Abstract
Empirical studies provide evidence that economic freedom, as measured by the Economic Freedom of the World Index, is related to economic growth. None the less, identifying which aspects of economic freedom are more conducive to growth has proven difficult, due to multicollinearity among the index areas. A possible explanation is that certain countries score high in all areas, whereas others tend do bad in all of them, simply because the former are more freedom-friendly than the latter. However, it is also true that each country presents a combination of freedoms, and restrictions to freedom, at the level of the individual indicators that make up each area. If some regularity exists with respect to these combinations, empirical detection of the most popular policy combinations would alleviate the collinearity problem, when assessing growth effects. Our article explores this possibility by means of cluster analysis, which we conduct at the individual indicator level. We show that multicollinearity can indeed be reduced in this way and identify policy packages that seem to be more conducive to economic growth than others. Results further indicate that certain policy packages may have only a short-term effect on growth, whereas others seem to have an enduring one.
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Along with different collaborators, Gwartney and Lawson (2001, 2002), Gwartney et al. (1996) have successively added more variables to their index and changed the areas a number of times. For a historical overview, Heckelman and Stroup (2005), as well as de Haan et al. (2006) offer a detailed and critical discussion on the past and present versions. According to the latter, some of the EFW versions are quite different when measured by rank correlation coefficients, reflecting the underlying changes that have been made in the index. Consequently, when comparing results from different studies on the relation between the EFW Index and growth rates, one has to consider carefully which version the authors are eventually using in each study, since this could make a substantial difference.
In what follows we dispense with the bulky literature focused on the growth effects of the size of the public sector, written outside the framework of the economic freedom indices. On this, see Bergh and Henrekson (2011) as well as Coll (forthcoming).
Other authors have resorted to theory in order to rearrange the EFW Index and, typically, to purge it from some of its components. Thus, de Haan and Sturm (2000) resorted to definitions by North (1981) to differentiate between variables that measure rules from those that measure outcomes. On these grounds, they purged the EFW Index from tax rates, government consumption variables and conscription, to produce their own version of the Index. A similar move in this same direction was recently made by Kapás and Czeglédi (2008), who also purge the EFW Index of certain variables. These authors let themselves be guided by a concept of economic freedom as defined by Hayek (1960), regrouping the EFW Index into freedom related variables, policy variables, and other variables.
De Haan and Siermann (1998), de Haan and Sturm (2000), like Sturm et al. (2002) only find the period increase to exert a positive impact on growth rates. Dawson (1998), Weede and Kämpf (2002), Pitlik (2002), and Doucouliagos and Ulubasoglu (2006) maintain that the increase in economic freedom is the principal cause of GDP growth, but they also find the relationship between the economic freedom level and growth to be positive and significant. Finally, Easton and Walker (1997), like Heckelman and Stroup (2000), Ali and Crain (2001, 2002), Cole (2003), Gwartney et al. (2004), and Weede (2006) encounter the level of economic freedom to be the main cause of growth, even though the increase in economic freedom also has a positive effect.
The idea that the full effect cannot be immediately felt is appealing, and those contributions that use cross sectional analysis currently take periods of 5 years or more as their time units. The use of such long periods somewhat alleviate the need of estimating time lags, but the fact remains that we still don’t know how long does it take for the effects of changes in economic freedom to operate. According to Chong and Calderón (2000) or Gwartney et al. (2004, 2006), these effects are highest after 10 years or so. However, this conclusion is entirely based on the sizes of lagged coefficients, which may be influenced by other factors.
A possible solution to this problem is estimating the effects of economic freedom on investment, although this leads to the next problem of formulating a proper equation for investment. Evidence is mixed up to date: While de Haan and Siermann (1998) claim that investment is not related to their indicator of economic freedom, Gwartney et al. (2004, 2006), Dawson (2006), Justesen (2008), and Aixalá and Fabro (2009) state that economic freedom has a positive influence on the investment rate. Gwartney et al. (2004, 2006) have tried to estimate the total growth effect (i.e., direct plus indirect) of economic freedom, but further research seems to be necessary to clarify this issue.
Total GDP in constant (year: 2000) US$. Source: The World Bank.
The idea that inspired such an attempt was that a country with a higher total GDP also has greater power in the world economy. Furthermore, it is possible that economically powerful countries create a model that other nations might want to imitate, in order to be economically successful themselves. Therefore, the global influence exercised by countries with a higher total GDP on what is defined as economic freedom should be greater.
Even authors that heavily criticize the theories of Lynn and Vanhanen (2002) on the heritability of intelligence agree that a correlation between their average national IQ estimates and GDP per capita exists (Whetzel and McDaniel 2006). Hunt and Wittmann (2008) conclude that although the Lynn and Vanhanen data set contains questionable points, their empirical conclusion, that there is a strong statistical relationship between GDP per capita and IQ, is correct.
In addition, we have run another eight sets of analogous regressions, where we have split the whole period into 5 year sub-periods with no time overlap whatsoever. The results do not change and therefore support our standpoint. For reasons of space, these are not shown, but they are available from the authors upon request.
These results are not shown for reasons of space, but they are available from the authors upon request.
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Acknowledgments
The authors thank the following persons for having made helpful comments: Niclas Berggren, Judith Clifton, Sven-Olov Daunfeldt, Daniel Díaz, Marcos Fernández-Gutierrez, Kevin Grier, James Gwartney, Santiago Lago, Felix Meier zu Selhausen, Julio Revuelta, Carmen Trueba and Erich Weede, as well as three anonymous referees. Preliminary versions of this paper were presented at the following conferences: The 2010 Annual Meeting of the Public Choice Society in Monterey, CA, the 2010 Annual Meeting of the European Public Choice Society in Izmir, Turkey, and the 2010 Annual Conference of the International Society for New Institutional Economics in Stirling, UK. Martin Rode would also like to thank the Spanish Ministry of Education for financial support in the framework of an FPU scholarship.
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Appendix
Appendix
Country list for regression
Albania | Japan |
Argentina | Jordan |
Australia | Kenya |
Austria | Korea, Rep. |
Bangladesh | Latvia |
Belgium | Lithuania |
Bolivia | Mexico |
Botswana | Netherlands |
Brazil | New Zealand |
Bulgaria | Nicaragua |
Canada | Norway |
Chile | Pakistan |
Colombia | Panama |
Costa Rica | Paraguay |
Croatia | Peru |
Czech Rep. | Philippines |
Denmark | Poland |
Dominican Rep. | Portugal |
Ecuador | Romania |
Estonia | Russia |
Finland | Singapore |
France | Slovak Rep |
Germany | Slovenia |
Greece | South Africa |
Honduras | Spain |
Hong Kong | Sweden |
Hungary | Switzerland |
Iceland | Thailand |
India | Tunisia |
Indonesia | Turkey |
Ireland | United Kingdom |
Israel | United States |
Italy | Uruguay |
Jamaica | Venezuela |
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Rode, M., Coll, S. Economic freedom and growth. Which policies matter the most?. Const Polit Econ 23, 95–133 (2012). https://doi.org/10.1007/s10602-011-9116-x
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DOI: https://doi.org/10.1007/s10602-011-9116-x