political versus economic institutions in the growth process

Journal of Comparative Economics 42 (2014) 212–229

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Journal of Comparative Economics

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Political versus economic institutions in the growth process

0147-5967/$ - see front matter � 2013 Association for Comparative Economic Studies Published by Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.jce.2013.05.001

⇑ Corresponding author.E-mail addresses: [email protected] (E. Flachaire), [email protected] (C. García-Peñalosa), maty.konte@uni

(M. Konte).1 See, amongst others, Hall and Jones (1999),Acemoglu et al. (2001), Easterly and Levine (2003) and Eicher et al. (2006) on the impact of institu

development and growth. Barro (1996),Persson (2004) and Persson and Tabellini (2006) examine the effect of democracy on growth rates.2 See Galor (2005) for a discussion of deep and proximate causes of growth.

Emmanuel Flachaire, Cecilia García-Peñalosa ⇑, Maty KonteAix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS, GREQAM-EHESS, Centre de la Vieille Charité,2 rue de la Charité, 13002 Marseille, France

a r t i c l e i n f o

Article history:Received 28 June 2012Revised 30 April 2013Available online 16 May 2013

JEL classification:O43O47

Keywords:GrowthInstitutionsMixture regressions

a b s t r a c t

Flachaire, Emmanuel, García-Peñalosa, Cecilia, and Konte, Maty—Political versus eco-nomic institutions in the growth process

After a decade of research on the relationship between institutions and growth, there is noconsensus about the exact way in which these two variables interact. In this paper were-examine the role that institutions play in the growth process using data for developedand developing economies over the period 1975–2005. Our results indicate that the datais best described by an econometric model with two growth regimes. Political institutionsare the key determinant of which regime an economy belongs to, while economic institu-tions have a direct impact on growth rates within each regime. These findings supportthe hypothesis that political institutions are one of the deep causes of growth, setting thestage in which economic institutions and standard covariates operate. Journal of Compara-tive Economics 42 (1) (2014) 212–229. Aix-Marseille University (Aix-Marseille School ofEconomics), CNRS & EHESS, GREQAM-EHESS, Centre de la Vieille Charité, 2 rue de la Charité,13002 Marseille, France.� 2013 Association for Comparative Economic Studies Published by Elsevier Inc. All rights

reserved.

1. Introduction

Over the last decade a heated debate has taken place over the role of institutions for economic growth. Although simplecorrelations indicate that growth and institutional quality are closely related, there is no consensus about the exact way inwhich these two variables interact. On the one hand, the evidence on cross-country income gaps has found that income lev-els are strongly correlated with economic institutions, while political institutions have been argued to be ‘deep’ causes ofdevelopment, acting through their impact on policies and economic institutions. On the other, studies of the determinantsof growth rates have focused on the role of political institutions, particularly that of democracy, and find that while the levelof institutional quality has no impact on growth rates, changes in the political framework do.1 In this paper we reconsider therelationship between institutions and growth and argue that both political and economic institutions are crucial determinantsof growth rates albeit in very different ways.

The motivation for our approach is the idea that there are deep and proximate causes of growth, and that political insti-tutions are likely to be part of the deep causes of economic performance.2 Such an argument has been proposed by Acemoglu

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E. Flachaire et al. / Journal of Comparative Economics 42 (2014) 212–229 213

et al. (2005) through the hierarchy of institutions hypothesis which argues that political institutions ‘set the stage’ in which eco-nomic institutions can be devised. As such, their role is indirect and operates through their impact on the economic institutionsthat a country chooses or on the effect that economic policies and institutions have on growth.

Several authors, such as De Long and Shleifer (1993),Jones and Olken (2005) and Larsson and Parente (2011), have arguedthat autocrats are not all alike in their objectives and policies, and that their choices have a major impact on economic per-formance. Meanwhile, Glaeser et al. (2004) emphasize that often poor countries growth because of the policies implementedby dictators. In fact, given the restrictions that autocratic regimes impose on economic agents, it is possible that the impor-tance of the economic institutions that they choose is greater than in democratic regimes, where individuals have greaterfreedom to pursue growth-enhancing activities. The idea that institutions may operate at various levels has also been for-malized by Davis (2010) who models the difference between institutional quality and institutional flexibility, where the lat-ter permits improvements in institutional quality in response to economic conditions. Davis (2010) then argues that thesetwo concepts capture, respectively, economic and political institutions. The role of political institutions in shaping the effectof other variables has also been explored by Aidt et al. (2008), who examine how the impact of corruption on growth variesacross political regimes.

While there is evidence that political institutions determine the choice of economic institutions,3 no work has been doneon whether the impact of the latter on growth depends on the broader institutional context. We hence focus on this secondmechanism and argue that while economic institutions affect growth rates in the same way as standard variables such asinvestment or education, political institutions are one of the deep causes of growth. To test this hypothesis we follow recentwork which emphasizes the existence of different growth regimes such that standard growth determinants have different mar-ginal effects on growth across regimes.4

Our approach consists of using a finite mixture of regression models, a semi-parametric method for modeling unobservedheterogeneity in the population that allows us to relax the hypothesis of one growth model. This method offers much greaterflexibility than alternative approaches that divide the data into groups. Indeed, using a dummy variable to divide the sampleis equivalent to arbitrarily allocating each country to one specific group with probability one. Rather than splitting the sam-ple based on a priori arbitrary choices, mixture models generate endogenous group membership and permit explaininggroup membership with several covariates. Countries are hence endogenously allocated to a group, and each has its ownprobability of belonging to one or another group. This framework allows us to test whether political institutions rather thanhaving a direct impact, determine which regime a country belongs to.

We estimate mixture regressions on a panel of developed and developing countries over the period 1975–2005. Our re-sults indicate that the data is best described by a two-regime model, with roughly a third of countries in a stable-growthgroup and the rest in a group with much more dispersed growth rates. Political and economic institutions play very differentroles. The former are the key determinant of regime membership, while economic institutions are important in determininggrowth rates within each of the two regimes, supporting the hypothesis that political institutions are one of the deep causesof growth but economic institutions are not. The impact of economic institutions on growth is substantial, although its mag-nitude differs across groups. In the high-democracy group, an increase of one standard deviation of the economic institutionsindex results in an increase in growth of 0.3 percentage points, while in the low-democracy group the same increase raisesgrowth by 1.3 percentage points. Our results hence suggest that when political institutions are weak, growth is more sen-sitive to the choice of economic institutions than when the former are strong.

The paper contributes to the literature on the relationship between growth and institutions, dating back to the work ofNorth (see North, 1981). Empirical analyses linking institutions and growth rates have focused extensively on the effect ofthe degree of democratization, yet the evidence for a significant effect is weak; see Barro (1996). Recent work, such asPersson and Tabellini (2006),Persson and Tabellini (2008), Rodrik and Wacziarg (2005) and Nannicini and Ricciuti (2010),has found that it is not being a democracy but rather becoming one what matters for growth. For example, Persson and Tabel-lini (2008) find that the transition from autocracy to democracy increases a country’s annual growth rate by 1 percentagepoint. Moreover, Persson and Tabellini (2006) maintain that the difficulty in identifying the impact of political regimes fromwithin-country variations is that democracy is too broad a concept. They focus on three specific situations in which demo-cratic reform impacts growth: the correlation between democratizations and economic liberalizations, instances wheredemocratic institutions influence fiscal and trade policies, and allowing for ‘expected political reforms’ that anticipate actualreforms. In all these cases they find a stronger growth effect of democracy than is obtained in more standard growth regres-sions. 5 Our approach is complementary to these studies in two aspects. On the one hand, our aim is to find a role for the level ofpolitical institutions in the growth process, rather than for changes. On the other, we postulate that their impact is not a directone but rather operates indirectly as they determine to which growth regime a country belongs to.

Our work is also related to empirical analyses of the effect of institutions on cross-country income differences, such asKnack and Keefer (1995),Hall and Jones (1999) and Acemoglu et al. (2001), which although methodologically different,ask conceptually similar questions. A major difference between the two approaches is that while looking at the level of

3 See Persson (2004) and Eicher and Leukert (2009).4 See Owen et al. (2009),DiVaio and Enflo (2011) and Bos et al. (2010).5 Some of this work has also considered the question of parameter heterogeneity when examining the impact of transitions on growth rates. Persson and

Tabellini (2008) and Nannicini and Ricciuti (2010) examine the different impact of transitions into and out of democracy. There is also evidence that democracymay induce changes in the level of economic institutions and through these affect growth; see the discussion and the references in De Haan et al. (2006).

214 E. Flachaire et al. / Journal of Comparative Economics 42 (2014) 212–229

GDP allows us to identify the ‘deep’ causes of development, such analyses tend to have limited policy implications becausethose causes are difficult to change in the short or medium term. In contrast, growth regressions, especially when exploitingthe panel dimension, focus precisely on short-term policies. The multiple-regime model that we propose has the advantagethat it allows us to combine both approaches: time varying factors have a direct impact on growth, while ‘deep’ determinantspush countries into one or another regime and hence determine long-run output by affecting the way in which policies orinputs affect growth.6

Most studies looking at cross-country income differences have found that economic institutions are strongly correlatedwith the level of development, while political institutions tend to be insignificant.7 As a response, Acemoglu et al. (2005) haveargued that different types of institutions act at different levels, with political institutions being part of the deep causes of devel-opment and economic institutions belonging to the set of proximate causes. Eicher and Leukert (2009) find support for thishypothesis when they use political institutions as an instrument for economic institutions, which in turn have a significant ef-fect on output levels. The literature on the determinants of growth rates has compared the impact of the two sets of institutionsand arrived to similar conclusions. For example, Glaeser et al. (2004) highlight the fact that, when controlling for education, thelevel of economic institutions has a significant coefficient in growth regressions while political institutions do not, and Giavazziand Tabellini (2005) find that economic liberalizations foster growth, and this effect is magnified if they are accompanied bypolitical liberalizations.

Methodologically, the paper builds on the extensive literature on growth regimes, starting with Durlauf and Johnson(1995), which tries to deal with the problem of parameter heterogeneity.8 Some recent papers have proposed the use of mix-ture regressions models to identify different growth regimes, and here we follow this approach. DiVaio and Enflo (2011) usehistorical data to identify the role of trade openness, while Bos et al. (2010) examine whether, in a world with different growthregimes, countries change regime over time. Neither of these analyses considers the role of institutions. Owen et al. (2009) allowfor the possibility that institutions affect group membership, and some of our results will revisit their empirical analysis. Thecrucial difference with their work is that we allow institutions to have both a direct and an indirect impact on growth, and indoing so find that economic and political institutions play very different roles. Using a different approach, Aidt et al. (2008)examine the impact of corruption on growth across different political regimes, dividing the data into two groups accordingto the level of political institutions and obtaining a threshold level of institutions. They find that the effect of corruption ongrowth is only significant in good-institutions economies.

The paper is organized as follows. Section 2 discusses the role of institutions in the growth process. Section 3 describesthe data, focusing on the measurement of institutions and the correlation between political and economic institutions, andshows that standard regression methods indicate that although economic institutions have a positive and significant effecton growth, political institutions play no role. We then move onto the central analysis of the paper, with Section 4 testing thehypothesis that political institutions determine to which regime a country belongs to and Section 5 performing a number ofrobustness analyses. The last section concludes.

2. Institutions in the growth process

In order to think about the role of institutions in the growth process, consider the following growth regression model:

6 Wehighly sbe sensusing a

7 See8 See

parameexamin

growth ¼ d0 þ d1 logðgdp0Þ þ d2 logðpopÞ þ d3 logðinvÞ þ d4 logðeduc0Þ þ d5eco0 þ d6dem0 þ e: ð1Þ

The dependent variable is the average annual growth rate of real per capita GDP (growth), and the core covariates are initialGDP per capita (gdp0), the average annual population growth rate (pop), the average investment to output ratio (inv) and ini-tial years of schooling of the labor force (educ0). The standard approach is to also add a term that captures the rate of growthof total factor productivity (TFP) which, in a Solow-type world, would be exogenous and common to all countries.

Alternatively, we can think of TFP growth as varying across countries and overtime, according to some country specificvariables. Eq. (1) postulates that TFP growth is driven by the institutional framework of the economy as captured bymeasures of economic institutions (eco) and of political institutions (dem). As discussed earlier, starting with Barro (1996)and Hall and Jones (1999), institutions have been argued to have a direct effect on growth and productivity. In recent yearsa number of theoretical arguments have been put forward to support this relationship. For example, Aghion et al. (2008) main-tain that political and economic freedoms are correlated with freedom of entry in product markets, which in turn encouragesinnovation. More generally, the entire literature on R&D-driven endogenous growth relies on the existence of high qualityeconomic institutions such as free markets, the protection of property rights, particularly intellectual property rights, andindividual freedom to move across sectors or occupations. Our prior, in the light of this literature, is that economic institutions

acknowledge, nevertheless, that one of the disadvantages of focusing on growth rates rather than output levels is that growth regressions are oftenensitive to the period and sample of countries included; this issue has been discussed by Durlauf et al. (2005). However, our key results do not seem toitive to these choices, and we refer the reader to the working paper version of this paper, which obtains equivalent results on the role of institutionsslightly different period and country sample.Acemoglu et al. (2002),Easterly and Levine (2003), Dollar and Kraay (2003),Glaeser et al. (2004), Acemoglu et al. (2005),Glaeser et al. (2007).also Brock and Durlauf (2001) and Eicher et al. (2007), amongst others. Huynh and Jacho-Chávez (2009) focus on the importance of allowing for non-tric analysis of the relationship between growth and governance, indicating that the relationship is highly non linear, while Sturm and De Haan (2005)e the impact of outliers. The differences between these and our approach are discussed in Section 4.


are a key factor determining productivity growth. Arguments for a direct impact of political institutions on productivitygrowth have also been put forward, such as those in Aghion et al. (2008) or Acemoglu (2008), who maintain that democraciesmay be better able to take advantage of new technologies. Yet these authors also argue that in the early stages of developmentautocracies may result in faster growth if they allow the adoption of suitable but costly policies,9 thus introducing ambiguitieson the relationship between political institutions and growth.

It is also possible that the impact of political constraints is not direct but rather determines the coefficients on the covar-iates in Eq. (1). This idea has been recently formalized by Larsson and Parente (2011) who model the way in which demo-cratic and autocratic regimes choose policies and examine their impact on growth. They maintain that all democraciesimplement similar policies, chosen by voters, which are conducive to moderate growth. In contrast, autocrats choose policiesaccording to their own preferences which are heterogeneous across regimes. Some autocrats impose policies that are con-ducive to fast growth even if this entails costs for some sectors of the electorate, while others prefer to dampen growth inorder to benefit minority groups, an idea that is supported by the historical evidence in De Long and Shleifer (1993) and Jonesand Olken (2005). As a result, the average performance of democratic and non-democratic regimes may not be very different,but the variance of growth rates would be much larger in the latter.

In order to test this hypothesis, we will consider the possibility that political institutions determine the type of growthregime a country belongs to. Our expectation is that democratic countries exhibit rather homogeneous growth rates whilenon-democratic ones are in a regime where growth is highly sensitive to policy choices. In particular, we will examinewhether in non-democratic regimes the economic institutions imposed by the government become a mayor factor determin-ing growth.

3. The data

3.1. Description of the data

Most of the data we employ has been extensively used by the empirical literature on the determinants of growth rates.We use a panel comprising 79 developed and developing countries for the period 1975–2005. Observations are averagedover 5-year periods, yielding (at best) six data points per country and totaling 450 observations. Table 1 presents descriptivestatistics and the data sources.

We measure economic institutions by the index of Economic Freedom of the World (EFW) from the Fraser Institute; seeGwartney and Lawson (2003) for the original description of the data. Economic freedom measures the extent to which prop-erty rights are protected and the freedom that individuals have to engage in voluntary transactions. This measure takes intoaccount the respect of personal choices, the voluntary exchange coordinated by markets, freedom to enter and compete inmarkets, and protection of persons and their property from aggression by others. The index is an unweighted average of 5elements: the size of the government in the economy, the legal structure, security of property rights, the access to soundmoney, the freedom to trade internationally, and the regulation of credit, labor and business. The country with the lowestaverage value in our sample is Algeria (2.3) and the one with the largest is Singapore (7.92).

Our main measure of political institutions is the degree of democracy obtained from Polity IV. This measure takes intoaccount the competitiveness of executive recruitment, the openness of executive recruitment, the constraints on the exec-utive, and the competitiveness of political participation. It ranges between 0 and 10, with a value of 0 denoting an autocraticgovernment and a value of 10 full democracy.

Measuring institutions is controversial.10 A first problem we encounter is the possibility of endogeneity as income levels andgrowth may themselves affect the quality of institutions. Although a number of instruments have been proposed in the literature,we will not use them as they have the drawback of not having a time dimension and being available for only a small number ofcountries; see Acemoglu et al. (2001). Instead we will use initial values of our measures of institutions, as suggested by De Haanet al. (2006). Second, although our two core measures of institutions have been widely used in the growth literature they havebeen questioned on a number of grounds. As De Haan et al. (2006) point out, one serious drawback of the Fraser Institute measureof economic freedom is that it captures not only institutions but also policies, and it is hence not clear whether it measures whatresearchers have in mind. The Polity IV data have also been criticized, notably by political scientists, on the grounds that in anattempt to build an indicator that is not binary, subjective perceptions have been introduced. For these reasons most of ourrobustness analysis will be concerned with using alternative measures of institutions. In particular, we will examine the effectof the different components of the Fraser Institute measure and will employ alternative measures of political institutions, such asthe binary democracy-Autocracy index proposed by Przeworski et al. (2000) and updated by Cheibub et al. (2010).

The correlation matrix in Table 2 presents some well-established facts. First, the two measures of institutions are onlymoderately correlated (see Glaeser et al., 2004). Second, the correlation between growth and institutions is stronger inthe case of economic institutions than political ones, which exhibit a correlation with growth of only 0.19 compared to0.29 for the former. Lastly, democracy covaries with education, raising the question of to what extent these two variableshave independent explanatory power in growth regressions.

9 See also Acemoglu et al. (2006).10 For further discussions on the measurement of institutions see De Haan (2003) and Glaeser et al. (2004).

Table 2Correlation amongst main variables.

growth log (gdp0) log (pop5) log (inv5) log (educ0) eco0 dem0

growth 1.00log (gdp0) 0.16 1.00log (pop5) �0.25 �0.70 1.00log (inv5) 0.32 0.68 �0.45 1.00log (educ0) 0.19 0.78 �0.61 0.53 1.00eco0 0.29 0.60 �0.43 0.44 0.49 1.00dem0 0.19 0.66 �0.59 0.44 0.64 0.47 1.00

Note: Output growth, population growth and investment are averaged over 5-year periods, GDP per capita, education and institutions are measured at thestart of each 5-year period. Sources are given in Table 1 above.

Table 1Descriptive statistics and data sources.

Variable Obs. Mean SD Min Max Description Data source

growth 450 1.51 2.81 �13.60 14.18 Average annual growth rate over 5 year period PWT 6.3log (gdp0) 450 8.69 1.09 6.36 10.64 Log of initial real GDP per capita PWT 6.3log (pop5) 450 1.89 0.16 1.49 2.328 Log of population growth PWT 6.3log (inv5) 426 2.76 0.53 0.62 3.92 Log of investment rate PWT 6.3log (educ0) 450 1.59 0.68 �1.47 2.57 Log of initial average years of education

of the total population aged over 25Barro and Lee (2010)

dem0 440 5.68 4.09 0 10 Initial index of political institutions www.systemicpeace.org, Polity IV projecteco0 450 5.83 1.18 2.3 8.78 Initial index of economic institutions www.freetheworld.com, version 2009dem75 79 4.15 4.44 0 10 Index of political institutions in 1975 www.systemicpeace.org, Polity IV projecteco75 58 5.48 1.01 3.39 7.35 Index of economic institutions in 1975 www.freetheworld.com, version 2009

Table 3Institutional patters.

Variable Mean SD

dem0

Overall 5.68 4.08Between 3.47Within 2.12

eco0

Overall 5.83 1.18Between 0.94Within 0.72

Note: Decomposition into between-country and within-country components.


To further understand the differences between our two institutional variables, Table 3 decomposes the data into theirbetween-country and within-country components. We can see that the two variables have roughly the same mean butthe dispersion of dem0 is substantially greater than that of eco0. The within country component is smaller than the be-tween-country one in both cases, but the difference between the two is much larger for dem, indicating the greater relativestability over time of this measure. Fig. 1 gives some country examples of the evolution of the two variables over time, withdemocracy being depicted by the continuous line and economic institutions by the dashed one. The figure indicates substan-tial variations over time as well as very different country patterns. In some cases, such as Botswana, the two institutions arevirtually identical, but for most countries this is not the case. There are many instances in which there is a gap between thetwo variables, but the time trend is the same for both (France, India, Tunisia). The size of the gap between the two types ofinstitutions varies, being small in France and large in Tunisia, where economic institutions are much better than politicalones. In the US and in Egypt political institutions have remained stable while economic ones have improved over time,but the difference between the two countries is that in the former eco0 has been catching up with dem0, while in the latterthe two measures have diverged over time. The overall tendency has been for an improvement in institutional quality butthere are exceptions, with Brazil, Peru, Venezuela and Zimbabwe being examples of countries that experienced a deteriora-tion of one or the other measure.

3.2. Standard regression models

The data discussed above indicates that our two measures of institutions are not only conceptually different but also di-verse in terms of their evolution over time and the degree of correlation with economic performance, raising the question of

http://www.systemicpeace.org

http://www.freetheworld.com

http://www.systemicpeace.org

http://www.freetheworld.com

Fig. 1. Institutions over time. Note: Democracy and economic institutions in selected countries are measured at the start of each five-year period.

Table 4Standard regression models.

Variable Pooled-OLS Fixed-effects Random-effects IV-Random-effects

(i) (ii) (iii) (iv) (v)

intercept 11.88⁄⁄⁄ 13.19⁄⁄⁄ 60.10⁄⁄⁄ 20.81⁄⁄⁄ 14.55⁄⁄⁄

(3.205) (3.208) (5.724) (3.721) (3.798)log (gdp0) �1.419⁄⁄⁄ �1.631⁄⁄⁄ �7.030⁄⁄⁄ �2.402⁄⁄⁄ �1.232⁄⁄⁄

(0.253) (0.256) (0.581) (0.476) (0.308)log (pop5) �4.425⁄⁄⁄ �4.876⁄⁄⁄ �5.044⁄⁄⁄ �6.661⁄⁄⁄ �6.692⁄⁄⁄

(1.068) (1.093) (1.718) (1.275) (1.495)log (inv5) 1.769⁄⁄⁄ 2.152⁄⁄⁄ 2.774⁄⁄⁄ 2.613⁄⁄⁄ 2.176⁄⁄⁄

(0.318) (0.328) (0.611) (0.403) (0.391)log (educ0) 0.590⁄ 0.554⁄ �1.298⁄ 0.775⁄ 0.575

(0.309) (0.312) (0.722) (0.402) (0.495)eco0 0.735⁄⁄⁄ 0.752⁄⁄⁄ 0.757⁄⁄⁄ 0.830⁄⁄⁄ 0.747⁄⁄

(0.145) (0.144) (0.198) (0.165) (0.371)dem0 0.038 0.054 0.079⁄ �0.222

(0.042) (0.052) (0.0460) (0.136)

Nb obs 450 440 440 440 353Nb countries 79 79 79 79 79R2 0.18 0.22 0.38 0.2 0.17BIC 2163.65 2103.78 2295.82 2129.4 –

Note: The dependent variable is the output growth. Estimations include time dummies. For IV estimation, first lags of log (educ0), dem0 and eco0are used asinstruments. Standard errors are shown in parentheses.The BIC (defined in Section 4.1) is provided to attest the goodness of fit of the estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.


whether their role in the growth process is also different. To highlight these differences, we consider the standard ap-proaches that have been used to examine the determinants of growth rates and ask to what extent these are able to satis-factorily identify the impact of institutions. We first run Ordinary Least Squares (OLS) regressions, and then exploit the paneldimension of the data, using Fixed-Effects (FE) and Random-Effects (RE) models, and show that a similar pattern emergesfrom all the estimated models.

The first column in Table 4 presents pooled OLS estimation results using five-year averages of annual growth rates as ourdependent variable and examines the effect of economic institutions. We include time dummies in the covariates to take into


account time-effects. Initial GDP (gdp0), the population growth rate (pop5) over the five-year period, and investment (inv5) allhave significant coefficients with the expected signs. Initial education (educ0) is significant only at the 10% level, and this islikely to be due to the fact that institutions and education are strongly correlated, as indicated by a number of papers that tryto disentangle the effect of these two factors.11 Alternatively, it could be the result of parameter heterogeneity due to the factthat education and institutions have different effects on growth for different groups of countries. The coefficient on economicinstitutions (eco0) is significant and positive, as expected.

The second column in Table 4 presents results for the pooled estimation including both economic and political institu-tions, and is followed by fixed-effects and random-effects models (columns (iii) and (iv)). In these three specifications wefind, consistently, that the coefficient on economic institutions (eco0) is significant and positive, while that on political insti-tutions (dem0) is insignificant. The literature has extensively discussed the fact that both institutional quality and educationalattainment may be determined by economic performance. To deal with the possible endogeneity of these variables, the lastcolumn in Table 4 presents estimation results for fixed-effects models with the IV estimator used by Balestra and Varadha-rajan-Krishnakumar (1987).12 We use the first lags of education and institutional variables as instruments and find that theconclusions on the role of economic and political institutions remain unchanged: coefficient estimates are significant for eco,not for dem. A number of alternative specifications, such as the Arellano-Bond and the Arellano-Bover/Blundell-Bond estima-tions, give equivalent results (not reported).

4. Finite-mixture models

The results using standard regression models give a consistent picture: economic institutions have positive and signifi-cant coefficients, while those on political institutions are not significant. One possible interpretation is that the level ofdemocracy has no impact on growth rates. An alternative is that the implicit assumptions imposed by the standard approachare too constraining and do not allow the identification of the impact of this type of institutions. In particular, we have beenestimating regression models in which all countries follow the same growth process. But what if they do not?

Both Acemoglu et al. (2005) and Persson (2004) have argued that political institutions set the stage for economic activityand the creation of economic institutions. It is hence possible that political institutions do not affect growth rates per se butrather the way in which different covariates impact growth. That is, they could be a determinant of the type of growth re-gime in which a country finds itself. To investigate this hypothesis, we need to go beyond standard regression models.

A number of approaches have been proposed in the literature to deal with the possibility of multiple growth regimes. In aseminal paper Durlauf and Johnson (1995) use regression-tree analysis to explain growth and look at the role of education indetermining growth regimes, and this approach has been more recently employed by Paap (2002) and Kourtellos et al.(2010). Desdoigts (1999) explores a method based on the growth trajectory followed by countries, Brock and Durlauf(2001) use the variable coefficients model, Canova (2004) proposes a technique based on the predictive density of the data,while Sturm and De Haan (2005) use robust estimators. In general, this body of work finds that the data is best explained bya multiple regime model and allocates countries to groups on the basis of a threshold value of one or more indicators. Ourproposed approach is to use a finite mixture of regression model which, as we discuss below, presents important advantagesover alternative procedures.

4.1. Econometric specification

Finite mixture of regression models are semiparametric methods for modeling unobserved heterogeneity in the popula-tion. They allow us to relax the hypothesis of one growth model and to assume that there may exist several growth paths,that is, different groups such that the growth determinants may have different marginal effects across groups. In the regres-sion model (1), this is equivalent to relaxing the hypothesis that the coefficients d1,d2,d3,d4,d5 and d6 are common to all coun-tries. To illustrate the approach, let us consider the simple case of two groups, or two growth paths. A mixture of linearregressions assumes that an observation belonging to the first group and one belonging to the second group would notbe generated by the same data-generating process. The mixture model can be written as follows:

11 See12 The

paper, wgrowth

Group1 : y ¼ xb1 þ e1; e1 � Nð0;r21Þ;

Group2 : y ¼ xb2 þ e2; e2 � Nð0;r22Þ;

ð2Þ

where y is the dependent variable, x a set of covariates, and e1 and e2 are independent and identical normally distributederror terms within each group, with variances of r2

1 and r22, respectively. Since the sets of coefficients b1 and b2 are not (nec-

essarily) equal, covariates x do not explain in the same way differences in y between observations belonging to the firstgroup and between observations belonging to the second group.

Glaeser et al. (2004) and Bhattacharyya (2009).re are alternative ways of estimating this model, such as the Arellano-Bond dynamic panel data estimator. Given that this is not the main focus of thee refrain from expanding on this issue and refer the reader to, for example, Caselli et al. (1996) and Bhattacharyya (2009) for alternative approaches to

regressions on panel data.


In the context of growth regressions, this mixture model assumes that countries can be classified into two groups, asso-ciated to two different growth paths, and at least one covariate does not explain identically growth discrepancies within thetwo groups. Note that such assumption can be taken into account in the standard regression model (1) if we include addi-tional covariates computed as cross-products of the variables of interest with a dummy variable that specifies group mem-bership. However, in this case the groups have to be defined a priori according to some prior believe of the researcher, such asthe hypothesis that the convergence coefficient is different for OECD countries than for other economies. In contrast, in afinite-mixture model, group membership is not imposed but rather estimated so as to create classes that are homogeneousin terms of the relationship between y and x. Moreover, the number of groups is not fixed but endogenously determinedaccording to an econometric criterion or test.

A set of additional covariates, called concomitant variables, can be used to characterize group profiles. Concomitant vari-ables play the same role as covariates in a multinomial regression model designed to explain group membership. The roles ofstandard covariates and of concomitant variables are different: standard covariates help to explain variations within groups,whereas concomitant variables explain variations between groups. As a result the values of the concomitant variables willpartly determine the probability that a particular country belongs to one class or another. Note that it is possible to allowa variable to play simultaneously the role of standard covariate and of concomitant variable. 13

A general mixture regression model can be written as follows:

13 Foralso the

14 The

f ðyjx; z;HÞ ¼XK

k¼1

pkðz;akÞfkðyjx; bk;rkÞ; ð3Þ

where K is the number of components or groups, pk(z, ak) is the probability of belonging to group k with a set of specificconcomitant variables z, and fk(yjx; bk, rk) is the distribution of growth rates conditional on belonging to class k and on covar-iates x. The parameters ak,bk and rk are unknown and hence estimated. If we consider fk as Gaussian distributions with con-ditional expectations equal to E(yjx) = xbk, for K = 1 this model reduces to (1) and for K = 2 this model reduces to (2).

The probability of belonging to a given group k0 is assumed to follow a multinomial logit model, i.e.

pk0 ðz;ak0 Þ ¼exp ak0 þ zak0ð Þ

PKk¼1 expðak þ zakÞ

; ð4Þ

and assesses the likelihood that a given country’s observed growth rates are generated by the process described by param-eters ðbk0 ;rk0 Þ, given the values of x and z. This expression is noteworthy in two respects. First, it is possible to estimate themodel without any concomitant variable, in which case a country’s probabilities of being in the various regimes depend onlyon the observations of growth rates y and growth determinants x. In other words, countries are allocated to the regime thatbest fits their data. Second, when concomitant variables are included to explain group membership, countries are sorted intoregimes according to a combination of the values of the variables included in z as well as the pattern of growth rates y andthe values of x.

The procedure hence differs from standard approaches that use individual indicators and either interact these with theregressors or split the sample according exclusively to the value of the indicator. Alternative procedures allocate countriesto groups on the basis of a threshold value of one or more indicators. For example, methods such as regression-tree analysisimply that all countries with, say, a high value of political institutions will be allocated to the same group by assumption andlooks for the threshold level of institutions that divides the data into regimes. In contrast, the finite-mixture of regressionsmodel uses simultaneously information on the variables z and on the growth process itself to allocate a country to a regime,and consequently there is no ’threshold’ value of the concomitant variable. In fact, it is possible that although a high value ofz increases the probability of countries to be in regime k0 some countries with high z find themselves in other groups becausetheir data is best described by the parameters in another regime. This means that the finite-mixture of regressions modelreverses the way in which the econometrician explains the data. With alternative procedures, an indicator is used to splitthe data and then the econometricians estimates both the threshold value of the indicator and the best fit within each re-gime. The finite-mixture of regressions model jointly estimates the parameters of the growth regression for each regime andthe determinants of regime membership. In other words, the model first identifies the extent of heterogeneity and then ex-plains the sources of systematic heterogeneity by variables z.

Mixture models then have two desirable features. First, covariates are allowed to have different marginal effects acrossgroups. Second, mixture models have the ability to evaluate the profile of the different groups, or growth paths, using con-comitant variables and the resulting classification is in terms of probabilities, so that some countries will be part of a groupwith a high probability but others may have features that imply a more nuanced position. What the model does not allow isfor a country to be in different groups at different times as the concomitant variables must be constant over time.14

For a given number of components K, finite mixture models are often estimated by maximum likelihood with the EM algo-rithm of Dempster et al. (1977), and this is the procedure we will follow. The log-likelihood function can be highly non-linearand a global maximum can be difficult to obtain. It is then recommended to perform the estimation with many different start-ing values. The number of components K can be selected minimizing a criterion, such as the Bayesian Information Criterion,

more details on finite mixture models see Frühwirth-Schnatter (2006), pp. 274–275, McLachlan and Peel (2000) and Ahamada and Flachaire (2010). Seediscussion in Owen et al. (2009), Section 3, for an application to growth regressions.question of regime migration is a complex one and has been recently addressed by Bos et al. (2010).

Table 5Mixture models.

Mixture 1 Mixture 2 Mixture 3 Mixture 4 Statistic p-value

Group 1 (57%) Group 2 (43%) Group 1 (50%) Group 2 (50%) Group 1 (52%) Group 2 (48%) Group 1 (43%) Group 2 (57%)

Intercept �0.515 26.575⁄⁄⁄ 0.307 23.965⁄⁄⁄ 0.053 23.488⁄⁄⁄ 8.522⁄⁄⁄ 11.171⁄⁄ 0.19 0.67(1.669) (6.986) (1.519) (6.072) (1.441) (6.070) (3.199) (4.988)

log (gdp0) �0.558⁄⁄ �2.073⁄⁄⁄ �0.674⁄⁄ �1.984⁄⁄⁄ �0.651⁄⁄ �2.019⁄⁄⁄ �1.098⁄⁄⁄ �1.381⁄⁄⁄ 0.35 0.55(0.274) (0.433) (0.283) (0.399) (0.277) (0.395) (0.269) (0.389)

log (pop5) 0.496 �10.441⁄⁄⁄ 0.482⁄ �10.519⁄⁄⁄ 0.485⁄ �10.124⁄⁄⁄ �2.365⁄⁄⁄ �5.222⁄⁄⁄ 1.793 0.18(0.31) (2.523) (0.266) (2.322) (0.267) (2.245) (1.063) (1.759)

log (inv5) 2.489⁄⁄⁄ 3.722⁄⁄⁄ 1.742⁄⁄⁄ 3.208⁄⁄⁄ 1.807⁄⁄⁄ 3.171⁄⁄⁄ 1.895⁄⁄⁄ 1.560⁄⁄⁄ 0.29 0.59(0.395) (0.737) (0.392) (0.689) (0.386) (0.695) (0.369) (0.477)

log (educ0) �0.303 0.844 �0.303 0.802 �0.263 0.779 0.044 1.037⁄⁄ 2.72 0.099(0.359) (0.562) (0.292) (0.537) (0.279) (0.531) (0.266) (0.527)

eco0 0.294⁄⁄ 0.807⁄⁄⁄ 0.295⁄⁄ 0.806⁄⁄⁄ 0.298⁄⁄ 1.074⁄⁄⁄ 8.38 0.004(0.134) (0.279) (0.134) (0.281) (0.132) (0.232)

dem0 �0.067 0.066 0.031 �0.039(0.042) (0.085) (0.055) (0.074)

Concomitant variablesIntercept – 1.419 – �0.221 – 1.298⁄⁄ – 1.539⁄⁄⁄

(1.659) (1.868) (0.519) (0.498)dem75 – – – �0.298⁄⁄⁄ – �0.271⁄⁄⁄ – �0.282⁄⁄⁄

(0.083) (0.075) (0.073)eco75 – �0.311 – 0.307

(0.297) (0.359)

Nb obs 342 342 342 450Nb countries 33 25 29 29 30 28 34 45R2 0.31 0.36 0.27 0.37 0.27 0.37 0.27 0.22BIC 1551.19 1533.09 1529.28 2089.44CAIC 1575.19 1560.09 1546.29 2113.44

Note: The dependent variable is the output growth rate. Estimations include time dummies. Standard errors are shown in parentheses. The criteria BIC and CAIC (defined in Section 4.1) are provided to attest thegoodness of fit of the different estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.

220E.Flachaire

etal./Journal

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ics42

(2014)212–

229


denoted BIC and developed by Schwarz (1978), or the Conditional Akaike Information Criterion (CAIC, see Sugiura, 1978;Hurvich and Tsai, 1989; Burnham and Anderson, 2002). More specifically, the BIC is defined as

15 As dcountrithis que

16 Oweconomreverseresultsthe role

17 Thefor 197

18 On19 Thi

institut

BIC ¼ �2‘þ ð#paramÞ log n; ð5Þ

where ‘ is the estimated value of the log-likelihood and n is the number of observations. A similar expression defines theCAIC, which also includes a penalty determined by the effective degrees of freedom.

4.2. Empirical results

Our hypothesis is that political institutions do not have a direct effect on growth but rather determine the growth regimein which a country finds itself. In contrast, economic institutions are similar to other growth determinants such as invest-ments in physical or human capital. In order to test this hypothesis, we estimate a series of mixture models of regressionsin which we allow the political institutions variable (dem) and the economic institutions variable (eco) to act as either con-comitant variable, as standard covariate, or both.

Table 5 presents estimation results of the mixture models. Our dependent variable is the annual rate of growth averagedover five years. For each five-year period the standard covariates are averaged over the period, except for gdp and educationfor which we use the first observation in the period. When used as a covariate, institutions are also measured at the begin-ning of each 5-year period. In contrast, since in our specification we do not allow countries to change regime over time andrequire a single observation per country, when they act as concomitants institutions are measured at the start of the sampleperiod, i.e. in 1975. 15 An alternative would be to use the average value over the period, an approach that we have found to giveequivalent results and which we briefly discuss in the robustness section below.

The table reports results for two groups, i.e. K = 2. We estimated the mixture models for K = 1 as well as for larger values ofK, but the BIC and CAIC criteria were minimized for K = 2, leading us to select the mixture model with 2 groups. The first twocolumns estimates a model in which political institutions have a direct effect on growth, while economic institutions act asconcomitant. In this case, both variables have insignificant coefficients. The second model considers the most general case, inwhich both types of institutions are allowed to play both roles. Columns 3 and 4 indicate that only economic institutionshave a significant effect as standard regressors and only political institutions have a significant coefficient as concomitants.

The results imply that both types of institutions affect growth, with the coefficients on eco and dem being both significantat 1%. Their roles are nevertheless different. Political institutions affect group membership, with the negative coefficient ondem indicating that when this variable increases, the probability of belonging to the second group decreases. Economic insti-tutions are, just as in the standard regressions in Section 3.2, a determinant of the growth rate, with better institutionsincreasing growth in both groups but having a larger effect in economies with low levels of democracy. These results thussupport the hypothesis that economic and political institutions operate at different levels of the growth process.16

The next two columns present our selected specification, with economic institutions being the only type of institutionsused as standard regressor and political institutions the only concomitant. Both variables are highly significant and the BICand CAIC criteria improve with respect to previous specifications. Note that the data used in the first three mixture modelsincludes only 57 countries, the reason being that data for initial economic institutions, i.e. for 1975, is often missing from oursample. Since our selected specification does not include economic institutions, we can reestimate it using the entire sampleas data on political institutions is available for the 79 countries included.17 Model 4 reproduces the results in the previousspecification for the larger sample. Note that the value of the BIC for the model with K = 2 is equal to 2089.44, implying thata significant improvement in fitting the data is obtained with the selected finite-mixture model as compared to the model withK = 1 of Table 4 (BIC of 2163.65).

The last two columns (Wald test) test the null of equal coefficients in the two groups. We can see important differences inthe determinants of growth rates across the two groups. The coefficients on initial gdp, population and investment are notstatistically significantly different across groups. In contrast, human capital has a significant effect in group 2 but not ongroup 1,18 while the impact of eco is much larger for group 2,19 which suggests that, ceteris paribus, an improvement in eco-nomic institutions would have a smaller effect on growth in the high-democracy group than in low-democracy countries. Morespecifically, an improvement in economic institutions of one standard deviation would increase the growth rate by 0.35 per-centage points for group 1 countries and by 1.27 percentage points for those in group 2.

iscussed in the introduction, (Bos et al., 2010) use a mixture of regressions model in which they allow for regime changes, and find that although mostes do not change regime over the period of study (1970–2005), a few do. Such regime changes would in principle be possible in our setup, but addressingstion would require careful consideration of the exogeneity of concomitant variables, and is thus beyond the scope of this paper.

en et al. (2009) estimate a model with both economic and political institutions as concomitants but not as standard regressors, and find that onlyic institutions have a significant coefficient. We obtain the same result when eco and dem are used in this way, but as the table shows the results are

d once institutions are allowed to play both roles, with democracy having a significant coefficient as concomitant while eco has an insignificant one. Oursuggest that the specification without economic institutions as standard covariate suffers from omitted variable bias and hence cannot properly identify

played by the two types of institutions.panel is unbalanced, hence for some countries in the larger sample we are missing observations for the first one or two periods due to the absence of eco

5 and 1980. However, the data for initial political institutions, dem75, is available and can be used whenever dem is the only concomitant.the empirical relationship between growth and human capital see, amongst others, De la Fuente and Domenech (2001) and Temple (2001).s is in line with the findings of Eicher and Leukert (2009) who divide their sample between OECD and non-OECD countries and obtain that economicions have a stronger effect on output levels in the latter.

Table 6Country classification.

Group 1 Group 2

Country Probability Country Probability Country Probability Country Probability

Australia 0.99 Norway 0.98 Algeria 0.99 Malawi 0.98Austria 0.99 Pakistan 0.99 Argentina 0.99 Malaysia 0.99Bangladesh 0.98 Senegal 0.96 Bolivia 0.99 Mexico 0.95Belgium 0.99 South Africa 0.99 Botswana 0.99 Nicaragua 1Benin 0.63 Sri Lanka 0.91 Brazil 0.53 Niger 1Canada 0.99 Sweden 0.99 Cameroon 1 Panama 0.64Colombia 0.93 Switzerland 0.99 Central Africa, 1 Papua New Guinea 1Costa Rica 0.56 Tunisia 0.88 Chile 1 Paraguay 0.99Denmark 0.99 Turkey 0.99 Cyprus 1 Peru 1Fiji 0.93 UK 0.99 Ecuador 1 Philippines 0.99Finland 0.95 US 0.99 Egypt 0.99 Poland 0.98France 0.99 El Salvador 0.99 Sierra Leone 1Germany 0.96 Guatemala 0.99 Singapore 0.99Ghana 0.93 Haiti 1 Tanzania 0.99Greece 0.95 Honduras 0.99 Thailand 1India 0.99 Hungary 0.56 Togo 1Israel 0.87 Indonesia 0.98 Trinidad Tobago 1Italy 0.99 Iran 1 Uganda 0.97Japan 0.98 Ireland 0.99 Uruguay 1Mali 0.99 Jamaica 0.99 Venezuela 0.99Nepal 0.65 Jordan 1 Zambia 1Netherlands 0.99 Kenya 0.90 Zimbabwe 1New Zealand 0.99 South Korea 1

Means of key variables by group

Group 1 Group 2

growth 1.77 1.18(1.29) (3.51)

log (inv) 2.84 2.79(0.58) (0.65)

log (educ) 1.67 1.39(0.75) (0.59)

eco 5.83 5.38(1.39) (1.24)

dem 7.33 3.88(3.46) (3.71)

Note: The classification is obtained from the mixture model with group membership posterior probabilities in Table 5, model 4. Countries are allocated to agroup if their probability of belonging to this group is higher than 0.5. The means of key variables are calculated for each group and standard errors are givenin parenthesis.


The relative impacts of growth determinants also vary across groups. In group 1, education plays no role and economicinstitutions only a moderate one, while investment is a key determinant of growth. A one standard deviation increase in theformer and latter variables raise by 0.35 and 1.00 percentage points respectively. In contrast, in group 2 human and physicalcapital have roughly similar effects, while that of economic institutions is well above that of other factors. An increase of onestandard deviation in each of these variables raises annual growth by 0.71, 0.83 and 1.27 percentage points, respectively.

4.3. Regime membership

Consider now the composition of the two groups. The probability that a specific country belongs to a given group can becomputed using Bayes rule, with the posterior probability that country i belongs to group k0 being equal to

pik0 ¼pk0 ðzi; ak0 Þfk0 ðyijxi; bk; rkÞPKk¼1pkðzi; akÞfkðyijxi; bk; rkÞ

: ð6Þ

We use the model reported in Table 5 to compute pik0 , and allocate country i to group 1 if and only if the probability of beingin that group is greater than that of being in group 2. The first group includes 34 countries and the second 45. Table 6 reportsthe classification of the countries with their group membership posterior probability. In general, these probabilities are closeto one, yet there are some exceptions such as Costa Rica, Brazil or Hungary which have roughly the same probability of beingin one or the other group. A majority of the countries belonging to the first group are rich countries, although it also includesBangladesh, Benin, Colombia, Ghana, Mali, Nepal, Pakistan, South Africa, Sri Lanka, Tunisia and Turkey. In the second groupwe find mainly middle- and low-income countries, however the classification does not exactly coincide with the ones ob-tained by ad hoc ex ante divisions. For example, it includes several OECD members (Chile, Ireland, Mexico, and Poland).


It is important to understand the role played by the concomitant variables. The finite-mixture of regression models dividethe data into groups according to similarities in their data generating process, even in the absence of concomitant variables.Our estimation without concomitants (not reported) also implies that the data is best described by a model with two growthregimes. Concomitants then help us understand what variables make it more likely that a country is in a particular group. Inour case, countries with high levels of democracy are more likely to be in group 1, although not all democratic countries fol-low that process and not all countries in group 1 are highly democratic.

The bottom panel of Table 6 reports the average values of growth and institutions for the two groups, as well as thewithin-group standard deviations. These averages have been calculated by giving countries in a group a weight equal totheir probability of being in it. There are important differences across groups in some variables but not in others. The aver-age growth rate is only 0.6 percentage points higher for group 1 than for group 2. However, the latter exhibits a muchlarger standard deviation (3.51 against 1.29 for group 1), indicating that the data is split into a stable-growth regimeand one where growth is highly variable. This is not surprising given that the second group includes both some of theso-called ‘growth miracles’ and ‘growth disasters’; see Durlauf et al. (2005). Moreover, the large coefficients found for somevariables, notably economic institutions, and the role played by others such as education imply that in group 2 differencesin policy choices result in major gaps in long-run economic performance. That is, the larger coefficients on growth deter-minants obtained for group 2 help us understand the much larger variance of growth in those countries since they implythat a given difference in, say, economic institutions would result in a larger difference in growth performance in the sec-ond group than in the first one. Interestingly, differences across groups in economic institutions are small, with averagevalues of 5.83 and 5.38 and similar standard deviations. In contrast, group 1 exhibits an average value of our index ofpolitical institutions which is almost twice that observed in group 2 (7.33 and 3.88, respectively). Although the standarddeviation of dem is somewhat lower for group 1 than for group 2, it nevertheless indicates a substantial variation in thequality of political institutions even for this group.

Table 7Alternative measures of political institutions.

Variable Mixture 1 Mixture 2 Mixture 3 Mixture 4

Group 1(43%)

Group 2(57%)

Group 1(41%)

Group 2(59%)

Group 1(45%)

Group 2(55%)

Group 1(42%)

Group 2(58%)

Intercept 8.251⁄⁄⁄ 11.283⁄⁄ 9.148⁄⁄⁄ 10.764⁄⁄ 8.855⁄⁄⁄ 11.214⁄⁄ 8.618⁄⁄ 10.843⁄⁄

(3.117) (4.985) (3.403) (4.891) (3.359) (5.071) (3.769) (4.987)log (gdp0) �1.093⁄⁄⁄ �1.339⁄⁄⁄ �1.239⁄⁄⁄ �1.328⁄⁄⁄ �1.076⁄⁄⁄ �1.401⁄⁄⁄ �1.021⁄⁄⁄ �1.356⁄⁄⁄

(0.275) (0.381) (0.332) (0.379) (0.277) (0.396) (0.315) (0.388)log (pop5) �2.256⁄⁄ �5.316⁄⁄⁄ �2.299⁄⁄ �5.148⁄⁄⁄ �2.589⁄⁄ �5.199⁄⁄⁄ �2.572⁄⁄ �5.074⁄⁄⁄

(1.048) (1.768) (1.102) (1.727) (1.175) (1.793) (1.235) (1.760)log (inv5) 1.891⁄⁄⁄ 1.552⁄⁄⁄ 1.960⁄⁄⁄ 1.591⁄⁄⁄ 1.866⁄⁄⁄ 1.545⁄⁄⁄ 1.789⁄⁄⁄ 1.549⁄⁄⁄

(0.365) (0.474) (0.392) (0.473) (0.371) (0.483) (0.371) (0.477)log (educ0) 0.051 0.970⁄ 0.206 0.922⁄ 0.012 1.102⁄⁄ �0.027 1.069⁄⁄

(0.270) (0.523) (0.332) (0.526) (0.285) (0.540) (0.303) (0.533)eco0 0.302⁄⁄ 1.048⁄⁄⁄ 0.319⁄⁄ 1.048⁄⁄⁄ 0.301⁄⁄ 1.079⁄⁄⁄ 0.300⁄⁄ 1.045⁄⁄⁄

(0.135) (0.227) (0.136) (0.228) (0.137) (0.237) (0.145) (0.233)

Concomitantvariables

Intercept – 2.421⁄⁄⁄ – 1.500 ⁄⁄⁄ – 2.098⁄⁄⁄ – 2.733⁄⁄⁄

(0.754) (0.554) (0.717) (0.946)xconst75 – �0.490⁄⁄⁄

(0.136)demautoc75 – �2.448⁄⁄⁄

(0.664)pr75 – �0.434⁄⁄⁄

(0.137)cl75 – �0.547⁄⁄⁄

(0.183)


Note: The dependent variable is output growth. Estimations include time dummies. The criteria BIC and CAIC defined in Section 4.1 are provided to attestthe goodness of fit of the different estimations. Standard errors are shown in parentheses.The criteria BIC and CAIC defined in Section 4.1 are provided toattest the goodness of fit of the different estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.


5. Robustness analysis

5.1. Alternative measures of institutions

In order to test the robustness of our results we conduct a number of further estimations. We start by considering alter-native measures of institutions, and we do that in two steps. First we proceed to use different measures of political institu-tions as concomitants, and we then decompose the Economic Freedom index into its five components.

As we have already discussed, measuring institutions is not straight forward. We hence consider alternative measures ofpolitical institutions. We use the index of executive constraints (xconst) provided by Polity IV to measure the extent to whichthe decision making powers of chief executives are constrained and a binary dummy that measures whether a country is ademocracy or an autocracy denoted demautoc, from the Democracy-Dictatorship data. The former measure is used, amongothers, by Glaeser et al. (2004) who argue that, together with dem it is one of the least flawed measures of institutions, whilethe latter has been employed by Cheibub et al. (2010), amongst others. A variable that has been widely used is the FreedomHouse Political Rights index, which is a measure of political liberties such as free and fair elections, party competition, andwhether the opposition has power. Our last measures are its two components, an index of property right protection (pr) thatmeasures how free people are to participate in the political process and an index of civil liberties (cl) which is an indicator offreedom of expression and belief, rule of law, and personal autonomy without interference from the state.

The results are reported in Table 7. In all four models, economic institutions remain highly significant and exhibit verydifferent coefficients for the two groups. The four measures of political institutions all exhibit the same pattern, with good

Table 8Disaggregated components of economic institutions.

Mixture 1 Mixture 2 Mixture 3 Mixture 4 Mixture 5 Mixture 6

Group 1(43%)

Group 2(57%)

Group 1(42%)

Group 2(48%)

Group 1(43%)

Group 2(57%)

Group 1(44%)

Group 2(56%)

Group 1(42%)

Group 2(58%)

Group 1(44%)

Group2(56%)

Intercept 8.758⁄⁄⁄ 7.958 11.211⁄⁄⁄ 4.077 8.838⁄⁄⁄ 11.256⁄⁄ 7.936⁄⁄ 15.212⁄⁄ 8.4617⁄⁄ 7.345 8.111⁄⁄⁄ 11.135⁄⁄

(3.216) (5.033) (3.634) (5.013) (3.371) (5.246) (3.249) (5.979) (3.377) (5.002) (3.109) (5.084)log (gdp0) �0.794⁄⁄⁄ �0.881⁄⁄ �1.175⁄⁄⁄ �0.999⁄⁄⁄ �0.909⁄⁄⁄ �1.040⁄⁄⁄ �1.701⁄⁄⁄ �1.188⁄⁄⁄ �0.968⁄⁄⁄ �0.940⁄⁄ �1.047⁄⁄⁄ �1.188⁄⁄⁄

(0.243) (0.377) (0.319) (0.381) (0.259) (0.386) (0.427) (0.406) (0.249) (0.383) (0.251) (0.39)log (pop5) �3.04⁄⁄⁄ �3.732⁄⁄ �2.965⁄⁄ �2.016 �2.592⁄⁄ �4.921⁄⁄⁄ �1.077 �6.264⁄⁄⁄ �2.811⁄⁄ �4.641⁄⁄⁄ �2.454⁄⁄ �5.363⁄⁄⁄

(1.036) (1.748) (1.195) (1.741) (1.157) (1.855) (0.827) (1.989) (1.126) (1.765) (1.035) (1.806)log (inv5) 1.772⁄⁄⁄ 2.064⁄⁄⁄ 2.046⁄⁄⁄ 1.739⁄⁄⁄ 1.819⁄⁄⁄ 1.679⁄⁄⁄ 2.602⁄⁄⁄ 1.607⁄⁄⁄ 1.905⁄⁄⁄ 1.961⁄⁄⁄ 1.911⁄⁄⁄ 1.644⁄⁄⁄

(0.351) (0.492) (0.345) (0.476) (0.373) (0.504) (0.371) (0.485) (0.367) (0.487) (0.357) (0.484)log (educ0) �0.069 0.948⁄ 0.2 1.069⁄ �0.027 1.107⁄⁄ 0.169 0.673 �0.092 0.438 0.023 0.972⁄

(0.261) (0.533) (0.319) (0.550) (0.274) (0.551) (0.409) (0.527) (0.261) (0.560) (0.259) (0.533)eco10 0.100 0.062

(0.073) (0.143)eco20 0.021 0.484⁄⁄⁄

(0.085) (0.155)eco30 0.062 0.298⁄⁄⁄

(0.065) (0.108)eco40 0.499⁄⁄⁄ 0.416⁄⁄

(0.109) (0.194)eco50 0.291⁄⁄ 0.818⁄⁄⁄

(0.137) (0.249)eco13450 0.313⁄⁄ 0.757⁄⁄⁄

(0.125) (0.219)

Concomitantvariables

Intercept – 1.589⁄⁄⁄ – 1.817⁄⁄⁄ – 1.517⁄⁄⁄ – 1.549⁄⁄⁄ – 1.548⁄⁄⁄ – 1.531⁄⁄⁄

(0.477) (0.524) (0.525) (0.458) (0.557) (0.481)dem75 – �0.283⁄⁄⁄ – �0.322⁄⁄⁄ – �0.277⁄⁄⁄ – �0.299⁄⁄⁄ – �0.272⁄⁄⁄ – �0.276⁄⁄⁄

(0.071) – (0.076) (0.074) (0.070) (0.075) (0.071)

Nb obs 449 424 450 445 435 450Nb countries 34 45 33 46 34 45 35 44 33 46 35 44R2 0.26 0.15 0.28 0.18 0.25 0.18 0.39 0.17 0.28 0.19 0.28 0.19BIC 2110.05 1966.24 2107.33 2074.82 2006.24 2097.48CAIC 2134.05 1990.24 2131.33 2098.82 20130.24 2121.48

Note: The dependent variable is the output growth rate. Estimations include time dummies. The variables eco10, eco20, eco30, eco40 and eco50 are thedifferent component of our main measure of economic institutions eco0, eco13450 is the unweighted average of the four policy variables (eco10, eco30, eco40

and eco50). Standard errors are shown in parentheses.The criteria BIC and CAIC defined in Section 4.1 are provided to attest the goodness of fit of thedifferent estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.

Table 9Additional concomitants.

Variable Mixture 1 Mixture 2 Mixture 3 Mixture 4

Group 1 (53%) Group 2 (48%) Group 1 (41%) Group 2 (59%) Group 1 (41%) Group 2 (59%) Group 1 (42%) Group 2 (58%)

Intercept 6.590⁄⁄⁄ 20.501 ⁄⁄⁄ 6.834⁄ 11.645⁄⁄ 6.719⁄ 11.648⁄⁄ 7.249⁄⁄ 11.422⁄⁄

(3.314) (6.319) (3.7) (4.947) (3.746) (4.936) (2.883) (4.905)log (gdp0) �0.889⁄⁄⁄ �1.968⁄⁄⁄ �1.03⁄⁄⁄ �1.322⁄⁄⁄ �1.015⁄⁄ �1.319⁄⁄⁄ �1.012⁄⁄⁄ �1.309⁄⁄⁄

(0.294) (0.428) (0.388) (0.373) (0.394) (0.372) (0.270) (0.366)log (pop5) �1.917⁄ �8.340⁄⁄⁄ �1.8⁄ �5.448⁄⁄⁄ �1.777⁄ �5.448⁄⁄⁄ �1.948⁄⁄ �5.396⁄⁄⁄

(1.100) (2.327) (1.055) (1.765) (1.058) (1.762) (0.949) (1.750)log (inv5) 1.816⁄⁄⁄ 2.468⁄⁄⁄ 1.873⁄⁄⁄ 1.518⁄⁄⁄ 1.858⁄⁄⁄ 1.515⁄⁄⁄ 1.830⁄⁄⁄ 1.508⁄⁄⁄

(0.431) (0.702) (0.375) (0.463) (0.376) (0.463) (0.347) (0.465)log (educ0) �0.206 0.841 0.005 1.014⁄⁄ �0.002 1.016⁄⁄ 0.032 0.991⁄⁄⁄

(0.277) (0.583) (0.360) (0.507) (0.365) (0.505) (0.2628) (0.501)eco0 0.264⁄⁄ 0.98⁄⁄⁄ 0.332⁄⁄ 1.016⁄⁄⁄ 0.331⁄⁄ 1.012⁄⁄⁄ 0.291⁄⁄ 1.027⁄⁄⁄

(0.131) (0.312) (0.139) (0.225) (0.140) (0.224) (0.135) (0.217)time dummies

Concomitant variablesIntercept – 1.010 – 2.541⁄⁄⁄ – 2.618⁄⁄⁄ – 0.847

(0.629) (0.891) (0.965) (0.595)dem75 – �0.384⁄⁄⁄ – �0.145⁄ – �0.147⁄ – �0.265⁄⁄⁄

(0.121) (0.088) (0.088) (0.084)educ75 – 0.628

(0.558)oecd – �1.16 – �1.149

(1.155) (1.151)lat – �4.00 – �4.109

(3.289) (3.337)landlock – �0.202

(0.897)subafric – 0.532

(0.793)latincar – 4.473⁄

(2.314)


Note: The dependent variable is the output growth rate. Estimations with time dummies. Standard errors are shown in parentheses.The criteria BIC and CAICdefined in Section 4.1 are provided to attest the goodness of fit of the different estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.


political institutions reducing the probability of being in group 2. The number of countries in each group changes slightlyacross specifications, which is not surprising given that on Table 6 we saw that some countries have probabilities close to0.50 of being in one or the other group. As a result, some of these countries move across groups depending on thespecification.

We turn next to our measure of economic institutions. Economic freedom measures the extent to which property rightsare protected and the freedom that individuals have to engage in voluntary transactions. However, as argued by De Haanet al. (2006) amongst others, it is not entirely clear whether this measure captures institutions or policies. One of its com-ponents, the legal structure, is clearly part of the institutional context, but the others can be seen as policy choices ratherthan institutions. For our purposes, whether we interpret the index eco as policies or as institutions does not alter ourkey hypothesis that political institutions act as a different level from other constraints on economic agents, whether the lat-ter are in the form of economic institutions or policies. It is nevertheless useful to decompose the index into its componentsto examine the effect of each of them.

Recall that eco is an unweighted average of 5 elements: the size of the government in the economy (eco10), the legal struc-ture and the security of property rights (eco20), the access to sound money (eco30), the freedom to trade internationally(eco40), and the regulation of credit, labor and business (eco50). Table 8 presents the results for our mixture model for eachof these components. Legal institutions and property rights, i.e. eco20, have an equivalent effect to that obtained with theindex as a whole, namely they have an insignificant impact on growth in group 1 but a positive and significant one on group2. In contrast, the effect of policy varies depending on the indicator. Government size does not have an effect on growth ineither of the two regimes (mixture model 1), while access to sound money is important only for group 2 (mixture model 3).The other two policy measures have significant coefficients for both groups, with the effect being the same across regimes forfreedom to trade but much larger in group 2 than in group 1 for the degree of regulation. This indicates that although the

Table 10OECD vs Non-OECD.

Variable [1] [2]

Group 1 (42%) Group 2 (58%) OECD Non-OECD

Intercept 6.863⁄⁄ 11.768⁄⁄ 16. 97⁄⁄⁄ 11.85⁄⁄⁄

(3.027) (4.934) (5.363) (4.273)log (gdp0) �1.019⁄⁄⁄ �1.290⁄⁄⁄ �2.845⁄⁄⁄ �1.134⁄⁄⁄

(0.313) (0.366) (0.471) (0.320)log (pop5) �1.847⁄⁄ �5.577⁄⁄⁄ �3.138⁄⁄ �5.261⁄⁄⁄

(0.938) (1.765) (1.411) (1.574)log (inv5) 1.873⁄⁄⁄ 1.486⁄⁄⁄ 3.310⁄⁄⁄ 1.387⁄⁄⁄

(0.348) (0.462) (0.618) (0.393)log (educ0) 0.024 1.001⁄⁄ 0.829 0.458

(0.304) (0.506) (0.572) (0.378)eco0 0.318⁄⁄ 1.015⁄⁄⁄ 0.896⁄⁄⁄ 0.849⁄⁄⁄

(0.136) (0.218) (0.232) (0.192)

Concomitant variablesIntercept – 1.925⁄⁄⁄

(0.679)dem30 – �0.343⁄⁄

(0.148)oecd – �0.811

(1.084)latincar – 4.81⁄

(2.599)

Nb obs 450 141 309Nb country 33 46 24 55R2 0.3 0.22 0.41 0.17BIC 2076.77 513.08 1570.98CAIC 2102.77 524.08 1581.98

Note: The dependent variable is the output growth rate. Estimations include time dummies. Standard errors are shown in parentheses.The criteria BIC andCAIC defined in Section 4.1 are provided to attest the goodness of fit of the different estimations.⁄ Significant at 10%.⁄⁄ Significant at 5%.⁄⁄⁄ Significant at 1%.


absence of trade openness harms growth in all regimes, the costs of regulation are particularly high in non-democratic re-gimes where powerful groups may engage in rent extraction at a scale that would not be possible in a democracy. Theseresults are consistent with the findings by Bhattacharyya (2009) who considers a four-way classification of economic insti-tutions: market creating, market regulating, market stabilising, and market legitimising institutions. His measures are clo-sely related to the various elements into which we decompose Economic Freedom. Market creating institutions are proxiedby the ICRG law and order index, a measure close to eco20, market stabilising institutions are proxied by sound money, i.e.eco30, market regulating institutions by eco50, and market legitimising institutions by the Polity IV democracy index. Bhat-tacharyya (2009) finds that market legitimising institutions have no direct impact on growth, in line with our results onpolitical institutions as standard variables, while the other three types of institutions have a significant effect. The last col-umn of Table 8 (mixture model 6) includes the unweighted average of all the policy variables (eco10, eco30, eco40, and eco50)and finds a significant effect of the resulting index in both groups, although the coefficient is over twice as large for group 2than for group 1.

To sum up, our robustness analysis indicates that, just as in our benchmark estimation, political institutions play a keyrole in determining class membership in all specifications. When we decompose our measure of economic freedom into acore institutional component and the policy components we find that both types of variables have different impacts acrossgroups. The legal structure seems to be important only in the low-democracy regime, while policy measures have an effect inboth regimes albeit stronger in group 2.

5.2. The determinants of group membership

Our next robustness test consists in estimating alternative specifications for the concomitant variables. Table 9 includesseveral concomitant variables that have been considered in previous work as possible determinants of group membership:education, OECD membership and geographical characteristics. There are two reasons for including education. First, some ofthe early literature on multiple growth paths postulated that education levels were a key determinant of which group acountry belonged to; see Durlauf and Johnson (1995). Second, it has been argued that education and political institutionsare closely related and that higher education levels lead to good political institutions.20 Glaeser et al. (2004) show that there

20 See Lipset (1960) for the seminal work, and Eicher et al. (2009) for a model of the relationship between education, institutions and economic performance.


is a strong correlation between some measures of political institutions, such as executive constraints, and educational attain-ment, and their results indicate that the coefficient on political institutions looses its significance once education is included.This evidence indicates that it is possible that education also erodes the effect of political institutions as a determinant of groupmembership, and we hence include education as a concomitant variable. In order to avoid the potential bias due to the endo-geneity of education we will use as concomitant variable the initial level of education, i.e. education in 1975, rather than theaverage over the entire period.

Geography has been argued to have an effect on institutions and hence it is possible that our measure of political insti-tutions is simply capturing the impact of location on group membership. Similarly, regressions are often estimated sepa-rately for OECD and non-OECD countries which are argued to follow a different growth process. It is thus conceivablethat the measure of institutions is acting as a proxy for OECD membership. Our regressions in Table 9 hence include latitude,a dummy for OECD countries and one for whether or not the country is landlocked.

The regressions in Table 9 show that our measure of political institutions retains a significant coefficient even when edu-cation is included as concomitant. The coefficient on education is insignificant, indicating that political institutions ratherthan initial education is the key variable determining to which growth regime a country belongs. The next two specificationsinclude latitude and the OECD dummy, and these two together with a dummy for countries that are landlocked. In both casesthe additional variables have insignificant coefficients. Our measure of democracy retains its significance although only atthe 10 percent level, a result that can be explained by the correlation across concomitants. Mixture model 4 includes regionaldummies, one for Sub-Saharan African countries and another for Latin American and Caribbean ones. Only the latter is sig-nificant, and being a Latin American economy tends to increase the probability of being in group 2. The coefficient on polit-ical institutions remains significant and the effects of the various standard regressors are equivalent to those obtained in ourcore specification.

It is important to emphasize that the classification resulting from the mixture model differs from that obtained when wedivide the sample into OECD and non-OECD economies. Table 10 hence reports two alternative models. The first one is amixture model in which political institutions, the OECD dummy and the Latin-American-Caribbean dummy are included.It reproduces our earlier results, with dem and latincar being significant respectively at the 1% and 10% levels respectivelyand OECD having an insignificant coefficient. The coefficients on education and ecovary across groups, as we found before,with education having an insignificant coefficient and ecoa moderate effect on group 1 and both variables having significantand economically sizeable impacts for group 2. The next two columns present the pooled regressions we obtain when wedivide the sample into OECD and non-OECD economies, as is often done in the literature. Unfortunately, there is no straightforward way of comparing the goodness of fit of the two models. Note, however, that their implications differ substantially.The main differences across regimes that we found, notably that education has a significant coefficient in group 2 and thatthe impact of eco differs across groups, have evaporated and both variables have the same impact across the two groups, asignificant and positive one in the case of eco and an insignificant one for educ. As a result, this ex ante sample division wouldlead to the conclusion that education has no effect on growth for either subset of countries and that economic institutionsare equally important across the two groups, both of them results that the mixture model contradicts.

6. Conclusions

This paper has tried to shed light on the debate concerning the role of institutions in the growth process by testing theidea that political institutions are one of the deep determinants of growth which set the stage in which economic institutionsand other variables affect growth. Our hypothesis is that there exist multiple growth regimes such that the marginal impactsof the determinants of growth vary across regimes, and that political institutions are the key factor determining to what re-gime a country belongs to.

The data supports the existence of two growth regimes. The first exhibits slightly higher growth rates, averaging 1.8% perannum, while the second is characterized by lower but highly dispersed growth rates, with a mean of 1.2% and a standarddeviation which is almost three times as high as in the first group. Membership of the first regime is more likely when polit-ical institutions are strong but is unaffected by economic institutions. In fact, in the two regimes the average level of eco-nomic institutions is rather similar, indicating that the two types of institutions operate at different levels. These resultsare in line with recent work that emphasizes how, in autocratic regimes, economic performance is highly sensitive to policychoices.

When we focus on the determinants of growth rates within regimes, it is economic rather than political institutions thatplay a role. The coefficient on economic institutions is systematically larger for the low-democracy regime, being about threetimes that found in the high-democracy group. As a result, an improvement in economic institutions of one standard devi-ation increases annual growth by 1.2 percentage points in the former group of economies. The low-democracy regime is alsocharacterized by a high return to human capital accumulation, a variable that seems to have no significant effect on growthfor the other group.

Our results shed light on two open debates. The first concerns the impact of the level of political institutions on growth,and our findings indicate that indeed such institutions do not have a direct impact on growth rates. The second is the debateon the proximate and deep determinants of growth. Our results show that political institutions belong to the latter class ofvariables, and that as such influence the environment in which growth occurs. Because they determine the marginal impact


of standard factors such as investment in human and physical capital, they are a central element in the growth process evenin the absence of a significant direct effect.

The main policy implication of our analysis is that economic and political institutions can be substitutes in the growthprocess. Countries in which the latter are strong tend to exhibit high growth rates and a low return to improvements in eco-nomic institutions. In contrast, countries with weak political institutions have lower average growth rates but a high returnto economic institutions. As a result, economies where democracy is weak but where autocratic governments improve eco-nomic institutions can attain fast growth, and the example of several East-Asian economies comes to mind. 21 Does this meanthat good political institutions are unnecessary for successful growth strategies? In the short-run the answer seems to be yes,although the question of whether this is so also in the medium term remains open. It is possible that growth strategies that aresuccessful at early stages of development are not able to sustain growth in mature economies. If so, our results indicate that agrowth regime change can only occur if there is a political regime change.

Acknowledgments

The paper has benefited from the comments of Alain Desdoigts, Theo Eicher, Stephan Klasen, Michel Lubrano, and audi-ences at the EEA meetings, the AFSE meetings and Greqam, as well as from those by two referees. This work was partly sup-ported by the French National Research Agency Grant ANR-08-BLAN-0245-01.

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