challenges. Therefore, the export-led industrialisation of manufactured-resource-based-

exports would increase the demand of traditional bank finance, i.e. long-term bank loans to firms to

accumulate physical capital and invest in infrastructure. Generally, a norm of 5-6% of GDP for

infrastructure financing to sustain growth is promoted (Gutman, 2015); in particular, power-

infrastructure has been responsible for more than half of SSA’s improved growth performance.

However, SSA’s infrastructure investment (electricity, water, sanitation, and transport) generally lags

behind other developing regions in the world, a funding gap of 31 bn$ a year, subsequently 30 countries

in SSA face regular power shortages, (Foster, and Briceno-Garmendia, 2009; Gutman et al 2015).

Besides, Gutman (2015) reports that SSA accounts for only 3% of total world project finance between

2003 and 2013, the extractive industries (oil, gas, mining) share 64% of total sub-Saharan Africa’s

project finance and four countries (Nigeria, Ghana, and South Africa and Angola) share 70% of the

total SSA project finance. Gutman (2015) points out that lack of power is a major bottleneck in SSA’s

industrialisation, together with low power access (only 30% of the population) and higher supply costs.

Financing the required infrastructure investment and accumulating physical capital requires more

domestic finance as well as external borrowings; in fact SSA countries have increasingly accessed

international capital markets with 13 countries issuing 15 bn$ worth of international sovereign bonds

since 2006. The resource-poor Island-states in the region may choose diversification instead of

concentration in their export-structure (agriculture-manufacturing-services). The Island-states have

been positively linked the impact of finance on economic growth, one of the financial factors, the

‘private credit by deposit money banks to GDP’ reached 135% (averaged from Cape Verde, Comoros,

Madagascar, Mauritius, Sao Tome and Principe, and Seychelles) by 2011 while for other LI economies

in SSA, it is less than 20% during the same period (Appendix, A3).

5. CONCLUDING REMARKS

This study re-examined the finance-growth relationship in SSA over the period 1996-2014. The

results of the capital mobility (measured by the FH coefficients) seem consistent with previous studies.

However, it is clear that the coefficients rely on the degree of data consolidation – the more numbers in

the pooled or consolidated data from the entire individual country samples, the lower the FH coefficients

due to a strong heterogeneity in each country’s specific gap between savings, dis-savings and current

account deficits/surpluses. Therefore, the validity and interpretation of the FH coefficients as a measure

of the degree of capital mobility should take account of the nature of capital flows such as FDI, PI, bank

loans, official debts or ODA, the fiscal deficits/surpluses and other characteristics of financial systems

and institutions of each economy in SSA.

The estimates on the finance-growth relationship show that money supply and CFS are positive

drivers for long-run growth in SSA. However, a negative role of FDI and the banking sector on long-

run growth is identified. The findings show more linkages in the finance-growth relationship in the

short-run rather than in the long-run, and in the MI-panel than the LI-panel in SSA. The regional

heterogeneity in the estimates is potentially due to the substantial diversity in terms of the fiscal and

monetary policies, income levels, resource endowments, and political stability in the sample economies.

Some imminent challenges to enhance the growth are narrowing the interest rate spread between

lending and deposit as it is much higher than that of other MI developing countries as well as boosting

traditional investment-banking to finance the accumulation of physical capital as bank-lending has been


18

limited compared to the increasingly expanding cell-phone based payment services. In particular,

transforming export-structure would raise the income-level and growth rate; in turn, that would raise

the demand of financial depth, access and efficiency as well as the demand of skilled labour forces.


REFERENCES

Adedeji O, J Thornton (2006). Saving, investment and capital mobility in African countries. Journal of African Economies, Vol. 16, No.3,

393-405. Adusei M (2013). Finance-growth nexus in Africa: a panel Generalized Method of Moments (GMM) analysis. Asian Economic and Financial

Review, 2013, 3(10):1314-1324.

African Economic Outlook, 2006-2015, African Development Bank Group. Agbetsiafa D K (2003). The finance growth nexus: evidence form sub-Saharan Africa. International Advances in Economic Research 9 (2).

Akinlo A E, T Edbetunde (2010). Financial development and economic growth: The experience of 10 sub-Sharan African countries revisited.

Review of Finance and Banking, Vol 02, Issue 1. Allen S, L Ndikumana (1998). Financial intermediation and economic growth in southern Africa. The Federal Reserve Bank of ST Louis,

Working Paper 1998-004.

Andrianaivo M, C A Yartey (2009). Understanding the Growth of African Financial Markets. IMF Working Paper, 09/182, Washington: International Monetary Fund.

Arslanalp S, F Bornhorst, S Gupta, E Sze (2010). Public capital and growth. The IMF Working Papers.

Artis M, T Bayoumi (1991). Global financial integration and current account imbalances. In: Alogoskoufis, G., Papademos, L., Portes, R. (Eds.), External Constraints on Macroeconomic Policy: The European Experience. Cambridge University Press, Cambridge.

Atindehou R B, J. P. Guyeie, E K Amenounve (2005). Financial intermediation and economic growth: evidence from Western Africa. Applied

Financial Economics 15, 777—790. Bangake C, J C Eggoh (2010). The Feldstein–Horioka puzzle in African countries: A panel cointegration analysis. Economic Modelling 30.

Barajas A, R Chami, R Y Seyed (2013). The Finance and Growth Nexus Re-examined: Do All Countries Benefit Equally? IMF Working

Paper 13/130, International Monetary Fund, Washington.

Barro R J (1990). Economic growth in a cross section of countries. NBER Working Paper 3120, September 1989.

Bayoumi, T (1990). Savings–investment correlations: immobile capital, government policy, or endogenous behavior? IMF Staff Papers 37,

360–387. Balasubramanyam V N, Salisu, M, D Sapsford (1996). Foreign Direct Investment and Growth in EP and IS Countries. Economic Journal,

Royal Economic Society, vol. 106(434), 92-105.

Beck T, A Demirguc-Kunt, R Levine (2007). Finance, inequality and the poor. World Bank. Beck T, S M Maimbo, I Faye, T Triki (2011). Financing Africa: Through the Crisis and Beyond. Washington: World Bank.

Beck T, Degryse H, C Kneer (2012). Is more finance better? Disentangling intermediation and size effect of financial systems. Journal of

Financial Stability, 10. Bigsten A, P Collier, S Dercon, B Gauthier, J W Gunning, A Isaksson, A Oduro, R Oostendorp, C Pattilok, M Soderborn, M Sylvain, F Teal,

A Zeufack (1999). Investment in Africa’s manufacturing sector: a four country panel data analysis. Oxford Bulletin of Economics and

Statistics Vol. 61, Issue 4, 489-512. Bloom D E, J D Sachs (1998). Geography, demography, and economic growth in Africa. Brookings Papers on Economic Activity, 2.

Bresser-Pereira L C, P Gala (2008). Foreign savings, insufficiency of demand, and low growth. Journal of Post Keynesian Economics, 30 (3),

315-334. Bresser-Pereira L C (2016). Why the rest doesn’t need foreign finance. Working Paper 415, Sao Paulo School of Economics.

Cass D (1965). Optimum Growth in an Aggregative Model of Capital Accumulation. The Review of Economic Studies, Vol. 32, No. 3, 233-

240. Choe J I (2003). Do foreign direct investment and gross domestic investment promote economic growth? Review of Development Economics,

7(1), 44-57.

Čihák M, A Demirgüç-Kunt, E Feyen, R Levine (2012). Benchmarking Financial Development around the World. World Bank Policy

Research Working Paper 6175. The World Bank, Washington, DC.

Carkovic M, R Levine (2002). Does foreign direct investment accelerate economic growth? University of Minnesota Department of Finance,

Working Paper. Coakley J, F Kulasi, R Smith (1998). The Feldstein-Horioka Puzzle and Capital Mobility: A Review. International Journal of Finance and

Economics 3, 169-188.

Coakley J, A M Fuertes, F F Spagnolo (2004). Is the Feldstein-Horioka Puzzle History? The Manchester School, 72(5), 569-590. Collier P, D Dollar (1999). Aid allocation and poverty reduction. The World Bank.

Collier P, J W Gunning (1999). Why has Africa grown slowly? The Journal of Economic Perspectives, Vol. 13, No. 3, 3-22. Collier P (2000). Economic causes of civil conflict and their implications for policy. The World Bank.

Cooray A, D Sinha (2007). The Feldstein-Horioka model revisited for African countries. Applied Economics 39 (12), 1501–1510.

Cyrille S M (2010). Saving-Investment Correlation and Capital Mobility in Sub-Saharan African Countries: A Reappraisal through Inward and Outward Capital Flows’ Correlation. International Journal of Economics and Finance, Vol. 2, No. 2.

De Wet A, R Van Eyden (2005). Capital mobility in sub-Saharan Africa: A panel data approach. South African Journal of Economics 73 (1),

22- 35. Durham J B (2004). Absorptive Capacity and the Effects of Foreign Direct Investment and Equity Foreign Portfolio Investment on Economic

Growth. European Economic Review 48(2), 285-306

Dicky D A, W A Fuller (1979). Distribution of the estimators of autoregressive time series with a unit root. Journal of the American Statistical Association.

Dollar D (1992). Outward-oriented developing economies really do grow more rapidly: evidence from 95 LDCs, 1976-1985. Economic

Development and Cultural Change, 40, 3, ABI/INFORM Global, 523.

Dooley M, J A Frankel J A, D Mathieson (1987). International Capital Mobility in Developing Countries vs. Industrial Countries: What Do

the Savings-Investment Correlations Tell Us? IMF Staff Papers 34, 503-529.

Easterly W, R Levine (1997). Africa’s Growth Tragedy: Policies and Ethnic Divisions. The Quarterly Journal of Economics, Vol. 112, No.4, 123-1250.

Efayena O (2014). Financial intermediaries and economic growth: the Nigerian evidence. Acta Universitatis Danubius, Economica Vol 10,

no 3, 2014. Esso L J, Y Keho (2010). The Savings-Investment Relationship: Cointegration and Causality Evidence from UEMOA Countries. International

Journal of Economics and Finance, Vol. 2, No.1.

Favara G (2003). An empirical reassessment of the relationship between finance and growth. IMF Working Paper, WP/03/123. Feldstein M, C Horioka (1980). Domestic saving and international capital flows. Economic Journal 90, 314–329.

Devarajan S, W Fengler (2012). Is Africa’s recent economic growth sustainable? IFRI, Programme Moyen-Orient/Maghreb.


20

Foster V, C Briceno-Garmendia (2009). Africa’s Infrastructure: A Time for Transformation. Africa Development Forum Series. Washington, DC: World Bank.

Ghosh A R (1995). International Capital Mobility amongst the Major Industrialised Countries: Too Little or Too Much. The Economic

Journal, 105, 07–128. Gramlich E (1994). Infrastructure investment: A review essay. Journal of Economic Literature, Vol. 32, No. 3, 1176-1196.

Granger C W J (1969). Investigating causal relations by economic models and cross-spectral methods. Econometrica, Vol. 37, No. 3, 424-

438. Granger C W J (1988). Causality, Cointegration and Control. Journal of Economic Dynamics and Control, 12, 551-559.

Greene W H (2008) Econometric Analysis, 6th ed. Upper Saddle River, New Jersey: Prentice Hall.

Gutman J, Sy, Amadou, S Chattopadhyay (2015). Financing African Infrastructure. Global Economy and Development Brookings. Hall, A R (2002005). Generalized method of Moments. Oxford University Press.

Hampel F R, E M Ronchetti, P J Rousseeuw, W A Stahel (1986). Robust Statistics: The approach based on influence functions. J. Wiley,

New York. Hansen L P (1982). Large sample properties of generalized method of moment estimators. Econometrica 50.

Haque M S (2000). Significance of accountability under the new approach to public governance. International Review of Administrative

Sciences, 66 (4). Hausmann J (1978). Specification tests in econometrics. Econometrica, 1251-1271.

Hussein K A, L R Mello Jr (1999). International capital mobility in developing countries: theory and evidence. Journal of International

Money and Finance, 18, 367-381. Ibrahim S (2012). Financial intermediation and economic growth in Nigeria. British Journal of Arts and Social Sciences ISSN: 2046-9578,

Vol.4 No.2.

IMF (2011). Regional Economic Outlook. Sub-Saharan Africa, Sustaining the Expansion. World Economic and Financial Surveys. Im K S, M H Pesaran, Y C Shin (1997). Testing for Units Roots in Heterogeneous Panels. Journal of Econometrics, 115, 53-74.

Kasuga H (2004). Saving-investment correlations in developing countries. Economics Letters 83, 371-376.

Kamara Y U (2013). Foreign Direct Investment and Growth in Sub-Saharan Africa, What are the Channels? University of Kansas. Kao C, M Chiang (2000). On the estimation and inference of a cointegrated regression in panel data. Advance in econometrics, 15, 179-222.

Kao C (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90, 1-44. Khan M, A S Senhadji (2000). Financial development and economic growth: an overview. The IMF working papers.

King R, R Levine (1993). Finance, Entrepreneurship, and growth: theory and evidence. Journal of Monetary Economics. Vol. 32, 513-542.

King R, R Levine (1993). Finance and Growth: Schumpeter might be right. The Quarterly Journal of Economics, Vol. 108, No.3, 717-737. Korner T, I Schnabel (2010). Public Ownership of Banks and Economic Growth: The Role of Heterogeneity. CEPR Discussion Paper 8138.

Koopmans T C (1963). On the concept of optimal economic growth. Cowles foundation for research in economics at Yale University.

Discussion Paper No. 163. Kumar S, R Sen, S Srivastava (2014). Does economic integration stimulate capital mobility? An analysis of four regional economic

communities in Africa. Department of Economics, Faculty of Business and Law, AUT, New Zealand.

Kool C J M, L M Keijer (2009). International capital mobility: linking the Feldstein–Horioka puzzle to the trade and equity home bias puzzles. Cambridge Journal of Region, Economy and Society, Vol.2, Issue 2, 211-227.

Law S. and Singh, N (2014). Does too much finance harm economic growth? Journal of Banking and Finance Vol. 41 (2014) 36-44.

Levin A, F Lin, C Chu (2002). Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties. Journal of Econometrics, 108, 1- 24.

Levine R, N Loayza, T Beck (2000). Financial Intermediation and Growth: Causality and Causes. Journal of Monetary Economics, Vol.

46, 31-77. Levin R (2005). Finance growth, theory, evidence, and mechanism. In Handbook of Economic Growth, edited by Aghion P and S Durlauf,

New York, Elsevier, 865-934.

Lewer H (2016) Private equity in Sub-Saharan Africa. Private Equity International, PEI Books, 2016. Lucas R E Jr. (1988). On the mechanics of economic development. Journal of Monetary Economics. XXII. 3-42.

Mamingi N (1997). Saving-Investment Correlations and Capital Mobility: The Experience of Developing Countries. Journal of Policy

Modeling 19, 605-626. Mark N C, D Sul (2003). Cointegration vector estimation by Panel DOLS and long-run money demand. Oxford Bulletin of Economics and

Statistics, 65, 655-680.

McDonald C, L Schumacher (2007). Financial deepening in Sub-Saharan Africa: Empirical evidence on the role of creditor rights protection and information sharing. IMF working paper WP/07/203.

Mlachila M, S G Park, M Yabara (2013). Banking in Sub-Saharan Africa, the Macroeconomic context. The IMF, 13/03.

Mlachila M, L Q Cui, A Jidoud, M Newiak, B Radzewicz-Bak, M Takebe, Y Ye, J Zhang, A-M G Wolf (2016). Regional economic outlook, Sub-Saharan Africa. The IMF.

Murphy R G (1984). Capital mobility and the relationship between saving and investment in OECD countries. Journal of International Money

and Finance 3, 327-342. Obstfeld M (1986). Capital Mobility in the World Economy: Theory and Measurement. Carnegie-Rochester Conference Series on Public

Policy, 24 55–104

Obstfeld M, K Rogoff (2000). The six major puzzles in international macroeconomics: is there a common cause? NBER Macroeconomics Annual 15, 340–390. National Bureau of Economic Research, Cambridge: Massachusetts.

Okonkwo R I, Egbunike F C, F N Udeh (2015). Foreign direct investment and economic growth in Nigeria. Developing Country Studies,

Vol 5, No 9. Oluitan R (2012). Financial development and economic Growth in Africa: Lessons and Prospects. Business and Economic Research, ISSN

2162-4860, 2012, Vol. 2, No. 2.

Onyeiwu S, H Shrestha (2004). Determinants of foreign direct investment in Africa. Journal of Developing Societies, Vol 20 No1-2. Ozmen E (2007). Financial development, exchange rate regimes and the Feldstein-Horioka puzzle: Evidence from the MENA region. Applied

Economics 39 (9), 1133-1138.

Padawassou S (2012). Capital mobility in African countries. International Journal of Business and Management. Vol. 7, No. 11. Payne J, R Kumazawa (2005). Capital mobility, foreign aid, and openness: Further panel data evidence from sub-Saharan Africa. Journal of

Economics and Finance 29, 123-127.

Pedroni P (2000). Fully Modified OLS for Heterogeneous Cointegrated Panels. In Baltagi, T.B. Fomby and R.C. Hill (eds), Nonstationary Panels, Panels Cointegration, and Dynamic Panels. Advances in Econometrics, 15.

Pedroni P (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP

Hypothesis. Econometric Theory, 20(3), 597-625.


Pelgrin F, S Schich (2008). International Capital Mobility: What Do National Saving-Investment Dynamics Tell Us? Journal of International Money and Finance, 27, 331-344.

Prasad E, R Rajan, A Subramanian (2007). The paradox of capital. Finance and Development Vol.44, No. 1 the IMF.

Rao B, A Tamazian, S Kumar (2010). Systems GMM Estimates of the Feldstein-Horioka Puzzle for the OECD Countries and Tests for Structural Breaks. Economic Modelling, 27, 1269-1273.

Rogoff K, C Rienhart (2003). FDI to Africa: The role of price and stability and currency instability. Working paper. International Monetary

Fund. Romer P M (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, Vol. 94, No.5, 1002-1037.

Sachs J D (1983). Aspects of the current account behaviour of OECD countries. In: Claassen, E., Salin, P. (Eds.), Recent Issues in the Theory

of Flexible Exchange Rates. North-Holland, Amsterdam. Sachs J D (1981). The current account and macroeconomic adjustment in the 1970s. Brookings Papers on Economic Activity 12, 201-268.

Sachs J D, A M Warner (2001). The curse of natural resources. European Economic Review 45, 827-838.

Sahay R, M Cihak, P N’Diaye, A Brarajas, R Bi, D Ayala, Y Gao, A Kyobe, L Nguyen, C Saborowski, K Svirydzenka, S R yousefi (2015). Rethinking financial deepening: stability and growth in emerging markets. IMF SDN/15/08.

Seetanah B, A J Khadaroo (2007). Foreign Direct Investment And Growth: New Evidences from Sub-Saharan African countries. Conference

paper 169. EDiA-La WBiDC. Shen C, C Lee (2006). Same financial development yet different economic growth-why? Journal of Money, Credit and Banking, 38(7): 1907-

1944.

Sloman J (2007). Essentials of economics, 4th edition, Prentice Hall. Solow R M (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, LXX, 65-94.

Songwe V, D Winkler (2012). Exports and export diversification in sub-Saharan Africa. Working paper 3, Africa Growth Initiative at

Brookings. Straub S (2008). Infrastructure and growth in developing countries: recent advances and research challenges. Policy research working paper

series 4460, the World Bank.

Swan T W (1956). Economic growth and capital accumulation. Economic Record, Vol. 32, Issue 2, 334-361. Umar M, Dayyabu, S, Gambo, A, Danlami, M, A Ahmad (2015). An empirical study on the relationship between financial intermediaries and

economic growth in Nigeria: A cointegration and causality analysis. Journal of Economics and Finance. Volume 6, Issue 4. United Nation (2005). Economic development in Africa, rethinking the role of Foreign Direct Investment Vol. 2. United Nation, New York

and Geneva.

United Nation (2005). World Investment Report. Ch.1. Vamvakidis A, R Wacziarg (1998). Developing Countries and the Feldstien–Horioka Puzzle. IMF, Washington, DC. IMF Working Paper.

Wood A, J Mayer (1998). Africa’s export structure in a comparative perspective. UN conference on trade and development. Study No. 4.

UN Geneva. Wong D Y (1990). What do savings-investment relationships tell us about capital mobility? Journal of International Money and Finance 9,

60-74.

World Bank (2015). Africa competitiveness report 2015: transforming Africa’s economies. Washington. World Bank (2014). Global financial development report. Washington.

World Bank (2012). Africa’s Pulse, Washington.

World Bank (2013). Global Financial development report: rethinking the role of the state in finance. Washington. Yartey C A, K A Adjasi (2007). Stock Market Development in Sub-Saharan Africa: Critical Issues and Challenges. IMF Working Paper.

Zamfir I (2016). Africa’s economic growth, Taking off or slowing down? European Parliamentary Research Service.


22

APPENDICIES

Appendix 1. Data description

A1. All data are sourced from the World Development Indicators database and the IMF IFS database Variable name Variable Description

AG Agriculture, value added (% of GDP) , consolidated data from SSA 46 countries

ATF Air transport, freight (million ton-km) , consolidated data from SSA 46 countries

ATP Air transport, passengers carried, consolidated data from SSA 46 countries

BA Deposit money banks’ assets to GDP (%)

BC (CBD) Bank credit to bank deposits (% bank deposits)

BD Bank deposits to GDP (% of GDP)

CA Current Account balance, percent of GDP. Current account balance (U.S. dollars)

CB (CPS) Domestic credit to private sector by banks (% of GDP), excludes non-bank credit to the private sector

CFS (CF) Domestic credit provided by financial sector (% of GDP). CF (domestic credit provided by financial system)

CGS Credit to government and state owned enterprises to GDP (%)

CPS Domestic credit to private sector (% of GDP)

EB External balance on goods and services (% of GDP) , consolidated data from SSA 46 countries

EC Electric power consumption (kWh per capita) , consolidated data from SSA 46 countries

ELDBB External loans and deposits of reporting banks vis-à-vis the banking sector (% of domestic bank deposits)

ELDBNB External loans and deposits of reporting banks vis-à-vis the nonbanking sectors (% of domestic bank deposits)

ENG Energy use (kg of oil equivalent per capita) , consolidated data from SSA 46 countries

ENR Adjusted net enrolment rate, primary, both sexes (%), consolidated data from SSA 46 countries

FCB Consolidated foreign claims of BIS reporting banks to GDP (%)

FCF Consolidated foreign claims of BIS reporting financial institutions to GDP (%)

FDII Foreign direct investment, net inflows (% of GDP), consolidated data from SSA 46 countries

FDIO Foreign direct investment, net outflows (% of GDP) , consolidated data from SSA 46 countries

FSD Financial system deposits to GDP (%)

GC General government final consumption expenditure (% of GDP) , consolidated data from SSA 46 countries

GDPPC (GDP) GDP per capita (Constant 2005 USD); GDPG (annual % growth of GDP per capita)

HE Health expenditure, total (% of GDP) , consolidated data from SSA 46 countries

I (CF; FCF) Gross investment % of GDP. (CF: Gross capital formation; FCF: Fixed capital formation, infrastructure)

IND Industry, value added (% of GDP) , consolidated data from SSA 46 countries

INF Inflation, GDP deflator (annual %), consolidated data from SSA 46 countries

INS Insurance company assets to GDP (%)

ISP (BS) Bank lending-deposit spread (%)

LL Liquid liabilities to GDP (%), M3

LNRB Loans from non-resident banks amounts outstanding to GDP (%)

M Imports of goods and services (% of GDP) , consolidated data from SSA 46 countries

M2 Money and quasi money (M2) (% of GDP)

MF Manufacturing, value added (% of GDP) , consolidated data from SSA 46 countries

NIPPC (NI) Adjusted net national income per capita (constant 2005 US$). NIPPCG (NIG) annual growth of NIPPC

ODA Official development assistance

OIPDS Outstanding international private debt securities to GDP (%);

OIPBS Outstanding international public debt securities to GDP (%)

PCBF Private credit by deposit money banks and other financial institutions to GDP (%)

PE Portfolio equity inflows

RE Remittance inflows to GDP (%)

S Gross national savings, % of GDP, the sum of private sector and government savings (GDS).

SM (STV) Stock market capitalization to GDP (%); STV(total value of stocks traded % of GDP)

SV (SRV) Services, etc., value added (% of GDP) , consolidated data from SSA 46 countries

TR Trade (% of GDP) , consolidated data from SSA 46 countries

X Exports of goods and services (% of GDP)

Financial

infrastructure

SFI: Saved at a financial institution in the past year (% age 15+)

BA1000: Bank accounts per 1,000 adults

FBF: Firms using banks to finance investments, (%)

IFB: Investments financed by banks, (%)

IFE: Investments financed by equity or stock sales, (%)

SSA

46 sub-Saharan Africa counties including Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African

Republic, Chad, Comoros, Congo Dem. Rep., Cote d’ Ivoire, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya,

Lesotho, Liberia, Madagascar, Malawi, Madagascar, Mauritania, Mauritius, Mayotte, Mozambique, Namibia, Niger, Rwanda, Sao Tome

and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, and

Zimbabwe.

Low income

Benin; Burkina Faso; Burundi; Central African Rep; Chad; Comoros; Congo D.R.; Eritrea; Ethiopia; Gambia; The Guinea; Guinea Bissau;

Liberia; Madagascar; Malawi; Mali; Mozambique; Niger; Rwanda; Senegal; Sierra Leone; Somalia; South Sudan; Tanzania; Togo;

Uganda; Zimbabwe.

Middle income Angola;; Botswana; Congo R.; Equatorial Guinea;; Ghana; Mauritania; Mauritius Sao Tome & Principe; Sudan; Swaziland; Yemen;

Zambia.

Landlocked

SSA

Botswana; Burkina Faso; Burundi; Mali; Swaziland; Malawi; Lesotho; Niger; Rwanda. Landlocked areas overlaps with CEMAC

countries. Data in red are excluded subject to data availability. (WDI 1996-2013).

Island State Comoros; Madagascar; Mauritius; and Seychelles. (1996-2013)

West region Benin; Burkina Faso; Ivory Coast; Ghana; Guinea; Nigeria; Senegal; Sierra Leone, and Togo, ECOWAS (1975)/UEMOA (1994)

(WDI 1996-2013) South region Angola; Mozambique; Swaziland; Namibia; South Africa; and Zimbabwe. Overlaps with some listed in COMESA & SACU (1969)

members. (WDI 1996-2013)

East region Burundi; Djibouti; Eritrea; Kenya; Rwanda; Tanzania; and Uganda. EAC/COMESA (1994) members, (WDI 1996-2013)


Appendix 2: Trend and Descriptive Statistics of Data

A2.1 Qualitative Category and Factor Ranking Financial Market Sophistication, Macroeconomic Stability, and Savings; Current Account, Investment; and Interest

rate Spread & Revealed Random Linkages: Sub-Saharan Africa 2005-2011 (Source: World Development Indicators database)

+

Notes: fms, mes, gdp, gs, ca, gcf, and irs refers financial market sophistication, macroeconomic stability, GDP per capita, gross savings, current account, gross capital formation (investment), and interest rate spread. Data shows are averaged between 2005 and 2011 (subject to data availability).

South Africa5.32 Namibia 5.13 Botswana 4.09 Botswana 35.79 Nigeria 15.14 Cape Verde40.10 Ethiopia 3.42

Mauritius 4.82 Cameroon 5.02 Mauritius 4.06 Cape Verde 28.42 Angola 11.08 Mauritania36.02 South Africa3.72

Kenya 4.71 Nigeria 4.92 South Africa3.97 Namibia 27.11 Botswana 8.51 Madagascar30.65 Namibia 4.86

Botswana 4.61 South Africa4.92 Namibia 3.75 Lesotho 25.03 Namibia 3.99 Tanzania 29.97 Mauritius 5.71

Namibia 4.59 Botswana 4.91 Swaziland 3.72 Angola 24.95 Cote d'Ivoire2.21 Senegal 29.50 Swaziland 6.24

Zambia 4.49 Lesotho 4.91 Angola 3.66 Zambia 20.94 Gambia -0.40 Botswana 28.25 Botswana 6.79

Ghana 4.26 Benin 4.58 Cape Verde3.50 Senegal 19.38 Zambia -1.11 Lesotho 26.52 Cape Verde 7.18

Malawi 4.25 Senegal 4.51 Mauritania3.34 Tanzania 19.24 Cameroon -1.88 Mauritius 24.81 Nigeria 7.31

Nigeria 4.25 Cote d'Ivoire4.51 Cameroon 3.31 Uganda 18.97 Lesotho -3.76 Malawi 23.94 Mozambique7.67

Rwanda 4.21 Rwanda 4.48 Nigeria 3.29 Ethiopia 18.47 South Africa-4.74 Burkina Faso23.72 Lesotho 7.86

Gambia 3.97 Mali 4.31 Senegal 3.23 Mauritius 16.72 Kenya -5.24 Chad 23.42 Tanzania 8.11

Tanzania 3.97 Cape Verde4.30 Cote d'Ivoire3.22 South Africa15.19 Rwanda -5.47 Ethiopia 23.32 Kenya 8.75

Uganda 3.93 Uganda 4.28 Gambia 3.20 Benin 15.14 Ethiopia -6.82 Uganda 22.97 Rwanda 10.11

Swaziland 3.93 Mauritius 4.25 Kenya 3.16 Kenya 14.55 Swaziland -7.25 Zambia 22.46 Uganda 10.37

Zimbabwe 3.76 Swaziland 4.09 Benin 3.15 Malawi 14.46 Uganda -7.36 Namibia 22.06 Cameroon 11.50

Benin 3.71 Tanzania 4.08 Ghana 3.14 Cameroon 14.44 Benin -7.59 Ghana 21.95 Chad 11.50

Cape Verde3.60 Burkina Faso4.05 Lesotho 3.13 Ghana 13.81 Burkina Faso-7.88 Mali 21.85 Mauritania12.63

Senegal 3.58 Ethiopia 3.97 Chad 3.12 Rwanda 13.61 Mauritius -8.65 Gambia 20.09 Zambia 13.19

Cote d'Ivoire3.47 Mozambique3.97 Zambia 3.11 Burkina Faso13.17 Mali -8.70 Benin 20.00 Gambia 14.85

Lesotho 3.43 Gambia 3.96 Tanzania 3.07 Gambia 12.97 Ghana -8.79 South Africa19.94 Angola 16.66

Burkina Faso3.42 Zambia 3.95 Burkina Faso3.03 Mali 11.90 Senegal -8.99 Rwanda 19.49 Malawi 21.33

Mozambique3.30 Chad 3.95 Uganda 3.03 Cote d'Ivoire11.67 Tanzania -10.19 Kenya 19.34 Madagascar27.33

Cameroon 3.20 Angola 3.92 Rwanda 2.99 Swaziland 10.17 Burundi -10.24 Mozambique18.60 Zimbabwe298.38

Ethiopia 3.20 Kenya 3.84 Mali 2.97 Mozambique6.99 Cape Verde-12.03 Cameroon 16.57

Mali 3.15 Mauritania3.71 Madagascar2.95 Burundi 1.88 Mozambique-13.34 Burundi 16.41

Madagascar3.04 Madagascar3.62 Ethiopia 2.90 Malawi -13.90 Angola 13.32

Mauritania2.90 Malawi 3.46 Mozambique2.88 Swaziland 11.49

Chad 2.79 Ghana 3.27 Malawi 2.85 Cote d'Ivoire11.35

Angola 2.78 Burundi 3.12 Burundi 2.71 Zimbabwe 11.08

Burundi 2.52 Zimbabwe 2.00

irsfms mes gdp gs ca gcf


24

A2.2 Trend of GDP, Private Credit provided by Banks and Credit to Public Sector Sub-Saharan Africa 1996-2014

Sources: Averages computed based on the underlying data from IMF’s International Financial Statistics, 2016. Trend line from the left-hand-side column indicates GDP per capita constant 2005 USD, private credit by bank to GDP %, and public credit to GDP%. The

average GDP, private credit and public credit all shows increased for 2005-2014 than those for 1996-2004 except Zimbabwe (-9.4), Eritrea (-

9.1), Burundi (-2.3), Djibouti (-7.6) and Lesotho (-0.9) on private credit; Ghana (-2.4), Lesotho (-5.8), Ethiopia (-1.8), Central Africa Republic (CAF: -0.2), Ivory Coast (-0.1) and Chad (-0.5) on public credit; and Burundi (-3.0), CAF (-10.6), Ivory Coast (-65.6), Eritrea (-44.5), Togo

(-11.3) and Zimbabwe (-235.8). The data indicates that the level of per capita GDP is related with private credit for Burundi and Eritrea while

public credit is more related with the GDP per capita in Djibouti and Ivory Coast, and the case of Lesotho, the public and private credit are not related with the GDP per capita.


A2.3 Trend of GDP Growth (%) and Exports (% of growth): Sub-Saharan Africa 2005-2014

Sources: Averages computed based on the underlying data from World Development Indicators (World Bank), 2016. The data shows that

between the periods C and D above, the annual economic growth declined about 1% in Benin, Cameroon, Gambia, Rwanda, Senegal and (-

2%) in Nigeria while the annual growth increased (+4%) in Burundi, Mali, and Sierra Leone. The export for the corresponding period increased in Mauritania (+1.4%) and Sierra Leone (+8) while decreased in Botswana (-2), Ghana (-6), Cameroon (-1), Gambia (-2), Kenya (-

2), Nigeria (-7) and Senegal (-1). That indicates the growth was related with export policy in Sierra Leone, Cameroon, Nigeria, Gambia, and Senegal while the growth is related rather than other factors in Burundi, Mali, Benin, Botswana, Rwanda, Sudan and Kenya.

A2.4 Descriptive statistics of selected SSA financial indicators (1996-2014) Notes: Indicators: BCBD (Bank credit to bank deposits %); BD (Bank lending-deposit spread); CBA (Central bank assets to GDP %);

CFCB (Consolidated foreign claims of BIS reporting banks to GDP (%)); CGSE (Credit to government and state owned enterprises to GDP %); CPS (Domestic credit to private sector % of GDP) DMBA (Deposit money banks' assets to GDP %); DCPS (Domestic credit to private sector

(% of GDP)); ELDBA (External loans and deposits of reporting banks vis-à-vis all sectors (% of domestic bank deposits)); ELDBB (External

loans and deposits of reporting banks vis-à-vis the banking sector (% of domestic bank deposits)); ELDBNB(External loans and deposits of reporting banks vis-à-vis the nonbanking sectors (% of domestic bank deposits)); FSD (Financial system deposits to GDP %); GDPPC(GDP

per capita (Constant 2005 USD)); LL (Liquid liabilities to GDP %); LNRB(Loans from non-resident banks (amounts outstanding) to GDP %);

PCDB (Private credit by deposit money banks to GDP %); RI (Remittance inflows to GDP, %). SFI (Saved at a financial institution in the past year (% age 15+)), BA1000 (Bank accounts per 1,000 adults), FBF (Firms using banks to finance investments, %); IFB (Investments

financed by banks, %); IFE (Investments financed by equity or stock sales, %). EAP (East Asia & Pacific); LAC (Latin America & Caribbean;

LI (low income); Hi (high income); SSA (sub-Saharan Africa); MENA (Middle East and North Africa); and EU (Euro region). Sample period (top panel: 1960-69; 1970-79; 1980-89; 1990-99; 2000-2013; bottom panel: 2001-2014).

A B C D A B C D

Country Name1970-79 1980-95 1996-2004 2005-14 Gap D-C 1970-79 1980-95 1996-2004 2005-14 Gap D-C

Burundi 4.47 2.15 0.38 4.08 3.71 10.52 10.85 6.75 8.14 1.39

Benin 2.28 3.84 4.79 4.20 -0.59 14.98 17.46 22.66 23.66 1.00

Botswana 15.70 9.02 4.43 5.19 0.76 38.42 58.32 52.25 50.53 -1.73

Cameroon 7.29 1.58 4.40 3.71 -0.69 23.01 24.22 21.39 20.45 -0.94

Congo, Rep. 5.47 4.48 2.79 5.16 2.38 36.68 49.45 77.02 79.81 2.79

Gabon 9.86 2.48 0.76 3.21 2.45 57.23 52.77 57.10 57.74 0.65

Ghana 1.45 2.79 4.54 7.32 2.77 18.95 12.07 38.57 32.10 -6.47

Gambia, The 4.96 3.34 4.33 2.97 -1.36 37.68 43.96 24.52 22.15 -2.37

Kenya 7.16 3.40 2.58 5.29 2.71 29.77 26.61 23.22 21.33 -1.89

Lesotho 8.50 3.58 3.15 4.59 1.44 14.49 18.10 43.41 48.15 4.74

Madagascar 1.51 0.34 3.06 2.91 -0.15 15.52 14.68 24.43 28.03 3.60

Mali 5.21 2.48 6.05 9.71 3.66 11.36 14.92 23.39 24.38 0.99

Mauritania 2.65 2.29 2.91 5.63 2.71 42.97 42.90 31.60 45.66 14.06

Malawi 6.25 2.53 3.05 5.81 2.75 26.60 23.96 23.72 24.11 0.40

Niger 2.16 0.20 3.35 5.71 2.37 15.24 20.39 16.92 19.71 2.79

Nigeria 7.00 0.07 7.62 6.04 -1.58 15.41 25.49 37.85 30.37 -7.49

Rwanda 5.27 0.63 9.10 7.68 -1.42 11.07 10.76 7.45 13.24 5.79

Sudan 4.28 3.38 6.09 4.58 -1.51 14.12 7.14 11.25 16.09 4.84

Senegal 3.02 2.10 4.26 3.73 -0.54 28.08 27.26 27.67 26.37 -1.30

Sierra Leone 2.69 -0.66 4.14 7.97 3.83 26.49 21.98 13.77 21.69 7.91

Togo 3.20 1.81 3.02 3.69 0.67 46.51 44.41 34.16 40.71 6.55

Uganda 4.83 6.39 6.88 0.50 11.33 11.63 18.28 6.65

South Africa 3.25 1.65 3.10 3.01 -0.09 25.40 28.35 26.49 30.14 3.65

Averages 5.17 2.54 4.10 5.18 1.08 25.48 26.41 28.57 30.56 1.98

GDP growth (annual %) Exports of goods and services (% of GDP)

GDP growth Export growth

1996-2014

M2G CFG DCPG DCBG EBG DSG SG GCG FCFG FDIIG FDIOG ELEC ENG ATRNSF ATRNSP

Mean 38.62 18.85 56.29 33.12 -1.07 18.17 16.87 15.50 18.40 2.60 0.79 517 668 1852 3E+07

Med 38.06 18.20 57.13 33.11 -0.96 17.86 16.53 15.48 17.81 2.56 0.56 515 670 1804 2E+07

StdDev 3.70 2.03 5.50 2.99 2.33 2.17 2.22 0.49 2.06 0.79 1.14 18 11 452 1E+07 Skewness 0.81 0.37 -0.38 -0.04 0.16 -0.36 0.09 0.27 0.27 0.47 1.91 0.52 -0.57 0.43 0.78Variat ion 0.10 0.11 0.10 0.09 -2.18 0.12 0.13 0.03 0.11 0.30 1.45 0.04 0.02 0.24 0.43

AGVAG MFG SRVG INDG TRADEG IMG EXG GDPP GDPPG NIPCG NIPC5 ENR HEPG HEPBG HEG

Mean 17.75 12.59 52.14 30.08 63.58 32.33 31.25 891.76 1.56 1.59 685.63 67.92 3.37 2.45 5.82

Med 17.46 12.94 51.65 30.41 63.18 32.22 30.84 886.24 1.45 0.76 666.20 68.55 3.33 2.47 5.79

StdDev 2.07 1.19 3.46 1.87 3.64 1.88 2.41 97.21 1.61 2.31 76.72 8.60 0.22 0.12 0.25 Skewness -0.02 -0.12 0.52 0.03 0.73 0.41 0.37 0.19 1.34 0.87 0.35 -0.19 0.17 -0.30 -0.04Variat ion 0.12 0.09 0.07 0.06 0.06 0.06 0.08 0.11 1.04 1.45 0.11 0.13 0.07 0.05 0.04


26

A2.5 Descriptive statistics of selected regional SSA financial Indicators (1996-2013)

Island satesBCBD BD CBA CFC GSE CPS DMBA ELDBA ELDBB ELDBNB FSD GDPPC LL LNRB PCDB RI

Mean 66.7 45.6 8.0 46.9 12.6 31.1 44.4 186.9 56.7 129.7 45.8 3603 53.1 1.8 29.1 5.3

Std. Dev. 33.7 28.7 7.2 59.6 14.1 24.6 33.2 297.3 48.1 283.3 28.6 4272 28.5 6.3 23.3 6.2

Skew 1.5 0.2 1.5 1.9 1.6 1.2 0.5 3.6 1.9 3.7 0.2 1.3 0.2 2.3 1.1 1.1

Mean 66.0 24.8 7.9 7.1 5.2 16.1 20.7 92.4 49.5 43.1 24.9 449.0 30.9 3.7 15.6 2.1

Std. Dev. 22.7 17.1 6.0 7.3 3.7 9.2 11.4 55.1 29.2 33.0 17.1 270.1 19.2 3.0 9.6 1.7

Skew 0.6 1.4 1.0 1.7 0.7 0.7 0.5 1.0 0.9 1.3 1.4 0.8 1.3 1.2 0.8 0.8

Mean 75.7 19.3 5.4 6.0 3.3 16.9 15.4 68.9 34.5 34.5 19.6 1055 24.6 4.3 13.9 5.7

Std. Dev. 24.1 9.1 5.5 7.3 3.3 7.0 6.1 56.7 31.9 35.2 9.0 1632 9.0 6.8 6.0 12.6

Skew 0.1 0.8 1.6 2.3 2.0 0.2 0.2 1.6 1.5 2.1 0.8 2.2 0.7 4.2 0.2 3.0

Mean 78.7 28.0 7.1 14.2 5.4 38.6 31.2 67.1 40.9 26.9 28.9 2260 29.3 8.7 25.8 1.0

Std. Dev. 32.3 15.5 15.7 11.3 3.2 44.0 23.2 54.0 37.3 24.0 15.4 1790 12.4 7.2 21.9 1.3

Skew 0.1 0.9 2.9 1.1 0.3 1.6 0.9 1.5 1.5 1.4 0.7 0.6 0.6 2.3 0.9 1.8

Mean 75.6 16.6 9.9 11.3 4.4 14.1 17.1 76.2 30.0 46.2 16.6 559.7 23.9 5.7 13.0 3.3

Std. Dev. 24.1 6.6 12.2 8.7 3.1 7.1 7.8 48.4 25.6 31.8 6.6 235.3 7.7 4.2 6.8 3.4

Skew -0.5 0.6 2.7 0.9 1.2 0.5 0.4 1.3 1.7 2.0 0.6 0.7 0.5 1.3 0.4 1.4

East region

Landlocked

South-region

West-region

Descriptive stattistics Island states in SSA


Mean 66.7 45.6 8.0 46.9 12.6 31.1 44.4 186.9 56.7 129.7 45.8 3603 53.1 1.8 29.1 5.3

Std. Dev. 33.7 28.7 7.2 59.6 14.1 24.6 33.2 297.3 48.1 283.3 28.6 4272 28.5 6.3 23.3 6.2

Skew 1.5 0.2 1.5 1.9 1.6 1.2 0.5 3.6 1.9 3.7 0.2 1.3 0.2 2.3 1.1 1.1

Mean 66.0 24.8 7.9 7.1 5.2 16.1 20.7 92.4 49.5 43.1 24.9 449.0 30.9 3.7 15.6 2.1

Std. Dev. 22.7 17.1 6.0 7.3 3.7 9.2 11.4 55.1 29.2 33.0 17.1 270.1 19.2 3.0 9.6 1.7

Skew 0.6 1.4 1.0 1.7 0.7 0.7 0.5 1.0 0.9 1.3 1.4 0.8 1.3 1.2 0.8 0.8

Mean 75.7 19.3 5.4 6.0 3.3 16.9 15.4 68.9 34.5 34.5 19.6 1055 24.6 4.3 13.9 5.7

Std. Dev. 24.1 9.1 5.5 7.3 3.3 7.0 6.1 56.7 31.9 35.2 9.0 1632 9.0 6.8 6.0 12.6

Skew 0.1 0.8 1.6 2.3 2.0 0.2 0.2 1.6 1.5 2.1 0.8 2.2 0.7 4.2 0.2 3.0

Mean 78.7 28.0 7.1 14.2 5.4 38.6 31.2 67.1 40.9 26.9 28.9 2260 29.3 8.7 25.8 1.0

Std. Dev. 32.3 15.5 15.7 11.3 3.2 44.0 23.2 54.0 37.3 24.0 15.4 1790 12.4 7.2 21.9 1.3

Skew 0.1 0.9 2.9 1.1 0.3 1.6 0.9 1.5 1.5 1.4 0.7 0.6 0.6 2.3 0.9 1.8

Mean 75.6 16.6 9.9 11.3 4.4 14.1 17.1 76.2 30.0 46.2 16.6 559.7 23.9 5.7 13.0 3.3

Std. Dev. 24.1 6.6 12.2 8.7 3.1 7.1 7.8 48.4 25.6 31.8 6.6 235.3 7.7 4.2 6.8 3.4

Skew -0.5 0.6 2.7 0.9 1.2 0.5 0.4 1.3 1.7 2.0 0.6 0.7 0.5 1.3 0.4 1.4

East region

Landlocked

South-region

West-region



Mean 66.7 45.6 8.0 46.9 12.6 31.1 44.4 186.9 56.7 129.7 45.8 3603 53.1 1.8 29.1 5.3

Std. Dev. 33.7 28.7 7.2 59.6 14.1 24.6 33.2 297.3 48.1 283.3 28.6 4272 28.5 6.3 23.3 6.2

Skew 1.5 0.2 1.5 1.9 1.6 1.2 0.5 3.6 1.9 3.7 0.2 1.3 0.2 2.3 1.1 1.1

Mean 66.0 24.8 7.9 7.1 5.2 16.1 20.7 92.4 49.5 43.1 24.9 449.0 30.9 3.7 15.6 2.1

Std. Dev. 22.7 17.1 6.0 7.3 3.7 9.2 11.4 55.1 29.2 33.0 17.1 270.1 19.2 3.0 9.6 1.7

Skew 0.6 1.4 1.0 1.7 0.7 0.7 0.5 1.0 0.9 1.3 1.4 0.8 1.3 1.2 0.8 0.8

Mean 75.7 19.3 5.4 6.0 3.3 16.9 15.4 68.9 34.5 34.5 19.6 1055 24.6 4.3 13.9 5.7

Std. Dev. 24.1 9.1 5.5 7.3 3.3 7.0 6.1 56.7 31.9 35.2 9.0 1632 9.0 6.8 6.0 12.6

Skew 0.1 0.8 1.6 2.3 2.0 0.2 0.2 1.6 1.5 2.1 0.8 2.2 0.7 4.2 0.2 3.0

Mean 78.7 28.0 7.1 14.2 5.4 38.6 31.2 67.1 40.9 26.9 28.9 2260 29.3 8.7 25.8 1.0

Std. Dev. 32.3 15.5 15.7 11.3 3.2 44.0 23.2 54.0 37.3 24.0 15.4 1790 12.4 7.2 21.9 1.3

Skew 0.1 0.9 2.9 1.1 0.3 1.6 0.9 1.5 1.5 1.4 0.7 0.6 0.6 2.3 0.9 1.8

Mean 75.6 16.6 9.9 11.3 4.4 14.1 17.1 76.2 30.0 46.2 16.6 559.7 23.9 5.7 13.0 3.3

Std. Dev. 24.1 6.6 12.2 8.7 3.1 7.1 7.8 48.4 25.6 31.8 6.6 235.3 7.7 4.2 6.8 3.4

Skew -0.5 0.6 2.7 0.9 1.2 0.5 0.4 1.3 1.7 2.0 0.6 0.7 0.5 1.3 0.4 1.4

East region

Landlocked

South-region

West-region


I_S I_L I_W I_I S_S S_L S_W S_I Y_S Y_L Y_W Y_I YG_S YG_L YG_W YG_I

Mean 19.8 22.2 17.9 22.7 13.5 3.4 9.5 7.0 5520 2994 2005 8760 2.3 2.4 2.0 1.6

Std. Dev. 8.5 10.4 7.2 8.6 16.1 20.9 9.1 14.0 3603 3701 996 7931 5.6 3.6 4.2 4.1

Observations 106.0 160.0 162.0 70.0 106.0 160.0 162.0 70.0 108 162 162 72 108.0 162.0 162.0 72.0

Skewness 1.1 1.4 0.0 0.4 0.9 -0.9 -0.1 -0.4 0 2 1 0 -0.1 -0.5 2.6 -0.6


A2.6 Descriptive statistics of selected financial indicators, world income groups, world

regional groups (1996-2014) Stocks, Savings, Fixed Capital Formation (Investment), Broad Money, Credit provided by Financial Sector, Credit to

Private Sector by Bank Notes: SSA, SA, EAP, EU, LAC, MEA, HI, MI, and LI is sub-Saharan Africa, South Asia, East Asia and Pacific, European Union, Latin

America and Caribbean, Middle East and North Africa, High Income countries, Middle Income countries, and Low Income countries respectively. Each raw indicates the mean and variation (mean/standard deviation) values.

Stocks

traded, total value (% of

GDP)

Gross

domestic savings (%

of GDP)

GDP per capita

(constant 2010 US$)

Gross fixed

capital formation

(% of GDP)

Broad money

(% of GDP)

Domestic

credit provided by

financial

sector (% of GDP)

Domestic

credit to private

sector by

banks (% of GDP)

Mean|Variation M V M V M V M V M V M V M V East Asia Pacific 80.7 0.4 33.6 0 6419.9 0.2 30 0 172 0.1 209.1 0.1 120.7 0.2

European Union 58.9 0.4 22.6 0 31890.1 0.1 21.2 0 135.9 0.2 101.3 0.1

Hi Income 117.9 0.3 22.6 0.1 37247.2 0.1 21.8 0 109.2 0.1 187.8 0.1 87.6 0.1

Latin AC 14.5 0.5 20.6 0.1 8111.2 0.1 19.5 0.1 47.3 0.2 54.9 0.2 31.1 0.3

Low Income 7.4 0.2 467.4 0.1 20.1 0.2 27.3 0.1 21.2 0.1 13.8 0.2

MENA 47.7 0.7 34.6 0.2 6250.8 0.1 22.7 0.1 65.1 0.1 49.9 0.1 41.4 0.1

Middle Income 36 0.6 30.6 0.1 3180.8 0.2 27.2 0.1 80.3 0.2 77.1 0.2 57.1 0.2

South Asia 36.9 0.7 25.7 0.1 1025.2 0.3 26.7 0.1 60.9 0.2 57.3 0.2 36 0.3

SSA 24.3 0.3 18.9 0.2 1367.7 0.1 18.1 0.1 61.5 1.1 69.5 0.1 33.1 0.1

External

balance on goods and

services (%

of GDP)

Foreign

direct investment,

net inflows

(% of GDP)

Foreign direct

investment, net outflows (% of

GDP)

Interest rate

spread (lending rate

minus

deposit rate, %)

Net ODA

received (% of gross

capital

formation)

Personal

remittances, received (%

of GDP)

Portfolio

equity, net inflows

(BoP,

current US$ bn)

East Asia Pacific 2.5 0.4 2.2 0.3 1.6 0.3 4.8 0.1 0.3 0.4 0.4 0.2 95.1 0.8 European Union 1.1 0.6 4.3 0.5 5.2 0.5 4.5 0.2 0 0.5 0.5 0.2 300.3 0.7

Hi Income 0.4 0.8 2.8 0.4 3.4 0.4 4.5 0.1 0 1.1 0.2 0.1 511.5 0.6

Latin AC 0.2 10.2 3.2 0.2 0.7 0.5 7.7 0.1 1.1 0.3 1.3 0.3 11.9 1.2

Low Income -14 -0.2 3 0.4 -0.1 -9.1 12.7 0.1 52.9 0.2 3.8 0.3 -0.3 -3.3

MENA 9 0.5 2.4 0.7 1 0.9 4.4 0.1 3 0.5 1.7 0.1 3.1 0.9

Middle Income 1.6 0.8 2.8 0.2 0.7 0.5 7.5 0.1 1.6 0.4 1.5 0.1 47 1.1

South Asia -3.6 -0.6 1.3 0.6 0.4 1 5.4 0.2 2.8 0.3 3.6 0.2 9.5 1.3

SSA 0.1 61.1 2.7 0.3 0.4 1.3 11 0.2 21.6 0.2 2.1 0.4 6 0.9

A2.7 Figure 1. Investment-Savings Plots in World Income Groups: 1996-2014 Notes: Figure 1.1, from the left panel (Hi-income countries group), middle (Middle-income countries group); and on the right (Low-income

countries group). Saving/GDP (%), investment/GDP (%), and per capital GDP (base=2010) are plotted for three income groups in the world. Interestingly, the higher the income, the more consistent positive relationship between savings and investment, in contrast, the lower the

relationship between investment and GDP. Data: World Development Indicators database and the IMF IFS database.

Figure 1.2, the case of world high-income country group, there is no evidence that the cpsb or FDI inflows would promote the growth although the result indicates credit to private capital provided by banks and FDI inflows shows a positive relationship. The case of middle income

group, the FDI inflows show a weakly positive relationship with growth, in contrast, the FDI inflows are not transmitted through the banking

channel the credit to private sector. Interestingly, there shows a positive relationship between CPSB and FDI inflows in low-income countries group that positively related to the growth of the group.

Figure 1.1 Savings/GDP (&), Investment/GDP (%), and GDP (base=2010)

[i] High-income countries group [i] Middle-income countries group [i] Low-income countries group

Figure 1.2 GDP growth per capita (growth), credit to private sector provided by banks (cpsb) and foreign direct investment inflows (fdii).

[i] High-income countries group [i] Middle-income countries group [i] Low-income countries group


28

A2.8 Figure 2. Investment-Savings Plots in Sub-Saharan Africa: 1996-2014 Notes: Figure 2.1, the scatter plots based on the consolidated datasets including savings/GDP (%); investment/GDP (%), current account

deficits, capital account surpluses, and per capita GDP (base=2010) show that the savings-investment relationship is negligible and inconsistent

while between investment and growth, it shows a positive and consistent over time. Savings are positively related with capital surplus on the

other hand an inverse relation with current account deficits. Figure 2.2., the growth of GDP per capita, credit to private sector provided by banks, and FDI inflows are not clearly nor consistently shown

a meaningful correlations in the three factors.

Figure 2.1 Savings-Investment-Growth in SSA

Figure 2.2 Growth-Credit to private sector provided by banks-foreign direct investment inflows in SSA


A2.9 Figure 3. Heterogeneity in Investment-Savings in SSA Panel Regions: 1996-2014 Notes: Some of East region countries show missing data. Some of panel countries show negative savings (in red). Most of countries show

current account deficits (in green). Some of South region countries show negative savings and economic growth. 3.1 Sub-Saharan Africa Island States Panel 3.2 Sub-Saharan Africa Landlocked-States Panel

3.3 Sub-Saharan Africa Eastern Region Panel 3.4 Sub-Saharan Africa South-Region Panel

3.5 Sub-Saharan Africa West-Region Panel 3.6 Sub-Saharan Africa Low-Income Panel

3.7 Sub-Saharan Africa Middle-Income Panel:

Investment, GDP and Savings 1996-2013 Notes: Most of middle income countries show current account

deficits except few among 22 SSA economies. The current account shows extremely volatile, inconsistent between middle-

income countries. The current account deficit that shows an

inverse relationship with investment implying financial account surplus.

-30

-20

-10

0

10

20

30

40

50

1 -

96

1 -

98

1 -

00

1 -

02

1 -

04

1 -

06

1 -

08

1 -

10

1 -

12

2 -

96

2 -

98

2 -

00

2 -

02

2 -

04

2 -

06

2 -

08

2 -

10

2 -

12

3 -

96

3 -

98

3 -

00

3 -

02

3 -

04

3 -

06

3 -

08

3 -

10

3 -

12

4 -

96

4 -

98

4 -

00

4 -

02

4 -

04

4 -

06

4 -

08

4 -

10

4 -

12

Investment GDP growth per capita Savings SSA island states 1996-2013

-80

-60

-40

-20

0

20

40

60

80

1 -

96

1 -

02

1 -

08

2 -

96

2 -

02

2 -

08

3 -

96

3 -

02

3 -

08

4 -

96

4 -

02

4 -

08

5 -

96

5 -

02

5 -

08

6 -

96

6 -

02

6 -

08

7 -

96

7 -

02

7 -

08

8 -

96

8 -

02

8 -

08

9 -

96

9 -

02

9 -

08

Investment Savings GDP growth

SSA Landlocked states 1996-2013

-40

-30

-20

-10

0

10

20

30

40

50

1 -

96

1 -

00

1 -

04

1 -

08

1 -

12

2 -

98

2 -

02

2 -

06

2 -

10

3 -

96

3 -

00

3 -

04

3 -

08

3 -

12

4 -

98

4 -

02

4 -

06

4 -

10

5 -

96

5 -

00

5 -

04

5 -

08

5 -

12

6 -

98

6 -

02

6 -

06

6 -

10

7 -

96

7 -

00

7 -

04

7 -

08

7 -

12

Investment Savings GDP growth SSA East region 1996-2013

-40

-20

0

20

40

60

80

1 -

96

1 -

00

1 -

04

1 -

08

1 -

12

2 -

98

2 -

02

2 -

06

2 -

10

3 -

96

3 -

00

3 -

04

3 -

08

3 -

12

4 -

98

4 -

02

4 -

06

4 -

10

5 -

96

5 -

00

5 -

04

5 -

08

5 -

12

6 -

98

6 -

02

6 -

06

6 -

10

7 -

96

7 -

00

7 -

04

7 -

08

7 -

12

Investment GDP growth Saving SSA South Region (1996-2014)

-80

-60

-40

-20

0

20

40

60

1 -

04

1 -

10

1 -

16

2 -

08

2 -

14

3 -

06

3 -

12

4 -

04

4 -

10

4 -

16

5 -

08

5 -

14

6 -

06

6 -

12

7 -

04

7 -

10

7 -

16

8 -

08

8 -

14

9 -

06

9 -

12

10

- 0

4

10

- 1

0

10

- 1

6

11

- 0

8

11

- 1

4

Investment Savings Current account

SSA West panel 1996-2013

-80

-60

-40

-20

0

20

40

60

80

1 -

04

1 -

14

2 -

10

3 -

06

3 -

16

4 -

12

5 -

08

6 -

04

6 -

14

7 -

10

8 -

06

8 -

16

9 -

12

10

- 0

8

11

- 0

4

11

- 1

4

12

- 1

0

13

- 0

6

13

- 1

6

14

- 1

2

15

- 0

8

16

- 0

4

16

- 1

4

17

- 1

0

18

- 0

6

18

- 1

6

19

- 1

2

20

- 0

8

21

- 0

4

21

- 1

4

22

- 1

0

Investment Savings Current account SSA low-income panel 1996-2013

-40

-20

0

20

40

60

80

1 -

04

1 -

10

1 -

16

2 -

08

2 -

14

3 -

06

3 -

12

4 -

04

4 -

10

4 -

16

5 -

08

5 -

14

6 -

06

6 -

12

7 -

04

7 -

10

7 -

16

8 -

08

8 -

14

9 -

06

9 -

12

10

- 0

4

10

- 1

0

10

- 1

6

11

- 0

8

11

- 1

4

12

- 0

6

12

- 1

2

13

- 0

4

13

- 1

0

13

- 1

6

14

- 0

8

14

- 1

4

Investment Savigns

Curernt account

SSA middle-income panel 1996-2013


30

Appendix 3: Results of Testing the Capital Mobility

A3.1 Table 1 Panel Cointegration Test Results from SSA regional panel samples Model 1: Null Hypothesis: No cointegration in the system of investment and saving.

SSA Low

income

Middle

income

West region South region East region Landlocked Island^

Test statistics: Pedroni Alternative hypothesis: common AR coefs. (within-dimension)

Panel v-Statistic 0.3036 -1.3144 0.2295 -0.3459 0.0060 -0.030 1.6953***

Panel rho-Statistic -0.6309 0.7505 -1.6243* -0.3304 -1.2505 2.1731 -0.7476 Panel PP-Statistic -1.8097** -1.6244* -2.3442*** -0.5453 -4.4574*** -0.9336 -0.5699

Panel ADF-Statistic -1.7542** -1.8360** -2.4579*** -0.3780 -4.3812*** -1.3654* -1.1611

Pedroni Weighted

Panel v-Statistic -1.0006 -0.8789 -0.2911 -1.1741 0.2156 -2.8921 0.0735 Panel rho-Statistic -0.9034 0.4199 -1.829 -0.52372 -0.9786 1.8181 -1.8507***

Panel PP-Statistic -2.6373 -2.6339*** -2.866*** -0.9757 -3.7608*** -1.7418** -2.3222***

Panel ADF-Statistic -3.7772 -3.8287*** -3.4759*** -0.5545 -3.7216*** -1.5903* -2.7244***

Pedroni Alternative hypothesis: individual AR coefs. (between-dimension)

Group rho-Statistic 0.9845 1.9106 -0.839 1.1412 0.0379 2.6426 -0.8210

Group PP-Statistic -1.6640** -3.9228*** -2.775*** -0.2194 -3.5577*** -5.744*** -1.6491*** Group ADF-Statistic -2.5265*** -5.4271*** -2.6935*** -0.0361 -3.56528*** -1.9144** -1.9714***

Kao Residual Cointegration Test^^

ADF -1.3822* -6.7922*** -2.7939*** -1.1236 2.8401*** -2.423*** -1.0495

Fisher Panel cointegration test Trace/max-eigen

No of CE: none

184***/158**

* 90***/75*** 41***/37*** 32***/26*** 27***/24*** 52***/42*** 9.22/8.41

At most 1 106***/106**

* 63***/63*** 27*/27* 25***/25*** 21*/21* 28*/28* 10.2/10.2

Model 2: Null Hypothesis: No cointegration in a system I, S, and CA, GDPPCG and

GDPPC in a system SSA Test statistics

Low income

Middle income

West region South region East region Landlocked Island ^

Test statistics: Pedroni Alternative hypothesis: common AR coefs. (within-dimension)

Panel v-Statistic -0.9649 1.2432 0.4637 0.5656 -1.3662 -0.0309 -0.010 Panel rho-Statistic 0.1213 -0.6415 0.1421 1.1112 0.7626 2.1731 2.1731

Panel PP-Statistic -6.1621*** -7.0069*** -1.282* 0.9489 -1.7646*** -0.9336 -0.9336

Panel ADF-Statistic -6.0309*** -6.5730*** -3.1225*** -1.2813 -1.7752*** -1.3654* -1.36654*

Panel v-Statistic -1.6650 -0.4057 -1.7074 0.2244 -0.9357 -2.8921 -2.8921

Panel rho-Statistic 1.2393 -0.9943 0.1688 -0.2347 0.5052 1.8181 1.8181 Panel PP-Statistic -4.4749*** -6.6181*** -1.9272*** -2.8392*** -2.6582*** -1.7418*** -1.7418***

Panel ADF-Statistic -5.9272*** -6.1529*** -1.9143*** -5.0600*** -2.5974*** -1.5903*** -1.5903**

Pedroni Alternative hypothesis: individual AR coefs. (between-dimension)

Group rho-Statistic 2.6823 0.6185 1.067 0.8184 1.0397 2.6426 2.6426

Group PP-Statistic -7.4346*** -7.7634*** -3.0185*** -3.146*** -4.2937*** -5.7440*** -5.7440***

Group ADF-Statistic -6.4591*** -6.9834*** -2.4004*** -5.2357*** -4.0256*** -1.9144** -1.9144*** Kao Residual Cointegration Test^^

ADF -8.4443*** -11.256*** -3.502*** -0.9625 2.0199*** -2.4236*** -2.423***

Fisher Panel cointegration test

Trace/max-eigen No of CE: none

273*** /215***

106*** /83.8***

179*** /130***

169*** /222***

203*** /337***

150*** /100***

150*** /100***

At most 1

108***

/78***

43***

/31**

76***

/59***

58.8***

/39.6***

74***

/62***

74***

/48***

74***

/48***

At most 2

82***

/82***

49***

/49***

33***

/29***

28***

/22***

27***

/22***

46***

/36***

46***

/36***

At most 3 17.9

/17.9 15.87 /15.87

15.4 /15.4

31** /31**

31*** /31***

Notes: The test statistics are normalised to approximate, asymptotically, a standard normal distribution. *, **, and *** represent 10, 5, and 1%

levels of significance based on critical values of 1.281, 1.644 and 2.326 respectively. The trend assumption of the Pedrony test: Deterministic intercept and trend (east region, landlocked), no deterministic (island states, west region), no deterministic intercept or trend (south region);

the Kao test: no deterministic trend (eat, south, west, islands and landlocked regions); Fisher test: linear deterministic trend (east, south, west,

landlocked, low-income, and middle-income), no deterministic trend (landlocked) for the system of I and S. No determinist trend (east, south,

and west), deterministic intercept and trend (landlocked); Kao test: no deterministic (east, landlocked, south, west, Islands); linear deterministic

trend (east, south, low- and middle income), no deterministic trend (landlocked, islands) for the system of I, S, gdppc, and gdppcg.


A3.2 Table 2. Panel FMOLS and DOLS Results of Savings Retention Coefficients 2.1 Panel Estimates for the Feldstein-Horioka Equation: (GDIi/GDPi) =α0 +α1 (GNSi/GDPi) +ui The empirical findings for the sample period for geographical regions that overlapped for the economic and monetary

integrated group of the SADC, ECOWUS, and EAC. Model 1

The empirical findings for the sample period for geographical regions that overlapped for the economic and monetary

integrated group of the SADC, ECOWUS, and EAC. Model 1 Panel

estimation

Low

income

Middle income West SSA South SSA East SSA Landlocked

States

Island State

1. Panel Fully Modified Least Squares (FMOLS) Savings 0.3111

(4.54)***

0.3544

(3.85)***

0.387

(4.298) ***

R-squd 0.8109 0.7438 0.584 SE 4.4085 4.9179 4.839

2. FMOLS added variables of current account (-0.7, low income; -1.0 middle income); and capital account (-0.18; 0.1415) Savings 0.2853

(2.25)**

0.3132

(2.37)**

R-squd 0.9217 0.8508 SE 3.4333 4.6315

3. Robust LS-M estimation Savings 0.47

(11.22) *** 0.64 (21.172) ***

0.2093 (5.15)***

0.175 (3.067) ***

0.083 (2.512) ***

0.298 (4.645) *** R-sq 0.322 0.428 0.1178*** 0.036 0.017

0.210 Rn-sq 0.471*** 0.61*** 2.4773 9.41*** 6.315*** 3.868***

4. Panel LS-fixed effect model Savings 0.1486

(2.5795)**

0.3146

(5.0982)***

0.317

(4.832) ***

0.200

(2.696) ***

0.335

(3.427) ***

R-squrd 0.6077 0.7255 0.583 0.3549 0.465 F-stat 20.0025*** 34.1401*** 23.59*** 9.080*** 14.51***

5. PLS-random effect model Savings 0.2318

(4.4131)***

0.3824

(6.9112)***

0.289

(4.546) ***

0.195

(2.895) ***

0.1806

(3.427) ***

R-squrd 0.0579 0.2181 0.113 0.075 0.032 F-stat 18.7773*** 46.3302*** 20.40*** 8.463*** 14.508*** Houseman test

12.4035*** 6.1444** 3.087* 0.0280 6.655*** 6. Panel generalised linear models

Savings 0.04***

(39.07)

0.042***

(31.18)

0.18

(3.660)***

0.262

(3.868) *** LR stat 2008*** 1011*** 13.398*** 14.963*** LL -724 -515 -370.61 -242.723

7. Panel generalised MM (instrument variables, c & ca for low-and middle income; c & gdppcg for all regions) Savings -0.919

(-3.592) *** 0.464

(2.410) *** 0.2511 (2.726) ***

R-sq -2.44 0.425 0.340 J-st -2.30E-28 5.24E-30 7.04E-30

8. Panel GMM (instrument variables, c & fa for low-and middle income; c & gdppc for all regions) Savings 1.590

(8.865) ***

1.47

(11.68) ***

0.494

(2.037) ***

0.2511

(2.726) ***

0.690

(2.133) *** R-sq -1.29 -1.08 0.492 0.340 -1.082 J-st 1.70E-27 -1.57E-29 9.72E-29 7.04E-30 7.08E-30

Notes: The t-ratios are in the parentheses. *, **, and *** denotes significance at the level of 10%, 5% and 1% respectively. The dependent variable is domestic investment (proxied by Gross fixed capital formation) for all groups. Testing random effects of Hausman test, was carried

out and the Chi-squared statistics and the probability of rejecting the null hypothesis at the significant level of 0.05 is reported above. Robust

least squares M setting, weight=Bisquare, tuning =4.685, scale=median centred, tol=0.0001, iters=500, Huber type 1 standard errors and

covariance. Generalized linear model (Newton-Raphson/Marquardt steps), setting tol=0.0001, initial values c=17.38, S=0.17907. For the

GLM, z-statistics are used for the significance of the coefficient. GLM estimates for current account is -0.034 (-42.76***), -0.037 (-31.65***)

for low-income and middle-income respectively at the level of 0.01 significance. The average coefficient of each group is 0.17 (East); 0.196 (South); 0.199 (landlocked); 0.28(Islands); 0.244 (West); 0.32 (low income and 0.36 (middle income). The GMM methods using instrumental

variables of gdppc (gdp per capita) and gdppcg (annual growth of gdppc, %) shows a high coefficient for landlocked (0.47) and east (0.48).

Methods 7 and 8 for the low- and middle-income groups’ instrument variables entered are ca (current account) and fa (capital account). The countries in each group is listed in Appendix, A1 data description. For the group of low-income and middle-income, the sample period is

2004-2016; and the region group sample period is 1996-2013.

Table 2.2: Panel data estimates from the GMM, DLS, FMOLS

Estimation equation: I=c + S + .

GMM: Cross sections included 19 periods included. (1990-2014 adjusted). Constant added to instrument list.

Panel dynamic LS, fixed leads (=1) and lags (=1) specification, Bartlett kernel, Newey-West fixed bandwidth

coefficient covariance is used. Panel GMM

Model1 Panel GMM

Model 2

Panel GMM

Model 3

Panel GMM

Model 4

Panel GMM

Model 5

Panel DLS

(1,1)

Panel FMOLS

Variable 0.23 (2.867)***

0.36 (5.612) ***

0.39 (5.514) ***

0.40 (5.650) ***

0.44 (6.987) ***

0.40 (2.93) ***

0.42 (4.107) ***

Constant 29.74 27.28 27.28 27.26 26.22

Instrument

specification

CA, growth,

m2, FDI, ri, ODA, Trade

CA, GDP, M2 FDI, ODA,

CA, growth, GDP

FDI, ODA,

growth, ri, GDP, CA,

M2, Trade

CA, GDP,

M2, ODA, Trade

R-squared 0.11 0.14 0.14 0.12 0.13 0.51 0.37

Notes: Panel data: Angola; Botswana; Burundi; Cameroon; Central African Republic; Chad; Congo; Gabon; Gambia; Madagascar; Malawi; Mauritania; Mauritius; Namibia; Nigeria; Rwanda; Seychelles; Sierra Leone; South Africa; Swaziland; Uganda; and Zimbabwe. Sample

period: 1990-2014.


32

Table 2.3. Panel FMOLS and DOLS Results of Savings Retention Estimates SSA MENA SA LAC EAP EU HI MI LI RLS 0.108 0.214** 1.275*** 0.324* 0.847*** 1.277*** 1.0714*** 0.925*** 0.388** 0.782 2.104 14.588 1.684 4.284 6.5064 14.259 7.291 2.363 FMOLS 2.515** 3.429*** 1.405*** 1.277* 3.08*** -0.766 -0.836 2.492*** 0.6*** 2.378 3.595 6.8079 2.055 5.456 -1.973 -2.117 6.096 8.817 GMM 0.152 -28.75 13.255*** 17.978 47.155** 58.387*** 45.796*** 24.309** 9.004** 2.139 -2.083 3.694 0.2765 2.41 3.517 3.212 2.222 2.661

Notes: The correlation adjusted FH regression = (GDI/GDP)it=β0i+(β1+β2Corrit)(GNS/GDP)it+εit

Table 2.4. Estimates of individual SSA countries Savings-Investment relationship Notes: The standard FH regression (GDIi/GDPi) =α0 +α1 (GNSi/GDPi) +ui. The equations used are (i) I/Y=+β(S/Y)+ and

(ii)I/Y=+β(S/Y)+ (CA/Y) +. For the robustness of the estimates 4 different methods are used: Autoregressive distributed lag model

(ARDL) with dynamic regressors (savings), fixed regressors (current account) evaluated with maximum dependent variation (investment) lag

of 1; a panel individual Dynamic Least Squares (DOLS) and Fully Modified Least Squares (FMOLS) and the Two-Stage Least Squares

instrument specification constant added to instrument list. ARDL model:𝑖𝑡−1 = 𝛼𝑡 + 𝛽𝑡+𝛽𝑡−1+𝛾𝑡−1 + 휀𝑡, and refers 𝛽𝑡and 𝛽𝑡−2 of the

model. The t-ratios are in the parentheses. *, **, *** refers 10%, 5% and 1% level of significance. Source from IMF IFS database of savings and investment, and current account for 40 sub-Saharan Africa countries for 1980-2015. ADI 25 2005-2011.

1980-1999 2000-2014 1996-2013 2005-2011 (ADF data)

Angola 0.363(2.885)** 0.626(6.356)*** 0.333(2.283) ** LS1 LS2 GMM Benin 0.730(2.316)** 0.780(4.107)*** 0.120(0.251) 0.002(0.005) 0.048(0.139) -0.088(-0.296) Botswana 0.665(3.417)*** 0.700(18.90)*** Burkina Faso 0.628(3.685)*** 0.387(2.405)*** 0.766(9.483) *** 0.665(1.393) 1.232(2.565) 0.103(0.376) Burundi 0.446(0.90) 0.583(8.428)*** -0.533(-2.895) *** -0.488(-2.528)* -0.118(-0.249) -0.439(-1.96) Cabo Verde -0.258(-1.170) 0.408(1.953)** -0.390(-2.136)* 0.376(0.912) 1.179(5.914)*** 0.185(0.632) Cameroon; 0.614(2.393)** 0.637(1.837)* -0.198(-0.720) 0.701(2.168) -0.434(-1.759) C. African R. 0.386(2.318)** 0.059(0.260) Chad 0.506(3.108)*** -0.684(4.237)*** Comoros 0.431(3.043)*** 0.547(2.749) -0.283(-2.487)** D.R. Congo 0.627(2.9926)** 0.287(2.195)** R. Congo 0.490(2.486)** 0.365(2.5490)** Cote d'Ivoire 0.760(4.281)*** 0.872(15.800)*** 0.301(0.746) 0.509(1.563) 1.412(1.738) 0.346(4.232)*** Eq. Guinea 0.825(3.394)* 0.716(2.604)** Eritrea 0.661(2.248)* 0.449(1.559) -0.642(-3.423)*** Ethiopia 0.653(1.939)* 0.839(4.476)*** 0.121(0.977) 0.214(0.587) 0.132(1.203) Gabon 0.626(2.255)*** 0.870(8.446)*** Gambia 0.509(9.270)*** 0.696(8.278)*** 0.522(3.753)** 0.504(2.956)** 0.600(3.255)** Ghana 0.909(9.467)*** 0.428(1.067) 0.432(2.435)** 0.768(5.762)*** 0.862(16.829)*** 0.621(3.180)** Guinea 0.583(3.430)*** 0.494(3.753)*** 0.163(2.437)** 0.395(2.106)* 0.624(5.806)*** 0.353(1.165) Guinea-

Bissau

0.663(2.395)** 0.762(4.696)*** Kenya 0.583(3.430)*** 0.151(0.574) 0.596(2.523)** -0.579(-2.399)* 0.068(0.295) -0.709(-2.275)* Lesotho 0.196(1.144) 0.830(10.193)*** 0.328(2.125)* -0.141(-0.634) 0.561(2.814)** -0.398(-1.722) Madagascar 0.280(2.967)** 0.931(4.041)*** 0.877(2.721)** Malawi 0.236(1.151) 0.336(3.203)*** 0.588(2.996)*** 0.193(0.776) 0.190(0.623) 0.250(2.151)* Mali 0.736(14.2499)*

**

0.6999(3.583)*** 0.361(1.773)* Mauritius 0.312(2.2118)** 0.092(0.600) -0.007(-0.006) 0.441(1.397) 0.955(5.63)*** 0.208 (1.281) Mozambique 0.256(0.919) 0.563(2.522)** 0.130(0.186) 0.952(5.921)*** 0.651(7.337) *** 1.275(5.561)*** Namibia 0.860(6.323)*** 0.516(1.877)* -0.234(-1.019) 0.257(2559)* 0.363(2.804) ** 0.127(2.135)* Niger 0.532(2.441)** 0.855(3.545)*** 0.963 (2.184)*

(2.184)*

Nigeria 0.616(1.960)* 0.673

(2.68

07)**

-0.037(-0.540) Rwanda 0.523(2.919)** 0.748(4.566)*** 0.880(8.801)*** 0.371(0.703) 1.190(12.482)*** -1.006(-1.815) Senegal 0.950(6.609)*** 1.514(4.860)*** -0.977(-1.280) -0.413(-0.506) 0.837 (3.130)* -0.113(-0.802) Sierra Leon 0.128(3.043)*** 0.877(9.260)*** 0.747(2.965)*** 1.766(2.416)* 0.730(5.348)* 4.641(6.289)*** South Africa 0.973(32.313)*** 0.294(1.141) 0.298(0.643) -0.269(-0.27) 0.931(15.276)*** -1.04(-1.899) Swaziland 0.540(3.702)*** 0.634(5.432)*** 0.313(2.246)** 0.175(4.340)** 0.354 (4.389)** 0.150(6.738)*** Sudan -0.156(-0.324) 0.415(1.602) -1.370(-2.132) Tanzania 0.245(2.756)** 0.768(3.739)*** 1.017(4.940)*** 0.693(1.377) 0.612(2.64)* 1.466 (1.077) Togo 0.548(4.5455)*** 0.507(4.6610)*** 0.517 0.363 (2.13) -0.497(-0.47) 0.338(4.46)** Uganda 0.197(0.8164) 0.349(4.1874)*** 0.366 0.07(0.376) 0.083(0.472) 0.739(0.170) Zambia 0.747(4.1416)*** 0.443(5.2140)*** 0.413 Average 0.54 0.59 0.45 0.64 0.68 Panel 24 Fixed effect Random effect GMM 0.216 [3.92] *** 0.542 [13.7] *** -0.399 [-3.24] *** Fixed effect (ca) Random effect

(ca)

FMOLS 0.252 [4.95] *** 0.597 [16.8] *** 0.212 [3.09] ***


A3.3 Table 3. Estimates of the level and growth of GDP per capita SSA (1995-2014) Notes: Two estimators (the GMM and the FMOLS) on growth are used with the explanatory variables which was categorised factors

considered such as financial indicators (M2, DCFS, DCB, INF), industry (AG, MF, SER, X, M), investment (CF, FCF), infrastructure (APT,

APF, ENG, ELE), welfare (HPE, HPB, ENR), and growth (NI, GDP, NIg, GDPg). For the descriptions of the variables see A1 above for the

data descriptions in Appendix. The estimates on the dependent variables of NI and NIG, the GMM estimates were not available as they were

recorded as statistically insignificant for all coefficient values. The significant level at 1% (in pink), 5% (in

yellow) and 10% (in green) shows to reject the null hypothesis of insignificant coefficient values.

A3.4 Table 4. Estimates of SSA GDP with explanatory financial variables: RLS M-estimator (i)Sample period 1: 1980-1989 (ii)Sample period: 1990-2014

Variable Coef Std. Error z-Stat Coef Std. Error z-Stat

BC 0.089* 0.048 1.865 0.024 0.092 0.265

BS 0.138*** 0.029 4.781 0.051 0.045 1.139

CBA -0.056*** 0.016 -3.607 -0.116*** 0.031 -3.765

CGS 0.078** 0.031 2.494 0.026 0.044 0.600

CPS 0.010 0.041 0.247 0.172*** 0.062 2.752

IDI -0.003*** 0.002 -1.345 0.000 0.003 0.136

RE -0.013* 0.007 -1.775 0.006 0.010 0.574

LNRB -0.011*** 0.001 -7.765 -0.002 0.002 -0.861

OIPDS 0.002 0.001 1.320 -0.001 0.002 -0.820

OIPBS 0.002 0.002 1.408 -0.001 0.003 -0.232

DMBA 0.302*** 0.046 6.605 0.182** 0.071 2.561

PCMB -0.062 0.045 -1.380 -0.131 0.087 -1.507

C 2.448*** 0.070 34.885 2.664*** 0.124 21.437

Robust Statistics

R-sqrd 0.741. SIC 101.667. AIC 75.316 R-sqrd 0.880. SIC 54.321. AIC 25.756

Notes: Dependent Variable: GDPPC, M settings: weight=Bisquare, tuning=4.685, scale=MAD (median centered), settings: tol=0.0001,

iters=500, Huber Type I Standard Errors & Covariance, Convergence achieved after 43 iterations for Sample1, and Convergence achieved

after 3 iterations for Sample 2. Coefficients in***, **,* indicate statistically significant at 1%, 5%, and 10%. Descriptions of financial variables, see Appendix A1. Data descriptions.

Dependent var. AG MFG SRV EX I M M2 D CFS D CB I NF CFG FD I I FD I O C

GDPP Coef -18.7 -42.1 0.316 -6.95 12.22 9.027 -0.46 -7.15 1.938 -11 -18.1 -2.09 1721

t-st -1.92 -2.17 0.032 -1.89 3.322 2.868 -0.21 -2.16 0.387 -1.16 -2 -0.27 2.45

p-val 0.104 0.073 0.976 0.107 0.016 0.029 0.838 0.074 0.712 0.289 0.092 0.798 0.05

Coef -26.9 -29.6 -3.79 -8.06 13.34 8.985 -0.09 -7.72 -2.9 -3.44 -12.3 -1.84 1792

FMOLS t-st -4.91 -2.09 -1.04 -3.55 4.333 4.377 -0.11 -5.01 -1.39 -0.58 -2.56 -0.69 5.049

p-val 0.003 0.082 0.34 0.012 0.005 0.005 0.912 0.002 0.215 0.583 0.043 0.517 0.002

GMM: Model based significant coeffs: MNF(-); SRV (+); FCF(+); M2(+/-);FDII(+);DCFS(-); DCP(+); DCB(+/-);EX(-);ENG(+);ENR(+);HE(-)

FMOLS: Model based significant coeffs:FCF(+);IND(-);IM(-);EX(+); DCP(+); DCB(-);DCFS(-);FDII(+);M2(+);MNF(-);SRV(+)

GDPPG Coef -2.19 4.906 -0.78 0.35 0.942 0.748 0.399 -0.82 -0.42 1.535 -2.17 -0.18 -72.1

GMM t-st -1.63 2.413 -0.49 1.476 1.333 2.606 1.136 -1.23 -0.6 0.741 -1.87 -0.36 -1.1

p-val 0.154 0.052 0.64 0.19 0.231 0.04 0.299 0.264 0.568 0.487 0.111 0.734 0.312

Coef -1.83 3.624 -0.29 0.056 0.604 0.634 0.175 -0.48 -0.21 0.292 -1.56 -0.1 -38.5

FMOLS t-st -4.45 3.41 -1.07 0.331 2.616 4.118 2.858 -4.12 -1.32 0.656 -4.33 -0.49 -1.45

p-val 0.004 0.014 0.326 0.752 0.04 0.006 0.029 0.006 0.235 0.536 0.005 0.639 0.198

NIP5 Coef -7.41 -41.2 -0.29 -2.44 7.52 2.007 1.078 -4.46 -5.19 2.887 -15.3 1.814 1196

t-st -1.67 -3.59 -0.1 -1.33 3.017 1.208 1.634 -3.57 -3.06 0.6 -3.95 0.839 4.163

FMOLS p-val 0.145 0.012 0.926 0.232 0.024 0.273 0.153 0.012 0.022 0.571 0.008 0.434 0.006

NIPG Coef 1.192 -2.62 0.004 0.688 -0.21 0.012 0.389 -0.4 -0.39 0.595 -2.69 -0.68 -17

FMOLS t-st 1.8 -1.53 0.009 2.509 -0.57 0.05 3.942 -2.13 -1.54 0.827 -4.63 -2.11 -0.4

p-val 0.122 0.177 0.993 0.046 0.592 0.962 0.008 0.077 0.174 0.44 0.004 0.079 0.706

FMOLS: Model based significant coeffs:IND(+);SRV(+);M2(+/-);HE(+);IM(+/-);EX(+/-);FDIIG(-);FDIO(-); DCB(-);MNF(+);ENR(-

);S(+);DCFS(+);AVG(+);FCF(+);ELEC(+);GC(-).

GMM: Model based significant coeffs: DCBG(-);M2(+); ELEC(+);IM(-).

GMM: Model based significant coeffs: dcbg(-)

FMOLS: Model based significant coeffs: SRVG(+);ELEC(+);INF(+);M2(+);FDIO(-);DCB(-);FCF(-);FDII(-);ENR(+);HE(+).

FMOLS: Model based significant coeffs: ELEC(+);S(+);FDIO(-);ATP(+);ENR(+);IND(-);MNF(-); AG(+);FCF(+); M2(-/+);TR(-); DCFS(+/-);HE(-);SRV(+);INF(-

);FDII(+);DCP(+);DCB(-/+);ENR(+).

GMM: Model based significant coeffs: S(+);FCF(+);INF(-);TR(-);DCP(+);DCB(-);DCFS(-);FDII(+);M2(+);AG(-);ELEC(+).

GMM:Instrument

specification: HEP HEPB ENR

DCP TR IND YPG NIP5 NIPG FCF

ENG

Instrument specification: HEP

HEPB ENR DCP TR IND YP NIP5

NIPG FCF ENG ELE


34

A3.5 Table 5. Estimates of World high, middle, low income, and SSA (1996-2014) High Income Middle Income Low Income SSA

RLS Coef z-stat Coef z-stat Coef z-stat Coef z-stat CB 0.082 0.474 -1.528*** -3.871 0.267*** 15.378 -0.237* -1.972

EB 0.165** 1.983 -0.010*** -8.535 -0.011*** -3.830

FCF -4.06*** -3.608 1.227*** 4.994 -0.127*** -2.748 -0.217** -1.964

ISP 0.02** 2.064 -0.0005 -0.611 0.002*** 5.403

STV 0.386*** 2.584 0.036*** 4.228

PEI 0.096** 2.558 0.051*** 3.017 -0.001* -1.856 0.017*** 3.525

CFS 2.384*** 6.225 -0.104*** -6.554 -0.628*** -4.278

S 0.240 0.915 0.163*** 10.310 0.519*** 3.951

M2 0.730*** 6.092

ODA -0.040** -2.309 -0.038*** -5.175 -0.016 -0.618

REM 0.225*** 2.662 -0.650*** -8.193 0.123*** 10.242

FDII 0.055*** 7.898 0.005*** 4.429 0.009 0.912

R-sq 0.84 0.82 0.72 0.78

Rn-sq 222*** 13055*** 9779*** 7819***

FMOLS Coef t-stat Coef t-stat Coef t-stat Coef t-stat

CB -0.274 -1.437 0.341 0.408 0.342*** 6.093 EB -0.036* -2.076 -0.009 -1.830

FCF -0.695 -0.730 0.257 0.446 0.121 1.537 0.375 1.925

ISP 0.098 0.446 -0.014 -0.198

STV 0.067* 2.35 -0.001 -0.07

PEI 0.032** 3.403 -0.009 -0.255 -0.002 -0.532 -0.004 -0.457

CFS -0.307* -2.015 0.631 0.776 -0.239** -3.865 0.07 0.667

S 0.283 0.272 1.119 1.973 0.028 1.082 0.456* 2.286

M2

ODA -0.041*** -4.017 -0.065 -1.876 -0.159** -2.989

REM 0.247*** 5.271 -0.235 -1.349 0.143*** 6.210

FDII 0.037* 2.604 0.003 0.497

R-sq 0.97 0.98 0.95 0.98

GMM Coef t-stat Coef t-stat Coef t-stat Coef t-stat

CB -0.413*** -3.956 1.000*** 8.338 0.017 0.208 EB

FCF -0.456 -0.741 0.099 0.684 0.719*** 16.675

ISP

STV

PEI 0.09*** 7.169 0.015 0.395

CFS

S -0.351 -1.630 0.874* 2.047 0.047** 5.530 0.106** 2.531

M2

ODA 0.133* 2.655 -0.153*** -4.922

REM 0.083 1.714 0.147*** 8.873

FDII 0.016 0.729 -0.021* -2.633

R-sq 0.79 0.98 0.51 0.97


A3.6 Table 6. Results of Regional Panel Estimation on Finance-Growth (1996-2013) Notes: The statistically significant level at 1% and 5% are in red and yellow respectively. Coef, SE, and t-st refers coefficients, standard errors,

and t-statistics respectively. Estimates of GDPPC for grouped samples (1) landlocked states; (2) south-region; (3) east-region; (4) west-region

and (5) Island states for the period of 1996-2013 Variables entered as explanatory factors: Consolidated foreign claims of BIS reporting banks

to GDP (%), domestic credit to private sector (% of GDP), external loans and deposits of reporting banks vis-à-vis all sectors (% of domestic bank deposits), external loans and deposits of reporting banks vis-à-vis the banking sector (% of domestic bank deposits). Remittance inflows

to GDP (%), private credit by deposit money banks to GDP (%), loans from non-resident banks (net) to GDP (%), liquid liabilities to GDP

(%), GDP per capita (Constant 2005 USD), financial system deposits to GDP (%), deposit money banks' assets to GDP (%), credit to government and state owned enterprises to GDP (%), and central bank assets to GDP (%). For the robustness, four different methods are

applied for the estimation: Panel least square (PLS), Panel cross section fixed methods; robust LS with M estimation; and Panel FMOLS.

6.1 Estimates on dependent variable GDP per capita: landlocked states

6.2 Estimates on dependent variable GDP per capita: South region

Method: Panel LS

Var Coef SE t-st Coef SE t-st Coef SE z-st Coef SE t-st

BCBD -3.99 9.43 -0.42 0.53 2.53 0.21 2.83 7.99 0.35 21.22 7.02 3.02

CBA -85.69 20.27 -4.23 -7.17 7.52 -0.95 -78.47 17.16 -4.57 -1.81 16.58 -0.11

CFC 109.80 17.91 6.13 -28.80 6.36 -4.53 139.05 15.16 9.17 -12.45 17.55 -0.71

CPS 205.55 88.60 2.32 -10.72 25.60 -0.42 149.79 75.01 2.00 118.52 94.49 1.25

DMBA -5.24 43.09 -0.12 -18.83 11.08 -1.70 -7.53 36.48 -0.21 -98.61 34.03 -2.90

ELDBA 738.86 1072 0.69 73.56 240.92 0.31 717.07 907.44 0.79 -86.72 493.35 -0.18

ELDBB -740 1072 -0.69 -74.31 240.89 -0.31 -719 907.46 -0.79 87.28 493.73 0.18

ELDBNB -736 1072 -0.69 -72.24 240.90 -0.30 -715 907.29 -0.79 88.91 493.17 0.18

FSD 193.91 47.19 4.11 36.03 14.47 2.49 198 39.95 4.95 110.71 42.92 2.58

GSE -196 76.00 -2.58 1.55 18.38 0.08 -130 64.33 -2.02 30.16 69.57 0.43

LL -128 30.87 -4.16 -1.89 10.40 -0.18 -136 26.13 -5.19 4.29 21.91 0.20

LNRB -101 20.60 -4.91 22.37 6.42 3.49 -132 17.44 -7.59 19.65 22.17 0.89

PCDB -163 117.77 -1.38 33.67 31.62 1.07 -128 99.70 -1.29 -128.6 112.26 -1.15

RI -27.40 9.37 -2.92 3.43 5.59 0.61 -22 7.93 -2.81 24.83 31.72 0.78

C 524.50 720.07 0.73 431.48 196.22 2.20 378 609.57 0.62

R-sqrd 0.83 R-sqrd 0.99 R-sqrd 0.23 R-sqrd 0.97

Ad R-sqrd 0.82 Ad R-sqrd 0.99 Ad R-sqrd Ad R-sqrd 0.96

F-st 48.26 F-st 733.08 Rw-sqrd 0.89

Prob 0.00 Prob 0.00


BCBD -15.53 12.12 -1.28 9.67 5.53 1.75 12.63 10.47 1.21 1.3 10.71 0.12

CBA -30.83 15.30 -2.02 -3.36 5.60 -0.60 -15.32 13.22 -1.16 -100.6 76.58 -1.31

CFC -83.32 10.39 -8.02 -12.14 5.43 -2.24 -77.22 8.98 -8.60 -10.0 10.71 -0.93

CPS 13.56 7.02 1.93 17.49 5.66 3.09 22.54 6.06 3.72 17.3 8.95 1.93

DMBA 14.69 93.93 0.16 -62.90 33.75 -1.86 -48.75 81.15 -0.60 36.6 66.38 0.55

ELDBA 553 1107 0 -62 432 0 671 956.50 0.70 -392.4 627.15 -0.63

ELDBB -547 1107 0 61 432 0 -670 956.66 -0.70 392.0 627.46 0.62

ELDBNB -540 1107 0 62 432 0 -668 956.67 -0.70 396.8 626.98 0.63

FSD 5 41 0 84 19 4 93 35.75 2.60 48.3 34.29 1.41

GSE 185 82 2 79 31 3 239 70.85 3.37 1.8 66.80 0.03

LL -20.54 20.56 -1.00 -25.95 8.80 -2.95 -34.15 17.76 -1.92 -8.0 13.54 -0.59

LNRB 45.83 22.99 1.99 31.07 9.38 3.31 31.83 19.86 1.60 55.2 15.41 3.58

PCDB 73.57 87.86 0.84 18.34 34.59 0.53 35.19 75.90 0.46 -76.3 61.64 -1.24

RI 274.03 80.03 3.42 -18.88 45.56 -0.41 377.58 69.14 5.46 -137.2 77.39 -1.77

C 251.90 1070 0 506 434 1 -1616 923.98 -1.75


Ad R-sqrd 0.89 Ad R-sqrd 0.99 Rw-sqrd 0.95 Ad R-sqrd 0.98

F-st 50 F-st 337

Prob(F-st) 0 Prob(F-st) 0

Pane-cross-section fixed Robust LS M Panel-FMOLS

2. Dependent Var: GDPPC_South region

1. Dependent Var: GDPPC-landlocked states

Method: Panel LS


BCBD -3.99 9.43 -0.42 0.53 2.53 0.21 2.83 7.99 0.35 21.22 7.02 3.02

CBA -85.69 20.27 -4.23 -7.17 7.52 -0.95 -78.47 17.16 -4.57 -1.81 16.58 -0.11

CFC 109.80 17.91 6.13 -28.80 6.36 -4.53 139.05 15.16 9.17 -12.45 17.55 -0.71

CPS 205.55 88.60 2.32 -10.72 25.60 -0.42 149.79 75.01 2.00 118.52 94.49 1.25

DMBA -5.24 43.09 -0.12 -18.83 11.08 -1.70 -7.53 36.48 -0.21 -98.61 34.03 -2.90

ELDBA 738.86 1072 0.69 73.56 240.92 0.31 717.07 907.44 0.79 -86.72 493.35 -0.18

ELDBB -740 1072 -0.69 -74.31 240.89 -0.31 -719 907.46 -0.79 87.28 493.73 0.18

ELDBNB -736 1072 -0.69 -72.24 240.90 -0.30 -715 907.29 -0.79 88.91 493.17 0.18

FSD 193.91 47.19 4.11 36.03 14.47 2.49 198 39.95 4.95 110.71 42.92 2.58

GSE -196 76.00 -2.58 1.55 18.38 0.08 -130 64.33 -2.02 30.16 69.57 0.43

LL -128 30.87 -4.16 -1.89 10.40 -0.18 -136 26.13 -5.19 4.29 21.91 0.20

LNRB -101 20.60 -4.91 22.37 6.42 3.49 -132 17.44 -7.59 19.65 22.17 0.89

PCDB -163 117.77 -1.38 33.67 31.62 1.07 -128 99.70 -1.29 -128.6 112.26 -1.15

RI -27.40 9.37 -2.92 3.43 5.59 0.61 -22 7.93 -2.81 24.83 31.72 0.78

C 524.50 720.07 0.73 431.48 196.22 2.20 378 609.57 0.62


Ad R-sqrd 0.82 Ad R-sqrd 0.99 Ad R-sqrd Ad R-sqrd 0.96

F-st 48.26 F-st 733.08 Rw-sqrd 0.89

Prob 0.00 Prob 0.00


BCBD -15.53 12.12 -1.28 9.67 5.53 1.75 12.63 10.47 1.21 1.3 10.71 0.12

CBA -30.83 15.30 -2.02 -3.36 5.60 -0.60 -15.32 13.22 -1.16 -100.6 76.58 -1.31

CFC -83.32 10.39 -8.02 -12.14 5.43 -2.24 -77.22 8.98 -8.60 -10.0 10.71 -0.93

CPS 13.56 7.02 1.93 17.49 5.66 3.09 22.54 6.06 3.72 17.3 8.95 1.93

DMBA 14.69 93.93 0.16 -62.90 33.75 -1.86 -48.75 81.15 -0.60 36.6 66.38 0.55

ELDBA 553 1107 0 -62 432 0 671 956.50 0.70 -392.4 627.15 -0.63

ELDBB -547 1107 0 61 432 0 -670 956.66 -0.70 392.0 627.46 0.62

ELDBNB -540 1107 0 62 432 0 -668 956.67 -0.70 396.8 626.98 0.63

FSD 5 41 0 84 19 4 93 35.75 2.60 48.3 34.29 1.41

GSE 185 82 2 79 31 3 239 70.85 3.37 1.8 66.80 0.03

LL -20.54 20.56 -1.00 -25.95 8.80 -2.95 -34.15 17.76 -1.92 -8.0 13.54 -0.59

LNRB 45.83 22.99 1.99 31.07 9.38 3.31 31.83 19.86 1.60 55.2 15.41 3.58

PCDB 73.57 87.86 0.84 18.34 34.59 0.53 35.19 75.90 0.46 -76.3 61.64 -1.24

RI 274.03 80.03 3.42 -18.88 45.56 -0.41 377.58 69.14 5.46 -137.2 77.39 -1.77

C 251.90 1070 0 506 434 1 -1616 923.98 -1.75



F-st 50 F-st 337


Pane-cross-section fixed Robust LS M Panel-FMOLS

2. Dependent Var: GDPPC_South region

1. Dependent Var: GDPPC-landlocked states


36

6.3 Estimates on dependent variable GDP per capita: East region

6.4 Estimates on dependent variable GDP per capita: West region


BCBD -1.51 0.89 -1.70 1.12 0.59 1.89 -0.39 0.72 -0.53 5.90 2.28 2.59

CBA -21.55 1.81 -11.88 -1.14 1.60 -0.72 -24.42 1.47 -16.61 -4.36 10.42 -0.42

CFC -1.87 2.08 -0.90 -1.48 0.96 -1.53 -2.22 1.69 -1.32 -2.28 2.06 -1.11

CPS -8.14 4.41 -1.85 0.15 2.02 0.07 -5.81 3.57 -1.63 10.37 5.52 1.88

DMBA -18.98 8.54 -2.22 -6.23 4.42 -1.41 -13.83 6.92 -2.00 9.52 11.15 0.85

ELDBA -740 231 -3 -224 98 -2 -881 187.32 -4.70 -703 486.58 -1.44

ELDBB 739.98 231.08 3.20 223.43 97.63 2.29 879.80 187.28 4.70 701.25 486.49 1.44

ELDBNB 738.99 231.10 3.20 222.60 97.60 2.28 879.66 187.30 4.70 701.94 486.58 1.44

FSD -3.05 4.64 -0.66 9.80 2.50 3.91 -5.30 3.76 -1.41 -1.21 9.77 -0.12

GSE 9.32 8.63 1.08 1.35 3.68 0.37 4.40 7.00 0.63 0.75 9.53 0.08

LL 9.73 3.80 2.56 -1.21 2.31 -0.52 13.50 3.08 4.39 13.56 9.15 1.48

LNRB 11.41 4.59 2.48 2.09 1.91 1.10 5.13 3.72 1.38 6.49 4.08 1.59

PCDB 28.06 11.29 2.49 5.73 6.35 0.90 17.21 9.15 1.88 -36.20 18.21 -1.99

RI 46.60 4.84 9.62 0.61 3.62 0.17 42.43 3.93 10.81 -45.30 16.43 -2.76

C 464.52 68.39 6.79 264.67 47.29 5.60 450.36 55.43 8.13



F-st 113 F-st 506



BCBD 2.57 1.76 1.46 -0.93 0.90 -1.04 2.81 1.54 1.82 2.10 1.72 1.22

BD -20.50 14.35 -1.43 4.02 7.53 0.53 -30.02 12.54 -2.39 -0.70 17.04 -0.04

CBA -8.30 1.74 -4.77 -1.22 0.85 -1.44 -7.24 1.52 -4.76 2.60 1.12 2.32

CFC 11.71 1.76 6.64 0.30 1.37 0.22 12.88 1.54 8.35 -3.59 1.38 -2.60

CPS -27.03 11.11 -2.43 -10.09 5.06 -1.99 -7.51 9.71 -0.77 -3.74 5.03 -0.74

DMBA 41.16 17.70 2.33 29.19 7.94 3.68 20.40 15.47 1.32 11.33 11.22 1.01

ELDB 130 1425 0.09 65.57 621.08 0.11 -645 1246 -0.52 1313 680 1.93

ELDBS -128 1425 -0.09 -65.59 621.08 -0.11 646 1246 0.52 -1313 680 -1.93

ELDNBS -129 1425 -0.09 -65.86 621.11 -0.11 646 1246 0.52 -1313 680 -1.93

FSD 8.40 2.91 2.88 8.36 2.76 3.03 11.34 2.55 4.45 7.27 2.44 2.98

GSE -13.98 16.56 -0.84 -14.08 6.97 -2.02 -5.61 14.48 -0.39 -4.01 5.36 -0.75

LL 8.39 6.46 1.30 -16.65 3.36 -4.95 18.12 5.65 3.21 -5.51 10.82 -0.51

LNRB -11.65 3.63 -3.21 -4.47 1.67 -2.67 -12.10 3.17 -3.81 0.95 1.43 0.66

PCDB -5.45 23.60 -0.23 -8.49 10.69 -0.79 -4.02 20.63 -0.20 -20.09 12.10 -1.66

RI 7.52 5.52 1.36 8.19 2.94 2.79 5.15 4.83 1.07 6.74 2.47 2.73

C 100.22 177.42 0.56 658.84 87.69 7.51 -84.38 155.08 -0.54

R-sqrd 0.65 R-sqrd 0.94 R-sqrd 0.57 R-sqrd -4.73

Ad R-sqrd 0.61 Ad R-sqrd 0.93 Rw-sqrd 0.76 Ad R-sqrd-17.90

F-st 17.35 F-st 95.13

Prob(F-st) 0.00 Prob(F-st) 0.00


BCBD 5.76 16.10 0.36 -14.07 7.63 -1.84 -15.12 13.80 -1.10 466.71 28.37 16.45

BD -392 239.02 -1.64 -115 85.64 -1.34 -724 205 -4

CBA 87.32 30.95 2.82 -34.88 17.36 -2.01 121 26.52 4.55 116.24 58.45 1.99

CFC 11.90 5.91 2.01 7.26 1.90 3.81 1.71 5.06 0.34 82.47 3.51 23.50

CPS 90.54 38.72 2.34 15.82 13.25 1.19 56.19 33.18 1.69 42.09 27.58 1.53

DMBA 19.37 56.48 0.34 -71.87 19.49 -3.69 12.42 48.41 0.26 35.99 50.67 0.71

ELDBA 30.28 24.49 1.24 34.04 7.75 4.39 47.86 20.99 2.28 14513 232.17 62.51

ELDBB -44.82 23.89 -1.88 -35.60 7.48 -4.76 -58.13 20.47 -2.84 -14583 233.38 -62.49

ELDBNB -23.85 24.40 -0.98 -33.42 7.69 -4.35 -40.51 20.91 -1.94 -14497 231.62 -62.59

FSD 595.90 224.14 2.66 46.94 81.63 0.58 992.41 192.10 5.17 1522 92.99 16.37

GSE 60.09 46.33 1.30 60.12 14.44 4.16 64.10 39.71 1.61 -51 39.93 -1.27

LL -143 62.75 -2.28 50.21 24.10 2.08 -229 53.78 -4.25 -670 74.12 -9.03

PCDB -140 64.54 -2.18 110.45 25.31 4.36 -49 55.31 -0.89 -1252 79.40 -15.77

RI -118 25.79 -4.56 -22.24 18.34 -1.21 -176 22.11 -7.95 -787 43.73 -17.99

C 279 1325 0 3438 649.84 5.29 1837 1136 1.62


Ad R-sqrd 0.96 Ad R-sqrd 1.00 Rw-sqrd 0.98 Ad R-sqrd -2.48

F-st 132.14 F-st 1069 Rn-sqrd statistic2610


4. Dependent Var: GDPPC_West region

5. Dependent Var: GDPPC_Island states

3. Dependent Var: GDPPC_East region


BCBD -1.51 0.89 -1.70 1.12 0.59 1.89 -0.39 0.72 -0.53 5.90 2.28 2.59

CBA -21.55 1.81 -11.88 -1.14 1.60 -0.72 -24.42 1.47 -16.61 -4.36 10.42 -0.42

CFC -1.87 2.08 -0.90 -1.48 0.96 -1.53 -2.22 1.69 -1.32 -2.28 2.06 -1.11

CPS -8.14 4.41 -1.85 0.15 2.02 0.07 -5.81 3.57 -1.63 10.37 5.52 1.88

DMBA -18.98 8.54 -2.22 -6.23 4.42 -1.41 -13.83 6.92 -2.00 9.52 11.15 0.85

ELDBA -740 231 -3 -224 98 -2 -881 187.32 -4.70 -703 486.58 -1.44

ELDBB 739.98 231.08 3.20 223.43 97.63 2.29 879.80 187.28 4.70 701.25 486.49 1.44

ELDBNB 738.99 231.10 3.20 222.60 97.60 2.28 879.66 187.30 4.70 701.94 486.58 1.44

FSD -3.05 4.64 -0.66 9.80 2.50 3.91 -5.30 3.76 -1.41 -1.21 9.77 -0.12

GSE 9.32 8.63 1.08 1.35 3.68 0.37 4.40 7.00 0.63 0.75 9.53 0.08

LL 9.73 3.80 2.56 -1.21 2.31 -0.52 13.50 3.08 4.39 13.56 9.15 1.48

LNRB 11.41 4.59 2.48 2.09 1.91 1.10 5.13 3.72 1.38 6.49 4.08 1.59

PCDB 28.06 11.29 2.49 5.73 6.35 0.90 17.21 9.15 1.88 -36.20 18.21 -1.99

RI 46.60 4.84 9.62 0.61 3.62 0.17 42.43 3.93 10.81 -45.30 16.43 -2.76

C 464.52 68.39 6.79 264.67 47.29 5.60 450.36 55.43 8.13



F-st 113 F-st 506



BCBD 2.57 1.76 1.46 -0.93 0.90 -1.04 2.81 1.54 1.82 2.10 1.72 1.22

BD -20.50 14.35 -1.43 4.02 7.53 0.53 -30.02 12.54 -2.39 -0.70 17.04 -0.04

CBA -8.30 1.74 -4.77 -1.22 0.85 -1.44 -7.24 1.52 -4.76 2.60 1.12 2.32

CFC 11.71 1.76 6.64 0.30 1.37 0.22 12.88 1.54 8.35 -3.59 1.38 -2.60

CPS -27.03 11.11 -2.43 -10.09 5.06 -1.99 -7.51 9.71 -0.77 -3.74 5.03 -0.74

DMBA 41.16 17.70 2.33 29.19 7.94 3.68 20.40 15.47 1.32 11.33 11.22 1.01

ELDB 130 1425 0.09 65.57 621.08 0.11 -645 1246 -0.52 1313 680 1.93

ELDBS -128 1425 -0.09 -65.59 621.08 -0.11 646 1246 0.52 -1313 680 -1.93

ELDNBS -129 1425 -0.09 -65.86 621.11 -0.11 646 1246 0.52 -1313 680 -1.93

FSD 8.40 2.91 2.88 8.36 2.76 3.03 11.34 2.55 4.45 7.27 2.44 2.98

GSE -13.98 16.56 -0.84 -14.08 6.97 -2.02 -5.61 14.48 -0.39 -4.01 5.36 -0.75

LL 8.39 6.46 1.30 -16.65 3.36 -4.95 18.12 5.65 3.21 -5.51 10.82 -0.51

LNRB -11.65 3.63 -3.21 -4.47 1.67 -2.67 -12.10 3.17 -3.81 0.95 1.43 0.66

PCDB -5.45 23.60 -0.23 -8.49 10.69 -0.79 -4.02 20.63 -0.20 -20.09 12.10 -1.66

RI 7.52 5.52 1.36 8.19 2.94 2.79 5.15 4.83 1.07 6.74 2.47 2.73

C 100.22 177.42 0.56 658.84 87.69 7.51 -84.38 155.08 -0.54



F-st 17.35 F-st 95.13



BCBD 5.76 16.10 0.36 -14.07 7.63 -1.84 -15.12 13.80 -1.10 466.71 28.37 16.45

BD -392 239.02 -1.64 -115 85.64 -1.34 -724 205 -4

CBA 87.32 30.95 2.82 -34.88 17.36 -2.01 121 26.52 4.55 116.24 58.45 1.99

CFC 11.90 5.91 2.01 7.26 1.90 3.81 1.71 5.06 0.34 82.47 3.51 23.50

CPS 90.54 38.72 2.34 15.82 13.25 1.19 56.19 33.18 1.69 42.09 27.58 1.53

DMBA 19.37 56.48 0.34 -71.87 19.49 -3.69 12.42 48.41 0.26 35.99 50.67 0.71

ELDBA 30.28 24.49 1.24 34.04 7.75 4.39 47.86 20.99 2.28 14513 232.17 62.51

ELDBB -44.82 23.89 -1.88 -35.60 7.48 -4.76 -58.13 20.47 -2.84 -14583 233.38 -62.49

ELDBNB -23.85 24.40 -0.98 -33.42 7.69 -4.35 -40.51 20.91 -1.94 -14497 231.62 -62.59

FSD 595.90 224.14 2.66 46.94 81.63 0.58 992.41 192.10 5.17 1522 92.99 16.37

GSE 60.09 46.33 1.30 60.12 14.44 4.16 64.10 39.71 1.61 -51 39.93 -1.27

LL -143 62.75 -2.28 50.21 24.10 2.08 -229 53.78 -4.25 -670 74.12 -9.03

PCDB -140 64.54 -2.18 110.45 25.31 4.36 -49 55.31 -0.89 -1252 79.40 -15.77

RI -118 25.79 -4.56 -22.24 18.34 -1.21 -176 22.11 -7.95 -787 43.73 -17.99

C 279 1325 0 3438 649.84 5.29 1837 1136 1.62









6.5 Estimates on dependent variable GDP per capita: Island states

A3.7 Table 7. Panel Estimates from the PLS, RE, FMOLS, RLS, 1990-2014

PLS RE

Cross-section random

effects

PLS FMOLS RLS PLS FMOLS

Dep. V GDP GDP GDP GDP GDP D(GDP) D(GDP) k 2.8E-05

(0.022)

0.0001

(0.081)

0.0151

(2.339) **

0.0003

(0.151)

0.016

(2.45) **

3.8E-05

(0.09)

0.002

(0.48)

fdi 4.3E-06 (0.953)

4.5E-06 (0.999)

7.7E-05 (3.120) ***

8.2E-06 (1.163)

8.2E-05 (3.19) ***

8.4E-07 (0.60)

3.2E-06 (1.75) *

g -0.0044

(-1.802) *

-0.0044

(-1.829) *

-0.0049

(-0.343)

-0.0051

(-1.341)

-0.001

(-0.08)

-0.0012

(-1.40)

-0.001

(-1.51) l 0.0058

(2.804) ***

0.0059

(2.842) ***

0.0175

(1.431)

0.0072

(2.258) **

0.019

(1.53)

0.002

(2.51) **

0.002

(2.54) **

ODA 6.90E-07 (0.14)

4.1E-06 (0.74)

CA

0.00046

(2.86) ***

0.0002

(1.21)

TRADE

6.16E-05

(0.75)

0.0001

(1.066)

M2 -0.00026 (-2.38) **

-0.0003 (-2.22) **

RI

6.86E-05

(1.12)

6.1E-06

(0.08) C 2.818

(217) ***

2.811

(34.40)***

2.366

(34.8)***

2.24

(31.7) ***

-0.0072

(-0.92)

R-squared 0.98 0.04 0.21 0.98 0.22 0.23 0.21 F-stat 942*** 4.134*** 26.35*** 3.37***

Hausman

Chi-Sq.

31.53***

Rn-sq. 148***

Notes: Panel data: Angola; Botswana; Burundi; Cameroon; Central African Republic; Chad; Congo; Gabon; Gambia; Madagascar; Malawi;

Mauritania; Mauritius; Namibia; Nigeria; Rwanda; Seychelles; Sierra Leone; South Africa; Swaziland; Uganda; and Zimbabwe. Sample period: 1990-2014.


BCBD -1.51 0.89 -1.70 1.12 0.59 1.89 -0.39 0.72 -0.53 5.90 2.28 2.59

CBA -21.55 1.81 -11.88 -1.14 1.60 -0.72 -24.42 1.47 -16.61 -4.36 10.42 -0.42

CFC -1.87 2.08 -0.90 -1.48 0.96 -1.53 -2.22 1.69 -1.32 -2.28 2.06 -1.11

CPS -8.14 4.41 -1.85 0.15 2.02 0.07 -5.81 3.57 -1.63 10.37 5.52 1.88

DMBA -18.98 8.54 -2.22 -6.23 4.42 -1.41 -13.83 6.92 -2.00 9.52 11.15 0.85

ELDBA -740 231 -3 -224 98 -2 -881 187.32 -4.70 -703 486.58 -1.44

ELDBB 739.98 231.08 3.20 223.43 97.63 2.29 879.80 187.28 4.70 701.25 486.49 1.44

ELDBNB 738.99 231.10 3.20 222.60 97.60 2.28 879.66 187.30 4.70 701.94 486.58 1.44

FSD -3.05 4.64 -0.66 9.80 2.50 3.91 -5.30 3.76 -1.41 -1.21 9.77 -0.12

GSE 9.32 8.63 1.08 1.35 3.68 0.37 4.40 7.00 0.63 0.75 9.53 0.08

LL 9.73 3.80 2.56 -1.21 2.31 -0.52 13.50 3.08 4.39 13.56 9.15 1.48

LNRB 11.41 4.59 2.48 2.09 1.91 1.10 5.13 3.72 1.38 6.49 4.08 1.59

PCDB 28.06 11.29 2.49 5.73 6.35 0.90 17.21 9.15 1.88 -36.20 18.21 -1.99

RI 46.60 4.84 9.62 0.61 3.62 0.17 42.43 3.93 10.81 -45.30 16.43 -2.76

C 464.52 68.39 6.79 264.67 47.29 5.60 450.36 55.43 8.13



F-st 113 F-st 506



BCBD 2.57 1.76 1.46 -0.93 0.90 -1.04 2.81 1.54 1.82 2.10 1.72 1.22

BD -20.50 14.35 -1.43 4.02 7.53 0.53 -30.02 12.54 -2.39 -0.70 17.04 -0.04

CBA -8.30 1.74 -4.77 -1.22 0.85 -1.44 -7.24 1.52 -4.76 2.60 1.12 2.32

CFC 11.71 1.76 6.64 0.30 1.37 0.22 12.88 1.54 8.35 -3.59 1.38 -2.60

CPS -27.03 11.11 -2.43 -10.09 5.06 -1.99 -7.51 9.71 -0.77 -3.74 5.03 -0.74

DMBA 41.16 17.70 2.33 29.19 7.94 3.68 20.40 15.47 1.32 11.33 11.22 1.01

ELDB 130 1425 0.09 65.57 621.08 0.11 -645 1246 -0.52 1313 680 1.93

ELDBS -128 1425 -0.09 -65.59 621.08 -0.11 646 1246 0.52 -1313 680 -1.93

ELDNBS -129 1425 -0.09 -65.86 621.11 -0.11 646 1246 0.52 -1313 680 -1.93

FSD 8.40 2.91 2.88 8.36 2.76 3.03 11.34 2.55 4.45 7.27 2.44 2.98

GSE -13.98 16.56 -0.84 -14.08 6.97 -2.02 -5.61 14.48 -0.39 -4.01 5.36 -0.75

LL 8.39 6.46 1.30 -16.65 3.36 -4.95 18.12 5.65 3.21 -5.51 10.82 -0.51

LNRB -11.65 3.63 -3.21 -4.47 1.67 -2.67 -12.10 3.17 -3.81 0.95 1.43 0.66

PCDB -5.45 23.60 -0.23 -8.49 10.69 -0.79 -4.02 20.63 -0.20 -20.09 12.10 -1.66

RI 7.52 5.52 1.36 8.19 2.94 2.79 5.15 4.83 1.07 6.74 2.47 2.73

C 100.22 177.42 0.56 658.84 87.69 7.51 -84.38 155.08 -0.54



F-st 17.35 F-st 95.13



BCBD 5.76 16.10 0.36 -14.07 7.63 -1.84 -15.12 13.80 -1.10 466.71 28.37 16.45

BD -392 239.02 -1.64 -115 85.64 -1.34 -724 205 -4

CBA 87.32 30.95 2.82 -34.88 17.36 -2.01 121 26.52 4.55 116.24 58.45 1.99

CFC 11.90 5.91 2.01 7.26 1.90 3.81 1.71 5.06 0.34 82.47 3.51 23.50

CPS 90.54 38.72 2.34 15.82 13.25 1.19 56.19 33.18 1.69 42.09 27.58 1.53

DMBA 19.37 56.48 0.34 -71.87 19.49 -3.69 12.42 48.41 0.26 35.99 50.67 0.71

ELDBA 30.28 24.49 1.24 34.04 7.75 4.39 47.86 20.99 2.28 14513 232.17 62.51

ELDBB -44.82 23.89 -1.88 -35.60 7.48 -4.76 -58.13 20.47 -2.84 -14583 233.38 -62.49

ELDBNB -23.85 24.40 -0.98 -33.42 7.69 -4.35 -40.51 20.91 -1.94 -14497 231.62 -62.59

FSD 595.90 224.14 2.66 46.94 81.63 0.58 992.41 192.10 5.17 1522 92.99 16.37

GSE 60.09 46.33 1.30 60.12 14.44 4.16 64.10 39.71 1.61 -51 39.93 -1.27

LL -143 62.75 -2.28 50.21 24.10 2.08 -229 53.78 -4.25 -670 74.12 -9.03

PCDB -140 64.54 -2.18 110.45 25.31 4.36 -49 55.31 -0.89 -1252 79.40 -15.77

RI -118 25.79 -4.56 -22.24 18.34 -1.21 -176 22.11 -7.95 -787 43.73 -17.99

C 279 1325 0 3438 649.84 5.29 1837 1136 1.62









38

A3.8 Table 8 Estimates of Financial Sector on Growth in 9-SSA Panel Data

Notes: The dependent variable is growth of GDP per capita. The t-ratios or

z-ratio are in parentheses. DDMBA, INS and SM denotes deposit money

bank assets, insurance assets, and stock market capitalization in 9 countries (Botswana, Cote d'Ivoire, Ghana, Kenya, Mauritius, Namibia, Nigeria, South

Africa, and Zambia). Methods used: PLS; GLS; RLS-M; RLS-S; RLS-MM.

*, **, *** refers 10% (in green), 5% (in yellow) and 1% (in pink) level of significance. Results: LS Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 2.96619 0.157 18.825 <2e-16 ***

ins 0.717 0.060 11.941 <2e-16 ***

sm 0.033 0.034 0.969 0.334

dmba -0.255 0.128 -1.982 0.050 .

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2201 on 100 degrees of freedom

(13 observations deleted due to missing ness). Multiple R-squared: 0.754

7, Adjusted R-squared: 0.7474. F-statistic: 102.6 on 3 and 100 DF, p-value: < 2.2e-16

[R-code: fsm<-read.csv("fsm.csv", header=TRUE); df<- data.frame(fsm); View(df); names(fsm)

reg<- lm(gdpp ~ins+sm+dmba); summary(reg)] Plots below, from left-hand-side: dmba, ins, sm on GDP. [DBMA vs GDP] [SM vs GDP] [INS vs GDP]

Models PLS (F)1 PLS(F)2 EGLS ® FMOLS RLS-M RLS-S RLS-MM

Variable

DDMBA -0.2628 0.3659 0.3565 0.4648 -0.1848 0.852 0.55578

t/z-stat -2.041 5.1505 5.0547 5.5275 -1.461 31.94 8.24758

INS 0.721 0.1023 0.1334 0.1384 0.684 0.1678 0.36106

t/z-stat 11.994 2.0636 2.7659 2.2519 11.586 13.473 11.4788

SM 0.0328 -0.0136 -0.0144 -0.0222 0.0365 -0.0125 0.00641

t/z-stat 0.9582 -0.985 -1.041 -1.274 1.0849 -1.754 0.35728

C 6.8493 6.0377 5.9729 6.6979 4.2932 4.79953

t/z-stat 18.868 28.212 20.139 18.786 57.094 25.2664

R-sqd 0.76 0.99 0.39 0.99 0.67 0.85 0.43468

Adj R-

sqd0.75 0.99 0.37 0.99 0.66 0.85 0.41772

S.E. 0.51 0.10 0.10 0.10 133.88 5201.18 258.341

Coefficinets


Appendix 4: Principal Component Analysis (PCA)

A4.1 Figure 4. Biplot of Development Factor PCA from Panel Datasets (1996-2014) Notes: The sample period of the variables entered is between 1996 and 2014. Left-hand-side top, right-hand-side top, left-hand-side bottom, and right-

hand-side bottom represents High Income countries (HI), Middle Income countries (MI), Low Income countries (LI), and sub-Saharan African countries (SSA).

The 2nd component of HI, MI, LI, and SSA is 28%, 21.3%, 22.2%, and 27.1%. The principal component analysis (PCA) is an exploratory method that can reduce

the data into a lower number of orthogonal synthesized factors from proximities of a change of variables space: ∑ (𝑝𝑖 − 𝑝�̃�2 = 𝑚𝑖𝑛,𝑖

refers minimisation of the loss of variability for the cloud of points (P). The PCA plot provides the relations between financial and economic variables in the

multivariate space in such a way that the first direction explains about 60% of all the variables for MI, 52% for Hi, 55% for LI and 25% for SSA group. The second

direction explains the remainder while being orthogonal to the first as principal two components. The two components are seen to be 81% for MI, 77% for LI,

81% for Hi and 62% for SSA. GDP and ISP, and ODA show an outlying behaviour where the loadings in the score scatter bi-plot above. The real sector variables

and financial variables show in the first and the 2nd directions in HI and MI group however, however, LI and SA are more diversified distance among all variables

entered in the system. Above bi-plot shows the first component (F1) on X-axis and the 2nd component (F2) on Y-axis. There are outliers such as GDP in Hi and

Li and ISP in MI and SSA as expected. Official development assistant (ODA) and interest rate spread (lending – deposit) variables behaviour more independent

in HI, MI and SSA. Only HI shows the GDP behaviours independently evolve over the period while GDP is integrated in MI and SSA. LI and SSA show fixed

capital formation (i.e. infrastructure investment) are more closely evolve with GDP. For SSA, gross domestic savings are closely evolve with GDP. For HI, credit

to bank assets (CB) and FCF are closely evolved while credit to financial sector assets are related with equity portfolio investment (PE). For MI, FCF is closely

related with CFS while PE is closely related in LI through presumably credit rating effect among others. For SSA, foreign direct investment inflows are closely

evolve with money supply (M2) due to relatively small size of M2 and the effect of FDI to the region.

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

CBHI

CFSHI

EBHI

FCFHI

ISPHI

M2HI

ODACFHIPEIHI

REMHI

SHI

STVHI

GDPHI

Component 1 (52.4%)

Co

mp

on

en

t 2

(2

8.0

%)

Orthonormal Loadings Biplot

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

GDPMI

CBMICFSMI

EBMI

FCFMI

FDIIMI

ISPMI

M2MI

ODACFMI

PEIMI

REMMI

SMI

STVMI

1996

Component 1 (59.9%)

Co

mp

on

en

t 2

(2

1.3

%)


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CBLI

CFSLI

EBLI

FCFLI

FDIILI

GDPLI

ISPLI

M2LI

ODACFLI

PEILI

REMLI

SLI

Component 1 (55.1%)

Co

mp

on

en

t 2

(2

2.2

%)


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CBSSACFSSSA

EBSSA

FCFSSA

FDIISSA

GDPSSA

ISPSSA

M2SSA

ODACFSSAPEISSA

REMSSA

SSSA

1996

Component 1 (34.9%)

Co

mp

on

en

t 2

(2

7.1

%)



40

Figure 5. Biplot of Investment and Savings PCA in Panel Samples (1996-2014) Below, the left-hand-side top, right-hand-side top, left-hand-side bottom, and right-hand-side bottom represents saving, investment, and

savings and investment, and savings, investment, and GDP factor behaviours for global regions and income group along with SSA group that

the corresponding 2nd component is 18.7%; 13.6%; 17.1%, and 12.3%. Year/s in blue indicates outliers.

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

SEAP

SEU

SHI

SLAC

SLI

SMENA

SMISSA

SSSA

2009

Component 1 (57.3%) global savings 1996-2013

Co

mp

on

en

t 2

(1

8.7

%)


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CFEAP

CFEU

CFHICFLAC

CFLI

CFMENACFMI

CFSA

CFSSA

2007 2008

Component 1 (68.6%) investment world income groups & SSA

Co

mp

on

en

t 2

(1

3.6

%)


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CFEAP

CFEU

CFHI

CFLAC

CFLICFMENA

CFMI

CFSA

CFSSA

SEAP

SEU

SHI

SLAC

SLI

SMENASMI

SSA

SSSA

Component 1 (58.0%)

Co

mp

on

en

t 2

(1

7.1

%)


-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

CFEAP

CFEU

CFHI

CFLAC

CFLI

CFMENA

CFMI

CFSA

CFSSA

GDPEAP

GDPEUGDPHI

GDPLAC

GDPLI

GDPMENA

GDPMIGDPSA

GDPSSA

SEAP

SEU

SHI

SLAC

SLI

SMENA

SMISSA

SSSA

Component 1 (68.7%) global investment, savings, GDP

Co

mp

on

en

t 2

(1

2.3

%)



A4.2 Figure 6. Biplot of Financial Factors PCA from Panel Datasets (1996-2014) The sample period of the variables entered is between 1996 and 2014. Below, the left-hand-side top, right-hand-side top, left-hand-side

bottom, and right-hand-side bottom represents SSA region, middle-income group, high-income group and low-income group in the world

samples that the corresponding 2nd component is 21.1%; 21.8%; 24.4%, and 22.2%. Year/s in blue indicates outliers.

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CBSSA

CFSSSA

EBSSA

FDIISSA

GDPSSA

M2SSA

ODASSA

PEISSA

REMSSA

SSSA

STVSSA

1996

Component 1 (50.5%)

Co

mp

on

en

t 2

(2

1.1

%)


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CBMICFSMI

EBMI

FCFMI

FDIIMI

GDPMIM2MI

ODACFMI

PEIMI

REMMI

SMI

STVMI

Component 1 (63.8%) middle-income global

Co

mp

on

en

t 2

(2

1.8

%)

Orthonormal Loadings Biplot 1996-2014

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

CBHI

CFSHI

EBHI

FCFHI

GDPHI

M2HI

ODACFHI

PEIHI

REMHI

SHI

STVHI

20122014

Component 1 (53.0%) hi-income global 1996-2014

Co

mp

on

en

t 2

(2

4.4

%)


-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

CBLI

CFSLI

EBLI

FCFLI

FDIILI

GDPLI

M2LI

ODACFLI

PEILI

REMLI

SLI

2002

2014

Component 1 (51.4%) low-income global 1996-2014

Co

mp

on

en

t 2

(2

2.2

%)



42

A4.3 Figure 7. Biplot of Financial Factor PCA from Panel SSA datasets (2996-2014) Below, the left-hand-side top, right-hand-side top, left-hand-side bottom, and right-hand-side bottom represents East-region, West-region,

Island-states, landlocked-states and South-region samples that the corresponding 2nd component is 20.3%; 13.5%; 9.5%, 16.3%and 12.8%.

Figures in blue indicates the outliers among the panel cross-id and the year.

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

BCBD

BD

CBA

CFC

CPS

DMBA

ELDBB

ELDBNB

FSD

GDPPC

GSE

LL

LNRB

PCDB

RI

2 - 06 2 - 07

2 - 08 2 - 09

2 - 10 2 - 11

2 - 12 2 - 13

3 - 01 3 - 02 3 - 03

Component 1 (46.5%) SSA East f inancial indicators PCA 1996-2013

Co

mp

on

en

t 2

(2

0.3

%)


-10.0

-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

-10.0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0

BCBD

BD

CBA CFC

CPS

DMBA

ELDBS

ELDNBS

FSDGDPPC

GSE

LL

LNRB

PCDBRI

4 - 99

4 - 00

4 - 01 4 - 02 4 - 03

4 - 04

6 - 09 7 - 12 7 - 13

8 - 96 8 - 97 8 - 98

8 - 00 9 - 12 9 - 13

Component 1 (44.9%) SSA West panel 1996-2013

Co

mp

on

en

t 2

(1

3.5

%)


-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

BCBD

BD

CBA

CFC

CPS

DMBA

ELDBA

ELDBNB

FSDGDPPC

GSE

LL

LNRB

PCDB

RI

Component 1 (82.1%) SSA Island panel 1996-2013

Co

mp

on

en

t 2

(9

.5%

)


-10.0

-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

-10.0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0

BCBD

BD

CBA

CFC

CPS

DMBA

ELDBB

ELDBNB

FSD

GDPPC

GSE

LL

LNRB

PCDB

RI

1 - 08 1 - 09 1 - 10 1 - 11

1 - 12 1 - 13

4 - 03

5 - 96 5 - 98

5 - 99 5 - 00 5 - 01 5 - 02 5 - 03

5 - 04

8 - 99

Component 1 (42.8%) SSA landlocked panel 1996-2013

Co

mp

on

en

t 2

(1

6.3

%)


-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

BCBD

BD

CBA

CFCB

CGSE

CPS

DMBA

ELDBB

ELDBNB

FSD

GDPPCLL

LNRB

PCDB

RI

1 - 96

2 - 13

4 - 08 4 - 09

Component 1 (55.0%) SSA South panel 1996-2013

Co

mp

on

en

t 2

(1

2.8

%)



Appendix 5. Short-Run Directional Causality Map (time lag=1)

A5.1 Figure 8. Diagram of Causality in Regional Financial Development Indicators Notes: All variables illustrated below are statistically significant at least 10% or less for the F-test statistics in order to reject the null hypothesis

of no directional causality at time-lag=1. Variables in bold is the coefficient size >10. Bi-directional causality indicates the financial indicators are endogenous with the growth equation. For the descriptions of the variable names, see Appendix A1 data descriptions above.

8.1: The entire SSA economies, overall, ODA and growth and infrastructure investment and broad money supply are inter-linked.

8.2: For SSA west region, the financial indicators influence on the growth in a way exogenous way. 8.3: For landlocked region, the growth influences on the financial indicators in particular on the consolidated foreign claims.

8.4: For south region, financial system and bank deposits and foreign credit and deposit indicators are endogenous variables with the growth.

8.5: For east region, financial system and bank deposits, bank assets and private credit provided by bank are endogenous to the growth. 8.6: For islands states in SSA, external liability and deposit to bank and non-bank and bank assets are endogenous to the growth. The growth

influences on foreign claims similar to the case of landlocked SSA states.

8.1 SSA consolidated effects of financial indicators 8.2 SSA West region

8.3 SSA landlocked-states 8.4 SSA South region

8.5 SSA East region 8.6 SSA Island states

GROWTH

ODA

ISP

CB

EB

CFS

FCF

M2

RE

S

FDI

GROWTH

BD

CFC

CGS

BAELDNB

FSD

LNRB

SSA WEST PANEL

ELDB

GROWTH L

BD

CFC

BA

FSD

CPS

LL

PCDB

GROWTH S

BD

BA

FSD

CPS

LLPCDB

GROWTH E

BD

BA

FSD

LL

CPS

PCDB

GROWTH I

ELDNB

ELDBA

GSE

BCBD

BD

CFCBA

FSD

LL


44

A5.2 Figure 9. Diagram of Directional Causality sub-Saharan Africa:1996-2014 Notes: All variables illustrated below are statistically significant at least 10%, 5% and 1% or less for the F-test statistics in order to reject the

null hypothesis of no directional causality between two variables, up to time-lag=1. Variables in bold is the coefficient size >10. Bi-directional

causality indicates the financial indicators are endogenous with the growth equation. For the descriptions of the variable names, see Appendix

A1 data descriptions above.

A5.3 Figure 10. Diagram of Investment-Saving in sub-Saharan Africa: 1996-2014 Notes: The directional diagrams below are based on the pairwise causality test with at least 10%, 5% and 1% significant level of the hypothesis

of no causality exists between two variables, all coefficients and F-test statistics were recorded with the p-value.

BD

BS

BA

GROWTH

PCB

RE

CPS

FSD

OIPBS

OIPDS

CGS IDI

LNRB

CBA

investment

gdp growth

savings

gdp level

SSA South

ca fa

investment

gdp growth

savings

gdp level

SSA landlocked

ca fa

investment

gdp growth

savings

gdp level

SSA West

ca fa

investment

gdp growth

savings

gdp level

SSA East

ca fa

investment

gdp growth

savings

gdp level

SSA Islands

ca fa


A5.4 Figure 11. Diagram of Causality in Financial & Economic Datasets SSA: 1996-2014

Notes: All variables illustrated below are statistically significant at least 10%, 5% and 1% or less for the F-test statistics in order to reject the

null hypothesis of no directional causality between two variables, up to time-lag=1. Variables in bold is the coefficient size >10. Bi-directional

causality indicates the financial indicators are endogenous with the growth equation. For the descriptions of the variable names, see Appendix A1 data descriptions above. Variable-box in yellow indicates financial factors, in purple, infrastructure, in light blue, industry, in blue, welfare,

and economic growth indictors are income, income growth, GDP and GDP growth.

A5.5 Figure 12. A simplified model of SSA rapid-sustainable economic growth (sefm) Notes: sefm: (𝑔𝑖 )

𝑛 = ∑ (𝑝𝑖 , 𝑝𝑚𝑒𝑖 , 𝑒𝑡𝑖 , 𝑓𝑖, 𝐺𝑖−1, 𝑔𝑖−1)𝑇

𝑖=1 where pi is primary resources capacity; pmei is processed and manufactured products

of primary resources for exporting, eti is educated and skilled labour forces and technology; Gi-1 is government policy for pme; and fi is

financial systems; and gi-1 is previous year ex-ante growth. This model is different from the existing ones as it combines the short-run growth

trigger and the long-run growth trigger based on the results in this study therefore, the growth is optimised through time. Two particular new elements in this model is first, the negative effects of financial system is converted into a positive ones, and second, the demand of educated

and skilled labour forces and technology (et) is required from the processed and manufactured primary products for exporting. These two

elements will lead a sustainable growth framework.

m2

s

cf

cb

cp

eb

cfs

fcf

fdii

gc

atp

ele

eng

atf

ag

mf

srv

indm

x

ni

nig

gdpg

gdp

enr

hp

hpb


capital mobility, financial development & growth: an ... · an empirical evidence from...

Documents