Download Financial Development, Economic Growth and Collateral Effect of Capital Account Liberalization

Financial Development and Collateral Effect of Capital
Account Liberalization
Daili Wanga
The paper employs an 81-country datasets to address the collateral
effect of capital account liberalization on financial development.
Besides directly providing foreign capital and transferring technology
spillovers, capital account liberalization might foster the development
of other sectors, enhancing their importance to economic growth. This
is named collateral effect. Previous researches in delving the collateral
effect have focused on studying the transmitting mechanism from
capital account liberalization to financial development. In this paper we
circumvent the difficulty of specifying such mechanism. We test
whether different status of liberalization results in varying importance
of financial development to economic growth. We find that the less
open economies gain more than their more liberalized neighbors, once
the financial development takes place. Thus the paper provides a new
perspective on investigating the collateral effect.
Field of Research: International Finance, Economic Development
1. Introduction
Since the building-up of Washington Consensus, debates on pros and cons of
capital liberalization have never been as fierce as in present days. Market
liberalists suggest capital account controls might impede the optimization of
social welfares, thus freeing the constraint is indispensable. On the other hand,
countries and regions suffering in the financial turmoil, such as Mexico,
Southeastern Asia and Chile, provide straightforward caveats to an inappropriate
liberalization of capital account. For politicians, the best response might be a
conditional acknowledgement to capital controls, just as reflected by Axel Weber
in the Per Jacobsson Foundation Lecture, that “In circumstances of high and
a
Daili Wang, Ph.D candidate in China Center for Economic Research (CCER), National School of
Development, Peking University. The author appreciates comments from Professor Yiping Huang,
Assistant Professor Ping Yan, discussion panel in National School of Development and Young
Scholar Forum in Xiamen University. The author takes sole responsibility to his opinions and
views. Corresponding address: China Center for Economic Research, Peking University, Beijing.
P.R China. 100871. Cellphone: +86-134-6671-6699. Email: [email protected].
volatile capital flows … countries need room for maneuver to formulate their own
policy … in order to enhance financial stability”.1
Turning from the political perspective to the economists‟ view, neither the
neoclassical “laissez-faire”, that liberalization benefits, nor the Keynesian
“interference”, that control works better, is strong enough to overwhelm the other.
Theoretically, formal discussions are far less than those in trade affairs.
Empirically, studies on the impact of capital account liberalization on economic
growth at best provide mixed findings. It is no shame to say that, “[Capital
account liberalization] remains one of the most controversial and least
understood policies of our day.” (Eichengreen, 2003: p.49).
In this paper, we plan to elucidate the collateral effect from capital account
liberalization on economic growth. The term “collateral” is first proposed by Kose,
et al. (2006), referring to the indirect benefit resulting from financial integration. In
addition to directly influencing the economic growth by providing necessary
capital as well as professional know-hows, freeing international capital flows
could nurture the development of financial sector and further benefit the domestic
economy.
Unlike previous work mainly researching on the transmitting mechanism or
relations between capital flows and financial development explicitly, we implicitly
address the issue by studying the change in the importance of financial
development to economic growth after liberalizing the capital account. To our
best knowledge, only Braun and Raddatz (2007) had researched similar
questions. While their work was based on the de jure index of capital account
liberalization, we presume the de facto index functions better 2 . Quinn (2010)
denoted that no consensus has been reached on which measurement of capital
account liberalization (nor on the intensity of capital account controls) is superior.
We defend our choice of de facto index for following reasons. First, noise
embedded in the de facto index could be averaged out in the long run. Besides,
by adopting the de facto index, we could distinguish different impact brought by
different types of capital flows (e.g. FDI, debt flows and aggregate cross-country
capital flows). Last, compared with the de jure index, the de facto one provides
more objective measurement on the status of capital account.
We expect to detect either a positive effect, implying that opening capital account
allows for more investment opportunity which levels up the importance of
financial sector, or a negative and neutral effect, indicating that economic agents
suffer less from finance constraint after liberalizing cross country capital flows,
and premature financial system no long exists as a burden to economic growth.
We refer to Levine (2002) to construct the financial development index. Despite
the difficulty to distinguish whether or not the index is made under researchers‟
subjective judgment, we focus mainly on the change of status of these indices,
rather than delving the transmitting mechanism. We adopt a two-stage
regression so as to eliminate the endogeneity occurs with financial development
and economic growth. Origins of the country‟s legal system are adopted as an
instrument variable3.
To preview the main findings, we categorize the research into three types. We
first run the OLS and two stage regression so as to confirm the relation between
financial development and economic growth. Then we distinguish the crisis and
non-crisis period, finding that more liberalized countries suffer in the crisis
regardless of their level of financial development. We further find that less open
economies would benefit more than more liberalized economies from financial
development. This varying importance of financial development to economic
growth under different status of liberalization is the reflection of the collateral
effect brought by capital account liberalization.
2. Literature Review
In this section we connect our research with previous studies. We first
summarize the measurement of capital account liberalization, so as to recall the
caveat proposed by Quinn (2010) about the measurement error. We then brief
the work employing country-wide or industry-wide datasets in the field of capital
account liberalization and economic growth. The third subsection reviews
literatures on the role of financial development.
2.1. Measurement of Capital Account Liberalization4
Generally speaking, two types of methods are employed in assessing the extent
to which capital account has been liberalized. The de jure approach is based on
legislative or policy restrictions. It often draws on data from the IMF‟s Annual
Report on Exchange Arrangements and Exchange Restrictions (AREAER) and
the OECD‟s Code of Liberalization of Capital Movement. The simplest method is
to use a 0–1 dummy for each category, with 1 representing full control and 0
representing no control, or the proportion of categories without restriction (see,
for instance, Epstein and Schor 1992; Klein and Olivei 1999). Some studies try to
distinguish different degrees of the controls, such as 0.25 implying light control
and 0.75 indicating heavy control (for instance, Quinn, 1997; Montiel and
Reinhart, 1999; Gou, forthcoming). The de jure method provides good
information about the policy framework or policy intension.
However, it may not be a good indicator of the actual controls, as the observed
capital flows often exceed the legal limit (Edwards, 2005). Therefore, some
researchers prefer the de facto approach, which is literally „outcome based‟. The
most popular-utilized de facto indicator is called „quantity index‟, which measures
the ratio of gross cross-border capital flows to GDP (Lane and Milesi-Ferretti,
2007). One potential problem of this indicator is that actual flows of international
capital are often results of not only capital controls but also several other factors,
such as expectations of investment returns and perceptions of sovereign risks.
Since the paper is designed to delve the long term impact from capital account
liberalization on the importance of financial development to economic growth, the
noise brought with implementing de facto index might be averaged out. Besides,
it would risk losing generality if we rely on the statute-based de jure index. Thus,
we adopt the de facto index as the proxy to capital account liberalization, in
contrast to the earlier work done by Braun and Raddatz (2007).
2.2. Capital Account Liberalization and Economic Growth
Early studies employed aggregate datasets to track the impact from capital
account liberalization. As one of the most cited works, Rodrik (1998) reported no
significant relation between capital account liberalization and economic growth.
On the other hand, Quinn (1997) employed a larger sample to indicate a
significant positive correlation. Klein and Olivei (1999) further proved that in
developed countries, removing capital control might facilitate growth. One
explanation might be that developed countries are equipped with appropriate
institutional environment to capture the positive effects.
Henry (2007), however, criticized the previous findings. He claimed that sample
selection and different definition of capital control might result in the
heterogeneity of findings. He further indicated it might be invalid to conclude from
a cross-sectional regression, which simply test the long term relationship.
Considering the marginal capital diminishing, it is the dynamic of capital account
liberalization that needs emphasis.
The problems addressed by Henry (2007) are not easy to settle down by merely
connecting capital account policies with economic growth. Consequently,
disaggregated analyses have emerged. The first strand of literatures turns to
investigate capital account management and domestic investment. Kraay (1998)
found no direct influence from capital account liberalization on investment. Since
interest rate is a straightforward factor influencing investment, McKinnon (1991)
reported that low real interest rate is associated with countries implementing
capital controls, which he referred to as financial repression.
The second strand of disaggregated researches is based on the notion that
capital type matters. As a common belief, FDI is more stable than other crossborder capital flows5. Kose, et al. (2006) provided a comprehensive survey on
FDI and growth, concluding that by taking a more nuanced approach, particularly
after accounting for the initial conditions, FDI and economic growth exhibits a
positive linkage. Researches on FPI reach similar conclusion, which might be
explained as that liberalization to portfolio flows would be adopted only if nations
have been moderately developed. A more interesting question might be delving
the impact on growth volatility from liberalizing different types of capital flows,
since lower volatility might facilitate the growth (Ramey and Ramey, 1995).
2.3. Role of Financial development
Many researchers have argued that financial development played an important
role in facilitating economic growth. Levine (1997) mentioned in his sage work
that financial system might influence the economic growth via producing
information to allocate capital, monitoring firms and exerting corporate
governance, providing risk amelioration and pooling the savings.His claim
suggested that a better-functioned financial sector would reap more benefit from
capital account liberalization. Following the ideology, Allen and Gale (2000)
argued that different financial system, either bank-based or market-based,
exerted different influences on the economic growth.
At the other end of the line, researchers have shed light on the nexus between
capital account liberalization and financial development. Levine and Zervos
(1998) discovered that even in low-income nations, capital account liberalization
might develop the stock market development, although the systemic impact on
financial system is ambiguous. Rajan and Zingales (2003) provided an “interestgroup” theory to illustrate the positive link between trade openness, financial
openness and development of financial sector. Balgati, et al. (2009) further
elaborated on the issue, claiming that a nation only needed to accomplish one
type of openness so as to attain gains in financial development.
The third branch of work offered a more synthetic view on the relation between
capital account openness, financial development and economic growth. Chinn
and Ito (2002) extended the previous findings by providing new datasets of
financial development, measurement of openness (the famous KAOPEN) and
legal institutions. Eichengreen, et al. (2011) analyzed the effects of capital
account liberalization on industry growth while controlling for financial crises,
domestic financial development and the strength of institutions. They found no
significant difference between liberalized and closed nations when trapped in
financial turmoil.
To summarize, previous researches have drawn disproportionate attention on
uncovering the transmitting mechanism of either capital account liberalization on
financial development or the former on economic growth. However, short of
acknowledged measurement of liberalization and available datasets hinder the
research progress. In this paper we take another view to circumvent the dilemma.
3. Data and Methodology
3.1. Theoretical Reflection
With the liberalization of capital account, international capital is expected to flood
into the domestic country, promoting economic growth by either increasing the
domestic investment, leading to investments with positive spillovers, and/or
increasing financial intermediation. We would first illustrate the relationship
between capital account liberalization with economic growth in the context of a
simple AK endogenous growth model.
The model presented here is based on Bailliu (2000). In the closed economy, the
aggregate production of the economy is given by:
Yt  AKt
(1)
Assume no population growth and only one good exists to be consumed or
invested. The capital depreciation rate is given as  per period. The gross
investment equals to:
It  Kt 1  (1   ) Kt
(2)
Financial system could transform the savings into investment with efficiency of
  (0,1] , which implies that a unit of saving would leave 1   absorbed by the
financial system. We could regard the loss of efficient saving allocation as the
interest rate spread charged by the financial intermediaries. In the closed
economy, the equilibrium must satisfy:
 St  It
(3)
Combining equation (1) and (3), we could easily derive the steady state output
growth rate as:
I
g  A( )    A s  
Y
(4)
Here we have s as the saving rate. Equation (4) suggests two main channels
through which financial development impact on the economic growth. The first
one involves saving-investment transformation efficiency, which is measured as
 , or the residual of saving after financial intermediaries‟ claim. With the
development of financial sector, as well as structural changes taking place in the
economy (say, interest rate liberalization), the transformation efficiency would
change. The second channel involves the investment allocation efficiency. We
implicitly assume the investment allocation efficiency would allocate capital into
the sector with highest marginal productivity, thus increasing overall productivity
of capital A .6
Once the capital account has been liberalized, international capital could move
freely between local economy and their domestic nations. Assume capital flows
into the local economy. We might simply have new market equilibrium:
 * (St  NCFt )  It*
(3‟)
Where NCFt denotes the scale of net capital inflows. The steady state is simply
adjusted to:
g *  A* (
I*
( s  NCF )
)    A* *
   A* * s*  
Y
Y
(4‟)
Comparing the new equilibrium (4‟) with the one in closed-economy (4), we find
that capital inflow might increase the domestic investment by providing more
capital ( s*  s ). This is also the most popular arguments held by the advocates of
capital account liberalization. Surely more restrictions are needed in order to
achieve the higher investment, such as not all the foreign capital inflows are
transformed into consumption instead of saving, and the investment financed by
the foreign capital might not crowd out the domestic investment.
The second benefit carried by capital account liberalization is to lead investments
with positive spillovers. Since the FDI literature has provided abundant findings to
understand the vertical externality as well as horizontal externality brought by
capital inflows (see Alfaro, et al. 2010), we would not elaborate too much on this
aspect. In the aforementioned framework, the spillover effect might be reflected
as a larger productivity A* , compared with the productivity A in closed economy.
The third way in which capital flows could influence economic growth is to
increase the domestic financial intermediation. In other words, the extent that
capital flows are intermediated by domestic financial institutions would lead the
financial intermediations themselves work more efficiently (that  *   ). To sketch
the mechanism, Yang and Li (2010) had constructed a more detailed threesector growth model.
Before we end the description of the simple endogenous growth model 7, one
thing we need to point out. The first and second channels described above are
ordinary financing and spillover effect of international capital flow. The third
channel, despite its simplicity, is the very effect we called “collateral”. In following
sections, we would focus on this third aspect.
3.2. Model Setup and Datasets
We have constructed two types of models: one OLS and one two-stage (2SLS)
panel regression. The subsection would brief the two approaches respectively.
For the OLS model, we would estimate the following equation:
gi   y0i   FDi   X i   i
(5)
Where g i is the average GDP per capita growth rate of country i ; y0i is the initial
level of real GDP per capita in logarithm; FDi is the corresponding level of
financial development. Referring to Levine (2002), we define FDi as ratio of bank
credit to private sector to GDP. Alternatives might be: ratio of total value traded in
markets to GDP; market capitalization ratio; ratio of bank deposit to GDP).
X i in equation (5) depicts a conditioning sets including CPI inflation, government
expenditure, gross domestic investment, volume of trade to GDP, level of
democracy and primary education enrollment 8 . The government spending
variable and gross investment variable might capture the net capital formation
effect on economic growth. We expect the government spending would
negatively correlate with the economic growth, by crowding out the more
productive private investment. The gross domestic investment is positively
related to the growth, as shown in equation (4) in section 3. According to Alesina,
et al. (1994), higher trade openness would lead to higher economic growth.
Institutions such as democracy and autocracy might also influence the effect
from capital account liberalization and economic growth.
For the two-stage regression model, we would estimate:
gi ,t ,t   i  vt   y i ,t   Vi ,t    X i ,t ,t    1 FDi ,t  2 LIBi ,t   i ,t
(6)
FDi ,t  i   t   yi ,t   Vi ,t    X i ,t ,t     LIBi ,t  Zi ,t   i ,t
(7)
First look at equation (6). gi ,t ,t  is the averaged growth rate of real GDP per
capita in country i and time period (t   , t ) . This is a standard panel regression
setup, with Vi ,t  denotes variables measured at the beginning of period (t   , t ) ,
and X i ,t  denotes the growth determinants measured as averages over the period
(t   , t ) . Considering the span of data is not sufficiently large, we define   5 ,
which is long enough to eliminate short term business cycle noise and also short
enough to capture the changes taking place in the economy. It needs to be
notified that the selection of explanatory variables is based on neoclassical
growth theory (see Barro, 1991) instead of ad hoc decision. yi ,t  is the initial
condition of per-capital GDP and LIBi ,t denotes the measurement of capital
account liberalization. As discussed in Lane and Milesi-Ferretti (2007), we
employ the stock of capital flows including aggregated data and disaggregated
data (total scale, FDI, and debt) 9 .  i , vt in the model are set to control the
country-specific and time-specific effect respectively.
Table 1, Data Description
Variable
Obs.
Mean
Std.
Dev.
Min
Max
Real GDP per Capita Growth
initial GDP level
2542
2542
2.08%
11675.8
5.42%
9988.2
-33.78%
982.5
57.53%
97643.2
Total Value Traded (% of GDP)
Bank Credit to Private (% of GDP)
Market Capitalization Ratio
Bank Deposit Ratio
1333
2224
1269
2237
18.4%
43.0%
35.0%
43.1%
36.2%
33.5%
43.2%
29.9%
0.0%
1.4%
0.0%
1.7%
326.3%
200.6%
303.4%
230.1%
Gross Capital Stock to GDP
Gross Debt Stock to GDP
Gross FDI Stock to GDP
2722
2722
2722
114.5%
93.1%
21.4%
152.7%
132.5%
29.0%
0.0%
0.0%
0.0%
1873.0%
1687.0%
270.0%
Inflation
Government Spending (% of GDP)
Domestic Investment (% of GDP)
Trade Openness
Primary Education
Democracy Index
Autocracy Index
2408
2342
2442
2835
1002
2539
2539
0.37
15.6%
21.8%
56.6%
100.4%
5.93
2.27
3.25
6.6%
5.8%
40.3%
13.4%
4.15
3.27
-0.22
3.0%
3.0%
0.0%
24.0%
0.00
0.00
117.50
76.0%
48.0%
424.0%
154.0%
10.00
10.00
Secondary Education
Black Market Premium
Private Investment
985
1120
788
74.8%
0.7%
14.5%
30.0%
4.8%
6.1%
5.0%
-0.6%
1.0%
162.0%
139.0%
36.0%
Notes: financial development aggregate and financial structure aggregate are built upon a
principal component analysis on the disaggregated indicators.
The reason why we have differentiated variables measured at the beginning of
period, Vi ,t  , between variables measured as averages over period, X i ,t  , is to
accommodate two major types of growth model, namely neoclassical model and
endogenous-growth model 10 . To enunciate the idea, notice Vi ,t  captures the
initial condition of the economy, while X i ,t  accounts for the steady-state level.
By explicitly distinguishing the two effects, we might explain both the transition
path to the steady states (as in a neoclassical model), and the steady states of
the economies (or difference in stead-state growth rate) as in an endogenous
growth model.
With respect to the data measured at the beginning of period, we expect the
initial per-capita GDP level yi ,t  is negatively associated with the following-year
growth rate, which is consistent with the classical growth theory. Human capital,
measured as primary education enrollment, included in Vi ,t  , which we expect to
be found a positive sign.
Equation (6), which has been detailed explained above, is the second step in the
2SLS regression. At the first stage, we seek for the instrument variable Zi ,t  to
provide a consistent estimation (as depicted in equation (7)). As argued in Beck
and Levine (2005), the origins of the country‟s legal system perform well when
taking account of the financial development and economic growth. We employ
these datasets to finish our regression model setup.
Table 1 offers the summary statistics of the data. Appendix 1 and 2 further
discuss the sources and definition of these datasets.
4. Discussions of Findings
In this section, we first provide the regression results from the OLS model.
Despite its obvious drawbacks, it is still a suitable start point for further analysis.
We then turn to the 2SLS model for remedying the endogeneity and offer
discussions with respect to the findings.
4.1 Findings from OLS Model
The findings of the OLS model are summarized in table 2. We could find that the
coefficients on the initial GDP level are all significantly negative, indicating that a
country with higher initial income would growth slower than those countries
starting with a lower level. Government spending is negatively related to the
economic growth, as what we expect. The improvement of investment rate and
trade openness both facilitate the economic growth significantly. The coefficient
of education is against our expectation, which is assumed to facilitate the growth
but turns out to be insignificant. To the author‟s knowledge, the puzzle has long
existed in the cross-country growth literature (see Pritchett 1996). The
democracy index also behaves against the expectation. We further run the
regression according to the classification of income levels, finding that the
coefficient is negative only in the low income group. Within high income and
emerging economies, democracy actually facilitates growth.11
Interestingly, capital account liberalization, as measured by gross capital stock to
GDP, is associated with lower growth rate. We again run the regression across
groups, discovering the negative impact exists in both high income and low
income group, while an insignificant positive effect is found in emerging
economies. In the last two columns we report replacing gross capital stock with
debt stock and FDI stock respectively. The pool effects across countries are
insignificant, while the debt flows exerts negative impact in low income economy
and FDI stock are significantly associated with growth in emerging economies. In
all, the simple regression reveals that benefit of capital account liberalization is
nonlinear, which might be consistent with Kose, et al. (2006)‟s “threshold effect”,
and the composition of capital flows matter.
Table 2, OLS Results
Dependent Variable
Real GDP per Capita growth
Initial GDP level
CPI Inflation
Government spending (in Ln)
-0.05***
-0.011*
-0.018
-0.054***
-0.038***
-0.035***
-0.042**
-0.01
-0.023*
-0.054***
-0.04***
-0.036***
-0.059***
-0.037***
-0.039***
-0.057***
-0.04***
-0.034***
Domestic Investment (in Ln)
Primary Education
0.069***
0.073
0.048***
-0.023
0.076***
0.088**
0.051***
-0.024
0.051***
-0.014
0.053***
-0.029
Trade Openness (in Ln)
Democracy index
0.064***
-0.001
0.028***
-0.003**
0.069***
-0.001
0.029***
-0.002**
0.024**
-0.002**
0.02*
-0.002*
Gross Capital Stock to GDP
Debt Stock to GDP
-0.036***
-0.014***
-0.035***
-0.013**
FDI Stock to GDP
Total Value Traded
-0.01**
-0.001
0.008***
Bank Credit to Private
0.003
Market Capitalization Ratio
Bank Deposit Ratio
0.001
0.001
0.006*
0.001
Constant
0.522***
0.531***
0.416**
0.538***
0.562***
0.573***
Observation
607
779
592
781
778
777
Notes: ***, **, * signal respectively the significant level at 1%, 5% and 10% level
With respect to financial development, whatever measurement index we adopt,
the result is at least insignificantly positive with economic growth. Since we do
not care about the transmitting mechanism, we would not go any further in
discovering why employing different index results in different consequence.
Before closing the subsection, we might emphasize the purpose of the OLS
regression. As is well known in development literature, growth happens in long
term. Employing annual data to estimate the impact seems inappropriate.
However, we do not draw our definite conclusion from the regression above.
Indeed we merely expect to see the sign of both control variables and interested
variables appear in consistence with the expectation, so as to confirm the
datasets and regression techniques are suitable. In the next subsection, we
would adopt the five-year average period datasets and 2SLS regression to detect
the change of importance of financial development to economic growth.
4.2. Findings from 2SLS Model
We perform three regressions in this subsection. First we run the 2SLS to
provide the benchmark results after controlling the endogeneity. We then
consider the status in crisis period, which is emphasized in Eichengreen, et al.
(2011). To better understand the change of importance, we divide the sample in
accordance with their extent of capital account openness, testing if the more
open economy gains more from the development of financial sector.
Table 3, Two-stage Regression
Dependent Variable
Real GDP per Capita Growth (5-year period)
Initial GDP level
CPI Inflation
Government spending (in Ln)
Domestic Investment (in Ln)
Primary Education
Trade Openness (in Ln)
Democracy index
-0.086***
-0.014**
0.006
0.064***
-0.102
0.006
0.001
-0.088***
0.03
-0.039**
-0.01
-0.061
0.027
0.002
-0.085***
-0.006
-0.009
0.046***
-0.113
0.006
-0.002
-0.083***
0.035
-0.051**
-0.016
-0.09
0.033
-0.002
Gross Capital Stock to GDP
Debt Stock to GDP
FDI Stock to GDP
Total Value Traded
Bank Credit to Private
Market Capitalization Ratio
Bank Deposit Ratio
Constant
-0.017
-0.032
-0.025
-0.037
1.072***
0.908***
1.027***
Anderson‟s CC Test
Hansen‟s J Test
Observation
4.77***
4.928
199
54.18***
7.461
322
15.7***
2.054
194
-0.09***
0.034
-0.039**
-0.01
-0.06
0.026
0.002
-0.092***
0.022
-0.046**
-0.004
-0.099
0.009
0.002
-0.032*
-0.008
0.016*
0.112**
0.112**
0.102**
0.162**
0.931***
0.917***
0.939***
138.8***
8.29*
323
52.45***
7.21
322
69.65***
7.89*
322
0.03*
Note 1: ***, **, * signal respectively the significant level at 1%, 5% and 10% level. IV variable:
origin of the country‟s legal system and lag of financial development.
Note 2: GMM-IV regression is adopted since the instrument variables‟ dimension is larger than
the explanatory variables‟. Heteroskedasticity and autocorrelation in error terms are taken
account of by implementing regression with robust standard errors.
Note 3: Anderson‟s CC test evaluates the strength of instrument variables, with null hypothesis
implying under-identification. Hansen‟s J test reflects the validity of instrument variables, with null
hypothesis indicating exact-identification.
Table 3 summarizes the 2SLS regression results. For the model specification, we
evaluate the strength of IV by Anderson‟s CC test and the over-identification
issue is addressed by Hansen‟s J test. 12 Initial GDP level again appears
negatively related to the economic growth. CPI inflation behaves insignificant
across model setups. Government spending and domestic investment influence
the economic growth in a way consistent with the model predicted. At this stage,
however, trade openness, education and democracy do not appear significantly
across samples.
In every case but one, capital account liberalization does not appear significantly
associated with the economic growth. When measuring the openness with the
ratio of debt to GDP, we obtain negative result, which is consistent with the
argument that debt flows are more volatile, thus hinder the economic growth. In
all cases, financial development, whatever measures are taken, is found to
facilitate economic growth.
We then ask what would happen in the crisis period if the nation adopts more
aggressive attitude towards freeing capital flows. Since de facto index performs
poorly under extreme condition, taking account of crisis also ameliorate the
misuse of measurement. In the estimation we revise equation (6) so as to detect
the effect of crisis:
gi ,t ,t    i  vt   y i ,t   Vi ,t    X i ,t ,t    1 FDi ,t   2 LIBi ,t
 1crisisi ,t  2crisis  FDi ,t  3crisis  LIBi ,t   i ,t
(8)
Table 4 summarizes the findings when we take account of the crisis period. We
have adopted two types of financial development index and three types of capital
account openness index to provide the results. Among the six models, we find
initial GDP level and domestic investment are both significant and consistent with
the expectation. Inflation, government spending and trade openness are not all
significant, while the signs of coefficient match the prediction as well. Model 2,
which proxies financial development with the ratio of bank credit to private sector
to GDP, in general produces more significant results compared with Model 1.
With respect to the coefficient of financial development and capital account
liberalization, both models yield similar results. Regardless of the choice of
capital account openness measurement, it exerts negatively impact on the
economic growth. Financial development is significantly positively associated
with the economic growth. Crisis does not change the level of economic growth
unless we employ FDI to GDP ratio. The interaction terms suggest that in crisis,
more liberalized economies suffer from slower economic growth, while the level
of financial development does not matter either in or out of crisis period.
Table 4, Capital Account Liberalization and Crisis
Real GDP per Capita growth (5-year average)
Dependent Variable
Model 1
Model 2
Initial GDP level
CPI Inflation
Government spending (in Ln)
Domestic Investment (in Ln)
Primary Education
Trade Openness (in Ln)
Democracy index
-0.112***
-0.012*
0.003
0.032***
-0.08**
-0.009
-0.001
-0.121***
-0.01
0.006
0.042***
-0.096**
-0.003
-0.001
Gross Capital Stock to GDP
Debt Stock to GDP
FDI Stock to GDP
Total Value Traded
Bank Credit to Private
-0.021**
Crisis
Crisis * FD
Crisis * LIB
Constant
-0.024
-0.002
-0.021*
1.268***
-0.028
-0.002
-0.02*
1.377***
Observation
199
199
-0.086***
-0.023***
0.001
0.021**
-0.054*
0.009
0.001
-0.117***
-0.002
-0.005
0.026***
0.01
0.04***
0.002**
-0.112***
-0.013**
-0.014*
0.027***
-0.036
0.017**
0.003***
-0.038***
-0.015**
0.027***
-0.12***
0.002
-0.007
0.026***
0.009
0.038***
0.002**
0.028***
-0.036***
-0.025*
0.025*
-0.011*
0.279***
0.276***
0.239***
-0.059**
-0.001
-0.021*
0.934***
-0.012
-0.011
-0.025**
0.989***
-0.016
-0.012
-0.024**
1.004***
-0.059**
-0.001
-0.022**
0.949***
199
338
338
335
Notes: ***, **, * signal respectively the significant level at 1%, 5% and 10% level. IV variable:
origin of the country‟s legal system and lag of financial development. The forth to last and third to
last rows indicate the interaction between crisis and financial development, as well as
liberalization respectively.
With respect to the coefficient of financial development and capital account
liberalization, both models yield similar results. Regardless of the choice of
capital account openness measurement, it exerts negatively impact on the
economic growth. Financial development is significantly positively associated
with the economic growth. Crisis does not change the level of economic growth
unless we employ FDI to GDP ratio. The interaction terms suggest that in crisis,
more liberalized economies suffer from slower economic growth, while the level
of financial development does not matter either in or out of crisis period.
The third regression involves testing changes in importance of financial
development to economic growth. We first add an interacting term into equation
(8) to construct the following model.
gi ,t ,t    i  vt   y i ,t   Vi ,t    X i ,t ,t    1 FDi ,t   2 LIBi ,t  3 LIBi ,t  FDi ,t
 1crisisi ,t  2 crisis  FDi ,t  3crisis  LIBi ,t   i ,t
(9)
We are interested in coefficient  3 . If 3  0 holds, it suggests financial
development is less important to economic growth in the more liberalized
countries, and vice versa if 3  0 . Table 5 summarizes the estimation results.
Table 5, Capital Account Liberalization, Crisis and Interaction Effect
Real GDP per Capita growth (5-year average)
Dependent Variable
Model 1
Initial GDP level
CPI Inflation
Government spending (in Ln)
Domestic Investment (in Ln)
Primary Education
Trade Openness (in Ln)
Democracy index
Model 2
-0.084***
-0.085***
-0.08***
-0.121***
-0.124***
-0.119***
-0.015**
0.009
0.065***
-0.119**
0.004
0.001
-0.015**
0.009
0.065***
0.002
0.001
-0.016**
0.007
0.068***
-0.11**
-0.001
0.001
-0.002
-0.006
0.027***
0.008
0.039***
0.002**
0.002
-0.008
0.027***
0.009
0.038***
0.002**
-0.013*
-0.016*
0.028***
-0.037
0.018**
0.003***
Gross Capital Stock to GDP
Debt Stock to GDP
FDI Stock to GDP
Total Value Traded
Bank Credit to Private
-0.008
-0.038***
Crisis
Crisis * FD
Crisis * LIB
FD * LIB
Constant
-0.029
-0.001
-0.02
-0.007***
1.0619***
-0.039
-0.002
-0.018
-0.0007**
1.075***
Observation
199
199
-0.008
0.009
0.009
-0.036***
0.001
0.007
-0.012*
0.286***
0.285***
0.255***
-0.565***
0.006
-0.026
-0.0004
0.999***
-0.011
-0.019
-0.023*
-0.011
1.019***
-0.016
-0.012
-0.022**
-0.006
1.032***
-0.059*
-0.012
-0.022**
-0.009
1.001***
199
322
322
319
Notes: ***, **, * signal respectively the significant level at 1%, 5% and 10% level. IV variable:
origin of the country‟s legal system and lag of financial development. The fifth to last and forth to
last rows indicate the interaction between crisis and financial development, as well as
liberalization respectively.
By first glance we might conclude no convincingly significant relation between the
financial development and capital account liberalization exists. At best, from the
first two columns we find the term FD*LIB is significantly negative, which
suggests that more liberalized country would suffer from slower economic growth,
given the level of financial development. However, the results do not hold for all
six regressions. From the third to last rows, we contend that the partial effect of
financial development to economic growth does not enhance with the
liberalization of capital account.
Instead of adding the interaction term, we classify the sample into “highly
liberalized” (HH), “relatively liberalized” (RH), “relatively closed” (RL) and “highly
closed” (LL) groups referring to the observation‟s level of capital account
liberalization13. Quartiles are taken as the threshold points. Notice the sample
here is not grouped merely by their country features, but by their country-time
features14. Due to the data availability, we estimate the following model rather
than the one in equation (9):
gi ,t ,t    i  vt   y i ,t   Vi ,t    X i ,t ,t    1 FDi ,t   2 LIBi ,t
 3 LIBi ,t  FDi ,t   i ,t ( within noncrisis subsample)
(10)
Table 6, Within Group Regression
LL
FD
LIB
FD*LIB
Obs
Measurement on Financial Development
Total Value Traded
Bank Credit to Private Sector
Total
Debt
FDI
Total
Debt
FDI
0.402***
0.353***
0.594***
-0.041**
-0.043**
-0.038**
0.038***
0.039***
0.001
72
72
72
RL
FD
LIB
FD*LIB
Obs
0.005
-0.018
-0.001
60
-0.005
-0.018
-0.001
63
0.028
-0.053
-0.001
72
-0.033
0.001
0.003
68
-0.089
-0.01
0.004
68
0.088
0.007
-0.001
68
FD
LIB
-0.002
-0.009
0.03**
-0.081***
0.131
-0.031
0.144
-0.06**
0.081
0.008
FD*LIB
Obs
-0.001**
69
-0.001**
67
0.081**
-0.085
0.0007*
55
-0.007
81
-0.005
81
0.008
81
FD
LIB
FD*LIB
Obs
0.025
-0.033
-0.0008*
59
0.0019
0.0046
-0.001*
58
0.01
-0.016
-0.002**
61
0.146
-0.013
-0.003
85
0.151
-0.012
-0.003
85
0.162
-0.008
-0.013**
85
Group
RH
HH
Note 1: ***, **, * signal respectively the significant level at 1%, 5% and 10% level. LL, RL, RH, HH
denotes “highly closed”, “relatively closed”, “relatively open” and “highly open” respectively. Total,
Debt, FDI defines the logarithm form of gross capital stocks to GDP, debt stocks to GDP and FDI
stock to GDP respectively. The model refers to equation (6) and equation (7). We report the
coefficients and observations of the financial development (FD), financial liberalization (LIB) and
their cross term (FD * LIB) in the regression.
Note 2: Due to the data constraints, we do not have enough observations to conduct within group
regression when measuring financial development with total value traded to GDP (the first three
columns). To make the estimation consistent, we combine the first two groups, i.e. LL and RL,
instead of regrouping the datasets. The report in RL rows in the first three columns reflects the
estimation from the combined group.
Instead of explicitly estimating the effect in crisis periods, we drop the
observation in crisis era (which accounts for 5% of the whole sample), focusing
on the “normal” period. Table 6 provides brief summary of the regression
results.15 In the first three columns, we define the financial development as total
value traded to GDP. Since capital markets are not mature in many less
liberalized countries, we do not have enough observations to estimate LL group.
To make the estimation consistent, we combine LL and RL groups and run a
pooled regression.
We first check the impact from the first order effect of financial development and
capital account liberalization.16 Though not convincing, the regression reveals a
positive connection between financial development with economic growth and a
negative relation between liberalization and growth. By detecting the second
order effect, it is interesting to notice the interaction terms are significantly
negative in RH and HH groups regardless of the choice to measure capital
account liberalization. The forth to sixth columns reflect measuring financial
development as bank credit to private sectors to GDP. If we deem capital
account liberalization as openness to gross capital flows or debt capital flows, the
regression suggests a significant positive  3 in LL and insignificant results from
other groups. However, one different phenomenon occurs once we define the
liberalization as openness to FDI flows.  3 is insignificant in LL, while it is
significantly negative in HH.
What could we conclude from the findings? The results suggest that, financial
development weighs less important in more liberalized countries. The impact on
economic growth does not only reflect on the first order positive effect from
financial development. Moreover, with the liberalization of capital account, the
second order effect from financial development on economic growth shrinks in
the more integrated countries.
4.3. Discussions
In this section we have offered three types of empirical evidence. We first
evaluate the effect of financial development and capital account liberalization
from an OLS model and two-stage regression model. Both regressions reveal
that financial development does facilitate the economic growth as previous
literature suggests, while the effect from capital account liberalization is mixed.
We further distinguish between the crisis period and non-crisis period, detecting
the role played by financial development and capital account openness. We
contend that more liberalized countries suffer more during the crisis, while the
financial development does not perform significantly different between the two
periods. In the third regression, we evaluate if capital account liberalization could
bring along more investment opportunity to enhance the role played by financial
development, or providing more fund sources so as to de-emphasize its
importance. Upon taking account of the interaction of two variables, we find no
convincingly significant impact from the interacting term. By dissecting the
samples into groups characterized by different level of liberalization, we come
with the result that, countries with highly closed capital account could benefit
more from further financial development.
We summarize three implications from our research. First, during crisis era we
observe the more liberalized countries suffer more, while financial development
does not matter. The implication is that even a nation is equipped with a fully
developed financial sector; it might risk the stability to push forward the capital
account liberalization. The finding challenges the opponents for capital account
liberalization, who believe a mature financial sector is the precondition of capital
account openness. To make it clearer, we do not argue for the liberalization
without prudent consideration. What we propose here is to provide a caveat to
those who hesitate over reforms. Nothing comes from nothing. The best strategy
towards capital account liberalization is learning by doing.
Secondly, we find that the status of capital account liberalization is associated
with the importance of financial development to economic growth. Why the less
liberalized countries appear to gain more with the development of financial sector?
Many factors could be named and we hereby refer to one proposed by Braun
and Raddatz (2007), who employed de jure index to reach the similar findings. In
closed countries, domestic capital misallocation could not get remedied unless
financial frictions being removed. On the contrary, in more liberalized countries,
entrepreneurs could access the international capital market so as to circumvent
the domestic financing restraints. As the result, the status of capital account
liberalization influences the importance of financial development to economic
growth.
Last, we provide a new perspective on the collateral effects from liberalizing
capital account. We have not only explicitly confirmed the positive influence from
financial development on economic growth and mixed effect from capital account
liberalization, as previous studies have done. One step further has been made to
detect the second order effect from capital account openness. With liberalization,
we ask what occurs on the importance of other sectors (say, financial sector) to
economic growth. We conclude that, besides directly financing and bringing
technology spillovers to the domestic economy, capital account openness
influences further on the financial sectors, i.e. the collateral effect varies across
different status of liberalization.
5. Conclusion
For quite a long time, capital account liberalization remains a contentious topic.
Researchers employing macro- and micro-level datasets find ambiguous effect of
liberalization on the economic growth. However, most previous literatures either
focus on the transmitting mechanism or directly test the relationship between
capital account liberalization and economic growth. The collateral effect from
liberalization has long been overlooked.
In this paper, we adopt two-stage regression to investigate if different status of
liberalization would affect the importance of financial development. To our
knowledge, the perspective is innovative. Our findings are twofold. On the one
hand, we find more liberalized countries would suffer more in crisis regardless of
their level of financial development. On the other hand, the less open countries
would gain more than more liberalized neighbors once the financial development
takes place. We contend the findings might be enlightening to the policy
decision-makers and further research in the field of capital account liberalization.
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Appendix 1, Sample Countries and Categories
81 countries, 1970 – 2004 annual data
Since there existsbreak in education series between 1996 and 1997, due to
change from International Standard Classification of Education (ISCED76) to
ISCED97, the sample span for the OLS regression is: 1970 -1995 and 1998
– 2004. The sample span for the panel regression is: 1970-1995 (average
on five years) and 1998 – 2002 (average on five years)
33 High income nations (OECD and Non-OECD countries):
Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark,
Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy,
Japan, Korea, Kuwait, Netherlands, Norway, Portugal, Saudi Arabia,
Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Trinidad
and Tobago, United Kingdom, United States
23Emerging economies (Upper middle income countries):
Argentina, Brazil, Bulgaria, Chile, Costa Rica, Croatia, India, Cote d'Ivoire,
Georgia, Indonesia, Kazakhstan, Latvia, Lithuania, Malaysia, Mauritius,
Mexico, Panama, Poland, Romania, Russian Federation, South Africa,
Turkey, Venezuela
25Developing countries (Low income and lower middle income countries):
Bangladesh, Bolivia, Columbia, Côte d'Ivoire, Ecuador, Egypt, El Salvador,
Ghana, Guatemala, Iran, Jordan, Kenya, Kyrgyz Republic, Macedonia,
Moldova, Morocco, Nepal, Pakistan, Paraguay, Peru, Philippines, Sri Lanka,
Thailand, Tunisia, Zambia
Appendix 2, Sources and Definition of Variable

Black Market Premium: Levine and Renelt; World's Currency Yearbook (for
1985, 1990-93); Adrian Wood, Global trends in real exchange rates: 1960-84,
WB Discussion paper no. 35. 1988 (filling in missing observations); Global
Development Finance & World Development Indicators (for 1996-1997,
calculated as (parallel Xrate/official Xrate-1)*100 ); values for industrial
countries are added as 0)

Financial Crisis Episode: drawn from Laeven and Valencia (2008)

Democracy and autocracy: research in George Mason University

Financial development: from Beck and Demirgüç-Kunt (2009)

General government final consumption expenditure): from Global
Development Network Growth Database (GDNGD), New York University

Gross capital formation (% of GDP):from Global Development Network
Growth Database (GDNGD), New York University

Growth rate of GDP per capita:from Global Development Network Growth
Database (GDNGD), New York University

Inflation: from Global Development Network Growth Database (GDNGD),
New York University

capital account liberalization: from Lane and Milesi-Ferretti (2007)
( FAi ,t  FLi ,t )
ratio of the sum of the stock of financial assets and the
K i ,t 
GDP
stock of financial liability to gross domestic product (GDP)

Origins of the Country’s Legal System: from Global Development Network
Growth Database (GDNGD), New York University. Classified as: British
common law; Germany civil law; French civil law; Scandinavia civil law;
Socialist law.

Primary Education Enrollment:from Global Development Network Growth
Database (GDNGD), New York University

Real Growth of GDP per Capita (Laspyre): from Global Development
Network Growth Database (GDNGD), New York University

Trade share of GDP: from Global Development Network Growth Database
(GDNGD), New York University
Endnotes
1
http://www.imf.org/external/np/speeches/2011/092511.htm
Generally, de jure index is based on the government statutes, which subject to the enforcement
of laws. De facto index is based on economic variables, such as scale of capital flows, which
might be influenced by the economic fundamental instead of capital account policies.
3
See Beck and Levine (2005) for the discussion of validity of regarding the variable as reliable
instrument variable in the financial development literature
4
The subsection is mainly based on Huang, et al. (2011)
5
See Sula and Willett (2009). However, Claessens, et al. (1995) believed the two types of capital
flows are in essence identical.
6
We could also understand the allocation efficiency as better risk diversification effect, thus more
capital could flow to originally risky but higher return projects.
7
For discussion of the dynamics of a random AK model, see Obstfeld and Rogoff (1996). The
model could also take distortions into account. Future research would address the issue of
imperfect market.
8
Upon regression, we have substituted the original setting with secondary education enrollment
(vis-à-vis primary education enrollment), private investment (vis-à-vis gross domestic investment),
black market premium and level of autocracy (vis-à-vis level of democracy). There appears no
material change.
9
Taking account of the critiques from Kose, et al. (2006), we exclude samples that might distract
the research interests, such as Hong Kong, China and Luxembourger. These regions are in
essence international financial centers and the capital flows cannot be regarded as an
appropriate proxy to the capital account liberalization.
10
For more detailed discussion on the growth theory, see Barro and Sala-i-Martin (1995)
11
Due to the words limit, we do not provide the detailed report. The results are available upon
request.
12
In the last three columns we observe the IVs are strong; and the over-identification does not
matter in the regression. We actually adopt the IV-GMM regression since the dimension of IVs is
larger than that of explanatory variables‟. The exclusion restriction implies lag of financial
development and origin of legal system are independent of current economic growth, which is
argued in Beck and Levine (2005)
13
For instance, when we detect the FDI liberalization and change of importance of financial
development to economic growth, we classify the sample with respect to their level of FDI to GDP
ratio. The same applies to debt and gross capital stock.
14
This means we could find a country with status HH in given time, while in another time span
might be defined with status LL. By this means, we could employ the information more completely
and not miss the change in the status of liberalization within country.
15
For full reports of the regression results, readers could reach the author by email.
16
First order effect here means how much the 1 percentage point change in financial
development would directly influence the economic growth, if we do not take other indirect effects
(such as the interaction from crisis and liberalization) into account. Second order effect, which is
quoted in the following passage, denotes the indirect effect from financial development to
economic growth, once considering other variables. The reader could regard the second order
effect as the marginal effect.
2