Job Market Paper
Inflation Threshold Effects in the Finance-Growth Nexus and
Transmission Mechanism Analysis
Min Li, University of Alberta
October 2007
ABSTRACT: In this paper, we use two econometric approaches, an endogenous threshold
model and a rolling regression method, to examine the interaction among inflation, finance,
and economic growth. Using data from 90 countries for the period 1961-2005, we find
evidence of a nonlinear effect of inflation on the link between finance and growth. While
finance can stimulate economic growth in a low-inflation environment, it does not do so
when inflation exceeds a threshold of around 15%. This paper also analyzes the inflation
transmission mechanism in the financial market, through which inflation can affect the
marginal effect of finance on economic growth. The empirical findings are consistent with
the hypothesis that when inflation is sufficiently high, financial intermediaries become less
efficient in allocating resources and monitoring investment projects and this in turn lowers
the productivity of capital. As a result, during high inflation-periods, financial
development has less ability to stimulate economic growth. The main implication of these
findings is that the adverse effect of inflation on economic growth in high-inflation
environments can be mitigated only if financial intermediaries improve their efforts in
collecting information, allocating funds, and monitoring projects.
JEL Classification: E31, E44, O16, O47
Keywords: Financial Development; Economic Growth; Inflation; Investment; Productivity;
Cross-country regression
Acknowledgement: We are grateful for the valuable comments and suggestions from Dr.
R. Todd Smith and Dr. Stuart Landon. Any remaining errors and omissions are the author's
responsibility.
Correspondence: Min Li, Department of Economics, University of Alberta, 8-14 HM
Tory, Edmonton, AB, Canada T6G 2H4; Tel: 780-428-9361; E-mail: minl@ualberta.ca.
1. Introduction
Over the last two decades, the relationship between financial development and economic growth
has been receiving increased attention in the macroeconomics and development literature.
Theoretical studies (Townsend, 1979; Greenwood and Jovanovic, 1990; Bencivenga and Smith,
1991; and Gurley and Shaw, 1995) suggest that the development of the financial system may be
important for economic growth because such development facilitates the provision of liquidity
services, improves the productivity of capital and investments, ameliorates the adverse effects of
informational frictions, and improves the allocation of funds to investment projects. Empirical
studies document a positive relationship between the size of a country’s financial system and its
rate of economic growth (e.g., Atje and Jovanovic, 1993; King and Levine, 1993a, b; Levine and
Zervos, 1998; Bell and Rousseau, 2001; and Rousseau and Vuthipadadorn, 2005).
A shortcoming of the empirical literature is that it has assumed a constant relationship
between finance and growth in different types of economic environments. For example, the
financial system is considered in some studies to be the key channel through which inflation can
adversely affect economic growth (Azariadis and Smith, 1996; Choi, et al, 1996, Huyben and
Smith, 1999, and Bose, 2002), but the finance-growth relationship might be very different in
different inflationary environments. In high-inflation environments, the financial system might
not function as well, and may have less ability to promote economic growth than it does in a low
inflation environment. Some recent studies (Andres et al., 1999; Rousseau and Wachtel, 2002)
have found evidence of an adverse inflation effect on the finance-growth nexus, but a major
question remains: why is it that inflation affects the finance-growth nexus? This transmission
mechanism of inflation in financial markets is a main focus of this paper. In other words, we
examine the cause of the weaker finance-growth nexus during high-inflation periods. This issue
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is important because it may help policy makers to understand why inflation is harmful to
economic activity.
There are many possible reasons why inflation might affect the finance-growth relationship.
Intuitively, we know that when inflation rates are very high, the usefulness of money assets is
eroded and there will be considerable uncertainty about future prices and interest rates. This
uncertainty, in turn, may make financial intermediation — standing between lenders and
borrowers—less efficient in allocating funds for investment, and may affect the ability of lenders
to monitor projects. As a result, high inflation may weaken the link between finance and growth.
More precisely, inflation could alter the link between finance and growth in two key ways. First,
inflation could affect the financial system’s ability to accumulate capital — the amount of
investment. In particular, when inflation is sufficiently high, the ability of financial
intermediaries to raise capital may decrease, and thus the positive effect of financial development
on capital accumulation may diminish. This channel, represented as ① in Figure 1, is referred to
in this paper as the “capital accumulation channel.” Second, inflation could affect the
productivity of capital investment financed through the financial system. Intuitively, in highinflation environments, even if the level of financing provided for capital investment is not
affected, high inflation may decrease the productivity of accumulated capital, and this decrease
will reduce the link between investment and economic growth. This second channel, referred to
in this paper as the “productivity channel,” is represented as ② in Figure 1. In contrast to the first
channel, the second channel focuses on the ability of financial intermediaries to allocate credits
efficiently, possibly because it impairs the ability of financial institutions to manage effectively
informational frictions with borrowers.
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Figure 1. Inflation Transmission Mechanism in the Financial System
Productivity of Capital
1
Finance
Development
2
Investment
Long-Run Economic
Growth
Inflation
To address these questions, we apply the endogenous threshold model developed by Hansen
(1996, 1999) and a rolling regression technique suggested by Rousseau and Wachtel (2002).
Using a five-year-average panel dataset with 90 countries from 1961-2005, we first examine
whether the positive relationship between finance and growth varies with the inflation rate. We
find that an inflation threshold exists at around 15 percent. Below this threshold, financial
activity stimulates economic growth, but when inflation exceeds the threshold, then the link
between finance and growth is severed. Second, we examine whether inflation exerts this effect
on the finance-growth nexus through the capital accumulation channel ① or the productivity
channel ②.To our knowledge, this study is the first to examine this issue.
A main finding of this paper is that the effect of inflation on the finance-growth nexus is
transmitted through the productivity channel (②) rather than the capital accumulation channel
(①). In other words, during high-inflation periods, the ability of financial intermediaries to
accumulate capital may not be significantly affected, but the productivity of capital may be
significantly impaired. This reduction in the productivity of capital might come from an adverse
effect of inflation on financial intermediaries’ ability to allocate resources and monitor projects
efficiently as theoretical studies have suggested (e.g., Townsend, 1979; Greenwood and
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Jovanovic, 1990) . In sum, a decline in the productivity of capital, rather than the level of capital
accumulation, may be the key reason inflation impairs economic activity.
The paper is organized as follows. Section 2 briefly reviews the literature on the relationship
between inflation, financial development, capital accumulation, and economic growth. Section 3
discusses the data including the construction of the panel dataset and the measures of financial
and real activity. Section 4 outlines the econometric methodology and presents our estimation
results for the inflation threshold in the finance-growth nexus. Section 5 focuses on the inflation
transmission channel through the financial system. Section 6 analyzes the nonlinear relationship
between the productivity of capital and inflation. Section 7 offers some concluding remarks and
policy implications.
2. Related Literature
The finance-growth relationship has been examined intensively in the past two decades. The
traditional views of the finance-growth nexus are pioneered by Schumpeter (1911), who
emphasized a proactive role of financial services in promoting growth and development.
Goldsmith (1969) and McKinnon (1973) provided some analytic foundations for this view and
supported it with simple but persuasive observations. These treatments focused on the role of
financial repression as manifested in government interventions in the financial sector, such as
ceilings on interest rates and directed credit programs, in hampering financial development and
thereby reducing rates of capital accumulation and productivity growth.
The more recent financial intermediation literature stems from seminal works by Townsend
(1983a, 1983b), Diamond (1984), and Boyd and Prescott (1986), which demonstrated that, in the
presence of private information and costly state verification, it is optimal for borrowing and
lending to be conducted through financial intermediaries rather than bilateral contracts. Recent
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contributions to the endogenous growth theory, typified in models by Greenwood and Jovanovic
(1990) and Bencivenga and Smith (1991), have characterized the role of the services provided by
financial intermediaries in stimulating economic growth.
In sum, the theoretical literature identifies two distinct channels linking finance and long-run
economic growth. The first “factor accumulation” channel emphasizes the link between the
ability of financial intermediaries to provide liquidity services, mobilize resources, help firms to
overcome project indivisibilities, and improve the rate of capital accumulation, and in turn
promote economic growth (Gurley and Shaw, 1955; Bencivenga and Smith, 1991). The second
so-called “productivity channel” emphasizes that the financial system may be important for the
productivity of capital investment because it is important for resolving informational
asymmetries, improving the quality of project selection, and monitoring financing (Townsend,
1979; Greenwood and Jovanovic, 1990).
The empirical literature concludes that there is a strong positive relationship between
measures of financial market development and real economic performance. In a classic study,
Goldsmith (1969), using data from 1860 to 1963, showed that a positive relationship exists
between economic development and the size of the financial sector. Kuznets (1971), in a crosssectional study of 57 developed and developing economies, found that the share of banking,
assurance, and real estate in GDP rises as income increases. Another well-known study is
McKinnon (1973) who showed that the ratios both of private credit to GDP and a broad
monetary aggregate to GDP are positively related to per capita income. More recently, King and
Levine (1993a) utilized more sophisticated techniques and reconfirmed the correlation between
growth rates and various measures of financial development in a cross-section of more than 80
countries (see also Levine and Zervos (1998)).
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Various studies have reached similar conclusions by using time series rather than crosssectional data. Jung (1986) applied the method of vector autogregressions (VARs) on post-1960
annual time series for financial and real variables, and found bi-directional causality in most
cases. More recently, Rousseau and Wachtel (1998) applied the VAR approach to five
industrialized countries over the 1870-1929 period and found strong uni-directional links from
finance to growth. Rousseau and Vuthipadadorn (2005) used the same approach for 10 Asian
economies and reached similar conclusions.
While intensive studies have examined the relationship between finance and growth, very
few efforts have been made to identify why a link exists between finance and growth. Empirical
studies on the issue of whether the finance-growth relationship is due to the “capital
accumulation channel” or the “productivity channel” are mixed and surprisingly scarce. Barro
(1995) employed a cross-sectional dataset to examine the impact of inflation on the amount of
capital investment. Barro (1995) found a negative effect of inflation on the INV-GDP ratio and
interpreted this finding as evidence for the presence of a capital accumulation channel. McClain
and Nichols (1994) studied time series for the U.S. from 1929-1987 and found that the amounts
of investment and inflation are positively correlated. Due to data difficulties, no empirical
research so far has explicitly examined the “productivity channel.”
The previous studies have generally assumed a constant relationship between finance and
growth. That is, they have not considered whether economic conditions, such as the rate of
inflation, are associated with a stronger or weaker finance-growth relationship. Andres et al.
(1999) pointed out that empirical studies have focused on either the finance-growth relationship
or the inflation-growth relationship, but not linked the two. These researchers tried to bring
inflation into the picture of the finance-growth relationship by using a data set mainly from
industrialized (OECD) countries and found that inflation affects growth through its interaction
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with financial market conditions. Rousseau and Wachtel (2002) went one step further and
examined the variation in the strength of the finance-growth relationship with respect to the rate
of inflation. Using a rolling regression technique and data for 84 countries from 1960 to 1995,
Rousseau and Wachtel (2002) found that the positive effect of financial deepening1 on growth is
weakened when inflation exceeds a threshold, which they estimated to be between 13 and 25
percent.
A shortcoming of these few studies of the effect of inflation on the finance-growth nexus is
that they have not examined whether and why this relationship is nonlinear and, in particular,
whether and why a threshold inflation rate exists above which this relationship kicks in. In our
study, we examine inflation effects on two potential channels which connect finance and
growth—the capital accumulation channel and the productivity channel—in order to bring
together the inflation-growth nexus and the finance-growth nexus with a focus on their nonlinear
features. In particular, the rest of this paper will examine the evolution of the capital
accumulation channel and the productivity channel with respect to the inflation rate in an attempt
to explain the nonlinear inflation effect on the finance-growth nexus.
3. Data
Our study is based on a panel dataset constructed mainly from the World Development
Indicators (WDI) and International Financial Statistics (IFS) databases. The dataset includes 90
countries and generally covers the period from 1961-2005. The selection of countries is based on
data availability.
To examine the interaction among inflation, finance, and real economic performance, we use
the growth rate of the CPI index as a measure of inflation and the growth rate of real per capita
1
Three measures of financial sector depth were used: the broad money supply (M3), M3-M1, and the total credit, each as a
percentage of GDP.
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GDP as a measure of real economic performance. To ensure comparability with previous studies,
we use three measures of financial development: the ratios of liquid liabilities (M3) to GDP,
quasi-liquid liabilities (M3-M1) to GDP, and domestic credit to GDP. M3 as a ratio of GDP has
become a standard measure of financial depth and an indicator of the overall size of financial
intermediary activity in cross-country studies. M3 less M1 removes the pure transactions
component and focuses on the intermediation activities of depository institutions. Domestic
credit includes all credit to domestic sectors with the exception of credit to the central
government. The credit is provided by monetary authorities and deposit money banks, as well as
other banking institutions where data are available (including institutions such as credit unions
and mortgage loan companies). Since banking institutions play the most important role in many
countries’ economies, the ratio of domestic credits to GDP is used as an indicator of the overall
level of financial intermediation in an economy.
In addition, we measure the level of investment in an economy as gross fixed capital
formation (formerly gross domestic fixed investment) as a share of GDP. The investment-GDP
ratio is generally considered to be the best variable measure of the level of investment in crosscountry studies since this ratio accounts for country size. The productivity of capital is typically
measured by the Marginal Product of Capital (MPK). We use two measures of the productivity
of capital: the growth rate of Total Factor Productivity (TFP) and the growth rate of the average
productivity of capital (Y/K). The dataset for the growth rate of TFP is constructed based on the
assumption that the production function follows a Cobb-Douglas specification with constant
returns to scale between capital and labor. In other words, we assume a production
function Y = AK α L1−α across countries, where Y is output, A is an index of total factor
productivity, and K and L are the stocks of physical capita and labor, respectively. The growth
rate of TFP can be derived as
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TFP Growth= gY − α * g K − (1 − α ) * g L ,
(1)
where g Y denotes the growth rate of real GDP, and g K and g L denote the growth rates of the
total physical capital stock and labor, respectively. In practice, we calculate the TFP growth
index on the basis of equation (1) by using a value of 0.4 for α .2 We also consider that α = 0.3
to ascertain the robustness of the results. An alternative measure of the productivity of capital is
the growth rate of the average productivity of capital, which is measured by the difference
between the growth rate of real GDP and the growth rate of physical capital stock, that is,
gY − g K .
Our main source of data for the above two measures of the productivity of capital is the
database on physical capital stocks (K), working-age population (L), and gross domestic product
(GDP), constructed by Nehru and Dareshwar (1993). They derived the data for physical capital
stocks by using the perpetual inventory method applied to gross domestic fixed investment series.
This data base covers the period 1950-1990, but we updated it through 2005 by using data from
the World Development Indicators (WDI) and International Financial Statistics (IFS). The
specific procedures used in this updating are described in the Appendix.
Other control variables required for our study include the real GDP measured as GDP per
capita in 2000 constant U.S. dollars, the secondary school enrollment rate, government
consumption expenditure as a share of GDP, and the growth rate of the terms of trade. The
secondary school enrollment rate, which comes from Barro and Lee’s (2000) education dataset,
is more widely available than more specific measures of human capital. Therefore, we use this
rate as an overall indicator of the commitment towards investment in human capital. The growth
2
The 0.4 average capital share in output is used by Fischer 1993，Nehru and Dareshwar (1993), Marfan and Bosworth (1994),
and Fajnzylber and Lederman. Collins and Bosworth (1996) use a capital share of 0.35 in their study of TFP growth and assert
that “we believe, from the existing literature, that a plausible range for the capital share is 0.3 to 0.4; and there is also
considerable evidence that the capital elasticity is higher in developing countries than in industrial economies” (p.155).
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rate of the terms of trade (TOT) is included to control for international trade effects on economic
growth.
The use of data averaged over a number of years is a standard approach for analyzing the
long-term determinants of growth. Since our interest is the long-term interaction among inflation,
finance, and economic growth, we use five-year-average data. Therefore, the time dimension is
reduced to nine observations: 1961-65, 1966-70, 1971-75, 1976-1980, 1981-1985, 1986-1990,
1991-1995, 1996-2000, and 2001-05. The potential dimension of the panel is 90 × 9=810
observations but data for a number of developing countries are not available for the entire period.
Because of the uneven coverage, the analysis is conducted using unbalanced panels.
4. Inflation thresholds in the Finance-Growth Nexus
We first focus on how the finance-growth nexus is affected by the inflation rate. This section
uses two econometric methods, a rolling regression approach and an endogenous threshold
technique, to test for the existence of an inflation threshold in the finance-growth relationship.
The endogenous threshold model has the advantage of providing a confidence interval for the
estimate of the threshold inflation rate.
4.1 Methodology
(1) Baseline Growth Regressions
The following specification captures the basic relationship between financial development and
growth:
growthit = β 0 + β 1 * FIN it + θ ′ ∗ X it + υ t + eit ,
(2)
where the dependent variable, growthit , is the growth rate of the real per capita GDP, and the
explanatory variables include one of three measures of financial sector depth ( FIN it ), and a
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vector of control variables ( X it ) including the inflation rate (Inflation), the logarithm of the
initial income per capita (Income), the logarithm of initial secondary school enrollment rate
(SSER), the government consumption expenditure share of GDP (GOV), and the growth rate of
the terms of trade (TOT). The contemporaneous five-year inflation rate (Inflation) is included in
the growth equation in order to control for the direct effect of inflation on growth. The growth
rate of the terms of trade (TOT) is used to control for external supply shocks. υ t is a countryinvariant time-specific intercept that captures omitted time effects, and eit is the error term.3 The
index "i" is the cross-sectional index, and "t" is the time-series index.
Since coefficients in the baseline specification for the growth model may be influenced by
the simultaneity between growth and contemporaneous measures of financial depth, inflation,
and other control variables, we use instrumental variables to extract their predetermined
components. The instruments used for financial depth, inflation, government expenditure, and
the growth rate of terms of trade are their initial values in each five-year period. The instrument
used for income is the value of GDP per capita at the beginning of the data set. In addition, we
include initial values of TOT and the INV-GDP ratio as well as the initial values of the financial
depth measures, which are not included as regressors, as additional instruments. Since TwoStage-Least-Square (2SLS) estimation might be sensitive to different sets of instrumental
variables, the growth equation is estimated using various sets of instruments.
(2) Rolling Regression
After estimating the baseline growth equation (2), we employ a rolling regression technique to
examine the manner in which inflation alters the impact of financial development on growth. A
rolling regression technique is also used by Rousseau and Wachtel (2002). Their approach has
3
The country dummies are not included in the growth regression because the logarithm of the initial secondary school enrollment
rate (SSER) is a country invariant variable. If SSER is included together with country dummies as regressors, we would have a
multicollinearity problem.
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three steps: first, they order panel observations by the magnitude of the inflation rate; second,
they estimate the growth equation sequentially, starting with 50 panel observations with either
the lowest or highest inflation rates, and then add one observation at a time until the full sample
is included; finally, they present a graphical illustration of the evolution of the coefficient on the
financial depth variable as the sample grows. This graph provides a view of the influence of
inflation on the finance-growth nexus.
A drawback of the above rolling regression technique is that the sequential regressions have
different sample sizes. Rousseau and Wachtel (2002) use 50 observations as the starting number
of observations and then add one observation at a time until the full sample is included. With this
technique, the regression coefficients are estimated from different sample sizes. A potential
problem is that the finance coefficients might not be comparable since the coefficients estimated
with small sample sizes might have large standard errors and be insignificant purely due to the
sample size. To avoid this drawback, this paper uses a rolling regression with a constant number
of observations —namely, a 300—observation rolling window. This approach requires first
ordering observations by the size of the inflation rate, estimating the growth equation (1)
sequentially starting with the first 300 observations with the lowest inflation rates, and then
rolling in an additional observation and rolling out the initial observation one-by-one so that each
finance coefficient is estimated from a 300-observation window. Finally, we present a graph of
the evolution of the finance coefficient as the average inflation rate of the 300-observation
rolling window grows. This graphical presentation then provides us with a view of how the
finance-growth linkage evolves as the inflation rate increases. For example, a downward sloping
curve, with the finance coefficient on the vertical axis and the average inflation rate of the 300observation rolling window on the horizontal axis, would suggest that the finance-growth nexus
is weakened in a high inflationary environment.
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(3) Endogenous Threshold Model
a. Threshold Model Specification
A drawback of the rolling regression technique is that it does not precisely determine whether
there is an inflation threshold above which finance ceases to stimulate growth. To precisely
estimate the inflation threshold in the finance-growth nexus, we use Hansen’s (1996, 1999)
endogenous threshold model. In particular, we estimate the following regression:
growth
it
= β 0 + β 1 ∗ FIN
it
∗ I ( Inf it < Pistar ) + β 2 ∗ FIN
it
∗ I ( Inf it ≥ Pistar )
+ θ ′X it + λ ′Tit + eit ,
(3)
where the dependent variable growthit and the vector of control variables X it are defined as in
Equation (2), Tit represents a vector of time dummies, and Pistar is the inflation threshold. 4
I ( Inflation < Pistar ) and I ( Inflation ≥ Pistar ) are indicator functions which take the value of
one if the term between the parentheses is true, and zero otherwise. The finance effect on growth
is measured by β1 and β 2 , which, respectively, capture the effect of finance on growth when
inflation rates are below and above the threshold level Pistar .
b. Estimation and Inference
The Two-Stage-Least-Square (2SLS) method is used to deal with potential endogeneity among
growth and financial depth, inflation, and the other control variables.5 We use the same set of
instrumental variables as in (2). For any Pistar , (3) is estimated by 2SLS, which yields the error
sum of squares (ESS) as a function of Pistar . The least square estimate of Pistar * is found by
4
⎧1, periodit = t
Tit = ⎨
⎩0, otherwise
, where
Tit
denotes the time dummies. The varaible
periodit
denotes the nine periods from 1961-
65 to 2001-05, each of which is assigned a number from 1 to 9. For example, number 1 denotes the period 1961-65, 2 denotes the
period 1966-1970 and so on. The time dummy
time dummy
Tit
Tit
is equal to 1 when
periodit equals t , and is zero otherwise. In such a way, a
carries the information for the period t .
5
The fixed effect estimation method cannot be used in our panel data analysis since the secondary school enrollment rate (SSER)
is a country invariant variable.
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selecting the value of Pistar which minimizes ESS, thus maximizing the R 2 of the regression.
Stacking the observation in vectors, we can write:
growth = Z γ ( Pistar ) + e
(4)
Pistar ∈ ( Pistar ,......, Pistar ) ,
where γ ( Pistar ) =( β 0 , β1 , β 2 , θ ′, λ ′ ) is the vector of parameters, and Z is the corresponding matrix
of observations on the explanatory variables. Note that the coefficient vector γ ( Pistar ) is indexed
by Pistar to show its dependence on the threshold levels of inflation, the range of which is given
by Pistar and Pistar . Define ESS ( Pistar ) as the error sum of squares with the threshold level
fixed at some value, Pistar . Then, the threshold estimate Pistar * is chosen by minimizing
ESS ( Pistar ) ; that is:
Pistar * = arg
min { ESS ( Pistar ) }, s.t.
Pistar = ( Pistar ,......, Pistar ) .
(5)
( Pistar )
After the inflation threshold Pistar * is estimated, it is important to determine whether the
threshold effect is statistically significant. The hypothesis of no threshold effect in (3) can be
represented by the linear constraint H 0 : β1 = β 2 . Then the Likelihood Ratio (LR) test statistic
of H 0 : β1 = β 2 is based on
LR = ( S 0 − S1 ) / σ 2 ,
(6)
where S 0 and S1 are the error sum of squares under H 0 : β1 = β 2 , and H 1 : β1 ≠ β 2 , respectively,
and σ 2 is the variance of the residuals under H 1 . Under H 0 , the threshold Pistar is not
identified, so the asymptotic distribution of LR is non-standard and strictly dominates
the χ k2 distribution. Hansen (1996) suggests the use of a bootstrap method to simulate the
asymptotic distribution of the likelihood ratio test. Given the panel nature of our dataset, we
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implement the following bootstrap procedure. First, treat the regressors Z it and threshold
variable Pistar * as given, holding their values fixed in repeated bootstrap samples. Second, take
the regression residuals eit* , and group them by individual: eˆi* = (eˆi*1 , eˆi*2 ,..., eˆiT* ) . We treat the
sample {eˆ1* , eˆ2* ,..., eˆn* } as the empirical distribution to be used for bootstrapping. Third, draw (with
replacement) a sample of size n from the empirical distribution and use these errors to create a
bootstrap sample under H 0 . Fourth, using the bootstrap sample, estimate the model (3) under the
null hypothesis β1 = β 2 and the alternative β1 ≠ β 2 , and calculate the bootstrap value of the
likelihood ratio statistic LR. Next, we repeat this procedure a large number of times (i.e.,10,000
times) and calculate the percentage of draws for which the simulated statistic exceeds the actual.
The result is the bootstrap estimate of the asymptotic p-value of LR under H 0 . Finally, we can
reject the null of no threshold effect if the p-value is smaller than the desired critical value (e.g.,
0.05).
c. Confidence Intervals
It is an interesting question whether the estimated inflation threshold (e.g., 10 percent) is
significantly different from a threshold of, say, 8 percent or 15 percent. In other words, can the
concept of confidence intervals be generalized to threshold estimates? Hansen (1999) suggested
that the best way to form confidence intervals for Pistar is to form the “no-rejection region” by
using the likelihood ratio statistic for tests on Pistar . To test the hypothesis H 0 : Pistar * = Pistar ,
where Pistar is the true value of Pistar * , the likelihood ratio test rejects for large values of
LR ( Pistar * ) ,
where LR ( Pistar * ) = ( S 2 ( Pistar ) − S 2 ( Pistar * )) / σ 2 .
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(7)
Hansen (1999) showed that the asymptotic distribution of the likelihood ratio statistics
LR ( Pistar * ) is non-standard but free of nuisance parameters. In addition, Hansen (1999) forms
asymptotic confidence intervals by using the inverse of the asymptotic distribution function
of LR ( Pistar * ) :
c (α ) = −2 log(1 − 1 − α ) .
(8)
With this method, critical values can be calculated easily. For example, the 10% critical
value is 5.94, the 5% is 7.35, and the 1% is 10.59. A test of H 0 : Pistar * = Pistar rejects at the
asymptotic level α if LR ( Pistar * ) exceeds c (α ) . To form an asymptotic confidence interval
for Pistar * , the “no-reject region” of confidence level 1 − α is the set of values of Pistar * such
that LR ( Pistar * ) ≤ c(α ) .
4.2 Estimation Results
(1) Baseline growth regressions
Table 1 reports the Instrumental variables (IV) estimates from our baseline growth regression (2).
Panels A, B and C present the estimation results of (2) with finance measured by M1/GDP, (M3M1)/GDP, and domestic credit/GDP, respectively. The second and the third columns of each
panel present the estimation results of the Two-Stage-Least-Square (2SLS) estimation with two
different sets of instrumental variables. For comparison purposes, we also include the simple
OLS estimation results in the first column of each panel.
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Table1. Baseline Growth Specification [Equation (1): growthit = β 0 + β1 * FIN it + θ ′ ∗ X it + υ t + eit ]
Panel A
M3/GDP
Financial variable:
Financial Variable
Inflation
Log initial GDP Per Capita
log initial SSER
Gov/GDP
TOT
constant
R Square Adjusted
Number of Observations
Number of Countries
Panel B
(M3-M1)/GDP
Panel C
Credit/GDP
OLS
0.024*
(5.883)
-0.002*
(-17.35)
-0.319*
(-2.571)
0.524*
(3.904)
-0.061*
(-3.224)
0.132*
(6.828)
2.878*
(3.691)
2SLS-1
0.028*
(7.128)
-0.002*
(-3.459)
-0.615*
(-4.861)
0.741*
(5.559)
-0.049**
(-2.459)
0.139*
(4.562)
4.284*
(5.295)
2SLS-2
0.029*
(7.149)
-0.002*
(-3.426)
-0.634*
(-4.991)
0.759*
(5.673)
-0.050**
(-2.514)
0.141*
(4.596)
4.387*
(5.409)
OLS
0.037*
(5.567)
-0.002*
(-14.27)
-0.319*
(-2.593)
0.472*
(3.446)
-0.057*
(-3.001)
0.130*
(6.776)
3.203*
(4.057)
2SLS-1
0.043*
(7.231)
-0.002*
(-3.531)
-0.620*
(-4.913)
0.682*
(5.109)
-0.045**
(-2.277)
0.133*
(4.417)
4.730*
(5.712)
2SLS-2
0.043*
(7.268)
-0.002*
(-3.531)
-0.642*
(-5.048)
0.701*
(5.225)
-0.046**
(-2.330)
0.134*
(4.356)
4.858*
(5.848)
OLS
0.007**
(2.127)
-0.002*
(-13.68)
-0.148
(-1.111)
0.538*
(3.796)
-0.061*
(-3.105)
0.137*
(6.936)
2.122*
(2.540)
2SLS-1
0.014*
(3.955)
-0.002*
(-4.579)
-0.557*
(-4.206)
0.812*
(5.833)
-0.049**
(-2.361)
0.170*
(5.417)
4.010*
(4.712)
2SLS-2
0.014*
(4.173)
-0.002*
(-4.478)
-0.574*
(-4.336)
0.820*
(5.898)
-0.047**
(-2.270)
0.148*
(4.682)
4.218*
(4.957)
0.2751
660
90
0.2671
660
90
0.2661
660
90
0.2755
660
90
0.2671
660
90
0.2659
660
90
0.2353
660
90
0.2111
660
90
0.2138
660
90
Notes: The dependent variable is the five-year average growth rate of real per capita GDP. We use two sets of instrumental variables to estimate the finance
coefficient in each panel. The Two-Stage-Least-Square (2SLS) estimates with the initial values of those financial depth measures not included as repressors as
additional instruments are reported in the second column under the title 2SLS-1 in each panel, while the results reported in the third column (2SLS-2) in each
panel are the 2SLS estimates excluding the initial values of those financial depth measures not included as repressors from the set of instruments. The t-statistic
for each coefficient, given in the parentheses, is computed from Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate
statistical significance at 1, 5, 10 percent, respectively. All equations include time dummy variables, but the estimated coefficients for the time dummies are not
reported. The number of countries and observations are shown in the last two rows.
-17-
The first impression from Table 1 is that finance coefficients are all positive and highly
significant. These results are consistent with those in the existing literature, which has found that
the positive finance-growth relationship is robust with respect to different measures of financial
depth. Note that a 10% increase in a financial depth measure is associated with a 0.1-0.4
percentage point increase in the annual growth rate. After corrections are made for the
endogeneity problem, the positive and significant finance coefficients in the standard growth
equation regression support the finance-leads-growth view.
The coefficients on the control variables in the growth equation are consistent with those in
previous studies. In particular, the direct inflation effects on growth are estimated to be
numerically small but all negative and significant at a 1 percent significance level. It would take
an increase in the inflation rate of more than 500 percentage points to depress the growth rate by
1 percentage point. In addition, in all equations, the initial secondary school enrollment rate—a
measure of human capital investment —has a significant and positive effect on growth; the initial
GDP has a negative growth effect as suggested by Neoclassical Growth Theory; government
expenditure as a share of GDP has a negative effect on growth, but its statistical significance is
questionable. Finally, the growth rate of the terms of trade is shown to have a very significant
and positive relationship with growth.
(2) Rolling Regressions
Table 2 shows the finance coefficients from the baseline growth equation for the above and
below median inflation observations. Clearly, the positive effects of financial depth on growth
are dampened substantially when we use the above median inflation observations. When we use
domestic credit/GDP as a measure of financial depth, the finance coefficient even turns out to be
insignificant for the high-inflation group.
-18-
Table 2. Sample Divided by Inflation Rate
Financial variable:
Full Sample [660]
< Median
[330]
> Median [330]
M3/GDP
OLS
0.024*
(5.883)
0.026*
(5.257)
0.016
(1.476)
2SLS-1
0.028*
(7.128)
0.030*
(5.583)
0.023**
(2.388)
(M3-M1)/GDP
2SLS-2
0.029*
(7.149)
0.029*
(5.334)
0.022**
(2.322)
OLS
0.037*
(5.567)
0.035*
(4.195)
0.029**
(2.555)
2SLS-1
0.043*
(7.231)
0.043*
(5.674)
0.034*
(2.723)
2SLS-2
0.043*
(7.268)
0.044*
(5.672)
0.036*
(2.864)
Credit/GDP
OLS
0.007**
(2.127)
0.008***
(1.806)
0.001
(0.276)
2SLS-1
0.014*
(3.955)
0.013**
(2.308)
0.007
(1.295)
2SLS-2
0.014*
(4.173)
0.015*
(2.723)
0.007
(1.390)
Notes: The dependent variable is the five-year average growth rate of the real per capita GDP. Financial coefficients reported here are estimated from the base line
growth equation, which includes the same set of explanatory variables as those in Table 1, while only the finance coefficient estimates are reported. All equations
include time dummy variables, but the estimated coefficients for the time dummies are not reported. OLS estimation and 2SLS estimation with different sets of
instruments are used. 2SLS-1 and 2SLS-2 are defined the same way as those in Table 1. The t-statistic for each coefficient, given in the parentheses, is computed
from Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate statistical significance at 1, 5, 10 percent, respectively. The
median inflation rate of the sample with 660 observations is 8.5%.
-19-
Figure 2 shows the evolution of the finance coefficients as the average inflation rate of a
constant 300-observation rolling window increases. Specifically, Figures 2-1, 2-2, and 2-3 show
these plots when the growth equation is estimated with M3/GDP, (M3-M1)/GDP, and domestic
credit/GDP as financial depth measures, respectively. In each figure, the solid line gives the
finance coefficient estimates from the rolling window, and the 5-percent-confidence intervals are
represented by the dotted lines. To shed light on where the inflation thresholds lie, we draw a
solid bold horizontal line (the “benchmark line”) passing through the vertical axis at the point
which equals the finance coefficient estimate from the full sample by using 2SLS-1 estimation.
In Figure 2-a, for example, this line is at the point 0.28 on the vertical axis.
Figure 2 suggests that, generally, the relationship between financial depth and growth is
stronger when inflation rates are below 14 percent, and tends to weaken as the average inflation
rate increases. In Figure 2-1, for example, the coefficient on M3/GDP is about 0.02 points above
the benchmark line when inflation rates are lower than 14 percent, but about 0.01 points below
the benchmark line when inflation rates are higher than 16 percent. This suggests a threshold
inflation rate of somewhere between 14 and 16 percent. In other words, only when inflation is
beneath the threshold is M3/GDP important for growth. The conclusions are similar when
alternative measures of financial depth are used.
(3) Endogenous Threshold Model
Next, we use the endogenous threshold model to estimate more precisely the inflation threshold.
The inflation threshold Pistar * is estimated to be 16.0%, 15.3%, and 14.1% with M3/GDP, (M3M1)/GDP, and domestic credit/GDP serving as the financial depth measure, respectively. Note
that the inflation threshold estimates from the endogenous threshold model are consistent with
our observations from the rolling regressions.
-20-
Figure 2. Rolling Regression
Figure 2-1.
0.09
0.07
0.05
22
18
15
14
12
11
10
10
9
8
8
7
7
6
6
5
5
4
0.03
4
Coefficient on M3 (%of GDP)
Rolling Regression - M3/GDP
(Inflation Ordered by Increasing Inflation)
0.01
-0.01
Ave rage Inflation Rate in 300 Obs e rvations Rolling Window
Figure 2-2.
Rolling Regression - (M3-M1)/GDP
(Observations Ordered by Increasing Inflation)
0.08
95
22
18
16
14
13
12
11
10
9
9
8
8
7
7
6
6
5
5
0.04
4
0.06
4
Coefficent on (M3-M1)/GDP
0.10
0.02
0.00
Average Inflation in 300 observations Rolling Window
Figure 2-3.
0.05
0.03
22
18
15
14
12
11
10
10
9
8
8
7
7
6
6
5
5
4
0.01
4
Coefficent on Credit/GDP
Rolling Regression - Credit/GDP
(Observations Ordered by Increasing Inflation)
-0.01
Average Inflation Rate in 300 Observation Rolling Window
Notes: These figures show the evolution of coefficients on financial variables in cross-country 5-year growth regressions
as the 300-observation rolling window moves from the lowest inflation to the highest inflation. Estimation is by 2SLS with
initial values of those financial depth measures not included as repressors as additional instruments. The estimation results
for the full sample are reported in the second column (2SLS-1) of each panel in Table 1.
-21-
Table 3. The Endogenous Threshold Model
[Equation (3): grow thit = β 0 + β 1 ∗ FIN it ∗ I ( Inf it < Pistar ) + β 2 ∗ FIN it ∗ I ( Inf it ≥ Pistar ) + θ ′ X it + λ ′Tit + eit ]
Financial variable:
Financial Variable
(Inflation<Pistar)
Financial Variable
(Inflation>=Pistar)
Inflation
log initial GDP
log initial SSER
GOV/GDP
% change of TOT
constant
Threshold Searching Range
*
Inflation Threshold Pistar
Likelihood Ratio (LR)
No Threshold against one
Threshold 5% Confidence Interval
R Square Adjusted
Number of Observations
Number of Countries
Panel A
M3/GDP
OLS
0.023*
(6.023)
[411]
-0.006
(-0.742)
[149]
-0.001*
(-5.173)
-0.272**
(-2.407)
0.478*
(3.832)
-0.064*
(-3.508)
0.127*
(8.274)
2.817*
(3.819)
{1.0,120.0}
2SLS
0.027*
(6.815)
[411]
0.003
(0.296)
[149]
0.001*
(-2.943)
-0.561*
(-4.457)
0.690*
(5.215)
-0.051*
(-2.591)
0.135*
(4.498)
4.146*
(5.190)
{1.0, 120.0}
Panel B
(M3-M1)/GDP
OLS
2SLS
0.037*
0.043*
(5.490)
(7.249]
[504]
[504]
0.003
0.013
(0.221)
(1.041)
[156]
[156]
-0.002*
0.002*
(-13.21)
(-3.218)
-0.295**
-0.590*
(-2.397)
(-4.686)
0.457*
0.663*
(3.363)
(4.998)
-0.058*
-0.045**
(-3.036)
(-2.301)
0.127*
0.131*
(6.709)
(4.397)
3.105*
4.583
(3.951)
(5.574)
{1.0, 120.0}
{1.0, 120.0}
16.0%
17.311
1%
[13.2, 22.4]
0.2929
660
90
16.0%
17.305
1%
[13.2,22.4]
0.2852
660
90
15.3%
41.794
1%
[13.0, 35.0]
0.2851
660
90
15.3%
10.078
1%
[13.0, 35.0]
0.2773
660
90
Panel C
Credit/GDP
OLS
2SLS
0.011*
0.017*
(3.355)
(4.869)
[486]
[486]
-0.006***
-0.0001
(-1.244)
(-0.030)
[174]
[174]
-0.002*
-0.002*
(-5.335)
(-3.319)
-0.175
-0.559*
(-1.515)
(-4.286)
0.495*
0.752*
(3.861)
(5.472)
-0.061*
-0.050**
(-3.237)
(-2.457)
0.134*
0.165*
(8.534)
(5.345)
2.358*
4.127*
(3.078)
(4.914)
{1.0, 120.0}
{1.0, 120.0}
14.1%
15.100
1%
[11.7, 22.4]
0.2517
660
90
14.1%
20.310
1%
[13.2, 22.4]
0.2340
660
90
Notes: The dependent variable is the five-year average growth rate of real per capita GDP. The Two-Stage-Least-Squares (2SLS) estimation, reported in the second column of
each panel, uses the initial values of those financial depth measures not included as repressors as additional instruments. The t-statistic for each coefficient, given in the parentheses,
is computed from Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate statistical significance at 1, 5, 10 percent, respectively. All
equations include time dummy variables, but the coefficient estimates for the time dummies are not reported. The number of countries and observations are shown in the last two
rows. The inflation threshold Pistar * is the threshold estimate, which achieve the minimum value of ESSs in the threshold search range.
-22-
Table 3 presents the estimation results of (3) with the inflation threshold Pistar * . Specifically,
panels A, B, and C of Table 3 show the estimates of (3) with M3/GDP, (M3-M1)/GDP, and
domestic credit/GDP serving as the financial depth measure, respectively. For comparison
purposes, the OLS estimates are also reported. Table 3 suggests that while the finance
coefficients ( β1′ s ) are all positive for the low-inflation group ( Inf it < Pistar * ), none of the
finance coefficients ( β 2′ s ) are statistically significant for the high-inflation group
( Inf it ≥ Pistar * ). The chief conclusion from these results is that the depth of the financial system
plays a positive role in real economic performance only when inflation is less than around 15
percent.
To test if the threshold estimate Pistar * , reported in each column of Table 3, is statistically
significant, we use the Likelihood Ratio (LR) test suggested by Hansen (1999). The row labeled
LR in Table 3 gives the observed value of the likelihood ratios for testing the hypotheses of no
threshold against one threshold. The significance levels of the LR test were computed by using
the bootstrap distributions of LR . Our results suggest that the null hypothesis of no threshold
effects can be rejected at a 1 percent significance level for all regressions. This evidence strongly
supports the existence of an inflation threshold. Hansen (2000) also suggests that under the
alternative hypothesis of the existence of a threshold effect, the t-statistic for each coefficient has
the usual distribution and thus the t-tests presented in Table 3 are valid.
Next, we compute the confidence intervals around the threshold estimates. If a confidence
interval shows that the threshold estimate is not significantly different from a large number of
other potential threshold levels, the implication is that substantial uncertainty exists about the
threshold level. The confidence intervals for the estimated thresholds at the 5 percent
significance level are [13.2, 22.4], [13.0, 35.0], and [13.2, 22.4] when M3/GDP, (M3-M1)/GDP,
-23-
and domestic credit/GDP, respectively, serve as the measure of financial depth. This finding
implies that the thresholds are reasonably precisely estimated. In addition, the confidence
intervals for the inflation threshold estimates are also consistent with the estimates of Rousseau
and Wachtel (2002), which suggest that an inflation threshold for the finance-growth relationship
lies between 13 and 25 percent.
4.3 Summary of Inflation Effects on the Finance-Growth Nexus
Our major findings in this section are summarized in Figure 3, which demonstrates how inflation
affects the finance-growth relationship nonlinearly. As Figure 3 shows, the inflation threshold
Pistar * divides the axes of inflation into two parts. As inflation rises from zero percent up to the
threshold Pistar * , the effect of finance on economic growth is stable and significant at the level
of β 1 ; when inflation rises beyond the threshold Pistar * , the finance coefficient decreases
dramatically and, in some cases, is not significantly different from zero. Inflation threshold
estimates Pistar * are quite robust to the use of different measures of financial depth.
Figure 3. Demonstration of Inflation Thresholds
Finance Coefficient
β1
β2
Inflation Threshold
Pistar *
-24-
Inflation Rate
5. The Inflation Transmission Mechanism in the Finance-Growth Nexus
The depth of financial sector development can promote economic growth in two ways: by
encouraging savings and investment (through the capital accumulation channel) and by
improving the allocation of funds among investment projects (through the productivity channel).
In Section 4, it was found that inflation severs the finance-growth nexus in high-inflation
environments. This section studies how inflation impacts the two channels mentioned above,
which are involved in the finance-growth nexus.
5.1 Methodology
(1) Baseline Models
Our study employs the following two baseline specifications (9) and (10) to shed light on the
ability of finance to promote economic growth through the capital accumulation channel and the
productivity channel, respectively.
INVit = α 0 + α1 * FIN it + δ ′Z it + υt + eit
(9)
growthit = γ 0 + γ 1 * INVi ,t + φ ′ X it + υt + eit ,6
(10)
where vt and eit , shown in both equations, represent country invariant time dummy variables
and the error term, respectively.
The dependent variable INVit in Equation (9) represents the level of investment, which is
measured as gross fixed capital accumulation as a share of GDP. The explanatory variable FIN it
is one of the three measurements of financial depth: M3/GDP, (M3-M1)/GDP, and domestic
credit/GDP. The coefficient α1 measures the link between financial intermediation and the
accumulation of capital. In other words, the magnitude of α1 reflects the strength of the capital
6
The model specification is selected based on the Ramsey Reset Test. The results are reported in the Appendix.
-25-
accumulation channel. For example, a positive and significant α1 implies that financial
intermediation encourages a higher level of capital formation. Equation (9) also includes a set of
control variables Z it to control for the effects on investment from sources other than financial
development. Using the Ramsey Reset Test, a model specification test, we choose the following
variables as additional explanatory variables in (9): inflation (INF), the logarithm of initial
income (INCOME), government expenditure as a share of GDP (GOV), and Openness,
measured as the ratio of exports plus imports as a share of GDP. In addition, we include the first
lag of the growth rate of per capita GDP (lag_growth) in order to control for the effect of
economic conditions in the previous period on the current level of investment.
Equation (10) is estimated to measure the productivity channel. Equation (10) is a standard
specification of the growth equation, which regresses the average rate of real per capita GDP
growth for the five-year period on a set of conditioning variables. The explanatory variable INVit ,
measured by the INV-GDP ratio, is included to examine the growth effect of investment. Then
the investment coefficient γ 1 measures the productivity channel. A vector of control
variables X it is also included in (10). To maintain consistency, we use the same control variables
in (2). 7
The Two-Stage-Least-Square (2SLS) estimation method is used to address the endogeneity
problem associated with (9) and (10). The instruments used for (9) are the initial values of
financial depth, inflation, government expenditure, and openness in each five-year period, the
initial value of the logarithm of income over the entire period, and the initial value of gross fixed
7
The vector of the control variables ( X it ) in Equation (10) includes the inflation rate (Inflation), the logarithm of the initial
income per capita (Income), the logarithm of the initial secondary school enrollment rate (SSER), the government consumption
expenditure share of the GDP(GOV), and the growth rate of income terms of trade (TOT).
-26-
capital accumulation (INV). In addition, the one period lag of growth (lag_growth) is included in
the 2SLS estimation equation as an exogenous variable. The instrumental variables used for (10)
are the initial values of gross fixed capital accumulation, inflation, the secondary school
enrollment rate, government expenditure, the growth rate of the terms of trade in each five-year
period, and the initial value of the logarithm of income. In addition, we use the initial values of
those financial depth measures not included as regressors as instruments.
(2) Rolling Regressions
The baseline specifications (9) and (10) describe the finance-growth relationship as arising from
two channels: the capital accumulation channel and the productivity channel. We use a rolling
regression technique to examine inflation effects on these two channels. In particular, we
examine the evolution of the finance coefficient α 1 in (9) and the investment coefficient γ 1 in (10)
with respect to the inflation rate. We do so with 300-observation rolling windows, by using the
same technique as in Section 4.1.(2). Graphical presentations of the evolution of the finance
coefficient α 1 and the capital accumulation coefficient γ 1 , as the average inflation rate of the
300-observation rolling window increases, shed light on effect of inflation on the financeinvestment nexus and the investment-growth nexus, respectively.
(3) Endogenous Threshold Model
An alternative method that can be used to identify nonlinear inflation effects on the capital
accumulation channel and the productivity channel is the endogenous threshold model. We apply
the same technique as described in Section 4.1.(3) to examine whether a weaker capital
accumulation channel and/or a weaker productivity channel are associated with high inflation
rates. To do so, we employ the following two threshold model specifications:
INVit = α 0 + α1 * FINit * I ( Infit < Pistar ) + α 2 * FINit * I ( Inf ≥ Pistar ) + δ ′Z it + ζ ′Tit + eit
-27-
(11)
growthit = γ 0 + γ 1 ∗ INVit ∗ I ( Inf it < Pistar ) + γ 2 ∗ INVit ∗ I ( Inf it ≥ Pistar ) + φ ′ X it + λ ′Tit + eit .
(12)
In
each
equation,
Pistar
is
the
inflation
threshold; I ( Inflation < Pistar )
and
I ( Inflation ≥ Pistar ) are indicator functions which take the value of one if the term in
parentheses is true, and the value of zero otherwise. Equation (11) specifies the finance effect on
the level of investment with two discrete coefficients: α1 and α 2 , which denote the effect of
finance on investment when inflation rates are below and above the threshold level Pistar ,
respectively. In other words, α1 and α 2 measure the link between financial intermediation and
the accumulation of capital in low- and high-inflation environments, respectively. In contrast,
Equation (12) specifies the investment effect on growth with two discrete coefficients, γ 1 and γ 2 ,
which capture the productivity of investment when inflation rates are below and above the
threshold level Pistar , respectively.
5.2 Estimation Results
(1) Baseline Models
Table 4 presents the estimation results of Equation (9), which describes a baseline relationship
between financial depth and the level of investment. For comparison purposes, the OLS
estimation results are reported as well as the 2SLS estimation results when each of the three
measures of financial depth is employed. Consistent with the existing literature, the finance
coefficients are significant in all regressions. This finding supports the theoretical models which
characterize the role of financial intermediaries to mobilize unproductive resources and stimulate
the level of capital accumulation. In particular, a-one-percentage-point increase in
-28-
Table 4. Baseline Investment Specification (Investment-Finance Nexus)
[Equation (9): INVit = α 0 + α1 * FINit + δ ′Zit + υt + eit ]
Financial variable:
Financial Variable
Inflation
lag of Growth
Log of initial income
Openness
Gov/GDP
Constant
R Square Adjusted
Number of Observations
Number of Countries
Panel A
Panel B
Panel C
M3/GDP
(M3-M1)/GDP
Credits/GDP
OLS
0.027**
(2.100)
-0.001
(-1.582)
0.667*
(8.373)
0.262
(1.496)
0.053*
(4.896)
0.018
(0.376)
10.458*
(8.966)
2SLS
0.025*
(2.653)
-0.001
(-0.698)
0.676*
(9.875)
0.116
(0.581)
0.055*
(9.133)
0.038
(0.811)
11.224*
(7.449)
OLS
0.030***
(1.882)
-0.001
(-1.444)
0.715*
(7.575)
0.473**
(2.506)
0.052*
(6.382)
0.042
(0.961)
9.188*
(6.509)
2SLS
0.032**
(1.949)
-0.001
(-0.531)
0.733*
(8.552)
0.326
(1.511)
0.052*
(8.012)
0.049
(0.930)
10.122*
(6.046)
OLS
0.023*
(3.544)
-0.0001**
(-2.002)
0.745*
(8.244)
0.344**
(1.970)
0.048*
(6.612)
0.043
(0.944)
9.938*
(7.437)
2SLS
0.024*
(3.256)
-0.001
(-1.011)
0.754*
(9.158)
0.203
(0.999)
0.049*
(8.533)
0.049
(0.943)
10.795
(6.774)
0.3130
810
90
0.3120
810
90
0.3486
564
89
0.3481
564
89
0.3507
569
89
0.3498
569
89
Notes: The dependent variable is the five-year average capital accumulation as a share of GDP. The Two-Stage-Least-Square (2SLS) estimation, reported in the
second column of each panel, uses the initial value of financial depth measure, inflation rate, government expenditure, and Openness in each five-year period and
initial values of GDP per capita (Income) and capital accumulation in the whole period as instruments. In addition, the growth rate of previous period
(lag_growth) enters into the 2SLS estimation equation as an exogenous variable. Since we include the previous period growth rate (Lag_growth) as an
explanatory variable, we lose the initial observation for each country. The t-statistic for each coefficient, given in the parentheses, is computed from
Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate the statistical significance at 1, 5, 10 percent, respectively. All
equations include time dummy variables, but the coefficient estimates for the time dummies are not reported. The number of countries and observations are
shown in the last two rows.
-29-
Table 5. Growth and Investment: Baseline Model
[Equation (10): growthit = γ 0 + γ 1 * INVi ,t + φ ′ X it + υt + eit ]
Estimation Methods
OLS
2SLS-1
2SLS-2
Investment/GDP
0.171*
(12.89)
-0.001*
(-5.861)
-0.236*
(-2.720)
0.585*
(5.592)
-0.058*
(-3.599)
0.105*
(7.705)
0.424
(0.729)
0.3765
755
90
0.144*
(8.569)
-0.002*
(-3.304)
-0.452*
(-4.007)
0.726*
(5.822)
-0.060*
(-3.196)
0.143*
(4.905)
1.649**
(2.260)
0.3522
659
90
0.130*
(8.588)
-0.002*
(-3.760)
-0.464*
(-4.771)
0.814*
(7.211)
-0.034***
(-1.931)
0.143*
(5.255)
1.860*
(2.915)
0.3527
755
90
Inflation
log initial GDP per capita
(Income)
log initial SSER
GOV/GDP
% change of the TOT
Constant
R Square Adjusted
Number of Observations
Number of Countries
Notes: The dependent variable is the five-year average growth rate of real per capita GDP. We use two sets of instrumental variables to estimate the finance
coefficient in each panel. The Two-Stage-Least-Squares (2SLS) estimates with the initial values of those financial depth measures not included as repressors as
additional instruments are reported in the second column under the title 2SLS-1 in each panel, while the results reported in the third column (2SLS-2) in each
panel are the 2SLS estimates excluding the initial values of those financial depth measures not included as repressors from the set of instruments. The t-statistic
for each coefficient, given in the parentheses, is computed from Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate
statistical significance at 1, 5, 10 percent, respectively. All equations include time dummy variables, but the estimated coefficients for the time dummies are not
reported. The number of countries and observations are shown in the last two rows.
-30-
financial depth leads to a 0.024-0.032 percent increase in capital accumulation as a share of GDP.
All control variables in (9) have the expected sign and significance level. For example, inflation
enters into the investment equation negatively and significantly; the previous period growth rate
(lag_growth) shows a positive and significant effect on capital accumulation.
Table 5 presents the estimation results of (10), which describes the baseline relationship
between investment and economic growth. Again, we present 2SLS estimates with two different
sets of instruments as well as OLS estimates. The investment coefficients are significant and
positive in all regressions. In particular, a one-percentage-point increase in the INV/GDP ratio
leads to a 0.130-0.144-percentage-point increase in economic growth. All control variables in
(10) have the same signs and significance levels as those estimated from the baseline growth
equation (2).
In sum, estimation results from (9) and (10) support the view that finance promotes economic
growth through the capital accumulation channel as well as the productivity channel.
(2) Rolling Regressions
Figure 4 reports the rolling regression results of (9) for the three measures of financial depth. For
example, Figure 4-1 shows the evolution of the finance coefficient ( α1 ) as the average inflation
rate of the 300-observation rolling window increases when M3/GDP serves as a measure of
financial depth in (9). The three graphs in Figure 4 suggest that, generally, the finance coefficient
( α1 ) is not adversely affected by the increasing inflation rate. In fact, when M3/GDP or (M3M1)/GDP is used as the measure of financial depth, the finance coefficient tends to increase
when inflation rates are higher. Thus, the ability of financial intermediaries to promote the
accumulation of capital does not appear to be damaged by high inflation.
-31-
Figure 4. Rolling Regressions of Equation (9)
Figure 4-1:
Rolling Re gre ssiion - INV&M 3/GDP
(Obse rv ations orde re d by incre asing inflaiton)
0.15
0.10
0.05
20.8
21.7
38.5
16.7
18.1
14.3
12.8
11.7
10.7
10.0
9.3
8.6
8.0
7.5
6.9
6.4
5.8
5.4
4.9
4.4
4.0
3.6
-0.05
3.1
0.00
2.6
Coefficient on M3/GDP
0.20
-0.10
Ave rage Inflation Rate in 300 Obse rvation Rolling Window
Figure 4-2:
Rolling Regression - Inv and (M3-M1)/GDP (Observations
Ordered by increasing inflation)
0.15
0.10
15.5
13.7
12.4
11.4
-0.05
10.6
9.8
9.2
8.6
8.1
7.5
7.0
6.6
6.1
5.6
0.00
5.2
0.05
4.7
Coefficient on(M3-M1)/GDP
0.20
-0.10
Ave r age Inflation Rate in 300 Obs e rvation Rolling Window
Figure 4-3:
Rolling Regression - Inv & Credit/GDP
(Observations Ordered by Increasing Inflation)
0.10
0.05
22.1
18.1
15.4
13.5
12.2
11.2
10.3
9.56
8.92
8.33
7.76
7.21
6.69
6.19
5.71
5.23
0.00
4.72
Coefficient on Credit/GDP
0.15
-0.05
Ave rage Inflation Rate in 300 Obs e rvation Rolling Window )
Notes: These figures show the evolution of coefficients on the financial development in Equation (9) regressions as the
300-observation rolling window moves from the lowest inflation to the highest inflation. Estimation is by 2SLS with
initial values of those financial depth measures not included as repressors as additional instruments. The estimation results
for the full sample are reported in the second column (2SLS) of each panel in Table 4.
-32-
Figure 5 presents the evolution of the investment coefficient ( γ 1 ) from the rolling regressions
by using (10) with two different sets of instrumental variables. In contrast to Figure 4, Figure 5
shows a decreasing trend of the investment coefficient as the inflation rate increases. In order to
have a clearer view of where the inflation thresholds lie, we draw a benchmark line as a solid
bold horizontal line passing through the vertical axis at the point which equals the investment
coefficient estimated by using 2SLS estimation and the full sample. In Figure 5-1, for example,
the benchmark line is a horizontal line passing through the vertical axis at the point 0.144, which
equals the investment coefficient from the 2SLS-1 estimates of (10).
With the benchmark lines, Figure 5 not only shows the trend of the investment coefficient as
the inflation rate increases, but also provides indications of where the inflation thresholds lie in
the relationship between investment and economic growth. Consistently, both Figure 5-1 and 5-2
suggest the existence of inflation thresholds between 8 and 9 percent in the investment-growth
nexus. In particular, when inflation rates are below 8 percent, the investment coefficients, which
measure the productivity channel in different inflationary environments, are above the
benchmark lines by around 0.15 or higher; however, when inflation goes beyond 9 percent, the
investment coefficients are all below the benchmark lines. Our finding is consistent with the idea
that the ability of financial intermediaries to allocate credits efficiently is dampened during highinflation periods.
To summarize, the rolling regression estimation results suggest that during high-inflation
periods, the link between financial intermediation and the accumulation of capital is not deterred,
but the productivity of investment is reduced. Thus, a reduction in the productivity of investment
appears to be the major source of the harmful effects of inflation on growth.
-33-
Figure 5. Rolling Regression of Equation (10)
Figure 5-1. 2SLS-1
Rolling Regression -INV/GDP
(Observations Ordered by Increasing Inflation)
0.2
48.4
20.7
16.8
14.4
12.8
11.6
0.1
10.6
9.8
9.1
8.4
7.7
7.2
6.6
6.0
5.5
4.9
4.4
0.15
3.9
Coefficient on Inv/GDP
0.25
0.05
0
Average inflation in 300 Observation Rolling Window
Figure 5-2. 2SLS-2
Rolling Regression - INV/GDP
(Observations Ordered by Increasing Inflation)
0.2
21.0
16.6
14.1
12.6
11.5
10.5
9.8
9.0
8.3
7.7
7.1
6.5
5.9
5.4
4.9
4.4
3.9
0.1
3.5
0.15
2.9
Coefficien on INV/GDP
0.25
0.05
0
Average Inflation in 300 observation Rolling Window
Notes: These figures show the evolution of coefficients on the capital accumulation in Equation (10) regressions as the
300-observation rolling window moves from the lowest inflation to the highest inflation. Estimation is by 2SLS with two
different sets of instrumental variables. The estimation results for the full sample are reported in the second (2SLS-1) and
the third column (2SLS-2) in Table 5.
-34-
(3) Endogenous Threshold Model
As Figure 5 implies an inflation threshold in the investment-growth relationship, we use (12), an
endogenous threshold model, to precisely estimate this threshold. Table 6 presents the estimation
results for (12). As Table 6 shows, a significant inflation threshold Pistar * is estimated to exist
at 14.8-16.0 percent. In particular, the investment coefficient ( γ 1 ) is strongly positive at 0.1290.142 for the low inflation group ( Inf it < Pistar * ), but its magnitude decreases to 0.08-0.09 for
the high-inflation group ( Inf it ≥ Pistar * ). As γ 1 and γ 2 measure the ability of financial
intermediaries to allocate credits in a low- and high-inflation environment, respectively, the
smaller magnitude of γ 2 possibly indicates the inefficiency of financial intermediaries in project
selection and monitoring during high-inflation periods. This finding is consistent with that from
the rolling regressions.
As well, we estimate (11) to examine if inflation affects the link between financial
intermediation and the accumulation of capital. Consistent with the results from the rolling
regressions, we find that no significant inflation threshold exists significantly in the relationship
between finance and the level of investment (these results are not reported for brevity).
5.3 Conclusions
The existence of an inflation threshold in the investment-growth relationship suggests that
inflation impacts financial markets mainly through the productivity channel. Thus, the cost of
inflation appears to stem from a reduction in the ability of financial intermediaries to allocate
credits efficiently. One explanation for this finding is that during high-inflation periods, the
usefulness of money assets is eroded, and considerable uncertainty exists about price and interest
-35-
Table 6. The Endogenous Threshold Model (Growth-INV Nexus with One Inflation Threshold)
[Equation (12): growthit = γ 0 + γ 1 ∗ INVit ∗ I ( Inf it < Pistar ) + γ 2 ∗ INVit ∗ I ( Inf it ≥ Pistar ) + φ ′ X it + λ ′Tit + eit ]
Estimation Methods
Dependent Variable = Growth Rate
INV/GDP
(Inflation<Pistar)
INV/GDP
(Inflation>=Pistar)
Inflation
log initial GDP
log initial SEC
GOV/GDP
% change of TOT
constant
Threshold Searching Range
Inflation Threshold Pistar
Likelihood Ratio (LR)
Significance Level by using Bootstraping Distributions
Threshold 5% Confidence Interval
R Square Adjusted
Number of Observations
Number of Countries
OLS
0.169*
(9.907)
[596]
0.129*
(6.048)
[159]
-0.001*
(-14.88)
-0.213**
(-2.225)
0.543*
(4.988)
-0.069*
(-3.954)
0.104*
(6.580)
0.575
(1.021
{1.0,120.0}
15.3%
13.380
1%
[11.7, 20.3]
0.3867
755
90
2SLS-1
0.142*
(8.513)
[494]
0.088*
(4.203)
[165]
-0.001**
(-2.507)
-0.393*
(-3.505)
0.661*
(5.338)
-0.075*
(-3.953)
0.140*
(4.864)
1.699**
(2.358)
{1.0, 120.0}
14.8%
17.8686
1%
[11.7, 19.3]
0.3687
659
90
2SLS-2
0.129*
(8.559)
[603]
0.080*
(4.073)
[152]
0.001*
(-2.977)
-0.408*
(-4.208)
0.739*
(6.559)
-0.047*
(-2.694)
0.140*
(5.201)
1.894*
(2.995)
{1.0, 120.0}
16.0%
15.257
1%
[11.7, 20.3]
0.3649
755
90
Notes: The dependent variable is the five-year average growth rate of real per capita GDP. The Two-Stage-Least-Squares (2SLS-1) estimation, reported in the second column, uses
the initial values of those financial depth measures not included as repressors as additional instruments. The 2SLS-2 estimation, reported in the third column, uses a different set of
instrumental variables, which exclude the two financial depth measures as additional instruments. The t-statistic for each coefficient, given in the parentheses, is computed from
Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate statistical significance at 1, 5, 10 percent, respectively. All equations include time
dummy variables, but the coefficient estimates for the time dummies are not reported. The number of countries and observations are shown in the last two rows. The inflation
threshold Pistar * is the threshold estimates, which achieve the minimum value of ESSs in the threshold search range.
-36-
rates, and uncertainty, in turn makes financial intermediation less able to ameliorate
informational asymmetries, allocate resources, and monitor projects efficiently. Consequently,
the productivity of capital investment decreases during high-inflation periods. Thus, a given level
of investment has less of a positive effect on economic growth than is the case in a low
inflationary environment.
The above transmission mechanism conclusions provide an explanation for our findings in
Section 4 that the positive finance-growth relationship is reduced during high-inflation periods.
The weaker finance-growth nexus appears to stem from the ineffectiveness of the financial sector
in allocating credits in high-inflation environments. The inflation threshold estimate 14.8-16.0%
in the investment-growth nexus is quite close to the inflation threshold estimate 14.1-16.0% in
the finance-growth nexus. As the inflation rate increases, the effect of inflation on the link
between investment and growth shows a similar pattern to the effect of inflation on the financegrowth nexus. This result implies that the effect of inflation on the finance-growth nexus is likely
transmitted through the productivity channel rather than the capital accumulation channel.
6. Inflation and the Productivity of Capital
In Section 5, we have shown that the investment coefficient γ 1 varies as the inflation rate
increases and argued that this variation appears to stem from the negative effect of inflation on
the productivity of capital. Next, we explicitly examine the empirical relationship between
inflation and the productivity of capital.
6.1 Methodology
-37-
The following dynamic equation is used as a baseline to measure the relationship between the
productivity of capital and inflation:
PROit = β 0 + β1 * INFit + θ ′ X it + eit ,8
(13)
where the dependent variable PROit is the productivity of capital measured as the growth rate of
the Total Factor Productivity (TFP) with the elasticity of capital α = 0.4 ; INFit is the annual
inflation rate measured as the growth rate of the Consumer Price Index (CPI). The set of control
variables denoted as X it in (13) includes the first lag of the productivity measure, PROi ,t −1 , and
the first lag of the growth rate of real per capital GDP, growthi ,t −1 . These two lagged variables
are included to control for any possible growth trend in the productivity of capital and economic
conditions in the previous period. In addition, the logarithm of initial income, the logarithm of
the secondary school enrollment rate, and the growth rate of the Terms of Trade (TOT) are also
included in X it to control for other possible factors which could have effects on PROit .
To investigate threshold effects in the inflation-productivity relationship, we use the
following endogenous threshold model:
PROit = β0 + β1 ∗ Infit ∗ I ( Infit < Pistar ) + β 2 ∗ Infit ∗ I ( Infit ≥ Pistar ) + θ ′ X it + eit ,
(14)
which can measure the effect of inflation on productivity. The two coefficients β1 and β 2
measure the effect of inflation on productivity when inflation is below and above the
threshold Pistar , respectively.
6.2 Results
Table 7 presents estimates of (13) by using OLS and 2SLS with two alternative sets of
instrumental variables. The coefficients on inflation are negative and significant (at the 0.001
8
The model specification is selected based on the Ramsey Reset Test. The results of the Reset Test are reported in the Appendix.
-38-
level) in all regressions. This result suggests that a 10-percentage-point increase in the inflation
rate will cause a 0.01- percent-point decrease in the growth rate of productivity. Although the
inflation coefficient in (13) seems small, the long-run effects on productivity might be substantial.
The negative relationship between inflation and productivity supports our earlier contention that
a weaker investment-growth nexus during high-inflation periods results from a decrease in the
productivity of capital. Ten percent annual inflation causes the price level to rise by a factor of
45 in 40 years; even 3 percent inflation causes it to triple over that period. As a result, inflation
may result in the deterioration in financial intermediaries’ ability to effectively allocate funds.
For example, during high-inflation periods, financial intermediaries may have reduced ability to
collect accurate information on borrowers and may allocate funds inefficiently, and this in turn
lowers the productivity of capital. A lower marginal productivity of capital means that a given
level of capital accumulation has less effect on economic growth.
The estimation results for (14) are reported in Table 8, which shows that a significant
threshold exists at 9.1% in the relationship between the productivity of capital and inflation. In
particular, when inflation rates are below 9.1%, inflation has a positive effect on productivity,
but when inflation rates exceed 9.1%, the effect of inflation turns significantly negative. In other
words, the ability of financial intermediaries to allocate funds efficiently appears to be weakened
only when inflation rates exceed 9.1%.
6.3 Robustness
To check the robustness of our results, we consider two alternative measures of the productivity
of capital. The first is the growth rate of Total Factor Productivity (TFP) with the elasticity of
capital α = 0.3 ( gTFP 0.3 ). The second is the growth rate of the average productivity of capital
-39-
Table 7. Productivity and Inflation
[Equation (13): PROit = β 0 + β1 * INFit + θ ′ X it + eit ]
Estimation Methods
OLS
2SLS-1
2SLS-2
Inflation
-0.001*
(-11.29)
0.300**
(2.336)
-0.202**
(-2.187)
-0.114
(-1.174)
0.348*
(2.756)
0.093*
(4.390)
0.913
(1.388)
0.168
684
90
-0.001*
(-2.589)
0.270*
(3.285)
-0.198*
(-2.892)
-0.152
(-1.415)
0.370*
(2.681)
0.153*
(4.944)
1.101
(1.622)
0.1382
684
90
-0.001*
(-2.670)
0.265*
(3.203)
-0.197*
(-2.869)
-0.182***
(-1.667)
0.397*
(2.848)
0.162*
(5.140)
1.276***
(5.140)
0.1282
684
90
Lag(TFP)
Lag(Growth)
log initial GDP per capita
log initial SEC
% change of the TOT
constant
R Square Adjusted
Number of Observations
Number of Countries
Notes: The dependent variable is the five-year average growth rate of total factor productivity. The Two-Stage-Least-Squares (2SLS-1) estimates, reported in the
second column, use the following variables as instruments: the initial values of inflation, the growth rate of TOT, Domestic Credit as a share of GDP in each
five-year period; the initial values of the logarithm of secondary school enrollment rate, the logarithm of initial income, Openness, and domestic fixed capital
accumulation/GDP in the entire period. In addition, the first lag of the growth rate of real GDP per capita and the first lag of the growth rate of TFP are also
included as exogenous variables. The 2SLS-2 estimation, reported in the third column, uses the same sets of instrument variables as those used in 2SLS-1 but
excludes the initial value of Domestic Credit/GDP in each five-year period from the instruments set. The t-statistic for each coefficient, given in the parentheses,
is computed from Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate statistical significance at 1, 5, 10 percent,
respectively. All equations include time dummy variables, but the coefficient estimates for the time dummies are not reported. The number of countries and
observations are shown in the last two rows.
-40-
Table 8. Productivity and Inflation with a Threshold
[Equation (14): Pr odit = β0 + β1 ∗ Infit ∗ I ( Infit < Pistar ) + β 2 ∗ Infit ∗ I ( Infit ≥ Pistar ) + θ ′ X it + eit ]
Estimation Methods
Productivity of Capital
Inflation
(Inflation<Pistar)
2SLS-1
0.136**
(2.387)
2SLS-2
0.140**
(2.454)
Inflation
(Inflation>=Pistar)
-0.001**
(-2.085)
-0.001**
(-2.152)
Lag(TFP)
0.263*
(3.238)
-0.218*
(-3.197)
-0.180***
(-1.691)
0.361*
(2.647)
0.148*
(4.820)
1.076
(1.604)
{1.0, 120.0}
9.1%
10.741
1%
[7.4, 9.7]
0.1599
684
90
0.258*
(3.157)
-0.218*
(-3.183)
-0.210***
(-1.942)
0.387*
(2.811)
0.156*
(5.013)
1.245***
(1.831)
{1.0, 120.0}
9.1%
11.198
1%
[7.3, 9.7]
0.1512
684
90
Lag(Growth)
log initial GDP per capita
log initial SEC
% change of the TOT
constant
Threshold Searching Range
Inflation Threshold Pistar
Likelihood Ratio (LR)
No Threshold against one
Threshold 5% Confidence Interval
R Square Adjusted
Number of Observations
Number of Countries
Notes: The dependent variable is the five-year average growth rate of the total factor productivity. The Two-StageLeast-Squares (2SLS-1) estimates and 2SLS-2 estimates reported in the first and the second column are defined the
same way as they are in Table 8. The t-statistic for each coefficient, given in the parentheses, is computed from
Heteroskedasticity-Consistent Standard Errors (HCSE). The asterisks “*”, “**”, “***” indicate statistical
significance at 1, 5, 10 percent, respectively. All equations include time dummy variables, but the coefficient
estimates for the time dummies are not reported. The number of countries and observations are shown in the last two
*
rows. The inflation threshold Pistar is the threshold estimate, which achieve the minimum value of ESSs in the
threshold search range.
-41-
( g Avgprod ), which is measured by the difference between the growth rate of real GDP and the
growth rate of physical capital stock, that is, gY − g K . In our panel dataset, the three commonly
used measures of the productivity of capital are highly correlated. The correlation with gTFP 0.4 is
0.994 for gTFP 0.3 and 0.814 for g Avgprod .
We employ the same methodology as we described in Section 6.1 to estimate the relationship
between inflation and productivity, with gTFP 0.3 and g Avgprod alternatively serving as measures of
the productivity of capital. While the results (not reported for brevity) are slightly weaker when
we use g Avgprod as the measure of productivity, the inflation effect has a similar pattern for all
three measures of productivity.
7. Conclusions and Policy Implications
7.1 Conclusions
Recent literature has shown the finance-growth relationship to be strong, positive, significant and
robust. However, there is little consensus regarding the effect of inflation on the finance-growth
relationship, because this relationship appears to be nonlinear. In this paper, we use a modified
rolling regression technique along with an endogenous threshold model to investigate the
interaction among inflation, finance, and economic growth.
Using a rolling regression technique and an endogenous growth model, our study suggests
that the strength of the finance-growth relationship, which is commonly assumed to be constant,
varies with the inflation rate. In particular, an inflation threshold exists in the finance-growth
relationship. The positive link between finance and growth decreases substantially as inflation
-42-
rises above a threshold level. This threshold is estimated to lie in the tight range of 14.1-16.0%
depending on the measure of financial depth employed.
This paper further analyzes the inflation transmission mechanism through the financial
system and, in turn, on economic growth. In particular, we examine the impact of inflation on the
ability of financial intermediaries to facilitate the accumulation of capital (the “capital
accumulation channel”) as well as their ability to allocate funds efficiently (the “productivity
channel”). Our study implies that during high-inflation periods, while the ability of financial
intermediaries to accumulate capital may not be affected, there does appear to be harmful effects
on the productivity of capital investments, which may arise from impairing financial institutions’
ability to allocate funds and monitor projects effectively. In other words, the cost of inflation
appears to derive from a reduction in the productivity of capital accumulated through the
financial system.
Finally, we explicitly examined the hypothesis that the productivity of capital decreases in a
high-inflation environment. Using three different productivity measures, we found a robust,
significant, and negative effect of inflation on the productivity of capital. In addition, a threshold
is estimated to exist at a 9.1% rate of inflation in the relationship between the productivity of
capital and the inflation rate. The adverse and nonlinear effect of inflation on the productivity of
capital provides an explanation for our finding that the finance-growth relationship is impaired
during high inflation. When the inflation rate increases above 9.1%, inflation may inhibit the
ability of the financial sector to allocate funds efficiently and, thus, results in finance having less
ability to promote economic growth.
-43-
7.2 Policy Implications
The existing literature finds a positive relationship between finance and growth and commonly
assumes a constant relationship between these two variables, whereas this paper finds that the
finance-growth nexus varies with the inflation rate. A chief finding of the paper is that a
reduction in the productivity of capital during high inflation appears to be the main route by
which inflation weakens the finance-growth relationship.
Furthermore, our study provides a possible explanation for the nonlinear relationship
between inflation and economic growth. As discussed by Khan and Senhaji (2001) and Li (2005),
while the relationship between inflation and growth is not significant and may even be positive at
low inflation rates, inflation has a significantly negative effect on growth when inflation is
sufficiently high. The nonlinear relationship between inflation and growth could stem from a
nonlinear effect of inflation on the finance-growth nexus. For example, during high inflation,
information about investment projects and returns, which is used by intermediaries in allocating
funds, may become less accurate. As a result, the productivity of capital accumulated through the
financial system may decrease. High inflation may also depress the efficiency of financial
intermediation by eroding money assets. Consequently, in high-inflation environments, inflation
may adversely affect economic growth. In contrast, during low to moderate inflation, financial
intermediaries may be better able to promote economic growth due to their improved ability to
analyze information, monitor projects, and allocate funds efficiently.
The key policy implications of this paper are as follows. First, policymakers should
recognize that there may be no reason to keep the inflation rate at a very low level since singledigit inflation (below the threshold level of 14-16%) may not affect the ability of financial
intermediaries to stimulate economic performance and may even have a positive effect on the
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productivity of capital (below the threshold level 9.1%). Second, inflation above single digits
should be recognized as potentially quite harmful to economic growth in large part by adversely
impacting the ability of financial institutions to allocate funds efficiently among investment
projects. Therefore, in order to reduce inflation costs, policymakers may assist financial
intermediaries in collecting information, allocating resources, and monitoring projects.
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APPENDIX 1: Complete List of Countries (90 Countries)
Developing Countries
Africa
Algeria
Benin
Botswana
Burundi
Cameroon
Central African Republic
Congo, Rep.
Egypt, Arab Republic of
Gambia, The
Ghana
Kenya
Lesotho
Malawi
Mali
Mauritania
Mauritius
Mozambique
Niger
Rwanda
Senegal
Sierra Leone
South Africa
Sudan
Swaziland
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Asia
Bangladesh
China
India
Indonesia
Malaysia
Pakistan
Barbados
Philippines
Sri Lanka
Thailand
Jamaica
Caribbean
Barbados
Jamaica
Europe
Hungary
Malta
Poland
Turkey
Latin America
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominican Republic
Ecuador
El Salvador
Guatemala
Guyana
Haiti
Honduras
Mexico
Nicaragua
Panama
Paraguay
Peru
Trinidad and Tobago
Uruguay
Venezuela
Middle East
Cyprus
Iran, Islamic Republic of
Jordan
Kuwait
Syrian Arab Republic
United Arab Emirates
Oceania
Fiji
Papua New Guinea
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Developed Countries
Australia
Canada
Denmark
Hong Kong
Iceland
Israel
Japan
Korea, Republic of
New Zealand
Norway
Portugal
Singapore
Spain
Sweden
Switzerland
United States
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APPENDIX 2: Updating the Nehru and Dareshwar (1993) Data Base
The data base constructed by Nehru and Dareshwar (1993) for the period 1950-1990 was
updated until 2005 for the 90 countries considered in this paper. We used information from the
World Bank’s “World Development Indicators Data Set” (WDI) and the International Monetary
Fund’s “International Financial Statistics” (IFS). A brief description of the procedures used in
this updating is described in this appendix.
(1) Capital Stocks
This series was calculated by using the perpetual inventory method, which is based on the
following accumulation equation:
t −1
K t = (1 − d )t K 0 + ∑ (1 − d )i I t −i
,
(A1)
i =0
where K t is the capital stock at time t (in 1987 prices), K 0 is the initial capital stock (in period
0), I t −i is the Gross Domestic Fixed Capital Accumulation in period t − i , and d is the
depreciation rate. Nehru and Dareshwar (1993) estimated K 0 by a modification of a technique
proposed by Harberger (1978). The procedure is based on the assumption that in steady state,
the rate of growth of output (g) is equal to the rate of growth of capital stock. The depreciation
rate is assumed to be 4 percent, and g is derived from the series of real GDP at market prices.
Equation (A1) is then applied to calculate the rest of the values of K t . To continue this
procedure for the post-1990 values, we used data on Gross Domestic Fixed Capital
Accumulation from IFS.
(2) Gross Domestic Product (GDP)
While comparing the IFS data for this series with the data from Nehru and Dareshwar (1993),
we found considerable discrepancies in the levels but not in growth rates of the series. Thus, we
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performed the updating by multiplying the 1990 levels from the original source by the
subsequent years’ rates of growth, as derived from the IFS data base.
(3) Labor Force
Nehru and Dareshwar (1993) use the population aged 15-64 years as a proxy for the labor force.
Their data covers the period from 1960 to 1990. We updated this series with WDI data for the
period 1991-2005.
APPENDIX 3: Reset Test for Equation (10) and Equation (13)
1. Reset Test Results of the Equation 10 (Growth-Investment Specification)
Regression: ols Growth Inv Pi Lincome LSSERT GOV TOTC T2-T9 / coef=b
RAMSEY RESET SPECIFICATION TESTS USING POWERS OF YHAT
RESET(2)= 0.20929
- F WITH DF1=
1 AND DF2= 739 P-VALUE= 0.647
RESET(3)= 0.89116
- F WITH DF1=
2 AND DF2= 738 P-VALUE= 0.411
RESET(4)=
1.4268
- F WITH DF1=
3 AND DF2= 737 P-VALUE= 0.234
DEBENEDICTIS-GILES FRESET SPECIFICATION TESTS USING
FRESET(1)=
1.6817
- F WITH DF1=
2 AND DF2=
FRESET(2)= 0.94413
- F WITH DF1=
4 AND DF2=
FRESET(3)= 0.84626
- F WITH DF1=
6 AND DF2=
FRESETL
738 P-VALUE= 0.187
736 P-VALUE= 0.438
734 P-VALUE= 0.534
DEBENEDICTIS-GILES FRESET SPECIFICATION TESTS USING
FRESET(1)= 0.32014
- F WITH DF1=
2 AND DF2=
FRESET(2)= 0.74185
- F WITH DF1=
4 AND DF2=
FRESET(3)= 0.56801
- F WITH DF1=
6 AND DF2=
FRESETS
738 P-VALUE= 0.726
736 P-VALUE= 0.564
734 P-VALUE= 0.756
2. Reset Test Results of the Equation 13 (TFP-Inflation Specification)
Regression: ols TFP Pi LTFP Lgrowth Lssert Lincome TOT / het
RAMSEY RESET
RESET(2)=
RESET(3)=
RESET(4)=
SPECIFICATION
0.68326
0.54768
0.43230
-
TESTS USING POWERS OF YHAT
F WITH DF1=
1 AND DF2= 676 P-VALUE= 0.409
F WITH DF1=
2 AND DF2= 675 P-VALUE= 0.579
F WITH DF1=
3 AND DF2= 674 P-VALUE= 0.730
DEBENEDICTIS-GILES FRESET SPECIFICATION TESTS USING
FRESET(1)=
1.0258
- F WITH DF1=
2 AND DF2=
FRESET(2)=
2.5652
- F WITH DF1=
4 AND DF2=
FRESET(3)=
3.7221
- F WITH DF1=
6 AND DF2=
FRESETL
675 P-VALUE= 0.359
673 P-VALUE= 0.038
671 P-VALUE= 0.001
DEBENEDICTIS-GILES FRESET SPECIFICATION TESTS USING
FRESET(1)= 0.22574
- F WITH DF1=
2 AND DF2=
FRESET(2)= 0.47336
- F WITH DF1=
4 AND DF2=
FRESET(3)= 0.43461
- F WITH DF1=
6 AND DF2=
FRESETS
675 P-VALUE= 0.798
673 P-VALUE= 0.755
671 P-VALUE= 0.856
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