WPS A139
POLICY RESEARCH WORKING PAPER 213 9
Sm all States, Small states, no different from
large states in income and
Small Problems? growth, should receive the
same policy advice large
states do Because of their
William Easterly greater openness, they may
Aart Kraay be more vulnerable to
volatility in terms-of-trade
shocks - but their openness
pays off in growth
The World Bank
Development Research Group
Macroeconomics and Growth
June 1999
POLICY RESEARCH WORKING PAPER 2139
Summary findings
Small states have attractecl a good deal of research. Their annual growth rates are more volatile, partly
Easterly and Kraay test wlhether microstates are any because of their greater volatility in responses to terms-
different from other states in income, growth, and of-trade shocks - to which they are exposed because of
volatility. their greater openness. But on balance their greater
They find that, controlling for location, smaller states openness pays off positively in growth.
are actually richer than other states in per capita GDP. Easterly and Kraay do recommend that srmall states
This income advantage largely reflects a productivity diversify their risk by opening up more to interniational
advantage - evidence against the idea that microstates capital markets, although the benefits of doing so are still
are unable to exploit increasing returns to scale. unresolved in the literature.
Small states do not have different per capita growth In general, they conclude, small states are no different
rates, with or without controls. from large states and should receive the same policy
advice large states do.
This paper - a product of Macroeconomics and Growth, Development Research Group - is part of a larger effort in the
group to study the needs of small states. Copies of the paper are available free from the World Bank, 1818 H Street NW,
Washington, DC 20433. Please contact Kari Labrie, room MC3-456, telephone 202-473-1001, fax 202-522-1155,
Internet address klabrie@o!worldbank.org. Policy Research Working Papers are also posted on the Web at http:,'/
www.worldbank.org/htmi/dec/Publications/Workpapers/home.html. The authors may be contacted at weasterly
(aworldbank.org or akraay@worldbank.org. June 1999. (36 pages)
The Policy Research WVorking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers canry the names of the authors and should be cited accordingly. The finidings, interpretations, and conclusions exp^ essed in this
paper are entirely those of the authors. They do not necessarily represent the vietc of the World Bank, its Execsitive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center
Small States, Small Problems?
William Easterly and Aart Kraayl
The World Bank
X Views expressed here are not necessarily those of the World Bank or its member governments.
2
"Smallness is neither a necessary nor sufflcient condition for slow economic development"
T.N. Srinivasan (1986)
"Economic storm clouds are gathering over paradise and the outlook is undeniably gloomy. "
A. Dolman (1985)
Do small states suffer from their smallness? There are good theoretical reasons to believe
that they do. The provision of public services may be subject to indivisibilities that lead to
increasing returns to scale (Alesina and Spolaore 1997), especially fiscal institutions (Easterly
and Rebelo (1993)) and defense (Kuznets (1960), Harden (1985)). Many theories of economic
growth suggest increasing returns to scale in the private economy as well (Romer 1986, Barro and
Sala-I-Martin 1995, Aghion and Howitt 1998), which may be difficult to realize in small states.
Small economies may also be at a disadvantage because their size prevents them from
diversifying into a wide range of activities, making them more vulnerable to terms of trade shocks
than large states (Commonwealth Secretariat (1998)). Many small states suffer from poor
location in that they are remote and/or landlocked, and many are located in regions prone to
hurricanes and volcanic activity (Srinivasan (1986)). Public officials in small states may be much
more likely to be subjected to conflicting pressures (Farrugia (1993)), and it may be difficult to
recruit a high-quality civil service given the limited pool of candidates in small states (Streeten
(1993)).
These potential difficulties facing small states have not been lost on policymakers or
academics. Numerous conferences and seminars on the special difficulties of small states have
been convened over the past forty years (Robinson (1960), Benedict (1967), Dobozi et. al. (1982),
Commonwealth Consultative Group (1985, 1997), Small States Financial Forum (1987,1988),
Kaminarides et. al. (1989)). International organizations such as the United Nations have
commissioned studies on the problems confronting small states for many years (United Nations
(1971), Doumenge (1983)), and the United Nations has formally recognized the special
difficulties of small states in a resolution to that effect (Briguglio (1995)). Titles of papers on
small states (see our bibliography) frequently feature ominous terms and phrases such as
"Problems", "Vulnerability", "Small is Dangerous", and even (twice) "Paradise Lost".
In this paper, we look for empirical evidence of alleged disadvantages of size by
examining microstates with population 1 million or less. (We will use the terms "small states"
and "microstates" interchangeably). If smallness is a disadvantage, then microstates must suffer
with a vengeance. In particular, we would expect that microstates must on average be less
3
developed and grow less rapidly than larger states. In this paper, we test this hypothesis using
cross-country data ia a large sample that includes many microstates. In light of the grim
predictions of theory, the picture of microstates which emerges from this analysis is somewhat
surprising. After controlling for a range of factors, we find that microstates have on average
higher income and productivity levels than small states, and grow no more slowly than large
states. Per capita GDP growth rates are more volatile in microstates, due to their much greater
exposure to international trade and fluctuations in their terms of trade. However, any growth
disadvantages of this greater volatility are more than outweighed by the growth benefits of trade
openness reaped by microstates by virtue of their necessarily large trade volumes. Finally,
microstates are well-positioned to take advantage of opportunities international risk sharing, since
the correlation of economic fluctuations in microstates with the world business cycles is
surprisingly low.
These resulls contribute evidence in support of the growing view in the literature that
small size might not be a disadvantage after all. Kuznets (1960) notes that small states also have
advantages: primarily that many are lucky to have good natural resources and have a small and
more cohesive populations which allows them to adapt better to change. Srinivasan (1986) and
Streeten (1993) argue without systematic empirical evidence that small may also be beautiful.
Using a sample of 48 countries Millner and Westaway (1993) fail to find evidence that the effect
of a number of growth determinants varies with country size. Armstrong et. al. (1998) uses cross-
sectional regressions covering a large number of small states and independent regions to argue
that population size. does not significantly affect growth, controlling for initial income and
regional effects.
The remainder of this paper proceeds as follows. In the next section we document that
small states are richer and have higher productivity levels than small states. In the following
section we observe that small states suffer no obvious growth rate disadvantage, and attribute this
to a number of offsetting advantages and disadvantages of small states. In Section 3 we
document that although trade openness contributes significantly to the greater volatility of growth
in microstates, this is not the whole story. In Section 4 we note that microstates are relatively
well-positioned to take advantage of opportunities to diversify away their special risks since they
currently are not particularly financially open and the shocks they receive are relatively
uncorrelated with those experienced by the rest of the world. Section 5 offers concluding
remarks.
I
4
1. Microstates and per capita income levels
In this paper, we consider a large cross section of 157 countries for which at least 10
years of annual data on per capita GDP adjusted for differences in purchasing power parity is
available. Of these, 33 are microstates defined as having an average population over the period
1960-1995 of less than one million. These countries are listed in Table 1, and range in size from
tiny St. Kitts and Nevis with population of 42,000 to Mauritius with 912,000. The income range
is similarly wide, from very poor African countries such as Guineau-Bissau and Comoros with
real PPP-adjusted per capita GDPs around $600 to wealthy oil-exporting countries such as Qatar
with per capita GDP of over $18,000.
If microstates suffer from the disadvantages of smallness, they should be poorer on
average than larger states. What do we actually find? Not controlling for any other characteristic,
microstates have the same range of per capita incomes as the rest of the samnple (Figure 1).
However, if we control for the location by continent of all countries, whether they are oil
producers, and whether they belong to the OECD, then microstates are actually significantly
richer than other states (Regression 1). Microstates are 50 percent (=-exp(.4025)-1) richer than
other states, controlling for location. We note that this result does not reflect the obvious outliers
in the sample, since the oil exporting countries Qatar and Bahrain are picked up by the OIL
dummy, and Luxembourg and Iceland are picked up by the OECD member dummy. Even if we
exclude two other particularly wealthy microstates (Bermuda and Bahamas), we still find that
microstates are nearly 40 percent richer than other states. Figure 2 shows the income residual by
quintile of population, and we see the very strong income effect in the microstate bottom quintile
of population. We also note that the favourable performance of small states carries over to other
quality of life indicators. For example, if we in tum use under-five infant mortality and life
expectancy at birth as the dependent variable in the above regression, we find that infant mortality
is significantly lower in small states by 22 per thousand, while life expectancy is about four years
higher. We are not sure why microstates are so much richer than their regional neighbours and
have so much better human development indicators, but we see this as a decisive refutation of the
macro arguments that microstates suffer from a development disadvantage.
Are microstates richer than others, controlling for location, because they save more or
because they have a higher productivity level? We use the Mankiw-Romer-Weil (1992)
5
regression to answer this question. In the steady-state of the Solow model, output per person is
given by:
(1) Y/L = A (s/(x+&+n))x('-O')
where Y/L is output per person, A is the level of labor-augmenting productivity, s is the
investment to GDP ratio, x is the rate of labor-augmenting productivity growth, 8 is depreciation,
n is population growth, and a is the share of capital income in GDP. We assume productivity
growth of 2 percent and a depreciation rate of 7 percent. Following MRW, we take logs of both
sides and regress the log of output per person on the same dummies as above (capturing
continental and other productivity differences) and the log of the second multiplicative term in
(1):
(2) ln(Y/L) = In A + a/(1-a) [In s - ln(x+5+n)]
We call this second term MRW, and the results of estimating this specification are in Regression
2. We find that small states' productivity advantage accounts for two-thirds of their income per
capita advantage. Again this decisively refutes the notion that small states have a productivity
disadvantage due to increasing returns to scale. We also refute the original MRW idea that
productivity levels are the same across countries, as Asia and especially sub-Saharan Africa have
significantly lower productivity than other regions. Once we allow the productivity level to vary,
the coefficient on MRW implies a capital share of .28 - which is in line with most estimates
from national income accounting.
When we decompose MRW into its numerator and denominator from equation (1), we
find that small states have significantly higher (log) investment rates - see Regression 3 - but not
lower population growth rates (not shown). Hence, the one third of the small-state income effect
in Regression 1 that is not attributable to productivity differences is attributable to higher
investment in small states.
However, we should take with more than a grain of salt the result that investment
accounts for even as much as one-third of the income advantage of small states. The significance
of the MRW term in Regression 2 may reflect reverse causality - richer states can afford to invest
more and are usually thought to choose lower population growth than poor states. Or it may
6
reflect an omitted third factor, like incentive policies that affect both investment and income.
One way of dealing with omitted third factors is to estimate equation 2 in chages. The results are
not encouraging to the MRW explanation of income and growth. In Regression 4 we estimate the
change in the MRW steady-state level using population growth and investment rates for 1960-70
and then 1985-95. The change in the MRW steady state levels does not explain the cross-country
differences in growth rates over 1960-95. Figure 3 shows the variation in growth rates and in
MRW steady state changes across small states. A view that aU countries have the same
productivity growth but have different long-run growth rates because of changes in steady state
levels doesn't work in the data, either for small states or aU states.
Moreover, the Solow/MRW sources of growth accounting implied in (2) sometimes gives
unreasonable predictions. For example, figure 4 shows actual income in Guyana compared to
Guyanese income assuming a constant productivity growth rate (x=.02) and allowing capital per
person growth to evolve using actual investment rates and population growth.2 The sources of
growth exercise based on the Solow/MRW model cannot account for the boom in the 70s or for
the collapse in the 80s. Nor can the sources of growth exercise based on the Solow/MRW model
account for the negative growth in Guyana over the 40 years 1950-90. Capital growth per person
was so rapid that Guyana should have had six times the income in 1990 that it actually had. Even
if we assumed that productivity growth was at a lower bound estimate of 1 percent over 40 years
(grey-shaded area), we still arrive at 4 times the actual income in 1990. Nor is there is a negative
level shift of steady state income in Guyana, because the MRW change is barely different from
zero (see the Guyana -GUY-- point in Figure 3). Clearly the assumption of a constant (and
positive!) productivity growth rate is untenable for Guyana. But negative productivity growth
does not make sense in the Solow/MRW framework if x is interpreted as technological progress -
- it's hard to believe that Guyana had technological regress. Nor does the Solow/MRW
framework give us any explanation as to why productivity growth rates might differ across
countries. We have to move outside of the model to recognize that capital growth sometimes does
not pass into output growth as the SolowIMRW predicts. This only strengthens the presumption
that income differences like the small state positive income differential have primarily to do with
differing levels of A - however that is interpreted - and little to do with capital growth per
person.
7
2. Microstates and Macro Growth
Even if the microstates do not have a disadvantage in levels, they may nevertheless grow
more slowly over time. Several endogenous growth theories have a scale effect on per capita
growth. Moreover, rmicrostates exhibit greater output volatility which has negative effects on
growth (Ramey and Ramey (1996)). Small states have the same range of growth experiences as
other states (Figure 5), suggesting that there is no obvious scale effect for growth rates. There is
also no growth difference for small states after controlling for continental location, oil, and
OECD dummies, as shown in Regression 5.
As in other work, sub-Saharan Africa has lower growth than the rest of the world
(Easterly and Levine 1997), and Asia has higher growth. However, there is no evidence that
microstates either grow faster or slower than non-microstates
Why do small states not suffer any apparent growth disadvantages due to their small size?
To answer this question, we consider a parsimonious cross-country growth regression which
captures two of the factors prominent in the small states debate: openness to international trade
measured as the shaLre of imports and exports in GDP, and volatility measured as the annual
standard deviation of growth rates within each country (Regression 6). We also include initial
income to capture convergence effects, and secondary school enrollment rates as a measure of
human capital. All of the non-dummy variables are significant of the expected sign: there is
conditional converg,ence (negative coefficient on initial income), a positive effect of secondary
enrollment and trade openness, and a negative effect of the standard deviation of annual growth.
This regression framework provides some useful clues as to why the microstate dummy
is not significant in the basic regression 5. In particular, we can see from this regression that
small states will have several offsetting advantages and disadvantages. They are richer than other
countries controlling for continent dummies (see previous section) and hence will have slower
growth than average by the conditional convergence effect. They have slightly higher secondary
enrollment, which would give therm higher growth, but the difference is not statistically
significant (results not shown). Most important, microstates tend to have much higher trade
shares (which is good for growth), offset by much higher volatility of growth rates (which is bad
2 We follow the usuaL conventions, using the perpetual inventory method to calculate the capital stock and
calculating the initial y/k as (x+5+nr/s where n and s are average population growth and investment rates,
8
for growth).3 The insignificance of the microstate dummy therefore suggests that the negative
effects of high initial income and high volatility are roughly offset by the positive effects of trade
openness and better educational attainment.
In order to document the magnitude of these offsetting effects, we first need to know how
different microstates are from non-microstates in terms of their trade volumes and volatility. We
first document the well-known fact that microstates typically have much higher trade ratios than
larger states, as illustrated by Regression 7. The consequences for openness of being a small state
are truly remarkable. Small states have a ratio of trade to GDP that is 54 percentage points (1.2
standard deviations) higher than the average economy controlling for continent dummies!
Second, real per capita GDP growth rates tend to be much more volatile in small states, as
illustrated by Regression 8.4 In particular, the standard deviation of annual real per capita GDP
growth is 1.4 percentage points higher in microstates than in non-microstates.
We have already shown that small states have higher income, which is a growth
disadvantage because of the convergence effect. They also have higher secondary enrollment
controlling for the usual dummies, which is a growth advantage, although the effect is not
statistically significant. If the small state dummy is not significant in the overall growth
regression (Regression 5 earlier), then the advantages and disadvantages of smallness must be
roughly offsetting. Interestingly, the positive growth effect of openness (0.012x0.54=0.65
percent) is 2.5 times larger than the negative growth effect (-1.79x0.014-0.25 percent) of small
states' greater output volatility. If output volatility is one of the consequences of openness (on
which more below), this suggests that small states' greater openness is still on balance a positive
factor for small states' growth. This finding is of particular interest, given the widely held view
that small states suffer from their openness.5 Any source of growth volatility that is not related to
openness, on the other hand, is detrimental to small states' growth.
In summary, there is no evidence that small states suffer any growth disadvantage from
their small size. This finding can be explained by several offsetting advantages and
respectively, over the entire 40 year period.
3 The model of the aforementioned Alesi;,m and Spolaore 1997 has the prediction that openness will make
small states more viable.
4In interesting historical footnote is that the greater volatility of small states has not always been accepted.
Tarshis (1960) finds little evidence of a relationship between the coefficient of variation of per capita
income and size across US states, and poses this as a puzzle.
9
disadvantages of small states. Although they are richer and experience more volatile shocks tha
non-microstates, they reap substantial growth advantages from their exposure to international
trade.
Finally, it is interesting to note that one often-heard benefit of microstates does not
appear to be empirically very important. It is often argued that one of the advantages of
microstates is that they tend to be ethnically very homogeneous, which may make it easier for
such states to forge the political consensus required to adjust to a changing environment (for
example, Kuznets (1]960)). Easterly and Levine (1997) and Alesina, Baqir, and Easterly (1999)
find that measures of ethnic fractionalization are associated with a lower level of public goods
provision and lower growth. However, the mean value of the ethnolinguistic indicator of ethnic
diversity among those microstates for which data is available is insignificantly different from that
among non-microstates, suggesting that the benefits of homogeneity may not be especially
pronounced for microstates.
3. Openness and V'olatility
In the previous Section we saw that microstates reap growth benefits from their openness
to international trade, but suffer growth costs due to their greater volatility of growth rates. In
this section we consider in more detail the relationship between trade openness and volatility in
microstates. A significant portion of the growth rate volatility experienced by small states can be
attributed to volatility in their terms of trade, but this is not the entire story. Even after
controlling for terms of trade volatility, growth rates in microstates are significantly more volatile
than in non-microstates.
We note first that the volatility of terms of trade shocks experienced by microstates is
much greater than for larger states. We define terms of trade shocks as the growth in the local
currency price of exports times the share of exports in GDP less the growth in the local currency
price of imports less; the share of iimports in GDP, which captures both the magnitude of price
fluctuations (changes in export and import prices) and their importance for the domestic economy
(weighted by the shares of exports and imports in GDP). We then regress the standard deviation
of this measure of terms of trade shocks on the same set of regional dummies as before, dunmny
5 This view of small states dates back at least to Scitovsky (1960). Dolman (1985) goes so far as to suggest
that many small island states would be better off reverting to auaic subsistence economies.
10
variables to capture oil exporters and commodity exporters who are more likely to suffer extreme
fluctuations in their terms of trade, and the microstate dummy (Regression 9). We find that there
is a highly significant microstate effect, with the standard deviation of terms of trade shocks
larger by 0.0 13 (or about one-third of one standard deviation of the dependent variable) in
microstates.
This terms of trade volatility might be due to two factors. First, we have already seen
that the share of trade in GDP is especially large in microstates, and this may contribute to the
magnitude of our measure of terms of trade shocks (since it weights changes in import and export
prices by the shares of imports and exports in GDP). Second, microstates' exports are likely to be
more specialized than those of large states, both in terms of products exported and in terms of
export markets (Kuznets (1960), Knox (1967), Annstrong and Read (1998)). The distinction
between these factors is important because there is little that microstates can do about their
overall trade volumes - autarky is simply not an option for small states that produce a much
narrower range of goods and services than they consume, and moreover we have already
documented the substantial growth benefits accruing to small states due to their openness. If in
contrast the greater volatility of growth is due to excessive reliance on a few export products and
a few export markets, then policies designed to help diversify exports may help to dampen
economic fluctuations.
We can get a rough idea of the relative importance of these two factors by redefining the
terms of trade shock as the unweighted difference between the growth in export prices and the
growth in import prices. When we use this alternative measure of terms of trade shocks as the
dependent variable in Regression 9, we find that the microstates dummy is negative and
insignificant (Regression 10). That is, the volatility of changes in the price of exports relative to
imports are if anything lower in microstates relative to larger states. Although this is not
conclusive evidence, it does cast doubt on the notion that microstates are especially vulnerable to
external shocks simply because their international trade is more specialized. Rather, the greater
volatility of terms of trade shocks in microstates is primarily due to their unavoidably large trade
shares.
Finally, it is worth noting that greater volatility of growth in microstates is not solely due
to their greater susceptibility to terms of trade shocks. To illustrate this point, we re-estimate
Regression 8, but include the volatility of the terms of trade as an explanatory variable
11
(Regression 11). We find that the microstate dummy remains significant even after controlling
for the effect of greater terms of trade volatility on the volatility of overall growth. This
additional volatility may be due tb several factors. Many of the microstates in our sample are
located in areas prone to natural disasters such as hurricanes, and the higher growth volatility in
small states may simply reflect the devastating effect of these natural forces. However, it is also
possible that some of this observed volatility reflects difficulties in measuring per capita incomes,
which may be particularly acute in small states where statistical institutions may be weaker than
average.
4. Opportunities fDr Diversification
In the previous section we have seen that microstates experience much more volatile
growth rates than non-microstates. This in part reflects their greater vulnerability to terms of
trade shocks, and also the tendency of many microstates to suffer heavily from natural disasters.
In this section we briefly consider the potential of small states to mitigate the adverse effects of
this largely-unavoidable volatility by sharing risks with the rest of the world.
One of the potential benefits of financial openness is that it allows countries to share risks
with the rest of the world, by holding claims on assets located outside their borders whose returns
are not perfectly cor.related with returns to domestic assets. The magnitude of these benefits
depends on how volatile are shocks to the domestic economy, and the extent to which they are
uncorrelated with shocks abroad. Small states are particularly well-situated to benefit from such
risk sharing arrangements, for two reasons. First, small states suffer large shocks, as documented
in Section 3. Second, in contrast to the often-heard view that small states are particularly
susceptible to cyclical fluctuations in large states, we find that the shocks experienced by small
states are not unusually correlated with the world business cycle. We illustrate this point with
Regression 12, which regresses the correlation of per capita GDP growth in a country with OECD
average real per capita GDP growth on the same set of dummies as before, as well as the
logarithm of average per capita CiDP (to capture the stylized fact document by Kraay and Ventura
(1998) that business cycles in poorer countries tend to be less correlated with the world average
cycle), and a microstate dummy. The microstate dummy is insignificant, suggesting that
microstates are not unusually correlated with the OECD cycle. However, it is important to note
that growth rates in neighbouring microstates may be highly correlated, especially to the extent
that growth rate volatility reflects natural disasters such as hurricanes. This suggests that regional
12
arrangements to share risk among microstates will be much less valuable than pooling risks with
a wider range of countries.
Despite the potential benefits of risk sharing through participation in intemational
financial markets, microstates do not appear to be especially open financially. We illustrate this
with Regressions 13-14, which regress two alternative measures of financial openess on a set of
regional dummies as well as the logarithm of average per capita income. In Regression 13 the
dependent variable is the fraction of years for which data is available in which the IMfF reports
restrictions on capital account transactions in that country.6 The coefficient on the microstate
dummy is positive, although insignificantly so. This suggests that microstates are not particularly
open to financial flows, as measured by legal impediments to such flows. Combining this
observation with the empirical results of Lewis (1995), who finds that consumption risks are less
diversified in countries with this measure of capital controls, this suggests that microstates are not
taking full advantage of the opporunities for risk diversification afforded by international capital
markets. The outcome measure of financial openness paints a somewhat more favourable picture,
as the microstate dummy is positive and statistically significant at conventional levels. This
suggests that the volume of capital flows is slightly larger for microstates than for non-
microstates, although the magnitude of this effect is small - only about 2 percentage points of
GDP. Overall, this evidence suggests that microstates are not as financially open as they might
be given the high volatility they face, and hence are not fully exploiting opportmities for
international risk diversification.
We conclude this section with the observation that although greater financial openness
may help microstates insure against the large shocks they receive, financial openness is itself no
panacea. Grilli and Milesi-Ferretti (1995) and Rodrik (1998) both note that there is no evidence
that financially-open economies grow faster or enjoy higher investment rates. On the other hand,
there is also no systematic evidence in favor of the popular view that by opening up financially,
countries expose themselves to greater volatility due to the vagaries of international financial
markets (Kraay (1998)). In summary, although financial openness may provide a valuable means
for small states to diversify some of the large risks they face, existing evidence does not support
the view that there will be a large growth payoff from such policies.
6 As reported in the Annual Report on Exchange Arrangements and Exchange Restrictions. The
disadvantages of this measure are well-known. First, it captures only the presence, and not the intensity of
controls. Second, it captures only controls on residents, and not on non-residents, although there is some
presumption that these two types of controls are correlated across countries.
13
5. Conclusions
Our analysis suggests that small states have perhaps received excessive attention from the
literature - notwithstanding our own addition to the literature! -as special cases calling for special
policy measures. We find that microstates have, if anything, significantly higher per capita
income than others in their region. There is no significant difference in growth performance
between large and small states. It is true that growth volatility and volatility of terms of trade
shocks as percent of GDP is higher in small states, but this is due entirely to their greater trade
openness - and the net benefits of openness on growth are positive. The one missing piece in the
current situation of rnicrostates is that they are not fully exploiting the potential to diversify their
risks by opening up to international capital movements. But even the payoff to filling in this last
missing piece is unclear from evidence in the literature.
This is not to say that microstates are free of economic problems! Many microstates are
still poor, and promoting growth out of that poverty is as important as it is in other poor countries.
The good news is that the lessons of experience from all countries are applicable to small states,
so they can benefit from the large amount of cross-country evidence on the determinants of long-
run growth.
14
Tables and Figures
Table 1 - Small States
Population Average Per Capita
(Thousands) GDP, 1985 PPP-Adjusted Dollars
ATG Antigua and Barbuda 63 5329
BHR Bahrain 419 10342
BHS Bahamas, The 237 11136
BLZ Belize 178 3548
BMU Bermuda 58 15356
BRB Barbados 247 5341
BWA Botswana 880 1516
COM Comoros 340 632
CPV Cape Verde 295 746
CYP Cyprus 638 5084
DJI Djibouti 344 1479
FJI Fiji 602 3149
GAB Gabon 777 3853
GMB Gambia, The 628 803
GNB Guinea-Bissau 739 644
GRD Grenada 92 2632
GUY Guyana 719 1630
ISL Iceland 223 9689
KNA St. Kitts and Nevis 42 4399
LCA St. Lucia 148 3264
LUX Luxembourg 358 11934
MDV Maldives 201 1908
MLT Malta 341 4049
MUS Maurtius 916 4092
QAT Qatar 384 18278
REU Reunion 496 2253
SLB Solomon Islands 299 1845
SUR Suriname 378 2877
SWZ Swaziland 556 2358
SYC Seychelles 59 2214
VCT St. Vincent and the Grenad 107 3312
VUT Vanuatu 145 1633
WSM Samoa 160 1844
15
Regression 1
Dependent Variable: log of average income 1960-95
Method: Least Squares
Included observations: 157
White Heteroskedasticity-Consistent Standard Errors & Covanance
Variable Coefficient Std. Error t-Statistic Prob.
SMALLSTATE 0.402504 0.108228 3.588670 0.0005
ASIA 7.517973 0.111549 52.12283 0.0000
AFRICA 6.691796 0.085404 81.37846 0.0000
WESTERN 7.932229 0.098881 85.54604 0.0000
HEMISPHERE
MIDDLE EAST & N. 7.863013 0.169759 45.58837 0.0000
AFRICA
EUROPE & 8.100983 0.116053 76.49556 0.0000
CENTRAL ASIA
OIL 0.814728 0.178268 4.900633 0.0000
OECD 1.168653 0.148174 9.309253 0.0000
R-squared 0.708909 Mean dependent var 7.855922
Adjusted R-squared 0.695233 S.D. dependent var 0.982946
16
Regression 2
Dependent Variable: log of average income 1960-95
Method: Least Squares
Included observations: 139
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA 7.438718 0.142546 52.18481 0.0000
EUROPE & 7.714671 0.133412 57.82584 0.0000
CENTRAL ASIA
WESTERN 7.800517 0.094056 82.93488 0.0000
HEMISPHERE
MIDDLE EAST & N. 7.763342 0.143795 53.98906 0.0000
AFRICA
OECD 1.122059 0.113640 9.873815 0.0000
OIL 0.691713 0.150642 4.591781 0.0000
AFRICA 6.865222 0.093269 73.60652 0.0000
SMALLSTATE 0.267340 0.132294 2.020799 0.0454
MRW 0.389346 0.101618 3.831453 0.0002
R-squared 0.761487 Mean dependent var 7.795722
Adjusted R-squared 0.746809 S.D. dependent var 0.994449
17
Regression 3
Dependent Variable: Log of average investment rate/GDP (PPP) 60-95
Method: Least Squares
Included observations: 139
White Heteroskedasticity-Consistent Standard Errors & Covanance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA -1.867634 0.117983 -15.82967 0.0000
AFRICA -2.581324 0.117818 -21.90938 0.0000
WESTERN -1.968057 0.072648 -27.09038 0.0000
HEMISPHERE
MIDDLE EAST & N. -2.064563 0.158454 -13.02945 0.0000
AFRICA
EUROPE & -1.493982 0.084612 -17.65692 0.0000
CENTRAL ASIA
OIL 0.137961 0.160770 0.858126 0.3924
OECD 0.198926 0.098043 2.028980 0.0445
SMALLSTATE 0.381396 0.107068 3.562201 0.0005
R-squared 0.440238 Mean dependent var -1.956707
Adjusted R-squared 0.410327 S.D. dependent var 0.680181
18
Regression 4
Dependent Variable: Average per capita growth 1960-95
Method: Least Squares
Included observations: 133
Variable Coefficient Std. Error t-Statistic Prob.
C 0.018962 0.001613 11.75912 0.0000
DMRW6095 0.003758 0.002847 1.319799 0.1892
R-squared 0.013122 Mean dependent var 0.019050
Adjusted R-squared 0.005589 S.D. dependent var 0.018633
19
Regression 5
Dependent Variaible: Average per capita growth 1960-95
Method: Least Siquares
Included observations: 154
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA 0.026679 0.004888 5.457927 0.0000
AFRICA 0.006639 0.002874 2.310184 0.0223
WESTERN 0.016090 0.002569 6.263201 0.0000
HEMISPHERE
MIDDLE EAST & N. 0.018039 0.006376 2.829106 0.0053
AFRICA
EUROPE & 0.020941 0.004848 4.319413 0.0000
CENTRAL ASIA
OIL -0.014381 0.007439 -1.933011 0.0552
OECD 0.006522 0.004430 1.472347 0.1431
SMALLSTATE 0.002222 0.004983 0.445971 0.6563
R-squared 0.195323 Mean dependent var 0.016336
Adjusted R-squared 0.156743 S.D. dependent var 0.021323
20
Regression 6
Dependent Variable: Average per capita growth 1960-95
Method: Least Squares
Included observations: 130
White Heteroskedasticity-Consistent Standard Errors & Covarance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA 0.137565 0.024502 5.614343 0.0000
AFRICA 0.117585 0.023412 5.022435 0.0000
WESTERN 0.134214 0.026701 5.026535 0.0000
HEMISPHERE
MIDDLE EAST & N. 0.137746 0.024181 5.696354 0.0000
AFRICA
EUROPE & 0.136707 0.025164 5.432535 0.0000
CENTRAL ASIA
OIL -0.000407 0.004736 -0.085912 0.9317
OECD 0.009192 0.005901 1.557607 0.1220
LQIN60 -0.017360 0.003702 -4.689944 0.0000
SECONDARY 0.000342 0.000111 3.069972 0.0027
ENROLLMENT 60-95
Share of Trade in 0.012076 0.002746 4.397740 0.0000
GDP 60-95
Standard Deviation of -0.179370 0.082047 -2.186187 0.0308
Growth 60-95
R-squared 0.535076 Mean dependent var 0.016972
Adjusted R-squared 0.496006 S.D. dependent var 0.019612
21
Regression 7
Dependent Variable: Share of Trade in GDP 60-95
Method: Least Squares
Included observations: 158
White Heteroskedasticity-Consistent Standard Errors & Covanance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA 0.657933 0.128117 5.135396 0.0000
AFRICA 0.522459 0.042598 12.26481 0.0000
WESTERN 0.613623 0.063168 9.714153 0.0000
HEMISPHERE
MIDDLE EAST & N. 0.698416 0.081605 8.558546 0.0000
AFRICA
EUROPE & 0.788507 0.059614 13.22688 0.0000
CENTRAL ASIA
OIL 0.145042 0.094684 1.531846 0.1277
OECD -0.178562 0.077809 -2.294883 0.0231
SMALLSTATE 0.538525 0.070860 7.599897 0.0000
R-squared 0.285557 Mean dependent var 0.739223
Adjusted R-squared 0.252217 S.D. dependent var 0.436784
22
Regression 8
Dependent Variable: Standard Deviation of Growth 60-95
Method: Least Squares
Included observations: 154
White Heteroskedasticity-Consistent Standard Errors & Covarance
Variable Coefficient Std. Error t-Statistic Prob.
ASIA 0.047770 0.002558 18.67561 0.0000
AFRICA 0.061945 0.002457 25.21095 0.0000
WESTERN 0.049090 0.002689 18.25762 0.0000
HEMISPHERE
MIDDLE EAST & N. 0.060996 0.006357 9.595790 0.0000
AFRICA
EUROPE & 0.055067 0.004943 11.14022 0.0000
CENTRAL ASIA
OIL 0.015740 0.004851 3.244719 0.0015
OECD -0.025540 0.004311 -5.924337 0.0000
SMALLSTATE 0.014339 0.003346 4.285854 0.0000
R-squared 0.467655 Mean dependent var 0.055809
Adjusted R-squared 0.442131 S.D. dependent var 0.021426
23
Regression 9
Dependent variable: Standard Deviation of Terms of Trade Shocks 1960-95
Number of observations: 114
Mean of dep. var. .038019 LM het. test = 1.87192 [.171]
Std. dev. of dep. var. .021479 Durbin-Watson = 2.07124
[<.8691
Sum of squared residuals = .026595 Jarque-Bera test = 2.17388 (.337]
Variance of residuals = .250897E-03 Ramsey's RESET2 = .137189 (.712]
Std. error of regression .015840 F (zero slopes) = 14.5415 [.000]
1R-squared = .489871 Schwarz B.I.C. = -8.03086
Adjusted R-squared = .456183 Log likelihood = 314.945
Estimated Standard
Variable Coefficient Error t-statistic P-value
SSA .04:2971 .419929E-02 10.2330 [.000]
ASIA .022997 .323085E-02 7.11797 [.000]
ECA .016725 .219593E-02 7.61616 (.000]
MENA .039738 .690534E-02 5.75471 [.000)
LAC .037033 .317373E-02 11.6685 [.000]
OIL .02:3363 .503847E-02 4.63685 [.000]
COMMOD .740058E-02 .452971E-02 1.63379 [.105]
MICROSTATE .01:3328 .473321E-02 2.81585 [.006]
Standard Errors are heteroskedastic-consistent (HCTYPE=2).
24
Regression 10
Dependent variable: Standard Deviation of Unweighted Terms of Trade Shocks
1960-95
Number of observations: 114
Mean of dep. var. = .126255 LM het. test = 3.77342 [.052]
Std. dev. of dep. var. = .072712 Durbin-Watson = 2.36931 [<.9971
Sum of squared residuals = .314207 Jarque-Bera test = 3.85662 (.145]
Variance of residuals = .296422E-02 Ramsey's RESET2 = .084551 [.772]
Std. error of regression = .054445 F (zero slopes) = 13.6498 (.000]
R-squared = .474073 Schwarz B.I.C. = -5.56154
Adjusted R-squared = .439342 Log likelihood = 174.193
Estimated Standard
Variable Coefficient Error t-statistic P-value
SSA .146951 .011390 12.9012 t.0003
ASIA .094062 .011595 8.11254 [.000]
ECA .046957 .553333E-02 8.48622 [.000)
MENA .138424 .029647 4.66910 (.000]
LAC .140449 .014181 9.90428 [.000]
OIL .074365 .023821 3.12183 [.002]
COMMOD .023068 .012845 1.79589 [.075]
MICROSTATE -.011439 .011375 -1.00558 [.317]
Standard Errors are heteroskedastic-consistent (HCTYPE=2).
25
Regression 11
Dependent variable: Standard Deviation of Real Per Capita GDP Growth 1960-1995
Number of observations: 114
Mean of dep. var. = .052993 LM het. test = .058422 [.809]
Std. dev. of dep. var. = .021039 Durbin-Watson = 1.82486 [<.466]
Sum of squared residuals = .023269 Jarque-Bera test = 7.39687 (.025]
Variance of residuals = .221607E-03 Ramsey's RESET2 = .32S584E-02 (.955]
Std. error of regression = .014886 F (zero slopes) = 15.0879 [.000]
R-squared = .534787 Schwarz B.I.C. = -8.12293
Adjusted R-squared = .499342 Log likelihood = 322.561
Estimated Standard
Variable Coefficient Error t-statistic P-value
SSA .045460 .524191E-02 8.67245 [.000]
ASIA .0376574 .424625E-02 8.87235 [.000]
ECA .028!505 .330153E-02 8.63375 [.000]
MENA .046493 .842420E-02 5.51904 [.000]
LAC .034650 .462190E-02 7.49686 (.000]
OIL .012151 .580664E-02 2.09255 [.039]
COMMOD .709771E-02 .396159E-02 1.79163 [.076]
MICROSTATE .020540 .518460E-02 3.96181 (.000]
Standard .249983 .105027 2.38017 (.019]
Deviation Of
Terms of Trade
Standard Errors are heteroskedastic-consistent (HCTYPE=2).
26
Regrssion 12
Dependent variable: Correlation of Real Per Capita GDP Growth with OECD
Average Real Per Capita GDP Growth, 1960-95
Number of observations: 155
Mean of dep. var. = .246648 LM het. test = .866619 [.352]
Std. dev. of dep. var. = .267756 Durbin-Watson = 1.95295 (<.711]
Sum of squared residuals = 7.23486 Jarque-Bera test = .769712 (.681]
Variance of residuals = .049896 Ramsey's RESET2 = .034630 [.853]
Std. error of regression = .223373 F (zero slopes) = 8.47520 (.000]
R-squared = .344712 Schwarz B.I.C. = -2.73913
Adjusted R-squared = .304039 Log likelihood = 17.5644
Estimated Standard
Variable Coefficient Error t-statistic P-value
SSA -.717189 .199481 -3.59529 [.000]
ASIA -.712958 .231572 -3.07877 (.002]
ECA -.638812 .239380 -2.66861 (.0081
MENA -.822765 .233648 -3.52138 (.001]
LAC -.692913 .232984 -2.97408 [.003]
OECD .082437 .068673 1.20043 [.232]
LOGQAV6095 .119230 .029067 4.10185 (.000]
OIL -.077165 .065326 -1.18123 (.239]
COMMOD .026310 .057571 .456996 [.648]
MICROSTATE .013354 .053808 .248181 (.804]
Standard Errors are heteroskedastic-consistent (HCTYPE=2).
27
Regrssion 13
Dependent variable: Fraction of Years in Which Capital Controls in Place,
1960-1995
Number of obse:rvations: 139
Mean of dep. var. = .787387 LK het. test = 12.2811 J.0001
Std. dev. of dep. var. = .342953 Durbin-Watson = 2.03770 [<.867]
Sum of squared residuals = 11.7237 Jarque-Bera test = 35.1819 [.000]
Variance of residuals = .090881 Ramsey's RESET2 = 2.65476 (.106]
std. error of regression = .301465 F (zero slopes) = 5.51078 (.000)
R-squared = .277703 Schwarz B.I.C. = -2.11787
Adjusted R-squared = .227311 Log likelihood = -25.3683
Estimated Standard
Variable Coefficient Error t-statistic P-value
SSA 2.09580 .328127 6.38717 [.000]
ASIA 2.0)6680 .375284 5.50729 (.000]
ECA 2.39422 .403090 5.93967 [.000]
MENA 2.13384 .384726 5.54640 (.000]
LAC 2.0)8108 .393251 5.29200 (.000]
OECD -.149049 .100129 -1.48857 (.139]
COMMOD .541426E-02 .062675 .086386 [.931]
OIL -.097368 .115647 -.841936 [.401]
LOGQAV6095 -.170939 .049805 -3.43213 t.001]
MICROSTATE .025574 .080311 .318431 (.751]
Standard Errors are heteroskedastic-consistent (HCTYPE=2).
28
Regression 14
Dependent variable: Average Capital Inflows Plus Capital Outflows as a Fraction of GDP,
1960-95
Number of observations: 132
Mean of dep. var. = .062080 LM het. test = 12.0349 [.001]
Std. dev. of dep. var. = .058247 Durbin-Watson = 2.03507 [<.866]
Sum of squared residuals = .329045 Jarque-Bera test = 111.565 [.000]
Variance of residuals = .269709E-02 Ramsey's RESET2 = .463370 (.497]
Std. error of regression = .051933 F (zero slopes) = 4.75418 [.000]
R-squared = .259653 Schwarz B.I.C. = -5.62445
Adjusted R-squared = .205037 Log likelihood = 208.328
Estimated Standard
Variable Coefficient Error t-statiozic P-value
SSA -.109303 .089036 -1.22763 [.222]
ASIA -.123786 .091137 -1.35824 [.177]
ECA -.126912 .102485 -1.23834 [.218]
MENA -.116162 .103124 -1.12643 (.262]
LAC -.153697 .104193 -1.47512 [.143]
OECD .016410 .028921 .567432 [.571]
COMMOD -.013906 .011405 -1.21930 [.225]
OIL -.027007 .015827 -1.70637 [.090]
LOGQAV6095 .023934 .013285 1.80152 (.074]
MICROSTATE .027312 .013028 2.09646 [.038]
Standard Errors are heteroskedaBtic-consistent (HCTYPE=2).
Per capita income
r'~~~~~~~~~~~~~~~~E~~~r
la
z
P8 | 8 f~~
| I ' . , ,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%
30
Figure 2: Unexplained income level and population size
0.3 -
Small states
C 0.25 -
O 0.2-
0
0.15 -
0
0.1
A)
M -0.05
-0.1
-0.1505
1 4 9 22 935
- ~~~~Population quintiles less thn or equal to (in millions):
GDP Per Capita Growth, 1960-9'
42 I al 1,r
a aa I
o o 0 0 0 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~r
Cl rA~~~~~~~~
9A
0 0 0 0 0 0~~~~~~~~~~~~~~~~r
.. . .......... ........ 0...... ..
Figure 4: Predicted Income in Guyana from Solow/MRW model and actual
income
6000
Predicted income from
sources of growth
5500 accounting with
productivity growth 2%
5000 -
4500 -
34000 -
3500
3000
0
2500
2000
1500
S / _ ~~~~~~~~~~~ACUl 11 wcoESMl#c
1000 -1 i i I I I I I I I_ _ .
2500 4/ _p 0b - c . -- -- c
NC)-.,'', -l .C -.' -.--' .q .''- "'q "') NC NC IC C C
Per capita growt}
o In I
PO 0 t-) 0 C
0 I I I IX ;I I
H
o H
0~~~~~~~~~~~~~~~~
34
References
Aghion, Philippe and Peter Howitt (1998). Endogenous Growth Theory. Cambridge: MIT Press
Alesina, Alberto, Reza Baqir, and William Easterly (1999). "Public goods and ethnic divisions,"
forthcoming, Quarterly Journal of Economics.
Alesina, Alberto, and Enrico Spoalare (1997). "On The Number and Size of Nations". Quarterly
Journal ofEconomics. November.
Armstrong, H R-J. de Kervenoael, X. Li and R. Read (1998). "A Comparison of the Economic
Performance of Different Microstates, and Between Microstates and Larger Countries".
World Development. 26(4):639-656.
Barro, Robert and Xavier Sala-I-Martin (1995). Economic Growth. New York, NY: McGraw-
Hill.
Benedict, Burton (1967). Problems of Smaller Territories. London: Athlone Press.
Briguglio, Lino (1995). "Small Island Developing States and Their Economic Vulnerabilities".
World Development. 23(9): 1615-1632.
Cashin, Paul and Norman Loayza (1995). "Paradise lost? Growth, convergence, and migration in
the South Pacific". AMFStaffPapers. 42:608-41.
Commonwealth Consultative Group (1985). Vulnerability: Small States in the Global Society.
London: Commonwealth Secretariat.
Commonwealth Consultative Group (1997). A Future for Small States: Overcoming
Vulnerability. London: Cormmonwealth Secretariat.
Dobozi, Istvan, Clare Keller and Harriet Matejka (1981). Small Countries and International
Structural Adjustment: A Collection of Hungarian and Swiss Views.
Dolman, Antony (1985). "Paradise Lost? The Past Performance and Future Prospects of Small
Island States", in Edward Dommen and Philippe Hein, eds. States, Microstates and
Islands. London: Croom Helm.
Doumenge, Francois (1983). Viability of Small Island States. United Nations Conference on
Trade and Development.
Easterly, William and Ross Levine (1997). "Africa's Growth Tragedy: Policies and Ethnic
Divisions". Quarterly Journal of Economics. November.
Easterly, William and Sergio Rebelo (1993). "Fiscal Policy and Economic Growth: an Empirical
Investigation". Journal ofMonetary Economics32:417-57.
Farrugia, Charles (1993). "The Special Working Environment of Senior Administrators in Small
States". World Development. 21(2):221-226.
35
Grilli, Vittorio and Gian Maria Milesi-Ferretti (1995). "Economic Effects and Structural
Determinants of Capitail Controls". International Monetazy Fund Staff Papers. 42(3):517-
551.
Harden, Sheila (1985). Small is Dangerous: Micro States in a Macro World. London: Frances
Pinter.
Kaminarides, John, Lino Briguglio and HenkN. Hoogendonk (1989). The Economic
Development of Small Countries: Problems, Strategies and Policies. Delft: Eburon.
Kraay, Aart and Jaume Ventura (1998). "Comparative Advantage and the Cross-Section of
Business Cycles". World Bank Policy Research Department Working Paper No. 1948.
Kraay, Aart (1998). "In Search of the Macroeconomic Effects of Capital Account
Liberalizalion". Manuscript, World Bank.
Kuznets, S. (1960). "Economic Growth of Small Nations", in E.A.G. Robinson, ed. (1960). The
Economic Consequences of the Size ofNations: Proceedings of ofA Conference Held by
the Interrational Economic Associations. Toronto: MacMillan.
Lewis, Karen (199,6). "What Can Explain the Apparent Lack of International Consumption Risk
Sharing?". Journal of 'olitical Economy. 104:267-297.
Mankiw, N. Gregory, David Romer and David Weil (1992). "A Contribution to the Empirics of
Economic Growth". Quarterly Journal ofEconomics.
Milner, Chris and Tony Westaway (1993). "Country Size and the Medium-Term Growth
Process: Some Cross-Country Evidence". World Development. 21(2):203-211.
Rainey, Garey and Valerie A. Ramney. (1995). "Cross-country Evidence on the Link Between
Volatility and Growth". American Economic Review. 85:1138-5 1.
Robinson, E.A.G., ed. (1960). 7he Economic Consequences of the Size ofNations: Proceedings
of ofA Conference Held'by the International Economic Associations. Toronto:
MacMillan..
Rodrik, Dani (1998). "Who Needs Capital Account Convertibility?", in Peter Kenen, ed. "Should
the IMF Pursue Capital-Account Convertibility?". Princeton Essays in International
Finance, No. 207.
Romer, Paul M. (1986). "Increasing Returns and Long-Run Growth" Journal ofPolitical
Economy. 94:1002-37.
Scitovsky, Tibor (1960). "International Trade and Economic Integration as a Means of
Overcoming the Disadvantages of a Small Nation", in E.A.G. Robinson, ed. (1960). The
Economic Consequences of the Size ofNations: Proceedings of ofA Conference Held by
the International Economic Associations. Toronto: MacMillan.
Small States Financial Forum (1987). Round Table on Foreign Investment and Commercial
Finance for Small States. London: Crown Agents Financial Advisory Service.
36
Small States Financial Forum (1987). Round Table on Some Practical Possibilities of Financial
Co-operation Between Small States. London: Crown Agents Financial Advisory
Service.
Streeten, Paul (1993). "The Special Problems of Small Countries". World Development
21(2):197-202.
Srinivasan, T.N. (1986). "The Costs and Benefits of Being A Small, Remote, Island, Landlocked
or Ministate Economy". World Bank Research Observer. 1(2):205-218.
Tarshis, L. (1960). 'The Size of the Economy and Its Relation to Stability and Steady Progress",
in E.A.G. Robinson, ed. (1960). The Economic Consequences of the Size of Nations:
Proceedings of of A Conference Held by the International Economic Associations.
Toronto: MacMillan.
United Nations Institute for Training and Research (1971). Small States and Territories: Status
and Problems. New York: Amo Press.
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2124 Social Exclusion and Land Robin Mearns May 1999 G. Burnett
Administration in Orissa. India Saurabh Sinha 82111
WPS2125 Developing Country Agriculture and Bernard Hoekman May 1999 L. Tabada
The New Trade Agenda Kym Anderson 36896
WPS2126 Libert6, Egaiite, Fraternite: Monica Das Gupta May 1999 M. Das Gupta
Exploring the Role of Governance 31983
In Fertility Deciine
WPS2127 Lifeboat Ethic versus Corporate Monica Das Gupta May 1999 M. Das Gupta
Ethic: Social and Demographic 31983
Implications of Stem and Joint
Families
WPS2128 Learning Outcomes and School Gladys Lopez Acevedo May 1999 M. Geller
Cost-Effectiveness in Mexico: 85155
The PARE Program
WPS2129 Agricultural Extension: Generic Gershon Feder May 1999 P. Kokila
Challenges and Some Ingredients Anthony Willett 33716
for Solutions Willem Zijp
WPS2130 Deep Integration, Nondiscrimination, Bernard Hoekman May 1999 L. Tabada
and Euro-Mediterranean Free Trade Denise Eby Konan 36896
WPS2131 Efficiency Wage and Union Effects William F. Maloney May 1999 T. Gomez
In Labor Demand and Wage Eduardo Pontual Ribeiro 32127
Structure in Mexico: An Application
Of Quantile Analysis
WPS2132 A Regime-Switching Approach to Maria Soledad Martinez June 1999 A. Yaptenco
Studying Speculative Attacks: Peria 38526
A Focus on European Monetary
System Crises
WPS2133 Resolution of Corporate Distress: Stijn Claessens June 1999 R. Vo
Evidence from East China's Simeon Djankov 33722
Financial Crisis Leora Klapper
WPS2134 interlinkage, Limited Liability, and Kaushik Basu June 1999 M. Mason
Strategic Interaction Clive Bell 30809
Pinaki Bose
WPS2135 Hungary's integration into Bartlomiej Kaminski June 1999 L. Tabada
European Union Markets: 36896
Production and Trade Restructuring
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2136 An Empirical Analysis of Competition, Scott J. Wallsten June 1999 P. Sintim-Aboagye
Privatization, and Regulation in 38526
Telecommunications Markets in
Africa and Latin America
WPS2137 Globalization and National Andr6s Solimano June 1999 D. Cortijo
Development at the End of the 84005
20th Century: Tensions and Challenges
WPS2138 Multilateral Disciplines for Bernard Hoekman June 1999 L. Tabada
Investment-Related Policies Kamal Saggi 36896
, /