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Does Free Trade Really Reduce Growth?
Further Testing Using the Economic Freedom Index
*
Niclas Berggren
†
and Henrik Jordahl
‡
October 23, 2003
Abstract
While studies of the relationship between economic freedom and economic growth have shown it to be
positive, significant and robust, it has rightly been argued that different areas of economic freedom may
have quite different effects on growth. Along that line, Carlsson and Lundström (2002) present the
surprising result that “International exchange: Freedom to trade with foreigners” is detrimental for
growth. We find that “Taxes on international trade” seems to drive this result. However, using newer data
and a more extensive sensitivity analysis, we find that it is not robust. Least Trimmed Squares-based
estimation in fact renders the coefficient positive.
JEL-Classification: E61, F13, F43, O24, O40, P17
Keywords: free trade, economic freedom, economic growth
*
The authors wish to thank Mikael Bengtsson for excellent research assistance, Susanna Lundström,
participants at the Public Choice Meetings in Nashville (2003), participants at seminars at the Department
of Economics, Uppsala University, and the Trade Union Institute for Economic Research (FIEF), as well as
an anonymous referee for helpful comments and suggestions, John Ekberg for introducing SAS, and
Torsten och Ragnar Söderbergs stiftelser (Berggren) and Jan Wallanders och Tom Hedelius Stiftelse
(Jordahl) for financial support. This working paper is forthcoming in Public Choice.
†
The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden; E-mail: niclas.berggren@ratio.se.
‡
Department of Economics, Uppsala University, P.O. Box 513, SE-751 20 Uppsala, Sweden; E-mail:
henrik.jordahl@nek.uu.se.
2
1. Introduction
In a recent article, Carlsson and Lundström (2002) advanced the literature using the
Economic Freedom of the World Index (EFI) by investigating the growth effects of the
various areas of the index.
1
They reported a surprising finding, namely that the area
“International exchange: Freedom to trade with foreigners” exerts a negative influence
on economic growth.
2
Here, we show that this result is not robust and caution against
using the negative result in offering policy advice.
Even though most economists have argued for a positive effect of free
trade, there are theoretical arguments both to support the contention that free trade
improves economic performance and the opposite view.
3
Hence, this is, in the end, an
empirical issue. And the bulk of the literature supports the view that free trade and
trade openness does have, at least some, positive effects on efficiency and growth.
4
This
1. For a survey of this line of research, see Berggren (2003).
2. Cf. Ayal and Karras (1998).
3. See Bhagwati (1994). Cf. Krugman (1987), Srinivasan (1999), and Bhagwati and Srinivasan (2001).
Among the arguments pointing at a possible negative relationship between free trade and growth, the
following can be mentioned: free trade might reduce growth in countries that do not specialize in research
and development or other growth-promoting activities; higher growth rates could lead to higher tariffs
rather than the other way around, perhaps due to some political logic, or they could be jointly determined;
the effect of one variable, such as free trade, is not always fully manifested in the coefficient of the variable
itself but through other variables that are themselves related to growth, e.g. investment; less free trade
could induce more growth if trade and foreign direct investment (FDI) are substitutes and if it is combined
with freedom for FDI; and perhaps some countries are able to act as price makers on the international
market, using trade policy strategically, and it may be that they have higher growth rates.
4. See e.g. the survey provided in Berg and Krueger (2003). Rodriguez and Rodrik (2000) claim that the
results in this literature are less trustworthy than has been claimed due to poor measures and methods; but
Baldwin (2003) maintains that there are credible studies to the effect that openness is growth-enhancing
in combination with a stable and nondiscriminatory exchange rate system, responsible fiscal and monetary
policies and an absence of corruption.
3
accentuates the need to scrutinize the negative finding of Carlsson and Lundström
(2002).
Along that line, the contribution of this paper is threefold: first, we use a
new version of the EFI and conduct extensive sensitivity analysis to see if the negative
result on trade openness holds; second, we decompose the index even further, in order
to get more information on what, exactly, drives the result; and third, by using the EFI,
we are able to control for the growth effects of other market-oriented policy changes
that often take place at the same time as trade liberalization (and hence we avoid a
methodological problem encountered by many other cross-country studies in this area,
as pointed out by e.g. Baldwin, 2002, and Clemens and Williamson, 2002).
We run cross-country regressions, encompassing 78 countries over the
period 1970–2000. The results indicate that the area “Freedom to exchange with
foreigners” is, indeed, detrimental for growth. In this regard, we replicate the result of
Carlsson and Lundström (2002), as in finding that the area “Legal structure and
property rights” exerts a strongly positive influence. When decomposing the index
further in the area “Freedom to exchange with foreigners,” we find that one of its
components, “Taxes on international trade,” seems to be the decisive factor behind the
result. That is, the higher these taxes, the higher the growth rate.
However, our sensitivity analysis reveals that the negative result for
“Freedom to exchange with foreigners” is not robust to changes in the sample or the
specification of the model. In fact, using Least Trimmed Squares to identify outliers and
Reweighted Least Squares to perform estimations without the outliers (these robust
estimators are explained in section 3.2), we get the result that “Freedom to exchange
with foreigners” exerts a positive influence on growth! Likewise, looking at various
subsamples of countries reveals that the negative effect primarily holds for some types
(such as democratic and poor countries) but not for others. This should make one
cautious in accepting the finding of a negative relationship.
4
2. The data
Our data set consists of averages of economic freedom measures (1970−1995) and
macroeconomic variables (1975−2000) for 78 countries. The variables used are
specified in Appendix.
The estimations are made on the basis of country averages of annual data
for the time periods mentioned, except for Y75 and SCHOOL, which measure initial
values, and except in the case of EFI data, which are only available at, and thus
averaged over, five-year intervals. The use of levels instead of changes is consistent with
endogenous growth theory, where certain policy variables are assumed to affect
economic growth. Since institutional variables, such as the EFI, are likely to have a
long-run influence on economic growth, we have chosen to work with a cross-section
rather than with a panel of countries. The EFI spans only a period of 30 years with no
more than seven observations for each country. This leaves little time-series variation,
especially if we would have used ten- or fifteen-year averages to avoid problems of
short-run dynamics; and of course any fixed-effects specification throws away the
between-country variation.
The choice of explanatory variables is such as to include those that have
generally been shown to be significantly and robustly related to growth (see e.g. Levine
and Renelt, 1992 and Sala-i-Martin, 1997; cf. de Haan and Sturm, 2000, 2001). The
EFI is added, in various ways, to investigate if it adds explanatory power, as we
hypothesize it might.
In central respects, the choice of variables, as well as the model
specifications, mirror the Carlsson and Lundström (2002) study. Unlike their study,
our include data for the EFI from 1995 and data for the other variables for the period
1996−2000. Moreover, the Fraser Institute constantly tries to improve the quality of
the EFI, and new parts have been added in the latest version.
5
3. The results
3.1 The regression results
In order to get a picture of what in the EFI that affects growth we regress real per capita
GDP growth (∆Y
i
) on the five areas that together make up the summary index. Our
baseline specification is written
∆Y
i
= α + β
1
Y75
i
+ β
2
INV
i
+ β
3
SCHOOL
i
+Σ
j
δ
j
EFI
ji
+ ε
i
, (1)
where economic growth (∆Y) and the investment share of GDP (INV) are country
averages between 1975 and 2000 and percentage of “secondary school complete” in the
total population in 1975 (SCHOOL) is an initial value.
5
EFI
ji
is area j (j=1,…, 5) of the
EFI in country i averaged between 1970 and 1995 (we expect economic freedom to have
a lagged effect on growth). To control for convergence, GDP per capita in 1975 (Y75) is
also included.
We use the average GDP per capita between 1970 and 1974 (Y7074) and
the average investment share of GDP between 1970 and 1974 (INV7074) as instruments
for Y75 and INV. This is to ensure that β
1
is not biased due to measurement error and
that β
2
is not overestimated due to endogeneity (as one can easily imagine that growth
causes investment as well as the other way around).
6
5. Since the initial (1975) percentage of “secondary school complete” in the total population is
predetermined, it enters as its own instrument. For empirical arguments on why a stock rather than a flow
is preferable for this kind of human-capital proxy, see Gemmell (1996) and Pritchett (1996).
6. Cf. Barro and Sala-i-Martin (1995: 431) and Temple (1999: 129).
6
Table 1. Estimation with the five areas of the EFI
Coefficient
(std. error)
Variance inflation
factor
EFI
1
Size of government .0965
(.1258)
1.33
EFI
2
Legal structure and property rights .8050**
(.1341)
2.93
EFI
3
Sound money .3720*
(.1556)
2.16
EFI
4
Freedom to exchange with foreigners -.4043*
(.1727)
2.71
EFI
5
Regulations .1179
(.2940)
1.97
Y75
IV
-.1403*
(.0204)*
2.94
INV
IV
.0943
(.0586)
1.20
SCHOOL .0364
(.0292)
2.11
Constant -5.6037**
(1.4988)
R-squared .58
# obs. 78
Condition number 4.2
Note: The dependent variable is ∆Y. The two variables with the superscript IV refer to instrumented
variables with EFI
j
, j = 1,…,5, SCHOOL, Y7074 and INV7074 as instruments. Huber-White robust standard
errors are used. * indicates significance at the 5 percent and ** at the 1 percent level.
According to the estimates in Table 1, three of the five areas of the EFI have a
statistically significant effect on growth. In particular, we reproduce Carlsson and
Lundström’s (2002) surprising negative effect of area 4 “Freedom to exchange with
foreigners,”
7
as well as the positive effect of area 2 “Legal structure and property
7. It has been argued by Bhagwati (1999) that free trade and freedom for capital are two distinct
phenomena with different effects on e.g. growth. Consequently, we ran a regression like the first
specification in Table 4 but excluding components 4B (for reasons outlined below in connection with Table
5) and 4E “International capital market controls.” The effect of this new variable on growth is negative but
insignificant.
7
rights”. Contrary to Carlsson and Lundström we also find that the positive effect of the
third area “Sound money” attains statistical significance, but that the first area “Size of
government” does not.
8,9
Table 1 also includes variance inflation factors and the
condition number for the explanatory variables.
10
Neither of these indicators suggests
that severe multicollinearity (presumably due to close resemblance of certain areas) is
at hand.
The surprising finding that area 4 “Freedom to exchange with
foreigners” reduces growth calls for further examination. A natural step is to
disaggregate this area into its five components. Table 2 contains the estimation results
from such a disaggregation, where component 4B “Regulatory trade barriers” is
excluded since it is only available for 37 countries.
8. We get very similar results if we instead use PPP-adjusted or chain-weighted growth rates. The most
notable difference is that the negative effect of EFI
4
only attains statistical significance at the ten percent
level with PPP-adjusted growth rates.
9. The effect of one variable, such as free trade, is not always manifested in the coefficient of the variable
itself but through other variables that are themselves related to growth. One such candidate is investment.
If we estimate the specification in Table 1 without investment, the coefficient for EFI
4
becomes less
negative (-.27) and statistically insignificant. Thus free trade might promote growth through investment.
The correlation coefficient between EFI
4
and INV is .29 and when regressing INV on EFI
4
and a constant,
the coefficient for EFI
4
(1.28) is highly statistically significant. The same is true if we also include the other
areas of the EFI in the regression.
10. Variance inflation factors are indicators of multicollinearity. As a rule of thumb a value greater than 10
indicates that the significance of the other variables is sensitive to the inclusion of the variable in question.
8
Table 2. Estimation with the components of area 4 of the EFI
First specification Second specification
Coefficient
(std. error)
Variance
inflation
factor
Coefficient
(std. error)
Variance
inflation
factor
EFI
1
Size of government .0513
(.1531)
1.50 .1146
(.1186)
1.32
EFI
2
Legal structure and property rights .7546**
(.1342)
3.00 .8002**
(.1357)
2.99
EFI
3
Sound money .2801*
(.1307)
2.03 .2709
(.1307)
2.00
EFI
4A
Taxes on international trade -.2172
(.1098)
2.67 -.2316*
(.1090)
2.64
EFI
4C
Actual size of trade sector
compared to expected size
-.1368
(.1037)
1.63
EFI
4D
Difference between official
exchange rate and black market rate
.0534
(.0806)
1.76
EFI
4E
International capital market
controls
-.0040
(.0902)
2.21
EFI
4CDE
-.0662
(.1309)
2.60
EFI
5
Regulations .0515
(.2883)
1.97 .0263
(.2894)
1.96
Y75
IV
-.1405**
(.0246)
3.88 -.1244**
(.0246)
3.26
INV
IV
.1408*
(.0614)
1.38 .1169*
(.0566)
1.24
SCHOOL .0435
(.0308)
2.13 .0458
(.0304)
2.12
Constant -5.9795**
(1.5218)
-5.8214**
(1.4444)
R-squared .62 .59
# obs. 78 78
Condition number 5.09 4.6
Note: The dependent variable is ∆Y. The two variables with the superscript IV refer to instrumented
variables with EFI
j
, j = 1, 2, 3, 5, EFI
4k
, k = A, C, D, E (in the first specification; in the second, C, D, and E
are measured as a composite), SCHOOL, Y7074 and INV7074 as instruments. Huber-White robust
standard errors are used. * indicates significance at the 5 percent and ** at the 1 percent level.
Focusing on the first specification in Table 2, we see that none of the four components
in area 4 of the EFI turns out statistically significant; but component 4A “Taxes on
9
international trade” is very close (with a significance level of 5.2 %). The second
specification, where the components C, D, and E of area 4 are put together into a
composite measure, renders component 4A statistically significant. Hence, this variable
appears to be behind the negative effect of free trade on growth: the higher the tariffs,
the higher the growth rate (as economic freedom and tariffs are negatively related by
definition). Furthermore, 4A is the only component that attains statistical significance
if we include component 4A to 4E one at a time.
11
Table 2 also includes variance
inflation factors and the condition number for the explanatory variables. Neither of
these indicators suggests that severe multicollinearity is at hand.
3.2. Sensitivity analysis
We carry out two types of sensitivity analysis in order to detect whether the EFI results
are robust: a test of the sensitivity of the results to the specification of the model and
some tests of the sensitivity of the results to the sample.
The first test uses the Extreme Bounds Analysis applied by Levine and
Renelt (1992) and the less strict robustness test of Sala-i-Martin (1997). The former
report an upper and a lower bound for parameter estimates based on a number of
regressions with different combinations of regressors; a coefficient is defined to be
robust if its two bounds have the same sign. The latter thinks this approach too
demanding and instead argues in favor of analyzing the entire distribution of the
parameter estimates, defining robustness as holding when the averaged 90 percent
confidence interval of a coefficient does not include zero. Like Sturm and de Haan
(2002a) we use an unweighted version of this test.
12
This sensitivity analysis includes 16
of the 22 variables that according to Sala-i-Martin (1997) appear to be “significant,” as
11. The estimates are available upon request.
12. See Sturm and de Haan (2002b) for a critique of Sala-i-Martin’s weighted approach.
10
well as Life Expectancy. We have excluded the variables that are similar to the EFI
variables. This gives rise to the following list of included variables:
1. Regional variables: Sub-Saharan Africa (dummy), Latin America (dummy),
Absolute Latitude.
2. Political variables: Political Rights, Civil Liberties, Number of Revolutions and
Coups, War dummy.
3. Religious variables: Fraction Buddhist, Fraction Muslim, Fraction Catholic,
Fraction Protestant. (We have not been able to find Fraction Confucian.)
4. Types of investment: Equipment Investment, Non-Equipment Investment.
5. Primary sector production: Fraction of Primary Products in Total Exports,
Fraction of GDP in Mining.
6. Former Spanish Colonies.
7. Life Expectancy.
13
For each regression we add one of the 680 possible triplets of the above variables to
equation (1). The results are reported in Table 3, with and without the Type of
investment variables, which, when included, reduce the sample to almost half the size.
13. For more detailed information on the variables included in the robustness analysis, see Berggren and
Jordahl (2003).
11
Table 3. Significance shares for the EFI variables when altering the model specification
N=680 N=455
10 % sign 5 % sign 10 % sign 5 % sign 10 % sign 5 % sign 10 % sign 5 % sign
% % # # % % # #
EFI
1
3.971 .294 27 2 EFI
1
4.654 .440 21 2
EFI
2
95.294 87.794 648 597 EFI
2
99.560 98.462 453 448
EFI
3
58.824 34.412 400 234 EFI
3
84.176 51.429 383 234
EFI
4
40.441 23.088 275 157 EFI
4
51.868 40.230 236 183
EFI
5
.147 .000 1 0 EFI
5
1.099 .000 5 0
Note: The first five columns include equipment and non-equipment investment whereas the latter five do
not. “N” refers to the number of regressions run.
EFI
4
“Freedom to exchange with foreigners” is not robustly related to
growth. Even when excluding the Type of investment variables and using the 10 percent
significance level, the share of statistically significant coefficients is a meager 52
percent. The only area of the EFI that passes the test (of significance at the 10 percent
level in at least 90 percent of the regressions) is EFI
2
“Legal structure and property
rights.”
14
In their sensitivity analysis, Carlsson and Lundström (2002) only varied the
included areas of the EFI.
15
We have shown that their claim that “Freedom to exchange
with foreigners” is negatively and robustly related to growth does not appear to stand
when other explanatory variables are incorporated in the sensitivity analysis.
The second type of test investigates whether only certain countries drive
the results, i.e. if outliers that are not representative have a decisive influence on the
estimated coefficients. First we use Least Trimmed Squares (LTS), the idea of which is
to fit the majority of the data and, after that, to identify outliers as the cases with large
14. According to the strict form of the Extreme Bounds Analysis, none of the five EFI areas is robustly
related to growth.
15. We have performed this type of analysis as well (although it might be problematic to use a method
which looks at the effect of eliminating variables thought to be of relevance for growth). When eliminating
up to three of the EFI variables and re-estimating the model (14 times per EFI area), we only found EFI
2
to
be robust at the 10 and 5 percent levels. EFI
4
only obtained a significance share of 21.4 % (5 % level) and
35.7 % (10 % level).
12
residuals (see Sturm and de Haan, 2002a).
16
After this identification, we use
Reweighted Least Squares (RLS) for inference by giving outliers (defined as countries
with a residual the absolute value of which is greater than 2.5 times the standard error
of the LTS regression) the weight zero and other countries the weight one. This
procedure concentrates on the observations that best approximate the estimated
model. The advantage of LTS compared with single-case diagnostics like Cook’s
distance and DFITS is that it can handle cases with several jointly influential
observations.
The estimates in Table 4 reveal that EFI
4
is positively correlated with
growth when 24 outlying observations are excluded. The sign of EFI
3
(now negative)
also changes with the exclusion. The estimates in Table 4 should of course not be seen
as evidence of a positive relationship between free trade and growth, but at least they
indicate that measurement errors (which are common in the national accounts of less
developed countries) or parameter heterogeneity (which is likely in cross country
regressions) might explain the negative coefficient for EFI
4
in Table 4.
17
16. We minimize the sum of the 44 smallest residuals.
17. The definition of outliers is of course arbitrary. If we instead include the 61 countries with a residual
that is smaller than 4 times the standard error of the LTS regression, the coefficient for EFI
4
is positive but
not statistically significant. The smallest number of countries that we can drop in this procedure and still
get a positive coefficient is 14. To do away with the statistical significance of the negative coefficient for
EFI
4
we only need to drop Egypt and the Democratic Republic of Congo.
13
Table 4. Least Trimmed/Reweighted Least Squares estimation with the five areas of the EFI
Coefficient
(std. error)
EFI
1
Size of government .0845
(.0585)
EFI
2
Legal structure and property rights .4134**
(.0719)
EFI
3
Sound money -.4324**
(.0590)
EFI
4
Freedom to exchange with foreigners .2675**
(.0695)
EFI
5
Regulations .6949**
(.1273)
Y75
IV
-.0546*
(.0169)*
INV
IV
.2294**
(.0257)
SCHOOL .0231
(.0125)
Constant -9.0788**
(.0731)
R-squared .87
# obs 54
Note: The dependent variable is ∆Y. The two variables with the superscript IV refer to instrumented
variables with EFI
j
, j = 1,…,5, SCHOOL, Y7074 and INV7074 as instruments. Huber-White robust standard
errors are used. * indicates significance at the 5 percent and ** at the 1 percent level. The following 24
countries are given weight zero: Bangladesh, Bolivia, Chile, Democratic Republic of Congo, Egypt, India,
Indonesia, Jamaica, Malta, Mauritius, New Zealand, Nicaragua, Pakistan, Papua New Guinea, South
Africa, South Korea, Sri Lanka, Syria, Thailand, Tunisia, Turkey, United Arab Emirate, Venezuela, Zambia.
All observations are used to construct the instruments in the first-stage regressions.
In addition, we have varied the sample manually in various ways, dividing the sample
into different groups in order to see if the results hold only for countries with certain
characteristics. Some of the divisions that have been undertaken, and the basic results,
are the following:
18
18. All estimations are available on request.
14
1. Rich or poor: The negative effect of EFI
4
holds for poor countries (with Y75 less
than the median) and is positive but not statistically significant for rich
countries.
2. Democratic or non-democratic: The effect of EFI
4
is positive in less democratic
countries, as measured by the variables Political Rights (not statistically
significant) and Civil Liberties (statistically significant at the 10 percent level);
and the effect is negative and statistically significant for more democratic
countries. For variable definitions see Berggren and Jordahl (2003).
3. Continents and groups of countries: The negative result for EFI
4
holds when
excluding Tiger economies in Asia (with a theoretical possibility of their being
closed but fast-growing); there is a particularly strong negative effect of EFI
4
in
Latin America; otherwise few interesting results are obtained.
4 Concluding remarks
It is widely believed that free trade is growth-promoting, and a number of studies
confirm this result. However, the relatively new dataset that forms the Economic
Freedom Index has been used to show the opposite result (Carlsson and Lundström,
2002). In using a newer version of the index, and hence partly new data, we likewise
find that the area “Freedom to exchange with foreigners” is associated with slower
growth. By decomposing the index even further, we can establish that the component
“Taxes on international trade” seems to drive this result – the higher these taxes, the
higher the growth.
However, performing a sensitivity analysis reveals that this negative result
is not robust. A robustness test of the model specification reveals that “Freedom to
exchange with foreigners” is significant in only 40 percent of the cases at the 5 percent
significance level and in only 52 percent of the cases at the 10 percent level.
Furthermore, the results are sensitive to the sample used. When using LTS to identify
15
outliers and RLS for inference, the variable turns out significant and positive. Likewise,
dividing the sample of countries into different groups reveals that the negative result
only holds for some types of countries whereas other types are characterized by a
positive result. (Carlsson and Lundström, 2002, do not perform these kinds of tests.)
The implication is that the negative result found in OLS and 2SLS regressions should
be interpreted with great caution.
Now, it could be that cross-country regression studies do not use a
methodology suitable to investigating the effect of free trade on growth, as Bhagwati
and Srinivasan (2001) have argued at length. For example, even if there is a partial
correlation between area 4 of the EFI and growth, the causality is unclear (cf. Dawson,
2003). So clearly, there is scope for more detailed, and various kinds of, studies of the
free trade-growth relationship (as well as between other areas of the index and growth).
In the paper, we have identified several actual and potential weaknesses of the tests
thus far. Although we have tried to resolve a few of these problems, it is still imperative
to be careful when offering policy advice. There is no robust and general relationship to
the effect that less free trade raises growth rates.
16
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Appendix
Table A1. Variable specifications and descriptive statistics for the countries of the Table 4 regressions
Variable
name
Variable definition #
obs
Mean Std
dev
Max
value
Min
value
Source
∆Y Average annual percentage change in 1995
constant USD per capita, 1975−2000
78 1.284 2.018 6.160 -4.808 WDI
Y75 Initial (1975) real GDP per capita in 1000
constant 1995 USD.
78 5.969 8.484 37.520 .149 WDI
Y7074 Average real GDP per capita in 1000
constant 1995 USD, 1970−1974
78 5.813 8.892 44.165 .134 WDI
INV Average annual gross capital formation,
per cent of GDP, 1975−2000
78 22.520 5.382 39.177 10.768 WDI
INV7074 Average annual gross capital formation,
per cent of GDP, 1970−1974
78 23.088 7.189 46.169 9.419 WDI
SCHOOL Percentage of “secondary school complete”
in the total population, 1975
78 7.609 8.534 49.100 .020 BL
EFI
1
Size of government: Expenditures, taxes,
and enterprises, average 1970−1995
78 5.440 1.512 9.535 2.418 GL
EFI
2
Legal structure and security of property
rights, average 1970−1995
78 5.091 1.619 8.410 2.023 GL
EFI
3
Access to sound money, average 1970−1995
78 6.311 1.702 9.580 1.795 GL
EFI
4
Freedom to exchange with foreigners,
average 1970−1995
78 5.660 1.450 9.608 2.512 GL
EFI
5
Regulation of credit, labor, and business,
average 1970−1995
78 5.445 .858 7.497 2.835 GL
EFI
4A
Taxes on international trade, average
1970−1995
78 5.813 2.252 9.900 .208 GL
EFI
4B
Regulatory trade barriers, average
1970−1995
37 6.691 1.624 9.300 3.330 GL
EFI
4C
Actual size of trade sector compared to
expected size, average 1970−1995
78 5.041 2.064 10.000 .207 GL
EFI
4D
Difference between official exchange rate
and black market rate, average 1970−1995
78 7.397 2.476 10.000 0 GL
EFI
4E
International capital market controls,
average 1970−1995
78 2.874 2.627 9.885 0 GL
EFI
4CDE
(EFI
4C
+ EFI
4D
+ EFI
4E
)/3 78 5.104 1.700 9.569 1.012 GL
Note: WDI = World Bank (2001); BL = Barro and Lee dataset at <http://www.nber.org/pub/barro.lee>;
GL = Gwartney and Lawson (2002) or <http://www.freetheworld.com>. All variables of the EFI range
from 0 (“no economic freedom”) to 10 (“full economic freedom”). The components of the EFI, as well as
weighting schemes, have changed in the various editions that have been published.
