The Path of Liberalization and Economic Growth

KYKLOS, Vol. 55 – 2002 – Fasc. 1, 57–80

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G4_HS:Aufträge:HEL002:13856_S_Kyklos_01/02:13856_4AK:Kyklos_2002-01_S-003-098 31. Januar 2002 07:38

The Path of Liberalization and Economic Growth

Hans Pitlik*

I. INTRODUCTION

Over the past decades economics has witnessed a progressively increasing in-terest in the political economy of growth. A substantial number of empiricalcontributions have studied the impact of economic freedom on the growth per-formance of nations. The underlying theoretical background goes back to clas-sical liberalism of Adam Smith and to the writings of Hayek (1960), Friedman(1962), Buchanan (1975), and others. The key argument is that more economicfreedom enhances individual incentives to engage in productive market activi-ties. Policies which curb the liberty of markets are thus supposed to be detri-mental to growth performance. Empirical studies so far strongly support thishypothesis1.

The obvious normative implication is that the state should confine to its coreactivities, abstain from interventionism, and ‘let markets rule’. Starting in the1970s, numerous countries followed this approach, at least to a certain extent(see, e.g., Tanzi and Schuknecht 2000). Bergsten and Williamson (1994, p. 3)claim that market-oriented reform

‘has become a global stampede in the last quarter of the 20th century. Countries of every geo-graphic region, income level, and ideology have joined the rush’.

Yet, the road towards a freer economy often proved to be difficult, and in manycases previously initiated reforms were partly and temporarily reversed. Al-

* University of Hohenheim 520D, 70593 Stuttgart, Germany, email: [email protected] author is indebted to Tobias Just, Harald Strotmann, Gerhard Wagenhals, Steffen Wirth,Manfred Wolz, the participants of the 15th Hohenheimer Oberseminar held at the HamburgischesWeltwirtschaftsarchiv on 17/18 November 2000 in Hamburg, and two anonymous referees forvery helpful comments on a previous version of this paper.

1. See, inter alia, Scully (1988), Spindler (1991), de Vanssay and Spindler (1994), Torstensson(1994), Keefer and Knack (1995), Barro (1996), Clague et al. (1996), Knack (1996), Bhalla(1997), Easton and Walker (1997), Dawson (1998), Hanke and Walters (1998), Nelson and Singh(1998), Gwartney, Lawson, and Holcombe (1999).

HANS PITLIK

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though today the world economy is more liberalized on average, some nationsexperienced a highly volatile liberalization path, whereas others undertookmuch more stable reform efforts. To which extent does the instability of mar-ket-friendly reforms affect growth performance as compared to a smoother lib-eralization process?

The paper shows that volatile liberalization policies depress growth even ifthe long-run trend is towards overall market-orientation. Section II is devotedto the topic of measuring the contents of market-friendly policies and of policyliberalization. Section III discusses the effects and the measurement of policyvolatility during the process of long-run liberalization. Section IV providessome cross-country empirical evidence on the long-term growth effects of thestability of liberalization efforts. In Section V problems of robustness and cau-sality are discussed and dealt with empirically. Some conclusions are drawn inthe final section.

II. ECONOMIC FREEDOM AND POLICY LIBERALIZATION

Examining the role of economic policies in determining GDP growth has beenthe subject of numerous empirical studies. Apparently, some public sector ac-tivities contribute to a better growth performance, whereas other policieshinder economic development. For example, it is conventional wisdom todaythat an inflationary environment is detrimental to long-run growth (Bruno andEasterly 1998). With respect to fiscal policies, Tanzi and Zee (1997) provide acomprehensive survey of the voluminous literature on growth depressing andstimulating effects and a treatment of the channels through which they influ-ence growth performance.

The core ingredients of economic freedom are personal choice, protectionof private property, and freedom of exchange, and the key proposition of eco-nomic liberalism is that the security of property rights and the freedom to en-gage in voluntary transactions are the basic sources of prosperity. Hence, thequality of government is not simply reflected by fiscal and monetary variables.Freedom from state control and expropriation of private property contains allareas of policies, including manifold regulations of private activities.

In measuring the degree of market friendliness, a growing number of studiesrely on some ‘index of economic freedom’ as developed by the Fraser-Institute(Gwartney, Lawson, and Samida 2000) or the Heritage-Foundation (Johnson,Holmes, and Kirkpatrick 1998). These numerical indexes are intended to esti-mate the whole range of government activities, i.e., they not only consider fiscal

THE PATH OF LIBERALIZATION AND ECONOMIC GROWTH

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and monetary policies but include additional measures for market regulations,adherence to the rule of law, international openness of the economy, and so on2.On the one hand, this clearly enhances information about the market orientationof a nation’s institutional environment, and is thus a more accurate way ofmeasuring the whole range of state activities. On the other hand, many of thesequalitative state activities are difficult to measure. It is not always clear how togauge the extent of economic regulations or the security of property rights. Be-sides that, finding an accurate method for the amalgamation of several variablesinto a single measure of government quality is always an uneasy task.

The Fraser-Index of Economic Freedom thereby provides the most compre-hensive measure, containing data from 1970 (57 nations) to 1997 (123 nations),in 5 year steps since 1970. This makes it possible to track the liberalization ef-forts of countries during three decades. The ratings are based on data of 23 pol-icy components, derived from international sources, that are merged into 7 ma-jor policy areas (Gwartney, Lawson, and Samida 2000, see Table 1). The indexallocates higher ratings on a zero-to-ten scale for countries with institutions andpolicies more consistent with smaller government expenditures and lower taxes(area I), greater reliance on markets (fewer government enterprises, less regu-lation and price controls: area II), price stability (area III), freedom to use al-ternative currencies (area IV), rule of law and secure property rights (area V),free trade and reliance on markets for capital allocation (areas VI and VII)3. Thecountry ratings reproduce the distribution of the original data values among theentire set of nations. If all nations reform their policies towards increasing mar-ket-friendliness, this is also reflected by an improvement of all country ratings.The authors derive the components’ weights in the construction of an area in-dex by a principal-component analysis (PCA)4. In short, PCA is an appropriateand objective method of aggregating several factors that are supposed to becentral elements of an overall variable into a newly constructed single indicator.The weights are determined such that the variation of the overall indicator ismaximized and therefore captures largely the variation of its basic components.A summary rating to obtain a comprehensive measure for the existence or theabsence of economic freedom is constructed as a weighted sum of these policyareas in the same manner. Area weights in the compound index are shown inTable 1. This summary rating is henceforth referred to as the ‘Fraser-rating’.

2. For a comparison of different indexes that are usually highly correlated, see Hanke and Walters(1998).

3. What is lacking, however, is an area that measures regulations of the labour market.4. For details see Gwartney, Lawson, and Samida (2000). On PCA in general refer to Tabachnik

and Fidell (1996, ch. 13).

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Table 1

Components of the Fraser-Index of Economic Freedom

The ‘high-score nations’ in 1997 were Hong Kong and Singapore, both with arating of 9.4 points. Further countries ranked at the top 10 of the 1997 list in-clude New Zealand, the United States, the United Kingdom, Ireland, Australia,Canada, Luxembourg, and the Netherlands. Germany actually ranks as No. 22(8.1 points), sharing this rank with Norway and Costa Rica. The economicallyleast free nations include Myanmar (No. 123, 2.1 points), the Democratic Re-public of Congo (122, 3.1), and Sierra Leone (121, 3.2). On average, the 123nations covered in 1997 attain 6.56 points, the standard deviation is 1.54.

The Fraser data also provide the opportunity to gauge the liberalization ef-forts of the nations. In this study, liberalization in the long run is calculated asthe difference between a country’s Fraser-ratings in 1995 and in 1975(LIB7595). The nations on which data were available as from 1975 to 1995 ob-served a liberalization during this period of +1.29 points (mean), ranging froma decline in economic freedom of –2.3 (Iran) to an increase of +4.7 points (Ar-gentina). The Appendix shows the entire list of countries included in the sampleused here. Among the nations with the strongest increase in economic freedomduring this 20 year period are the well known reforming countries of Chile,Turkey, and New Zealand. Russia faced an increase in the rating of +3.2 points,compared to the former U.S.S.R. Most successor states of the former SovietUnion and post-communist Central European countries are not included in theranking, as they have been rated for the first time in 1990 and in 1995.

Area Contents percentage weight in summary index

I Size of Government: Consumption, Transfers, and Subsidies 11.0%

II Structure of the Economy and Use of Markets 14.2%

III Monetary Policy and Price Stability 9.2%

IV Freedom to Use Alternative Currencies 14.6%

V Legal Structure and Property Rights 16.6%

VI International Exchange: Freedom to Trade with Foreigners 17.1%

VII Freedom of Exchange in Capital and Financial Markets 17.2%

Source: Gwartney, Lawson, and Samida 2000


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III. THE PATH OF LIBERALIZATION AND POLICY VOLATILITY

The long-run change in the rating indicates how economic freedom haschanged between two different points of time. Nevertheless, the path of liber-alization was clearly different for the countries. Whereas some nations ob-served a smooth and steady improvement of their freedom ratings, others actedin a ‘two-steps-forward-one-step-back’ mode. The long-run liberalizationmeasure crudely seizes a policy change over a lengthy period of time, but itdoes not capture the development of the institutional environment during therespective time period.

Different policy paths may however produce considerable disparities in thegrowth performance of nations. Eucken (1950) already noted that frequent pol-icy changes decrease government credibility. More recent theories claim thatan uncertain environment is a serious obstacle to private investment (see, e.g.,Pindyck 1991, Dixit 1996, and Aizenman 1997). Since unstable policies makefuture economic conditions less predictable, policy volatility reduces the incen-tives to engage in productive activities whose returns accrue in a longer timespan. Certainly, policy volatility has a two dimensional nature. Firstly, volatilityis related to the frequency of policy changes. The more often policy shifts areobserved, the less stable is the economic environment. Secondly, the predicta-bility of future economic conditions clearly depends on the intensity of the pol-icy changes. The private sector has to deal with severe uncertainty if abrupt andradical changes occur. In contrast, minor policy changes leave the economicenvironment more predictable. A number of empirical studies report evidencefor the hypothesis that policy instability is unfavorable for growth5.

In many papers the standard deviation of a time series of certain policy var-iables serves as an indicator for the volatility of policies (see Kormendi andMeguire 1985, and Easterly and Rebelo 1993, among others). In order to detectthe growth impact of different liberalization paths, this study refers to thestandard deviation of the time series of changes in freedom ratings during thetime period t = 0 . . . T, LIBSD, which is calculated as

LIBSD = (1)

with LIBt = difference in Fraser-ratings in t and (t – 1).

5. See, for example, Barro (1991), Aizenman and Marion (1993), Brunetti and Weder (1998), Bru-netti (1998).

1T

∑ (LIBt –T

t=1

1T

∑ LIBt)2T

t=1√

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The underlying idea is that the sequence of reforms shapes the expectationsof the private sector concerning the durability of a policy change. A sequenceof reform steps which are all directed towards a liberalization of the economyis of a more stable nature than a sequence which includes reversals of previousreforms. If the government ceases to liberalize, or even undoes reforms, expec-tations that the liberal re-arrangements will be continued may be revised.Hence, the credibility of reform efforts is probably influenced by the volatilityof the policy changes rather than by the variability of the policies themselves.If a country steadily improves its Fraser-ratings, LIBSD reports a small volatil-ity of policies. The same applies in the case of no liberalization at all. Contra-rily, a nation which experiences major policy swings has a high value of LIBSD.

To clarify the idea of the LIBSD measure of policy volatility, consider the fol-lowing fictitious time series of Fraser-ratings (fr1, fr2, fr3, fr4, fr5) of five coun-tries (A, B, C, D, E) in Table 2. All countries start with a rating of 2 at time t = 1and end with a rating of 10 at t = 5. The ‘long-run-liberalization indicator’(LIB15 = fr5 – fr1) is therefore +8 for every country. The mean of the nation’s Fra-ser-index over time is 6 in all cases. Columns LIB12 (= fr2 – fr1) to LIB45 (=fr5–fr4) report the countries’ liberalization efforts during the respective time periods.The countries follow different paths of liberalization. In country A four moderatepolicy changes occur, each directed towards a freer economy. In B, two intenseliberalization steps occur in t = 2 and t = 5, whereas C becomes fully liberalizedafter a moderate step in t = 2 and a major step in t = 4. D faces two liberalizationsteps in t = 3 (+6) and in t = 5 (+2). In contrast to countries A–D, E observes in-tense policy swings, i.e., three policy reversals, and a small step towards furtherliberalization in t = 5. Volatility is measured by LIBSD, the standard deviation ofliberalization efforts (LIB12 – LIB45) which is calculated as described above.

Table 2

Alternative Liberalization and Policy Volatility Measures: An Example

Although country A faces a moderate change of policies in every period,LIBSD = 0. Due to the radical policy changes, C and D have a higher LIBSD

fr1 fr2 fr3 fr4 fr5 LIB15 LIB12 LIB23 LIB34 LIB45 FRSD LIBSD

A 2 4 6 8 10 +8 +2 +2 +2 +2 2.83 0

B 2 6 6 6 10 +8 +4 ±0 ±0 +4 2.53 2.00

C 2 4 4 10 10 +8 +2 ±0 +6 +0 3.34 2.44

D 2 2 8 8 10 +8 ±0 +6 ±0 +2 3.34 2.44

E 2 8 2 8 10 +8 +6 –6 +6 +2 3.34 4.90


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than B. In country E the high value of the volatility measure indicates the im-pact of the policy U-turns6.

Real world policy volatility differed considerably from 1975 to 1995. Fin-land (0.04), Belgium (0.06), Australia (0.07), and the United States (0.11) ex-perienced the lowest degree of policy volatility as defined by LIBSD. Highestvolatility is observed for Nicaragua (2.20), Peru (1.60), Russia (1.25), and Ar-gentina (1.24). Although Chile witnessed a nearly similar long-run overall lib-eralization as compared to Argentina, its LIBSD value is 0.67, indicating asmoother reform path. The overall correlation coefficient between LIBSD from1975 to 1995 and LIB7595 for all countries is –0.0127.

IV. TESTING THE IMPACT OF THE LIBERALIZATION PATH ON GROWTH

1. Hypothesis, Model and Data

The central hypothesis of this paper is that economic liberalization is growthenhancing. Countries in which liberalization occurs on a smoother path are ex-pected to have a better growth performance than nations which observe volatileliberalization policies. In order to estimate the long-run effects of economicliberalization efforts and policy volatility on growth performance, standardcross-country OLS-regressions are run for the period 1975–957. The modeltested is of the following structure:

LRG7595 = α1 + α2LIB7595 + α3LIBSD + α4IEF75 + α5IGDP75 + ε (2)

LRG7595 denotes a country’s average annual real GDP growth rate between1975 and 1995. LIB7595 is the difference between the Fraser-ratings in 1995and 1975 and indicates the long-run liberalization efforts. Hence, α2 is ex-pected to have a positive sign. LIBSD measures the policy volatility during therespective time period. Consequently, α3 is expected to be negative. IGDP75represents initial real GDP per capita in 1975. If catching-up effects can be ob-

6. Note that the calculation of the standard deviation of the respective Fraser-ratings (i.e., the var-iability of policies and not of policy changes), shown in the column FRSD, indicates an identicaldegree of policy volatility for C, D, and E, which obviously appears to be unconvincing. For-mally,

FRSD =

7. To avoid distortions with the measurement of LIBSD, the 2 year time span from 1995 to 1997 isneglected.

1T

T

t=1∑ ( frt –

1T

T

t=1∑ frt )

2√

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served, α5 should have a negative sign. IEF75 is a country’s initial Fraser-ratingin 1975. As higher initial economic liberties supposedly represent a better ini-tial environment for growth, α4 is expected to be positive. Additionally, onemight want to control for a nation’s average level of economic freedom over therespective time period. Two countries that face identical initial freedom ratings,long-run liberalization, and policy volatility, may yet observe different timespans of liberalization. For example, if one nation’s liberalization efforts occurat the beginning and the other nation liberalizes at the end of the respective timeperiod, the first country experiences a longer era of higher economic freedom.This should obviously be reflected in a better growth performance of the firstcountry8. Including a measure for average economic freedom over time causesa serious problem of multicollinearity, however. For that reason it was decidedto employ only the IEF75 variable9.

Two separate regressions are run. The first regression – labeled (a) through-out the paper – drops the volatility measure LIBSD, whereas the second regres-sion (b) includes LIBSD. As in regression (a) the path of liberalization is ne-glected, the supposedly positive impact of growth enhancing economicliberties over time is expected to be understated. Thus, we expect coefficient α2

to be higher in regression (b) compared to (a). This may serve as an additionalconfirmation of the general impact of policy volatility.

LRG7595 is calculated on the basis of recent International Monetary Fund(2000) data (World Economic Outlook). IGDP75 represents real per capitaGDP in terms-of-trade-figures (1.000 US-$) and is taken from the Penn WorldTables Mark 5.6a (see Summers and Heston 1991). A country’s long-run liber-alization efforts LIB7595, the volatility measure LIBSD, and its initial eco-nomic freedom in 1975 IEF75 are calculated from the recent report of the Fra-ser-Institute. The total country sample is listed in the Appendix and is solelyrestricted by data availability over the respective time period.

2. Regression Results

The first two columns in Table 3 show the estimates for the entire sample of 80countries. All coefficients have the expected signs and are highly significant.

8. Thanks to an anonymous referee who pointed out this problem very clearly.9. As average economic freedom during 1975–95 is highly correlated (+0.85) with IEF75 it is in-

appropriate to include both measures in the same regression. Yet, in contrast to IEF75, the aver-age freedom rating is also highly correlated (–0.49) with the volatility measure LIBSD. None-theless, I ran alternative regressions using average freedom ratings instead of IEF75. Althoughthe regression coefficients and the P-values for LIB7595 and LIBSD drop down slightly, esti-mates are qualitatively unchanged. Results are available from the author on request.


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According to column (1a), a one point increase in the Fraser-Index during thetime span 1975 to 1995 produces a higher annual growth rate of about 0.9 per-centage points. In spite of that, even if two countries observed an identicallong-run increase in their Fraser-ratings and the same initial level of economicfreedom, their GDP growth performance may significantly differ depending onthe path of liberalization. A higher volatility of one point as measured byLIBSD depresses real GDP growth by 2.3 percentage points, see column (1b).Admittedly, a one point difference in the LIBSD value is unusually high, butnote that in the entire sample the values of LIBSD range between a minimumof 0.04 and a maximum of 2.20 with a mean of 0.50. Note that adding the vol-atility measure LIBSD improves the adjusted R2 to a reasonable level of 0.458.Yet, the expected (positive) effect of including the volatility measure on the co-efficient of LIB7595 cannot be observed. Furthermore, a higher per capita GDPof 1.000 US-$ in 1975 appears to be negatively correlated with the annualgrowth rate, the coefficients ranging from –0.4 to –0.48, depending on themodel specification.

Table 3

The Effect of Economic Liberalization and Policy Volatility on Growth

Dependent variable: Average Annual Growth Rates 1975–95

Entire country sample Country sample with increase in economic

freedom 1975–95

Country sample with steady increase in economic freedom

1975–95

(1a) (1b) (2a) (2b) (3a) (3b)

Independent Variables

Liberalization 1975–95 0.950(0.000)

0.930(0.000)

0.840(0.002)

1.131(0.000)

0.972(0.010)

1.813(0.000)

Policy Volatility 1975–95 –2.355(0.000)

–3.419(0.000)

–6.361(0.011)

Economic Freedom 1975 1.080(0.000)

1.071(0.000)

1.084(0.000)

1.006(0.000)

1.326(0.000)

1.195(0.000)

GDP per capita 1975 –0.403(0.000)

–0.479(0.000)

–0.463(0.000)

–0.557(0.000)

–0.654(0.000)

–0.609(0.000)

Const. –1.925(0.077)

–0.257(0.802)

–1.385(0.321)

0.662(0.585)

–1.097(0.588)

–0.610(0.735)

adj. R2 0.315 0.458 0.331 0.543 0.646 0.721

F 13.10(0.000)

17.71(0.000)

11.91(0.000)

20.59(0.000)

18.02(0.000)

19.06(0.000)

N 80 80 67 67 29 29

Note: P-values in parentheses

HANS PITLIK

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67 nations out of the total sample of 80 experienced an increase in their eco-nomic freedom ratings during the respective time period 1975–95. Columns(2a) and (2b) in Table 3 reproduce the results of similar regressions for thissmaller sample. In this sub-sample the maximum value of policy volatility(LIBSD) is 1.60. Again, throughout both regressions all coefficients show thepredicted signs, with P-values between 0.000 and 0.002. The hypothesis thatliberalization is in general good for growth performance, whereas a highly vol-atile path of liberalization is growth depressing, is strongly confirmed regard-less of the respective model specification. This country sample also shows theexpected positive impact of employing the volatility measure on the coefficientof liberalization efforts (LIB7595). Neglecting the time path of liberalizationleads to an underestimation of the growth promoting effects of enhancing eco-nomic freedom over time of about 0.3 real GDP growth percentage points.However, quantitative statements like ‘an increase in the economic freedom-in-dex by one point leads to higher average growth rates of 1 per cent’, must beinterpreted with caution, as the coefficients obviously depend on the construc-tion of the index. Therefore, it is clearly preferred to confine to a qualitative in-terpretation of the results.

The results may however largely depend on the impact of policy reversals.To test this, another set of regressions is run for a further reduced sample of 29nations which experienced a steady liberalization, or at least no reversal of lib-eralization efforts, between 1975 and 1995, see columns (3a) and (3b) inTable 3. All results are still confirmed. Even if no policy reversal can be ob-served the volatility of the liberalization path influences the growth perform-ance of nations. Yet, throughout this country sample, there are only 3 nationswith a policy volatility of LIBSD > 0.510. Robust regression estimates also con-firm the results in every case. Table 4 displays the results of an IRLS (iterativelyreweighted least square)-regression that lessens the influence of outliers by giv-ing them less weight11. As compared to the OLS-estimates the coefficients varyonly slightly. Results therefore do not appear to be determined by outliers.

10. Regressions with robust standard errors (White 1980) lead to almost identical P-valuesthroughout all 3 country samples. Results are available from the author on request.

11. The method employed is suggested by Beaton and Tukey (1974, p. 151).


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Table 4

The Effect of Economic Liberalization and Policy Volatility on GrowthIRLS-Estimates

V. SENSITIVITY AND CAUSALITY

1. Robustness of the Estimates

An essential problem of cross-country growth studies is that the central varia-bles often lose significance when combining them with an alternative set of con-trol variables (see Levine and Renelt 1992)12. Obviously, there exist a numberof other variables that had a positive or negative impact on economic growth inprevious analyses. For example, one might include different indicators for theinternational openness of the economy (Sachs and Warner 1995), the level andthe variability of inflation rates (Bruno and Easterly 1998), or the level of gov-ernment expenditures (Gwartney, Holcombe, and Lawson 1998) which all seemto affect growth rates considerably. These variables are, however, elements ofthe overall index of economic freedom. Thus, it appears to be inappropriate toinclude them as additional control variables. Yet, growth theory also predicts a


Entire country sample Country sample with increase in economic

freedom 1975–95

Country sample with steady increase in economic freedom

1975–95

Independent variables

Liberalization 1975–95

0.961(0.000)

1.128(0.000)

1.730(0.002)

Policy Volatility 1975–95

–2.433(0.000)

–3.391(0.000)

–6.088(0.028)

Economic Freedom 1975

1.084(0.000)

1.032(0.000)

1.169(0.002)

GDP per capita 1975

–0.488(0.000)

–0.562(0.000)

–0.603(0.000)

Const. –0.329(0.759)

0.527(0.688)

–0.422(0.837)

F 16.95(0.000)

17.66(0.000)

14.24(0.000)

N 80 67 29


12. With respect to the influence of economic freedom, see de Haan and Sturm (2000).

HANS PITLIK

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country’s investment ratio, the quality of human capital, and its populationgrowth to be decisive for GDP growth performance. In order to test for the ro-bustness of the previous estimates, these variables shall be included now.

The data for real investment (in 1985 international prices) are taken from thePenn World Tables Mark 5.6a. In the regressions I used the average real invest-ment quota during the time period 1975–92 as an explanatory variable13. Theexpected sign of its coefficient is positive. The data source for the educationlevel is the recent Barro and Lee (2000) data set. To measure the level of edu-cation the time series average of the average years of schooling-variable asshare of the entire population aged over 25 years is employed. The coefficientof this variable is expected to be negative. Next, the average annual populationgrowth in the respective time period is also included, derived from the Barroand Lee (2000) data set as well. To avoid the introduction of a lagged variable,I decided to make use of the ‘population aged over 25 years’ data14. The respec-tive coefficient’s sign is expected to be positive.

In addition, two political variables are introduced. Firstly, a measure for po-litical (as opposed to economic) liberties is included. The data used here are de-rived from the Freedom House (1999) report of political freedom which is ahighly accepted scale for the existence or absence of political and civil rightsin a nation. Adding up both measures one obtains an overall indicator for polit-ical freedom. Full democracies have a score of 2 points whilst totally autocraticregimes reach 14 points. For the present study, the average political freedomscore over the respective time period was calculated. However, it is not quiteclear whether more democratic or more autocratic regimes are beneficial foreconomic growth (Przeworski and Limongi 1992, Barro 1996). If democracywere better for growth performance, one should expect a negative sign of thecoefficient. Secondly, I included a dummy variable for internal and externalconflict risk15. Since a higher conflict risk appears to be negatively related toeconomic activities, the expected sign of the coefficient is negative. Finally,

13. The Penn World tables unfortunately do not report data for years later than 1992.14. Population growth appears to have a positive impact on long-run economic growth only if the

work force of the country increases. It is therefore more appropriate to use population datawhich reflect the development of the (potentially) productive subset of the population than touse lagged data of total population growth, which may also be affected by a variation of eco-nomic performance.

15. The data source is the IRIS (2000) compilation of 4 political risks, i.e., the danger of externalconflicts, racial tensions, political terrorism, and civil wars. Adding up the annual values ofthese variables gives an maximum sum of 28 points, indicating lowest conflict risks. As the dataare only available for a time period from 1985 to 1994, I decided to use the annual average scoreof the sum and allocate the conflict dummy a value of ‘1’ if a nation reaches, on average, a scoreof less than or equal to 14 points, otherwise ‘0’.


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continent dummies are included for Africa, Asia, Latin America, and an addi-tional dummy for OECD members.

Table 5

Regression Estimates with an Enlarged Set of Control Variables


Entire country sample Country sample with increase in economic freedom 1975–95

(1) (2) (3) (4) (5) (6)

Independent variables


0.667(0.000)

0.657(0.001)

0.653(0.000)

0.809(0.001)

0.627(0.018)

0.789(0.001)


–2.648(0.000)

–1.787(0.001)

–3.318(0.000)

–2.544(0.000)


0.573(0.002)

0.679(0.003)

0.607(0.004)

0.576(0.007)

0.642(0.023)

0.550(0.029)

GDP per capita 1975

–0.381(0.000)

–0.367(0.002)

–0.365(0.001)

–0.417(0.000)

–0.382(0.005)

–0.434(0.000)

Av. Investment1975–92

0.127(0.000)

0.092(0.025)

0.087(0.021)

0.106(0.001)

0.094(0.028)

0.093(0.016)

Population growth1975–95

0.896(0.000)

0.737(0.004)

0.884(0.000)

0.779(0.001)

0.719(0.009)

0.816(0.001)

Human Capital1975–95

–0.002(0.987)

–0.002(0.989)

0.024(0.867)

0.063(0.624)

Av. Political Freedom 1975–95

–0.136(0.121)

–0.092(0.252)

–0.116(0.247)

–0.114(0.200)

Conflict Dummy1985–1994

–1.271(0.011)

–0.841(0.072)

–0.684(0.281)

–0.301(0.598)

AfricaDummy

–0.636(0.466)

–0.931(0.249)

–0.678(0.470)

–0.416( 0.619)

Latin AmericaDummy

–2.126(0.004)

–1.495(0.032)

–2.114(0.006)

–1.179(0.099)

AsiaDummy

0.525(0.482)

0.228(0.741)

0.358(0.639)

0.098(0.885)

OECDDummy

–1.142(0.110)

–1.013(0.122)

–1.010(0.171)

–0.840(0.201)

Const. –91.659(0.000)

–74.809(0.004)

–88.854(0.000)

–79.032(0.001)

–73.035(0.010)

–81.694(0.001)

adj. R2 0.657 0.628 0.687 0.667 0.578 0.666

F 24.38(0.000)

11.14(0.000)

13.15(0.000)

22.04(0.000)

8.09(0.000)

10.51(0.000)

N 74 73 73 64 63 63


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Table 5 displays the results for the entire country sample and the sample ofcountries with overall positive liberalization efforts between 1975 and 199516.Adding only the regressors for investment and population growth does not af-fect the previous results in both samples, see columns (1) and (4). Both addi-tional variables have the expected signs at a 1% confidence level. The initialeconomic freedom measure as well as the liberalization and policy volatilitymeasures remain highly significant with the expected signs. The estimates in-cluding further variables also do not differ much with respect to these 6 basicvariables. In every case, economic liberalization, initial economic freedom,real investment quota, and population growth are positively related to growth,whereas initial GDP per capita and policy volatility show negative signs. Thisholds at least at a 5% confidence level throughout all regressions. The humancapital variable never shows significant results17. The political freedom varia-ble always shows a negative sign, i.e., more political and civil liberties are pos-itively correlated with growth, but is permanently insignificant. The conflictdummy has the expected negative sign, but is statistically significant only in thesample of all countries. Among the continent dummies only the Latin Americadummy shows a significant and negative relation to growth. The Africa dummyis always negative, but not statistically significant, as is the case for the OECDdummy.

In order to test further for the sensitivity of the liberalization variables inquestion, I ran an extreme bounds analysis (EBA) as proposed by Levine andRenelt (1992). The procedure of an EBA is to run first a base regression, includ-ing a set of explanatory variables that appear to be highly influential for growth.In the second step, several additional variables are added to the base regressionin all possible linear combinations. The extreme upper bound is defined as acombination of additional variables which provide the highest value of the co-efficient of the variables in question. An extreme lower bound is the combina-tion of variables which produces their lowest coefficients. If the coefficient ofthe variable to be checked in both the upper and lower bounds is statisticallysignificant it is said to be ‘robust’.

The variables of interest here to be tested for robustness are the change ofeconomic freedom (LIB7595), the policy volatility measure (LIBSD), and theinitial level of economic freedom (IEF75). The respective base regression em-ploys initial GDP per capita, average investment, and population growth as in-

16. The sample of countries with steady liberalization was excluded. With only 29 observations, itis not very meaningful to run a regression with up to 13 independent variables.

17. Alternatively, I used the time development of average schooling years and the ‘no schooling’-variable from Barro and Lee (2000), but the results were far from being significant, too.


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dependent variables, and is shown in Table 5, column (1). The education level,political liberties, the conflict dummy, and continent dummies serve as furthercontrol regressors. Table 6 shows the results.

Table 6

Results of an Extreme Bounds Analysis

According to this test, both the change in economic freedom and the policy vol-atility measure are robust. In neither case these measures lose significance,even at a 1% level of confidence. The coefficient of the policy volatility meas-ure, however, varies from –1.8 to –2.6, depending on the use of additional con-trol variables. The results for initial economic freedom in 1975 are slightly lessconvincing. Yet, if one accepts a P-value of 0.053, it is robust, too18.

Liberalizationmeasure

coefficient P-value adj. R2 othervariables

robust/fragile


high 0.763 0.000 0.693 continent dummies

base 0.667 0.000 0.657 robust

low 0.507 0.006 0.645 human capital,political freedom,conflict dummy


high –1.787 0.001 0.692 political freedom,conflict dummy,

continent dummies

base –2.648 0.000 0.657 robust

low –2.648 0.000 0.655 human capital


high 0.658 0.000 0.693 continent dummies

base 0.573 0.002 0.657 (robust)

low 0.408 0.053 0.645 human capital,political freedom,conflict dummy

18. In any case, the results are confirmed using only the sub-sample of countries with LIB7595>0.The same applies to all following tests of reverse causality.

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To sum up, the main hypotheses cannot be rejected on the basis of regres-sions with an enlarged set of standard control variables and with an EBA. Lib-eralization efforts are always significantly positively related to growth and pol-icy volatility is negatively related to growth, regardless of the additional controlvariables employed in the regressions.

2. The Problem of Reverse Causality

The robustness of the links between economic liberalization and the volatilityof liberalization policies on the one hand, and growth performance on theother, may nonetheless stem from a reverse causality. For example, if a coun-try’s economy became more free during a given time period and it experienceda better growth performance during that time, this may facilitate further eco-nomic liberalization. If growth performance is, however, not satisfactory, thiscan obviously lead to a reversal or a reduced speed of liberalization efforts(Gwartney, Holcombe, and Lawson 1999, p. 654). Yet, the political economyof reform suggests a different time path of policies that can work in the otherdirection. According to the ‘crisis hypothesis’ a severe economic downturn isoften said to be a pre-condition to market-oriented reform (Williamson andHaggard 1994). Hence, a bad growth performance during a previous time pe-riod may cause governments to undertake a liberalization of economic policiesin future periods. Slower growth may however cause a policy change per se,be it in the direction of more or less economic freedom. If a freer economyfaces a growth crisis it can change to more interventionist policies, and in aless free economy it may lead to liberalization. In a nutshell, the extent and thedirection of change in the liberalization variable – and, hence, the extent of pol-icy volatility – can be endogenous as both may depend on a country’s growthperformance.

Three alternative ways to test for reverse causality are provided. First a testsuggested by Maddala (1992, p. 395) is used as follows. In the first step, liber-alization is regressed on a set of variables which explain average economicgrowth plus some other variables that appear to be correlated with liberaliza-tion efforts19. Next, the predicted value of economic liberalization is includedin the basic growth regression. The same procedure applies to the volatilitymeasure. As coefficients of the predicted values are insignificant in the growth

19. Regressors employed in the base regression are initial GDP per capita, average investment, pop-ulation growth, and average years of education.


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regressions, the endogeneity hypothesis is rejected. According to this test, re-verse causality does not appear to be a problem for both the liberalization meas-ure and the policy volatility measure20.

Results of further estimations trying to explain shorter term liberalizationefforts are reported in Table 7. The dependent variable in this second test is thechange in economic freedom during the four 5-year-periods between 1975 and1995. The underlying idea is as follows. If higher growth causes liberalizationone should expect a significant and positive coefficient of GDP growth rates inthe previous 5-year-period on the change of the economic freedom index. Theexplanatory variables are a country’s liberalization efforts during the preceding5-year-periods, economic freedom at the beginning of the respective periods,and the growth rates in the preceding and in the respective periods. In addition,it is controlled for initial GDP per capita, political freedom in the preceding andin the present 5 year periods (estimates not reported here). In any case, initialeconomic freedom ratings are negatively related to liberalization. Liberaliza-tion efforts in the preceding 5-year-periods show a weakly significant (positive)correlation with liberalization efforts in the actual time period only in 1990–95. With respect to growth performance and liberalization we observe the ex-pected positive relation only for the combination of liberalization and growthin the present time periods. The correlation between growth performance dur-ing the preceding 5-year-period and the liberalization efforts in the followingperiod is negative and statistically significant (at least at the 10% level) in onlyone of four cases. If anything, lower GDP growth in the preceding period seemsto lead to stronger liberalization efforts in the next period, et vice versa. Thisapparently contradicts the thesis that higher growth rates cause liberalization,though the results are obviously too weak to be interpreted as a confirmation ofthe crisis hypothesis.

20. The P-values of the coefficients of the predicted values in the growth regressions are 0.284 forLIB7595 and 0.859 for LIBSD.

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Table 7The Impact of Previous Growth Records on Liberalization

In a third test, the results of which are displayed in Table 8, growth estimations arerun for the four 5-year periods regressed on liberalization efforts during the pre-vious, the present, and the following 5-year-periods. Further control variables areinitial economic freedom, initial GDP per capita, average investment and popu-lation growth in the respective actual 5-year-periods. If the future liberalizationefforts were correlated significantly with economic growth, this would be a sig-nal of reverse causality. Yet, in none of the four regressions this is the case, and intwo cases the coefficient of future liberalization even shows a negative sign.

Table 8The Effect of Future Economic Liberalization on Present Growth

Liberalization dur-ing preceding5 year period

Initial economicfreedom

Growth during preceding

5 year period

Growth during present

5 year period

independentvariable:

Liberalization1975–80

–0.140(0.202)

–0.243(0.004)

–0.137(0.000)

0.182(0.000)


0.104(0.207)

–0.106(0.150)

0.016(0.506)

0.086(0.004)


–0.114(0.390)

–0.255(0.002)

–0.008(0.817)

0.052(0.156)


0.233(0.083)

–0.370(0.001)

–0.015(0.677)

0.065(0.069)

Note: Other control variables are initial GDP per capita, political freedom in the previous and inthe present 5 year periods. P-values in parentheses.

Liberalizationduring previous

5 year period

Liberalizationduring present5 year period

Liberalizationfollowing

5 year period1

Initialeconomicfreedom

independentvariable:

Growth1975–80

0.487(0.254)

1.815(0.000)

0.197(0.730)

0.934(0.004)

Growth1980–85

–0.273(0.338)

1.255(0.002)

–0.215(0.608)

–0.014(0.956)

Growth1985–90

1.076(0.018)

0.454(0.311)

0.198(0.579)

0.977(0.002)

Growth1990–95

–0.118(0.792)

0.263(0.497)

–0.221(0.772)

0.946(0.013)

Note: Other control variables are initial GDP per capita, average investment and population growthin the actual 5 year periods. P-values in parentheses. 1 Only two year period from 1995–1997.


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Summing up, no evidence for reverse causality going from economic growth toliberalization efforts neither in the short-run nor in the long-run can be found.Changes in the degree of economic freedom over time, and, hence, the extentof policy volatility, do not seem to be caused by economic growth.

VI. CONCLUSION

Recent empirical work has shown that both increasing the extent of economicfreedom and a higher stability of policies are supportive for economic growth.Yet, economic liberalization is by definition a change of previous policies.Moreover, it can often be observed that market-oriented reforms are temporar-ily reversed during a nation’s long-run process of liberalization. Althoughthroughout the past 25 years various countries followed more or less stablepaths of liberalization policies the growth impact of the volatility of the overallliberalization process has not yet been examined.

This paper has focused on the effect of the stability of market-oriented re-form policies on the growth performance of nations. Using the Fraser-index ofeconomic freedom, which is aimed at providing an overall measure of the mar-ket-friendliness of a state’s policies, it is shown that a higher volatility of theliberalization path proves to be growth depressing, even if a nation experiencesmore economic freedom in the long-run. Growth performance is notably betterif liberalization follows a smoother path. The results obtained appear to be un-usually robust to changes in the model specification.

This suggests that a gradualist reform strategy of piecemeal liberalizationefforts may lead to higher long-run economic growth. This, however, cruciallydepends on the assumption that the reforms are not being reversed. From a po-litical perspective, it is often claimed that a sudden and comprehensive changeof previous policies improves the probability of a non-reversal of the reforms.Following a smoother liberalization path therefore frequently appears to be un-feasible. Yet, in terms of growth performance, a policy turn-back from a previ-ously enacted comprehensive reform program seems to be the worst thing pol-icy makers can do.

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APPENDIX

Country Sample and Liberalization 1975–95 (LIB7595)

Country LIB7595 Country LIB7595 Country LIB7595

Argentina +4.7 Israel +1.9 Syria –1.5

Australia +1.7 Italy +2.0 Taiwan +1.7

Austria +1.6 Jamaica +2.3 Tanzania +1.8

Bahamas –0.2 Japan +1.2 Thailand +2.0

Bangladesh +1.2 Jordan +0.5 Trinidad & Tobago +2.5

Barbados +0.9 Kenya +1.4 Tunisia +1.6

Belgium +0.4 Luxembourg –0.4 Turkey +3.0

Benin –0.4 Madagascar –0.2 United Kingdom +2.9

Botswana +2.9 Malawi –0.9 United States +0.8

Brazil +0.9 Malaysia +1.2 Uruguay +0.7

Burundi +0.3 Mali +0.3 Venezuela –2.0

Canada +0.7 Malta +0.9 Zambia +0.2

Chile +4.5 Mauritius +3.3

Colombia +1.7 Mexico +1.7

Congo (Dem. Rep.) ±0.0 Morocco +1.0

Costa Rica +0.8 Netherlands +1.0

Cyprus +0.3 New Zealand +3.1

Denmark +1.4 Nicaragua –1.9

Dominican Rep. +2.4 Niger –0.9

Ecuador +0.9 Nigeria –0.4

Egypt +1.8 Norway +2.0

Fiji +1.1 Pakistan +2.7

Finland +1.4 Panama +0.4

France +1.8 Papua New Guinea +1.3

Germany +0.5 Peru +4.0

Ghana +3.6 Philippines +2.8

Greece +1.0 Portugal +4.4

Guatemala +0.4 Russia +3.2

Honduras –1.2 Sierra Leone –0.2

Hong Kong +0.5 Singapore +1.8

Iceland +3.7 South Africa +0.4

India +1.4 South Korea +2.1

Indonesia +2.3 Spain +2.0

Iran –2.3 Sweden +1.7

Ireland +2.4 Switzerland +0.5

Source: Own calculations based on Gwartney, Lawson, and Samida (2000).


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SUMMARY

Empirical research has shown that both more economic freedom and a higher stability of policy var-iables are supportive for economic growth. Thus, the path of policy liberalization may have a consid-erable influence on a nation’s growth performance. Using the Fraser-index of economic freedom, thisarticle shows empirically that policy volatility proves to be growth depressing, even if the state lib-eralizes the economy in the long-run. Growth is significantly higher if liberalization occurs on asmoother path.


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ZUSAMMENFASSUNG

Empirische Arbeiten zeigen, dass mehr ökonomische Freiheiten und eine höhere Politikstabilitätwachstumsfördernd sind. Der Pfad einer Liberalisierungspolitik kann deshalb erheblichen Einflussauf das Wachstumsergebnis einer Nation haben. Unter Verwendung des Fraser-Index ökonomischerFreiheiten wird in diesem Artikel empirisch gezeigt, dass Politikvolatilität wachstumshemmendwirkt, selbst wenn der Staat langfristig die Volkswirtschaft liberalisiert. Das Wachstum ist signifikanthöher, wenn die Liberalisierung auf einem gleichmäßigen Pfad erfolgt.

RÉSUMÉ

Des recherches empiriques indiquent qu’un accroissement des libertés économiques et une politiquede plus grande stabilité favorisent la croissance économique. C’est pourquoi le chemin menant à unepolitique de libéralisation peut influencer considérablement les résultats de croissance économiqued’une nation. En s’appuyant sur l’index Fraser des libertés économiques, le présent article montreque des fluctuations de politique retardent la croissance économique, même si l’Etat liberalise l’éco-nomie à long terme. La croissance économique est nettement plus forte lorsque la libéralisation suitun sentier régulier.

Documents

The Path of Liberalization and Economic Growth