WPS 12032
Working Paper Series
Growth and Convergence in
South–South Integration Areas:
Empirical Evidence
Stefan Sperlich
Yvonne Sperlich
March 2012
Growth and Convergence in South–South
Integration Areas: Empirical Evidence
Stefan Sperlich*
Département des sciences économiques, Université de Genève
Yvonne Sperlich
Faculty for Economic Sciences, Georg-August University Göttingen
Abstract
It is empirically investigated whether South-South-regional integration areas such as the
MERCOSUR help to improve income growth and convergence among members. All
three world regions in question are considered: Latin America, South-East Asia, and
Africa. A comprehensive empirical study is done by different panel data analyses based
on data from at least one period before the beginning of the particular formal integration
processes. The models and methods applied are derived and discussed in detail. The
empirical findings shed some new light on an old discussion: for all regions
membership in South-South agreements shows indeed a positive impact on income
convergence and growth in most of the considered integration areas.
Keywords: South-South Agreements, regional integration, growth and development
economics, income convergence, panel HCA Solow-Model.
JEL code: F15, F43, O43, O11, E13
* Corresponding author: [email protected]. The authors appreciated a lot helpful comments of and
discussion with Marcelo Olarreaga (Université de Genève), Ricardo Mora (Universidad Carlos III de
Madrid), Walter Zucchini (Universität Göttingen, Research center for poverty, equity and growth), and
Inmaculada Martinez-Zarzoso (Universitat Jaume I), Ana Maria Alvarez Herrera (UNCTAD), the
participants of the workshop of the WTO, UNCTAD, UNIGE and the Centre for Trade and Economic
Integration, as well as the participants of the colloquium of the Center for European, Governance and
Economic Development Research, Göttingen.
1
1. Introduction
This paper is intended to contribute to the controversial discussion on the so called SouthSouth agreements regarding income convergence and growth. One may argue that this is an
old dispute, but it is only now that we are provided with data from reasonably long periods of
these areas. In the meanwhile, the discussion has not lost its political urgency as the number
of integration agreements and member states have been uninterruptedly increasing2.
Therefore, we have taken the chance to contribute some empirical evidence. To our
knowledge this is so far the most comprehensive and methodologically rigorous study,
looking at several South-South areas and at both, convergence and growth at once. This is
crucial due to the theory of possible convergence clubs from which directly follows that only
looking at convergence will fall short to assess the success of South-South integration.
While the neoclassical growth theory predicts general convergence in the sense that countries
which are farthest behind would grow faster, several empirical studies showed that in contrast
to that, the correlation between per capita growth and the initial income was close to zero in
world-wide samples. When Baumol (1986) tested the hypothesis of convergence for the
period from 1870 to 1979, his outcomes indicated the existence of different convergence
clubs, so that he claimed that developments would diverge world-wide. This was confirmed
by Prichett (1997). Milanovic (2006) added in his study that evidence for trade-induced
convergence would be weak, and contradicts to the idea that globalization promotes
convergence whereas import protection would hamper it.
Parts of the integration theory assume that the formation of regional integration areas (RIAs
henceforth) promotes the increase of welfare as well as the catching up to regional economic
leaders. At the same time it would stimulate the seemingly unrelated solving of trans-national
2
The WTO has registered more than 150 regional agreements until the end of 2010.
2
problems, like environmental and energy problems, the reduction of extreme poverty (like the
“pact of convergence, stability, growth and solidarity” of WAEMU). It might also support the
extension of bargaining power at multilateral level for developing countries, stability and
peace (but cf. also Limao, 2007). As the title announces, we are interested in the effect of
regional integration in southern areas, concentrating on the goal of per capita income growth
and convergence. Recently, the convergence and growth debate has been re-attracting
increasing attention in the integration theory. This is partly be due to the fact that now the
time periods are of sufficient length for such an evaluation, and partly because empirical
methods to test convergence have became reasonably well developed. To test for the impact
of membership on growth we have chosen a panel model based on the neoclassical augmented
Solow model, going back to Knight, Loayza and Villanueva (1993).
Ben-David (1993, 1996) showed a strong link between the timing of trade liberalization and
income convergence. He pointed out that countries converge most strongly in a transition
period after signing and implementing an agreement of trade liberalization. His studies
corroborated the catching-up hypothesis of neoclassical growth theory that poorer countries
benefit most from free trade and the associated technology transfer. They therefore were often
interpreted as a supporting argument for RIAs. In contrast, Slaugther (2001) disproved a
systematic link between trade liberalization and income convergence, saying that trade
liberalization could equally well lead to income divergence. Actually, the results of BenDavid (1993, 1996) and Slaughter (2001) were not necessarily contradictory even though they
came up with different conclusions. Income convergence between countries is caused by
complex but similar development paths, which may be based on formal agreements or not. No
one believes that the mere signing of integration agreements (or trade liberalization) is
sufficient to achieve income convergence and growth. The question to be answered in practice
3
is much simpler: do South-South RIAs help to improve growth and income convergence or
not? Bear in mind that although inside a South-South RIA the intra-regional trade flow might
be low, thanks to the RIA the member's international bargaining power and attractiveness
increases due to a larger domestic market with improved infrastructure, political stability, etc.
We consider a world-wide sample of economically relevant South-South RIAs, differentiate
inside each panel dynamically between membership and non-membership, ignored by all
previous studies, and fix the testing period along the commencements of contracts. The panel
structure enables us to identify and estimate the impact of membership on growth and
convergence in a South-South agreement, at least for the 'treated'.
The criticism against South-South RIAs revolves around different arguments. There is the
general criticism against regional integration agreements, which says that they lead to trade
creation but also to trade diversion; see Viner (1950). Whether trade creation exceeds trade
diversion (and thus gives positive welfare effects) depends on the trade share of the RIA and
on the share of intra-regional trade. Furthermore, often the South-South agreements are
characterized by different parallel and overlapping agreements which typically favour the
economically strongest members and hinder real trade liberalization rather than promote it;
see Bhagwati, Greenaway, and Panagariya (1998) or Limao (2007). A criticism specifically
directed towards the South-South agreements is the “poor stays poor” argument, saying that
since convergence in RIAs is mainly caused by knowledge- and technology transfer, stronger
stress of competition and efficient compensation mechanisms etc., success is only guaranteed
if strong economic leaders participate; cf. Quah (1993) or Shioji (2004). Moreover, the new
integration- and the economic geography theory suggest explanatory models which implicate
divergence for RIAs, at least in the short and medium term; see Krugman (1991a, 1991b) or
Baldwin, Martin and Ottaviano (2001). Krugman (1991a, 1991b) shows that economic
4
integration processes induce agglomeration, especially if at the initial state the industrial
development and industrial centres are unequally distributed. The liberalization coming along
with the regional integration then leads to a further concentration of economic activities; see
also Krugman and Venables (1996). Relevant parameters for the agglomeration tendencies in
these models are increasing returns to scale in the industrial sector, decreasing transport costs
over time, and boosting urban centripetal forces (localization economies). Following BenDavid and Loewy (2003), a key factor of convergence processes is the openness to foreign
ideas and technologies. Consequently, a closed regionalism between developing countries
causes divergence due to the lack of new technology and ability of innovation. Giannetti
(2002) adds that the intensified knowledge spill-over in integration areas entails the risk of
income divergence as unequal economic areas do not benefit uniformly from the knowledge
exchange; for example low developed agricultural areas do not. Venables (2003) argues with
comparative price advantages; his theoretical model predicts income divergence for
integration agreements among low-income countries owing to concentration processes of the
manufacturing industry into countries with intermediate comparative advantages.
We neither argue in favour of nor against the statements given above. But as many theoretical
studies abstract from the respective other aspects like the economically relevant targets of
political stabilization and collaboration, it would be interesting to see whether one observes
income convergence and stronger growth in Southern RIAs or not. We study both questions at
once to counter the argument that convergence is not enough - referring to the "poor stays
poor" argument. In order to do so we consider several of so-called South-South RIAs from all
world regions in question: South America, South Asia and Africa, namely of the
MERCOSUR, ASEAN, ECOWAS, CEMAC, and the WAEMU. We consider the states'
performances in each area during the whole study period, creating thereby a kind of
5
randomized design experiment to identify the impact of membership on income. A simple
comparison of members versus non-members would not work due to selection problems. Also
an inappropriate data handling and modelling can lead to uncontrolled biases and unfounded
comparative statics. Using panel data until 2010, we aim to test whether or not these areas
experienced accelerated convergence and whether membership added positively to growth.
Though we concentrate on Southern regions, we are aware of the importance of their
catching-up to word-wide economic leaders, and of multilateral liberalization. An argument in
favour of concentrating on the regional agreements for low and middle income countries is
the idea of gradual opening (trade liberalization), and the solving of intra-regional problems.
In other words, we understand the South-South agreements as a step or even a stepping stone,
to reach an international ability to compete or to gain access to technology transfers, etc., but
we want to assess empirically if this is a step forward or backward; cf. Limao (2007). As the
focus is growth and convergence, we consider only the per capita income. We are not
interested in progresses of deeper integration by an increase of intra-regional political
institutions; i.e. we are interested in the development of income, not in the specific integrative
stages. Also the empirical literature on convergence and integration is abundant. There are
many analyses on special regional integration areas or on continents, see Madariaga, Montout,
and Ollivaud (2004), Tsangarides (2005) or Camarero, Flôres, and Tamarit (2003) for recent
contributions; for world-wide samples ignoring RIAs we refer to Mankiw, Romer and Weil
(1992), Sala-i-Martin (1996), and Lee and Pearsan (1997), to mention only some of them.
What they have in common is the question: Do poor countries grow faster than richer
countries in the long-run, attaining a catching-up? While some papers ask this question in
general, others concentrate on a particular agreement area. Our study contributes to this
question by analysing to what extend South-South agreements are helpful for this process.
6
Jones (2002) studied the ECOWAS countries over the period of 1960 to 1990. He looked at
the cross sectional unconditional growth, and on time series methods for log income
disparities (cf. Bernard and Durlauf, 1995). For the CFA franc zone (basically WAEMU plus
CEMAC) Fielding and Shields (2003) investigated whether monetary unions augment the
extent of macroeconomic integration. While they found a positive impact on intra-regional
trade, the effect of business cycle synchronicity seemed to decline over time. They argued that
this was because of both the lack of a common central bank and of coordination of economic
policies. A recent study of integration processes in this region is from Hammouda, Karingi,
Njuguna, and Jallab (2009). Their hypothesis is that regional integration does not improve
income convergence in Africa. They show that COMESA3, SADC4, and ECOWAS have quite
low unconditional and conditional convergence, and conclude that regional integration has
made little progress there. Note that they tell us only that in African RIAs the convergence
rate is low, but not how African countries would do without RIAs. The members of SouthSouth-RIAs are prevailing low and lower- middle income states. One might expect low
convergence rates, because of the lack of intra-regional trade activities, small growth rates of
total factor productivity, and deficits in economic and social infrastructure; cf. Wane (2004).
Even if one member has an economic boom period, then positive developing effects to the
RIA-partners hardly transfer. All this, however, does not answer the question whether
membership in integration agreements matters or not.
For Latin America, a recent study has been performed by Camarero, Flôres, and Tamarit
(2003). They used time series methods on log-income disparities to test the MERCOSUR
3
COMESA: Angola, Burundi, Comoros, DR Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Madagascar,
Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, Zimbabwe.
4
SADC: Angola, Botswana, DR Congo, Lesotho, Malawi, Madagascar, Mauritius, Mozambique, Namibia,
South Africa, Swaziland, Tanzania, Zambia, Zimbabwe.
7
states for long-run convergence analysing data over the period 1960-1999, including
therewith 31 years before the foundation of MERCOSUR. Madariaga, Montout, and Ollivaud
(2004) tested for various types of convergence, including conditional and unconditional beta
convergence, in the MERCOSUR and NAFTA. They analyzed the integration processes
mainly along the pair-wise comparison of the economic leaders, namely Mexico and United
States for the NAFTA, and Argentina and Brazil for the MERCOSUR. They claimed that
MERCOSUR showed only strong convergence in the period 1985-1991 (but it was founded
not before 1991). Explanations for the slow down after 1991 are the devaluation of the Real in
Brazil and the recession in Argentina at the end of the nineties. More recently, Blyde (2006)
added a study on sigma convergence, the Gini- and the Theil-index (cross national and cross
regional) and found trends of increasing inequalities. He did not look at growth.
For Asia we refer to the studies of Park (2000), Lim and McAleer (2004), Ismail (2008), and
Evans and Kim (2005). The first one tested convergence of the whole ASEAN+55 for the
period from 1960 to 1995 via a Theil-inequality index. While his full sample did not indicate
convergences until 1995, excluding the non-open and non-market-oriented economies, in
particular VR China, Vietnam, and Burma (Myanmar), he found evidence for convergence in
the sub-period 1975-1995 (i.e. before the Asian crises). The problem in our opinion is again
that this does not justify drawing conclusions with respect to RIAs, as the ASEAN+3 and +5
were founded not founded before 1997. Even if looking just at ASEAN, we have to realize
that countries like, for example, Vietnam did not join ASEAN until 1995. So a particularity
here is the late entry of the non-open and non-market-orientated countries in the RIAs,
something that has to be accordingly modelled. This was done more adequately by the second
mentioned article. They found no beta convergence. The last mentioned article looks at the
5
ASEAN+3 and +5 are economic cooperation platforms since 1997. They include the ASEAN states plus Japan,
South Korea, and the VR China (+3), and also New Zealand and Australia (+5), respectively.
8
Asian region as a whole and found apart from the non-surprising strong growth also
significant convergence for the period of 1960 to 1992. Concentrating on the ASEAN
countries, but still looking at the wrong period, Ismail (2008) found beta and sigma
convergence based on the use of a particular growth model.
To conclude, most of these works have not studied the effect of RIAs but just a country set
related to them, independently from foundation and membership; none has made a worldwide
study of South-South agreements, paid attention to the exact dating, duration or modelling of
membership. Moreover, differently to them, our paper investigates both, whether members
exhibit stronger growth and beta convergence. In other words: does participation in this RIA
matter for growth and convergence? The question when agreements were in force, and which
countries entered at what time is treated carefully. In the next section we discuss in detail the
econometric model and methods, and present the results of an extensive panel data analysis.
2. The Econometric Model and Estimation
We study different panels with modified versions of the human capital augmented (henceforth
HCA) growth model. We concentrate on beta convergence without stressing the discussion
about the most appropriate convergence measure. The fundamental framework goes back,
among others, to Solow (1957), with the assumption of diminishing return on capital in the
neoclassical growth model. Here, the so-called unconditional or absolute convergence is
defined as follows: If countries are similar in their structural parameters related to preferences
and technologies, then poor countries grow faster until they catch up to the richer ones. The
hypothesis of conditional convergence extends this definition by including the relevant factors
that define the steady state of a country, like the level of technology, the propensity to save
and the population growth rate. The per capita incomes of countries converge to one another
in the long-run, independently of their initial conditions, if their structural characteristics are
9
similar. In other words, countries further away from their own steady state grow faster.
Obviously, the two concepts of convergence coincide if all economies converge to the same
steady state. We are aware of the controversy on convergence and divergence of growth rates,
see Durlauf (1996), going back to the fact that the alternative endogenous growth models
typically (depending on the assumptions) do not allow for convergence. A detailed derivation
of our panel models is given in the appendix, so we will directly give here the used
econometric form. A starting point of the HCA Solow model is the Cobb-Douglas production
α
function at time t; more specifically, Yt = K t H tβ ( At Lt )1−α − β with 0<(α+β)<1, where Y is real
output (i.e. income), K is the stock of physical capital, L is (raw) labour, H is the stock of
human capital, and A is the level of technology. Note that the model assumes constant returns
to factor inputs jointly and decreasing marginal returns for all production factors, necessary
for the existence of a steady state and convergence. In the following we will work with y=Y/L,
the output per capita, k=K/(AL), h=H/(AL), the capital and human capital per effective labour,
expressed in terms of their saving rates, sK, sH, and depreciation rates δK, δH, respectively. It is
assumed that technology and labour are growing with exogenous rates, such that Lt = L0 e nt
and At = A0 exp( Pt 'θ )e gt , where n denotes the exogenous growth rate of the labour force, and
g the one of technological progress. A standard argument for further modelling is that A0
reflects also resource endowments, climate, land locked, etc. which clearly differs over
countries and causes part of the unobserved heterogeneity in the model. In panel models this
is mainly reflected in country specific fixed or random effects (say γi), while an error term
(say ηit ) allows for additional shocks and time varying heterogeneity. Less typical is the
inclusion of a vector of dynamic variable Pt which stands for observable, and potentially
dynamic impacts e.g. for openness of the domestic economy with θ being a vector of
unknown coefficients. This can refer e.g. to trade and technology transfer; it is in any case the
10
way to control for membership in a particular RIA6. While some literature develops the
income per capita model by a first order Taylor approximation7 around the steady state
income y*, one can simply think of all income levels as being deviations around the steady
state income. One would start then from ln y it = ln y i* + γ ′X it + ε it , similar to Wane (2004) or
Evans and Kim (2005), with εit being a random zero mean deviation. Here, γ ′X
it
may
describe policy and structural changes including membership in a RIA, investment in (human)
capital, etc.; see De la Fuente and Domenech (2001). Under adequate conditions, cf. Santos
Silva and Tenreyro (2006) for possible pitfalls, all approaches yield the same growth
regression model. For τ=t2-t1 and λ = (n + g + δ )(1 − α − β ) this is
(
(
)
)
ln y i (t 2 ) − ln y i (t1 ) = γ i − (1 − e −λτ ) ln yi (t1 ) + g t 2 − t1e −λτ + θ ′ Pit2 − e −λτ Pit1 + η it +
(1 − e −λτ )
α
β
α +β
ln(s Ki ) + (1 − e −λτ )
ln(s Hi ) − (1 − e −λτ )
ln(ni + g + δ )
1−α − β
1−α − β
1−α − β
(1)
and the simplified (unconditional) model for ln yi (t 2 ) − ln yi (t1 ) becomes then
(
)
(
)
= γ i − (1 − e − λτ ) ln yi (t1 ) + g t 2 − t1e − λτ + θ ′ Pit2 − e − λτ Pit1 + ε it ,
(2)
where εit summarizes all the unspecified heterogeneity. We should note here on two points:
the assumption of constant saving rates sH, sK, and the time index of Pt. It is typically assumed
that the saving and depreciation rates are constant, thus automatically given for the steady
state so that a time index could be suppressed. It is evident that, no matter if one favours the
continuous or discretised version, for the econometric derivation this assumption can be
relaxed to be the saving rate of the considered period from t1 to t2, and therefore may vary
over time. Most of the literature sets g=0.02 and δ=0.03, basically all g+δ=0.05. Often,
6
Be aware of the dynamic nature of P as this accounts for the fact that all countries will be first out of, and later
in a RIA during the studied period. Recall that this in turn is necessary to identify the membership impact.
Openness could be measured by imports by GDP or collected duties, but our aim is to explicitly model
membership and years of membership whereas further heterogeneity is subsumed in fixed effects and shocks.
7
In some textbooks one can find the analogue for a discrete process yielding the same regression model.
11
g (t 2 − t1e − λτ ) is included via time fixed effects. Note, however, that for most countries this
term is almost linear in t when τ, δ and g are chosen to be constant, and a function of ln(n)
else. The same argument applies to years of membership when included in P. Consequently,
fixed effects for time seem to be an unnecessary over-parameterization of the model,
especially unfortunate for panels where the dimension of time is relatively large compared to
the cross section. Moreover, the estimates of the time fixed effects would rather reflect
business cycles than anything else. It is often argued that for equidistant time points this term
should be approximately of the same size over time, and is therefore skipped in many studies.
A nice side-effect, however, is that the liner inclusion of time leads to a positive Nickel bias
of the coefficient of ln(yt-1) (Phillips and Sul, 2007), so that testing for beta convergence
becomes conservative8. Another argument in favour of the inclusion of a linear time trend is
to correct for spurious regression due to trend-stationarity. For robustness reasons we checked
that our findings do not change if time is excluded, but for the sake of brevity only results
with linear time trend will be shown.
We established the panels for Africa, Latin-America and Asia with three-year intervals and
consider the average values over the years inside each interval. This way we achieve a prior
smoothing of the data over time which shall correct for business cycles. We ran all
regressions with two measures of growth rate per year and capita: first, approximating it by
the log differences divided by two such that our models are as given in equations (1) and (2);
second, calculating the yearly growth rate by ({ y i (t 2 ) − yi (t1 )} / y i (t1 ) )
1/ 2
. This makes a
difference only for countries with particularly high growth rates. The results were
nevertheless quite similar so that we will only report regression results based on the first
8
This means that the indicated p-values overestimate the true ones. If we reject we are on the save side
independently of the size of a potential Nickel bias.
12
measure. When we consider a three years interval, we have taken the first year as the basis
year, t1 to compare it with the last year of the same period t2. This differs from a classical
dynamic panel growth model, where each income (except the first and the last) appears twice
in the sample: with index t2 as (part of the) left-hand side variable, and in the next panel wave
with index t1 as initial income. Contrary to that, our model is a semi-dynamic panel where
each income appears in only one wave as we consider a panel of strictly separated periods
what reduces the correlation between the wave equations. For sH, sK, n we take the period
averages and constant rates of depreciation and technology. A further advantage of the semidynamic panel is that thanks to the almost constancy of e − λτ (0< e − λτ <0.9 for any reasonable
(
)
τ and λ) it is justified to replace Pit2 − e − λτ Pit1 by Pi(τ) indicating membership or membership
years. In all studies we are aware of, this has been ignored; either countries were always
counted as member or the modelling was questionable.
When estimating the different models to test for growth and convergence, one faces several
econometric problems which could lead to inconsistent results and misleading interpretations.
One can identify the following sources of inconsistency in estimation and inference: omission
of individual country specific effects, endogeneity due to simultaneity (of sH, sK, n, or
membership), possible inconsistency due to the dynamics in the fixed effects panel model,
inconsistent estimation of the standard errors; see for example Caselli, Esquivel, and Lefort
(1996), or Lee and Pearsan (1997) concerning our context. We solve the first problem by
considering fixed effects models. For comparison we also estimated the same panel models
without fixed effects and finally with random effects. While the first resulted in models with
mostly insignificant coefficients, the second came closer to our results from fixed effects
models. However, the necessary assumption of independence between included covariates
and the (country specific) random effects is rather questionable in both the conditional and the
13
unconditional model. In order to perform a robust inference we allow for additional country
specific heterogeneity by including panel specific AR(1) autocorrelation and arbitrary
heteroscedasticity. Ignoring these structures could result in invalid inference. By applying
feasible generalized least squares estimators (GLS) we make use of this covariance structure
to obtain efficient estimates. Due to the degrees of freedom, however, the small unbalanced
panels which we analyze in the sample split exercise, can only be estimated either the above
described GLS but then without country specific fixed effects or by standard (fixed effects)
within estimators. We did both but only show the latter one as the main difference in the
results is that the GLS estimates without fixed effects often become insignificant.
The problem of estimating dynamic fixed effects panel models, also known as Nickel-bias
problem, is pretty small for large time horizons as ours. Even if there were still a measurable
bias, recall that this would be positive for initial income, see above. Note that in our context
difference and system GMMs are little helpful for various reasons like too many but too weak
instruments, non-controllable bias due to the violation of necessary initial state conditions,
serial correlation, etc., see for example Roodman (2009), Bun and Windmeijer (2010),
Hayakawa (2007) or Gaduh (2002), to mention only some of the problems.
Note that we are only interested in estimating and testing the sign of the impact of
membership in South-South-Agreements on growth and convergence, so a small mean
squared error is more important than pretended unbiasedness9, especially if we can control for
the sign of our potential bias. Instead of sampling data from member and non-member
countries separately to compare the regression results, we decided to compare the effect of
membership versus non-membership for the same set of countries. So we included all
countries that stepped at some time into one of the considered RIAs but choosing the time
9
We say pretended because GMMs typically have a bias, too - but one has a less clear idea about its sign.
14
periods such that each countries experiencing both, being member and non-member. This is
possible thanks to the panel structure of our data. By this approach we avoid a selection bias
which is quite likely to occur when looking at exclusive members versus non-members –
though we emphasize that our approach is conservative in favour of the criticisms10 against
South-South RIAs. To our knowledge we are the first to perform such a counterfactual
exercise (cf. in particular our sampling split exercise) that compares the effect of membership
on growth and convergence for RIA-agreements empirically, in particular for South-South
agreements. There exist different options for comparing the effect of membership: the
inclusion of membership-dummies, the number of membership-years, comparing the subsamples comprising only the observations when membership was active with the subsamples
when membership was not active, etc. to mention only some of them11.
The problem of possible inconsistency due to potential simultaneity of growth and our
covariates is counteracted by looking at period averages for human capital, investment and
population growth. This coincides with the idea of fixed sH, sK, and n, see discussion above.
When we repeated the estimation with lagged interval averages as instruments, these turned
out to be rather weak instruments. For membership this problem is somewhat more involved.
Note first that the main criticism is against cross-sectional studies, while we consider a fixed
effect panel model with only countries having experiences both states, and therefore control
for and time invariant heterogeneity and selection bias. One could still argue that the decision
of membership depends on time varying variables that are related to growth but not captured
10
I.e. when estimating the beta coefficient we only allow for positive biases pushing beta towards zero.
11
We deliberately declined to include interaction terms with membership in the entire sample. The reasons are
twofold: First, it is not clear how the macroeconomic model can be modified to end up in a regression model
with such dynamic interactions, i.e. this would be a regression without a model. Second, even if such a model
would be derivable from macroeconomic theory, it either imposes the restriction that membership only affects
convergence speed but no other return, not even the fixed effects, or we would have to include all interaction
terms which is equivalent to sample splitting which we did indeed.
15
by the other regressors (initial income, human capital, investment, population growth and
time). This, however, does not necessarily entail a bias unless if the membership decision
remains time varying, i.e. countries step in and out. As we know, this is not the case. One may
also argue that there are other factors like democracy and stability that are correlated to both,
RIA membership and growth. This is doubtless true, and our argumentation is that it is
precisely the RIAs which promote a bunch of those factors and by this way contribute to
development, growth and convergence - recall our discussion in the introduction. For
example, to become an associated member of MERCOSUR, countries have to fulfil certain
standards of democracy. Many of these factors are hard to be measured correctly or are
simply not observed; neither it is clear how they have to be modelled econometrically. So,
instrumentalising membership by them would not make much sense as it (a) reverses the
causality chain, and (b) causes even more problems of endogeneity, not to mention our
concerns about keeping the mean squared error small. Apart from that, it is well known that
instrumental methods cannot identify treatment effects12. Controlling for these factors does
not make sense neither, as this blurs the impact and renders its identification even harder if
not infeasible13. And so we are back in the causality discussion bringing us to a brief
treatment effect consideration. There the typically used assumption would be that in our panel
the difference between the averages of observed growth in the periods before compared to the
periods after membership, given the periods' initial income and time (plus population growth,
investment and human capital in the HCA case) is only due to those factors which are caused
12
They give only the latent average treatment effect which is polemically discussed due to its dubious
interpretation. Even though the problem carries over to the IV methods in structural equations, these have so far
been escaping from the dispute due to its popularity. There exists, however, an increasing amount of articles
concerning endogenous covariates with discrete outcomes discussing indirectly this issue.
13
A quite popular example for this problem is, that in wage equations the inclusion of employment sectors and
maternity leaves renders the gender wage gap insignificant.
16
or promoted by membership. Note that here again one automatically corrects for country
specific heterogeneity by observing each country in as well as out of the particular RIA. This
corresponds also to the randomized design idea14 which requires that after controlling for the
set of covariates, each subject has equal chances to be observed with or without treatment (i.e.
to be in or out of the RIA). As we only consider countries that at some point decided for
membership, this might be interpreted as a so-called 'treatment effect for the treated'.
We conclude this discussion with a remark on the sign of a potential bias for the membership
impact estimate. As one can see from our review, there exists an important amount of
literature saying that South-South Agreements would obstruct growth and convergence
whereas there is no indication for that exactly those countries with expected strong growth
and convergence will form South-South RIAs keeping out the others. Consequently, the
expected biases in our sample are negative for membership and positive for initial income15.
Both are in line with the strategy of conservative testing, see comments above.
We use data selected from different data sources. The real GDP per capita in international
PPP$ and the growth rates of population are from the World Development Indicator database.
For the conditional growth model we added the covariates introduced by Mankiw, Romer, and
Weil (1992). In order to account for the criticism of Pritchett (2001) we work with the
schooling years of the working-age population as a proxy of human capital which is provided
in the education data set of Barro and Lee (2010). The rest is taken from the Penn World
Tables by Summer and Heston which includes real investment shares to GDP. Missings were
replaced by predictions; we ran a regression explaining schooling years syrit by literacy rates
litit in a time fixed effect panel model: syrit = β1 ⋅ litit + γ t + ε it , and for those countries for
14
A potential alternative could be the difference-in-difference approach, extended for panel data.
15
Not the prospering economies or those with positive expectancies tend to join a RIA, but the tumbling and
struggling ones, hoping for protection and recovering.
17
which we observed literacy rates but not schooling years we predicted the latter by this model.
This was done for Cambodia, Lao, Vietnam, and China in Asia, and for Burkina Faso, Cote
d’Ivoire, Cape Verde, Nigeria Chad and Mauritania in Africa16. For Gabon, Guinea and
Equatorial Guinea we had no information about schooling, literacy or enrolment rates. So we
predicted the schooling years from geographical neighbours with similar economic structures.
Summarizing, our inference is quite robust against possible misspecification, and conservative
in favour of the hypothesis of no convergence and no accelerated growth. We have opted for
keeping potential biases under control instead of applying complex econometric methods to
allegedly eliminate them at the cost of a serious efficiency loss. Given the controversial
discussion about the pros and cons of South-South RIAs, this has been done to counteract the
else obvious criticism that other specifications or methods will yield other conclusions. A
disadvantage is the small number of degrees of freedom. Therefore we consider also the
unconditional growth model, and disregard further covariates of integration depth which
inevitably would lead to over-parameterization. For a study of robustness of HCA Solow
models and consequences of misspecification see Temple (1998).
3. The South-South Integration Areas
We consider various samples, organized in three main data sets. The first set is composed of
ten countries, the founding states of MERCOSUR plus the associated members. Although the
16
The latest updates of the Barro and Lee dataset show escalating schooling years (exhibiting serious jumps) for
several Southern countries. Moreover, the distorted data record for example a much higher average of schooling
years for Bolivia than for Switzerland for the population above 15 in 2010. This indicates either measurement
errors for the recent waves or a substantial change of definition and scaling. We cleaned the suspicious data by
linear regression and calculated the lacking observations via linear inter- and extrapolation as we did for missing.
18
MERCOSUR17 was founded in 1991, we chose as the testing period 1985-2008 (not until
2010 due to data constraints) to have each country observed in as well as out of the RIA, as
explained above. Our second set contains the countries of ASEAN18, founded in 1967. At that
time the cooperation was only security politics oriented because of regional instabilities
(conflict of the Spratly-islands). Since 1975 annual meetings of the ministers for economic
affairs have been held, and the character of the cooperation has changed towards an
economically orientated one. Therefore our testing period is from 1972 to 2010, i.e. including
one period before the RIA started to be more than just a security council. Because of missing
data we had to exclude Brunei and Myanmar completely, while we could not include
Cambodia, Vietnam and Laos before the eighties. Though Papua New Guinea is not a full
member but only associated, it is included. The third set is composed of the twenty one
countries of the ECOWAS19, CEMAC20, and the WAEMU21. This panel starts one period
before the foundation of the ECOWAS (the oldest one of the here considered RIAs) in 1975
and goes until 2010. Due to lack of data we had to drop Liberia, and could include Equatorial
Guinea, Guinea and Cape Verde not before the mid eighties.
The introduced sample offers a reasonable coverage of the three relevant world regions for
our proposed study. With this selection we hope to control for specific factors like restrictions
17
Mercado Común del Sur: Argentina, Brazil, Uruguay, and Paraguay. Venezuela signed membership in 2006
but is waiting for official full admission. Associated members are Peru (2003), Chile (1996), Bolivia (1997),
Ecuador (2004) and Colombia (2004). An association is based on bilateral agreements entailing a free-trade-area.
18
Association of Southeast Asian Nations: Indonesia, Malaysia, Thailand, Singapore, Philippines, Brunei (1984),
Burma/Myanmar (1997), Vietnam (1995), Cambodia (1999), Laos (1997), Papua New Guinea (associated 1984).
19
Economic Community of West African States: Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, Gambia,
Ghana, Guinea Bissau, Guinea, Liberia, Mali, Mauritania till 2001, Niger, Nigeria, Senegal, Sierra Leone, Togo.
20
Economic and Monetary Community of Central Africa: Cameroon, Central African Republic, Chad, Rep.
Congo, Equatorial Guinea, Gabon.
21
West African Economic and Monetary Union: Benin, Burkina Faso, Cote d’Ivoire, Guinea Bissau, Mali, Niger,
Senegal, Togo.
19
due to the explicit grouping together of anglo-, francophone or Latino areas. An important
criterion to select the RIAs is the enabling clause of the GATT. Then one could also have
chosen COMESA or SADC, from which the latter is often referred to be quite successful.
However, trouble spots like Angola, and Zimbabwe yield a quite inhomogeneous pattern over
time and countries. There is also dissension about the role of South Africa in the SADC, and
finally, it is well known that some members did not even sign several of the most important
protocols for trade liberalization. COMESA has not been considered because this union with
its 24 countries spread out all over Africa with enormous distances, and the lack of
infrastructure to overcome them, is a different construct from what we aim to study.
4. The Empirical Analysis
Given the amount of numerical outcomes of our studies, we summarize here only on the most
relevant results. Concerning the full samples, we present the estimation results with a GLS
least squares dummy variable regression with panel-specific AR(1) and heteroscedastic errors.
We checked the robustness of our results with respect to the following alternatives: withinbetween fixed effects regression, random effects models, neglecting possible AR error
structure, assuming homoscedasticity, or neglecting a time trend. Further, all calculations
were repeated with different numerical procedures to obtain the weighting matrices of the
GLS. Although numerical results changed (for samples smaller than 100 observations), the
qualitative findings stayed always the same. Skipping the linear time trend typically increased
the speed and/or significance of convergence, and in particular the positive effects of
membership. Skipping the recent periods to see the potential impact of the recent crises
yields a similar effect. As our aim was a robustness check but not to exercise data snooping
until we get the estimates that support our hypothesis most, we only present estimates that
refer to the full time period, standard estimation procedures, and exactly models (1) and (2).
20
As we consider overlapping RIAs we had to proceed for Africa in a slightly different way
than we did else. We consider one data set containing all mentioned African countries, not
separated sets of each African RIA. Therefore, we expect only a clear significant positive
impact of membership on growth for the 37 years old ECOWAS, whereas for the two much
younger ones we only expect to find accelerated convergence. Moreover, one could interpret
the foundations of CEMAC and WAEMU even negatively as a post-cold-war split-off of the
CFA-zone. To identify a positive effect for growth is much harder for these two RIAs as we
contrast them with several countries belonging to the quite successful ECOWAS. The CFA
will not been considered in the sample-split exercise as it has been existing since 1949 so that
no information is available about states having been out of the CFA. Instead, in Tables 3 and
6 are included regression estimates which are only based on the CFA countries.
In the conditional model one expects positive signs for investment and schooling, but negative
ones for initial income and ln(n+g+δ), i.e. the log of the growth rate of labour force and
technological growth plus depreciation rate. The hypotheses to be tested are (i) whether initial
income has significantly negative impact (i.e. beta convergence), (ii) whether membership has
significant positive impact on growth, and in case of sample splitting according to
membership (iii) whether one observes a stronger slope for initial income in the 'member
samples'. One might suspect a decreasing marginal utility of membership over time giving
insignificant or negative coefficients for years of membership. We first discuss the findings
for the unconditional regression model, and in a second step for the conditional one.
Concerning the unconditional convergence, Tables 1, 2, 3, and A1-3c (left hand side), show
that the hypothesis of significant convergence, faster convergence and larger growth inside
nearly all considered RIAs is supported empirically in several respects. Certainly, one has
significant unconditional convergence in all world regions irrespective of membership.
21
However, when we consider the subsample including only observations for which
membership=1, then the coefficients of initial income is more negative in the subsample of
members than in the full sample. The same happens when we include membership in the full
sample; then initial income is more negative. This indicates heterogeneity between members
vs. non members with respect to convergence speed. Moreover, when we split each sample
into two unbalanced samples containing the same sets of countries but either including only
the data when being a member of the considered RIA, or only the data if not (Tables A1-3),
then we find stronger convergence in the first sub-sample than in the second one for African
RIAs. For MERCOSUR and ASEAN convergence is highly significant among members but
else not. One may argue that for South-East Asia the subsample of non-members is too small
to produce significant convergence; therefore we did a study where we additionally included
the PR China, South Korea, Japan, Australia, and New Zealand, i.e. the ASEAN+5 members.
Then, however, the estimates report a very low convergence in the non-members subsample,
probably due to the serious heterogeneity of that country set. We face there the additional
problem of sample selection which renders the member vs. non-member sample comparison
less credible. Summarizing, whatever we tried for ASEAN (see also discussion below
concerning the 1997/98 crises), the results shown represent very well our all-over findings.
Back with the results for un-split samples, we see that the effect of membership is positive
except for CEMAC when years is excluded or WAEMU if years (whose impact is positive
there) is included. As both negative coefficients are highly insignificant, this confirms our
guess that in this analysis we can only identify a strong significant positive growth effect for
ECOWAS membership. The impact of time is positive, as it is supposed to be according to
(2). Ambiguous are the signs of the coefficients for years. We observe decreasing marginal
utility effects for MERCOSUR, ASEAN, and CEMAC but even increasing ones for WAEMU
22
and ECOWAS. The estimates for CFA in Table 3 compared with those of Tables A3 confirm
that the whole CFA converges at the same speed as CEMAC and WAEMU separately do.
Table 1: Unconditional convergence and growth tests for Latin America, 1985-2008
Initial income
-.0828 (.026) -.1046 (.026) -.1003 (.026)
Time
.0056 (.002) .0037 (.003) .0088 (.004)
MERCOSUR mem
- .0269 (.010) .0230 (.010)
MERCOSUR years
- -.0022 (.001)
GLS-Regression with panel-specific AR(1), heteroscedasticity and fixed effects. Standard deviations
for coefficient estimates are given in parenthesis. mem stays for membership, years for the number of
years the country staid in the RIA. Sample size was 80. All regressions are highly significant.
Table 2: Unconditional convergence and growth tests for South-East Asia, 1972-2010
Initial income -.0538 (.017) -.0552 (.017)
Time
.0023 (.003) .0016 (.003)
ASEAN mem
- .0191 (.017)
ASEAN years
GLS-Regression with panel-specific AR(1), heteroscedasticity and
-.0483 (.019)
.0054 (.005)
.0192 (.016)
-.0016 (.001)
fixed effects. Standard deviations
for coefficient estimates are in parenthesis. mem stays for membership, years for the number of years
the country staid in the RIA. Sample size was 102. All regressions are highly significant.
Table 3: Unconditional convergence and growth tests for West- and Central Africa, 1972-2010
ECOWAS
WAEMU
CEMAC
-.0463
-.0538
-.0408
-.0433
-.0479
-.0476
(.012)
(.013)
(.012)
(.012)
(.013)
(.013)
Time
-.0001
-.0012
.0001
-.0002
.0007
.0008
(.000)
(.000)
(.000)
(.000)
(.000)
(.000)
mem
.0182
.0138
.0060
-.0047
-.0088
.0026
(.007)
(008)
(.006)
(.008)
(.009)
(.016)
years
.0006
.0012
-.0011
(.000)
(.001)
(.001)
GLS-Regression with panel-specific AR(1), heteroscedasticity and fixed effects. Standard deviations
Initial income
CFA
-.1372
(.018)
.0063
(.001)
-
for coefficient estimates are given in parenthesis. mem stays for membership, years for the number of
years the country staid in the RIA. Sample size was 261. All regressions are highly significant. For
CFA only the subset of member states has been used.
To summarize the results for unconditional growth regression: the exercise with the full
samples has shown that we find, among other things, not only significant beta convergence in
all considered regions, but also that membership has had mostly a significant positive impact
23
on growth. The exercise with split samples does confirm this finding and further, exhibited
accelerated convergence for members of the South-South RIAs.
Initial income
Time
Log investment
Log schooling
Ln(n+g+δ)
Fixed eff. aver.
Table A1: Sample split Latin America
MERCO=1
MERCO=0
MERCO=1
MERCO=0
-.1694 (.048)
-.1811 (.117)
-.2051 (.055)
-.2716 (.124)
.0083 (005)
.0097 (.013)
.0051 (.006)
.0343 (.019)
.0119 (.036)
.0253 (058)
.1061 (058)
-.1113 (.146)
.1825 (350)
.8238 (.474)
1.4585 (.403)
1.4957 (.938)
2.1020 (.960)
4.6154 (1.81)
No. of observ.
45
35
45
35
Prob > chi2
0.025
0.121
0.011
0.0310
Standard FE within estimation without error correlation. Standard deviations are given in parenthesis.
Initial income
Time
Log investment
Log schooling
Ln(n+g+δ)
Fixed eff. aver.
Table A2: Sample split East Asia
ASEAN=1
ASEAN=0
ASEAN=1
ASEAN=0
-.0629 (.025)
-.1480 (.162)
-.0707 (.028)
.6277 (.478)
.0018 (.005)
.0360 (.023)
.0075 (.008)
-.1107 (.085)
.0068 (.043)
.1894 (.106)
-.0797 (.072)
.0827 (.105)
-.0851 (.112)
1.4733 (.868)
.5362 (.177)
.9097 (.994)
.4668 (.363)
.4127 (1.03)
No. of observ.
86
16
86
16
Prob > chi2
0.001
0.051
0.002
0.267
Standard FE within-estimation without error correlation. Standard deviations are given in parenthesis.
Initial income
Time
Log investment
Log schooling
Ln(n+g+δ)
Fixed eff. aver.
Table A3a: Sample split West Africa: ECOWAS
ECOWAS=1
ECOWAS=0
ECOWAS=1
ECOWAS=0
-.1154 (.023)
-.0173 (.020)
-.1147 (.022)
-.0526 (.019)
.0004 (.001)
-.0018 (.002)
-.0013 (.002)
.0029 (.005)
.0296 (.008)
.0883 (.017)
.0160 (.017)
-.0551 (.050)
-.0156 (.064)
.0455 (.147)
.8139 (.162)
.1689 (.151)
.8231 (.226)
.7433 (.445)
No. of observ.
172
89
172
89
Prob > chi2
0.001
0.373
0.000
0.015
Standard FE within-estimation without error correlation. Standard deviations are given in parenthesis.
The conditional convergence estimation, see Tables 4 to 6 and A1-3c, mainly confirms and
underpins the evidence found before. We again see significant convergence in all regions.
Comparing the corresponding regressions for full sample and for subsets where
membership=1, the return of initial income is more negative for the subsample, same happens
when including membership in the full sample. The coefficient estimates for time have the
24
expected positive signs or are insignificant. Also the other estimates have either the expected
signs, or are insignificant, i.e. investment as well as schooling has positive impact and ln
(n+g+δ) has a negative one except for Africa, see discussion below.
Initial income
Time
Log investment
Log schooling
Ln(n+g+δ)
Fixed eff. aver.
Table A3b: Sample split West Africa: WAEMU
WAEMU=1
WAEMU=0
WAEMU=1
WAEMU=0
-.1424 (.046)
-.0335 (.014)
-.1932 (.054)
-.0452 (.013)
.0047 (.002)
-.0005 (.001)
.0042 (.005)
.0008 (.002)
.0181 (.020)
.0493 (.009)
.0042 (.025)
-.0221 (.024)
.1944 (.124)
-.0432 (.074)
.9476 (.316)
.2654 (.103)
1.8313 (.591)
.3362 (.220)
No. of observ.
48
213
48
213
Prob > chi2
0.014
0.000
0.024
0.001
Standard FE within-estimation without error correlation. Standard deviations are given in parenthesis.
Initial income
Time
Log investment
Log schooling
Ln(n+g+δ)
Fixed eff. aver.
Table A3c: Sample split Central Africa: CEMAC
CEMAC=1
CEMAC=0
CEMAC=1
CEMAC=0
-.1416 (.021)
-.0932 (.018)
-.1199 (.028)
-.0925 (.017)
.0038 (.004)
.0009 (.001)
.0019 (.010)
-.0004 (.002)
.0751 (.035)
.0282 (.008)
-.0154 (.063)
.0129 (.016)
.0802 (.209)
-.0133 (.063)
1.1212 (.156)
.6670 (.125)
1.3305 (.589)
.6779 (.204)
No. of observ.
35
226
35
226
Prob > chi2
0.000
0.000
0.000
0.000
Standard FE within-estimation without error correlation. Standard deviations are given in parenthesis.
In Tables 4 and A1 we see the results for South America. When we include membership, the
coefficient of initial income is more negative and the coefficient of membership is clearly
positive significant. Thus, there is again evidence that the membership promotes growth. In
the sample split the member sample converges faster than the full sample. In contrast, the
non-member-sample shows insignificant convergence. Any trial to increase significance
(variables selection etc.) led to similar results for this subsample. This suggests that the
explicit fixing of convergence criteria as it has happened in this RIA (concerning inflation
rate, budget deficit and national debt) is bearing fruits. But even though on the long run a
currency union is planned, agreements about coordination of exchange rates have been
advancing slowly, and adjustment payments have just started.
25
Table 4: Conditional convergence and growth test for Latin America 1985-2008
Initial income
-.1024 (.028) -.1318 (.027) -.1386
Time
.0031 (.003) .0015 (.003) .0122
Log investment
.0359 (.023) .0378 (.022) .0333
Log schooling
.0272 (.025) .0503 (.028) .0759
Log (n+g+δ)
-.1255 (.124) -.0319 (.135) .1449
MERCOSUR mem
- .0293 (.010) .0271
MERCOSUR years
- -.0039
(.026)
(.004)
(.020)
(.026)
(.134)
(.010)
(.001)
GLS-Regression with panel-specific AR(1), heteroscedasticity and fixed effects. Standard deviations
for coefficient estimates are in parenthesis. mem stays for membership, years for the number of years
the country staid in the RIA. Sample size was 80. All regressions are highly significant.
Table 5: Conditional convergence and growth test for South-East Asia, 1972-2010
Initial income
-.0526 (.018) -.0532 (.019) -.0510 (.020)
Time
.0006 (.005) .0004 (.006) .0020 (.009)
Log investment
.0174 (.018) .0132 (.022) .0095 (.025)
Log schooling
.0124 (.046) .0101 (.046) .0080 (.048)
Log (n+g+δ)
-.0236 (.075) -.0282 (.075) -.0294 (.075)
ASEAN mem
- .0082 (.021) .0112 (.024)
MERCOSUR years
- -.0006 (.002)
GLS-Regression with panel-specific AR(1), heteroscedasticity and fixed effects. Standard deviations
for coefficient estimates are in parenthesis. mem stays for membership, years for the number of years
the country staid in the RIA. Sample size was 102. All regressions are highly significant.
Table 5 presents the results for East Asia. They confirm convergence, i.e. the return of initial
income is significantly negative, but there seems to be no strong linkage between per capita
income growth and membership or years of membership. A simple reason could be the Asian
crisis 1997/98, because the ASEAN members were hard hit, and the negative economic
effects persisted the longest in the ASEAN founding members Indonesia and Malaysia. In
order to filter out the impact of that crisis, we corrected the data for 1997 and 1998 by linear
interpolation and repeated the regressions. Although the sign of the membership coefficient
became bigger then, it was still insignificant. A possible reason for this are the subsequent
new entries of poor countries in the nineties like Vietnam and Cambodia. One would expect
that the Asian crisis led to more convergence in the whole area, because the newly
industrialized countries with their high growth rates, as Thailand, were strongly affected by
26
the crisis, whereas the poorer late-comers were not. A comparison of the initial income
coefficients in regressions with (not shown) vs. without correction for the crisis does not
confirm this hypothesis. Finally, considering the conditional convergence in the split samples
(Table A2) we see once again that membership has had an acceleration effect on convergence.
Table 6: Conditional convergence and growth test for West- and Central Africa, 1972-2010
ECOWAS
WAEMU
CEMAC
-.0676
-.0667
-.0529
-.0525
-.0581
-.0557
(.013)
(.013)
(.013)
(.013)
(.013)
(.013)
Time
-.0013
-.0021
-.0006
-.0003
-.0002
-.0001
(.001)
(.001)
(.001)
(.001)
(.001)
(.001)
Log invest.
.0267
.0263
.0242
.0237
.0245
.0246
(.005)
(.005)
(.005)
(.005)
(.005)
(.005)
Log schooling
.0121
.0113
.0055
-.0040
.0079
.0090
(.008)
(.008)
(.009)
(.010)
(.008)
(.008)
Log (n+g+δ)
.0056
-.0075
.0018
.0039
.0047
-.0190
(.039)
(.039)
(.043)
(.038)
(.039)
(.040)
mem
.0253
.0219
.0071
-.0003
-.0079
.0052
(.007)
(.008)
(.006)
(.009)
(.009)
(.015)
years
.0004
.0010
-.0014
(.001)
(.001)
(.000)
GLS-Regression with panel-specific AR(1), heteroscedasticity and fixed effects. Standard deviations
Initial income
CFA
-.1400
(.019)
.0041
(.003)
.0296
(.010)
.0044
(.013)
.2022
(.052)
-
for coefficient estimates are in parenthesis. mem stays for membership, years for the number of years
the country staid in the RIA. Sample size was 261. All regressions are highly significant. For CFA
only the subset of member states has been used.
For the African RIAs (ECOWAS22, WAEMU, CEMAC, and the CFA-zone) we found
significant beta convergence for all (sub-) samples (see Tables 6, and A3a to A3c) like we did
before in the unconditional growth model. Investment and time have the model consistent
positive signs throughout or are insignificant in some cases. We again see positive influences
of membership on growth (except for CEMAC where it is insignificant), which supports the
22
In order to investigate the impact of ECOWAS membership, we faced a problem with the handling of the
status of Mauritania which officially left ECOWAS in 2001, having been member for more than 25 years. Not
surprisingly, when we treated it as a non-member after 2001, the estimates gave counterintuitive results as this
country and the ECOWAS have been benefiting by previous achievements. Looking at the data, one sees that
Mauritania made considerable economic progress during the 25 years, and it is clear that it is still using the
established structures. So we decided to not increase the number of years after 2001 but keeping member=1.
27
previous findings. Also when years are included in the model, we see the same effects as
before. In empirical studies on Africa it is quite common to find insignificant or even negative
returns for schooling, or positive ones for population growth. While several of the states with
relatively high p.c. income thanks to natural resources, hardly ever invest in human capital,
the poor countries without natural resources often do (or try). But until now the return of this
investment has not yet produced a catching up to the p.c. income of the others. We also
suspect that in Africa the marginal utility of labour force is still positive due to the low
technological standards and the high proportion of labour intensive production in gross
domestic income. While all this seems to be confirmed in our calculations for CEMAC and
WAEMU, this does no longer hold for the ECOWAS, see Table A3a.
All in all we have seen that membership in South-South RIAs has been promoting growth and
convergence. Most of these findings are in contrast to often made statements in the literature.
In particular, they contradict the argument that South-South agreements remain in the “poor
stays poor” trap even if the member states converge among themselves. All results are robust
to model and estimation method or any of the above discussed issues.
5. Conclusions
In our review of the controversial and mostly negative discussion about regional integration
areas, and in particular South-South agreements, we discussed also the hypothesis that the
latter not only exhibit convergence but even promote growth and faster convergence. This is
tested by studying a set of RIAs including the three main world regions in question, namely
West and Central Africa, South America, and South Asia. These sets were introduced and in
detail. Our analysis is based on the neoclassical growth model. The resulting econometric
panel model and methods with all its estimation and identification problems has been
extensively discussed. The possible econometric problems were carefully treated and taken
28
into account for a rigorous and robust empirical analysis. One might want to add a test for
endogenous growth, but this is clearly beyond the scope of this paper.
The estimates show that for most RIAs the coefficient of initial income is higher (in absolute
values with negative signs) for members compared to non-members. One therefore must
conclude that South-South-RIAs accelerate beta convergence. More importantly in our
opinion, since their returns on membership are positive, one could conclude that they produce
stronger growth -- but definitely do not hamper it. This contradicts the statement, that SouthSouth-Agreements would lead into the 'poor stays poor' trap. Particularities of the specific
regions were highlighted. Differences to existing studies are mentioned and discussed, but not
surprising as these mostly lack of econometric accurateness: for example, only a few handle
membership correctly in their model, they consider periods not corresponding to the existence
of a RIA, most report membership imprecisely in their data, econometric problems are often
ignored or inappropriately handled, and the performed inference is often questionable.
Reasons for our findings are discussed in the introduction and afterwards in Sections 3 and 4,
like for example sectoral coordination between the RIA-partners including the improvement
of infrastructure, political stability, the enforcement of international bargaining power,
coordinated treatment of transnational problems like AIDS or desertification and land
degradation, etc. These aspects are often neglected in economic theory models but substantial
for growth and development. The recent discussion is insinuating that most of the presently
existing South-South RIAs could even significantly better use its potentials by solving wellknown problems like e.g. the jumble of many overlapping RIAs and bi- or multilateral
agreements, and especially by improving their institutions (see the WTO action plan of 2010).
An also interesting but somewhat different point is to investigate the impact of RIAs on the
speed of convergence among members and to see what we can learn about the specific
29
differences in the speed of convergence. A growth theoretical approach forecasts for example
that land-locked countries such as Mali or Paraguay should show lower adjustment rates23.
Another politically crucial question is the convergence of income distributions inside a RIA,
see Sperlich and Sperlich (2012). It is argued that since the RIAs often help rather the small
and weak than the big and stronger members, South-South RIAs might not necessarily be
beneficial for all members, an especially hot topic in the MERCOSUR discussion. We hope
that this paper, together with the study of the above mentioned but still open questions, helps
to understand better the impact of South-South agreements on growth and convergence.
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Appendix I - Detailed introduction of the RIAs
MERCOSUR: The oil crises in the 70s caused inflation, recession and poverty in several
South American countries. At the same time, most industrialized countries strengthened their
34
policy of protectionism, such that potential regional trade partners became more important
alternatives. In 1986 Argentina and Brazil signed a bilateral agreement and started
negotiations with Uruguay and Paraguay. The MERCOSUR treatment of Asuncion was
concluded in 1991. The intended objectives at the time were the creation of a domestic market
with a free flow of goods, services, and any production factors between the member states.
This was to be achieved by the reduction of tariff and non-tariff trade barriers, a common
customs and trade policy, the coordination of macro-economic and sectoral policy in
agriculture and industry, as well as fiscal and monetary policy including exchange rates, etc.
After different crises the member states tried a restart in 2000 (Relanzamiento del Mercosur).
Basically, without changing the targets, they intensified their coordination, with some success
in the formation of an internal market and a customs union. Since 2005 there has also been
established a convergence fund to help the poorer regions from which mainly Paraguay has
been beneficiary. But continuing problems are the weak institutions of MERCOSUR, and the
differences of trade rules between full and associated members.
ASEAN: Officially the agreement started in 1967 with the declaration of Bangkok. But at that
time the motivation was driven by security policy. In 1976 followed the treaty of amity and
cooperation with principles like the one of non-intervention in internal affairs and national
sovereignty. This cooperation formed a basis for an economic opening and became more
dynamic after the end of the cold war, especially by new entries. In 1992 the ASEAN Free
Trade Agreement was signed, followed already in 1994 by the ASEAN Regional Forum. In
2007 the ASEAN Charter was signed, a common constitution accounting for the new
economic situation, the so called globalization. This Charter was to be the basis for national
reforms to reach rules of law and the protection of human rights. Finally, an ASEAN
investment area is planned for 2010. Besides, a free trade areas with the PR China, South
35
Korea and Japan, as well as with Australia and New Zealand, are envisaged. In fact,
ASEAN+3 was already founded 1997 in Kuala Lumpur going back to a Japanese initiative. In
2005 New Zealand and Australia commenced free trade agreements building the ASEAN+5.
As neither of them is a typical South-South RIA we limit our study to ASEAN.
According to UNECA, there exist in each African region at least three, partly overlapping
RIAs, e.g. in West Africa ECOWAS, WAEMU and the Mano River Union24, in Central
Africa CEMAC, ECCAS25 and CEPGL26. One goal of the Organization of the African Union
is the rationalization of RIAs like the enlargement of the CFA zone to comprise also the other
countries of ECOWAS. UNECA recommends that all RIAs of West- and Central Africa
institutionally join the framework of ECOWAS. But this is partly in conflict with the fact that
the different memberships have also varied functions.
The CFA-Zone: Originally, it goes back to the initiative of the former colonial power France
in 1945. The CFA had a fixed exchange rate to the French Franc and later (since 2001) to the
Euro. However, today the zone comprises two monetary areas: the one of the WAEMU with
the CFA-Franc BCEAO, and one of the CEMAC with the CFA-Franc BEAC, with two
central banks and two different monetary policies. Although Central African CFA francs and
West African CFA francs have the same peg to the other currencies (namely the Euro), West
African CFA coins and banknotes are not accepted in countries using Central African CFA
francs, and vice versa. Nevertheless, these two areas practice some economic cooperation.
24
Founded in 1973 by Liberia, Guinea, and Sierra Leone.
25
Economic Community of Central African States: founded in 1983, it entails Cameroon, Central African Rep.,
Chad, Equatorial Guinea, Congo, and Gabon.
26
Economic Community of the Great Lakes Countries: founded in 1976, it entails the former Belgian colonies
Burundi, Rwanda and the Democratic Republic of Congo.
36
ECOWAS: It was founded in 1975, ratified through the treaty of Lagos and includes 15
members. Since 1999 the ECOWAS is a free trade area and is planning a customs and
monetary union (deferred to 2009). An important cooperation field is the sectoral programs to
intra-connection of national electric grids and a regional pipeline for the distribution of natural
gas as well as the improvement of regional infrastructure like for example the Trans-AfricanHighway Dakar-Lagos. Since the end of cold war an effective regional security mechanism
has become important for this world region leading to further prosperity.
WAEMU: This union was founded in 1994 and has eight members. The common currency is
the CFA-France with the central bank BCEAO in Dakar. The WAEMU has a regional
parliamentary committee and a common court of law since 1998. The sectoral cooperation
concentrates on the improvement of the infrastructure, the efficient energy policy and
programmes for poverty reduction.
CEMAC: Like the WAEMU it was founded in 1994 and has six member countries. The
common currency is the CFA-Franc with the common central bank BEAC in Yaoundé. The
main targets of the RIA are macroeconomic coordination between all members, development
of a common market, improvement of the infrastructure and reduction of poverty and AIDS.
The main focus of this study is the question on per capita growth and convergence 'due to' or
'in spite of' South-South RIAs. The common features of the here considered Southern areas
show the importance and difficulties of such partnerships: (i) Cross-national problems such as
poverty, low per capita income, weak infrastructure and insufficient institutions etc. (ii)
Strong emphasis on sovereignty and non-intervention, e.g. non-uniform voting of members at
multilateral level as WTO or UN. (iii) Hardly any terms-of-trade gains, since the considered
integration areas are economically relatively small compared to the rest of the world. (iv)
37
Strong economic trade dependence to USA, Japan and Europe, which leads to relatively low
intra-regional trade and common economic activities.
Appendix II – An econometric panel model for exogenous growth
In this appendix we derive explicitly the panel model under consideration because we
detected many confusing and erroneous (at least imprecise) versions and statements
concerning the theoretical model in the related literature. Starting from the neoclassic Solow
model we consider the panel extension to the conditional income growth model. A model for
the income per capita is typically developed along the Cobb-Douglas model substituting the
steady state intensities of human and real capital for the present ones as an approximation. To
determine the steady state, we will briefly derive the human and real capital intensities. To
simplify the notation we will most of the time suppress the index t in the formula and only
include it where necessary. Let ke and he denote effective real capital, and human capital
intensities
respectively.
With
y=Y/L
per
effective
working
units
ye = Y / AL ,
At = A0 exp( Pt 'θ )e gt we can write
α
β
ye = ke he , and
ln y e (t ) = ln yt − ln A0 − θ ′Pt − gt .
(4)
The saving rates s K , s H are assumed to be exogenous, and let δ K , δ H denote the
depreciation rates of real capital and human capital. Let us denote the partial derivative of a
function g by ġ. The growths rates of he and ke result from differential equations:
k& e
s Y −δKK
s Y
K&
A& L&
=
− − = K
− g − n = K − (g + n + δ K )
ke
K
A L
K
K
k&e
y
− (1−α ) β
= sK e − ( g + n + δ K ) = sK ke
he − ( n + g + δ K )
ke
ke
⇔
(5)
With exactly the same steps we get
38
h&e
α − (1− β )
= sH ke he
− (g + n + δ H ) ,
he
(6)
Setting (5) and (6) to zero and inserting one into the other we end up with equations for two
unknown quantities, namely he and k e in each case. In the long-run steady state equilibrium,
one assumes the constancy of human and real capital intensity. To derive the formula of the
corresponding balanced income y e * , one equates both capital intensity equations under the
assumption of identical depreciation rates δ H = δ K = δ ,

sH
ke * = 
 g + n + δH



β /(1−α − β )

sK

 g + n + δK



(1− β ) /(1−α − β )
 s K 1− β s H β
= 
n + g +δ
1 /(1−α − β )




and
 s K α s H 1−α
he * = 
 n + g +δ




1 /(1−α − β )
leading to
α
β

sK sH
ye * = 
α +β
 (n + g + δ )
1 /(1−α − β )




,
cf. equations in (4). Taking the logarithm, the per capita steady state log output is thus
ln y e * =
α
β
α+β
ln s K +
ln s H −
ln(n + g + δ ) .
1−α − β
1−α − β
1−α − β
The transitional dynamics over the changes of capital intensities (5) and (6) are derived by
looking at the first order Taylor expansion around the steady state, i.e.
α
β
k&e
s k h
≡ G (k e , he ) = K e e − (n + g + δ K )
ke
ke
≅ G (k e *, he *) + G& ke * (k e *, he *)(k e − k e *) + G& he * (k e *, he *)(he − he *)
Because the effective capital intensity is in
(7)
k e *, he * constant, one can resume
G (k e *, he *) = 0 , and therefore the partial derivatives are (see upper line of equation (7))
G& ke * (k e *, he *) = s K (α − 1)k e *α −1 he *β / k e * and G& he * (k e *, he *) = s K β k e *α he * β −1 / k e *
Inserting this in the lower line of (7) gives
39
k&e s K (α − 1)k e *α −1 he *β
s β k *α he *β −1
≅
(k e − k e *) + K e
(he − he *) =
ke
ke *
ke *
s K k e *α he *β (k e − ke *) s K βk e *α he *β (he − he *)
(α − 1)
+
ke *
ke *
ke *
he *
(8)
sK ke *α he *β
In the steady state,
is equal to ( g + n + δ K ) , see equation (7), and for ke close
ke *
to ke * , and similarly he close to he *, one can approximate (8) by
k&e
≅ (α − 1)(n + g + δ K ) ln(k e / k e *) + β (n + g + δ K ) ln(he / he *)
ke
k&
⇔ e ≅ ( g + n + δ K )[(α − 1) ln(k e / k e *) + β ln(he / he *)]
ke
Analogously for the growth rates of effective human capital intensity we obtain
h&e
≅ (n + g + δ H )[α ln(k e / k e *) + ( β − 1) ln(he / he *)] .
he
Using both approximations in the partial derivatives gives for
y& e
k&
h&
= α e + β e , see equation (4), finally
ye
ke
he
y& e
≅ [(α − 1)(n + g + δ K ) + β (n + g + δ H )]α ln(ke / ke *)
ye
+ [α (n + g + δ K ) + ( β − 1)(n + g + δ H )]β ln(he / he *)
With the assumption δ H = δ K = δ this simplifies to
y& e
≅ (α − 1 + β )(n + g + δ )α ln(k e / k e *) + (α + β − 1)(n + g + δ ) β ln(he / he *) , and
ye
y& e
≅ −(1 − α − β )(n + g + δ )[α ln(ke / ke *) + β ln(he / he *)] , so that we end up with
ye
y& e
= −(1 − α − β )(n + g + δ ) ln( y e / y e *) , i.e.
ye
40
y& e
≅ −λ ln( ye / ye *)
ye
or
with
λ = (n + g + δ )(1 − α − β ) ,
d ln( ye (t )) y& e
=
≅ −λ ln( ye / ye *)
dt
ye
(9)
Factor λ is known as the so called convergence coefficient, which determines the speed of
adjustment to the steady state y e * . Clearly, the speed of convergence is not constant and
depends on the respective distance to the steady state, or, in other words, growth depends on
the “initial” state i.e. on the state at time t-1 for longitudinal data. The hypothesis is that the
further a country is distant from the steady state, the faster is its speed of adjustment. If we
integrate (9) with respect to time from t1 to t2 by using results from the analysis of differential
equations, referring to the differential equation
y& e ≅ −λ ln( y e / y e *) y e * , we obtain y e = y e * exp(e − λt ) and consequently
ln( y e (t 2 ) / y e *)
= e −λ ( t2 −t1 ) .
ln( y e (t1 ) / y e *)
This now is equivalent to
ln( y e (t2 )) = (1 − e − λτ ) ln( y e *) + e − λτ ln( y e (t1 )) , where τ = t2-t1 , or
ln( y e (t2 )) − ln( y e (t1 )) = (1 − e − λτ ) ln( y e *) − (1 − e − λτ ) ln( y e (t1 )) .
(10)
Recall that the steady state may vary over the countries depending on sH, sK leading to the
conditional growth, respectively income model. In the literature of panel analysis of growth
and income it has not been discussed in depth from which point in time t the factors n, sH , sK,
(and P in our case) have to be taken. Typically they are taken from period t2. In that case,
however, there might be a problem of endogeneity due to simultaneity. To avoid this, we
assume them to be constant in the period from t1 to t2 and work with its period averages, say
sK(t1,2), etc. Then, substituting for ye* in (10)
41
ln( y e (t 2 )) − ln( y e (t1 )) = (1 − e −λτ )
− (1 − e − λτ )
α
β
ln(s K (t1, 2 )) + (1 − e −λτ )
ln(s H (t1, 2 ))
1−α − β
1−α − β
α+β
ln(n(t1, 2 ) + g + δ ) − (1 − e − λτ ) ln( y e (t1 ))
1−α − β
for the conditional panel data model. We get finally the conditional growth model
ln(y(t 2 )) − ln(y(t1 )) = (1 − e −λτ )
− (1 − e −λτ )
α
β
ln(s K (t1,2 )) + (1 − e −λτ )
ln(s H (t1,2 ))
1−α − β
1−α − β
α +β
ln(n(t1,2 ) + g + δ ) − (1 − e −λτ ) ln y(t1 )
1−α − β
(
)
+ (1 − e −λτ ) ln A0 + (1 − e −λτ )θ ' P(t1, 2 ) + g t 2 − t1e −λτ ,
recall the definition of y e in equation (4). Note that in some steps one uses (linear)
approximations, so that this formula is not only subject to economic assumptions and countryspecific distortions but also to all simplifications in the model. Finally, when disregarding the
covariates controlling for capital and human capital, n, g and δ, we end up with equation (3).
42