W Ps XiD51
POILICY RESEARCH WORKING PAPER 2057
Finance and the Sources Development of the banking
sector exerts a large, causal
of Grow th impact on total factor
productivity growth, which in
turn causes GDP to grow.
Tho0rsten Beck
Whether banking
Ross Levine
Nornmani Loayz-a development has a long-run
effect on capital growth or
private saving remains to be
seen.
The World Bank
Development Research Group
Finance a
February 1999
|_POLICY RESEARCH WORKING PAPER 2057
Summary findings
Beck, Levine, and Loayza evaluate whether the level of controls for country-specific effects and endogeneity.
development in the banking sector exerts a causal impact They find that:
on economic growth and its sources-total factor * Banks do exert a large, causal impact on total factor
productivity growth, physical capital accumulation, and productivity growth, which feeds through to overall
private saving. GDP growth.
They use (1) a pure cross-country instrumental variable * The long-run links between banking development
estimator to extract the exogenous component of and both capital growth and private savings are more
banking development and (2) a new panel technique that tenuous.
This paper - a product of Finance, Development Research Group - is part of a larger effort in the group to understand
the links between the financial system and economic growth. Copies of the paper are available free from the World Bank,
1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-456, telephone 202-473-1001, fax
202-522-1155, Internet address klabrie@worldbank.org. Policy Research Working Papers are also posted on the Web at
http://www.worldbank.org/htmVdec/Publications/Workpapers/home.html. Thorsten Beck may be contacted at tbeck
C°worldbank.org. February 1999. (46 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
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Produced by the Policy Research Dissemination Center
Finance and the Sources of Growth
Thorsten Beck, Ross Levine, and Norman Loayza
Beck: University of Virginia and World Bank; Levine: University of Virginia; Loayza: Banco Central
de Chile. This paper's findings, interpretations, and conclusions are entirely those of the authors and do
not necessarily represent the views of the Banco Central de Chile, World Bank, its Executive Directors,
or the countries they represent.
1
I. Introduction
Joseph Schumpeter argued in 1911 that banks play a pivotal role in economic development
because they choose which firms get to use society's savings. According to this view, the banking
sector alters the path of economic progress by affecting the allocation of savings and not necessarily by
altering the saving rate. Thus, the Schumpeterian view of finance and development highlights the
impact of banks on productivity growth and technological change.' Alternatively, a vast development
economics literature argues that capital accumulation is the key factor underlying economic growth.2
According to this view, better banks influence growth primarily by raising domestic saving rates and
attracting foreign capital. Our paper empirically assesses the impact of banks on productivity growth,
capital accumulation, private saving rates, and overall growth.
This paper is further motivated by a rejuvenated movement in macroeconomics to understand
cross-country differences in both the level and growth rate of total factor productivity. A long
empirical literature successfully shows that "something else" besides physical and human capital
accounts for the bulk of cross-country differences in both the level and growth rate of real per capita
Gross Domestic Product (GDP). Nevertheless, economists have been relatively unsuccessful at fully
characterizing this residual, which is generally termed "total factor productivity." Recent papers by
Hall and Jones (1998), Harberger (1998), Klenow (1998), and Prescott (1998) have again focused the
profession's attention on the need for improved theories of total factor productivity growth. While we
l Recent theoretical models have carefully documented the links between banks and economic activity. By economizing on
the costs of acquiring and processing information about firms and managers, banks can influence resource allocation.
Better banks are lower cost producers of information with consequent ramifications for capital allocation and productivity
growth [Diamond 1984; Boyd and Prescott 1986; Williamson 1987; Greenwood and Jovanovic 1990; and King and Levine
1993b].
2 See discussion and citations in King and Levine (1994), Easterly (1998), and Easterly, Levine, and Pritchett (1999).
2
do not advance a new theory, this paper empirically explores one cause of cross-country differences in
total factor productivity growth: differences in the level of banking sector development.
Specifically, this paper examines whether the level of banking sector development exerts a
causal impact on real per capita GDP growth, capital per capita growth, productivity per capita growth
and private saving rates. For convenience, we refer to capital per capita growth, productivity per
capita growth and private saving as the "sources of economic growth." Recent industry- and firm-level
research suggests that the level of banking sector development has a large, causal impact on real per
capita GDP growth [Rajan and Zingales 1998; Demirgiiu-Kunt and Maksimovic 1999].3 Past work,
however, does not explore the channels via which banks affect economic growth. Thus, using a cross-
country dataset, we assess the causal impact of banks on capital accumulation, private saving rates and
productivity growth and trace these effects through to overall per capita GDP growth.
This paper improves on the existing literature both in terms of econometric technique and data.
First, while King and Levine (1993a) and Levine and Zervos (1998) empirically assess the connection
between banking sector development and the sources of economic growth, they do not explicitly
confront the issue of causality. We use two econometric techniques to control for the simultaneity bias
that may arise from the joint determination of banking sector development and (i) capital
accumulation, (ii) total factor productivity growth, and (iii) private saving rates. The first technique
employs a pure cross-sectional, instrumental variable estimator, where data for 63 countries are
averaged over the period 1960-95. The dependent variable is either real per capita GDP growth, real
per capita capital stock growth, productivity growth, or private saving rates. Besides a measure of
banking sector development, the regressors include a wide array of conditioning information to control
for other factors associated with economic development. To control for simultaneity bias, we use the
3
legal origin of each country as an instrumental variable to extract the exogenous component of banking
sector development. Legal scholars note that many countries can be divided into countries with
English, French, German, or Scandinavian legal origins and those countries typically obtained their
legal systems through occupation or colonization. Thus, we take legal origin as exogenous. Moreover,
LaPorta, Lopez-de-Silanes, Shleifer, and Vishny (1997, 1998; henceforth LLSV) show that legal origin
substantively affected (a) laws concerning bank credits, (b) systems for enforcing bank contracts, and
(c) standards for corporate information disclosure. Each of these features of the contracting
environment helps explain cross-country differences in banking sector development [Levine, Loayza,
and Beck 1998; Levine 1998, 1999]. Thus, after extending the LLSV data on legal origin from 49 to
63 countries, we use the legal origin variables as instruments for banking sector development to assess
the effect of banking development on economic growth, capital growth, productivity growth, and
private saving rates.
The second econometric technique that we use to control for simultaneity bias also eliminates
any omitted variable bias induced by country-specific effects. We use a panel dataset, with data
averaged over each of the seven 5-year periods between 1960 and 1995. We use a Generalized-
Method-of-Moments (GMM) estimator proposed by Arellano and Bover (1995) and Blundell and
Bond (1997) to extract consistent and efficient estirnates of the impact of banking sector development
on growth and the sources of growth. Specifically, dynamic panel procedures typically take first
differences of the observations in levels to eliminate country-specific effects [Arellano and Bond 1991;
Holtz-Eakin, Newy, and Rosen 1990]. Then, lagged values of the regressors from the levels regression
are used as instruments to eliminate inconsistency arising from simultaneity bias. This difference
dynamic-panel estimator, however, frequently suffers from weak instruments, which produces biases in
3 Also, see the time-series studies by Rousseau and Wachtel (1988) and Neusser and Kugler (1998).
4
finite samples and inefficiencies even asymptotically [Alonso-Borrego and Arellano 19963. To
mitigate this problem, we use a system estimator. Besides the difference dynamic-panel equations, we
simultaneously estimate the level equations where the instruments are lagged values of the differenced
regressors [Arellano and Bover 1995]. By ameliorating the weak instrument deficiency, this system
estimator dramatically improves the consistency and efficiency of the estimator [Blundell and Bond
1997]. Thus, this paper uses two econometric procedures - a pure cross-sectional instrumental variable
estimator and a GMM dynamic panel technique - to evaluate the impact of differences in banking
sector development on economic growth, capital accumulation, productivity growth, and private
saving.
The second major way in which this paper improves upon existing work is by using better
measures of saving rates, physical capital, productivity, and banking sector development. Private
saving rates are notoriously difficult to measure [Masson et al. 1995]. As detailed below, however, we
use the results of a recent World Bank initiative that compiled high-quality statistics on gross private
savings as a share of gross private disposable income for a broad cross-section of countries over the
period 1971-1995[Loayza, Lopez, Schmidt-Hebbel and Serven 1998]. We also use more accurate
estimates of physical capital stocks. Researchers typically make an initial estimate of the capital stock
in 1950 and then use aggregate investment data to compute capital stocks in later years [King and
Levine 1994; Nehru and Dhareshwar 1994]. These estimates use aggregate investment data that, for
example, combine investment in residential structures with investment in equipment and machines,
while employing a single depreciation rate. Recently, however, the Penn-World Tables (PWT) 5.6
constructed capital stock data based on disaggregated investment and depreciation data. While the
PWT 5.6 discusses remaining measurement problems, these data suffer from fewer shortcomings than
existing capital stock data. We also improve on existing measures of aggregate TFP growth.
5
Researchers typically define TFP growth as a residual: real per capita GDP growth minus real per
capita capital growth times capital share in the national income accounts, which is commonly taken to
be between 0.3 and 0.4. Thus, simply by using better capital data, we obtain more accurate measures
of TFP growth. Moreover, aggregate studies of TFP growth frequently ignore human capital
accumulation. In contrast, we use both the Mankiw (1995) and the Bis and Klenow (1996)
specifications to control for human capital accumulation. Thus, we obtain three improved measures of
TFP to examine the impact of banking sector development on productivity growth. Finally, this paper
also uses an improved measure of banking sector development. We measure banking sector credits to
the private sector relative to GDP. This measure more carefully distinguishes who is conducting the
intermediation, to where the funds are flowing, and we more accurately deflate financial stocks than
past studies [e.g., King and Levine 1993a,b]. Finally, we check our results using the King and Levine
(1 993 a,b) and Levine and Zervos (1998) measures of financial intermediation after extending their
sample periods and deflating correctly.
We find that banks exert a strong, causal impact on real per capita GDP growth and per capita
productivity growth. Using both the pure cross-sectional instrumental variable estimator and system
dynamic-panel indicator, we find that higher levels of banking sector development produce faster rates
of economic growth and total factor productivity growth. These results are robust to alterations in the
conditioning information set and to changes in the measure of banking sector development. Thus, the
data are consistent with the Schumpeterian view that the level of banking sector development
importantly determines the rate of economic growth by affecting the pace of productivity growth and
technological change.
Turning to physical capital growth and savings, the results are more ambiguous. We frequently
find a positive and significant impact of banks on the rate of capital per capita growth. Nonetheless,
6
the results are inconsistent across alternative measures of financial development in the pure cross-
sectional regressions. The data do not confidently suggest that higher levels of banking sector
development promote economic growth by boosting the long-run rate of physical capital accumulation.
We find similarly conflicting results on savings. Different measures of banking sector development
yield different conclusions regarding the link between banking sector development and private savings
in the both pure cross-section and the panel regressions. Thus, we do not find a robust relationship
between banking sector development and either physical capital accumulation or private saving rates.
In sum, the results are consistent with the Schumpeterian view of finance and development: banks
affect economic development primarily by influencing total factor productivity growth.
The rest of the paper is organized as follows. Section II describes the data and presents
descriptive statistics. Section III discusses the two econometric methods. Section IV presents the
results for economic growth, capital growth and productivity growth. Section V presents the results for
private saving rates. Section VI concludes.
HI. Measuring financial development, growth and its sources
This section describes the measures of (1) banking sector development, (2) real per capita GDP
growth, (3) capital per capita growth, (4) productivity per capita growth, and (5) private saving rates.
A. Indicators offinancial development
A large theoretical literature shows that banks can reduce the costs of acquiring information
about firms and managers and lower the costs of conducting transactions. ' By providing more
accurate information about production technologies and by exerting corporate control, better banks can
enhance resource allocation and accelerate growth [Boyd and Prescott 1986; Greenwood and Jovanovic
7
1990; King and Levine 1993b]. Similarly, by facilitating risk management, improving the liquidity of
assets available to savers, and reducing trading costs, banks can encourage investment in higher-return
activities [Obstfeld 1994; Bencivenga and Smith 1991; Greenwood and Smith 1997]. The effect of
better banks on savings, however, is theoretically ambiguous. Higher returns ambiguously affect
saving rates due to well-known income and substitution effects. Also, greater risk diversification
opportunities have an ambiguous impact on saving rates as shown by Levhari and Srinivasan (1969).
Moreover, in a closed economy, a drop in saving rates in the presence of a production function with
physical capital externalities induces a negative impact on growth. Indeed, if these saving and
externality effects are sufficiently large, an improvement in banking development could lower growth
[Bencivenga and Smith 1991]. Thus, we attempt to shed some empirical light on these debates and
ambiguities that emerge from the theoretical literature. Specifically, we examine whether economies
with better-developed banks (i) grow faster, (ii) enjoy faster rates of productivity growth, (iii)
experience more rapid capital accumulation, and (iv) have higher saving rates.
To evaluate the impact of banks on growth and the sources of growth, we seek an indicator of
the ability of banks to research and identify profitable ventures, monitor and control managers, ease
risk management, and facilitate resource mobilization. We do not have a direct measure of these
financial services. We do, however, construct a better measure of banking sector development than
past studies and we check these results with existing measures of financial sector development.
The primary measure of banking sector development is PRIVATE CREDIT, which equals the
value of credits by financial intermediaries to the private sector divided by GDP. Unlike many past
measures [King and Levine 1993a,b], this measure excludes credits issued by the central banks.
Furthermore, it excludes credit to the public sector and cross claims of one group of intermediaries on
4 For overviews of the literature see Gertler (1988) and Levine (1997).
8
another. PRIVATE CREDIT is also a broader measure of banking sector development than that used
by Levine and Zervos (1998) since it includes all financial institutions, not only deposit money banks.5
Finally, unlike past studies, we carefully deflate the banking statistics. Specifically, financial stock
items are measured at the end of the period, while GDP is measured over the period. Simply dividing
financial stock items by GDP, therefore, can produce misleading measures of financial development,
especially in highly inflationary environments.6 Thus, PRIVATE CREDIT improves significantly on
other measures of financial development.
To assess the robustness of our results, we use two other measures of financial development.
LIQUID LIABILITIES equals the liquid liabilities of the financial system (currency plus demand and
interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP.' Unlike
PRIVATE CREDIT, LIQUID LIABILITIES is just an indicator of size. The other measure is
COMMERCIAL-CENTRAL BANK, the ratio of commercial bank domestic assets divided by
commercial bank plus central bank domestic assets. COMMERCIAL-CENTRAL BANK measures the
degree to which the banks versus the central banks allocate society's savings. The intuition underlying
this measure is that commercial banks are more likely to identify profitable investments, monitor
managers, facilitate risk management, and mobilize savings than central banks.
Forexample, King and Levine (1993a,b) use a measure of gross claims on the private sector divided by GDP. But, this
measure includes credits issued by the monetary authority and government agencies, whereas PRIVATE CREDIT includes
only credits issued by banks and other financial intermediaries. Also, Levine and Zervos (1998) and Levine (I 998a) use a
measure of deposit money bank credits to the private sector divided by GDP over the period 1976-1993. That measure,
however, does not include credits to the private sector by non-deposit money banks.
6 Some authors try to correct for this problem by using an average of fnancial intermediary balance sheet items in year t
and t-l and dividing by GDP measured in year t [King and Levine 1993a]. This however does not fully resolve the
distortion. This paper deflates end-of-year financial balance sheet items by end of year consumer price indices (CPI) and
deflates the GDP series by the annual CPI. Then, we compute the average of the real financial balance sheet item in year t
and t- 1 and divide this average by real GDP measured in year t.
7Among others it has been used by King and Levine (1993a).
9
B. Economic'growth and its sources
This paper uses new and better data on capital accumulation, productivity growth and private
saving rates. This subsection describes our data on economic growth, capital per capita growth and
three different measures of productivity growth.8
GROWTH equals the rate of real per capita GDP growth, where the underlying data are from
the national accounts. For the pure cross-sectional data (where there is one observation per country for
the period 1960-1995), we compute GROWTHfor each country by running a least squares regression
of the logarithm of real per capita GDP on a constant and a time trend. We use the estimated
coefficient on the time trend as the growth rate. This procedure is more robust to differences in the
serial correlation properties of the data than simply using the geometric rate of growth [Watson 19921.9
We do not use least squares growth rates for the panel data because the data are only over five-year
periods. Instead, we calculate real per capita GDP growth as the geometric rate of growth for each of
the seven five-year periods in the panel data.
The capital accumulation data are from a study by Levine and Orlov (1998) and improve
significantly on figures from previous studies by using disaggregated data. Briefly, they construct
capital stock figures, K, from investment data, I, and depreciation estimates, 6, for five separate capital
categories: machinery, transportation equipment, business construction, residential construction and
other construction. These data are from revised Penn-World Tables (5.6). The capital stock number
for each category, i, is then computed using the following formula: Kj,+1 = K i, + lit - 6 j,K it. This
perpetual inventory method requires the estimate of an initial capital stock Ko. Levine and Orlov
(1998) use Harberger's (1978) suggestion for deriving a guess of the initial capital stock in 1950,
For a detailed description of sources and construction of the data see appendix B.
9 Using geometric growth rates yields virtually. identical results.
10
which assumes that each country was at its steady-state capital-output ratio in 1950. While this
assumption is surely wrong, it is probably better than assuming that an initial capital stock of zero,
which many researchers use.'" Despite remaining difficulties, these capital figures improve
significantly on previous studies. CAPGROWTH is the growth rate of the physical per capita capital
stock.
We use three different methods to construct measures of productivity per capita growth. All of
them are residuals from aggregate production functions. The first measure (PRODI) builds on the
neoclassical production function with physical capital K, labor L and the level of total factor
productivity A. We assume that this aggregate production function is common across countries and
time.
Yi = A, K;a Li'a (1)
Assuming a capital share a-0.3, the productivity per capita growth rate is given by
PROD1 = GROWTH- 0.3 * CAPGROWTH (2)
This first measure of total factor productivity growth ignores human capital accumulation. Our
other two productivity measures therefore include a measure of human capital, H, in the aggregate
production function. We use the average years of schooling in the total population over 15 [Barro and
Lee 1996] as proxy for the human capital stock in the economy.
PROD2 follows Mankiw (1995) and adds human capital to an augmented neoclassical
production function of the following form:
Y, = Ai Kia HT LI-a-, (3)
'°Note, alternative measures of capital growth based on assuming an initial capital stock of zero, tend to produce similar
11
We assume a=0.3, as above and y=O.5, as Mankiw." Thus, our second measure of productivity per
capita growth, PROD2, is then given by:
PROD2 = GROWTH - 0.3 * CAPGROWTH- 0.5 * GSCHOOL (4)
where GSCHOOL is the growth rate in average years of schooling.
Our third measure of total factor productivity growth follows Hall and Jones (1998) and Bils
and Klenow (1996) in specifying the role of human capital in the aggregate production function.
Specifically, let the aggregate production function be given by
1, = Kil(AiHi)'-
where H is human capital augmented labor and A is labor-augmenting productivity. Assuming that
labor L is homogeneous within a country and that each unit has received E years of schooling, we can
write human capital augmented labor as follows:
H1 = eO(Ei) Li (6)
The function g(E) reflects the efficiency of a unit of labor with E years of schooling relative to
one with no schooling (A(0)=0) and the derivative #'(E) is the return to schooling estimated in a
Mincerian wage regression [Mincer 1974]. Following Hall and Jones (1998) and estimations by
Psacharopoulos (1994) we assume that g(E) is piecewise linear, and the following rates of retun:
13.4% for the first 4 years, 10.1% for the following 4 years and 6.8% beyond the 8 year.2
cross-country characterizations of capital growth as discussed in King and Levine (1994).
" Mankiw presents two arguments for the assumption of r-0.5. First, in the U.S. the minimum wage is about one third of
the average wage rate, which suggests that about two thirds of the labor income is return to human capital. Second, the
return to schooling is at least 9.8% and the average American has 12 years of education (Psacharopoulos 1994 and Barro
and Lee 1996), which implies that the average worker earns almost three times as much as he would without any schooling.
Again, this suggests that two thirds of labor income are return to human capital.
12These rates of return are based on averages for sub-Saharan Africa, the whole world and the OECD, respectively.
12
Solving our model for the growth rate of A, we define PROD3 as:'3
PROD3 = [GROWTH - 0.3 * CAPGROWTH - 0.7 * A(E * 0' (E))] /0.7 (7)
C Private Saving Rates
The data on private saving rates draw on a new savings database recently constructed at the
World Bank, and described in detail in Loayza, Lopez, Schmidt-Hebbel and Serven (1998). This
database improves significantly on previous data sets on savings in terms of accuracy, and both
country- and year-coverage. These data draw on national accounts data, and are checked for
consistency using individual country sources.
The private saving rate is calculated as the ratio of gross private saving to gross private
disposable income. Gross private saving is measured as the difference between gross national saving
(gross national disposable income minus consumption expenditures, both measured at current prices)
and gross public saving (the public sector is defined as the consolidated central government).14 Gross
private disposable income is measured as the difference between gross national disposable income and
gross public disposable income (sum of public saving and consumption).
Due to data availability, the private saving rate sample is slightly different from the sample
used in the analysis of real per capita GDP growth, capital per capita growth and productivity per
capita growth. Specifically, we have data available from 1971 - 1995, so that we have five non-
overlapping five-year periods for the panel data set and 25 years for the cross-country estimations.
' We get this result a follows. We first divide (5) by Li and take logs. Note that due to the assumed functional form 0(E) =
Ei*o'(E1), so that ln(H,J Li)= Ei*o'(Ei). Solving the equation for the log of productivity per capita and taking first
differences results in (7).
"4 Using a broader measure of the public sector, instead of the consolidated central government, would be analytically
preferable. This, however, limits the sample size. Nonetheless, this definition of the public sector yields very similar
results to those presented below.
13
D. Descriptive Statistics and Correlations
Table 1 presents descriptive statistics and correlations between PRIVATE CREDIT and the
different dependent variables. There is a considerable variation in financial development across
countries, ranging from a low of 4% in Zaire to a high of 141% Switzerland. GDP per capita growth
and capital per capita growth also show significant variation. Korea has the highest growth rates, both
for real per capita GDP and for capital per capita, with 7.16% and 10.5 1%, respectively. Zaire has the
lowest GDP per capita growth rate with -2.8 1%, whereas Zimbabwe has the lowest capital per capita
growth rate with -1.84%. Private saving rates also show considerable cross-country variation. Sierra
Leone has a private saving rate of 1.05%, whereas Japan's rate is 33.92%. Notably, PRIVATE
CREDIT is significantly correlated with all of our dependent variables. Also, the three productivity
growth measures have cross correlations of at least 94%.
III. Methodology
This section describes the two econometric methods that we use to control for the endogenous
determination of banking sector development with growth and the sources of growth. We first use a
traditional cross-sectional, instrumental variable estimator. As instruments, we use the legal origin of
each country to extract the exogenous component of banking sector development in the pure cross-
sectional regressions. We also use a cross-country, time-series panel of data and employ dynamic panel
techniques to estimate the relationship between financial development and growth, capital
accumulation, productivity growth, and saving rates. We describe each procedure below.
14
A. Cross-county regressions with instrumental variables
1. Legal origin and financial development
To control for potential simultaneity bias, we first use instrumental variables developed by
LLSV (1998). Legal systems with European origins can be classified into four major legal families
[Reynolds and Flores 1996]: the English common law, and the French, German, and Scandinavian civil
law countries. l5 All four families descend from the Roman law as compiled by Byzantine Emperor
Justinian in the sixth century and developed by the Glossators, Commentators, and in Canon Law
through the 13' century. The four legal families developed distinct characteristics during the last 5
centuries. In the 17' and 18' centuries the Scandinavian countries formed their own legal codes. The
Scandinavian legal systems have remained relatively unaffected from the far reaching influences of the
German and especially the French Civil Codes.
The French Civil Code was written in 1804, under the directions of Napoleon. Through
occupation, it was adopted in other European countries, such as Italy and Poland. Through its
influence on the Spanish and Portuguese legal systems, the legal French tradition spread to Latin
America. Finally, through colonization, the Napoleonic code was adopted in many African countries,
Indochina, French Guyana and the Caribbean.
The German Civil Code (Burgerliches Gesetzbuch) was completed almost a century later in
1896. The German Code exerted a big influence on Austria and Switzerland, as well as China (and
hence Taiwan), Czechoslovakia, Greece, Hungary, Italy, and Yugoslavia. Also, the German Civil
Code heavily influenced the Japanese Civil Code, which helped spread the German legal tradition to
Korea.
'5 This does not include countries with "communist" or Islamic legal systems.
15
Unlike these civil law countries, the English legal system is common law, where the laws were
primarily formed by judges trying to resolve particular cases. Through colonialism it was spread to
many African and Asian countries, Australia, New Zealand and North America.
There are two conditions under which the legal origin variables serve as appropriate
instruments for financial development. First, they have to be exogenous to economic growth during
our sample period. Second, they have to be correlated with financial intermediary development. In
terms of exogeneity, the English, French and German legal systems were spread mainly through
occupation and colonialism. Thus, we take the legal origin of a country as an exogenous
"endowment." In terms of the links between legal origin and financial intermediary development, a
growing body of evidence suggests that legal origin helps to shape financial development. LLSV
(1998) show that the legal origin of a country materially influences its legal treatment of shareholders,
the laws governing creditor rights, the efficiency of contract enforcement, and accounting standards.
Shareholders' rights enjoy greater protection in common law countries than in civil law countries,
whereas creditors' rights are best protected in German origin countries. French Civil Law countries are
comparatively weak both in terms of shareholder and credit rights. In terms of accounting standards,
French origin countries tend to have company financial statements that are comparatively less
comprehensive than countries with different legal origins. These legal, regulatory and informational
characteristics affect the operation of financial intermediaries as shown in LLSV (1997), Levine (1998,
1999), and Levine, Loayza, and Beck (1998).
16
2. Cross-country estimation
In the pure cross-sectional analysis we use data averaged for 63 countries over 1960-95, such
that there is one observation per country. 16 The basic regression takes the form:
Yj = ct +/FINANCEi + 7'Xi + ei (8)
where Y is either GROWTH, CAPGROWTH, PRODI, PROD2, PROD3 or SA VING. FINANCE
equals PRIVATE CREDIT, and LIQUID LIABILITIES or COMMERCIAL-CENTRAL BANK in the
robustness tests. Xrepresents a vector of conditioning information that controls for other factors
associated with economic growth and s is the white noise error term. 7
To examine whether cross-country variations in the exogenous component of financial
intermediary development explain cross-country variations in the rate of economic growth, the legal
origin indicators are used as instrumental variables for FINANCE. Specifically, given the vector Z of
instrumental variables and assuming that E[s] = 0, this results in a set of orthogonality
conditions E[Z'e]=0. We can use standard GMM techniques to estimate our model, which produces
instrumental variable estimators of the coefficient in (8). After computing these GMM estimates, the
standard Lagrange-Multiplier test of the overidentifying restrictions assesses whether the instrumental
variables are associated with growth beyond their ability to explain cross-country variation in financial
sector development. Under the null-hypothesis that the instruments are not correlated with the error
terms, the test is distributed X2 with (J-K) degrees of freedom, where J is the number of instruments and
K the number of regressors. The estimates are robust to heteroskedasticity.
16The cross-country sample for private saving has 61 countries over the period 1971-95.
17 to the potential nonlinear relationship between economic growth and the assortmnent of economic indicators, we use
natural logarithms of the regressors in the regressions of GROWTH, CAPGROWTH, PROD 1, PROD2 and PROD3.
17
B. Dynamic panel techniques
The cross-country estimations help us determine whether the cross-country variance in
economic growth and the other dependent variables can be explained by variance in financial
development. But we also would like to know whether changes in financial development over time
within a country have an effect on economic growth through its various channels. We also gain
degrees of freedom by adding the variability of the time-series dimension: the "within" standard
deviation of PRIVATE CREDIT in our panel data set is 15.1%, compared to a "between" standard
deviation of 28.4%, whereas for real per capita GDP growth the values are 2.4% versus 1.7%.18 So
there is a considerable additional variability to exploit.
The panel consists of data for 77 countries averaged over the period 1960-95. We average the
data over seven non-overlapping five-year periods. 19 In the following, the subscript t therefore refers
to one of these five-year periods.
1. Dynamnic panel: Econometric problems
We want to explore regressions of the following form:
yi,t =a'X! 6 X2,Pi+At+ (9)
where y represents our dependent variable, XI represents a set of lagged explanatory variables and X2 a
set of contemporaneous explanatory variables, pi is an unobserved country-specific effect, X is a time-
specific effect, s is the white-noise error term, and i and t represent country and time period,
respectively. There are two econometric problems when estimating equation (9).
18
1. The unobserved country-specific effect is part of the error term. Therefore, a possible correlation
between ,. and other explanatory variables results in biased coefficient estimates. Furthermore, if
the lagged dependent variable is included inXI, the country-specific effect is correlated with it.
Under assumptions for the country-specific effect that we explain below, we control for the
presence of correlated specific effects.
2. A subset of the explanatory variables may not be exogenous. Thus, endogeneity bias may lead to
inappropriate inferences. Specifically, we assume that all the explanatory variables are weakly
exogenous. This means that the explanatory variables are uncorrelated with future realizations of
the error term and thus are not affected by future realizations of the dependent variable. The
explanatory variables, however, may be affected by current and past realizations of the dependent
variable. This assumption allows for the possibility of simultaneity and reverse causality.
2. Dynamic panels: A GMM estimator?
Arellano and Bond (1991) propose to first-difference the regression equation to eliminate the
country-specific effect.
- 1 = a'(Xl,_- Xl,, 2) + -l(X2, - l(I ) + (6j,t- c1) (10)
This procedure solves the first econometric problem, as described above, but introduces a correlation
between the new error term n j - e-st-I and the lagged dependent variable y i,t-l -Y Yit-2 if it is included
Is The "within" standard deviation is calculated using the deviations from country averages, whereas the "between"
standard deviation is calculated from the country averages. The fact that the "between" standard deviations in the panel are
not the same as in the cross-section sample results from the different country coverage.
" The panel sample for private saving includes 72 countries and five 5-year periods between 1971 and 1995.
20 Chamberlain (1984), Holtz-Eakin, Newey and Rosen, Arellano and Bond (1991) and Arellano and Bover (1995)
proposed the General Method of Moments (GMM) estimator. The GMM estimator has been applied to cross-country
studies, by, among others, Caselli, Esquivel and Lefort (1996), Easterly, Loayza and Montiel (1997) and Fajnzylber,
Lederman and Loayza (1998).
19
inXl X1t-I -X i, t-2. To address this and the endogeneity problem, Arellano and Bond propose using the
lagged values of the levels of the variables as instruments. Under the assumptions that there is no serial
correlation in the error term E and that the explanatory variables X are weakly exogenous, we can use
the following moment conditions:
E[X,,, s * (6', - C,,,)] = 0 for s 2 2;t = 3,...,T (11)
Using these moment conditions, Arellano and Bond propose a two-step GMM estimator. In the first
step the error terms are assumed to be independent and homoskedastic across countries and over time.
In the second step, the residuals obtained in the first step are used to construct a consistent estimate of
the variance-covariance matrix, thus relaxing the assumptions of independence and homoskedasticity.
We will refer to this estimator as diference estimator.
There are several conceptual and econometric shortcomings with the difference estimator.
First, by first-differencing we loose the cross-country dimension and exploit only the time-series
dimension within countries. Second, differences of the explanatory variables are often less correlated
over time than the levels. This may produce biases estimates if the dynamic structure of the estimated
model (the differenced equation) differs from the true model as noted by Barro (1997). Third,
differencing may decrease the signal-to-noise ratio thereby exacerbate measurement error biases (see
Griliches and Hausman, 1986). Finally, Alonso-Borrego and Arellano (1996) and Blundell and Bond
(1997) show that if the lagged dependent and the explanatory variables are persistent over time, lagged
levels of these variables are weak instruments for the regressions in differences. Simulation studies
show that the difference estimator has a large finite-sample bias and poor precision, especially in
samples with a small time-series dimension.
20
To address these conceptual and econometric problems, we use an alternative estimator that
combines in a system the regressions in differences with regression in levels, as proposed by Arellano
and Bover (1995). Blundell and Bond (1997) show that this system estimator reduces the potential
biases and imprecision associated with the difference estimator. The instruments for the regressions in
differences are the same as above. For the regressions in level we use lagged differences as
instruments. The latter are valid instruments under the following assumption. Although there might be
a correlation between p. and the levels of the other explanatory variables, this correlation is constant
over time:
E[X,,,+Puj = E[Xi,,+q - pi] for allp and q (12)
Under this assumption there is no correlation between the differences of the explanatory variables and
the country-specific effect, and lagged differences are therefore valid instruments. The moment
conditions for the regressions in levels are thus2":
E[(Xi, s- Xjl-S-1)* (i,t+,+i)] = 0 for s = 1; t = 3,..., T (13)
The system thus consists of the stacked regressions in differences and levels, with the moment
conditions in (11) applied to the first part of the system, the regressions in differences, and the moment
conditions in (13) applied to the second part, the regressions in levels. As with the difference estimator,
the model is estimated in a two-step GMM procedure generating consistent and efficient coefficient
estimates.22
The consistency of the GMM estimator depends on the validity of the assumption that s does
not exhibit serial correlation and on the validity of the instruments. We use two tests proposed by
21 Given that lagged levels are used as instruments in the difference regressions, only the most recent difference is used as
instrument in the level regressions. Using additional differences would result in redundant moment conditions (see
Arellano and Bover 1995).
21
Arellano and Bond (1991) to test for these assumptions. The first is a Sargan test of over-identifying
restrictions, which tests the overall validity of the instruments by analyzing the sample analog of the
moment conditions used in the estimation procedure. Under the null-hypothesis of the validity of the
instruments this test is distributed x2 with (J-K) degrees of freedom, where J is the number of
instruments and K the number of regressors. The second test examines the assumption of no serial
correlation in the error terms. We test whether the differenced error term is second-order serially
correlated.23 Under the null-hypothesis of no second-order serial correlation, this test is distributed
standard-normal. Failure to reject the null hypotheses of both tests gives support to our model.
IV. Finance and the channels to economic growth
This section presents the results of the cross-country and panel regressions of real per capita
GDP growth, productivity per capita growth, and capital per capita growth on financial development
and a conditioning information set.
A. The conditioning information sets
To assess the strength of an independent link between financial development and the growth
variables we use various conditioning information sets. The simple conditioning information set
includes the logarithm of initial real per capita GDP to control for convergence and the average
years of schooling as indicator of the human capital stock in the economy. The policy conditioning
information set includes the simple conditioning information set plus four additional policy variables,
2We are grateflul to Stephen Bond for providing us with a program to apply his and Arellano's estimator to an unbalanced
panel data set.
22
that have been identified by the empirical growth literature as being correlated with growth
performance across countries (Barro 1991; Easterly, Loayza, and Montiel 1997). We use the inflation
rate and the ratio of government expenditure to GDP as indicators of macroeconomic stability. We
use the sum of exports and imports as share of GDP and the black market premium to capture the
degree of openness of an economy. In our sensitivity analysis for the cross-country regressions, we
will also include the number of revolutions and coups, the number of assassination per thousand
inhabitants, and a measure of ethnic diversity.24
B. Finance and Economic Growth
The results in Table 2 show a statistically and economically significant relationship between the
exogenous component of financial intermediary development and economic growth. The first two
columns report the results of the pure cross-country regressions using the simple and the policy
conditioning information set. PRIVATE CREDIT is significantly correlated with long-run growth at
the 5% significance level in both regressions and the LM-test of overidentifying restrictions indicates
that the orthogonality conditions cannot be rejected at the 5% level. Thus, we do not reject the null
hypothesis that the instruments are appropriate. The strong link between finance and growth does not
appear to be driven by simultaneity bias. The variables in the conditioning information set also have
the expected sign, except for inflation. Consistent with Boyd, Levine, and Smith (1999), we find that
inflation affects growth by influencing financial sector performance. Specifically, when we omit
PRIVATE CREDIT from the regressions in Table 2, inflation enters with a negative, statistically
23 By construction the error tern is likely to be first-order serially correlated. We cannot use the error terms from the
regression in levels since they include the country-specific effect 1i.
24 We cannot use the full conditioning information set in the panel estimations since there is not enough time series
variation in the additional three variables.
23
significant, and economically large coefficient. However, when we control for the level of banking
sector development, inflation enters positively and insignificantly.
The results are economically significant. For example, Mexico's value for PRIVATE CREDIT
over the period 1960-95 was 22.9% of GDP. An exogenous increase in PRIVATE CREDIT that had
brought it up to the sample median of 27.5% would have resulted in a 0.4 percentage point higher real
per capita GDP growth per year.25
The dynamic panel estimates also indicate that banking sector development has an
economically large causal impact on economic growth. Columns 3 and 4 in table 2 report the results of
the panel regressions. PRIVATE CREDIT is significant at the 5% level with both conditioning
information sets. The variables in the conditioning information set have significant coefficients with
the expected sign. Furthermore, our tests indicate that our econometric specification and the
assumption of no serial correlation in the white-noise error terms cannot be rejected.
C. Finance and Productivity Growth
The results in Table 3 show that banking sector development has a large, significant impact on
productivity growth. Here we use the first of our productivity growth measures, PROD1. The LM-test
for overidentifying conditions shows that the data do not reject the orthogonality conditions at the 5%
level. The variables in the conditioning information set have the expected sign except for inflation, and
initial income, openness and the schooling variable in the simple conditioning information set are
significant at the 5% level.
25 This result follows from ln(27.5) - In(22.9) = 0.18 and 0.18*2.2=0.4, where 2.2 is the smaller of the two parameter
values on PRIVATE CREDIT in the cross-country regressions.
24
The results for the panel regressions confirm the pure cross-country estimates. The strong link
between PRIVATE CREDIT and productivity growth is not due to simultaneity bias of omitted
variable bias. The p-values for the Sargan test and the serial correlation test indicate the
appropriateness of our instruments and the lack of serial correlation in e.
Table 4 shows that the impact of PRIVATE CREDIT on productivity growth is robust to the
vse of alternative measures of productivity growth. Due to the lack of sufficient schooling data, we
loose five countries when controlling for human capital accumulation. PRIVATE CREDIT is
significant across all three productivity growth measures. This holds using both the simple and the
policy conditioning information set and using both cross-country and panel estimation techniques. The
test statistics do not reject our econometric specification for the cross-country and the panel
regressions.
D. Finance and Capital Growth
The empirical relationship between banking sector development and physical capital
accumulation is less robust than the link between banking sector development and productivity growth.
The Table 5 results indicate that PRIVATE CREDIT enters significantly at the 5% level in both the
pure cross-country and the dynamic panel regressions. In the case of the cross-section estimator, we
reject the LM-test of overidentifying restrictions when using the simple conditioning information set.
However, when we expand the conditioning information set, the cross-sectional estimator passes the
specification test. Thus, PRIVATE CREDIT exhibits a strong, positive link with capital growth that
does not appear to be driven by simultaneity bias. Nevertheless, other measures of banking sector
development do not produce the same results. In the pure cross-section results, none of the other
measures of financial sector development enjoy a significant link with capital growth as we discuss
25
below in the subsection on sensitivity results.26 The panel results are more robust. Banking sector
development is positively and significantly correlated with capital accumulation when using alternative
conditioning information sets and alternative measures of banking sector development. The test
statistic for serial correlation, however, rejects the null hypothesis of no serial correlation at the 5%
level when using the simple conditioning information set and at the 10% level when using the policy
information set. By including the private saving rate or lagged values of capital growth in the
conditioning information set, however, we (i) eliminate the serial correlation, (ii) find a positive impact
of banking sector development on physical capital growth, and (iii) obtain very similar coefficient
estimates to those reported in Table 5.27 The difference between the panel and cross-country results
may reflect data frequency. While the long-run relationship between capital accumulation and banking
development is not robust to alternations in different measures of banking development, the short-term
relationship - which may reflect business cycle activity - is positive and robust.
E. Sensitivity Analyses
Table 6 presents the coefficients on all three measures of financial development in cross-
country and panel regressions with real per capita GDP growth as dependent variable, using both
conditioning informnation sets. The coefficient estimates for both LIQUID LIABILITIES and
COMMERCIAL-CENTRAL BANK are significantly positive across both samples and both
conditioning information sets. All regressions pass the different specification tests. We also ran the
regressions with the full conditioning information set in the cross-country sample with similar results.
26 For completeness, we can get the other measures of financial sector development to enter with positive and significant
coefficients in the capital growth equations by using alternative conditioning information sets. But, the other measures are
not significant when using the simple or policy conditioning information sets.
27 The results are available on request. We do not include the results here because we wanted to keep a uniform set of
control variables across the growth and sources of growth equations. Since the resulting coefficient estimates are of similar
magnitude and significance, we merely want to make the point that the serial correlation reflected in Table 5 is not biasing
26
These results strengthen the hypothesis of a statistically and economically significant causal impact of
the exogenous component of financial development on economic growth.
Table 7 presents the coefficient estimates for the regressions with the three different measures
of productivity growth. The significant, positive coefficient estimates for our two alternative indicators
of financial development confirm our previous results. The results with the full conditioning
information set yields similar results as the policy conditioning information set.
Table 8 presents the corresponding results for capital per capita growth. Unlike PRIVATE
CREDIT, LIQUID LIABILITIES and COMMERCIAL-CENTRAL BANK do not seem to have a
significant impact on capital per capita growth in the cross-country sample. In the panel estimations,
both financial intermediary development indicators exert a causal impact on capital per capita growth.
But, only in the regressions with COMMERCIAL-CENTRAL BANK is the null hypothesis of no
serial correlation in the error term not rejected. Thus, while evidence suggests that banking sector
development positively influences physical capital accumulation, the pure, cross-sectional relationship
between physical capital growth and banking sector development is highly dependent on the measure
of banking sector development used.
V. Finance and Private Saving
This section explores the impact of the exogenous component of financial development on
private saving rates. As in the previous section, we will use both cross-country and panel samples, but
a different set of conditioning information.
the results in a meaningful way.
27
A. The conditioning information set
The set of conditioning information is determined by the different theories of consumption. The
permanent income hypothesis focuses on an infinitely lived representative agent, whereas the life-cycle
hypothesis derives its predictions from an overlapping generation model. We can distinguish between
two groups of determinants: income-related and demographic determinants.
Both income level and income growth (measured by the gross private disposable income) have
ambiguous signs in saving functions, depending on whether it is permanent or temporary income and
whether the increase takes place within a generation or across generations. The same argumentation
holds for the terms of trade, which can be considered an exogenous determinant of income. A higher
real interest rate has a negative substitution and a positive income effect on consumption, which
again results in an ambiguous sign. We include the inflation rate as a proxy for uncertainty, expecting
a positive correlation between saving and the inflation rate.
We include several demographic variables, as the old-age and young-age population
dependency ratios (defined as ratio of the population under 15 to the total population and ratio of
population over 65 to the total population, respectively) and the share of urban population in total
population. Including the dependency ratios helps to discriminate between the life-cycle and the
permanent income hypothesis. The life-cycle hypothesis predicts a negative sign, whereas the
permanent income hypothesis predicts insignificance. Since agents engaged in agricultural activities
face a higher income uncertainty, economies with a higher urban population share should have lower
private savings rates. The opposite effect, however, might result, if urban consumers have easier and
better access to financial intermediaries.
28
Finally we include the share of government savings in GPDI, to test for the validity of the
Ricardian equivalence theorem. A coefficient estimate of minus 1 would provide evidence in favor of
the Ricardian hypothesis.
B. Regressions Results
The results in tables 9 and 10 do not suggest that banking sector development exerts a strong,
positive effect on private saving rates. Whereas the coefficient on PRIVATE CREDIT is significantly
positive in the cross-country regression, it is insignificant in the panel regression. The results for the
cross-country regression indicate a small positive effect of financial development on private saving
rates. The LM-Test of overidentifying restrictions indicates that the orthogonality conditions cannot be
rejected at the 5% level and that the instruments are therefore appropriate.
The panel estimations, however, indicate an insignificant impact of PRIVATE CREDIT on
private savings rates. The econometric specification tests indicate that we cannot reject the null
hypotheses of the appropriateness of the instruments and the assumption of no serial correlation of the
differenced error terms. The results seem to indicate that there is a small long-run, but not a short-run
impact of financial intermediary development on private saving rates. As shown in Table 10,
alternative measures of banking sector development do not clarify the ambiguous relationship between
financial development and private savings.
29
VI. Conclusions
This paper examined the causal impact of financial development on growth and its sources. We
use two econometric methods. To assess the long-run impact of the exogenous component of financial
development on economic growth and its sources we use a cross-country sample with data averaged
over the period 1960-95 and the legal origin of countries as instruments. To exploit the time-series
nature of the data, we create a panel data set and use recent dynamic panel techniques as proposed by
Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and Bond (1997). This procedure
controls for the country-specific effect and the possible endogeneity of the regressors.
We find a significantly positive causal impact of financial development on real per capita
growth and productivity per capita growth. This result is robust to the use of different estimation
procedures, conditioning information sets, and indicators of financial development. The picture is
more ambiguous in regressions with physical capital per capita growth and saving. While there tends
be a positive link between banking sector development and both physical capital accumulation and
private saving rates, these results are sensitive to alterations in estimation techniques and measures of
banking sector development. This paper's results support the view that better functioning banks
improve resource allocation and accelerate total factor productivity growth with positive repercussions
for long-run economic growth.
30
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34
Data Appendix
A. Countries in the Sample
I in the 63 country cross-country sample for GDP, capital and productivity growth
2 in the 77 country panel sample for GDP, capital and productivity growth
3 in the 72 country panel sample for alternative productivity measures
4 in the 61 country cross-country sample for private saving
5 in the 72 country panel sample for private saving
Algeria (2,3) Guatemala (1-5) Nigeria (4,5)
Argentina (1,2,3) Guyana (1-3) Norway (1-5)
Australia (1-5) Haiti (1-3) Pakistan (1-5)
Austria (1-5) Honduras (1-5) Panama (1-3)
Bahamas (5) Iceland (4,5) Papua New Guinea (1-5)
Bangladesh (4,5) India (1-5) Paraguay (1-3)
Belgium (1-5) Indonesia (2,4,5) Peru (1-4)
Belize (5) Iran (2,3) Philippines (1-5)
Bolivia (1-3) Ireland (1-5) Portugal (1-5)
Brazil (1-3) Israel (1-3) Rwanda (2-5)
Cameroon (2,3,5) Italy (1-5) Senegal (1-5)
Canada (1-5) Jamaica (1-5) Sierra Leone (2-5)
Central African Republic (2,5) Japan (1-5) South Africa (1-5)
Chile (1-5) Jordan (5) Spain (1-5)
Colombia (1-5) Kenya (1-5) Sri Lanka (1-5)
Congo (2,3) Korea (1-5) Sudan (2,3)
Costa Rica (1-5) Lesotho (2,5) Swaziland (5)
Cote d"Ivoire (5) Luxembourg (5) Sweden (1-5)
Cyprus (1-5) Madagascar (4,5) Switzerland (1-5)
Denmark (1-5) Malawi (2,4,5) Syria (2-5)
Dominican Republic (1-3) Malaysia (1-5) Taiwan (1)
Ecuador (1-5) Malta (1-4) Thailand (1-5)
Egypt (2-5) Mauritius (1,2,4,5) Togo (1-5)
El Salvador (1-3,5) Mexico (1-4) Trinidad and Tobago (1-5)
Ethiopia (5) Morocco (5) United States of America (1-5)
Finland (1-5) Myanmar (4,5) Uruguay (1-4)
France (1-5) Nepal (5) Venezuela (1-5)
Gambia (2-5) Netherlands (1-5) Zaire (1-3)
Germany (1-3,5) New Zealand (1-5) Zimbabwe (1-3,5)
Ghana (1-5) Nicaragua (2,3)
Great Britain (1-5) Niger (1-5)
Greece (1-5)
35
B. Data Sources
The first ten variables are from Loayza, Lopez, Schmidt-Hebbel, and Serven (1998). These numbers
are revised and cross-checked National Account data.
1. Log level and growth rate of per capita GDP.
2. Log level and growth rate of per capita gross private disposable income (GPDI).
3. Private saving rates is the ratio of gross private savings and GPDI. Gross private saving is measured
as the difference between gross national saving (gross national product minus consumption
expenditure, both measured at current prices) and gross public saving. GPDI is measured likewise as
the difference between gross national disposable income (GNDI) and gross public disposable income
(sum of public saving and consumption).
4. Government size is real general government consumption as share of real GDP.
5. Openness to trade is the sum of real exports and real imports of goods and nonfinancial services as
share of real GDP.
6. Government saving is the ratio of gross public saving and gross private disposable income.
7. Real interest rate is defined as ln[(1+i)/I+lr)], where i is the nominal interest rate and X the inflation
rate. The inflation rate is the average of the current and year-ahead inflation.
8. Terms of Trade
9. Old and young dependency ratios are the shares of population under 15 and over 65, respectively, in
total population.
10. Urbanization ratio
11. Inflation rates are calculated using average annual CPI data from the International Financial
Statistics (IFS), line 64.
12. The average years of secondary schooling in the total population (15 years and over) come from
Barro and Lee (1996): They are for the initial year of the period
13. Data on the black market premium are from World's Currency Yearbook; and Adrian Wood,
Global trends in real exchange rates: 1960-84, WB Discussion paper no. 35. 1988.
The numbers are averages over the 5-year periods.
14. Data on Private Credit are calculated using IFS numbers and the following method:
{ (0. 5)* [F(t)/P_e(t) + F(t- 1 )/P_e(t- 1)] }/[GDP(t)/P_a(t)]
36
where F is credit by deposit money banks and other financial institutions to the private sector (lines
22d + 42d). If there are no data on 42d, we assume that the value is zero. GDP is line 99b, P_e is end-
of period CPI (line 64) and P_a is the average annual CPI.
15. Data on Liquid Liabilities are calculated using IFS numbers and the following method:
{(0.5)* [F(t)/P_e(t) + F(t- 1 )/P_e(t-1 )] }/[GDP(t)/P_a(t)]
where F is liquid liabilities (line 551) or money plus quasi money (line 351), if liquid liabilities is not
available. If neither liquid liabilities nor money plus quasi money are available, we use time and
savings deposits (line 25). GDP is line 99b, P_e is end-of period CPI (line 64) and P_a is the average
annual CPI. The numbers are averages over the 5 year periods.
16. Data on Commercial versus Central Bank are calculated using IFS numbers, using the following
method:
DBA(t) / (DBA(t) + CBA(t))
where DBA is assets of deposit money banks (lines 22a-d) and CBA is central bank assets (lines 12 a-
d).
Table 1: Summary Statistics: 1960-1995
Descriptive Statistics
Private Economic Capital Productivity Productivity Productivity Private
Credit Growth Growth Growth 1 Growth 2 Growth 3 Saving
Mean 40.15 1.95 3.13 1.01 0.13 0.74 19.21
Median 27.51 1.98 3.11 1.15 0.44 0.99 19.98
Maximum 141.29 7.16 10.51 5.14 4.87 7.01 33.91
Minimum 4.08 -2.81 -1.84 -3.39 -5.74 -5.59 1.05
Std. Dev. 29.16 1.92 2.22 1.52 1.82 2.11 7.65
Observations 63 63 63 63 63 63 61
Correlations
Private Economic Capital Productivity Productivity Productivity
Credit Growth Growth Growth 1 Growth 2 Growth 3
Private Credit 1.00
Economic Growth 0.43 1.00
(0.001)
Capital Growth 0.34 0.71 1.00
(0.006) (0.001)
Productivity Growth 1 0.39 0.95 0.46 1.00
(0.002) (0.001) (0.001)
Productivity Growth 2 0.44 0.87 0.36 0.94 1.00
(0.001) (0.001) (0.004) (0.001)
Productivity Growth 3 0.38 0.92 0.39 0.99 0.96 1.00
(0.002) (0.001) (0.002) (0.001) (0.001)
Private Saving 0.74
(0.001)
p-values are reported in parentheses
Productivity Growth 1 = output per capita growth - 0.3 * capital per capita growth
Productivity Growth 2 = output per capita growth - 0.3 * capital per capita growth - 0.5 * growth in average years of schooling
Productivity Growth 3 = [output per capita growth - 0.3 * capital per capita growth - 0.7 * change in (average years of schooling * return on schooling)] /0.7
Statistics for national saving and its correlation with measures of financial development are from a different sample
Table 2: Financial Intermediation and Growth
Cross-country Panel
Conditioning information set simple policy simple policy
Constant 6.571 2.643 1.272 0.082
(0.006) (0.527) (0.250) (0.875)
Initial income per capital -1.971 -1.967 -1.299 -0.496
(0.001) (0.001) (0.001) (0.001)
Average years of schooling2 1.936 1.548 2.671 0.950
(0.008) (0.078) (0.001) (0.001)
Openness to trade1 0.931 1.311
(0.042) (0.001)
Inflation2 4.270 0.181
(0.096) (0.475)
Government size' -1.207 -1.445
(0.132) (0.001)
Black market premium2 -0.139 -1.192
(0.914) (0.001)
Private Credit' 2.215 3.215 2.397 1.443
(0.003) (0.012) (0.001) (0.001)
LM-Test3 0.577 0.571
Sargan test4 (p-value) 0.183 0.506
Serial correlation test5 (p-value) 0.516 0.803
Countries 63 63 77 77
Observations 365 365
p-values in parentheses
1 In the regression, this variable is included as log(variable)
2 In the regression, this variable is included as log(l + variable)
3 The null hypothesis is that the instruments used are not correlated with the residuals.
Critical values for LM-Test ( 2 d.f. ): 1 0%=4.61; 5%=5.99
4 The null hypothesis is that the instruments used are not correlated with the residuals.
5 The null hypothesis is that the errors in the first-difference regression exhibit
no second-order serial correlation.
Table 3: Financial Intermediation and Productivity Growth
Cross-country Panel
Conditioning information set simple policy simple policy
Constant 3.527 -1.189 2.473 -1.611
(0.065) (0.717) (0.001) (0.033)
Initial income per capita1 -1.266 -1.171 -1.244 -0.353
(0.001) (0.001) (0.001) (0.001)
Average years of schooling2 1.375 1.241 3.043 1.174
(0.028) (0.060) (0.001) (0.001)
Openness to trade' 0.956 1.337
(0.015) (0.001)
Inflation2 3.223 -0.415
(0.096) (0.033)
Government size' -0.647 -0.431
(0.286) (0.088)
Black market premium2 -0.191 -1.003
(0.861) (0.001)
Private Credit1 1.500 1.986 1.332 0.297
(0.004) (0.021) (0.001) (0.001)
LM-Test3 2.036 3.472
Sargan test4 (p-value) 0.205 0.401
Serial correlation test5 (p-value) 0.772 0.865
Countries 63 63 77 77
Observations 365 365
p-values in parentheses
In the regression, this variable is included as log(variable)
2 In the regression, this variable is included as log(1 + variable)
The null hypothesis is that the instruments used are not correlated with the residuals.
Critical values for LM-Test ( 2 d.f. ): 10%=4.61; 5%=5.99
4 The null hypothesis is that the instruments used are not correlated with the residuals.
5 The null hypothesis is that the errors in the first-difference regression exhibit
no second-order serial correlation.
Table 4: Financial Intermediation and Different Measures of Productivity Growth
Panel
Conditioning information set PROD 1 PROD 2 PROD 3 Number of Number of
coefficient p-value coefficient p-value coefficient p-value countries observations
simple 1.177 0.001 1.616 0.001 1.992 0.001 72 294
policy 0.340 0.016 0.675 0.001 0.831 0.004 72 294
Cross-country
Conditioning information set PROD I PROD 2 PROD 3 Number of
coefficient p-value coefficient p-value coefficient p-value countries
simple 1.500 0.004 0.998 0.020 1.615 0.010 63
policy 1.986 0.021 1.271 0.036 1.907 0.038 63
PROD 1 = output per capita growth - 0.3 * capital per capita growth
PROD 2 = output per capita growth - 0.3 * capital per capita growth - 0.5 * growth in average years of schooling
PROD 3 = [output per capita growth - 0.3 * capital per capita growth - 0.7 * change in (average years of schooling * return on schooling)] / 0.7
simple conditioning information set: logarithm of initial income per capita, average years of schooling
policy conditioning information set: simple set plus government size, openness to trade, inflation, black market premium
Table 5: Financial Intermediation and Capital Growth
Cross-country Panel
Conditioning information set simple policy simple policy
Constant 8.448 8.349 -1.273 5.694
(0.004) (0.093) (0.219) (0.001)
Initial income per capital -2.075 -2.225 -0.933 -0.070
(0.001) (0.001) (0.001) (0.701)
Average years of schooling2 0.663 0.628 0.985 -0.340
(0.439) (0.559) (0.055) (0.552)
Openness to trade1 0.245 -0.448
(0.663) (0.097)
Inflation2 4.196 0.445
(0.236) (0.360)
Government size1 -1.619 -3.229
(0.082) (0.001)
Black market premium2 0.304 -0.748
(0.826) (0.001)
Private Credit1 2.832 4.038 3.435 3.005
(0.006) (0.012) (0.001) (0.001)
LM-Test3 6.747 3.039
Sargan test4 (p-value) 0.166 0.316
Serial correlation teste (p-value) 0.014 0.053
Countries 63 63 77 77
Observations 365 365
p-values in parentheses
I'n the regression, this variable is included as log(variable)
2 In the regression, this variable is included as log(1 + variable)
3 The null hypothesis is that the instruments used are not correlated with the residuals.
Critical values for LM-Test ( 2 d.f. ): 10%=4.61; 5%=5.99
4 The null hypothesis is that the instruments used are not correlated with the residuals.
The null hypothesis is that the errors in the first-difference regression exhibit
no second-order serial correlation.
Table 6: Financial Intermediation and Growth
Cross-country
Regression Set 1: simple conditoning information set
Financial variable coefficient p-value LM-test' Number of
countries
LIQUID LIABILITIES 1.667 0.023 1.575 63
COMMERCIAL - CENTRAL BANK 10.169 0.001 1.386 63
PRIVATE CREDIT 2.204 0.003 0.567 63
Regression Set 2: policy conditioning information set
Financial variable coefficient p-value LM-test1 Number of
countries
LIQUID LIABILITIES 2.173 0.020 2.394 63
COMMERCIAL - CENTRAL BANK 9.641 0.021 2.331 63
PRIVATE CREDIT 3.222 0.012 0.504 63
The null hypothesis is that the instruments used are not correlated with the residuals from the respective regression
Critical values for LM-Test (2 df.): 10%= 4.61; 5%= 5.99
Panel
Regression Set 1: simple conditoning information set
Financial variable coefficient p-value Sargan-test 2nd order serial Number of
(p-value)2 corr. test (p-value)3 observations
LIQUID LIABILITIES 2.093 0.001 0.227 0.522 365
COMMERCIAL - CENTRAL BANK 4.763 0.001 0.246 0.712 365
PRIVATE CREDIT 2.364 0.001 0.180 0.521 365
Regression Set 2: policy conditoning information set
Financial variable coefficient p-value Sargan-test 2nd order serial Number of
(p-value)2 corr. test (p-value)3 observations
LIQUID LIABILITIES 2.321 0.001 0.607 0.722 365
COMMERCIAL - CENTRAL BANK 3.361 0.001 0.390 0.958 365
PRIVATE CREDIT 1.420 0.001 0.506 0.808 365
2 The null hypothesis is that the instruments used are not correlated with the residuals from the respective regression
3The null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correbtion
simple conditioning information set: logarithm of initial income per capita, average years of secondary schooling
policy conditioning information set: simple set plus govemment size, openness to trade, inflation and black market premium
LIQUID LIABILITIES: liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and
nonbank financial intermediaries) divided by GDP
COMMERCIAL - CENTRAL BANK: assets of deposi money banks divided by assets of deposit money banks plus centrai bank assets
PRIVATE CREDIT: credit by deposit money banks and other financial instRtutions to the private sector divided by GDP
Table 7: Financial Intermediation and Different Measures of Productivity Growth
Cross-country
Regression Set 1: simple conditioning information set
Financial variable PROD 1 PROD 2 PROD 3 Number of
coefficient p-value coefficient p-value coefficient p-value countries
LIQUID LIABILITIES 1.787 0.002 1.416 0.010 2.269 0.003 63
COMMERCIAL - CENTRAL BANK 5.853 0.001 4.062 0.006 6.096 0.002 63
PRIVATE CREDIT 1.504 0.004 1.003 0.020 1.624 0.009 63
Regression Set 2: policy conditioning information set
Financial variable PROD I PROD 2 PROD 3 Number of
coefficient p-value coefficient p-value coefficient p-value countres
LIQUID LIABILITIES 2.168 0.006 1.869 0.013 2.395 0.012 63
COMMERCIAL - CENTRAL BANK 8.134 0.006 6.163 0.009 8.432 0.006 63
PRIVATE CREDIT 2.010 0.021 1.296 0.036 1.935 0.036 63
Panel
Regression Set 1: simple conditioning informnation set
Financial variable PROD 1 PROD 2 PROD 3 Number of Number of
coefficient p-value coefficient p-value coefficient p-value countries observations
LIQUID LIABILITIES 0.775 0.001 0.773 0.001 1.055 0.001 72 294
COMMERCIAL - CENTRAL BANK 1.733 0.001 2.120 0.001 2.918 0.001 72 294
PRIVATE CREDIT 1.171 0.001 1.611 0.001 1.985 0.001 72 294
Regression Set 2: policy conditioning information set
Financial variable PROD 1 PROD 2 PROD 3 Number of Number of
coefficient p-value coefficient p-value coefficient p-value countries observations
LIQUID LIABILITIES 0.570 0.032 0.937 0.001 0.883 0.007 72 294
COMMERCIAL - CENTRAL BANK 0.514 0.222 1.257 0.001 1.308 0.023 72 294
PRIVATE CREDIT 0.395 0.018 0.668 0.001 0.821 0.004 72 294
PROD 1 = output per capita growth - 0.3 * capital per capita growth
PROD 2 = output per capita growth - 0.3 * capital per capita growth - 0.5 * growth in average years of schooling
PROD 3 = [output per capita growth - 0.3 * capital per capita growth - 0.7 * change in (average years of schooling * return on schooling)] /0.7
LIQUID LIABILITIES: liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and
nonbank financial intermediaries) divided by GDP
COMMERCIAL - CENTRAL BANK: assets of deposR money banks divided by assets of deposit money banks plus central bank assets
PRIVATE CREDIT: credit by deposit money banks and other financial institutions to the private sector divided by GDP
Table 8: Financial Intermediation and Capital Growth
Cross-country
Regression Set 1: simple conditioning information set
Financial variable coefficient p-value LM-test' Number of
countries
LIQUID LIABILITIES -0.345 0.767 4.662 63
COMMERCIAL - CENTRAL BANK -1.046 0.832 4.599 63
PRIVATE CREDIT 2.779 0.007 6.867 63
Regression Set 2: policy conditioning information set
Financial variable coefficient p-value LM-test1 Number of
countries
LIQUID LIABILITIES 0.511 0.562 4.599 63
COMMERCIAL - CENTRAL BANK 1.018 0.755 4.725 63
PRIVATE CREDIT 3.979 0.013 3.15 63
The null hypothesis is that the instruments used are not correlated with the residuals from the respective regression
Critical values for LM-Test (2 d.f.): 10%= 4.61; 5%= 5.99
Panel
Regression Set 1: simple conditioning information set
Financial variable coefficient p-value Sargan-test 2nd order serial Number of Number of
(p-value)2 corr. test (p-value)3 countries observations
LIQUID LIABILITIES 3.666 0.001 0.192 0.031 77 365
COMMERCIAL- CENTRAL BANK 8.848 0.001 0.258 0.172 77 365
PRIVATE CREDIT 3.438 0.001 0.163 0.014 77 365
Regression Set 2: policy conditioning information set
Financial variable coefficient p-value Sargan-test 2nd order serial Number of Number of
(p-value)2 corr. test (p-value)3 countries observations
LIQUID LIABILITIES 5.162 0.001 0.494 0.076 77 365
COMMERCIAL - CENTRAL BANK 6.493 0.001 0.338 0.169 77 365
PRIVATE CREDIT 3.018 0.001 0.318 0.051 77 365
2 The null hypothesis is that the instruments used are not correlated wiff the residuals from the respective regression
3 The null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correlation
simple conditioning information set logarithm of initial income per capita, average years of secondary schooling
policy conditioning information set: simple set plus govemment size, openness to trade, inflation and black market premium
LIQUID LIABILITIES: liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and
nonbank financial intermediaries) divided by GDP
COMMERCIAL - CENTRAL BANK assets of deposit money banks divided by assets of deposit money banks plus central bank assets
PRIVATE CREDIT: credit by deposit money banks and other financial institutions to the private sector divided by GDP
Table 9: Financial Intermediation and Private Saving
Cross-country Panel
Constant -0.102 0.474
(0.387) (0.001)
Real per capita GPDI' 0.041 0.000
(0.005) (0.992)
Growth rate of real per capita GPDI 1.378 0.531
(0.001) (0.001)
Real interest rate2 0.172 -0.101
(0.282) (0.130)
Terms of trade' -0.024 -0.029
(0.534) (0.094)
Old dependency ratio -0.313 -0.940
(0.170) (0.001)
Young dependency ratio 0.012 -0.300
(0.884) (0.001)
Urbanization ratio -0.073 0.107
(0.054) (0.010)
Government Saving / GPDI -0.129 -0.273
(0.527) (0.001)
Inflation2 0.039 -0.327
(0.733) (0.001)
Private Credit 0.085 0.021
(0.027) (0.224)
LM-Test3 0.708
Sargan test4 (p-value) 0.311
Serial correlation test5 (p-value) 0.335
Countries 61 72
Observations 247
p-values in parentheses
In the regression, this variable is included as log(variable)
2 In the regression, this variable is included as log(1 + variable)
3 The null hypothesis is that the instruments used are not correlated with the residuals.
Critical values for LM-Test ( 2 d.f. ): 10%=4.61; 5%=5.99
4 The null hypothesis is that the instruments used are not correlated with the residuals.
5 The null hypothesis is that the errors in the first-difference regression exhibit
no second-order serial correlation.
Table 10: Financial Intermediation and Private Saving: Altemative Measures
Cross-country
Financial variable coefficient p-value LM-test1 Number of
countries
LIQUID LIABILITIES 0.075 0.102 3.106 61
COMMERCIAL - CENTRAL BANK 0.896 0.338 1.370 61
PRIVATE CREDIT 0.085 0.027 0,708 61
The null hypothesis is that the instruments used are not correlated with the residuals from the respective regression
Critical values for LM-Test (2 df.): 1 0%= 4.61; 5%= 5.99
Panel
Financial variable coefficient p-value Sargan-test 2nd order serial Number of Number of
(p-value)2 corr. test (p-value) countries observations
LIQUID LIABILITIES -0.012 0.588 0.631 0.286 72 247
COMMERCIAL - CENTRAL BANK 0.154 0.001 0.363 0.340 72 247
PRIVATE CREDIT 0.021 0.224 0.311 0.335 72 247
2The null hypothesis is that the instruments used are not correlated with the residuals from the respective regression
3The null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correlation
conditioning information set: logarithm of real per capita GPDI, the growth rate of real per capita GPDI, the real interest rate,
the log of terms of trade, the old and the young dependency ratios, the urbanization rate, the inflation rate and the ratio of government saving to GPDI
LIQUID LIABILITIES: liquid liabilifies of the financial system (currency plus demand and interest-bearing liabilfites of banks and
nonbank financial intermediaries) divided by GDP
COMMERCIAL - CENTRAL BANK: assets of deposit money banks divided by assets of deposit money banks plus central bank assets
PRIVATE CREDIT: credit by deposit money banks and other financial institutions to the private sector divided by GDP
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