okui causality between economic and political freedom

8/7/2019 Okui Causality Between Economic and Political Freedom

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Causality between Political Freedom

and Economic Freedom

March 7, 2005

Katsuyoshi Okui

Abstract

The aim of this paper is to investigate causality between political freedom and economicfreedom. Since economic freedom data do not have long time series, I conduct

causality tests that use panel data with only short time-series but many cross-sectionalcountries.

A glance at the data shows that political freedom promotes economic

freedom. Granger causality tests that do not take account of individual effects leadto the results that political freedom causes economic freedom. But my causality

tests that take account of individual effects lead to the results that there is nocausality between political freedom and economic freedom.

Otemon Gaku in University, 2-1-15, Nishiai, Ibarak i, Osaka , 567-8502, J apan E-ma il: [email protected]


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1. Introduction

The aim of this paper is to investigate causality between political freedom and

economic freedom. Here political freedom means how degree democratization has

progressed. Economic freedom means how degree marketization has progressed.

Does democratization cause marketization? Or, does marketization cause

democratization? I want to investigate them by estimating political-economic freedom

autoregressions and conducting causality tests.

When we think about development strategy of developing countries, we often

face the question which we should put priority on between democratization and

marketization. Results of this paper are considered to contribute to solving this

question. For example, if we get the result that democratization causes marketization

and marketization does not cause democratization, we get the implication that

democratization should be put priority on.

The paper is organized as follows. Section 2 presents a background of the

issue that I deal with, and refers to some prior researches. Section 3 reviews previous

works that pursue the relationship between political freedom and economic freedom.

Section 4 explains political and economic freedom data that I use and overviews the

data. It is shown that that there are more number of countries where marketization

progressed after democratization had progressed than the ones where democratization

progressed after marketization had progressed. To check it using econometric method,

I conduct Granger causality tests between political freedom and economic freedom in

section 5. In section 6, weak points of the Granger causality tests are pointed out and

another type of causality tests is explained. I conduct this type of causality tests and

show their results in section 7. In section 8, I conduct causality tests using different

data of political freedom and economic freedom. Section 9 discusses the conclusion

and policy implication.

2. BackgroundThe collapse of Soviet East Europe socialistic countries in 1980's, Asian

currency crisis in 1997, and default problems of emerging countries and/or the like have

made the following old questions be new and important. What kind of political and

economic state system is desirable? In order to head for this system, what sort of

process should be taken?

Around the questions, many people agree little resistance that developing

democratization and progressing marketization are preferable. But when it comes to

strategy of developing democratization and progressing marketization, opinions about


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the strategy are largely divided. One of main differences of the opinions is the one

about which should be put priority on between marketization or democratization.

Some say that marketization should be accelerated, thinking highly of efficie ncy. Theothers say that developing democratization and enlarging freedom of choice should be

more urgent than realizing economic growth. People who believe market force, who

advocate big-ban approach about transition to market economy such as Sachs, and who

support current IMF policy, development economists such as Lal who emphasize

government failure, and the like can be said to be close to the former. People who

consider market failure is important and who look much to government role, Sen who

thinks much of democracy instead of economic growth and insists that entitlement

should be given to the poor, and the like can be said to be close to the latter.

This difference is considered to start disappearing if causality between

marketization and democratization is known. For example, if democratization tend to

promote marketization and marketization does not tend to promote democratization, we

can say that the policy that put more priority on democratization than on marketization

is preferable. The aim of this paper is to deepen understanding causality between

political freedom and economic freedom and to get a clue to construct desirable regime.

As seen in "economic freedom is also indispensable means toward the

achievement of political freedom (Friedman, 1962, 8)," the relationship between

political freedom and economic freedom has been aware of long before. But it seems

that there have been few works that make theoretical models dealing with the

relationship between political freedom and economic freedom. Hence, taking

advantage of Granger-causality technique seems to be a good idea because the

technique enables us to explore the causality without theoretical models.

But there is one problem left. Since economic freedom data have just begun

to be made recently, it is difficult to collect enough time series data to conduct

Granger-causality tests. Economic freedom data of the Fraser Institute has the longest

time series. The data are reported every five years from 1970 to 2000, although thedata have been reported every year since 2000. Thus period that the data cover is so

short that we cannot conduct usual time-series version of Granger-causality tests.

However, the data are made for the purpose of country-by-country comparison and have

a relatively large cross-section part. By taking advantage of the characteristic of the

panel data that have a short time-series part and a large cross-section part, I want to

conduct causality tests. Holtz-Eakin, Newey, and Rosen (1988, 1989) propose a useful

method to conduct a causality test with panel data. They say that it is not necessary to

collect long time series data when applying their method into causality tests. Hence


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we conduct causality tests of political freedom and economic freedom by owing much

to their method.

3. Literature Review

Recently there have been many works that explore the relationship between

politico-economic regime and economic performance (for example, Scully and Slottje

(1991), De Haan and Sierman (1995, 1998), Barro (1996), Vanssay and Spindler (1994),

Knack and Keefer (1995), Eaton and Walker (1997), Durham (1999), Chong and

Calderon (2000), Ali and Crain (2001) and so on.) That is, they examine the

followings. Does political freedom affects economic growth and per capita GDP?

How about economic freedom? However, there have not been so many works that

explore the relationship between political freedom and economic freedom.

Wu and Otto (1999) investigate the relationship among the four (political

freedom, economic freedom, economic growth, and economic development) by using a

technique named log-linear model. They say that clear correlation between political

freedom and economic freedom is not found. Dethier et al. (1999) and De Haan and

Sturm (2003) investigate whether political freedom facilitates economic freedom for

former communist countries and developing countries respectively. Both conclude

that political freedom facilitates economic freedom. Dawson (1998) investigates the

relationship between economic performance and institutions by using standard

regression analysis. Dawson also checks the relationship between political freedom

and economic freedom in the paper and get the conclusion that the initial level of

political freedom affects economic freedom and that change in economic freedom

affects political freedom. Dawson (2003) investigates the similar relationship by using

Granger causality tests. The conclusion is that political freedom causes economic

freedom and that economic freedom does not cause economic freedom, although both

directions of causality are observed in the case of summary indexes of both freedom and

some other cases.Feng (2003) directly addresses the relationship between political freedom and

economic freedom and conducts Granger causality tests. Feng's result is that political

freedom causes economic freedom and that economic freedom does not cause political

freedom. But Feng does not take country-specific factors into account. It may occur

bias and hide the true relationship because the country-specific factors can make

significantly different levels of political freedom and economic freedom.

Farr, Lord, and Wolfenbarger (1998) investigate causality among political

freedom, economic freedom, and per capita GDP by conducting Granger causality tests.


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In an effort to deal with individual effects, country dummy variables are included in

their estimated regressions. Their conclusion is that there is no causality between the

two. They use economic freedom data of the old version of the Fraser Institute.They estimate vector autoregressions assuming that the lag length is one. And it is not

very clear how degree individual effects work in their results. The method proposed

by Holtz-Eakin, Newey and Rosen does not require us to assume lag length and enables

us to check whether we can neglect individual effects or not. I conduct causality tests

by using the new version of the Fraser Institute and the method by Holtz-Eakin, Newey

and Rosen.

4. Political Freedom Data and Economic Freedom Data

I would like to begin by explaining political freedom data and economic

freedom data that we use for our analysis.

4.1. Explanation about both freedom data

Political freedom data shows how degree a country progresses democratization. I use

political rights index and civil liberties index issued by Freedom House as political

freedom data. Political rights index shows how degree fair and meaningful elections

are executed. Civil liberties index implies freedom of press, freedom of speech,

freedom of religious belief, and the right to protest and organize. Higher values of

both indexes mean less democratic. Freedom House has issued these indexes annually

since 1973. Because there are seven years' economic freedom data such as 1970, 1975,

1980, 1985, 1990, 1995, and 2000, I prepare political freedom data of 1975, 1980, 1985,

1990, 1995, and 2000. In order to make each year's political freedom data, we average

five years before and after the year's data instead of using the year's data itself. For

example, when we make 1975 political right index, we average 1973, 1974, 1975, 1976,

1977 political right indexes. This is because evaluation of political institution seems to

be better by making use of information before and after the year's data. Now we getpolitical rights index and civil liberties index of 1975, 1980, 1985, 1990, 1995, and

2000. Then I add up these two indexes and multiply it by minus 1 so that freer

countries have higher value indexes. Next I standardize them so that their average

becomes zero and their standard deviation becomes 1. I use this political freedom

index as political freedom data.

Economic freedom data shows how degree a country progresses marketization.

I use economic freedom data issued by the Fraser Institute. The data see as

economically free the situation where personal choice, protection of private property,


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and freedom of exchange are ensured. Gwartney, Lawson et al. (2002) say that their

Chain-Weighted Summary index has the highest quality among some economic freedom

data. I use the Chain-Weighted Summary index and standardize it. I use the index aseconomic freedom data.

4.2. Overviewing both freedom data

Let us overview the data made in the above process. As explained before, I use the

political freedom index and the economic freedom index of 1975, 1980, 1985, 1990,

1995, and 2000 for our analysis. Because there are at best 123 countries that have

economic freedom data, I choose countries for the political freedom index taking

account of it. Basic statistics of the political freedom index are shown in Table 1.

Basic statistics of the economic freedom index are shown in Table 2.

Table 1- Basic statistics of the political freedom index

Year 1975 1980 1985 1990 1995 2000 AllN 113 114 114 121 123 123 708

M ean -0.25 -0.16 -0.12 0.04 0.18 0.27 0.00M ax 1.37 1.37 1.37 1.37 1.37 1.37 1.37M in -1.76 -1.76 -1.76 -1.71 -1.76 -1.76 -1.76SD 1.05 1.02 1.05 0.98 0.91 0.90 1.00

Table 2- Basic statistics of the economic freedom index

Year 1975 1980 1985 1990 1995 2000 AllN 79 111 114 119 123 123 669

M ean -0.32 -0.33 -0.29 -0.17 0.29 0.65 0.00M ax 2.20 2.28 2.02 1.95 2.52 2.36 2.52M in -2.24 -2.24 -2.74 -2.20 -1.61 -1.65 -2.74SD 1.01 0.95 0.98 0.96 0.91 0.80 1.00

Movement of averages of the political freedom index is shown in Figure 1. From it,we can tell development of democratization of entire world. And movement of

averages of the economic freedom index is shown in Figure 2. We can see that

marketization has been progressed rapidly since around 1990.


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Figure 1. Movement of the political freedom index Figure 2. Movement of the economic freedom index

What country has developed democratization well? What country has a high economic

freedom? Let us show the best and worst 10 countries of both 2000 indexes. Table 3

and Table 4 describe them. Japan is 22nd among 123 countries for political freedom

and 27th among 123 countries for economic freedom.

Table 3. High-democratic countries and low-democratic countries

Dem ocraticcountries

politicalfreedomindex

Non-dem ocraticcountries

politicalfreedomindex

Australia 1.37 Syria -1.76Austria 1.37 M yanm ar -1.76Barbados 1.37 C hina -1.54Belize 1.37 Rwanda -1.48C anada 1.37 C ongo,Dem .R. -1.43Denm ark 1.37 Burundi -1.39Finland 1.37 Bahrain -1.38Iceland 1.37 C am eroon -1.32Ireland 1.37 Iran -1.30Luxem bourg 1.37 Egypt -1.18M alta 1.37Netherlands 1.37New Zealand 1.37Norway 1.37Portugal 1.37Sweden 1.37Sw itzerland 1.37United States 1.37

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1975 1980 1985 1990 1995 2000

Political Freedom IndexEconom ic Freedom Index

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1975 1980 1985 1990 1995 2000


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Table 4. High- economic-free countries and low economic-free countries

Econom ic-fee

countries

econom ic

freedomindex

Non-

econom icfree

countries

econom ic

freedomindex

Hong Kong 2.36 M yanm ar -1.65Singapore 2.22 C ongo,Dem .R. -1.60United States 2.17 G uinea-B issau -1.21United Kingdo 2.03 Algeria -1.09Sw itzerland 1.95 Ukraine -0.75New Zealand 1.92 Russia -0.57Ireland 1.87 C ongo,Rep.of -0.54

C anada 1.80 M alawi -0.51Netherlands 1.79 Zim babw e -0.50Australia 1.78 Togo -0.49

4.3. The relationship between political freedom and economic freedom

Table 5 shows the relationship between movement of political freedom and movement

of economic freedom. In how many countries did the political freedom index or the

economic freedom index increase or decrease during each four period of 1975-1980,

1980-1985, 1985-1990, and 1990-1995? Among the countries where it increases or

decreases, in how many countries did the other freedom index increase or decrease

during the next 5-year-period? For example, there are 113 countries where political

freedom data exist in 1975 and 1980. Among the 113 countries, there are 56 countries

where political freedom index increased during 1975-1980. Among these 56 countries,

there are 32 countries where economic freedom index increased during 1980-1985.

Since the ratio is 21 countries among 31 countries, it is 32/56=0.57. Table 6 is the

same as Table 5 with the exception of criteria that freedom index increase or decrease

by 0.1. For example, there are 43 countries where political freedom index increasedby 0.1 during 1975-1980. Among these 43 countries, there are 21 countries where

economic freedom index increased by 0.1 during 1980-1985. Since the ratio is 21

countries among 43 countries, it is 21/43=0.49.

What is immediately obvious from the table is that the ratio of the countries

where economic freedom increased after democratization had been progressed is higher

than ones of the other cases. It seems to suggest that democratization promotes

marketization. There seems to be causality from political freedom to economic

freedom. Further below, we check causality between political freedom and economic


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freedom more rigorously by using econometric methods. In below analysis, I turn 79

countries into an object of our analysis since the 79 countries have complete data of

both freedom indexes for six years.

Table 5. The relationship between movement of political freedom and movement economic freedom

(criteria is just increase or decrease)

19751980 19801985 19851990 19901995 All

A number of countries113 114 114 121 462

The number of countries where democratization was progressed

56 43 61 52 212Among the above countries, the number of countries where

economic freedom increased during the next period32 32 50 45 159

Ratio 0.57 0.74 0.82 0.87 0.75


The number of countries where democratization was progressed 29 37 30 54 150

Among the above countries, the number of countries where


Ratio 0.34 0.35 0.17 0.26 0.28



Among the above countries, the number of countries where


Ratio 0.38 0.57 0.40 0.51 0.47



Among the above countries, the number of countries whereeconomic freedom increased during the next period 14 13 15 8 50

Ratio 0.50 0.31 0.43 0.40 0.40


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Each a and d is a parameter, m is a lag length, and ut is a disturbance. I conduct

panel type of Granger causality tests. Granger causality tests are conducted

through the following procedure. If the null hypothesis x does not cause y isrejected,x Granger-causesy. That is, if the null hypothesis: 1 =2 ==m=0

is rejected, we judge thatx affectsy. We can apply a standard F test into testing the

hypothesis. I conduct the F test taking the freedom variables of 1995 and 2000 as

explained variables. And I check the cases where the lag lengths are three, two,

and one, i.e., m=3, 2, 1.

Results about the hypothesis economic freedom does not cause political

freedom are shown in Table 7. In the case of m=3, I regress political freedom on

three lagged political freedom and three lagged economic freedom using ordinary

least squares technique. Obtained sum of squared residuals is 22.45. Degrees of

freedom in this case is 151 because the number of data 158 (79 observations for two

period) minus the number of parameters 7. Next I estimate the equation in which

three lagged economic freedom are excluded from the above equation. Thus

obtained sum of squared residuals is 23.55. The Fstatistics from these values is

.

This Fvalue has an F-distribution with 3 degrees of freedom of numerator and with

151 degrees of freedom of denominator under the hypothesis: 1=2=3=0 .

The value 2.47 is the one of which the upper side of the F distribution is 6.4 percent,

shown as p. Hence we cannot reject the null hypothesis at the 1-percent level and

5-percent level but at 10-percent level. Although we can slightly see causality from

economic freedom to political freedom, the causality is not so strong. The cases of

m=1 and m=2 show similar results. It is difficult to say that economic freedom

causes political freedom.

Table 7. Results of Granger-causality tests:

economic freedom political freedom

Sum of squared

residualsF

Degrees of

Freedom

m =3 22.45 -----

ExcludeE 23.55 2.47 3,151 0.064 *

m =2 22.94 -----

ExcludeE 23.73 2.65 2,153 0.074 *

m =1 23.46 -----

ExcludeE 24.00 3.52 1,155 0.063 *

***significant at the 1 -percent level

** Significant at the 5-percent level* Significant at the 10-percent level

F =

23.55-22.45 /3

22.45/151= 2.47


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Results about the hypothesis political freedom does not cause economic freedom are

shown in Table 8. The null hypothesis is strongly rejected in all cases ofm=3, 2, 1.

Political freedom causes economic freedom. These results of the simple F testsshow that political freedom affects economic freedom more largely than economic

freedom affects political freedom.

Table 8. Results of Granger-causality tests:

political freedom economic freedom

Sum of squared

residualsF

Degrees of

Freedom

m =3 27.02 -----

Exclude P 29.27 4.19 3,151 0.007 ***

m =2 27.16 -----

Exclude P 29.36 6.22 2,153 0.003 ***

m =1 27.18 -----

Exclude P 29.37 12.52 1,155 0.001 ***

***significant at the 1 -percent level** significant at the 5-percent level* significant at the 10-percent level

6. Another Type of Causality Tests Proposed by Holtz-Eakin=Newey=Rosen

Now we get the result that political freedom causes economic freedom, while it

is difficult to say that economic freedom causes political freedom. The result is

consistent with the fact in section 4 that there are more countries where economic

freedom increased after democratization had been progressed. And the result

supports the recent works that investigate causality between political freedom and

economic freedom, such as the Dawson (2003) and Feng (2003). Does political

freedom really cause economic freedom? Does political freedom really have more

effect on economic freedom than economic freedom does on political freedom? I will

look more carefully to these points.One of the biggest problems about the tests that I conducted above is to neglect

country-specific factors named individual effects. The tests assume that every country

has the same intercept. It means that the individual effects are not taken into account.

The individual effect summarizes the influence of unobserved variables which have

persistent effect on the dependent variables. Therefore the intercept and the dependent

variable are correlated if there is the individual effect. The correlation result in

inconsistent estimates. Consider the following equation that adds an individual effect

(fi) into equation the (1).


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yit = a o +

m

l=1

a lt yit-l +

m

l=1

dlt xtit-l + fi + uit

(2 )A standard method of eliminating the individual effect is to first difference the data to

eliminate fi and then use ordinary or generalized squares to estimate the differenced

equation:

yit yit-1 =

m

l=1

al yt-l yt-l-1 +

m

l=1

dl xtt-l xtt-l-1 + (uit uit-1 )

(3 )

This approach, however, induce a simultaneity problem. Becauseyit-1depends on uit-1,

(uit uit-1) is correlated with the regressor (yit-1 yit-2). A solution of this problem is to

employ instrumental variables that are uncorrelated with (uit uit-1) such asyit-2, yit-3, ,yi1,

x it-2, xit-3, , xi1. Holtz-Eakin, Newey, and Rosen (1988, 1989) propose estimation

technique that allows the existence of an individual effect and that employs the

instrumental variables. I will use the similar method to theirs.

Consider the following model:

yit = aot +

m

l=1

a lt yit-l +

m

l=1

dlt xtit-l + fi + uitt=1,...,T

i=1,...,N

(4)

where fi is an unobserved individual effect. Subtracting the t-1 period from the t

period of equation (4), we get the following equation.

D yit = at +

m+1

l=1

clt yit-l +

m+1

l=1

dlt xtit-l + vit t= m+2 ,...,T ,

where

a

D

c

c

c

d

d

d

t

1t

v

lt

m+1,t

t

y

1t

m+1

it

=

i

=

=

t

=

=

=

=

,t

a

d

a

=

a

ot

u

d

=

1t

ot

lt

it

y

1t

it y

a

it-1

a

a

m,t-1

ot-1

d

u

l-1,t-1

d

1-1,t-1

it

m,t-1

[

[

l=2,...,m

l=2,...,m

]

]


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(5)1

In the above equation, an individual effect disappears, and we can estimate theequation without considering the individual effect. And to avoid the simultaneous

problem, I use the following vector of instrumental variables as Holtz-Eakin, Newey,

and Rosen (1988, 1989) do:

Zt = [1, yt-2,...,y1, xt-2,...,x1]

Note that number of instrumental variables varies as the time period t changes. If

we can restrict that the lag coefficients are constant over time, the equation (5) can

be written:

D yit = at +

m

l=1

a l D yit-1 +

m

l=1

dl D xtt-1 + vit

(6).

Therefore in the method, it is important to check whether we can put restriction on

the equation (5).

Holtz-Eakin, Newey, and Rosen (1988, 1989) use the following estimation

procedure. i) Estimate the equation (5) for each time period using two-stage least

squares (2SLS), ii) using the residuals and the matrix of instruments, estimate the

joint covariance of the disturbance terms, iii) estimate all the parameters

simultaneously using generalized least squares on the stacked equations. This

procedure can be applied in the case where we put restriction on the equation (5).

The above procedure can be seen three-stage least squares (3SLS). And

their technique is GMM (Generalized Method of Moments Estimation) in that

instrumental variables are chosen so that they do not correlate with disturbance terms.

Hence I use 3SLS and GMM command of an econometric software package: TSP

when estimating the relevant equations. When I apply 3SLS and GMM, only the

commands 3SLS and GMM are different and the other part of the programs is the

same. Wooldridge (2002) refers to the difference between the GMM 3SLS andtraditional 3SLS. He says that GMM 3SLS is more rigorous. Therefore my

results of GMM are expected to be more reliable than the ones of 3SLS.

What we are the most interested in is causality between x and y. The

framework of the paper is convenient because we can test causality not estimating

the original parameters of the equation (4) but using the equation (5). 1=2==

m=0 in (4) means d1=d2==dm=0 in (5). Hence if the latter is rejected, the

former is rejected. Therefore if d1=d2==dm=0 is rejected, we can judge that "x

causesy." Moreover, if the lag coefficients are constant over time, we can check


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1=2 ==m =0 directly by using equation (6).

I estimate the equation (5) or (6), and conduct causality tests between

political freedom and economic freedom. Following Holtz-Eakin, Newey, andRosen (1988, 1989), I take the procedure as follows.

1) I check whether parameters are stationary over time.

I choose a relatively large value ofm , estimate the autoregressions with and

without the restriction of parameters stationary, and compare the sum of squared

residuals. If the sum of squared residuals does not become large with the

restriction, we can judge that parameters are stationary.

2) I check what is the correct lag length, m.

Next, I check how many lags the autoregression has. I estimate

autoregression that has one-smaller lag length, compare its sum of squared residuals

with the one that is gotten in the process 1). If the sum of squared residuals does

not become large in the case of smaller lag length, we can judge that the smaller lag

length is accepted. Then we reduce the lag length and repeat the same test. If the

sum of squared residuals in the case of smaller lag length becomes large and the

hypothesis of the smaller lag length is rejected, we choose the lag length in the

previous process.

3) I check causality between political freedom and economic freedom.

Now we have the selected autoregressions. I estimate the autoregression

with and without the restriction of non-causality: d1=d2==dm=0 or1=2 ==

m = 0, and compare the sums of squared residuals. For example, if sum of

squared residuals becomes large with the restriction that economic freedom does not

cause political freedom, we can judge that economic freedom causes political

freedom.

7. Estimated Results

7.1. Political freedom equation

Let us start estimating a political freedom equation: an explained variable is political

freedom in the equation (5). I will estimate parameters for the last two years: 1995

and 2000 and start the case ofm=2. Equation (5) in the case ofm=2 has three lags.

That is,


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D P1995 = a1995 + c1,1995 P1990 + c2,1995 P1985 + c3,1995 P1980 + d1,1995 E1990 + d2,1995 E1990 + d3,1995 E1980 +v1995

D P2000 = a2000 + c1,2000 P1995 + c2,2000 P1990 + c3,2000 P1985 + d1,2000 E1995 + d2,2000 E1990 + d3,2000 E1985 +v2000

( 7 ) ,

where Pt is political freedom of period t,Et is economic freedom of period t, and at

is constant of period t. For the 1995 equation, instrumental variables are political

freedom (P) of 1985, 1980, 1975 and economic freedom (E) of 1985, 1980, 1975

plus a constant term. For the 2000 equation, instrumental variables are P of 1990,

1985, 1980, 1975 andEof 1990, 1985, 1980, 1975 plus a constant term. I estimate

systems of these two periods equations by using three-stage least squares (3SLS).

The 3SLS estimation produced the sum of squared residuals Q: 1.721.

Next, let us put the restriction that all parameters are stationary. The equation on

that we put the restriction becomes

D Pt = a0 +a 1DPt-1+ a2D Pt-2 + d1DEt-1 + d 2,DEt-2 +v ( 8 ) .

I estimate the equation using the same instrumental variables as the case of equation

(7) and estimate it using 3SLS technique . As a result, I get the restricted sum of

squared residuals QR : 18.938.L= QR Q follows a chi-distribution with degrees of 8,

because

1,1990=1,1995=1,2000, 2,1990=2,1995=2,2000

1,1990=1,1995=1,2000, 2,1990=2,1995=2,2000

are restricted. Actually obtained L is 17.217, because L= QR Q =18.938

1.721=17.217. This value is the one of which the right side of the chi-distribution

is 2.8 percent, shown asp in Table 9. Therefore this hypothesis that all parameters

are stationary is rejected at the significance level 5 percent. Since the sum of

squared residuals increases very much when we put the restriction, we cannot accept

that all parameters are stationary.

Now, let us reduce lag length from m=2 and investigate how many lags ourselected model has. Obtained restricted sum of squared residuals QR is 9.027 when

we restrict m=1. Changing from m=2 to m=1 is the same as putting the restriction

that the coefficients of one lagged variable P and one lagged variable Eare zero for

each 1995 political freedom equation and 2000 political freedom equation. Hence

the number of restriction is 4. Therefore L, which is gotten by subtracting the sum

of squared residuals Q of the case m=2 from QR, follows 4 degrees of freedom.

Actually I get L= QR Q =9.027 1.721=7.038. This value is the one of which the

right side of the chi-distribution is 12.1 percent, shown as p. Therefore we can


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accept m=1, although the restricted sum of squared residuals becomes quite large.

How about the case of m=0. Obtained restricted sum of squared residuals

QR is 29.483. Changing from m=1 to m=0 is the same as putting the restriction thatthe coefficients of one lagged variable P and one lagged variableEare zero for each

1995 political freedom equation and 2000 political freedom equation as well.

Hence the number of restrictions is 4. Actually obtained L= QR Q =29.483

0.027=16.367 is the value of which the right side of the chi-distribution is 0 percent,

shown asp. Therefore we strongly reject m=0. Sum of squared residuals increase

very much when m=0 is restricted.

Then let us conduct causality tests assuming that parameters are not

stationary and that lag length is 1. Putting the restriction of no causality from

economic freedom to political freedom is identical with that the coefficients of all

lagged variable E are zero for each 1995 political freedom equation and 2000

political freedom equation. Hence the number of restrictions is 4. Actually

obtained L= QR Q =14.723 9.027=5.696 is the value of which the right side of

the chi-distribution is 22.3 percent, shown as p. Therefore we accept the no

causality restriction. We cannot say that economic freedom causes political freedom.

Table 9 summaries the above procedure.

Table 9. Model selection of political freedom equation using 3SLS

Q L Degrees of Freedom p

) m =2 1.721 ----) all param eters stationary 18.938 17.217 8 0.028 **

) m =1 (given i) 9.027 7.038 4 0.121) ExcludeE (given i) 13.117 11.396 6 0.077 *

V ) m =0 (given iii) 29.483 16.367 4 0.000vi) ExcludeE (given i) 14.723 5.696 4 0.223

a) given m=2, we reject the hypothesis: all parameters are stationary.

b) given that all parameters are not stationary, we accept the hypothesis: m=1.c) given that all parameters are not stationary, we reject m=0.

d) given that all parameters are not stationary and that m=1, we accept the

hypothesis: no causality from economic freedom to political freedom.

In sum, the following model is selected: all parameters are not stationary,

m=1, and no causality from economic freedom to political freedom That is, we get

the result that economic freedom does not affect political freedom.

When we put restriction m=1 from m=2, restricted sum of squared residuals


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18

becomes quite large. Hence we may see that hypothesis: m=1 is rejected. If it is

rejected, m=2 model is selected. Hence I conduct causality tests assuming m=2.

In this case, the hypothesis of no causality is neither rejected at the 1 percentsignificance level nor 5 percent significance level but 10 percent significance level.

In this case, we can see slight causality from economic freedom to political freedom.

I take the same procedure using GMM (Generalized Method of Moments

Estimation). Estimation by GMM is expected to be more rigorous than the one by

3SLS. I estimate parameters for the last two years: 1995 and 2000 and start the

case of m=2. Sum of squared residuals Q is 0.02026. Sum of squared residuals

QR when putting restriction that all parameters are stationary is 0.11227. L= QR

Q follows a chi-distribution with degrees of 8, and actually obtained L is 0.09201.

This value is the one of which the right side of the chi-distribution is almost 100

percent, shown as p in Table 10. Therefore this hypothesis that all parameters are

stationary is accepted. Next I put restriction of m=1 in addition to the restriction

that all parameters are stationary. Obtained restricted sum of squared residuals QR

is 0.24289. Changing from m=2 to m=1 is the same as putting the restriction that

the coefficients of one lagged variable P and one lagged variableEare zero for each

1995 political freedom equation and 2000 political freedom equation. Hence the

number of restriction is 2. Therefore L = QR Q follows 2 degrees of freedom.

Actually obtained L is 0.13062. This value is small enough for us to accept the

hypothesis: m=1. Then I put restriction of m=0 assuming that all parameters are

stationary. Sum of squared residuals does not increase even in the case of m=0.

Therefore we accept m=0. Table 10 shows summary of the procedure.

Table 10. Model selection of political freedom equation using GMM


) m =2 0.02026 ---- 2 0.98992) all param eters stationary 0.11227 0.09201 8 1.00000

) m =1 (given ii) 0.24289 0.13062 2 0.93678) m =0 (given ii) 0.47402 0.23113 2 0.89086

a) given m=2, we accept the hypothesis: all parameters are stationary.

b) given that all parameters are stationary, we accept the hypothesis: m=1.

c) given that all parameters are stationary, we accept m=0.

Such being the case, it follows from the results of estimating political

freedom equation that the model of m=0 is selected. That is, political freedom can

be explained neither by past political freedom nor by past economic freedom. It


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19

means that whether democratization progress or not has nothing to do with how

degree democratization and marketization progressed in past times. Of course,

economic freedom does not cause political freedom.

7.2. Economic freedom equation

Here I make the same analysis of economic freedom equation as I did of political

equation. That is, I estimate the following system equations.

D E1995 = a1995 + c1,1995 E1990 + c2,1995 E1985 + c3,1995 E1980 + d1,1995 P1990 + d2,1995 P1990 + d3,1995 P1980 +v1995

D E2000 = a2000 + c1,2000 E1995 + c2,2000 E1990 + c3,2000 E1985 + d1,2000 P1995 + d2,2000 P1990 + d3,2000 P1985 +v2000

or

D Et = a 0 +a 1DEt-1+ a2D EE-2 + d1DPt-1 + d 2,DPt-2 +v

And I make model selection and causality tests. Results of estimation using 3SLS

technique are shown in Table.11.

Table 11. Model selection of economic freedom equation using 3SLS



) m =1 (given i) 8.957 7.395 4 0.116) Exclude P (given i) 17.125 15.562 6 0.016 **

V ) m =0 (given iii) 29.451 20.494 4 0.000 ***

vi) Exclude P (given iii 15.249 6.292 4 0.178

We can see the following from Table 11.

a) given m=2, we reject the hypothesis: all parameters are stationary.

b) given that all parameters are not stationary, we accept the hypothesis: m=1.

c) given that all parameters are not stationary, we reject m=0.d) given that all parameters are not stationary and that m=1, we accept the

hypothesis: no causality from economic freedom to political freedom.

When we put restriction m=1 from m=2, restricted sum of squared residuals

becomes quite large like the political freedom equation case. Hence I conduct

causality tests assuming m=2. In this case, the hypothesis of no causality is not

rejected at the 5 percent significance level. Comparing the result of Table 9, we

can reject the no causality hypothesis with higher probability. We can see from the


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results that it is more probable that political freedom causes economic freedom than

vice versa.

Results of estimation using 3SLS technique are shown in Table 12.Table 12. Model selection of political freedom equation using GMM


) m =2 0.00120 ---- 2 0.99987) all param eters stationary 0.06273 0.06153 8 1.00000) m =1 (given ii) 0.06526 0.00253 2 0.99874) m =0 (given ii) 0.09733 0.03207 2 0.98409

We can see the following from Table 12.

a) given m=2, we accept the hypothesis: all parameters are stationary.

b) given that all parameters are stationary, we accept the hypothesis: m=1.

c) given that all parameters are stationary, we accept m=0.

The model of m=0 is also selected. That is, political freedom can be explained

neither by past political freedom nor by past economic freedom. Political freedom

does not cause economic freedom.

7.3. Summary of results of OLSQ, 3SLS and GMM

I conducted Granger causality tests in section 5. Estimation technique in

conducting Granger causality tests was ordinary least squares OLSQ. Now we

have the estimated results of political-economic equations and causality tests

between political freedom and economic freedom using three technique: Ordinary

least squares (OLSQ), three-stage least squares (3SLS), and GMM (Generalized

Method of Moments Estimation). Summary of the results is reported in Table 13.

Table 13. Summary of results of OLSQ, 3SLS and GMM

Notes. In OLSQ column, results of causality tests of the cases of m=3, m=2, and m=1 are shown. Stationary or not shows

results of the tests about the hypothesis: all parameters are stationary. Lag length shows results of the tests: how many lag

length estimated autoregreesions have. On causality, EP means economic freedom causes political freedom and EPmeans economic freedom does not cause political freedom, for example. Difference of Arrow thickness reflects

significance level of causality tests. Thicker arrow shows that no causality hypothesis is rejected at smaller rejection

region.

OLSQ 3SLS GMM

Political Freedom

Equation

Economic Freedom

Equation

Political Freedom

Equation

Economic Freedom

Equation

Political Freedom

Equation

Economic Freedom

Equation

m=3 E P P E stationary or not not sta tionary not tsat ionary stationary st ationary

m=2 E P P E lag length m=1 m=1 m=0 m=0

m=1 E P P E causaltiy E P P E E P P E


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From the results of OLSQ, we can see strong causality from political

freedom to economic freedom and weak causality from economic freedom to

political freedom. Political freedom causes economic freedom, but it is difficult tosay that economic freedom causes political freedom. These results are consistent

with the fact that we find overviewing data in section 4.

But the above estimation technique have a weak point that individual effects

are neglected. 3SLS and GMM estimation technique was used to overcome the

weak point. 3SLS estimation selected non-stationary and m=1 model for both

political and economic freedom equations. And in causality tests using these

models, we cannot see valid causality between political freedom and economic

freedom.

The results of GMM estimation technique is exp ected to be more rigorous

than 3 SLS technique. GMM estimation selected stationary and m=0 model for

both political and economic freedom equations. That is, both political and

economic freedom are neither explained by past political freedom nor by past

economic freedom. It also means that there is no causality between political

freedom and economic freedom.

. Causality among components of political freedom and economic freedom

Here I check causality between political freedom and economic freedom using

different data. As I explained in section 4, my political freedom index is composed

of political rights index and civil liberties index issued by Freedom House. I use

each index and transform it the same way as in section 4. That is, each index are

standardized and prepared for 1975, 1980, 1985, 1990, 1995, and 2000 respectvely,

meaning higher values means having more freedom.

Economic freedom data issued by the Fraser Institute have five components: 1)

Size of Government, 2) Legal Structure and Security of Property Rights, 3) Access to

Sound Money, 4) Freedom to Trade Internationally, 5) Regulation of Credit, Labor, andBusiness. Size of Government indicates the extent to which countries rely on

individual choice and markets rather than the political process to allocate resources and

goods and services. Legal Structure and Security of Property Rights indicates the

extent to which countries have a legal system consistent with economic freedom, such

as rule of law, security of property rights, an independent judiciary, and an impartial

court system. Access to Sound Money indicates the extent to which countries are

successful in stabilizing value of money. Freedom to Trade Internationally indicates

the extent to which countries do not restrain free international exchange by using tariffs,


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quotas, and so on. Regulation of Credit, Labor, and Business indicates the extent to

which countries do not restrain freedom of exchange in credit, labor, and product

markets by using control on interest rates, minimum wage, business entry regulation,and so on. I use each index and transform it the same way. That is, each index are

standardized and prepared for 1975, 1980, 1985, 1990, 1995, and 2000, meaning

higher values means having more freedom.

A List of components of political freedom and economic freedom is written

in Table 14. There are 10 combinations of political freedom and economic freedom.

I take the same procedure for each combination as taken in section 7. Table 15

shows summaries of results of OLSQ, 3SLS and GMM for the all combinations.

Details of the procedures are shown in Appendix.

Table 14. List of components of political freedom and economic freedom

Political Freedom Economic Freedom

Political Rights (PR)

Civil Liberties (CL)

Size of Government (SG)

Legal Structure and Security of Property Rights (LS)

Access to Sound Money (AS)

Freedom to Trade Internationally (FT)

Regulation of Credit, Labor, and Business. (RC)

Table 15. Summary of results of OLSQ, 3SLS and GMM

Causality between Political Rights (PR) and Size of Government (SG)

OLSQ 3SLS GMM

Political Freedom

E uat ion

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

m =3 E P P E stationary or not not stationary not tsationary stationary stationary

m =2 E P P E lag length m =1 m =1 m =0 m =0

m =1 E P P E causaltiy E P P E E P P E

Causality between Civil Liberties (CL) and Size of Government (SG)

OLSQ 3SLS GMM

Political Freedom

Equation

Economic Freedom

Equation

Political Freedom

Equation

Economic Freedom

Equation

Political Freedom

Equation

Economic Freedom

Equation





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23

Causality between Political Rights (PR) and Legal Structure & Security of Property Rights (SP)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality between Civil Liberties (CL) and Legal Structure & Security of Property Rights (SP)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality between Political Rights (PR) and Access to Sound Money (AS)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality between Civil Liberties (CL) and Access to Sound Money (AS)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation





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24

Causality between Political Rights (PR) and Freedom to Trade Internationally (FT)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality between Civil Liberties (CL) and Freedom to Trade Internationally (FT)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality between Political Rights (PR) and Regulation of Credit, Labor & Banking (RC)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Causality betweenCivil Liberties (CL) and Regulation of Credit, Labor & Banking (RC)

OLSQ 3SLS GMM

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation

Political Freedom

E uation

Economic Freedom

E uation




Using Table 15, I count the number of rejecting no causality hypothesis at 5 percent

re and 1 percent rejection region for each OLSQ and 3SLS and GMM. Table 16

shows the result. When I use OLSQ and 3SLS technique, I find some cases where


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economic freedom causes political freedom and political freedom causes economic

freedom. There are more cases where political freedom causes economic freedom

than the ones where economic freedom causes political freedom. It follows from itthat political freedom are more likely to cause economic freedom than vice versa.

There are not so many cases where economic freedom causes political freedom.

And the probability that political freedom causes economic freedom is higher than

the one that economic freedom causes political freedom. These facts are also

consistent with the ones that I find in section 4.

When I use GMM technique, on the contrary, no causality is found. OLSQ

has a weak point that individual effects are neglected. Hence I used 3SLS and

GMM to improve the precision of estimation. Because GMM technique gives more

accurate estimated results, the results of GMM are more reliable than the ones of

3SLS. But the results of GMM do not see any causality. Rigorous analysis shows

that there is no causality between political freedom and economic freedom.

Table 16. The number of cases where causality is accepted strongly.

OLSQ 3SLS GMM

E P

5

out of 30

P E

18

out of 30

E P

3

out of 10

P E

5

out of 10

E P

0

out of 10

P E

0

out of 30

8. Conclusion

The aim of this paper was to investigate causality between political freedom

and economic freedom. Since economic freedom data do not have long time series,

I conducted causality tests using panel data that have many countries. Some

previous works conducted causality tests without considering individual effects.

Hence I used the model that has individual effects. And I also consider the

possibility that coefficient parameters of political and economic freedom are

stationary and that lag length changes by using the method by Holtz-Eakin, Newey,

and Rosen (1988, 1989).

If we overview political freedom data and economic freedom data, in

many countries economic liberalization progressed after democratization had been

developed. Granger causality tests that do not take account of individual effects

support that democratization promotes marketization.

But the estimation that takes account of individual effects leads to the


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26

following result. Political freedom does not cause economic freedom. And,

economic freedom does not cause political freedom. That is, neither causality from

democratization to marketization nor causality from marketization todemocratization is found.

A glance at the data shows that democratization promotes marketization.

But more rigorous analysis says that there is no causality between political freedom

and economic freedom. This is the conclusion of the paper.

What policy implication is lead by the conclusion? At the beginning of

the paper, we mentioned that the result of the paper is considered to contribute to

solving the question which should be put priority on between democratization and

marketization. But it is with regret that this disappointing conclusion cannot lead

the answer. We may, therefore, confirm that we should be careful about which we

should put priority on when we think about development strategy of developing

countries. In any event, in a state where many researches that explore the

relationship between institutions and economic performance has come out,

researches that explore the relationship between institutions themselves, such as the

relationship between political freedom and economic freedom, seem to be weak.

The research of this field seems to be more important in the future.

Notes

1. Holtz-Eakin, Newey, and Rosen (1988, 1989) consider the parameter multiplying

the individual effect, which allows us to analyze the case where the individual effect

is stationary, but the other parameters are not. Because we are not interested in

whether the individual effect is stationary or not, I omit the parameter. That is,

the equation here is the special case of the one in Holtz-Eakin, Newey, and Rosen.


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Appendix .

Causality between Political Rights (PR) and Size of Government (SG)

1) OLSQPolitical Freedom Equation

Sum of

squared

residuals

FDegrees of

Freedom

m =3 42.18 -----

Exclude 42.78 0.96 3,203 0.412

m =2 42.83 -----

Exclude 43.37 1.29 2,205 0.278

m =1 42.86 -----

Exclude 43.44 2.80 1,207 0.096 *

Table Econom ic Freedom EquaSum of

squared

residuals

FDegrees of

Freedom

m =3 41.22 -----

Exclude 42.06 1.37 3,203 0.253

m =2 41.34 -----

Exclude 42.16 2.04 2,205 0.133

m =1 41.58 -----

Exclude 42.17 2.92 1,207 0.089 *

2) 3SLS

Political Freedom Equation


) m =2 3.733 ---- 2 0.423) all param eters stationary 30.991 27.258 8 0.001 **

) m =1 (given i) 4.712 0.979 4 0.913) m =0 (given iii) 43.784 39.071 4 0.000 ***

V ) ExcludeE (given iii 11.711 6.999 4 0.136

Econom ic Freedom Equation


) m =2 0.424 ---- 2 0.458) all param eters stationary 73.708 73.284 8 0.000 ***


V ) Exclude P (given iii 8.427 5.351 4 0.253

3) GMM



) m =2 0.03793 ----) all param eters stationary 0.25746 0.21954 8 0.99999) m =1 (given ii) 0.27197 0.01451 2 0.99277) m =0 (given ii) 0.27883 0.00685 2 0.99658




*** significant at the 1-percent level ** Significant at the 5-percent level * Significant at the 10-percent level


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30

Causality between Civil Liberties (CL) and Size of Government (SG)


Sum of

squared

residualsF

Degrees of

Freedom

m =3 28.95 -----

ExcludeE 29.14 0.46 3,203 0.713

m =2 28.97 -----

ExcludeE 29.17 0.71 2,205 0.493

m =1 29.24 -----

ExcludeE 29.31 0.50 1,207 0.479

Econom ic Freedom EquationSum of

squared

residualsF

Degrees of

Freedom

m =3 40.67 -----

Exclude P 42.06 2.30 3,203 0.078 *

m =2 41.08 -----

Exclude P 42.16 2.70 2,205 0.069

m =1 41.27 -----

Exclude P 42.17 4.51 1,207 0.035 **

2) 3SLS



) m =2 2.657 ----) all param eters stationary 79.953 77.296 8 0.000 ***








3) GMM









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31

Causality between Political Rights (PR) and Legal Structure & Security of Property

Rights (SP)


Sum of

squared

residualsF

Degrees of

Freedom

m =3 12.64 -----

ExcludeE 13.59 2.34 3,93 0.079 *

m =2 12.70 -----

ExcludeE 13.94 4.64 2,95 0.012 **

m =1 12.94 -----

ExcludeE 13.95 7.58 1,97 0.007 ***


squared

residualsF

Degrees of

Freedom

m =3 23.50 -----

Exclude P 24.90 1.84 3,93 0.145

m =2 24.43 -----

Exclude P 25.38 1.85 2,95 0.163

m =1 25.20 -----

Exclude P 26.12 3.55 1,97 0.062 *

2) 3SLS





V ) ExcludeE (given iii 29.214 18.741 4 0.001 ***






3) GMM









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32

Causality between Political Rights (CL) and Legal Structure & Security of Property

Rights (SP)


Sum of

squared

residualsF

Degrees of

Freedom

m =3 10.20 -----

ExcludeE 12.14 5.89 3,93 0.001 ***

m =2 10.72 -----

ExcludeE 12.19 6.49 2,95 0.002 ***

m =1 10.76 -----

ExcludeE 12.22 7.58 1,97 0.000 ***


squared

residualsF

Degrees of

Freedom

m =3 22.98 -----

Exclude P 24.90 2.58 3,93 0.058 *

m =2 23.68 -----

Exclude P 25.38 3.41 2,95 0.037 **

m =1 23.93 -----

Exclude P 26.12 8.87 1,97 0.004 ***

2) 3SLS




) m =1 (given i) 25.538 22.823 4 0.001 ***

) ExcludeE (given i) 27.318 24.604 6 0.000 ***




) m =1 (given i) 21.789 11.480 4 0.022 **

) m =0 (given iii) 53.958 32.169 4 0.000 ***


3) GMM



) m =2 0.01167 ----) all param eters stationary 0.31318 0.30151 8 0.99980







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33

Causality between Political Rights (PR) and Access to Sound Money (AS)

1) OLSQ

Political Freedom EquationSum ofsquared

residualsF

Degrees of

Freedom

m =3 44.02 -----

ExcludeE 45.15 1.84 3,215 0.140

m =2 44.62 -----

ExcludeE 45.70 2.62 2,217 0.075 *

m =1 45.62 -----

ExcludeE 45.91 1.41 1,219 0.236

Econom ic Freedom EquationSum ofsquared

residualsF

Degrees of

Freedom

m =3 95.77 -----

Exclude P 108.33 9.40 3,215 0.000 ***

m =2 96.42 -----

Exclude P 108.66 13.78 2,217 0.000 ***

m =1 98.29 -----

Exclude P 111.51 29.44 1,219 0.000 ***

2) 3SLS










V ) Exclude P (given iii 44.531 36.743 4 0.000 ***

3) GMM










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34

Causality between Political Rights (CL) and Access to Sound Money (AS)

1) OLSQ


residualsF

Degrees of

Freedom

m =3 32.52 -----

ExcludeE 32.73 0.46 3,215 0.712

m =2 32.56 -----

ExcludeE 32.78 0.73 2,217 0.483

m =1 33.01 -----

ExcludeE 33.08 0.45 1,219 0.501


residualsF

Degrees of

Freedom

m =3 93.23 -----

Exclude P 108.33 11.61 3,215 0.000 ***

m =2 96.46 -----

Exclude P 108.66 16.31 2,217 0.000 ***

m =1 97.79 -----

Exclude P 111.51 30.72 1,219 0.000 ***

2) 3SLS









) m =1 (given i) 15.428 10.420 4 0.033 **


3) GMM



) m =2 0.00464 ----) all param eters stationary 0.07064 0.06600 8 1.00000) m =1 (given ii) 0.16392 0.09329 2 0.95443

) m =0 (given ii) 0.24262 0.07870 2 0.96142 Econom ic Freedom Equation





35/38

35

Causality between Political Rights (PR) and Freedom to Trade Internationally (FT)

1) OLSQ


residualsF

Degrees of

Freedom

m =3 31.11 -----

ExcludeE 32.29 2.14 3,169 0.097 *

m =2 31.80 -----

ExcludeE 32.77 2.62 2,171 0.076 *

m =1 32.12 -----

ExcludeE 32.80 3.67 1,173 0.057 *


residualsF

Degrees of

Freedom

m =3 34.81 -----

Exclude P 36.51 2.75 3,169 0.044 **

m =2 34.83 -----

Exclude P 36.58 4.29 2,171 0.015 **

m =1 35.47 -----

Exclude P 37.51 9.95 1,173 0.002 ***

2) 3SLS





V ) ExcludeE (given iii 15.118 11.074 4 0.026 **





V ) Exclude P (given iii 16.532 11.094 4 0.026 **

3) GMM










36/38

36

Causality between Civil Liberties (CL) and Freedom to Trade Internationally (FT)

1) OLSQ


residualsF

Degrees of

Freedom

m =3 21.72 -----

ExcludeE 22.67 2.47 3,169 0.064 *

m =2 22.55 -----

ExcludeE 22.67 0.47 2,171 0.624

m =1 22.63 -----

ExcludeE 22.73 3.67 1,173 0.381


residualsF

Degrees of

Freedom

m =3 36.23 -----

Exclude P 36.51 0.44 3,169 0.725

m =2 36.29 -----

Exclude P 36.58 0.67 2,171 0.515

m =1 37.24 -----

Exclude P 37.51 9.95 1,173 0.260

2) 3SLS











3) GMM










37/38

37

Causality between Political Rights (PR) and Regulation of Credit, Labor & Banking

(RC)


Sum of

squared

residualsF

Degrees of

Freedom

m =3 27.02 -----

ExcludeE 27.23 0.35 3,139 0.792

m =2 27.50 -----

ExcludeE 27.53 0.08 2,141 0.926

m =1 27.56 -----

ExcludeE 27.57 0.07 1,143 0.791


squared

residualsF

Degrees of

Freedom

m =3 35.94 -----

Exclude P 40.30 5.62 3,139 0.001 ***

m =2 35.99 -----

Exclude P 40.30 8.46 2,141 0.000 ***

m =1 36.31 -----

Exclude P 40.41 16.16 1,143 0.000 ***

2) 3SLS










V ) Exclude P (given iii 19.883 12.168 4 0.016 **

3) GMM









38/38

Civil Liberties (CL) and Regulation of Credit, Labor & Banking (RC)

1) OLSQ


residualsF

Degrees of

Freedom

m =3 18.88 -----

ExcludeE 19.14 0.64 3,139 0.589

m =2 19.01 -----

ExcludeE 19.15 0.53 2,141 0.592

m =1 19.43 -----

ExcludeE 19.48 0.44 1,143 0.510


residualsF

Degrees of

Freedom

m =3 33.68 -----

Exclude P 40.30 9.11 3,139 0.000 ***

m =2 33.86 -----

Exclude P 40.30 13.42 2,141 0.000 ***

m =1 34.07 -----

Exclude P 40.41 26.61 1,143 0.000 ***

2) 3SLS

P olitical Freedom Equation


) m=2 1.809 ----) all param eters stationary 15.885 14.076 8 0.080 *) m=1 (given i) 3.283 1.474 4 0.831) m=0 (given iii) 25.902 22.619 4 0.000 ***

V) ExcludeE (given iii) 6.369 3.086 4 0.544

vi) m=1 (given ii) 20.348 4.463 4 0.347(vii) m=0 (given iii) 25.902 5.554 4 0.235






3) GMM


Q LDegrees of Freedom

p) m =2 0.02092 ----) all param eters stationary 0.14624 0.12533 8 1.00000) m =1 (given ii) 0.30297 0.15673 2 0.92463) m =0 (given ii) 0.33071 0.02774 2 0.98623




okui causality between economic and political freedom

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