Financial liberalisation and the relationship between finance and growth

How and Where Capital Account Liberalization Leads to Economic Growth
Dennis P. Quinn
Professor
McDonough School of Business
Georgetown University
Washington, D.C. 20057



Carla Inclan
Ernst & Young, LLP
Washington, D.C. 20036



A. Maria Toyoda
Research Scholar
Institute for International Studies
Stanford University
Stanford, California





© by the American Political Science Association. Prepared for Presentation at the 2001 Annual
APSA Convention, San Francisco, California. We thank the Georgetown University
McDonough School of Business and the National Science Foundation for their support (SBR-
9810410). Please direct comments to Dennis Quinn. (30 August 2001)
ABSTRACT

We utilize an empirical model of growth as a platform for examining the effects
of capital account liberalization on growth. While we test for the direct effects of liberalization,
we are equally interested in another facet of liberalization: sequencing. We ask what prior
political, social, or economic conditions were required for capital account liberalization to have
led to subsequent growth. Our key independent variable is a measure of capital account
openness that comes in the form of five-year time-series, cross-sectional observations for 80
nations, 1950 (or independence) to 1997. Our focus is on change indicators of liberalization, as
we argue that level indicators of government policies in political economic research are generally
too imprecisely specified to exclude the influence of other collinear political economic variables.
We focus not simply on the economic preconditions for beneficial liberalizations, but the
political and social preconditions as well. We find that capital account liberalization has a robust
and direct effect on subsequent economic growth in most countries. Capital account
liberalization does not, however, lead to higher growth in emerging market democracies that
have weak welfare states. We conclude that policymakers in emerging market democracies have
ample reason to be cautious about full capital account liberalization.

Policymakers in emerging market nations are routinely urged to liberalize their international
economic transactions. Scholars, journalists, and experts from international development agencies laud
the growth and development benefits of freer trade in goods and services in particular. (See Rodrik 2000
for a critical review of this advice.)
Is this advice sound regarding international capital flows? That is, if a government of an
emerging market nation were to liberalize its restrictions on inward and outward capital flows, what
would be the growth effects in the liberalizing economy?
We offer a partial answer to these questions in this paper. We proceed as follows. After a brief
review of the relevant literature, we utilize an empirical model of growth as a platform for examining the
effects of capital account liberalization on growth. While we test for the direct effects of liberalization,
we are equally interested in another facet of liberalization: sequencing. What prior political, social, or
economic conditions were required for capital account liberalization to have led to subsequent growth?
Our paper makes several contributions. We introduce a measure of capital account openness that

comes in the form of five-year time-series, cross-sectional observations for 80 nations, 1950 (or
independence) to 1997. We focus on change indicators of liberalization, arguing that level indicators of
government policies in political economic research are generally too imprecisely specified to exclude the
influence of other collinear political economic variables. We focus not simply on the economic
preconditions for beneficial liberalizations, but the political and social preconditions as well. We have
new results, which we discuss at the end of the paper.
I. A Brief Review of the Capital Account Liberalization and Growth Literature

The starting point of this paper on the growth effects of capital account liberalization is the recent
and comprehensive review essay on the topic by Barry Eichengreen (forthcoming). Eichengreen notes
that various theoretical models drawn from economics imply inconsistent effects from capital account
liberalization. One strand of literature, drawing on traditional “frictionless” factor market models,
proposes that capital account liberalization produces growth effects for many of the reasons trade
liberalization does. (See also Sweeney 1997.) A second strand of theory, however, proposes that many
types of policy-based distortions in a nation’s economy lead to suboptimal outcomes from capital account
liberalization as the distortions create “second-best” conditions. (See Stiglitz 2000, e.g.) To a very large
extent, the differences in the two perspectives derive from different starting assumptions about the policy
and economic environments under which liberalization occurs.
Given the conflicting starting assumptions and resulting theory, we might reasonably hope that
empirical studies of the many countries that have liberalized their capital accounts might narrow the
theoretical discourse. Unfortunately, the first round of “large n” empirical studies of the direct effects of
capital account liberalization produced indecisive results. Grilli and Milesi-Ferretti 1995 found no
association between the levels of capital account openness (hereafter Openness) and economic growth, a
finding that Rodrik 1998 replicated and extended. Quinn 1997 showed that changes in capital account
openness (hereafter Liberalization) were associated with higher long-run growth. Focusing on emerging
markets, Bekaert, Harvey, and Lundblad 2000 also find that incidences of financial liberalizations were
associated with subsequent economic growth.
Much of the difference is in the data used. Grilli and Milesi-Ferretti and Rodrik use a binary 0,1
indicator of the presence or absence of capital controls found in a table at the back of the International
Monetary Fund’s annual publication, Exchange Arrangements and Exchange Restrictions. The IMF 0,1

data contains too little information for it to be used to study Liberalization per se, but only levels of
Openness. The Quinn study is based on a coding of the text of the laws governments used to regulate
capital accounts, which are reported in the text section of Exchange Arrangements. (The Quinn/Toyoda
measure is described below.) These data contain ample information to generate a study of Liberalization,
but the range of countries and years for which data were available was limited. The Bekaert, Harvey, and
Lundblad 2000 study also used a 0,1 measure, but this measure, unlike the IMF’s, is linked to the date of
the liberalization. Eichengreen forthcoming reviews the data and methodological differences among most
of these and other studies. (For discussions of the different data measures, see also Edwards 2001 and
Quinn 1997.)
A second round of studies moves beyond the direct effects of Openness on growth to a focus on
the channels through which openness might produce growth. Kraay 1998 finds scant evidence of the
effects of Openness on investment. Klein and Olivei 1999 show Openness leads to financial “deepening,”
but only for advanced industrial nations. Klein and Olivei propose that emerging market nations lack
some key political economic institutions through which Openness might act beneficially, which implies
that Openness has a contingent relationship to growth. Klein and Olivei use a summed version of the 0,1
IMF indicator for 1986-95. Levine and his coauthors, in a series of papers (King and Levine 1993;
Levine and Zervos 1998; Beck, Levine, and Loayza 2000), find that financial or stock market
development leads to growth, though whether Openness or Liberalization contributes to that process was
not addressed in those papers.
Scholars undertaking a third round of studies test a version of the proposition that Openness or
Liberalization’s effects are contingent upon various economic and policy preconditions. Diaz-Alejandro
1985 notes that financial liberalization in emerging market nations often produced poor results, leading to
the question of what prior states distinguished emerging market nations from other nations. Kraay 1998
finds little evidence that Openness’ effects are contingent on various economic preconditions. Edwards
2001 finds that Liberalization lead to growth in middle to high-income countries. Arteta, Eichengreen,
and Wyplosz 2001 revisit Edwards’s study, and while they reject his findings on methodological and
other grounds, they suggest that Liberalization does indeed have a contingent relationship with growth.
The contingency that matters, they believe, is macroeconomic imbalances – as exemplified by black
market premia. Chandra 2001, investigating sociological contingencies, finds that countries with higher
levels of ethnic heterogeneity were actually harmed by Openness. More homogenous societies, in

contrast, benefited. Edwards and Arteta et al. used the same indicator as Quinn 1997, whereas Chandra
used the IMF 0,1 data.
These studies have advanced our understanding of capital account openness and its effects. The
search for contingencies under which liberalization produces beneficial effects is a particularly promising
avenue of research.
Even so, the prior studies have some limitations. For one, the 0,1 measure of capital account
openness taken from the IMF tables is of limited further use. Another lesson we can take from the prior
studies is that purely cross-sectional research designs are unlikely to reconcile some of the differences
among the studies because of the necessarily limited range of information that can be entered as
independent variables in purely cross-sectional models.
Perhaps the salient problem found in the prior studies is a central problem of empirical political
economic scholarship generally – nations at similar levels of political, social, and economic development
have similar “clusters” of political economic policies and structures. Of relevance to this study, the levels
of Openness are, without question, part of a broader cluster of policies and processes, and are therefore
collinear with many other variables. (We will develop this point below as we describe the “repression
syndrome” and the “liberalization cycle.”) For related discussions, see Arteta, Eichengreen, and Wyplosz
2001, 11; Rodríguez and Rodrik 2000, 28-34; and Eichengreen forthcoming, 6.
We explicitly address the first and third round of studies of capital account liberalization and
growth. We use annual indicators of capital account restrictions for 77 countries to construct five-year
panel averages, 1960-98 based on the same data and coding rules as Quinn 1997. The range of
information allows us to compute meaningful indicators of change in capital account regulation, and it
also allows us to enter a wide array of information into the analysis. We also use a robust method -
pooled-cross-section, time-series analysis. To guard against simultaneity, we use long lags of the key
independent variables.
We overcome in part the policy cluster problem by focusing on changes in capital account
regulation, while including levels (Openness), along with many other variables as control variables in a
pooled regression model. Both levels and changes in capital account regulation carry relevant
information, but in a time-series, cross-section research design, changes in Openness are less likely to
exhibit collinearity with other “cluster” variables. A further advantage of focusing on Liberalization
rather than Openness is that Liberalization is a topic of greater policy relevance for many emerging

market nations.
II. The Repression Syndrome and the Liberalization Cycle

Groups of countries have hard-to-measure, common attributes for many reasons. Nations share
strategic interests, citizens of nations at similar levels of development have similar tastes, and many
nations share common linkages from emigration and colonization. Other groups of nations, even with
otherwise different cultures and traditions, have shared ideological or religious beliefs. Of importance to
this investigation is that these common experiences and values lead nations to adopt similar – and related
– political economic policies.
Let us develop a relevant example. If the political elite of a group of nations were to share a
belief in the efficacy of markets, we might see these countries adopt similar institutions and policies. A
floating exchange rate, open trade accounts, an independent central bank, anti-inflationary policies, and
open capital accounts might jointly characterize these economies. As policies are hard to measure
precisely, the effects of mutually related policies are sometimes conflated. For example, capital account
openness might proxy for the above list of related policies across countries. Moreover, even if economic
policies could be measured precisely, because these economic policies are frequently collinear, the
econometric difficulty of estimating the effects of any one policy is great.
Regarding international financial liberalization, we find two distinct types of political economic
clusters. (This section draws on Quinn 2000.) Low levels of capital account openness are associated with
lower levels of per capita income, lower levels of trade openness, weaker financial development, higher
levels of inflation, fixed exchange rates, and higher premia on the black market for foreign currencies.
These are characteristics of what McKinnon 1973 called a financially repressed economy. These
economically repressed countries are frequently also politically repressed (Quinn 2000), and highly
vulnerable to political instability. Politically repressed economies are characterized also by low rates of
investment in human capital (Helliwell 1994) and high birthrates (Feng, Kugler, and Zack 2000). These
joint and cumulative attributes of political and economic repression we will henceforth call the
“repression syndrome” of which capital account closure is only one part. In an econometric cross-
sectional investigation, one of the indicators of the repression syndrome is highly likely to capture part of
the influence of the other indicators.
Let us turn to a “liberalization cycle.” From the 1950s onward, most democratic governments

have liberalized finance and liberalized trade (Quinn 2000). These economically open countries
developed strong financial sectors, produced low levels of inflation (comparatively speaking), and had
very limited black markets in currencies. These democracies also invested in human capital and had
lower birthrates than similar countries, and per capita income was higher than in authoritarian countries.
Democratic countries were far less vulnerable to revolutions, coups, and other forms of political
instability than other nations, in part because democratic nations followed economic policies that
decreased economic risk even at the expense, at times, of higher growth (Quinn and Woolley 2001).
These generalizations apply at least partially to both emerging market as well as advanced industrial
democracies.
We present some evidence on the repression syndrome and the liberalization cycle in Tables 1
and 2 where we report the simple correlations between variables, 1960-98 (using five year averaged data,
which will be described below).
[Tables 1 and 2 about here]
The pairwise contemporaneous correlations do not capture change over time, do not take into
account the effect of other variables, and tell is little about the direction of relationships. But, they do help
us make the point that political economic variables are correlated in line with the repression syndrome
and the liberalization cycle. Note that the repression syndrome and liberalization cycles also present
themselves, albeit in a weaker form, in the emerging market data.
An empirical implication for our project is that political economic clustering exposes to challenge
estimating the direct effects of Openness. We will show below that the levels of capital account openness,
lagged two periods, usually have a statistically significant and positive coefficient when entered in a
growth regression. But, because of the clustering of variables, we cannot directly tell whether the levels
of Openness, or some other aspects of the repression syndrome or the liberalization cycle, are what is
actually linked to subsequent growth.
In the second part of our analysis, we make use of the information found in these and other
political economic variables to examine whether Liberalization has a direct or contingent (or both)
relationship to economic growth. We turn now to a discussion of the possible contingencies.
III. Preconditions for the Benefits of Liberalization?

The Liberalization cycle and the repression syndrome are not simply econometric problems.

More importantly, these political economic states potentially reinforce, enable, or impede other policies.
Are features of the liberalization cycle necessary preconditions for capital account liberalization to
generate growth? Do features of the repression syndrome impede the positive effects of capital account
liberalization?
Economic States as Preconditions for Liberalization. This section draws on the prior work of Edwards
2001 and Arteta, Eichengreen, and Wyplosz 2001. The authors of these papers see Liberalization as
having a contingent, rather than direct, effect on growth. The core conclusion of the Edwards 2001 study
is that economic development, proxied by per capital income, was a precondition for the benefits of
Liberalization. The Arteta, Eichengreen, and Wyplosz 2001 paper examines a number of policy
preconditions for the benefits of Liberalization to occur. They use indicators of trade liberalization from
Sachs and Warner 1995 and the premiums paid in black markets for foreign currencies as indicators of
imbalanced macroeconomic conditions. They advance the theory that the benefits from Liberalization are
likely to occur only after key distortions have been eliminated to prevent either misdirection of the
resulting inflows or capital flight .
Two additional questions then emerge about related policies… The first is whether the prior
liberalization of international financial transactions associated with the underlying current account
transactions might produce benefits different from liberalizing the underlying transactions themselves.
The second is whether financial development is a precondition for the positive effects of capital account
liberalization. Arteta, Eichengreen and Wyplosz 2001 do not find such an effect, but we test for it.
Political and Legal States as Precondition for Liberalization. We draw on Quinn 2000 and Quinn and
Woolley 2001 for this section. Wealthy democracies have been relentless liberalizers of both capital and
current accounts. See Figure 1, which shows median levels of Openness over time for three groups of
countries: democratic OECD nations, continuously democratic emerging market nations,
1
and

1
We define continuously democratic emerging market nations as those whose summed democracy/autocracy Polity
98 scores were continuously above 7 on the –10 to 10 scale, 1960 (or after independence) to 1995. These nations
are Botswana, Colombia, Costa Rica, India, Israel, Jamaica, and Trinidad. If we were to stretch the definition of

democracy to include nations whose Polity 98 scores are continuously above zero, we would include Malaysia,
South Africa, Sri Lanka, and Venezuela.
continuously autocratic nations,
2
1950-97. Emerging market democracies, while generally more open
than authoritarian emerging market nations, were characterized by far lower levels of Openness than
OECD democracies.
The lower levels of Openness might be explained in part by a finding in Quinn 2000, which was
that capital account liberalization was a risk factor contributing to democratic reversals in emerging
market democracies. Capital account liberalization is robustly associated with subsequent increased
income inequality (Quinn 1997). Dixon and Boswell 1996 find that foreign investment “penetration,”
which follows from capital account liberalization, also increases income inequality (cf. Firebaugh 1996).
Increased income inequality, in turn, has deleterious effects on polities, particularly emerging market
polities (Muller and Seligson 1987).
Democracies, therefore, tend to compensate losers from market competition. These “side-
payments” take the form, in wealthier democracies, of direct transfer payments. In poorer democracies,
however, the ability to make direct transfer payments is limited by their ability to tax and fund a welfare
system. Consequently, emerging market democracies use other mechanisms of compensation, such as
employment in state-owned enterprises or trade protection. Even private firms in emerging market
democracies frequently provide social welfare benefits to society (Khanna 2000). Capital account
liberalization, however, constrains the ability of these democratic countries to maintain these other forms
of compensation. We might therefore expect that the transactional costs of dismantling traditional
methods of compensation and establishing new ones are very high. Non-democratic nations presumably
are less concerned with compensation. Hence, the growth benefits of Liberalization might be less for
emerging market democracies than other nations.
A second possible explanation for lower levels of Openness in emerging market democracies is
that voters in all forms of democracy are characterized by a high degree of risk aversion. Quinn and

2
We define continuously autocratic nations as those whose summed democracy/autocracy Polity 98 scores are

continuously zero or below, 1960 (or shortly after independence) to 1995. These nations are Algeria, Bahrain,
China (PRC), Egypt, Ethiopia, Indonesia, Iran, Iraq, Jordan, Kenya, Morocco, Rwanda, Syria, and Tunisia.
Woolley 2001 find that voters everywhere punished incumbent governments for increased growth
volatility. In advanced industrial democracies, Liberalization might lead to lower growth volatility from
portfolio diversification effects. In emerging market democracies, in contrast, it might lead to increased
growth volatility as capital account liberalization in financial repressed economies have tended to result in
speculative bubbles with subsequent financial crashes. (See Reinhart and Kaminsky 1998 and
Williamson and Mahar 1998 for a review of the evidence that international financial liberalization is
associated with subsequent economic crises.)
Third, democracies tend to be associated already with “virtuous” economic policies. As
democratic governments tend to reform their economies, the marginal economic benefits of capital
account liberalization are smaller. Autocratic countries, in contrast, tend not to reform, since
Liberalization might reduce an autocrat’s freedom to impose arbitrary policies.
Another possible political precondition for beneficial effects from Liberalization is investor
protection. La Port, Lopez-de-Silanes, Shleifer, and Vishny 1998 propose that nations with English
common law traditions will have deeper financial markets because that tradition offers investors strong
property rights protection. Liberalization might result in higher rates of growth in common law countries.
Political volatility, revolutions and coups, and other forms of social unrest are also part of the
fabric of many emerging market societies. In settings with higher levels of political uncertainty and
violence, a natural response of investors to Liberalization could well be capital flight. Moreover, in the
face of political uncertainty, foreign investors will avoid delayable investments (Rivoli and Salorio 1997).
Hence, liberalizing against the backdrop of violence and uncertainty is plausibly economically
counterproductive.
Social Development. A central question in the growth literature has been whether ethnically or
linguistically fractionalized societies suffer lower rates of growth because of the economic consequences
of social tensions. (See Easterly and Levine 2001.) Chandra 2001 tests directly for the contingent effects
of ethnic fragmentation on Openness in a growth regression. Her pessimistic findings suggest that
heterogeneous societies do not benefit from financial openness.
Another strand of growth theory has examined the growth consequences of social
underdevelopment. (See Feng forthcoming; Feng, Kugler, and Zack 2000.) Nations suffering from a

poverty trap are less able to use policy reforms to escape their situation. Lower levels of education and
higher rates of birth proxy for the existence of a poverty trap, which might prevent capital account
liberalization from producing growth.
Summary. We make three points above. 1) The level of capital account openness is part of a “clustering”
of political economic variables, which can be characterized as a “repression syndrome” or a liberalization
cycle. “Openness” as such is a less reliable regressor in a growth regression. Capital account
liberalization’s effects on growth, in contrast, are less vulnerable to the “clustering” problem, and offer a
more policy relevant study. 2) Liberalization’s effects on growth might be contingent on prior states. 3)
Prior economic states are not the only relevant contingencies: political, legal, and social conditions might
also matter.
IV. DESIGN
Our research design is simple. We begin with a base growth model in which we test for the direct
effects of capital account liberalization on growth. In the second stage of our analysis, we estimate
models including the interaction between Liberalization and prior levels of various independent variables
– e.g., when studying the effect of Black Market Premium we include the following three terms in the
model:
Capital Account Liberalization
s-1
+ Black Market Premium
s-2
+ CAL
s-1
*BMP
s-2

If Liberalization's effect is contingent, and the contingent variable is a dummy (indicator) variable, the
interaction of capital account liberalization with the "contingent" variable will be statistically significant,
but the base capital account liberalization

term will no longer be statistically significant. If neither the

interaction term nor the prior levels variable is statistically significant, then the direct effect of capital
account liberalization

is maintained (assuming its initial statistical significance). When the contingent
variable is a measurement (like black market premium, democracy, population growth, etc.) and the
interaction term is positive and statistically significant, than the interpretation is that the effect of capital
account liberalization is greater when preceded by higher values of the contingent variable. On the other
hand, if the interaction term is negative and statistically significant, it means the effect of capital account
liberalization on growth is smaller when preceded by higher values of the contingent variable.
The prior political economic variables are these:
A) prior economic states - national income; current account liberalization; trade liberalization (the
Sachs-Warner trade openness measure); black market premium; and financial depth;
B) prior political and legal states - democracy (Polity 98 scores); continuously democratic or
continuously autocratic countries; property rights protection (English common law tradition); and
political stability (revolutions and coups; volatility of democracy scores);
C) prior social states - educational attainment (secondary schooling); population growth; and ethnic
fractionalization (ELF60 or ETHFRAC).
Let us note three possible design problems. The first is possible endogeneity in the relationships
between growth and various independent variables. We focus on lagged changes in capital account
liberalization, which should not be influenced by subsequent growth. A second problem is that data
limitations for many of the prior state variables lead to large reductions in the sample. In particular, the
experiences of the 1960s are sometimes lost with the use of the prior state variables: for the financial
depth and black market premium measures, we lose up to 40% of the available sample. We can only
interpret cautiously some of the results of the prior state variables. A third problem, to which we have
already spoken, is the extensive collinearity among variables. When collinear variables are used to create
interaction terms, the collinearity problem is exacerbated. To reduce this problem, we “recenter” the
variables used to create the interaction terms, which has the effect of reducing the Variance Inflation
Factor in most cases.
3

V. METHODS, DATA, and MODELS
Methods.

3
We subtract the means of the variables in question from the observed values of the variables. For example, when
considering X1=democracy(s-2) and ∆capital(s-1), the three terms included in the model are: (X1-mean of X1),
(∆capital – mean of ∆capital), and (X1-mean of X1)*(∆capital – mean of ∆capital). Centering before obtaining the
interaction terms reduces the correlation between each of the variables and the interaction term without affecting the
coefficients of interest.
The dependent variable in this investigation is per capita ppp-adjusted economic Pooled,
cross-section, time-series (PCSTS) models are useful in evaluating the question of why, over
time, some nations grow quickly and others do not. That is, the variation in the dependent
variables comes from both the time series and the cross-sections, and some pooling of data is
necessary to address the questions. We estimate PCSTS models using five-year averaged data.
An explicit assumption in estimating PCSTS models is that the relationship between independent
and dependent variables are not simultaneously determined. We use very long lag periods to guard against
simultaneity. The pooled equations are estimated by ordinary least squares using panel corrected standard
errors, as suggested by Beck and Katz 1995.
4
All models are fixed effects models
5
in which country
dummy variables are used. (The coefficient estimates of the country dummy variables are not reported,
but are available from the authors.)
Data.
International Financial Regulation. We operationalize international financial regulation through two
indicators of change in international financial openness or closure, which are described in Quinn 1997.
CAPITAL and CURRENT are the main components of OPENNESS created from the text of an annual
volume published by the International Monetary Fund (IMF), Exchange Arrangements and Exchange
Restrictions. This IMF text reports on the laws governments use to govern international financial

transactions. The measure is available from 1950 to 1997 for 58 countries, and for a shorter period for an
additional 23. (See Eichengreen forthcoming for a review of this and other measures.)

4
All PCSTS estimations use the POOL command with HETCOV option in Shazam 9.0. Because of a matrix error
in the code for HETCOV, all residuals analysis is done with the OLS option in POOL, which gives accurate
residuals.
5
An alternative is to use random effects models. We replicate key results using S-Plus. These data are not from a
random sample, but are the universe of that which is available. For discussions, see Hsiao 1986, chapter 4, and
Pesaran, Shin, and Smith 1998, 4.
CAPITAL is scored 0-4, in half integer units, with 4 representing a fully open economy.
CURRENT is scored 0-8, in half integer units, which represents the sum of the two components of current
account scores: trade (exports and imports) and invisibles (payments and receipts for financial and other
services). We transformed each measure into a 0 to 100 scale taking 100*(CAPITAL/4) and
100*(CURRENT/8).
When using CAPITAL and CURRENT as independent variables, we need to model the potential
influences of changes and levels of these variables over many years. We use five-year averages, which
are calculated as follows:
CAP
s
=( X
t
+ X
t+1
+ X
t+2
+ X
t+3
+ X

t+4
)/5
where X
t
= 100*(CAPITAL
t
/4). The subscript s represents a five year period: s=1960-64, 1965-69,…, and
the subscript t identifies the first year in the five year period: t=1960, 1965,… Because we are interested
in isolating how changes in policy affect growth, and because we seek to avoid problems of endogeneity,
our primary focus is on lagged changes of CAPITAL, or:
∆
CAPITAL
s-1
= CAP
s-1
- CAP
s-2

We also create a contemporaneous change measure and a lagged levels measure, or,
∆
CAPITAL
s
and
CAPITAL
s-2
, respectively. Corresponding variables for CURRENT are defined similarly. In cases of
missing values, the averages are obtained over the number of observations available.
Economic, Political, and Social Data. Our focus in this investigation is on international capital account
variables. The other variables in the study are treated as control variables.
In estimating the unbalanced panel models, we use a beta version of Penn World Tables Mark

6.0.
6
The advantage of this data set is that we are able to estimate models using data from 1960 to 1998.
The disadvantage is that the data may contain errors.
7
To insure the robustness of the results, we repeat

6
Prof. Alan Heston provided the data. Email correspondence between Quinn and Prof. Heston, 28 Feb. 2001.

7
The PWT 6.0 growth series for Syria contain some unlikely numbers, as does the trade series for India before
1970. The data for Syria and India before 1970 are excluded from the analyses. The data for Argentina and
all analyses with data drawn from the Penn World Tables Mark 5.6. We report the results for key
equations using PWT 5.6 data, and note significant deviations between the PWT 6.0 and PWT 5.6 results.
The PWT 6.0 data contain more observations than the PWT 5.6 data, but when matched with the data for
other variables the resulting data set also contains fewer countries (70 vs. 76). Our core results are robust
to the choice of which PWT data set we use.
The educational attainment measures are Barro/Lee indicators from the World Bank 2001. The
data on revolutions, coups, etc. are updated Cross-National Times Series data from Banks 2001.
Educational data for Nigeria, Tunisia, and Morocco are unavailable, as are political data for Hong Kong.
In order to use the widest range of countries, we omit education from the base model, but we use
educational measures in our interaction models. Our core results are highly robust to the inclusion or
exclusion of educational attainment measures. The black market premium data and the Liquidity
measures are taken from Beck, Levine and Loayza 2001. Because the black market data have an
extremely skewed distribution but also contain negative numbers, we transform the series using a signLog
transformation (Atkinson 1985).
8

For a democracy indicator, we use the “Democracy” plus “Autocracy” indicators from the Polity

98 data set, which report data from 1800 to 1998 for the countries used in this investigation. (See Gurr
and Jaggers 2000.) Autocracy is scored on a –10 to 0 scale, Democracy is scored on a 0 to 10 scale, and
both are summed to produce the main political indicator of this investigation, DEMOCRACY. (This
indicator is also used the World Bank 1997, 112.) Missing data are interpolated linearly.

Singapore in PWT 6.0 contain a few missing data points. In constructing the five year panel averages used here, we
used the data from PWT 5.6 to interpolate the missing data points.
8
Taking logarithms is a common practice when fitting linear regression models for several reasons. However, when
X has negative values, or 0's, the logarithm is not defined. One alternative is to use the following transformation:
sign(x)log(abs(x)+1). This is a monotonic transformation which achieves the same objective of making the
distribution more symmetric and the relationship with Y better described by a straight line. This transformation is
like the power transformation with offset discussed in Atkinson (1985).
We use the levels of the index transformed into a 0 to 100 scale and lagged two five-year periods
as an explanatory variable, and we also consider its interaction with financial regulation. We also use
FULLDEM, a 0,1 Democracy indicator for countries that were continuously democratic during the period
studied, following Quinn 2000.
9
The 0,1 indicator, when interacted with the financial liberalization
variables (FULLDEM*
∆
CAPITAL
s-1
), allows us to estimate precise differences in coefficients between
continuously democratic and other nations. We further distinguish the seven continuously democratic
emerging market nations by (EMERGING*FULLDEM*
∆
CAPITAL
s-1
) and the eighteen continuously

democratic advanced industrial nations by (OECD*FULLDEM*
∆
CAPITAL
s-1
). To examine whether the
effects of capital account liberalization differ for continuously autocratic nations versus other nations we
create AUTOCRAT*
∆
CAPITAL
s-1
.
For social fragmentation, we use two different indicators. One is the linguistic fractionalization
(ELF60) index from Mauro 1995, which is used by Chandra 2001. The indicator is taken from a single
1960 survey. ELF60 is therefore available for only one point in time, which creates estimation difficulties
in a fixed effect model.
10
Krain 1997 offers a more nuanced measure of ethnic (rather than linguistic)
fractionalization over a number of points in time (ETHFRAC), and we use it also. (ELF60 and
ETHFRAC are highly correlated.)
Models.
We use a panel variant of the standard Barro 1991 economic growth model. The base model
includes per capita income measured at the beginning of the period, change in investment, lagged levels
of investment (as a share of GDP), annual rates of population growth, lagged levels of trade openness

9
These are: Botswana, Canada, Colombia, Costa Rica, the U.S., Iceland, India, Israel, Japan, Austria, Belgium,
Denmark, Finland, Germany, Ireland, Italy, Jamaica, the Netherlands, Norway, Sweden, Switzerland, Trinidad, the
United Kingdom, Australia, and New Zealand. The Polity 98 scores for France dropped from 10 to 5 in 1958 as the
Algerian war of independence intensified and the 4
th

Republic collapsed. (France’s score rises to 8 in 1969.)
Because of unobtainable data on financial regulation, all former Soviet-Bloc nations are excluded.