The Maturity Structure of Bank Credit:
Determinants and Effects on Economic Growth
National Bank of Serbia
Georgia State University
We investigate a new data set on the maturity of bank credit to the private sector in 74
countries. We show that credit maturity is longer in countries with strong institutions, low
inflation, large financial markets, and where banks share information about borrowers.
Furthermore, we extend the finance and growth literature by showing that credit maturity
matters for economic growth. Economic growth is enhanced in countries where agents
have access to long-term financing. Therefore, weak institutions, high inflation and other
variables that reduce credit maturity have an impact on economic growth via their
influence on credit maturity. The estimated effects are substantial in size.
Key words: financial development, economic growth, credit maturity, liquidity
JEL Classification: G21, O40, O16, O43
We would like to thank Thorsten Beck, Shiferaw Gurmu, Yuriy Kitsul, Vassil Mihov, Felix
Rioja, Sally Wallace, and the participants at the SEA conference in New Orleans for their helpful
comments and suggestions. Tasić: Monetary Policy and Strategy Division, Economic Analyses
and Research Department, National Bank of Serbia. E-mail: firstname.lastname@example.org. Valev:
Department of Economics, Andrew Young School of Policy Studies, Georgia State University,
Atlanta, GA 30303. Telephone: +1-404-413-0162. E-mail: email@example.com. The views expressed
in this paper are those of the authors, and do not necessarily represent the views of the National
Bank of Serbia.
The Maturity Structure of Bank Credit:
Determinants and Effects on Economic Growth
The literature on financial development and economic growth has established that
finance has a positive, statistically significant, and economically large causal effect on
economic growth (Levine 2005). There is, however, much less empirical evidence on the
channels through which this positive effect is obtained. Levine (2005) points out that
even “the organization of the empirical evidence advertises an important weakness in the
finance and growth literature: there is frequently an insufficiently precise link between
theory and measurement. Theory focuses on particular functions provided by the
financial sector, [while the empirical literature] pertains to the proxies for financial
Transforming liquid savings into illiquid assets that can fund long-term
investment projects is one of the important functions of the financial system. Levine
(1997) explains that economic growth is closely linked to the maturity transformation
function of the financial system, as high-return projects require a long commitment of
capital but savers do not like to relinquish control of their savings for long periods. The
financial system plays a key role in preserving the liquidity of savings of individual
savers while investing a portion of the funds into illiquid long-term projects. Historical
evidence supports this claim. According to Hicks (1969), the capital market
improvements that mitigated liquidity risks were the primary cause of England’s
industrial revolution as individual investors could hold liquid assets but at the same time
the financial system transformed these liquid financial instruments into long-term capital
investments. As England’s industrial revolution required large commitments of capital
for long periods, Levine (1997) goes as far as noting that the industrial revolution may
not have occurred without this liquidity transformation.
Our objective is to provide empirical evidence for that function of the financial
system. For that purpose we collect and analyze a unique data set on the maturity of
domestic bank credit to the private sector in 74 countries during the period from about
1990 to 2005. We ask two broad questions. First, what factors determine the differences
in credit maturity across countries? For example, only 24 percent of domestic private
credit in Mali has maturity longer than 1 year, whereas in Hungary 75 percent of credit
has maturity longer than 1 year. What explains that difference? Second, we investigate
whether credit maturity has an effect on economic growth.
Bencivenga and Smith (1991) develop an insightful model that formalizes the
relationship between the maturity transformation role of banks and economic growth.
There are two savings assets in the model: a liquid asset that matures early but returns
less of the consumption good and an illiquid asset that has a higher (but later) payoff.1 If
liquidated before it matures, the illiquid asset returns less than the liquid asset. Following
Diamond and Dybvig (1983), individuals are uncertain about their future liquidity needs
at the time they make capital allocation decisions and therefore they invest most of their
savings into the liquid low-return asset. Financial institutions emerge as groups of
individuals who pool their savings, keep a portion of the pooled savings in liquid assets to
meet the liquidity needs of their members, and invest the remaining amount in illiquid
1 The higher return on the illiquid asset captures the idea of the slow production cycle of high productivity
investments, as well as the long gestation periods in capital production as discussed by Böhm-Bawerk
(1891), Cameron (1967), and Kydland and Prescott (1982).
high-return project. By the law of large numbers, financial institutions can predict the
aggregate demand for liquidity across their members and, therefore, they invest a smaller
fraction of the savings in the liquid asset compared to the individual investors. As a
result, the proportion of society’s savings that are invested in projects with high
productivity increases and this enhances economic growth.
In Bencivenga and Smith, economic growth increases in the proportion of savings
invested in long-term assets.2 We provide empirical evidence in support of this
hypothesis. We show that, holding constant the level of credit, longer credit maturity
enhances economic growth. Our empirical evidence fits well with papers showing that
the effect of finance on growth depends on the economic and institutional environment of
a country. For example, Rousseau and Wachtel (2002), Choi, Smith, and Boyd (1996),
Haslag and Koo (1999), Khan and Senhadji (2000), and Boyd, Levine and Smith (2001)
show that the effect of credit on growth is diminished in high inflation countries. It is,
however, not clear what function of the financial system is blocked in high inflation
environments. Our results suggest that credit has a smaller effect on growth (at least
partly) because the financial system shifts resources toward short-term, less productive
Before we present that evidence and in order to become more familiar with credit
maturity, we investigate its determinants by testing a number of empirical hypotheses
2 The notion that long-term lending enhances growth is not universally accepted. Sissoko (2006) combines
the monetary and the financial role of intermediaries into a growth model where agents can buy and sell a
cash-in-advance constraint. This gives rise to growth enhancing short-term credit, but the author does not
test this prediction for lack of data on credit maturity. Also, in Flannery (1986) firms that are not concerned
about reevaluation by the credit markets (good firms) will borrow short-term, while firms that fear
reevaluation (bad firms) will want to borrow long-term. Therefore, short-term credit could have a positive
effect on growth as more short-term credit implies more efficient investments. However, the more realistic
setting of Titman (1992) with uncertain interest rate and financial distress costs motivates good firms to use
long-term credit despite the lower contractual cost on short-term debt. Diamond (1991) also shows that
good firms borrow short- and long-term to extract the benefits of good news while lowering liquidity risk.
drawn from the literature. The data show that credit maturity varies substantially across
countries, even if the countries have a similar level of financial and economic
development. We show that credit maturity is shorter in countries with lax rule of law,
high inflation, less developed financial markets, and greater economic volatility.
The rest of the paper is structured as follows. We describe the data in the
following section. Section 3 draws empirical hypotheses from the literature and
investigates the determinants of credit maturity. Section 4 present results for the effect of
credit maturity on economic growth and Section 5 concludes.
2. Data on credit maturity
We use data on lending by banks to the private sector in 74 countries spanning the
period from about 1990 to 2005, depending on data availability for the individual
countries. The data were collected from a variety of sources including publications by
central banks and multilateral organizations. Table 1 provides variable definitions and
details the sources of the data. The sample includes all countries for which we could
identify a consistent data source. The summary statistics of our private credit variable,
shown in Table 2, match closely those from the widely used World Bank data set on
financial structure (see Beck, Demirgüç-Kunt, and Levine 2000) for the entire sample and
for each individual country. However, because our sample spans only more recent years,
the summary statistics reveal a higher level of financial development compared to the
World Bank data that begin in 1960.
Credit is decomposed into two categories: short-term credit that has contractual
maturity of one year or less and long-term credit that has contractual maturity longer than
one year. Some countries, most notably many of the transition economies, provide more
detailed data on credit maturity – up to one year, one to five years and longer than 5
years. Some countries report maturity longer than 7 or even 15 years. While it would be
interesting to investigate credit with different maturity structures (e.g. medium-term,
long-term, and “very long-term” credit), the only categorization that is consistent across
all countries is the one that divides credit into short-term credit with maturity of one year
or less and other credits. Therefore, we proceed with this definition of short-term and
long-term debt but we also explore other maturity structures in a parallel paper with a
Table 3 shows large differences in terms of financial development measured as
private credit as percent of GDP. For example, in Albania, Azerbaijan, Chad and several
other countries, private credit is below 10 percent of GDP whereas in Ireland, the
Netherlands, Portugal, Taiwan and several other countries it is well over 100 percent of
GDP. Table 4, which reports the credit averages for three groups of countries based on
income, shows that private bank credit has the lowest level in low income countries
(25.01 percent of GDP), compared to middle income countries (58.31 percent of GDP)
and high income countries (93.81 percent of GDP).
On average, 54.14 percent of bank credit to the private sector has long-term
maturity. There are, however, large differences between countries. Long-term credit is
less than 30 percent of total credit in a number of countries including Bangladesh, The
Central African Republic, Niger, and Lesotho and it is greater than 70 percent of total
3 The data do not indicate what portion of short-term credit is rolled over and used to finance long-term
projects. Therefore, in some countries our measure of long-term credit is most likely an underestimate of
the actual amount of funding available for long-term financing. While this introduces a measurement error,
it also serves to produce more conservative estimates of a possible positive effect of credit maturity on
credit in Austria, Cyprus, Finland, Norway, and several other countries. Table 4 shows
that there are systematic differences in credit maturity between countries at different
levels of economic and financial development. In low income countries, the percent long-
term credit is 40.38 percent, whereas in middle income and high income countries it is,
respectively, 63.17 percent and 72.39 percent. More developed economies have more
private credit and, also, a greater portion of their credits have long-term maturity.
However, notice in Table 2 that the correlation coefficient of the level of credit and credit
maturity is not very large in magnitude (0.57), i.e. credit maturity differs across countries
with similar levels of credit to GDP. For example, credit is 95 percent of GDP in
Germany and Belgium. However, the percent long-term credit is 83 percent in Germany
and 66 percent in Belgium. Also, private credit is 40 percent of GDP in both Bangladesh
and Estonia. However, in Estonia long-term credit is 83 percent of total credit and in
Bangladesh it is only 14 percent of total credit.
3. The determinants of credit maturity
Building on Modigliani and Miller (1958), Stiglitz (1974) shows that in a perfect
world the maturity of credit, as any other financing decision, is irrelevant. Subsequent
research has added transaction costs, informational asymmetries, liquidation costs, and
taxes to that framework as a result of which maturity becomes an important factor in
financing decisions. There is a large empirical literature on the determinants of credit
maturity from individual (mostly industrialized) countries reviewed by Ravid (1996).
In terms of cross country evidence, Qian and Strahan (2007) and Demirgüç-Kunt
and Maksimovic (1999) investigate the determinants of credit maturity in samples of,
respectively, 43 and 30 countries with a particular focus on the effect of legal institutions.
We stay close to their analysis in terms of the selection of the country-level explanatory
variables but we expand the number of countries substantially and we also include
additional explanatory variables such as economic volatility and banking system
concentration. Furthermore, we use the maturity of bank credit to the entire private
sector whereas Demirgüç-Kunt and Maksimovic (1999) and Qian and Strahan (2007)
analyze the borrowing by publicly traded companies only. Using the total private bank
credit allows us to link the paper to the finance and growth literature where that variable
is used extensively.
3.1. Empirical hypotheses
Legal Institutions. The literature provides substantial evidence that weak legal
institutions are a primary reason for the underdevelopment of financial markets as lenders
cannot effectively monitor and exert control over borrowers (La Porta et al. 1997; 1998).
Inefficient protection of creditor rights leads to a reduction in the volume of external
financing provided by financial institutions to the private sector. Furthermore, institutions
affect the terms of credits and the maturity of credit in particular. Diamond (1991; 1993)
and Rajan (1992) show that short-term lending facilitates the enforcement of credit
contracts as it limits the period during which an opportunistic firm can exploit its
creditors without being in default. Diamond (2004) argues that “maturity acts as a
substitute contracting tool to control borrower risk,” and that bank loan maturity is
“especially sensitive to the legal environment.” Giannetti (2003) also argues that if the
law does not guarantee creditor rights, lenders would prefer short-term debt to control
entrepreneurs’ opportunistic behavior by using the threat of not renewing their loans. In
line with these theories, we expect to find that weak institutions contribute to shorter
High Inflation. Similar to weak legal institutions, high inflation is detrimental to
the development of the financial system as it limits the amount of external financing
available to borrowers (Huybens and Smith 1998, 1999). Furthermore, similar to legal
institutions, high inflation affects credit maturity. Boyd, Levine, and Smith (2001) point
out that financial intermediaries are less willing to engage in long-run financial
commitments in high inflation environments. Rousseau and Wachtel (2002) also argue
that high inflation will “discourage any long term financial contracting and financial
intermediaries will tend to maintain very liquid portfolios. In this inflationary
environment intermediaries will be less eager to provide long-term financing for capital
formation and growth.” Therefore, we expect that high inflation reduces the fraction of
credits with long-term maturity.
Stock Market Development. Stock market development has an ambiguous effect
on credit maturity. According to one view, a well functioning stock market could be a
substitute source of long-term financing and would therefore reduce the demand for long-
term bank financing. Diamond (1997) argues that increased participation in markets
causes the banking sector to shrink, primarily through reduced holdings of long-term
assets. An alternative view holds that a developed stock market increases the ability of
firms to obtain long-term financing as it helps reveal information about the borrowers and
reduces information asymmetries (Grossman 1976; Grossman and Stiglitz 1980).
Therefore, theoretically the effect of stock market development on long-term bank
financing is ambiguous.
Banking Sector Competition. Banking sector competition can have a dual effect
on the provision of external financing and the provision of long-term financing in
particular. A high level of concentration in the banking sector may raise the cost of funds
and thus reduce external financing (Pagano 1993). Alternatively, high concentration in
the banking industry may foster close relationships between banks and borrowers which
reduces information asymmetries and the cost of monitoring borrowers (Mayer 1988;
Mayer and Hubbard 1990; Petersen and Rajan 1995). Therefore, the theoretical effect of
banking system concentration on debt maturity is ambiguous.4 Testing the bank-firm
relationship hypothesis Giannetti (2003) finds that, contrary to (her) expectations,
maturity is shorter in countries where the banking system is more concentrated.
Overall Level of Bank Credit. Diamond (1984) highlights the function of banks as
delegated monitors that emerge to reduce the cost of monitoring borrowers by exploiting
economies of scale. In the absence of banks, individual savers would incur the cost of
assessing and monitoring investment projects. With economies of scale, a larger banking
system would have lower monitoring costs, which reduces lending risk and increases the
supply of long-term debt. There is, however, an additional effect related to the volume of
credit extended in an economy. Diamond and Rajan (2000) argue that a larger pool of
smaller, riskier, and less collateralized borrowers would obtain access to external
financing with the expansion of the financial system. As most of the credits to these
riskier borrowers are short-term, the proportion of short-term debt in total debt would
4 Cetorelli and Gambera (2001) investigate whether the market structure of the banking sector has empirical
relevance for economic growth, finding that banking system concentration has a non-trivial impact on
growth, but that competition in banking does not necessarily dominate monopoly and vice versa.
increase as overall lending increases. Thus, the theoretical effect of credit levels of credit
maturity is ambiguous.
Real Per Capita GDP. Ravid (1996) points to the “industry paradigm” of
matching maturities introduced by Morris (1976) where a firm with long-term assets
should use long-term debt. If the maturity of debt is longer than the asset life, the
borrower might have a problem finding new assets to invest in but will have to continue
servicing the debt. If debt maturity is shorter than the asset life, then the borrower is
exposed to the risk of being short on cash when debt payments are due. Stohs and Mauer
(1996) find evidence for this on the firm level. We use per capita GDP to proxy for the
amount of fixed assets in a country, with richer countries having a larger stock of long-
term assets. Thus, higher GDP per capita is expected to be associated with longer debt
Credit Information Sharing. Empirical researchers have shown that countries with
institutions that gather and share information about borrowers have higher private credit
to GDP ratios (Brown, Jappelli, and Pagano 2007; Djankov, McLiesh, and Shleifer 2007;
Jappelli and Pagano 2002).5 Furthermore, because lack of information reduces the supply
of long-term credit (Djankov, McLiesh, and Shleifer 2007), information sharing is also
expected to lengthen debt maturity. Zhang and Sorge (2007) provide a direct link
between credit information sharing and credit maturity in a model where information
sharing is used by banks as a screening device and leads to an equilibrium where short-
term contracts are not preferred. Empirically, Zhang and Sorge (2007) confirm their main
5 Information sharing overcomes adverse selection (Pagano and Jappelli 1993) and moral hazard problems
(Padilla and Pagano 2000) in the credit markets. While, theoretically, the impact of information sharing on
aggregate lending is ambiguous, the increase in lending to safe borrowers is certain.
hypothesis using data from publicly traded companies to show that information sharing
leads to longer credit maturity. We expect to find the same effect.
Real Per Capita GDP Growth. Smith and Watts (1992) note that GDP growth
rates can serve as a proxy for investment opportunities: the demand for external financing
would increase in boom times and will recede in recession periods. It is not clear,
however, whether expansions would stimulate the demand for long-term and short-term
credit in different ways. Nonetheless, we follow the literature (Demirgüç-Kunt and
Maksimovic 1999; Qian and Strahan 2007) and include the growth rate of per capita GDP
in our estimations.
Output Volatility. Booth, Demirgüç-Kunt, and Maksimovic (2001) look at the
variability of the return-on-assets to proxy for business risk expecting that an increase in
variability would shorten the maturity of credit as it proxies for the short-term operational
component of business risk. Giannetti (2003) notes that controlling for such risk has been
neglected in the previous cross-country research, at least partly because of lack of
suitable empirical proxies. The author uses a similar variable, but at the sectoral level,
and shows that the percent short-term debt increases with higher volatility of the return-
on-assets of the corresponding sector in that country. It is more difficult to account for
such risks at the country level. Nevertheless, if per capita GDP growth is a suitable proxy
for investment opportunities as noted in the previous literature, then its variability can be
used as a measure of business risk.6
Manufacturing Share of Output. Barclay and Smith (1995) and Scherr and
Hulburt (2001) show that the maturity of credit differs substantially across economic
6 In the context of international lending, Valev (2007) relies on the same proxy and shows that higher
volatility of per capita GDP growth in a country leads U.S. banks to shorten the maturity of credit to that
sectors with manufacturing firms having a larger fraction of long-term credit as percent
of their overall credit. We include the percent of manufacturing in total output as a proxy
for the importance of the manufacturing sector on the country level. We expect that credit
in countries with a larger manufacturing sector will have longer maturity.
In summary, the empirical hypotheses drawn from the literature are as follows:
ingmanufactur atility,output vol growth, GDP
capita,per GDP credit, ion, concentratindustry banking
sharing, info. credit market,stock inflation, law, of rule
Credit Term- Long
Some of the explanatory variables: legal institutions, inflation, banking sector
competition, financial development, and credit information sharing affect the availability
of long-term financing primarily through the supply side. Other variables: stock market
development, per capita GDP, economic growth, and the share of manufacturing affect
the maturity of credit primarily through the demand side.
The correlations in Panel B of Table 2 show that inflation and output volatility are
negatively and significantly correlated with the percent long-term credit. Also, rule of
law, credit information sharing, and GDP per capita are positively and significantly
correlated with the percent long-term credit. The correlation between economic growth
and the percent long-term credit is positive and significant as is the correlation between
the credit level and the percent long-term credit.
By construction private credit and the percent long-term credit are determined
jointly and, therefore, we need to control for the endogeneity of private credit. Following
the literature, we could use countries’ legal origins as external instruments for the level of
credit. However, for those to be valid instruments, we would have to assume that legal
origin does not have an impact on credit maturity, except through its effect on credit. This
may not be the case as Demirgüç-Kunt and Maksimovic (1999) and Qian and Strahan
(2007) find that legal origin has a direct influence on credit maturity. In addition, we
would be constrained to using a random effects model (since the legal origin does not
change over time) even though the Hausman test reveals that the explanatory variables
used in the random-effects model are correlated with the country specific effects and,
therefore, we have to use a fixed-effects estimation. To resolve these problems, we
implement the Hausman-Taylor (1981) estimator that corrects for correlation between the
explanatory variables and the country-level random-effects, and does not require the use
of outside instruments.7
When explaining the percent long-term credit one concern that arises is that the
dependent variable is a ratio (between 0 and 100 percent) making OLS problematic as the
predicted values might lay outside the unit interval (Papke and Wooldridge 1996). This
may require the transformation of the dependent variable using a log-odds transformation
(log(y/1−y)). However, the coefficient estimates using the log-odds ratio are difficult to
interpret in a panel setting and therefore we follow the previous literature (Demirgüç-
Kunt and Maksimovic 1999; Rodrik and Velasco 1999; Valev 2006; 2007) and do not
perform the transformation. Furthermore, less than 1 percent of the predicted values from
the models are outside the unit interval.
7 For robustness, Appendix B presents a set of empirical results where we use a random-effects estimator, a
fixed-effects estimator, GLS estimators that control for a heteroskedastic error structure and allow for
AR(1) autocorrelation, as well as a two-stage least squares random-effects estimator. The estimated effects
are similar across the various estimations.
Table 5 presents the empirical results regarding the determinants of credit
maturity. We start with a benchmark equation where the percent long-term debt is
explained by rule of law, inflation, financial and economic development, and economic
growth. Then we add, one at a time, a dummy variable for credit information sharing,
banking system concentration, stock market development measured by the stock market
turnover ratio, output volatility, and the share of the manufacturing sector in GDP. In
column (7) we report the estimations from a regression where we include all explanatory
It is immediately clear that the rule of law has a statistically significant and robust
effect on the maturity of credit. Greater rule of law is associated with longer debt
maturity. Looking at the estimations from the benchmark equation, a decrease in the rule
of law by one standard deviation leads to a decrease of the percent long-term credit by
5.57 percentage points (1.05*5.308). This result compares well with previous findings. In
Demirgüç-Kunt and Maksimovic (1999), a decrease of the Law & Order index by 1.05
index points decreases the percent long-term debt by 5.78 percentage points.8 To
illustrate, if the Slovak Republic (where the rule of law index is 0.288) had the rule of
law level of Austria (1.891), its long-term credit would increase by 8.51 percentage
8 Demirgüç-Kunt and Maksimovic (1999) use a different index to measure rule of law but their index has a
nearly identical definition to ours (“the degree to which citizens of a country are able to utilize the existing
legal system to mediate disputes and enforce contracts”). In addition, their index has a similar standard
deviation (1.597) and a similar range (4.286).
Inflation also affects credit maturity in significant ways with higher inflation
leading to shorter credit maturity in all specifications. We explore the size of the effect of
inflation in more detail later. Countries with deeper financial markets have a greater
fraction of long-term credits. The estimates from the benchmark equation in column (1)
suggest that if Slovakia (where private credit is 25.67 percent of GDP) had the level of
private credit of Hungary (72.22 percent), it would also have 11.38 percentage points
greater percent long-term credit. Thus, the process of financial deepening is accompanied
by lengthening of the maturity of credit as suggested by Diamond (1984).
To test whether information sharing affects credit maturity, we follow Qian and
Strahan (2007) and include a dummy variable that equals 1 if a country had either a
public credit registry or a private credit bureau in a particular year, and 0 otherwise.
Credit information sharing is statistically significant when included in the base estimation
model and in the full model. The more conservative yet statistically significant estimate
in column (7) suggests that if Luxembourg had established a credit information sharing
institution, the percent long-term credit would increase from 59.72 percent to 66.30
percent, bringing it to the same percentage long-term credit as in Belgium. Using the
same estimate, if China had not established a credit information sharing institution in
2003, the average percent long-term credit would have remained at 29.48 percent, a level
below Congo or Burkina. Instead, the percent long-term credit in China increased to
China is not the only country that established a credit information sharing
institution during the years covered by our data – Norway implemented one in 1998,
Bulgaria in 1999, and Romania in 2000, to name a few. Figure 1 shows that, perhaps not
coincidentally, the percent long-term credit increased in all countries that implemented a
credit information sharing institution (except Serbia, where the implementation was
preceded by macroeconomic and political turmoil and coincided with financial
liberalization, closure of major banks, and overall reduction in credit). This was
particularly true in countries that started at a relatively low percent of long-term credit.
For example, the percent long-term credit in Romania doubled after the introduction of a
public credit registry.
Economic development measured by per capita GDP, which was included to
proxy for the importance of long-term capital and to test the hypothesis of maturity
matching is not statistically significant. This result differs from Demirgüç-Kunt and
Maksimovic (1999) who find evidence for maturity matching on the firm level. The
difference in results may be attributed to the imprecise measure of fixed assets that we
employ compared to Demirgüç-Kunt and Maksimovic who use a direct measure of fixed
assets as a share of total assets.9 Similar to us, Qian and Strahan (2007) use per capita
GDP to control for economic development and report an insignificant impact on maturity.
GDP growth has mostly a positive coefficient, which implies that faster growing
countries have more long-term credit. However, the coefficient is significant at the
accepted confidence levels only when we control for the manufacturing share of output in
column (6) and therefore we refrain from making stronger claims. Nevertheless, with the
results on inflation, we interpret this finding in line with Booth, Demirgüç-Kunt, and
9 For robustness, we also tried to compile data on per capita capital stock and capital stock as share of GDP
to proxy for the fixed assets in a country. However, the initial income and investment data needed to
compute the capital stock are available only for a limited number of countries in our sample and are not
available for the last few years of the sample period.
Maksimovic (2001): agents can borrow to invest in more productive, longer gestation
projects against real, but not against inflationary growth prospects.
The rest of the results suggest that banking industry concentration, stock market
development, and output volatility do not affects bank credit maturity. Contrary to
expectations, a greater share of manufacturing is associated with less long-term credit.
Unfortunately data limitations prevent us from investigating whether this effect is driven
by particular non-manufacturing sectors, e.g. utilities, transportation, and/or construction.
3.4. Inflation and Credit Maturity
To examine further the relationship between inflation and credit maturity, we
reestimated the regression reported in column (7) using 40 subsamples ordered by the
rate of inflation as in Rousseau and Wachtel (2002) and Boyd, Levine, and Smith (2001).
Both papers investigate the effect of inflation on financial sector activity and not on the
maturity of credit specifically. However, the authors explain that the effect of financial
development on economic growth diminishes with inflation because high inflation limits
long-term financial contracting. Here we provide direct evidence for that idea.
Rousseau and Wachtel (2002) find that inflation reduces the availability of bank
credit at low inflation rates but after some threshold (which they estimate to be around 16
percent) the negative effect of additional inflation on credit activity disappears. Similarly,
Boyd, Levine, and Smith (2001) conclude that, while there is a statistically significant
and economically important negative relationship between inflation and banking sector
development, the marginal impact of inflation on bank lending activity diminishes
rapidly. The threshold inflation rate above which inflation has no effect on credit market
activity in Boyd, Levine, and Smith (2001) is very close to that in Rousseau and Wachtel
(2002): 15 percent. Boyd, Levine, and Smith (2001) conclude that until this threshold is
reached “the damage to the financial system has already been done, [and] further
increases in inflation will have no additional consequences for financial sector
performance or economic growth.” This is consistent with the anecdotal evidence from
Brazil provided by Demirgüç-Kunt and Maksimovic (1999) who explain that an
inflationary environment gives rise to the indexation of financial contracts reducing the
negative impact of additional high inflation on credit markets.
To examine these ideas using our data set, we sorted all observations according to
the rate of inflation and estimated repeatedly the full model from column (7) in Table 5
starting with observations 1 through 244, then on 2 through 245, continuing until the last
subsample that includes observations 40 through 284.10 The estimated coefficients of
inflation, along with the 95 percent confidence intervals, are plotted in Figure 2. Looking
at Figure 2, we can identify three regions in terms of the effect of inflation on the percent
long-term credit. Inflation significantly reduces the percent long-term credit until
inflation reaches about 14 percent. After that point, the effect of inflation on the percent
long-term credit declines markedly. When the inflation rate reaches about 25 percent, the
negative effect of inflation on credit maturity increases again.
The low range of inflation until about 14 percentage points is very close to the
ranges reported by Rousseau and Wachtel (2002) and Boyd, Levine, and Smith (2001).
However, our estimations suggest that the negative effect of high inflation reappears at
10 We need a large enough number of subsamples so that we can observe the variation of the inflation
coefficient estimate at different levels of inflation. However, we also need to have sufficient degrees of
freedom in each individual subsample. Forty subsamples seem to strike this balance well, but we also
performed the estimations with 30 to 50 subsamples. The results from these estimations suggest similar
relationships to the ones described here.
“high” inflation rates. It is possible that the indexations of financial contracts cannot
sufficiently reduce the uncertainty about the real value of nominal payments when
inflation becomes too high. In addition, Demirgüç-Kunt and Maksimovic (1999) note that
very high inflation rates reveal a deterioration of institutions other than central banking.
For example, even efficient legal systems take time to enforce contracts. As Demirgüç-
Kunt and Maksimovic argue, while payments can be indexed, borrowers and lenders
cannot “index judgment.”
To recount, the major determinants of the maturity composition of bank credit to
the private sector are rule of law, inflation, the existence of institutions for credit
information sharing, and the size of the financial system. These effects are robust across
various estimation techniques and specifications of the models. They are also robust to
substituting the rule of law measures with alternative indexes (e.g. the ICRG variables
and an index of corruption), to different definitions of the credit information sharing
variable (public vs. private agencies) and to the inclusion of additional control variables
such as the share of foreign banks and the share of government owned banks (which
reduce the sample size substantially and are not statistically significant). The next section
builds on these results to examine the effect of credit maturity on economic growth.
4. Credit Maturity and Economic Growth
Following the literature, e.g. Beck, Levine, and Loayza (2000) and Levine,
Loayza, and Beck (2000), we estimate the growth equations using dynamic panel
generalized-method-of-moments (GMM) techniques to address the potential endogeneity
of credit and other explanatory variables. This technique is fully described in Appendix
C. The literature usually investigates the effect of finance on growth by averaging data
over 5 years to reduce the impact of business cycles and to concentrate on long-term
growth. Proceeding in the same fashion would reduce the number of observations in our
data set substantially as the sample period for most countries is about 10 years long.
Fortunately, the literature has dealt with this issue. Bekaert, Harvey, and Lundblad (2005)
investigate the impact of equity market liberalization on economic growth by using
overlapping data. The five-year averages are constructed as 1990-95, then 1991-96, 1992-
97, and so on producing 6 five-year averages from any 10 years of annual data. While
this ingenious methodology increases the number of observations, it calls for the
adjustment of the moving average component in the residuals as introduced by Newey
and West (1987). Without the adjustment, the standard t-tests lead to a slight over-
rejection (Bekaert, Harvey, and Lundblad 2001). Although in the panel data context,
unlike in the single time series, we do not need the weighting matrix for the estimate of
central term in covariance matrix to be positive semi-definite (Petersen 2007), we follow
Newey and West (1987) assuming that as the distance between observations goes to
infinity, the correlation between corresponding residuals approaches zero.11 We adjust the
dependence for up to five lags (i.e. we set lmax to 5) and estimate correlations only
between lagged residuals in the same cluster.12 The procedure provides serial-correlation
and heteroskedasticity consistent standard errors.13
11 We use a weighting matrix which multiplies the covariance of lag l by
is the maximum lag order. A weighting matrix with such elements will weigh heaviest the adjacent
observation, while the weights decrease as the distance between observations increases.
12 As suggested by several papers, we have repeated the procedure by including up to T-1 lags, where T is
the maximum number of years per country, but doing so leaves our standard errors almost unchanged.
13 Ranciere, Tornell, and Westermann (2003) also use overlapping averages to provide long-term
predictions of the finance and growth relationship and adjust their standard errors according to Newey and
( ) (
, where lmax
Column (1) in Table 6 reports the results of an equation where economic growth
is explained by private sector credit, initial GDP per capita, government size, openness to
trade, and inflation. This is a standard specification from the finance and growth literature
(Beck, Levine, and Loayza 2000). Financial development is expected to lead to faster
economic growth. High inflation is an indicator of macroeconomic instability and is
expected to slow down economic growth. More open economies are expected to grow
faster. A large government size is taken as an indicator of inefficient use of resources and
is expected to reduce economic growth. Initial income is included to test for income
The results show that private credit has a positive and statistically significant
effect on economic growth. Besides being statistically significant, private credit also has
a large economic effect, similar to the effect reported in the previous literature. To
illustrate, we compare our results with the estimates of Beck, Levine, and Loayza (2000):
a 10 percent exogenous increase in private credit leads to an additional 0.216 percentage
points of economic growth per year using our estimated coefficient,15 and to 0.228
percentage point of additional yearly growth using the estimated coefficient of Beck,
Levine, and Loayza (2000). The coefficients on all control variables except government
size have the expected signs. Openness to trade and initial income per capita are
West (1987). Petersen (2007) finds that about 7 percent of authors who use panel data in the finance
literature adjust their standard errors using the Newey-West procedure.
14 We could not obtain recent data on education levels for many countries for the later years in our sample.
We carried out all estimations with a smaller sample including education and obtained qualitatively similar,
but less statistically significant results on all variables.
15 The calculation is as performed follows: 2.296 * ln(1.1) = 0.216.
statistically significant at the accepted confidence levels. The specification tests confirm
the validity of our results: we cannot reject the null hypothesis of the Sargan tests or of
the serial-correlation test at the accepted confidence levels in all specifications.
In column (2) we add the percent long-term credit. Credit maturity has a positive
and statistically significant effect on economic growth as predicted by Bencivenga and
Smith (1991). In terms of economic size a 10 percent increase in the portion of long-term
credit leads to an additional 0.574 percentage points of economic growth per year.16 As
the average growth rate in the sample is 2.98 percent, the impact of an increase in credit
maturity on growth is large (an increase of over 19 percent).
Consider the following example to illustrate the economic impact of credit
maturity. Private credit in Malaysia is 130 percent of GDP which is well above the
average of the middle income group: 58.31 percent. By this standard measure Malaysia
has above average financial development. However, only 46.73 percent of private credit
in Malaysia is long-term, which is below the average of the middle income group of
63.17 percent. Thus, Malaysian banks extend relatively large volumes of credit but much
of the credit is short-term compared to other countries. If private credit in Malaysia
declined to the average of the middle income group, economic growth in Malaysia would
decline by 1.616 percentage points. However, if the percent long-term credit in Malaysia
increased to the average of the middle income group, economic growth would increase by
1.815 percentage points. If these two changes happened simultaneously, economic
growth in Malaysia would increase by 0.199 percentage points. Therefore, if most of the
16 The calculation is as follows: 6.02 * ln(1.1) = 0.574; where 6.02 is the coefficient of the percent long-
term credit in column (2).
reduction in credit originated from a decline in short-term credits, the negative impact of
reduced credit to the private sector would be countered by the longer maturity of credit.17
4.2. The determinants of credit maturity and economic growth.
Section 3 shows that credit maturity is longer in countries that have strong
institutions, low inflation, and institutions for sharing credit information among financial
institutions. These characteristics also influence economic growth through their impact
on credit maturity. Furthermore, the impact is large. Using the estimations in column (2)
in Table 5, we obtained the predicted values for the percent long-term credit. Then, we
reestimated the growth equation using the predicted values for the percent long-term
credit. These results are reported in column (3) of Table 6.
Putting together the estimates from sections 3 and 4, we estimate that an increase
in the rule of law index by 1 index point would increase economic growth (via credit
maturity) by 0.586 percentage points a year.18 A decrease of inflation by 10 percentage
points leads to a 0.045 percentage points faster economic growth.19 The establishment of
a credit information sharing institution in a country would raise economic growth by
17 For robustness, we also added the stock market value traded as a share of GDP, the stock market turnover
ratio, and stock market capitalization as a share of GDP as measures of stock market development. The
stock market is an alternative source of long-term financing and its inclusion in the model might reduce the
effect of credit maturity on economic growth. Although the sample size decreases from 64 to 44 countries,
the coefficient on credit maturity remains statistically significant and similar in magnitude. All stock
market measures have positive coefficients, while only value traded and the turnover ratio are statistically
18 1.00 increase in rule of law leads to (5.308 * 1.00 =) 5.31 percentage points increase in the percent long-
term credit. At the average of 54.14 percent long-term credit, this leads to an increase in yearly GDP
growth of (6.27 * (ln(0.541+ 0.053) - ln(0.541))=) 0.586 percentage points.
19 0.10 decrease in inflation leads to (3.939 * 0.10 =) 0.394 percentage points increase in the percent long-
term credit. At the sample average of 54.14 percent long-term credit, this leads to an increase in yearly
GDP growth of (6.27 * (ln(0.5414 + 0.00394) – ln(0.5414)) =) 0.045 percentage points. Note that this
calculation is separate from the independent impact of inflation on growth. Such decrease in inflation
independently increases growth by 0.82 percentage points.
0.718 percentage points.20 These effects on economic growth via credit maturity are
separate from other channels through which strong institutions, low inflation and
institutions for credit information sharing might affect growth.
This paper investigates empirically one of the important functions of the banking
system: to transform short-term liquid deposits into long-term illiquid financial assets that
can fund long gestation activities and enhance economic growth. The paper shows that
the extent to which banks perform this function well has an important effect on the
relationship between the financial system and economic growth. Economic growth is
faster in countries where the banking system extends more long-term credits.
Furthermore, the paper shows that credit maturity depends on a number of
institutional and economic factors. Greater rule of law, low inflation, and credit
information sharing institutions contribute to lengthening the maturity of bank credit.
While policymakers can make improvements along each of these dimensions, the effect
of credit information sharing is probably most interesting form a policy perspective.
Improvements in the rule of law and sustained low inflation take decades, whereas a
credit information sharing institution can be established within a few years. We show that
these institutions provide valuable information to banks and are associated with longer
maturity of credit. This, in turn, raises economic growth.
In a parallel paper where we use a smaller data set primarily from the transition
economies, we show that the rule of law has greater effect on the portion of credit with
20The establishment of a credit information sharing institution would increase the percent long-term credit
by 6.573 percentage points. At the average of 54.14 percent long-term credit, this leads to an increase in
yearly GDP growth of (6.27*(ln(0.7604+0.06573)-ln(0.7604)=) 0.718 percentage points.
maturity longer than five years, whereas inflation has a greater effect on the portion of
credit with maturity longer than one year. In addition, per capita GDP becomes
significant determinant of maturity, as it has a positive impact of the portion of credit
with maturity longer than five years. Credit information sharing institutions remain
important determinant of maturity.
To our knowledge, the results presented in this paper are the first empirical test of
an important theoretical idea – that banks contribute to economic growth by providing
liquidity services and increasing the supply of long-term credit. Future work can examine
additional channels through which finance enhances economic growth: by producing
information about borrowers and allocating capital, by monitoring borrowers, by
aggregating savings into large-size investments, and by cross sectional risk
diversification. Ideally, we would be able to compare the channels through which finance
affects growth in various institutional and economic environments. We would also be
able to investigate whether lax rule of law diminishes the positive effect of finance on the
economy because banks: 1) cannot assess risk and monitor the behavior of borrowers,
and/or as we show here, 2) curtail long-term financing. We would also be able to
investigate how the relative importance of different channels evolves as the financial
system develops. In summary, investigating the channels through which finance affects
growth presents a number of exciting research opportunities.
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Appendix A. List of Countries
Below is the list of countries used in the estimations.
Albania Gabon Nicaragua
Armenia Georgia Niger
Austria Germany Norway
Azerbaijan Greece Poland
Bahamas, The Guinea Bissau Portugal
Bangladesh Hungary Romania
Belgium Iceland Russia
Benin Ireland Saudi Arabia
Bolivia Italy Senegal
Bosnia and Herzegovina Ivory Coast Serbia, Republic of
Bulgaria Jordan Singapore
Burkina Kazakhstan Slovak Republic
Cameroon Kyrgyz Republic Slovenia
Central African Republic Latvia Spain
Chad Lesotho Sri Lanka
China Lithuania Sweden
Congo Luxembourg Taiwan
Cyprus Macau Togo
Czech Republic Macedonia, FYR Tunisia
Denmark Malaysia Turkey
Equatorial Guinea Mali Ukraine
Estonia Mongolia United States
Finland Mozambique Uruguay
Netherlands, The Yemen
Appendix B. Determinants of Credit Maturity – Additional Estimators
Rule of Law
GLS GLS – AR(1)
(0.515) (0.000) (0.000) (0.000) (0.005)
-9.321 -3.034 -27.937
(0.360) (0.092) (0.000) (0.025) (0.323)
(0.248) (0.003) (0.001) (0.762) (0.038)
(0.000) (0.000) (0.000) (0.000) (0.000)
(0.268) (0.429) (0.001) (0.104) (0.729)
-8.452 -15.347 -10.670 -11.068
(0.038) (0.001) (0.022) (0.032)
(0.162) (0.016) (0.111) (0.051)
(0.955) (0.557) (0.788) (0.514)
(0.015) (0.006) (0.027) (0.002)
(0.000) (0.000) (0.000) (0.000) (0.000)
Inflation -1.600 -6.242
Growth 0.0270.251 0.362
Credit 10.758 14.238 14.463
Income 0.2000.050 -0.210
U.K. Legal Origin
French Legal Origin -6.992 -8.857
German Legal Origin -1.1642.929
Socialist Legal Origin 10.342 15.960
64.472 74.863 71.521
Hausman test: χ2
Notes: See Table 1 for variable definitions. P-values are reported in parentheses below the
coefficients. In 2SLS, legal origin dummies are used as instruments for endogenous credit.
Hausman test has a null hypothesis that the explanatory variables are not correlated with the
country-specific random-effects. Credit, Banking Industry Concentration, Stock Market Turnover
Ratio, and Output Volatility enter the regression as log(variable), while Income is in thousands.
Appendix C. GMM Methodology21
Let yit be the logarithm of real per capita GDP in country i at time t. We are
interested in the following equation:
i t i, t i
where yi,t - yi,t-1 is the growth rate in real per capita GDP, Xi,t is a set of explanatory
variables, including our measures for financial development,
i η captures unobserved
country-specific effects, and
ε is an error term. We rewrite equation (1) as:
,,1,, t i
iti t i
and take first differences to eliminate the country-specific effect, as it is correlated with
lagged dependent variable:
()( ) (
t it i t it i t it it i t i
By construction, in equation (3), the lagged difference in per capita GDP is
correlated with the error term, which along with the potential endogeneity of the
explanatory variables X, requires the use of instruments. The GMM difference estimator
uses the lagged levels of the explanatory variables as instruments under the conditions
that the error term is not serially correlated and that the lagged levels of the explanatory
variables are weakly exogenous (i.e., they are uncorrelated with future error terms). Then
the following moment conditions are used to calculate the difference estimator:
0 for 2;3,...., ,
i t s
i ti t
0for 2;3,...., .
i t s
i ti t
21 This method is fully described in Arellano and Bond (1991), Arellano and Bover (1995), and Blundell
and Bond (1998).
Since persistence in the explanatory variables may adversely affect the small-
sample and asymptotic properties of the difference estimator (Blundell and Bond 1998),
the difference estimator is further combined with an estimator in levels to produce a
system estimator. The inclusion of a levels equation also allows us to use information on
cross-country differences, which is not possible with the difference estimator alone.
The equation in levels uses the lagged differences of the explanatory variables as
instruments under two conditions. First, the error term is not serially correlated. Second,
although there may be correlation between the levels of the explanatory variables and the
case-specific error term, there is no correlation between the difference in the explanatory
variables and the error term. This yields the following stationarity properties:
i t p
i i t q
i i t p
i i t q
⎦ for all p and q.
The additional moment conditions for the regression in levels are:
i t s
i t s
i i t
− − −
i t s
i t si i t
In summary, the GMM system estimator is obtained using the moment conditions
in equations (4), (5), (7), and (8). In addition, as Beck and Levine (2004), we use
alternative procedure developed by Calderon, Chong, and Loayza (2002) and Loayza,
Chong, and Calerdon (1999) to control for the over-fitting by reducing the dimensionality
of instruments. This procedure has one shortcoming: in order to perform it we loose one
time period from the sample. Nevertheless, given the sample size, we are still able to
make robust estimates.
As our data are constructed using overlapping averages, we need to adjust the
moving average component in the residuals. We do this by adjusting standard errors
according to Newey-West (1987) procedure, modified for the use in panel data. Petersen
(2007) points that, unlike for the single time series, in the panel data context the
weighting matrix is not necessary for the estimate of central term in covariance matrix to
be positive semi-definite. Nevertheless, we follow Newey-West approach assuming that
as the distance between observations goes to infinity, the correlation between residuals
approaches zero. Therefore, we use a weighting matrix which multiplies the covariance
of lag l by
( ) (
, where lmax is the maximum lag order. Weighting matrix
with such elements will weigh heaviest the adjacent observation, while the weights
decrease as distance between observations increases. We adjust the dependence for up to
five lags (i.e. we set lmax to 5) and estimate correlations only between lagged residuals in
the same cluster. As suggested by several papers, we have repeated the procedure by
including up to T-1 lags, where T is the maximum number of years per country, but doing
so leaves our standard errors almost unchanged. This procedure provides serial-
correlation and heteroskedasticity consistent standard errors.
Figure 1: Credit Information Sharing Institutions and Credit Maturity
Plotted are the averages of the percent long-term credit for the period before and after the
establishment of credit information sharing institutions. The years included vary by country
depending on data availability. For The Czech Republic each period includes 5 years; for Latvia,
Poland, Romania, and Republic of Serbia 4 years; for Bulgaria and China 3 years; for FRY
Macedonia 2 years; and for Norway 1 year.
Percent Long-Term Credit .
Figure 2: Impact of Inflation on Credit Maturity at Different Inflation Levels
Plotted are the estimated coefficients of inflation and 95 percent confidence intervals when we
use subsamples ordered by inflation. Each subsample contains 244 observations. The values on
the abscissa correspond to the subsamples used in the estimations, while values on the ordinate
represent the coefficient (and confidence intervals) estimates of inflation for the corresponding
12345678 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Estimated Coefficient of Inflation
Table 1: Variable Definitions
Credit / GDP
Credit by deposit money banks and
other financial institutions to the private
sector divided by GDP.
Long-Term Credit is credit by deposit
money banks and other financial
institutions to the private sector with the
original contractual maturity longer
than one year divided by GDP.
Short-Term Credit is credit by deposit
money banks and other financial
institutions to the private sector with the
original contractual maturity of one
year or less divided by GDP.
Credit with an original contractual
maturity longer than one year divided
Central Bank of West African States:
Benin, Burkina, Guinea Bissau, Ivory
Coast, Mali, Niger, Senegal, and
Togo; Economic and Monetary
Community of Central Africa:
Cameroon, Central African R., Chad,
Congo, Equatorial Guinea, Gabon;
Eurostat: Austria, Belgium, Cyprus,
Czech R.*, Denmark, Finland,
France*, Greece*, the Netherlands*,
Norway, Poland*, Spain, and
Sweden; and FDIC Statistics on
Depository Institutions for the United
States. For the remaining countries
(and as second source for countries
with * above) source was
corresponding central bank (official
publications and website).
Credit / GDP
Credit / GDP
Real per capita
Per Capita GDP The real per capita GDP in US dollars.
Inflation The increase in the annual CPI.
The percent increase in real per capita
GDP from the previous year.
International Financial Statistics (IFS)
database of International Monetary
Fund (IMF). In some cases data were
retrieved from Eurostat database and
Euromonitor International's World
Marketing Data and Statistics (Plus)
which uses IMF’s World Economic
Outlook, United Nations, as well as
national statistics in addition to IFS.
Trade / GDP Sum of imports and exports of goods
and services as a share of GDP.
General government consumption as
share of GDP.
Index that measures “the extent to which
agents have confidence in and abide by
the rules of society, and in particular the
quality of contract enforcement.”
The assets of three largest banks as a
share of assets of all commercial banks.World Bank data set A new database
Stock market volume traded during a
year divided by the stock market
capitalization at the end of the year.
Dummy variable: 1 if public credit
registry or private credit bureau
operates in a country during a year, 0
Value added by manufacturing divided
by total value added.
Output Volatility Root mean squared errors from Growtht
= α + εt, using data from the preceding
Gov. / GDP
Rule of Law
World Bank data set Governance
Matters VI by Kaufmann, Kraay, and
on financial development and
structure by Beck, Demirgüç-Kunt,
and Levine (2000).
Author constructed from Djankov,
McLiesh, and Shleifer (2007), Miller
(2003), and Pagano, Brown, and
United Nations’ National Accounts
Main Aggregates Database.
Manuf. Share of
Author constructed from data on Real
per capita GDP Growth.
Table 2: Summary Statistics
Panel A: Descriptive Statistics
Panel B: Correlations
Credit / GDP
Per Capita GDP
Gov. / GDP
Trade / GDP
Rule of Law
Bank. Ind. Conc.
Stock Mkt. TOR
Manuf. Share of
Notes: * indicates statistical significance at the 5 percent level. See Table 1 for variable definitions.
0 0 -9.62
Table 3: Country Averages (Credit and Credit Maturity)
25.44 Guinea Bissau
73.77 Ivory Coast
30.39 Kyrgyz Rep.
35.28 Saudi Arabia 29.46
52.66 Serbia, Rep.
33.74 Slovak Rep.
65.21 Sri Lanka
24.24 United States 62.24
66.99 10.45 86.44
91.98 34.84 69.32
10.09 19.37 33.12
14.86 42.11 31.44
71.33 20.31 77.31
99.66 43.93 69.37
30.35 34.99 46.35
27.58 34.66 44.01
32.16 26.64 52.16
Cent. African Rep.
199.09 184.72 14.37
23.54 10.03 13.51
86.22 72.98 13.24
53.0434.88 18.16 54.14
Notes: Presented are country averages for the available years. See Table 1 for variable definitions.
Table 4: Income and Bank Credit Maturity
Growth Credit / GDP
Credit / GDP
Low income countries 2.6425.0112.08 12.93 40.38
Middle income countries 3.04 58.3139.9118.40 63.17
High income countries 2.20 93.8169.1624.65 72.39
Notes: Presented are the average values for each variable for three income groups defined as low
income if per capita GDP is below $1,715, middle income if it is between $1,715 and $10,800,
and as high income if it is above $10,800. See Table 1 for variable definitions.
Table 5: The Determinants of Credit Maturity (Hausman-Taylor Estimation)
The dependent variable is credit with an original contractual maturity longer than one year
divided by overall credit. The explanatory variables are defined as in Table 1.
Rule of Law
Stock Market Turnover
Manufacturing Share of
U.K. Legal Origin
French Legal Origin
German Legal Origin
Socialist Legal Origin
Hausman test: χ2 (d.f.) 6.41 (5) 5.43 (6)
2.09 (6) 0.88 (10)
Notes: The results are based on the Hausman-Taylor estimation where Credit is endogenous. P-
values are reported in parentheses below the coefficients. The Hausman test has a null hypothesis
that the explanatory variables are not correlated with the country-specific random-effects.
43 Download full-text
Table 6: Bank Credit Maturity and Economic Growth (GMM System Estimation, 5-year
The dependent variable is the average yearly increase in real per capita GDP. The variables are
defined as in Table 1. Credit, Percent Long-Term Credit, Government size, Openness to trade,
and Initial income per capita enter the regression as log(variable). Inflation enters the regression
as log(1 + Inflation).
(1) (2) (3)
Percent Long-Term Credit
Initial income per capita
Openness to trade
Sargan test (p-value)
Serial correlation test (p-value)
Notes: P-values based on the Newey-West adjusted heteroscedastic-serial consistent standard
errors are reported in parentheses below the coefficients. The Sargan test has the null hypothesis
that the instruments are not correlated with the residuals. The serial correlation test has a null
hypothesis that the errors in the first difference regressions do not exhibit second order serial