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POLICY RESEARCH WORKING PAPER 1708
Has Latin America s The response of economic
growth to reforms in Latin
Post-Reform Growth America has not been
Been Disappointing? disappointing. Because of
those policy changes, and
despite a global slowdown,
William Easterly Latin America did well to
Norman Loayza return to its historic growth
Peter Montiel rate of 2 percent per capita in
1990-93.
The World Bank
Policy Research Department m
Macroeconomics and Growth Division
January 1997
POLICY RESEARCH WORKING P5APER 1 7108
Summary findings
After years of poor macroeconomic performance, mana slowdown, lIatin America did w.ell to return to its
Latin American countries undertook ambitious programs historic growth r ate of 2 percent per capita in ] 990-93.
of macroeconomic stabilizar!on and structural reforrn in Latin American growth has responded to changes in
recent years. This change in policy crtated high policy variables, as would hiave been predicted by the
expectations for the region, and some observers have experience of other times and places, 'fThose earlier
questioned whether actual growth outcornes in several experiences are summarized by a panel regression
Latin American countries have lived up to thiese spanning manv countries and mnultivear periods from
expectations. 1960 to 1993.
Easterly, Loayza, and Montiel offer evidence that the Io get consistenit estimates ot the parameters linking
response of economic growth to reforms in Latin growth and policy variables, the autliors use a dynamic
America has not been disappointing. Because of those panel methodology that both conrtrols for unobserved
significant policy changes, and despite a global time- and country-specific effects and accounts for the
likely joint endogeneity of che explanatory variables.
This paper - a product of the Macroeconomics and Growth Division, Policy Research Department -- is part of a larger
effort in the department to understand the determinants of economic growth. Copies of the paper are available free from
the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Martin, room N1 1-059, telephone
202-473-1320, fax 202-522-3,518., Ilternet address rmartinl(Ii(tworldbaink.org. January 1997. (34 pages)
The Policy Research Working l'aper ˇeries disseminates the findings of ivork in progress to encourage the exchange of ideas aboutI
development issues. An objectie oj the serics is to get the findings out quickly, even if theR presentations are less than lisly polished. The
papers carry the names ot the authors and should he cited accordingly. IThe /:ndings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They (1o not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Imrodtuccd by the Policy Kesearch Disscmination Center
HAS LATIN AMERICA'S POST-REFORM GROWTH BEEN DISAPPOINTING?'
William Easterly, World Bank
Norman Loayza, World Bank
Peter MontieL Williams College
'We are very grateful for assistance from Raimundo Soto and Wanhong Hu and for
preliminary discussions with Sebastian Edwards. We thank Francesco Caselli, Raul Hopkins, Aart
Kraay, Ross Levine, Klaus Schmidt-Hebbel, participants of the Econometric Society Meetings in
Rio de Janeiro (August 1996), and three anonymous referees for their helpful comments and
suggestions. We kindly thank Stephen Bond for having provided us with the program to rnm
dynamic panel data estimation.
I
HAS LATIN AMERICA'S POST-REFORM GROWTH BEEN DISAPPOINTING?
L Introduction
After years of poor economic performance, many Latin American countries undertook
ambitious programs of macroeconomic stabilization and structural reform during recent years. 2 The
change in policy created high expectations for the region. Many observers question whether actual
growth outcomes in several Latin American countries have measured up to such expectations. Paul
Krugman (1995) used Latin American examples to note that "the real economic performance of
countries that had recently adopted Washington consensus policies ... was distinctly disappointing."(p.
41) The World Bank's Vice President and Chief Economist for Latin America noted in 1995 that
"after all the reforms, the efforts, and the accolades from the financial media, the region as a whole
is making little progress towards breaking out of the quagmire of poverty."(Burki and Edwards,
1995). Sebastian Edwards, the aforementioned World Bank Chief Economist for Latin America, said
in a separate publication about Latin America, "the results in terms of growth and social progress
have not yet met expectations" (Edwards 1995). In like vein, Dombusch and Edwards (1995)
conclude a review of Latin American (and Middle Eastem) reforms: "Even though structural reforms
appear to be a necessary condition for growth, they are not a sufficient one."
Economic growth has been faster in the region during the nineties than during the previous
decade (Figure 1). But the growth recovery in the aggregate can be judged inadequate by several
standards -- in comparison with contenporaneous growth in the East Asian Miracle countries (Figure
2Excellent overviews of the stabilization and structural components of reform in the region
are provided by Edwards 1995.
1
I again), or with "desirable" growth rates in the region itself-- that is, rates of growth sufficient to
reduce the incidence of poverty, or to restore previous levels of per capita income in a reasonably
short time. Would expectations of faster growth have been unreasonable, given the reforms actually
implemented? Is there a puzzle why Latin American growth has not been faster? Does general
disappointment about Latin American growth reflect overemphasis on particularly vivid cases, like
that of Mexico?
The simplest approach to these questions would be to analyze whether reforming countries
are growing faster than non-reforming ones and/or faster than before the reforms. Such an approach
is complicated, however, by a number of factors. First, non-reform determinants of growth may be
quite different across countries, so that reforming and non-reforming countries may have grown
differently even without the reforms. Second, Latin American countries differ in reform initiation and
duration, as well as in depth and breadth of the reforms. Third, we would like to evaluate how the
growth payoff to a given amount of reform in Latin America in the 1990s compares to reform
experiences in other regions and times.
These questions point toward a cross-country framework in which we relate countries'
changes in growth rates to changes in economic policies, controlling for other factors and for initial
conditions. Our procedure will be to estimate a growth equation based on this framework for all
regions and time periods for which data are available, and then ask how incremental growth achieved
by Latin America in the 1990s compares to the sample as a whole. The estimation of the average
effects of reforms on growth using this approach wil allow us to identify countries whose experience
differs significantly from what would be predicted on the basis of the reforms they have undertaken.
We will thus be able to address the question of whether there is a "growth puzzle" for specific
2
countries. We will examine the experience of all Latin American countries that have the complete
set of data on growth and policies.
This framework builds on the existing cross-country growth literature that quantifies the
effects of a variety of policies on long-term growth, after controlling for non-policy variables.
Previous exercises in this literature in the same spirit include Barro and Lee's (1993) examination of
winners and losers in economic growth; Easterly and Levine's (1995) examination of the low average
growth in Africa; Bouton, KigueL and Jones' (1994) examination of the payoffs to reform in Africa;
and Corbo and Rojas' (1993) and De Gregorio's (1992) examination of macroeconomic adjustment
and growth in Latin America (see also the early treatment of Cardoso and Fishlow 1989). Use of
panel data similar to ours in the literature is given in Barro and Sala-i-Martin (1995) and Caselli,
Esquivel, and Lefort (1995). Our framework makes a new contribution to this literature by
performing an evaluation of Latin America's reforms based on the changes in growth performance
associated with reforms, while explicitly addressing some of the econometric problems created by the
first-difference specification.
The rest of the paper is organized in three sections. Section II describes the empirical
methodology. The results are described in Section Im, and the concluding section summarizes the
implications of the exercise for the process of economic reform in Latin America.
IL Measuring the Average Effects of Reforms on Growth
1. Cross-section versus time series
The existing empirical literature on growth has mainly used cross-country data without much
consideration oftheir time-series dimension. In principle, it would be possible to apply the standard
techniques to our problem by relying on cross-country growth regressions based on observations
3
averaged only over the most recent (reform) years. For our purposes, however, the alternative of
employing panel estimation has several advantages. First, our question is inherently a time series
question: how did growth change when policy changed? Second, because the variables of interest
vary significantly over time, their time series provide a considerable wealth of information ignored
in cross-sectional averages. The gain in degrees of freedom by employing panel data is particularly
important when a relatively large number of explanatory variables are used, as will be necessary to
characterize the muliple dimensions of reform in Latin America. Third, the use of panel data allows
us to control for, and assess the importance of; time-specific effects (in the form of world economic
conditions) as well as country-specific effects. Fourth, the likely endogeneity of some explanatory
variables can be accounted for by using previous observations of the variables in the panel as
instruments. For these reasons, the estimates of policy (and non-policy) effects on growth obtained
using panel data will be more consistent and efficient than those using only cross-sectional data.
At the same time, the use of the time-series dimension presents some problems for growth
regressions. The effects of policies on growth are likely to exhibit complicated dynamics, which may
be obscured by temporal effects emanating, for example, from the business cycle. Cross-section
regressions avoid these problems by focusing on long-run effects. We will attempt to do the same
here, while exploiting the time-series variation in the data, by using panels based on five-year
averages.
Our estimation strategy will be based on the following regression equation:
GRi, = Y'RVI, + CVI, + TEt + YCE + y + rIl + E, j (1)
where the subscript i denotes a given country; time periods are normalized so that the subscript t
4
refers to a five-year interval; GR is the average growth rate of per capita GDP; RV is the set of
variables that measure the extent of economic reform; CV is the set of control variables for which
panel data are available; TE is the set of time-specific variables; CE is the set of country-specific
variables; u and il are, respectively, the unobserved country-specific and time-specific effects; and
E is a white-noise error term. The sets CV, TE, and CE all contain growth determinants arguably
independent of the reform process. These variables control for the non-reform determinants
described above.
2. Variables
As indicated in the introduction, the empirical cross-country growth literature has identified
a number of both policy and nonpoficy variables that are correlated with growth performance across
countries. Our strategy is to rely on such variables both to serve as indicators of the depth and
breadth of reform as well as to control for non-reform determinants of growth performance.
Operationally, the following set of variables provides a reasonably complete and objective measure
of the various dimensions of economic reform implemented in Latin America (the set RV):
a. With regard to macroeconomic stabilization, we rely on both the log of the average
inflation rate and the log of the average ratio of government consumption to GDP. Lowering
inflation has been the central objective of stabilization policy in Latin America. While Bruno and
Easterly (1995) have pointed out that growth has little relation to inflation at rates below about 40
percent annual Latin America has provided precisely the kind of one-time high inflation experiments
whose growth effects Bruno and Easterly found to be easily detectable in panel data like that used
by the current paper (Fischer 1993 and De Gregorio 1992 found such growth effects).3 Government
3We will also explore a dummy variable for whether inflation is above 40 percent annual.
5
consumption is arguably a good indicator of credible and permanent fiscal adjustment, and has played
a prominent role in previous empirical growth studies (Barro 1993, Barro and Sala-l-Martin 1995).
b. Our indicator offinancial reform is the traditional measure of financial deepening -- the
average ratio of broad money (M2) to GDP. The work of King and Levine (1993) has shown a
strong and robust association between financial depth and subsequent growth.4
c. We have tried to capture reform of the external sector, encompassing both trade reform
and liberalization of regulations governing foreign exchange transactions, by using the log of the
average black market premium and the average trade share of GDP as explanatory variables.
Among a large number of alternative measures of increased openness and international market
oxientation, the black market premium has proven to be robustly correlated with growth performance
in previous studies (e.g. Levine and Zervos 1993, Fischer 1993, Easterly 1994, Barro and Sala-I-
Martin 1995). The trade share has fared less well in empirical studies (e.g. Harrison 1996), but we
include it here to see if it works better under our econometric approach.
d. Finally, we have attempted to capture the effects of other structural reforms, such as
privatization of public enterprises, the resolution of debt-overhang problems, liberalization of the
foreign direct investment regime, etc., through the inclusion of the log of the average ratio of
investment to GDP. Investment to GDP is also obviously endogenous to unobserved characteristics
that influence growth (or possibly to growth itself), but we will see in the next section that we address
endogeneity of this variable as well as all the other variables by instrumenting with initial values.
'We deflate end-of-year nominal M2 by the end of year CPI, and nominal GDP by the
average-of-year CPI. This deflation is an important correction to the usual practice of taking the
ratio of end of year nominal M2 to nominal GDP -- otherwise the M2/GDP ratio wil be
artificially high for high inflation countries. Though known to practitioners, we are not aware that
this correction has been used in the literature before.
6
All told, then, our regression includes a total of six variables intended to measure the extent
of various types of economic reform. Undoubtedly, many other variables could have been used for
this purpose. Our choices were guided by the criteria that the variables chosen to represent the
reform phenomenon should be sufficiently varied and extensive as to capture the diverse aspects of
reform in Latin America, should be in wide use in the growth literature as indicators of policy stance,
and should be available for a large group of countries, particularly in Latin America. We consider
that an appropriate set of reform indicators should both capture the process of reform in Latin
America -- ie., they should move in the qualitative direction associated with reform for the countries
in our sample -- and should explain growth. As just mentioned, judgments regarding the latter are
based on performance in previous studies. With regard to the former, Table 1 presents changes in
the average values of each of our reform variables for all of the Latin American countries in the
sample from 1986-90 to 1991-93. Of sixteen such countries, thirteen registered a reduction in the
rate of inlation, nine registered declines in the share of government consumption in GDP, and fifteen
experienced an increase in the volume of trade as a ratio to GDP (openness). Twelve of the sixteen
achieved some extent of financial deepening by our measure. while the average black market premium
declined in eleven countries. Finally, eleven countries achieved an increase in the share of investment
in GDP. Overall, then, we conclude that the indicators we have chosen do seem to capture the
reform phenomenon for the Latin American countries in our sample.5
As mentioned above, we intend to control for non-reform determinants of economic growth.
5Some of the variables we have chosen to capture the reform process can best be
understood as intermediate targets of reform, rather than direct policy instruments. However, the
dearth of cross-country data on more direct measures of individual policies has rendered variables
such as those listed above the policy indicators of choice in the empirical growth literature.
7
Panel-data control variables (the CV set in regression equation (1)) are initial per capita GDP,
average population growth, initial average number of secondary-school years of the adult
population, and average terms-of-trade changes. The first three of these are standard variables in one
form or another in virtually all growth regressions. Including initial per capita GDP in the model is
necessary in order to control for the possibility of "rmean reversion" as an explanation for improved
growth performance following a recessionary period; controlling for "mean reversion" is especially
important for our purposes because reforms tend to be implemented in periods of poor growth
performance, and, thus, their effect on growth could otherwise be confused with the simple dynamics
of growth recovery.
The effect of terms-of-trade changes on growth appears in many studies (such as Easterly,
Kremer, Pritchett, Summers 1993; Fischer 1993; Barro and Sala-i-Martin 1995). We are careful to
include it here because of our relatively short time units (5-year periods) and because we want to
distinguish growth associated with reform from that associated with external factors. (Note that
except for initial per capita GDP and educational achievement, we use period averages for all
variables of interest.)
As explained next, time-specific ( the TE set) and country-specific variables (the CE set) are
eliminated in the process of controlling for their unobserved counterparts.
For both efficient estimation and comparison purposes, we intend to use all countries for
which data are available in the period 1960 to 1993. Our sample, dictated by data availability
considerations, consists of an unbalanced panel of 70 countries. Time periods will be defined as non-
overlapping 5-year intervals, except the last one which consists of only three years. As mentioned
above, we prefer to use 5-year rather than annual periods to smooth out data variability due to
8
transitory fluctuations. The influence of cyclical fluctuations and measurement error in the data,
which produces a bias in coefficient estimators, is reduced when averages over relatively long
horizons are used. The composition of the panel and data sources are described in the data appendix.
3. Econometric Procedures
The estimation of the parameters of interest poses two econometric problems that our
methodology can address: the presence of unobserved effects and the likely endogeneity of some of
the regressors. Controlling for unobserved time- and country-specific effects is necessary because
they may be correlated with the right-hand side variables, and thus bias the coefficients if omitted.
The unobserved time-specific effects are controlled for by using time-period dummies; this entails the
elimination of information related to those variables that vary across time periods but not across
countries. After controlling for time specific effects, regression equation (1) can be rewritten as
follows:6
YiJ.Y"IJI = (a-l)Yi,-1 + PXiJ + y's + pi + ei (2)
where y is the natural logarithm of per-capita GDP, x is the set of explanatory variables for which
both time and cross-sectional data are available, s is the set of explanatory variables for which only
cross-sectional data are available, and the time periods are normalized so that the time subscript t
6Renaming the sets of variables as in eq. (2) makes the explanation of the econometric
methodology, especially in the appendix, easier. Equations (1) and (2) are related as follows.
Concerning the dependent variable, if we normalize the period length to one, the growth rate,
GR,,, in eq. (1) is equal to the log difference of per capita GDP, yj, - Y,, ,, in eq. (2). Concerning
the explanatory variables, initial per capita GDP, yi, , in eq. (2) belongs to the set of panel control
variables, CVJ', in eq. (1) ; the rest of panel control variables and all reform variables, RF1,, are
included in the set x,, in eq. (2); lastly, the set of variables s, in eq. (2) is the same as the set of
country-specific variables, CE,, in eq. (1).
9
refers to a five-year interval.
Following Anderson and Hsiao's (1982) procedure to account for unobserved country-
specific effects, all variables in equation (2) are first-differenced. This eliminates not only the
unobserved country-specific effects but also all variables for which only cross-sectional information
is available. Using differences has an appealing intuition when we are trying to answer the question,
"how did growth respond to reform?", since we are actually relating changes in growth to changes
in policy.
After first-differencing and rearranging the terms of the dependent variable, regression
equation (2) becomes,
"X_Yi,t-I= a(y,1 l-yi,1i-2) + WU( x_-x_) + (Ejt-IEj_) (3)
Note that first-differencing introduces a correlation between the new error term and the
differenced lagged-dependent variable. Therefore, OLS estimation of equation (3) would produce
biased results, even when the set of variables x is strictly exogenous. Assuming that the ei, are serially
uncorrelated, that is, E(Ej,,e,) = 0 for t o s, values of y lagged two periods or more are valid
instruments in the equations in first differences.
The second econometric problem to be addressed is the likely endogeneity of some of the
regressors. Given that the problem of reverse causation applies to most variables in the set x,
assuming that they are strictly exogeneous would lead to inconsistent estimation. Assume, rather,
that x are only weakly exogenous in the sense that E(xL,E,S) * 0 for s 5 t and zero otherwise. Then,
values of x lagged two periods or more are valid instruments in the equations in first differences. (In
actual estimation, we assume that all variables are only weakly exogenous except for two, which are
10
taken as strictly exogenous; they are the average number of secondary school years in the adult
population and the change in the terms of trade.)
The assumptions that the error term is serially uncorrelated and that the explanatory variables
are weakly exogenous imply a set of moment restrictions that can be used in the context of the
Generalized Method of Moments (GMM) to generate consistent and efficient estimates of the
parameters of interest. This methodology, more fully described in Appendix I, follows work by
Chamberlain (1984), Holtz-Eakin, Newey, and Rosen (1988), and Arellano and Bond (1991) on
dynamic panel data estimation.
The consistency of the GMM estimator depends on whether lagged values of income and the
other explanatory variables are valid instruments in the growth regression. A necessary condition for
the validity of such instruments is that the error term e,, be serially uncorrelated. To address these
issues we present two specification tests, suggested by Arellano and Bond (1991). The first is a
Sargan test of over-identifying restrictions; it tests the overall validity of the instruments by analyzing
the sample analog of the moment conditions used in the estimation process. The second test
examines the hypothesis that the error term in the differenced regression, e, - ei, ,, is not second-
order serially correlated, which implies that the error term in the level regression, E,,, is not serially
correlated.' Under both tests, failure to reject the null hypothesis gives support to our model.
[IL Results
1. Interpretations with and without investment
7Actually, lack of second-order serial correlation in the differenced residual is also
consistent with the level residual following a random walk; in this case, however, there will also
be no first-order serial correlation in the differenced residual. Tests of first-order autocorrelation
applied to our data strongly reject the possibility of a random-walk level residual.
11
We estimated two versions of equation (3), with and without the investment variable. The
reason is that the interpretation of the role of this variable is problematic even after we address its
endogeneity. Including investment in the equation also has implications for the interpretations given
to the coefficients of the remaining variables. We can think of the investment variable as capturing
the effects on growth through the investment channel of all reforms, including those already in the
equation. The coefficients on the included policy variables in the regression with investment included
would then indicate the contribution of the relevant type of reform to the return on investment.
Investment could also be capturing the effects of reforms that are difficult to quantify and are not
included in the regression, like privatization, deregulation of labor markets, and so on. However,
investment could conceivably be changing for exogenous reasons unrelated to reform, so we could
be miscalculating the growth associated with reform when we call investment a reform variable.
Hence we define the set of reform variables with and without investment.
The estimation results are presented in Table 2. Column (1) presents the results of the
regression including the investment ratio; column (2) presents those obtained when the investment
ratio is excluded. The key finding is that the variables we have identified perform very well in
explaining changes in cross-country growth performance over five-year periods in our sample. In all
cases, the policy and control variables have the theoretically-expected sign, and except for the ratio
of government consumption to GDP, are statistically significant at conventional significance levels.8
'When we try a dummy variable for inflation above 40 percent instead of the continuous
inflation variable, we find it to be significant in a one-sided test at the 5 percent level in the
regression without investment and insignificant in the regression with investment. Brnno and
Easterly 1996 show stronger results with such a dichotomous treatment of inflation when they
test for growth differences between the high-inflation and low-inflation periods within each
country.
12
The procedure we are using improves the precision of estimates compared to conventional OLS
growth regressions, so we actually find significant results on variables like openness that do not
generally survive statistical tests in growth regressions. Overall, the explanatory power of the
regression is 0.46 when investment is included and 0.35 when it is not.
2. Coefficient estimates
For our purposes, the magnitudes of the parameter estimates are of central importance. In
what folows, we compare our results (in the model where investment is included) with those found
elsewhere in the growth literature. In particular, we focus on two recent studies that also relied on
panel data -- those of Barro and Sala-i-Martin 1995 (henceforth B-SM) and Caselli, Esquivel, and
Lefort 1995 (henceforth C-E-L). They are two of the best and most recent studies on growth using
panel data. B-SM use a "random-effect" model, which assumes that the unobserved country-specific
effects are uncorrelated with the explanatory variables. If as we believe, these country-specific
effects are correlated with the regressors, B-SM's estimates will be biased (upwards if the partial
correlation between the regressor and growth-enhancing specific effects is positive, and viceversa).
C-E-L's estimation procedure (based on time-differencing and the application of the generalized
method of moments) is very similar to ours. B-SM consider two ten-year periods: 1965-75 and
1975-85 (data for 1960-65 and 1970-75 are respectively used as instruments for the two ten-year
periods); and C-E-L consider five five-year periods from 1960 to 1985. Both B-SM and C-E-L
attempt to account for possible joint endogeneity by using previous observations of each endogenous
regressor as instruments for it.
Beginning with the policy variables, we are able to compare our results with those in the
previous studies for four of the five policy variables. We estimated the coefficient of the ratio of
13
government consumption to GDP as -0.46. Not being statistically significant, this estimate falls
between the negative estimate reported by B-SM and the positive one by C-E-L. The estimated
coefficient of the black-market premium on foreign exchange, - 1.12, is of the same sign but
smaller in absolute value than the estimates in B-SM and C-E-L, both of which are around -3.0.9
Our estimated coefficient of 1.71 for financial depth is similar to B-SM's 1.6. Finally, we estimated
a coefficient of 4.07 for the share of investment in GDP; this value is significant and somewhat
larger in size than C-E-L's estimate. B-SM also find a positive but smaller and statistically
insigficant estimate. B-SM claim that their result shows that the high positive correlation between
investment and growth is due to causation from (expected) growth to investment and not the reverse.
C-E-L's and our estimate show that there is also causation from investment to growth.
Turning to the control variables, the estimated coefficient of initial per capita GDP, -4.75,
implies a convergence rate of 5.42% per year and, thus, a half life of 12.8 years. The "traditionar'
convergence rate found in papers based on cross-sectional regressions, such as Barro and Sala-I-
Martin (1992) and Mankiw, Romer, and Weil (1993), is 2.0%. Convergence rates estimated through
panel-data regressions are higher than the "traditional" rate: B-SM's estimate is 3.0%, Loayza's 1994
estimate is 4.94%, and C-E-L's estimate is 10%. We obtained a larger (in absolute value) estimate
ofthe effect of population growth (-0.99), than did B-SM (-0.63). Finally, with regard to changes
9'here are two reasons why our estimate is smaller: First, we include inflation in the
regression, which is positively correlated with the black-market premium (the correlation
coefficient is about 0.23). Second, they only use data up to 1985; when we performed the
estimation with data from 1960 to 1985, we found an estimated coefficient of about -2.5; that is,
the partial correlation between the black-market premium and growth in the last two periods
(1986-1990 and 1991-1993) is weaker than in the previous five periods. For a given change in
each of these variables in the reforming countries, then, our results would generate smaller effects
of refoms on growth than would be obtained from previous estimates.
14
in the terms of trade, our estimated effect, associated with a coefficient of 7.76 is in between those
of C-E-L (5.66) and B-SM ( 11.0).
These comparisons show how sensitive the coefficient estimates are to the estimation
procedure and to time periods. We believe our procedure is preferable to that of B-SM because of
likely correlation between country effects and the righthand side variables, and preferable to C-E-L
for our purposes because of the inclusion of the reform-relevant last 8 years (1985-93). We
acknowledge some concern about the stability of the relationships we are estimating. This instability
is reminiscent of the results by Levine and Renelt (1992) about the sensitivity of coefficients to
regression specification. As in Levine and Renelt, however, the instability comes about because of
difficulty in separating out effects of different policies; the estimated aggregate effects of policy
packages are more stable than the estimated effect of each policy in isolation.
3. Validity of the instruments
As explained in section II, the validity of lagged values of income and the explanatory
variables as instrments is crucial to the consistency of the GMM estimator. According to the Sargan
and serial-correlation test statistics presented in Table 3, our econometric model specification cannot
be rejected. In fact, in the regression that includes investment as an explanatory variable, the p-value
for the Sargan test is 0.59, and that for the second-order serial correlation test is 0.67. When
investment is excluded, the p-value for the Sargan test, 0.41, is lower but still above standard
significance levels, and the p-value for the second-order serial correlation test, 0.64, clearly supports
the assumption of lack of serial correlation in the growth-regression error terrm 0
lOAs mentioned in a previous footnote, the hypothesis of no first-order serial correlation in
the differenced residual is strongly rejected, thus eliminating the possibility that the level residual
follow a random walk.
15
4. Is Latin America unider-achieving?
To calculate the contribution of the reforms to recent growth performance in Latin America,
we calculated fitted values using both versions of equation (1) for each of the sixteen Latin American
countries in our sample. These fitted values are reported in Table 3 for the version of eq. (1) that
includes the investment ratio, and in Table 4 for the version that does not. The short answer to the
question of whether Latin America's post-reform growth has been disappointing is given at the
bottom of the last column in each table, which provides the average value of the residuals from the
estimated regression for the sixteen countries in the table. The answer is no. The average value of
the growth residual for the region is positive, indicating that, when the effects of the reforms are
estimnated on the basis of international experience over the entire 1960-93 period, and when
"permanent" characteristics of the countries themselves as well as the state of the international
economy are controlled for, Latin American countries have performed on average better than would
have been expected."1 Thus the growth puzzle for the region as a whole, if there is one, would be
why it grew so rapidly, rather than so slowly, in the immediate aftermath of economic reform.
However, a more appropriate conclusion for the region as a whole may be that there is no puzzle at
all, since the average estimated residual for the sixteen countries is not significant in either table.
The experience of individual countries is of independent interest. Of the sixteen Latin
American countries in the worldwide sample, eleven produced positive residuals in Table 3, and ten
in Table 3. No negative residual was found to be significantly different from zero when the
investment ratio was included in the regression, and only one, in the case of Peru, when the
"Bruno and Easterly (1995) have a similar, even stronger finding for countries stabilizing
from high inflation: after stabilization, growth was significantly above both the world average
growth and the country's own pre-crisis average growth.
16
investment ratio was excluded. Thus the regional average reflects substantial uniformity across
countries. The largest growth improvements were predicted for Argentina and Peru, two relatively
recent and ambitious reformers. By contrast Chile, a very early reformer with little additional reform
in 1990-93, was predicted to experience a growth slowdown in 1990-93, albeit one entirely driven
by a less favorable international environment. It is interesting to note that Mexico, whose slow
growth during recent years probably triggered much of the concern with growth in the region, did
grow slower than predicted in Tables 3 and 4, although neither residual is significantly negative.
5. Comparing Latin America with the East Asia Miracle, 1990-93
Latin America is often unfavorably compared with East Asia. We can utilize our equation to
see how Latin America's change in growth in 1990-93 compared to East Asia's, and how much of
the relative change in growth between the two regions is explained by their respective policy changes.
In Table 5, we show a comparison between the East Asian Miracles and Latin America of the
changes in growth and in country characteristics for 1990-93 on the right-hand side on our growth
regressions in Table 2. (The six East Asian Miracle countries over which we average -- because they
have all the necessary data-- are Indonesia, Japan, Korea, Malaysia, Singapore. and Thailand. Latin
America means the average for the sixteen countries from Table 3.) Two facts jump out from the first
two columns of Table 5. First, Latin America closed the growth gap in 1990-93 compared to 1985-
90, since Latin America's growth went up and East Asia's growth went down. Latin American
growth was still below East Asia's in 1990-93 (figure 1), but not as far below as in the previous
period. Second, Latin America was not alone in reforming. The East Asian miracles also achieved
increases in trade volume, financial depth, and investment ratios to GDP in 1990-93. However, Latin
America's reduction in inflation and in the black market premia had no counterpart in East Asia, since
17
East Asia did not have high inflation or a high black market premium in the previous period.
On balance, the policy changes in Latin America relative to East Asia correctly predict that
Latin America's growth should have risen relative to East Asia's in 1990-93. The strongest effect
comes from Latin America's reduction in inflation, with another strong effect from its reduction in
the black market premiumm Another important. non-policy determinant of growth is the change in
initial level of income, which is strongly positive in East Asia and close to zero in Latin America.
According to the standard convergence result, the increase in initial income was a disadvantage for
East Asia in 1990-93.
Adding all effects together, our estimated equation explains most or all of the fall in the East
Asia - Latin America growth differential. Our resuhs suggest that there is nothing "miraculous" about
the relative growth performances of East Asia and Latin America during the period of Latin American
reform. Latin America's growth performance is not falling short relative to the degree of policy
change, even compared to the "miracle" economies of East Asia. If Latin American reform did not
close the gap with East Asia even more strongly, it was because financial depth, openness, and
investment were also improving in East Asia.
IV. Conclusions
The response of economic growth to reforms in Latin America has not been disappointing.
Latin American growth has responded to changes in policy variables as would have been predicted
by the experience of other times and places, as summarized by a panel regression spanning a large
number of countries and (mostly) 5-year periods from 1960 to 1993. Part of the perception of
disappointing growth in Latin America in the 1990s mav really be reflecting the disappointing growth
in the world as a whole, since the 1990s have registered poor growth rates in all countries on average,
18
including reforming economies, non-reforming economies, and those that did not need to reform.
Latin America did well to return to its historic rate of growth of 2 percent per capita in 1990-93 in
spite of the global slowdown. This growth recovery was associated with the significant changes in
policies achieved in Latin America by the 1990s.
Another part of the perception of disappointing growth may be because the standard of
comparison may be not the average performance in the worldwide sample, but the maximum
performance in the worldwide sample -- namely, the East Asian Miracles. But even if East Asia is
taken as the standard of comparison, the predicted change in the East Asia-Latin America growth gap
is the right size and sign. Latin America's growth rose relative to East Asia's because it reformed
more (since it had more to reform).
Perhaps when we compare Latin America's policy improvement with East Asia's, some
disappointment is valid. Still we wonder how useful are the incessant policy comparisons of any and
all industrial and developing regions with the best performing one, East Asia. Economic policy in the
world as a whole cannot be like Garrison Keillor's Lake Woebegon, "where all the children are above
average." By normal standards, Latin America's policy reforms and accompanying growth recovery
have been an impressive achievement.
19
Table 1: Indicators of Economic Reform in 16 Latin American Countries, 1991-93 Relative to
1986-90
Change in Change in Change Change in Change in Change in
ratio of rate of in ratio ratio of black market the ratio of
government inflation of M2 to investment to premium volume of
consumption GDP GDP trade to
to GDP GDP
Argentina 0.90 -518.73 1.80 -0.46 -0.35 6.05
Bolivia -0.21 -28.56 19.03 3.64 0.09 3.98
Brazil 1.78 358.80 7.58 -3.01 -0.41 4.32
Chile -0.88 4.69 0.08 1.33 -0.11 11.65
Colombia 0.84 -3.13 0.80 -1.05 -0.08 9.18
CostaRica 0.06 4.70 1.83 2.00 0.09 17.25
Ecuador -3.35 -1.73 -2.22 0.84 0.08 5.72
El Salvador -2.40 -8.99 4.33 2.39 -1.65 13.13
Guatemala -1.50 -3.57 2.36 3.65 -0.37 6.97
Honduras -2.62 7.04 -0.98 6.90 -0.45 -1.48
Mexico 0.77 -55.15 10.64 2.20 -0.10 13.15
Panama -4.28 -0.20 18.00 9.70 0 7.05
Paraguay 3.15 -11.52 7.71 -0.63 -0.02 36.21
Peru -1.68 -585.38 2.93 -3.38 -1.09 4.99
Uruguay 0.26 -8.77 -2.56 1.56 0.09 19.05
Venezuela -0.89 -6.68 -4.36 1.11 -1.30 5.13
20
Table 2: Growth Improvement Determinants'
Dependent Variable Real per capita GDP Growth
(1) (2)
Volume oftrade/GDP' 2.50 1.24
(7.13) (3.35)
Gov. casunnptip/GDP` -0.46 -1.33
(-1.32) (-3.68)
Mation' -3.35 -5.33
(-9.23) (-16.43)
M2/GDP 1.71 0.50
(4.44) (1.33)
InvesnetIGDP2 4.07
(10.55)
Initial GDP -4.75 -5.61
(-9.03) (-13.76)
Average Years of Secmdary Schooling 0.12 0.26
(1.24) (2.17)
Black Markd Premum' -1.12 -1.92
(-3.71) (-6.49)
Tenns of Trade Growth 7.76 7.39
(9.39) (12.04)
Population Growth -0.99 -0.64
(-11.93) (-11.04)
Canstant Tem -0.07 0.72
(-0.33) (3.76)
Period Dummy, 1991-93' -1.66 -1.97
(4-13) (-12.83)
Tests of GMM casistency (p-values):
Sargan test 0.59 0.41
Scial-correlaticn test' 0.67 0.64
R' 0.46 0.35
Waldtestofjointsigificance (p-value) 0.00 0.00
# of observatios 352 355
Mean of dependent variable 1.84% 1.85%
't-satistics givea in parenthesis
2In the regessicn, this variable is included as log (viable)
In the regressien, this variable is included as log ( I +variable)
4Dummies are included in the regressims for all time paiods
5The null hypthesis is that the errors in the first-diffacence regressicn exhibit no secm d-order serial correlatioi. that is,
E((K.,-EJ,) (Ev,-E1,.3)) 0
21
Table 3: Decomposition of Changes in Growth Rates from 1986-90 to 1991-93 - Investment
Included
Country Actual Predicted Contribution to predicted change in growth Regression
change change in rates from residuals
in growth rates
growth Six refonn Time effect' Other
rates variables variables
Argentina 7.464 5.619 6.304 -1.73 1.044 1.845
Bolivia 1.561 2.836 3.308 -1.73 1.258 -1.275
Brazil -0.252 -1.643 -0.344 -1.73 0.43 1.392
Chile 0.901 -1.501 0.944 -1.73 -0.715 2.402
Colombia -0.049 0.109 0.644 -1.73 1.195 -0.157
Costa Rica 0.923 -0.41 0.712 -1.73 0.608 1.333
Ecuador 1.584 0.069 0.291 -1.73 1.507 1.516
Guatemala 1.197 1.187 2.142 -1.73 0.775 0.01
Honduras 1.141 0.698 1.393 -1.73 1.035 0.443
Mexico 1.116 2.424 3.393 -1.73 0.761 -1.308
Panama 9.178 3.722 3.34 -1.73 2.112 5.455*
Peru 4.504 5.399 5.289 -1.73 1.84 -0.895
Paraguay -0.92 0.927 1.727 -1.73 0.93 -1.847
El Salvador 1.714 1.232 2.906 -1.73 0.056 0.482
Uruguay 0.255 -1.077 1.358 -1.73 -0.706 1.333
Venezuela 2.261 -0.408 1.346 -1.73 -0.024 2.668
Regional 2.036 1.199 2.172 -1.73 0.757 0.837
Average
1 The time effect corresponding to the last period is equal to the overall constant plus this period's dummy
coefficient.
* Statistically different from zero at the 0.10 level of significance on a one-tail test.
** Statistically different from zero at the 0.05 level of significance on a one-tail test.
22
Table 4: Decomposition of Changes in Growth Rates from 1986-90 to 1991-93 - Investment
Excluded
Country Actual Predicted Contribution to predicted change in Regression
change in change in growth rates from residuals
growth growth Five reform Time effect2 Other
rates rates
variables' variables
Argentina 7.464 8.818 9.036 -1.246 1.029 -1.354
Bolivia 1.561 1.384 1.55 -1.246 1.081 0.177
Brazil -0.252 -2.246 -1.222 -1.246 0.222 1.994
Chile 0.901 -1.584 0.722 -1.246 -1.059 2.485
Colombia -0.049 -0.023 0.52 -1.246 0.704 -0.026
Costa Rica 0.923 -1.076 -0.075 -1.246 0.246 1.999
Ecuador 1.584 0.512 0.465 -1.246 1.293 1.072
Guatemala 1.197 0.563 1.292 -1.246 0.518 0.635
Honduras 1.141 0.01 0.538 -1.246 0.719 1.13
Mexico 1.116 2.161 2.801 -1.246 0.607 -1.045
Panama 9.178 1.471 0.607 -1.246 2.11 7.707**
Peru 4.504 9.103 8.75 -1.246 1.599 4.598*
Paraguay -0.92 -0.049 0.639 -1.246 0.559 -0.871
El Salvador 1.714 1.766 2.795 -1.246 0.218 -0.052
Uruguay 0.255 -1.54 0.521 -1.246 -0.814 1.795
Venezuela 2.261 0.544 1.994 -1.246 -0.203 1.716
Regional 2.036 1.238 1.933 -1.246 0.552 0.798
Average
1 Set of reform variables does not include Investment /GDP
2 The time effect corresponding to the last penrod is equal to the overall constant plus this penrod's
dummy coefficient.
* Statistically different from zero at the 0.10 level of significance on a one-tail test.
** Statistically different from zero at the 0.05 level of significance on a one-tail test.
23
Table 5: Explaining the Difference in Growth-Rate Changes between East Asia and Latin
America (period 1991-93 compared to 1986-90)
Average change between Difference Predicted difference in growth-
the periods 1986-90 and East rate changes: East Asian
1991-93 Asian Miracles - Latin America
miiracles -
East Asian Latin Latin Including Excluding
miracles Amenrca Amenca investment investment
Per capita GDP growth -1.12 2.04 -3.16 -2.77 -3.42
Policy indicators (total) -1.15 -1.63
Volume of trade/GDP 14.58* 20.43* -5.85 -0.15 -0.07
Government -5.27* -5.10* -0.17 0.00 0.00
consumption/GDP
Inflation rate 0.55* -20.61* 21.16 -0.71 -1.13
M2/GDP 12.66* 17.13* -4.47 -0.08 -0.02
Black market premium -0.88* -22.14* 21.26 -0.24 -0.41
Investment/GDP 10.55* 10.03* 0.52 0.02
Other detenninants of
growth (total) -1.63 -1.79
Initial GDP per capita 28.65* 0.22* 28.43 -1.35 -1.59
Average number of 0.20 0.14 0.06 0.01 0.02
secondary-school years in
the labor force (initial)
Percent change in terms 0.68 1.75 -1.07 -0.08 -0.08
of trade
Population growth -0.42 -0.62 0.20 -0.20 -0.13
* Average percentage change (log difference) from 1986-90 to 1991-93. As in the estimation regression,
the variables inflation and black market premium are presented as one plus the respective rate.
24
Figure 1: Per capita growth rates by region
7.00
6.00 , - , -
E 5.00 l
c~~~~~~~~~~~~~~~~
C~~~
4.00 0
CL 3.00
.0 0 -. ....... . .. . . . . . . . . . . . .
2.00
2 1.00
0.00
- 1.00 - ------ World Mean
16 Latin American Countries
-2.00 - - - - - 7 East Asian Miracle Countries
-3.00
yr6165 yr6670 yr7175 yr7680 yr8l85 yr8690 yr9193
Time period
25
Appendix 1: Econometric Procedure.'2
Although the data used for estimation consist of an unbalanced panel, for expositional
purposes consider a data set that consists of N individual time series, each having T periods.
Consider regression equation (3),
yiJ-yij-l = a(Y-1 -Yiv-2) + P(xi,-x1-1) + (e;-i,J-I) (Al)
The error term and the lagged-dependent variable are correlated by construction. Therefore,
OLS estimation produces biased results, even when the set of variables x is strictly exogenous.
Assuming that the Ec, are serially uncorrelated, that is, E(e,e,) = 0 for t * s, values ofy lagged two
periods or more are valid instruments in the equations in first differences. Therefore, for T 2 3, the
model implies the following linear moment restrictions:
= 0 (j = 2,...,t- 1; t = 3,...,T) (A2)
Given that the problem of reverse causation applies to most variables in the set x, assuming
that they are strictly exogeneous would lead to inconsistent estimation. Assume, rather, that x are
only weakly exogenous in the sense that E(x; ,eJ " 0 for s ¸ t and zero otherwise. Then, values of
x lagged two periods or more are valid instrumnents in the equations in first differences. Therefore,
for T 2 3, the model implies the following additional linear moment restrictions:
= 0 (j=2,...,t-1; t=3,...,T) (A3)
Hansen (1982) and White (1982) propose an optimal estimator, the Generalized Method of
Moments (GMM) estimator, based only on moment restrictions, that is, in the absence of any other
knowledge concerning initial conditions or the distributions of the e,, and the ,. The moment
equations in (4) and (5) can be written invector form as EZ,'vj = 0, where v, = ((Ej3 - Ej ... (e,T -
ELT.) 'and Z,, the instrument matrix, is a matrix of the form Z, = diag (y, ** y, ,x,1 .X,9s (s = 1,
T-2). Note that the number of columns of Z,, say M, is equal to the number of available
instruments. Following Hansen (1982), the form of the GMM estimator of the k x 1 coefficient
vector 0 (a (i) 'is given by
o = (X'Z4-1' Zk'ZA1Ziy (A4)
12The presentation in the appendix follows Arellano and Bond (1991) and Caselli,
Esquivel, and Lefort (1995).
26
where a bar above a variable denotes that it is in first differences; X is a stacked (T-2)N x k matrix
of observations on x' and y'X,,; y is a stacked (T-2)N x 1 vector of y',,; Z = (Z,'... ZN ) ' is a (T-2)N
x M matrix; and A is any M x M, symmetfic, positive definite matrix.
For arbitrary A, a consistent estimate of the asymptotic variance-covariance matrix of Ohat
is given by
A VAR() N(XZA Z Z (Zi'viZ)A -'ZXXXZA Z (AS)
The most efficient GMM estimator for 0 is obtained when the matrix A is chosen such that
A is V = E[Z,'vyv,'ZJ, that is, when A is equal to the variance-covariance matrix of the moment
conditions. This variance-covariance matrix can be consistently estimated using the residuals
obtained from a preiminary, consistent estimation of 0.
Following this idea, Arellano and Bond (1991) suggest a two-step estimation procedure. In
the first step, it is assumed that the E,O be independent and homoskedastic both across units and over
time; under these assumptions, the optimal choice of A is, without loss of generality, A, = (f/N) Z.v=,
Z,'HZ, where H is a (T-2) square matrix that has twos in the main diagonal, minus ones in the first
subdiagonals, and zeroes otherwise. In the second step, the asumptions of homoskedasticity and
independence across units are relaxed. The residuals obtained in the first step are used to construct
a consistent estimate of the variance-covariance matrix of the moment conditions. This matrix, say
A2, becomes then the optimal choice of A and is used to reestimate the coefficients of interest.
Clearly, A2 = (I/A) E'=, Z1'~/),"' Z,, where 4, are the residuals estimated in the first step.
Specification Tests
1. Residual serial correlation test. The consistency of the proposed GMM estimator depends
crucially on the et, being serially uncorrelated. Since the vi, are first differences of Ej,, the consistency
of the GMM estimator does not require that E(v,, v,,,) be zero; however, consistency does hinge
heavily on the assumption that there is no second-order serial correlation in the residual of the
regression in first differences, that is, E(v,, v,,) = 0.
Consider the foliowing notation e(t)j / ., AVT' 4(t-2)- ["l, ,T-J' 4(t)
.'(t)J', v(t-2) - (t-2),, ..., )(t-2)j'. The statistic
mn2 = 2 (A6)
is standard normal (Q serves as the standardization factor) and can be used as a test of the null
27
hypothesis that the residuals in the first-difference regression are not second-order serially correlated,
that is, E[v,,v,,1J = 0.
2. Sargan Test. The second specification test is based on a Sargan test for over-identifying
restrictions. The null hypothesis of the Sargan test is that the instruments are not correlated with the
residuals in the first-difference regressions, that is, E[Z 'vj = 0. The test is based on the following
statistic,
s = v z vivi z (A7)
where 4 a A, ... 7' consists of the residuals estimated in the second stage. Under the null
hypothesis, the asymptotic distnbution of the statistic s is x2 with M-k degrees of freedom As
mentioned above, Mis the number of instrunents (equal to the number of cohlumns of Z) and k is the
number of explanatory variables.
28
Appendix n: Data
A. Country coverage
The following is the list of countries covered in our study. Since the panel data set is unbalanced, we
also indicate the time periods for which observations are available in each of the 70 countries.
Countrv 1961-65 1966-70 1971-75 1976-80 1981-85 1986-90 1991-93
Algeria x x x
Argentina x x x x x x x
Australia x x x x x x x
Austria x x x x x x x
Bangladesh x x x x
Bolivia x x x x x x x
Brazil x x x x x x x
Cameroon x x x x x
Canada x x x x x x x
Central African Rep. x x x x x x
Chile x x x x x x x
Colombia x x x x x x x
Costa Rica x x x x x x x
Cyprus x x x x x
Demnark x x x x x x x
Ecuador x x x x x x x
El Salvador x x x x x x x
Finland x x x x x x x
France x x x x x x x
Gambia x x x x
Germany x x x x x x x
Ghana x x x x x x
Greece x x x x x x x
Guatemala x x x x x x x
Haiti x x x x
Honduras x x x x x x x
India x x x x x x x
Indonesia x x x x x x
Ireland x x x x x x
Israel x x x x x x x
Italy x x x x x x x
Jamaica x x x x x x x
Japan x x x x x x x
Jordan x* x* x*
Kenya x x x x x x
Korea x x x x x x
Malawi x x x
Malaysia x x x x x x x
Mauritius x x x x
Mexico x x x x x x x
29
Country 1961-65 1966-70 1971-75 1976-80 1981-85 1986-90 1991-93
Netherlands x x x x x x x
Niger x x x x x
Norway x x x x x x x
Pakistan x x x x x x x
Paraguay x x x x x x x
Peru x x x x x x x
Philippines x x x x x x x
Portugal x x x x x x
Rwanda x x x x x x
Senegal x x x x x x
Singapore x x x x
South Africa x x x x x x
Spain x x x x x x x
SriLanka x x x x x x x
Sudan x x x
Swaziland x x x
Sweden x x x x x x x
Switzerland x x x x x x x
Thailand x x x x x x x
Togo x x x x x
Trinidad and Tobago x x x x
Tunisia x x x x x x x
Turkey x x x x x x
United Kingdom x x x x x x x
United States x x x x x x x
Uruguay x x x x x x x
Venezuela x x x x x
Zaire x x x x x x*
Zambia x x x x x x
Zimbabwe x x x
Note: * indicates that observations are available when investment data are not included.
A. Data sources
1. Data on the level and growti rate of per capita real GDP is calculated from the World Bank's real
GDP and population data.
2. Data on total real imports and exports come from the World Bank.
3. Data on the ratio of real government consumption to GDP come from the World Bank.
4. Inflation rates are calculated using the IFS's CPI data.
5. Data on M2 and CPI come from IFS. Statistic is [M2/(end of year CPI)]/[GDP/(average year
CPI)]
30
6. The average years of secondary schooling in the total population (15 years of age and over) for
1960, 65, 70, 75, 80, 85 and 90 come from the Barro-Lee data set.
7. Data on real investment shares of GDP come from the World Bank except for Jordan and Nepal
whose investment data come from Summers-Heston Penn World Table 5.6.
8. Data on black market premium for time periods 1960-1984 are from Wood, A., "Global Trends
in Real Exchange Rates, 1960 to 1984," World Bank, 1988. 1985's data are from World Currency
Yearbook (1987-89). Data for 1990-1993 are from International Currency Analysis (1990-1993).
Some missing observations are approximated by the data from the Barro-Lee data set.
9. Data on terms of trade are from the World Bank for 1965-1992. Data from Barro-Lee for the
period 1960-64 are also used.
10. Data on population growth are from the World Bank.
31
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Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS1692 Regulating Market Risk in Banks: Constantinos Stephanou December 1996 P. Infante
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Regulatory Regimes
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85734
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as the Population Ages in New 30182
Zealand
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WPS1705 The Polish Experience with Bank Fernando Montes-Negret January 1997 T. Ishibe
and Enterprise Restructuring Luca Papi 38968
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