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A joint initiative of Ludwig-Maximilians University’s Center for Economic Studies and the Ifo Institute for Economic Research
Ifo / CESifo & OECD Conference on Regulation:
Political Economy, Measurement
and Effects on Performance
29 – 30 January 2010 CESifo Conference Centre, Munich
Policy Complementarities and Growth
Jorge Braga de Macedo, Joaquim Oliveira Martins,
and Bruno Rocha
CESifo GmbH
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81679 Munich
Germany
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Policy complementarities and Growth
Jorge Braga de Macedo (FE-UNL and NBER)
Joaquim Oliveira Martins (OECD)*
Bruno Rocha (IERU-University of Coimbra)
Abstract
This paper investigates the impact of reforms and their complementarity on growth. Based
on reform data for six policy areas compiled by the Heritage Foundation’s Index of
Economic Freedom and the World Bank, we compute composite indicators of reform level
and complementarity during the period 1994-2006 for 130 countries. We provide
qualitative justification for the existence of pair-wise complementarities among policy
areas. We then use cross-section and panel data estimates to test the effect of reform level
and complementarity on GDP per capita growth. We found reforms to be positively related
and their dispersion negatively related to growth, controlling for initial conditions,
macroeconomic stabilisation and other variables, as well as possible endogeneity. The effect
of complementarity is stronger for the sub-sample of developing countries. Complementary
reforms appear to be a condition for sustainable growth.
Keywords: Second-best, Policy complementarities, Structural reforms, Reform
indicators, Growth, Developing countries, Cross-section and Panel data estimates
JEL classification: O40, O38, C30
OECD/CESifo/Ifo Workshop “Regulation: Political Economy, Measurement and
Effects on Performance”, Munich, Munich, 29-30 January 2010
(*) Contact author: [email protected]. Previous versions of this paper were presented at
our classes at Sciences Po, Paris, the IZA-FRDB Workshop on ‚Tracking Structural Reforms‛,
University Bocconi, Milan, March 2009 and Research seminar at the OECD Economics
Department. We are grateful for comments of Andrea Bassanini, Riccardo Rovelli and Giuseppe
Nicoletti. We remain solely responsible for the text. The views expressed are those of the authors
and do not reflect those of the OECD or its Member countries.
1
1. Introduction
Piecemeal approaches to reform have been justified on the grounds of political
constraints as political cycles are often too short to engage several reform fronts at the
same time. However, when there are many such distortions to begin with, eliminating
only few of them reduce in general welfare along the lines of the Second best theory
(Lipsey and Lancaster, 1956). In addition, gains arising from policy complementarities
will not be reaped. This could result in lower growth and generate frustration vis-à-vis
the reform process.
The complex relationship between reforms and growth has proven to be difficult to
capture empirically. However, the subject of policy complementarities seems to be
attracting a growing attention – for instance, Chang et al. (2005) found that trade
openness results in a larger increase in economic growth when the investment in human
capital is stronger, financial markets are deeper, public infrastructure is more available,
governance is better, labour market flexibility is higher, and firm-entry is easier. A
summary of recent work on policy complementarities and growth is provided in Table 1.
We have argued in previous work, that reform complementarities help to explain the
relationship between reforms and growth in countries under transition from plan to
market1 or experiencing the consequences of the Asian financial crisis.2 Even if igniting
growth may sometimes require focusing on the main distortions blocking the take-off of
a developing economy, as argued by Hausman, Rodrik and Velasco (2008), thereafter it
is important to reform other areas in order not to fall in Second Best costs and reap gains
from positive policy interactions. Deepening some reforms while maintaining other key
policy areas unreformed may generate an increasingly smaller return (even negative),
because the interaction among policies will not work fully. For instance, if a country
opens completely its economy to international trade, therefore changing in this way the
incentives for resource allocation, it will have to put in place mechanisms that allow for
such a reallocation to take place, e.g. good infrastructure, flexible business regulations or
added labour flexibility.
The remainder of the paper is as follows. In section 2 we recall the theoretical link
between 2nd-Best and Complementarity. Then we describe the reform indicators (mainly
from Heritage Foundation and the World Bank) and other variables. The time span is
1994-2006. Next we establish a ranking of countries according to these indicators and
compute simple correlations between these and both the level and growth of GDP per
capita.
Braga de Macedo and Oliveira Martins (2008) consider a set of structural reform indicators compiled by
the EBRD for Central and Eastern European countries in transition.
1
2
See Rocha (2007) and Table 1.
2
The following section provides new empirical tests on the link between growth and
reforms allowing for policy complementarities. In the first place we use cross-section
estimates for a number of different specifications with different controls, and also
consider a subsample without higher-income countries (hereafter, HIC). We also divide
the sample in two parts, according to the level of complementarity. A possible
endogeneity bias between reform indicators and growth is addressed through a
simultaneous equation approach using the Three-stage Least Squares Method (3SLS). We
also estimate a number of fixed- and random-effects panel models.
We find that increasing the level of reforms and its policy complementarity is positively
related to GDP per capita growth, given initial conditions, macroeconomic stabilisation
and other controls. Importantly, results hold after correction for endogeneity. We also
find that the estimated effect of policy complementarities is stronger in the non-HIC
countries sub-sample. The effects of reforming on growth are weakened when policy
dispersion (the inverse of complementarity) is high. Panel data regressions confirm the
main results.
2. Second-Best and Policy Complementarities: theory
As Bergstrom (2002) pointed out, one of the more disconcerting results in the theory of
welfare economics was articulated by Lipsey and Lancaster (1956) in their paper ‚The
General Theory of Second Best.‛ They demonstrated that if there are distortions in more
than one market, removing a distortion in a single market may not be beneficial if
distortions remain in other markets. This theory generates a disheartening result:
piecemeal reforms do not necessarily increase welfare and can even reduce it. The only
way to unambiguously ensure an increase in welfare is to eliminate all distortions at
once. In 1970, Foster and Sonnenschein proved that under reasonably general
circumstances, at least one kind of piecemeal reform—a radial one (i.e., made of
proportional reductions in all distortions)—would improve welfare. Foster and
Sonnenschein required that the production possibility set be the intersection of a halfspace with the non-negative orthant (for this to be the case, not only must there be
constant returns to scale, but essentially there also must be no more than one nonproduced factor of production). They also required convexity of preferences and
normality of all goods. Rader (1976) generalized this result, making it less dependent on
initial conditions. Rader’s theorem dispenses with all these assumptions (Bergstrom
2002).
A less demanding framework is thus required. According to Braga de Macedo and
Oliveira Martins (2008), engaging several reforms in parallel reflects the idea that
reforms are mutually interdependent and, therefore, complementary. This idea goes
back to Edgeworth and has been generalized in such a way that it does not require any
particular differentiability or convexity assumptions. The modern concept of
supermodularity (Topkis, 1998; Milgrom and Roberts, 1995; Amir , 2003) stipulates that a
3
change in only one coordinate of a system is less than the change associated with a
parallel move across several dimensions. This is to say that raising one variable increases
the return to raising another. The basic idea is easy to formalize. Assume an objective
function F depending on two policy instruments (x, y). A given policy can have two
possible states, either reform (x) or no-reform (𝑥). The two policies are complementary if:
𝐹 𝑥, 𝑦 − 𝐹 𝑥, 𝑦 ≥ 𝐹 𝑥, 𝑦 − 𝐹(𝑥, 𝑦)
(1)
This means that the return of implementing reform (x) is enhanced when reform (y) is
already in place (and symmetrically for x). In a system of n policies, F is said to be
supermodular if the relations above hold for every pair of reform areas. In such a system,
optimizing can be achieved by increasing all reforms in parallel (but not necessarily in
the same proportion, as in radial reductions in distortions).
3. Reform indicators: data, development stages and growth
3.1 Data and reform indicators
In order to compute our composite policy indicators, we use the following data sources:
for the period 1994-2006, the Heritage Foundation provides a number of (time-variant)
policy variables; in addition, we use the World Development Indicators (WDI) database to
obtain data on infrastructure. Data on GDP and immunization rate (DPT) came from
WDI as well. To include human capital in the set of controls, we use of the recent dataset
constructed by Lutz et al. (2007).
Six broad (structural) policy areas are considered are: 1) trade openness (less tariff and
non-tariff barriers to trade); 2) business regulations (ability to create, operate and close
an enterprise quickly and easily); 3) free flow of capital (especially foreign capital); 4)
openness of a country’s banking and financial system (e.g. independence of the central
bank, government influence on the allocation of credit, difficulty of opening and
operating financial services for domestic and foreign individuals); 5) protection of
private property rights (the ability of individuals to accumulate private property,
secured by clear laws that are fully enforced by the state); and, 6) infrastructure. The
latter index was computed for the purposes of this paper using the number of fixed line
and mobile phone subscribers per 100 inhabitants.3 All the Heritage Foundation
indicators are scaled from 0-100; the infrastructure index was also normalised with the
scale.
Table 2 provides a qualitative justification for the existing complementarities across the
six policy areas. For instance, the effects from better infrastructure on growth will
3
See Appendix for information on sources and definitions for each policy area.
4
increase if firms are able to operate in a more flexible environment in terms of business
regulations. Say, if a Paris-based group decides to reallocate a major part of its operation
in Lyon (because of lower costs), taking advantage of good infrastructure in terms of
transportation and telecommunications, it needs to be confident that all the procedures
to open the new Lyon subsidiary or obtaining the necessary licences are quick and nonexpensive.
The reform level (RL) is given by the simple average of the six broad areas4; a direct
measure of the reform ‚effort‛ is therefore given by the variation of RL. Policy
complementarities are captured a contrario by the standard deviation of the six
aforementioned individual policy indicators (DP). When the standard deviation equals
zero, there is no policy dispersion and complementarities can be fully exploited (for a
given reform effort). With higher the dispersion, less complementarities will be at work.
Our dataset contains 130 countries (with population is larger than one million) and
covers a time span of 13 years (1994-2006). We only considered countries whose. Table 3
provides descriptive statistics on policy indicators and other variables, for the complete
sample and some sub-groups.5 In this table reform level variation ΔRL is given by the
difference of RL in 2006 (or 2005, if unavailable) and 1994 (or 1995), policy dispersion DP
is the time-average standard deviation of individual policy indicators, and growth is a
simple average of GDP per capita growth rates between 1994 and 2006.
3.2 Reform, development stages and growth
The expected relations between the policy indicators above and the level of development
are broadly confirmed by descriptive statistics in our dataset. Richer countries have
larger reform level (RL) and less policy dispersion (see Figures 1 and 2 6). Figure 2
suggests that, while a certain level of policy dispersion is observable for poorer
countries, reducing it (or achieving higher complementarity) seems to be a decisive step
towards higher stages of development. Above a certain income threshold, the negative
relation between policy dispersion and GDP per capita is more pronounced.
We do not address in this paper the relative weights of policies according to the stage of development.
However, it could be argued that certain policies (e.g. enforcement of property rights) are key at the
beginning, while others may be particularly important when countries are in more advanced stages (e.g.
trade openness). We also do not include labour market policies, an omission we plan to address in future
work.
4
In particular, two subsample of 94 and 76 countries, to allow direct comparisons with the cross-section
econometric estimates.
5
6
Note that Figures 1 to 7 the sample includes Hong-Kong (n=95).
5
In contrast, there is no obvious, direct, relation between reform effort (ΔRL), as measured
by our simple reform metric, and the stage of development of a given country. Indeed,
Figure 3a displays a wide variety of reform dynamics, which do not appear to be related
to income levels.7 The same apply to change in the policy dispersion (Figure 3b) although
there seems to be a split in the sample between the less developed and the more
advanced countries. An increase in policy dispersion is associated with lower average
growth in less developed countries, while the contrary applies for more developed ones,
Tables 4 and 5 identify the top-5 and bottom-5 reformers, in terms of reform effort (ΔRL)
and policy dispersion (DP). For policy dispersion, we also give the bottom-10 countries
for the non-HIC group. On average, good reforms seems to pay off, as the best reformers,
both in terms of high ΔRL and low DP display higher growth performance.
For the whole sample (Table 6), there is a positive correlation between ΔRL and growth.
The inverse applies for policy dispersion (DP). The latter correlation is stronger for
countries where GDP per capita in 1993 was smaller than 14,000 dollars: -0.35 (against
-0.21). This suggests that there are important growth dividends to be gained if reforms in
the non-HIC are implemented in a coherent, non-fragmented way.
Figures 4 represent reform trajectories in the space (RL, DP) for selected countries.
Ideally, the optimal reform path should be an increase in reform levels, while decreasing
the dispersion of policies. Chile, India, Botswana, or Ireland, for instance, experienced
important increases in level and complementarity of reforms. The same is true for post2000 Latvia and Lithuania. China experiences a similar trajectory (although with a ‘oneshot’ policy reversal in 2000, according to the Heritage indicators). In all these economies
growth performances are well above average. On the contrary, in Russia and Turkey –
countries with a somewhat weak growth in the period considered – there were sizeable
and long-lasting policy reversals (negative ΔRL). Interestingly enough, the case of
Nicaragua suggest that growth is to remain moderated if RL is augmented but policy
dispersion increases as well. Not surprisingly, countries with more chaotic policy
trajectories (e.g. The Philippines or Guinea) display lower growth.
Results in table 3 suggest that poorer countries display higher ΔRL after controlling for political stability,
EU membership, geographical isolation (landlocked situation) and other variables.
7
6
4. Reforms and Growth: an empirical test
4.1 Cross-section estimates
We first carried out cross-section estimates as our time dimension is relatively limited (13
years) and many of the control variables are time-invariant. The reduced-form model
relating the policy variables to growth is as follows:
YN    POL '     YN ,1993  .MacroStab  Z '  
(2)
YN are the average 1994-2006 GDP per capita growth rates. POL stands for the vector of
policy indicators. YN ,1993 is the level of GDP per capita in 1993, capturing a possible
catching-up effect. Macroeconomic stabilisation is given by the respective Heritage
indicator8 and Z represents a vector of control variables (country dummies9, mean years
of schooling of 25+ age group in 1995, DPT immunization rate10, and the Heritage
measure of relative government size in the economy).
We tested the impact of the two policy indicators, both in levels and changes (RL, ΔRL,
DP, ΔDP). It turned out that the variables having the strongest explanatory power are
ΔRL, the difference of RL in 2006 (or 2005) and 1994 (or 1995), and DP the time-averaged
standard deviation of individual policy indicators in the period 1994-2006. To avoid
possible co-linearity problems, these were the only policy variables retained in the
baseline specification. All the pair-wise correlations across variables are provided in
Table 6 and the econometric results are in Table 7.
First, only the changes, not the level of reforms seems to affect growth. The coefficient of
ΔRL is always positive and significant. Countries where distortions were reduced grew
8
See Data Appendix.
Asia, (sub-Saharan) Africa, European and NIS transition economies, and mineral fuel exporters.
Regarding the later, we consider that in many of these countries growth can be to a large extent explained
by non-policy factors, namely the contemporary increases in the oil or gas global demand. The fuel
exporters are the countries where the proportion of mineral fuels and related materials (e.g. petroleum and
gas) in total merchandise exports is typically larger than 2/3 – Algeria, Azerbaijan, Congo, Gabon, Iran,
Nigeria, Oman, Saudi Arabia, Trinidad and Tobago, Turkmenistan, UAE, Venezuela, and Yemen (in italic
countries included in the sample n=94).
9
We address the need of considering an integrated concept of human capital (taking into account of
education as well as of health conditions) by including in many specifications the DPT (Diphtheria,
Pertussis or whooping cough, and Tetanus) immunization rate as a proxy. This variable can also be seen
as a proxy for the quality of the institutional environment. Data is derived from the World Bank World
Development Indicators database.
10
7
faster, but the reform effort does not appear to have a lasting effect on growth. In
contrast, the sign of DP is negative and significant. This implies that countries where
policy complementarities can unfold to a greater extent grow faster. Achieving a higher
level of policy complementarity has therefore a permanent effect on growth rates. Here,
it is the level not the change that affects growth.
These results extend the previous test carried out for transition countries in Braga de
Macedo and Oliveira Martins (2008), where the policy variables displaying a positive
impact on growth were the level of reforms (RL) and changes in complementarity (ΔDP).
These differences illustrate the impact of the sample in a growth regressions’ context. For
the case of transition countries, the idiosyncrasies of the transition process and the way
the reform indicators were constructed by the EBRD implied that at the beginning of
transition the system displayed a low dispersion, as all reform indicators were equally
low for all policy areas. The increase in the reform level had also a massive negative
impact on growth at the beginning of the transition. With a larger sample, it is the
changes in the reform level and the level of policy dispersion that appear to influence
growth in a significant way.
Noteworthy, the results for DP are stronger when higher-income countries (HIC) are
excluded from the sample (Table 7, column 6). The magnitude of the estimated
coefficients is larger in this sub-sample – this is to say that reducing policy dispersion
will result in more growth in the group of non-HIC economies. This suggests that policy
complementarities are not a luxury and the exploitation of policy complementarities is
important in promoting a more efficient allocation of resources for developing countries.
In the HIC group, our policy variables are not able to discriminate (see table 3) – in
general RL is high and policy dispersion is low, so the margin to obtain important
efficiency gains within the production frontier by removing those distortions is already
small. In this group of countries neo-Schumpeterian policies à la Aghion-Howitt (2005)
are to take a relatively more important role to expand the production frontier and those
are not well captured by set of policies considered here.
The coefficients of ΔRL and DP continue to be significant after the inclusion of several
controls. Importantly, these have the expected signs and are in general statistically
significant. We note that the coefficient of macroeconomic stabilisation improves after
controlling for transition – this is because in these ex-socialist economies a market system
(with free prices) was established and therefore demand pressures became fully
measurable, which resulted in important increases in the level of prices. Controlling for
transition is equally adequate in what concerns DP, as those economies moved from
highly coherent (low DP) – yet closed and market-unfriendly – economic systems to freemarket economies, experiencing a trajectory in which policy coherence arguably
decreased in the first phases of the transition right after 1989/90 (that is, policy dispersion
8
grew).11 We also highlight the fact that our measure of the stock of educational human
capital in 1995 – derived from the recent dataset of Lutz et al. (2007) – works well in the
model.
In the last two columns of Table 7 we divided the sample in two parts, according with
the size of the policy dispersion (DP). The threshold is around 18.1-18.3 (countries like
India, Malaysia and South Africa have a DP of 18.52, 18.35, and 18.77, respectively). In
countries where policy dispersion is large, there is not significant effect of increase in
reforms on growth; on the contrary, in those countries where policy dispersion is low
there is a strong association of reforming and growth. This provides an additional
argument for the importance of policy complementarity in developing countries.
To address a possible endogeneity of reforms, we estimated a system of equations using
the Three-stage Least Squares Method (3SLS). We consider a system of three equations.
One explaining GDP per capita growth; the other two have ΔRL as the dependent
variable and DP as dependent variables. In the later, as independent variables we
consider (aside from growth itself) elements that arguably exert a – positive or negative –
influence on the reform intensity observed in any given country, namely government
size, the political stability12, belonging to the EU and the OECD, and the origin of its legal
system13. We also consider population (in logs), assuming ceteris paribus that it is easier to
reform if population is smaller. Table 8 shows that the overall results continue to hold. In
particular, the coefficients of ΔRL and, in particular, DP are greater than in table 6. This
could be expected as the endogeneity bias is probably downward (the relationship of
reforms on growth and its reverse having the same sign). The coefficient of growth in the
equation of ΔRL is significant. This suggests that reforms and growth are selfreinforcing. The coefficient of DP is not significant – this is to say that there is a positive
effect of complementarity on growth, but the reverse does seem not to be true.
Interestingly, the initial GDP affects negatively the change in reforms, but reduces
dispersion. Richer countries have lower changes in the reform level, but they are more
coherent.
Policy dispersion decreased thereafter in new EU member states, as Braga de Macedo and Oliveira
Martins (2008) describe and our dataset confirms. See Figure 4 for Latvia and Lithuania. Figures for other
countries are available upon request.
11
Data on political stability comes from the Worldwide Governance Indicators of the World Bank. Origin
of the legal system is from Djankov et al (2007) and was completed with La Porta et al (2007) and the CIA
Factbook. Corruption (freedom from) is a Heritage indicator.
12
According to La Porta et al (2007), legal origins are closely related to the types of capitalism (more
market-focused Anglo-Saxon capitalism vs. the more state-centered capitalism of Continental Europe), and
differences in legal rules and regulations are accounted for to a significant extent by legal origins. In table 8
coefficients of the legal origin dummies are not significant, in what could be probably a sign – and here we
follow La Porta et al (2007: 65) – of the harmonizing forces towards (good) policy practises that
globalisation ‚imposes‛ to (competing) countries.
13
9
Some of the results for the additional control variables deserve to be noted. For example,
ceteris paribus, a smaller government increases growth, but its indirect effect through
reforms is negative as it increases policy dispersion and reduces the change in reforms.
Landlocked counties have lower change and less coherent reforms. The political stability
reduces the dispersion of reforms, as it could be intuitively expected. Larger countries,
reform less, but do it in a more coherent way.
4.2 Panel data estimates
In the previous section the emphasis is on cross-country variation of average growth
rates. Panel data will allow us to shed some light on how the relation between policy
indicators ΔRL and DP and economic growth over time during the period 1994 and 2006
(Table 9).
We first estimated the pooled model (columns 1 and 2). The results are comparable to
the cross-section of average growth rates, although the coefficient of ΔRL tends to be
larger. The introduction of (country) fixed-effects (column 3) destroys the significance of
the policy dispersion (DP) indicating that this effect is driven mainly by the cross-section
variance. We also estimated a two-way fixed-effect model with time dummies, which
produced the same results (column 4). However, estimating fixed-effect regressions is
not without serious limitations with relatively few years (only 12) compared with the
number of countries (104). Furthermore, in many countries policy indicators are only
available for recent years and there are time-invariant variables that vary widely
between countries and are crucial to explain growth (e.g. human capital and initial GDP
per capita).14
Consequently, we tested the simple random-effects model (column 5). The coefficients
both for ΔRL and DP are significant, but, according to the Hausman test the random
effect model is rejected. In the next model, we combined random effects with timedummies (column 6). In this case, the Hausman test indicates we can accept the
hypothesis that there is no significant correlation between the unobserved countryspecific random effects and the regressors. Therefore, the random-effect model being
more powerful and parsimonious seems a more appropriate framework in this context.
The coefficient on reform change and policy dispersion continues to be significant for the
subsample of lower-income countries (column 7).
14
When the coefficient of any time-invariant regressor is absorbed into the individual-specific effect, the
coefficients of time-varying regressors are estimable, but these estimates may be very imprecise if most of
the variation in a regressor is cross-sectional rather than over time. Prediction of the conditional mean is
not possible. Instead, only changes in the conditional mean caused by changes in time-varying regressors
can be predicted.
10
5. Conclusions
This paper relates growth performance in the period 1994-2006 to reforming and the
complementarity of structural reforms. Based on broad structural reform indicators
compiled by the Heritage Foundation’s Index of Economic Freedom and World Bank
data, we consider six areas – business regulations, trade openness, free flow of capital,
openness of the banking and financial system, protection of private property rights and
infrastructure – to compute two policy indicators: the reform level (RL) and the policy
dispersion (DP). The later is given by a time-averaged standard deviation of individual
reform indicators, while the former is a simple average of those six areas; a direct
measure of the reform ‚effort‛ is given by the variation of RL (i.e. ΔRL) between the
beginning and the end of the period. We also provide qualitative justification for the
existence of pair-wise complementarities among these areas.
Descriptive evidence suggests that stronger growth performances are related to larger
ΔRL and lower policy dispersion (DP) – that is, the countries where the reform process
allowed for the exploitation of policy complementarities. A high level of
complementarity has therefore a permanent effect on the growth rate, whereas only the
change in the reform level increases growth. In other words, the effect of increasing the
level of reforms per se is only transitory.
We then use cross-section estimates and confirm that reforming and policy dispersion
are, respectively, positively and negatively related to GDP per capita growth, given
initial conditions, macroeconomic stabilisation and other controls. Importantly, results
hold after correction of endogeneity. The estimated effect of policy complementarities is
stronger if we remove richer countries of the sample. We also found that there are not
effects of reforming on growth when policy dispersion is high. Finally, the panel data
regressions confirm the main results.
These results show that policy complementarities in structural reforms are to play an
important role in achieving higher sustainable growth rates, especially in developing
countries. Thus exploiting policy interactions should not be seen as a "luxury", which
could only be afforded by rich countries with mature institutions.
To be sure, we assume that growth can be ignited in different ways, namely on focusing
in some areas, as Hausmann et al. (2008) have propose – liberal economic reforms are not
the only path to achieving the goal of creating a full-fledged market economy capable of
sustaining growth. Such reforms are more likely to fall prey to the second-best argument
when there is a tendency for ready-made policy packages and much less is known about
local conditions.
But when starting a reform strategy that deliberately results, in its initial stages, in an
increase of policy dispersion, countries incur a cost. In the beginning, it may be rational
to bear it, as (short-term) gains arising from growth ignition can be larger. However, if a
country persists in that piecemeal approach and does not reform other areas, there will
11
be long-lasting growth losses, as policy interactions will not work and second-best costs
could be larger.
A low-coherence approach will probably result in a lower long-term growth trajectory or
in growth-and-crash experiences of unsustainable growth (e.g. crises) – to disentangle
how piecemeal reforms translate in the medium- and long-term into low and/or
unsustainable growth is a relevant issue with important policy implications, which
constitutes an item for future research.
12
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Research, Vol. 2007, pp. 193-235. See http://www.iiasa.ac.at/Research/POP/edu07/index.html
La Porta, R., F. Lopez-de-Silanes, and A. Shleifer (2007), ‚The Economic Consequences of Legal
Origins‚, NBER Working Paper No. 13608.
Rocha, B. (2007), "At Different Speeds: Recovering from the Asian Crisis", ADB Institute
Discussion Paper No. 60.
Staehr, K. (2005), ‚Economic reforms in transition economies: Complementarity, sequencing and
speed‛, European Journal of Comparative Economics, Vol. 2, No. 2, pp. 177–202.
13
Topkis, D.M. (1998), Supermodularity and Complementarity, Princeton: Princeton University Press
14
Table 1. Recent empirical work on policy complementarities and growth
Subject
Method for
capturing Policy
Complementarities
1990-2000 (only year
2000 for level
regressions); 98 (108))
Complementarity
between trade openness
and regulation (labour
and business entry
regulations)
Interaction term in
(cross-country) level and
growth regressions
Chang, Kaltani and Loayza
(2005)
1960-2000;
(5-year periods; 82)
Complementarities
between trade openness
and other policies
Interaction coefficients
in growth regressions
Staehr (2005)
1989-2001; 25
Growth in transition
Principal components in
growth regressions
Dennis (2006)
2 countries (Morocco
and Tunisia)
Calderón and Fuentes (2006)
1970-2000
(5-year periods); 78
Paper
Bolaky and Freund (2004)
Braga de Macedo and
Oliveira Martins (2008)
Rocha (2007)
Sample
(years; countries)
Complementarity
between trade openness
and labour market
flexibility
Complementarities
between trade and
financial openness and
the quality of
institutions
Conclusions
Increased trade does not stimulate growth in economies with
high regulation – trade may even hamper growth in those
with excessive regulation.
More trade openness accelerates economic growth when the
investment in human capital is stronger, financial markets are
deeper, public infrastructure is more abundant, governance is
better, labour market flexibility is higher, and firm-entry is
easier.
Broad-based reforms are good for output growth (but so is a
policy of liberalisation and small-scale privatisation without
structural reforms. Conversely, large-scale privatisation
without adjoining reforms, market opening without
supporting reforms and bank liberalisation without enterprise
restructuring affect growth negatively.
Simulation using GTAP
model
The gains of liberalising the trade regime are significantly
higher under the flexible market scenario: three times for
Morocco, six times for Tunisia.
Interaction coefficients
in growth regressions
The impact of increased financial openness becomes positive
for higher levels of institutional quality (nonlinear effect).
Trade openness has a larger impact on growth when
institutional quality is higher (monotonic relationship).
1989-2004; 27
Growth in transition
Hirschmann-Herfindhal
(reciprocal of) used in
growth regressions
1995-97-00-02-03;
4 (Korea, Thailand,
Malaysia and
Indonesia)
Recovery after the Asian
crisis
Hirschmann-Herfindhal
(reciprocal of); policy
groupings
Reform level and the change in reform complementarity are
positively related to output growth. The former provides a
long-run target for reforms, while the latter provides guidance
on the conduct of the transition process.
‚Orthodox‛ policies must be complemented with other
policies (e.g. unemployment benefits and good exit
mechanisms). The complementarity indicator and the reform
level indicator adjusted for complementarity are related to
better immediate reactions and faster recoveries.
15
Table 2. Matrix positive policy linkages for the six reform areas
Linkage from
lines to columns
Business
regulations
Business regulations
-
Trade openness
Improved entry-exit
mechanisms facilitate
trade-induced resource
allocation
Free flow of capital
(Investment)
Banking and financial
system
Property rights
Infrastructure (ICT,
transport, energy)
Enhances investment e.g. FDI
(increased attractiveness);
increases the return of this
investment (reduction of
costs)
Improved entry-exit
mechanisms enhance better
intermediation (e.g. better
selection of investment
projects); easier to create and
run financial services firms
(more competition and
efficiency in the sector)
Enhances investment and entrepreneurship;
simple and clear regulations gives less space to
corruption and favours effective protection of
private property
Effect of good infrastructure
increases when firms can
operate in a more flexible
environment;
easier to open and run
infrastructure-related firms
Increases the potential
profitability of investment
(larger markets);
more trade-related
investment projects
Increases the scope of (viable)
investment projects;
easier imports of
technological equips. (favours
financial innovation and
banking efficiency)
Protection of property rights favours
investment and this is enhanced by easier
imports of investment goods (e.g. technologyintensive goods); eased imports of technologic
equips. for the court system; low and clear
NTBs reduce the probability of cases in courts
and eases courts’ work
Stimulates demand for (good)
infrastructure and logistics;
eases importation of
technological equips. and other
capital goods
Increased supply of funds;
increased competition
between domestic and foreign
banks improves credit
conditions and financial
intermediation
Protection of property rights favours
investment and this is enhanced by increased
supply of funding
Favours investment in the sector
(namely infrastructure-oriented
FDI);
more competition among
domestic and foreign firms (e.g.
ICT)
The effect of better property rights protection
(e.g. more firm creation) is more important
when there is a better financial intermediation
Favours (viable) investment in
the sector (improved credit
conditions, project finance, etc.)
Favours investment in the sector
(e.g. due to low risk of
expropriation)
Trade
openness
Increases the scope of
resource allocation;
more (trade-related) business
opportunities materialize
Free flow of
capital
(Investment)
More business opportunities
(e.g. FDI projects) materialize;
more competition among
domestic- and foreignowned/financed firms
banking and
financial
system
More (viable) business
opportunities (e.g. more
firms) materialize;
support of hard-budget
constraints
Financing of (viable) export
projects
Good financial intermediation
improves allocation of (more
available) capital;
more (viable) investment
opportunities materialize (e.g.
domestic suppliers to FDI
projects)
Property
rights
Incentives from lower and
better regulations are
enhanced by effective
property rights protection
(e.g. firm creation);
regulations are trustworthy
(and courts solve problems in
a transparent and quick way)
There are not extra costs to
tariffs (and NTBs)
associated with corruption
or inefficient courts (e.g.
delays)
Protection of investors’ rights
is important to attract capital
inflows
Enhances demand for credit;
effective creditor protection;
favours investment in the
financial sector (e.g. FDI)
-
Infrastructure
(ICT,
transport,
energy)
Enhances entry mechanisms
(reduction of costs); more
business opportunities
Increases attraction of
investment (e.g. FDI)
Development of capital
markets (new market
segments);
technology favours financial
innovation
Protection of property rights favours
investment and this is enhanced by better
infrastructure; favours court efficiency and law
enforcement (as well as broadening of the
knowledge of laws among economic agents).
Intra-industry trade
requires complementarity
between traded goods and
factor inputs; development
of trade (e.g. due to easier
international payments);
favours export-oriented FDI
Easy transportation of
traded goods
-
-
-
Table 3. Descriptive statistics
Obs.
Mean
Std. Dev
Min
Max
1. n =130 (complete sample)
Growth GDPpc (93-06)
130
2.540479
1.976704
-1.750668
9.593532
RL (avg 94-06)
130
52.03625
15.4599
23.96901
90.77924
DP (avg 94-06)
130
17.36944
4.497032
4.591103
24.70571
ΔRL (94/5 - 05/6)
116
3.107447
7.384614
-15.43958
25.80893
GDPpc93 (log)
130
7.425229
1.575565
4.674188
10.45244
GDPpc93
130
5205.16
7867.67
107.1455
34628.78
Monet. Stabi. (avg 94-06)
130
71.64188
12.64085
19.0729
90.35119
Mean yr. school age 25+ in 95
105
6.881905
3.067534
.7
12.8
Imm. rate DPT (avg 94-06)
129
82.88275
16.47611
21.30769
99
Gov. size (avg. 94-06)
130
67.81625
22.47176
1.051746
96.06029
Growth GDPpc (93-06)
94
2.578733
1.867435
-1.750668
9.593532
1.a) n =94
RL (avg 94-06)
94
54.70526
15.27408
26.25125
83.10872
DP (avg 94-06)
94
16.80782
4.776234
6.709916
24.70571
ΔRL (94/5 - 05/6)
94
3.395244
6.851461
-14.83538
25.80893
GDPpc93 (log)
94
7.615562
1.605558
4.674188
10.45244
GDPpc93
94
6052.502
8456.288
107.1455
34628.78
Monet. Stabi. (avg 94-06)
94
73.54722
11.27059
41.47557
90.35119
Mean yr. school age 25+ in 95
94
6.923404
3.064624
.7
12.8
Imm. rate DPT (avg 94-06)
94
83.73322
15.67394
30
99
Gov. size (avg. 94-06)
94
66.19392
24.15931
1.051746
96.06029
1.a)i. Countries GDPpc93<14,000
Growth GDPpc (93-06) |
76
2.629021
2.003227
-1.750668
9.593532
RL (avg 94-06) |
76
49.41156
11.63417
26.25125
81.63174
DP (avg 94-06) |
76
18.34547
3.781887
6.709916
24.70571
ΔRL (94/5 - 05/6) |
76
3.142961
7.23744
-14.83538
25.80893
GDPpc93 (log) |
76
7.060542
1.246225
4.674188
9.379306
GDPpc93 |
76
2330.894
2827.192
107.1455
11840.79
Monet. Stabi. (avg 94-06) |
76
70.61144
10.52588
41.47557
88.58868
Mean yr. school age 25+ in 95 |
76
6.096053
2.717766
.7
12.8
Imm. rate DPT (avg 94-06) |
76
81.66093
16.64417
30
99
Gov. size (avg. 94-06) |
76
73.40265
17.79286
18.97905
96.06029
1.a)ii. Countries GDPpc93>14,000
Growth GDPpc (93-06)
18
2.366405
1.146543
.9565369
5.88635
RL (avg 94-06)
18
77.05643
4.988251
66.66563
83.10872
DP (avg 94-06)
18
10.31552
2.549105
6.745528
15.24818
ΔRL (94/5 - 05/6)
18
4.460437
4.922059
-4.85601
14.61843
GDPpc93 (log)
18
9.958979
.242032
9.59381
10.45244
GDPpc93
18
21765.96
5707.522
14673.68
34628.78
Monet. Stabi. (avg 94-06)
18
85.94273
2.211926
82.67101
90.35119
Mean yr. school age 25+ in 95
18
10.41667
1.676919
7.3
12.6
Imm. rate DPT (avg 94-06)
18
92.48291
4.639966
82.53846
98.61539
Gov. size (avg. 94-06)
18
35.75707
24.14774
1.051746
91.09914
17
Table 4. Top and Bottom Reformers (1994-2006)
1. Top
ΔRL
DP
GrowthGDPpc
Lithuania
25.80893
14.90712
5.388146
Latvia
17.32871
13.93727
7.352433
Slovenia
16.39428
13.06846
3.942798
Nicaragua
15.73359
22.95574
2.429393
Estonia
15.19876
12.94249
7.304047
15.56
5.28
ΔRL
DP
GrowthGDPpc
Guinea
-14.83538
22.03294
1.346778
Russia
-10.22993
15.23484
2.608697
Argentina
-9.398083
17.72505
1.690825
Turkey
-8.195862
14.95749
2.284287
Paraguay
-7.976151
21.50127
-.1567812
18.29
1.55
average
2. Bottom
average
Note: n=95 (n=94 + Hong Kong).
Table 5. Top and Bottom Policy Dispersion (1994-2006)
1. Top
ΔRL
DP
GrowthGDPpc
Uganda
-3.193069
24.70571
3.190732
Bolivia
.2009506
23.61236
1.446205
Madagascar
14.69335
23.27308
.0343205
Mali
-2.98819
22.98728
2.470555
Nicaragua
15.73359
22.95574
2.429393
average
4.89
1.91
2. Bottom
ΔRL
DP
GrowthGDPpc
Hong Kong
5.668556
4.591103
3.045984
Spain
8.497734
6.709916
2.57883
Italy
-.0726013
6.745528
1.259798
UK
5.910286
7.425909
2.60656
US
3.108955
8.274014
2.131783
average
3. Bottom
4.62
2.32
ΔRL
DP
GrowthGDPpc
Spain
8.497734
6.709916
2.57883
Hungary
10.33104
9.932064
4.076752
Portugal
11.38499
10.17849
1.90343
New Zealand
2.175804
10.44017
1.948825
Korea
Bulgaria
1.762581
4.112518
11.01573
11.8401
4.488919
3.383185
Croatia
7.467712
12.77801
4.870755
Estonia
15.19876
12.94249
7.304047
Slovenia
16.39428
13.06846
3.942798
Latvia
17.32871
13.93727
7.352433
(GDPpc93<14,000)
average
9.47
4.18
Note: n=95 (n=94 + Hong Kong).
19
Table 6. Pair-wise correlations
1. n =94
Growth GDPpc
RL
DP
ΔRL
(93-06)
(avg 94-06)
(avg 94-06)
(94/5 - 05/6)
GDPpc93 (log)
RL (avg 94-06)
0.1415
DP (avg 94-06)
-0.2067
-0.7460
ΔRL (94/5 - 05/6)
0.3766
0.1796
-0.2714
GDPpc93 (log)
0.0035
0.8592
-0.7733
0.0735
Monetary Stability (avg 94-06)
-0.0506
0.5158
-0.3162
-0.0134
0.4898
Mean yr. schooling age 25+ in 95
0.2886
0.7819
-0.6341
0.2437
0.7705
Monet. Stabi.
(avg 94-06)
Mean yr.
School. age 25+
(95)
Imm. rate DPT
(avg 94-06)
0.2461
Imm. rate DPT (avg 94-06)
0.3571
0.5238
-0.4601
0.1410
0.5679
0.0703
0.6419
(small) Gov. size (avg. 94-06)
-0.1150
-0.6736
0.7313
-0.2720
-0.6522
-0.2374
-0.6416
Growth GDPpc
RL
DP
ΔRL
(93-06)
(avg 94-06)
(avg 94-06)
(94/5 - 05/6)
-0.4542
2. n =76 (GDPpc93 < 14,000)
GDPpc93 (log)
Monet. Stabi.
(avg 94-06)
Mean yr.
School. age 25+
(95)
RL (avg 94-06)
0.2519
DP (avg 94-06)
-0.3515
-0.5189
ΔRL (94/5 - 05/6)
0.3692
0.1629
-0.2819
GDPpc93 (log)
0.0781
0.7328
-0.6112
0.0410
Monetary Stability (avg 94-06)
-0.0219
0.2229
0.0645
-0.0623
0.1768
Mean yr. schooling age 25+ in 95
0.4304
0.7011
-0.4902
0.2542
0.6591
-0.0855
Imm. rate DPT (avg 94-06)
0.4100
0.5025
-0.4131
0.1358
0.5604
-0.0948
0.6504
(small) Gov. size (avg. 94-06)
-0.2784
-0.5466
0.6580
-0.2857
-0.4632
0.1560
-0.5196
Imm. rate DPT
(avg 94-06)
-0.4308
Table 7. Cross-section regressions
Dependent variable
GDP pc growth 1993-2006
Reform level (RL)
(2)
all
(3)
all
(4)
all
(5)
all
(6)
GDP(93)<$14,000
(7)
DP<18.2
(8)
DP>18.2
0.0504**
(0.0250)
-0.145***
(0.0520)
0.0676***
(0.0250)
-0.134***
(0.0458)
0.0529**
(0.0223)
-0.112**
(0.0501)
0.0517**
(0.0222)
-0.147**
(0.0594)
0.0389*
(0.0219)
-0.170**
(0.0746)
0.1000***
(0.0295)
0.0210
(0.0264)
-1.090***
(0.258)
0.0263
(0.0173)
0.263***
(0.0924)
0.0481***
(0.0121)
-1.023***
(0.232)
0.0197
(0.0168)
0.236**
(0.0917)
0.0326**
(0.0153)
-0.899
(0.640)
1.017**
(0.420)
-0.912***
(0.253)
0.0431**
(0.0204)
0.152*
(0.0796)
0.0297**
(0.0145)
-0.862
(0.588)
1.153***
(0.400)
1.694***
(0.502)
0.562
(0.499)
-0.927***
-0.984***
(0.256)
(0.300)
Monet. stabilisation
0.0463**
0.0493**
(0.0216)
(0.0226)
Education level
0.177*
0.195*
(0.0888)
(0.106)
Immunization rate
0.0316**
0.0321**
(0.0145)
(0.0150)
Dummy for Africa
-0.822
-0.800
(0.576)
(0.608)
Dummy for Asia
0.952**
0.893*
(0.399)
(0.465)
Dummy Transition
1.787***
1.841***
(0.500)
(0.688)
Dummy Fuel exporters
0.652
0.693
(0.541)
(0.608)
Govern. Size (smaller)
0.0125
0.0147
(0.00806)
(0.0145)
Constant
6.001***
5.363***
6.610***
4.247*
3.568
3.885
(2.178)
(1.994)
(2.187)
(2.428)
(2.401)
(2.612)
Observations
94
94
94
94
94
76
R2
0.420
0.404
0.476
0.536
0.545
0.567
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. All regressions were carried out by OLS.
-0.628*
(0.369)
0.0413
(0.0254)
0.0530
(0.115)
-0.0510
(0.0440)
0
(0)
1.449*
(0.801)
1.469**
(0.632)
0
(0)
0.00494
(0.00802)
8.468*
(4.427)
46
0.625
-0.665**
(0.246)
0.0470*
(0.0278)
0.0517
(0.119)
0.0359**
(0.0141)
-0.284
(0.534)
1.131**
(0.509)
6.960***
(0.598)
0.322
(0.735)
-0.0206
(0.0170)
1.351
(2.471)
48
0.651
Change reform level (ΔRL)
Policy dispersion (DP)
Change Policy dispersion (ΔDP)
Log GDP pc 1993
(1)
all
-0.00147
(0.0316)
0.0536**
(0.0236)
-0.160**
(0.0665)
-0.0514
(0.0378)
-1.179***
(0.260)
0.0273
(0.0217)
0.294***
(0.108)
0.0472***
(0.0123)
21
Table 8. Simultaneous equation estimates
Dependent variable:
Change reform level (ΔRL)
Policy dispersion (DP)
Log GDP pc 1993
Monet. stabilisation
Education level
Immunization rate
Dummy for Africa
Dummy for Asia
Dummy Transition
Dummy Fuel exporters
Govern. Size (smaller)
(1)
(2)
GDP pc
Change
growth
Reform
1993-2006 Level (ΔRL)
0.0887*
(0.0534)
-0.275**
(0.127)
-1.069**
(0.422)
0.0366
(0.0378)
0.187**
(0.0869)
0.0213*
(0.0118)
-1.100*
(0.581)
0.466
(0.661)
1.586**
(0.650)
0.927
(0.717)
0.0274**
(0.0123)
-2.405***
(0.858)
(3)
Policy
dispersion
(DP)
-0.942***
(0.363)
-0.0683*
0.0552***
(0.0407)
(0.0175)
1.464**
-0.238
Growth rate of GDP pc
(0.611)
(0.261)
Landlocked countries
-4.002**
0.810
(1.776)
(0.769)
Legal origin English
-0.00212
-1.406
(2.409)
(1.092)
Legal origin French
2.042
-0.645
(2.499)
(1.123)
Legal origin German
0.669
-1.558
(2.373)
(1.062)
Corruption Index (less)
0.0519
0.0109
(0.0637)
(0.0281)
Political stability
2.053
-1.329**
(1.353)
(0.603)
Log of population
-0.968**
-0.457**
(0.492)
(0.220)
Dummy OECD
1.437
-2.076**
(2.155)
(0.990)
Constant
7.354*
22.18***
23.24***
(3.895)
(7.212)
(3.025)
Observations
94
94
94
Pseudo R2
0.476
0.353
0.775
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. All
regressions were carried out by Three-stage Least square method.
Table 9. Panel regressions
Dependent variable
GDP pc growth
Reform level (RL)
Change reform level (ΔRL)
Policy dispersion (DP)
Change Policy dispersion (ΔDP)
Log GDP pc 1993
Monet. stabilisation
Education level
Immunization rate
Constant
(1)
Pooled
OLS all
-0.00448
(0.014)
0.147***
(0.046)
-0.133***
(0.026)
-0.0694
(0.057)
-1.132***
(0.14)
0.0213*
(0.012)
0.372***
(0.059)
0.0315***
(0.0087)
6.925***
(1.40)
1175
0.12
104
(2)
Pooled
OLS all
(3)
FE all
(4)
FE & Time effects
all
(5)
RE all
(6)
(7)
RE & Time effects RE & Time effects
all
GDP(93)<$14,000
0.132***
(0.046)
-0.138***
(0.025)
0.102***
(0.035)
-0.0488
(0.039)
0.0856**
(0.034)
-0.0437
(0.041)
0.112***
(0.034)
-0.0932***
(0.033)
0.101***
(0.034)
-0.0976***
(0.033)
0.0993***
(0.038)
-0.109***
(0.038)
-1.161***
(0.12)
0.0205*
(0.011)
0.362***
(0.054)
0.0316***
(0.0087)
7.129***
(1.40)
1175
0.12
104
--
--
0.0360***
(0.0094)
--
0.0453***
(0.010)
--
0.0339**
(0.015)
-1.957
(1.54)
1175
0.03
104
0.00678
(0.015)
0.128
(1.58)
1175
0.11
104
-1.154***
(0.19)
0.0265***
(0.0083)
0.403***
(0.095)
0.0334***
(0.010)
5.469***
(1.61)
1175
.
104
-1.187***
(0.18)
0.0303***
(0.0089)
0.447***
(0.086)
0.0229**
(0.0099)
7.145***
(1.52)
1175
.
104
-1.048***
(0.21)
0.0269***
(0.0099)
0.521***
(0.096)
0.0138
(0.011)
7.232***
(1.72)
968
.
86
Observations
R2
Number of countries
Hausman test:
Chi2(15)
8.27
2.39
1.99
2
Pr >Chi (15)
0.08
0.99
1.00
Notes: FE: fixed-effects; RE: Random-effects. Robust standard errors in parentheses; significance: *** p<0.01, ** p<0.05, * p<0.1. The coefficients of the time
dummies are not reported.
23
100
Figure 1. Reform Level (average 1994-2006) vs. GDP per capita in 1993 (logs)
Singapore
UK Denmark
NewZealand
Netherlands
Switzerland
Ireland
USA
Sweden
Australia
Finland
Estonia
Belgium
Austria
Germany
CanadaNorway
Czech
Israel
Spain Italy
Korea
Japan
Portugal
Chile
France
Hungary
Trinidad
Greece
Lithuania
Latvia
Slovenia
Slovakia
Jamaica
Uruguay
UAE
ElSalvador
Turkey
Poland
Panama
Mauritius
Jordan Botswana
CostaRica Argentina
Colombia
SouthAfrica
Thailand
Namibia
Bulgaria
Armenia
MalaysiaMexicoOman
BoliviaSwaziland
Peru
Paraguay Croatia
Mongolia
SriLanka Morocco
Lebanon
Zambia
Brazil
Moldova
Saudi
Guatemala
Uganda
Tunisia
Romania
Albania
Nicaragua
Philippines
Ecuador
Mali
Kenya
Russia
Honduras
Senegal
Venezuela
Algeria
Gabon
GeorgiaIndonesia
Ghana
Malawi
Kyrgyz
Tanzania
Pakistan Ukraine
Gambia
Lesotho
Burkina
Benin
Madagascar
Egypt
Dominican
Cambodia
Belarus
Guinea
Ivory
Mozambique
Kazakhstan
Nigeria
Mauritania
Yemen
Chad
China
Cameroon
Niger
Azerbaijan
Nepal
SierraLeone
Tajikistan
India
Ethiopia
TogoBangladesh
CongoR
Rwanda Haiti Turkmenistan
Syria
Uzbekistan
Vietnam
Iran
GuineaBissau
Laos
20
40
60
80
HongKong
5
6
7
8
Log GDP pc 1993
9
10
Uganda
Bolivia
Madagascar
Mali
Nicaragua
Panama
SriLanka
Namibia
Swaziland
Zambia
ElSalvador
Guinea
Haiti
Pakistan
Burkina
Tajikistan
Peru
Mongolia
Indonesia
Laos
Paraguay
Mozambique
Morocco
Tunisia Botswana
Cambodia
Oman
BeninArmenia Albania
Turkmenistan
Algeria
Thailand
Mauritania
Iran
Tanzania
Ivory
Guatemala
Yemen
Kenya
Malawi
Chad
Lesotho
Honduras Colombia
Kyrgyz
Saudi
Senegal
Nepal
SierraLeone
Ghana
Gambia
Chile
GuineaBissau
Moldova Philippines
Niger
Nigeria
Mexico
Georgia
Jordan
SouthAfrica
Gabon
India
Ecuador
Trinidad
UAE
Syria
Malaysia
Jamaica
Ethiopia TogoRwanda
Cameroon
Kazakhstan
CostaRica
Azerbaijan
Romania
Lebanon Argentina
Egypt
Uruguay
Ukraine
Uzbekistan
Venezuela
CongoR
Vietnam
Bangladesh
Belarus
Dominican
Poland
Norway
Russia
China
Mauritius
Turkey
Lithuania
Japan
Brazil
Slovakia
Greece
Czech
Latvia
Canada
Slovenia
Estonia
Croatia
Germany
Israel
Singapore
France
Bulgaria
Finland
Korea
NewZealand
Portugal
Sweden
Hungary
Belgium
Australia
Austria Switzerland
Netherlands
Ireland
Denmark
USA
UK
Spain Italy
5
10
15
20
25
Figure 2. Policy Dispersion (average 1994-2006) vs. GDP per capita in 1993 (logs)
HongKong
5
6
7
8
Log GDP pc 1993
9
10
25
30
Figure 3a. Variation in the Reform Level (1994-2006) vs. GDP per capita in 1993 (logs)
-10
0
10
20
Lithuania
Moldova
Latvia
Slovenia
Estonia
Ireland
Botswana
Jamaica
Ethiopia
Portugal Finland
Belgium
Hungary
Sweden
Chile
Dominican
Mexico
Spain
Trinidad
Vietnam
Romania
Croatia
India
GuatemalaSlovakia
Denmark
Lesotho
Poland Uruguay
Haiti
Honduras
UK
Ukraine ElSalvador
HongKong
Laos
Netherlands
Germany
Mozambique
KenyaIvory
Brazil
Australia Switzerland
Iran
Bulgaria SouthAfrica
Egypt
Senegal
Philippines
USA
ChinaAlbania
Nepal Benin Mongolia
France
NewZealand
AustriaNorway
Burkina
Czech Korea
Niger
Peru
Bolivia
Malawi Tanzania
Cameroon
Italy
CostaRica
Israel
Canada
Syria Tunisia
Algeria
Gabon
Nigeria
GreeceSingapore
Ecuador
Panama
Morocco
Indonesia
Yemen
Jordan
SriLanka
Mali
Uganda
Swaziland
CongoRThailand
Bangladesh
Zambia
Oman
Colombia
Japan
Pakistan
Saudi
Malaysia
Paraguay Turkey
Lebanon
Argentina
UAE
Russia
Nicaragua
Georgia
Madagascar
Armenia
Mauritania
Azerbaijan
Ghana
Belarus
Venezuela
-20
Guinea
5
6
7
8
Log GDP pc 1993
9
10
10
Figure 3b. Variation in the Policy Dispersion (1994-2006) vs. GDP per capita in 1993 (logs)
Armenia
Belarus
Croatia
Norway
Canada
Germany
LesothoNicaragua
Madagascar
Vietnam
Bangladesh
Azerbaijan
NewZealand
Ukraine
Georgia
Nepal
Lebanon
Kenya
Honduras
USA
Italy
Tanzania
Mongolia
Iran
Bulgaria
Philippines
Singapore
Switzerland
Uruguay
Latvia
Uganda
Japan
Indonesia Dominican
Malawi
Nigeria
Algeria
France
GreeceAustralia
Bolivia Guatemala
CongoR
Portugal
Niger
Belgium
Denmark
Ghana
Mauritania
Burkina
Netherlands
Cameroon
Mozambique
Zambia
Haiti
China
Moldova
CostaRica Korea Spain
SriLanka
Austria
Lithuania
Senegal
Ivory
Peru
Morocco
Romania
Finland
Swaziland
Ecuador
Panama
Slovenia
Egypt
Brazil
Mali
Pakistan
Tunisia Poland
Israel UAE
Mexico
Yemen
Albania
Russia
Malaysia
Benin
Jamaica
Guinea
Turkey
Botswana
Sweden
India
Slovakia
Colombia
Syria ThailandSouthAfrica
Laos
HongKong
Venezuela
UK
ElSalvador
Argentina Ireland
Paraguay
Oman
Chile
Hungary
Jordan
Czech
Estonia
Gabon
Saudi
-20
-10
0
Ethiopia
Trinidad
5
6
7
8
Log GDP pc 1993
9
10
27
Figures 4. Policy Dispersion and Reform Level in selected countries
20
China
18
1994
1995
1996
1997
16
1998
2006
1999
14
2005
2000
2001
2002
2004
12
2003
34
36
38
40
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 8.8
24
India
22
1994
20
1996
1997
1995
18
2001
2002
2003
1999
1998
2000
2004
16
2005
2006
30
32
34
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 5.0
36
38
Figures 4. Policy Dispersion and Reform Level (cont’d)
Russia
18
20
1994
1995
16
1998 1997
1996
2001
14
2005
2000
1999
2003
2002
2004
40
45
50
55
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 2.6
Brazil
18
1996
1997
1998
16
1994
1995
1999
14
2000
2003
2004
12
2001
2002
2005
10
2006
44
46
48
Reform Level (RL)
50
52
GDP per capita growth rate (average 94-06) = 1.4
29
Figures 4. Policy Dispersion and Reform Level (cont’d)
20
Turkey
18
1995
1994
1996
16
1997
1998
1999
2004
14
2000
2005
2001
12
2006
10
2003
2002
50
55
60
65
Reform Level (RL)
25
GDP per capita growth rate (average 94-06) = 2.3
1994
1995
Chile
1996
1997
1998
1999
20
2000
2001
15
2002
2003
2005 2004
2006
62
64
66
68
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 3.5
70
72
Figures 4. Policy Dispersion and Reform Level (cont’d)
18
Latvia
16
1996
1997
1998
1999
2000
14
2001
2002
12
2003
2004
2005
2006
10
1995
50
55
60
Reform Level (RL)
65
70
GDP per capita growth rate (average 94-06) = 7.4
17
Lithuania
16
19962000
1997
1999
1998
15
1995
2001
2003
14
2004
13
2005
2002
12
2006
50
55
60
65
Reform Level (RL)
70
75
GDP per capita growth rate (average 94-06) = 5.4
31
Figures 4. Policy Dispersion and Reform Level (cont’d)
The Philippines
1997
21
22
1996
1999
1995
1998
20
2004
2005
19
2006
2003
2002
2000
18
1994
2001
40
42
44
46
Reform Level (RL)
48
50
GDP per capita growth rate (average 94-06) = 2.2
Nicaragua
26
2002
24
2001
2003
2004
2005
1997
1998
2000
2006
20
22
1995
1996
1999
18
1994
35
40
45
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 2.4
50
55
Figures 4. Policy Dispersion and Reform Level (cont’d)
Botswana
25
1996
1995
1997
1998
1994
1999
2004
2002
2003
20
2000
2001
2005
15
2006
50
55
Reform Level (RL)
60
65
GDP per capita growth rate (average 94-06) = 4.3
24
Guinea (Conakry)
1997
1996
2002
1994
2004
1998
1999
2000
22
2003
20
1995
18
2001
16
2005
30
35
40
Reform Level (RL)
45
50
GDP per capita growth rate (average 94-06) = 1.3
33
Figures 4. Policy Dispersion and Reform Level (cont’d)
16
France
14
2005
1996
1997
19951998
1994
2006
12
2002
2001
10
2000
1999
2004
8
2003
65
66
67
Reform Level (RL)
68
69
GDP per capita growth rate (average 94-06) = 1.7
UK
1997
1995
1996
1998
8
10
12
1994
2000
2001
2003 2002
6
1999
2005
4
2004
2006
80
82
84
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 2.6
86
88
Figures 4. Policy Dispersion and Reform Level (cont’d)
13
Portugal
1994
12
1995
1996
11
2006
10
1997
2002
2003
2005
2001
2004
2000
9
1998
8
1999
60
65
70
75
Reform Level (RL)
GDP per capita growth rate (average 94-06) = 1.9
Ireland
14
1994
1995
1996
12
1997
10
1998
8
1999
2005
6
2004
2000
2006
4
2003
2002
2001
70
75
80
Reform Level (RL)
85
90
GDP per capita growth rate (average 94-06) = 5.9
35
APPENDIX – Data sources and definitions
variable name*
(original database)
Description (original database)
Sources (in order of priority)**:
1. Trade
(Heritage)
Trade Freedom score is based on two inputs: the trade-weighted average tariff rate (weights for each tariff
are based on the share of imports for each good) and non-tariff barriers (NTBs). The higher the rate, the
worse (lower) the score. This is given by TF = (50-Tariff)/50-NTB. The NTB penalty can be of 5, 10, 15 or 20
percentage points. The NTBs considered are: quantity restrictions (e.g. import quotas or export limitations),
price restrictions (e.g. antidumping or countervailing duties), regulatory restrictions (licensing; domestic
content and mixing requirements; SPSS; safety and industrial standards regulations; packaging, labelling,
and trademark regulations; advertising and media regulations), investment restrictions (exchange and
other financial controls), customs restrictions (advance deposit requirements; customs valuation
procedures; customs classification procedures; customs clearance procedures), direct government
intervention (e.g. subsidies and other aids, government procurement policies or state trading).
2. Business Regulations
(Heritage)
Business Freedom is a quantitative measure of the ability to start, operate, and close a business that
represents the overall burden as well as the efficiency of government regulations. The score is based on ten
components, all weighted equally, based on objective data from the World Bank’s Doing Business study:
World Bank, Doing Business; Economist Intelligence
Starting a business—procedures (number), time (days), cost (% of income per capita), and minimum capital
Unit, Country Report and Country Profile; U.S.
(% of income per capita); Obtaining a license—procedures (number), time (days), and cost (% of income
Department of Commerce, Country Commercial Guide.
per capita); Closing a business—time (years), cost (% of estate), and recovery rate (cents on the dollar). This
method for business freedom is recent; a subjective assessment was used in previous issues of the Index of
Economic Freedom [data relative to years 1994-2004 in our dataset]
3. Free flow of capital
(Heritage)
This factor scrutinizes each country’s policies toward foreign investment, as well as its policies toward
capital flows internally, in order to determine its overall investment climate. Questions examined include
whether there is a foreign investment (FI) code that defines the country’s investment laws and procedures;
whether the government encourages FI through fair and equitable treatment of investors; whether there
are restrictions on access to foreign exchange; whether foreign firms are treated the same as domestic firms
under the law; whether the government imposes restrictions on payments, transfers, and capital
transactions; and whether specific industries are closed to FI. In a country that receives a 100 score, FI is
encouraged and treated the same as domestic investment, with a simple and transparent FI code and a
professional, efficient bureaucracy. There are no restrictions in sectors related to national security or real
estate. No expropriation is allowed. Both residents and non-residents have access to foreign exchange and
may conduct international payments. Transfers or capital transactions face no restrictions.
World Bank, World Development Indicators and Data on
Trade and Import Barriers: Trends in Average Tariff for
Developing and Industrial Countries 1981-2005; World
Trade Organization, Trade Policy Reviews; Office of the
U.S. Trade Representative, National Trade Estimate
Report on Foreign Trade Barriers; World Bank, Doing
Business; others.
International Monetary Fund, Annual Report on
Exchange Arrangements and Exchange Restrictions; official
government publications of each country; Economist
Intelligence Unit, Country Commerce, Country Profile, and
Country Report; Office of the U.S. Trade Representative,
National Trade Estimate Report on Foreign Trade Barriers;
and U.S. Department of Commerce, Country Commercial
Guide.
variable name*
(original database)
Description (original database)
Sources (in order of priority)**:
4. Banking and Finance
(Heritage)
The financial freedom factor measures the relative openness of each country’s banking and financial
system. The authors score this factor by determining the extent of government regulation of financial
services; the extent of state intervention in banks and other financial services; the difficulty of opening and
operating financial services firms (for both domestic and foreign individuals); and government influence
on the allocation of credit.
Economist Intelligence Unit, Country Commerce, Country
Profile, and Country Report; official government
publications of each country; U.S. Department of
Commerce, Country Commercial Guide; others.
5. Property Rights
(Heritage)
This factor scores the degree to which a country’s laws protect private property rights and the degree to
which its government enforces those laws. It also assesses the likelihood that private property will be
expropriated and analyzes the independence of the judiciary, the existence of corruption within the
judiciary, and the ability of individuals and businesses to enforce contracts. The less certain the legal
protection of property, the lower a country’s score; similarly, the greater the chances of government
expropriation of property, the lower a country’s score. In a country that receives a maximum, 100% score,
the court system enforces contracts efficiently and quickly. The justice system punishes those who
unlawfully confiscate private property. There is no corruption or expropriation.
Economist Intelligence Unit, Country Commerce; U.S.
Department of Commerce, Country Commercial Guide;
U.S. Department of State, Country Reports on Human
Rights Practices; and U.S. Department of State,
Investment Climate Statements.
6. Infrastructure index –
authors’ calculations
A simple infrastructure index was computed for the purposes of this article, using (fixed line and mobile)
phone subscribers as a proxy.
World Bank, World Development Indicators.
7. Monetary Stability
(Heritage)
The weighted average inflation rate (WAI) for the most recent three years (weights for t, t-1, and t-2 are
0.665, 0.245, and 0.090, respectively) is the primary input in the equation that that generates the base score
for monetary freedom MF = 100-α√WAI. The α coefficient is set to equal 6.333, which converts a 10 percent
inflation rate into a freedom score of 80.0 and a 2 percent inflation rate into a score of 91.0. There is also a
price controls penalty of up to 20 percentage points subtracted from the base score (in the face of
widespread price controls, the measured inflation has not the same meaning, because the price signal can
no longer equate supply and demand).
International Monetary Fund, International Financial
Statistics On-line; International Monetary Fund,
World Economic Outlook; others.
8. Government size
(Heritage)
Scoring of the government size factor is based on government expenditures as a percentage of GDP. The
following non-linear quadratic cost function is used to calculate the expenditures score
GE = 100- α(Expenditure)2 where Expenditure represents the total amount of government spending at all
levels as a portion of GDP (between 0 and 100), and α is a coefficient to control for variation among scores
(set at 0.03). The minimum component score is zero.
World Bank, World Development Indicators, and Country
at a Glance tables; official government publications of
each country; Economist Intelligence Unit, Country
Report and Country Profile; OECD data (for member
countries); others.
* All the variables are in a 0-100 scale. In italic – variables used to compute policy indicators RL and DP.
**See the 2008 Index of Economic Freedom for more detailed information www.heritage.org/index (except for variable 6)
37