Discussion Paper No.

583

UNLUCKY OR BAD?
ECONOMIC POLICY AND ECONOMIC GROWTH

Evan Osborne

May 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Unlucky or Bad? Economic Policy and Economic Growth
Evan Osborne
Institute of Social and Economic Research, Osaka University, andWright State University, Dayton, Ohio, USA
Contact Information:
Wright State University, Dept. of Economics, 3640 Col. Glenn Hwy., Dayton, OH,
45435, USA.
Phone: +1 937 775 4599
Fax: +1 937 775 2441
evan.osborne@wright.edu

Unlucky or Bad? Economic Policy and Economic Growth
EVAN OSBORNE
May, 2003
Abstract
Cross-country growth regressions have in recent years become a major growthindustry. The most common purpose of this work has been to investigate thedeterminants of economic growth. But it is also possible to use the existing growth data
to determine the extent to which policy choices help or hinder growth. This paper
generates estimates of the net contribution of policy to growth for a wide variety ofcountries. It also provides the ability to “see” in an almost photographic way whatsubstantial economic reform looks like, to test whether countries have meaningfullyreformed. The findings also provide grist for speculation on the virtues of gradual versus
dramatic, total economic reform.
Key words: growth, growth regression, development, economic reform.
Journal of Economic Literature classification numbers: O11, O47, O57.

The problem of economic growth is perhaps one of the most fundamental in all of
economics. Thinking about why nations become (and, more compellingly, do not
become) wealthy has occupied many of the greatest minds in the field at least since thedays of Hume, Smith and Ricardo. In much of the twentieth century, with the advent ofmodern neoclassical techniques, the thinking about development was dominated by theneed to spur adequate investment. The basic description of the problem took several
forms technically, but whether seen as mobilizing adequate savings to achieve a target
investment rate (Domar, 19461), of moving through a sequence of stages, the most criticalof which is an investment “takeoff” (Rostow 1960), of adding enough capital stock to
draw away surplus labor (Lewis, 1954; Fei and Ranis, 1964), or of convergence via
capital accumulation to a steady-state equilibrium (Solow, 1956), the key task was always
explicitly or implicitly to accumulate physical capital. This is an idea that is pregnant
with implications, because it is not much of a leap from this characterization of the
problem to a belief that a command authority, e.g. a government ministry or a multilateralaid organization, ought to be in charge of that mobilization.
More recently, owing to the increase in the amount and quality of the available
data, a substantial and rapidly growing empirical literature has emphasized human capital(Lucas, 1988, Romer, 1986). The presence of both specific and general human capital isaccording to this literature critical although not necessarily sufficient to achieving
economic growth. In addition, the role of policy has in recent years commanded attention.
1. Easterly (1999) argues that Harrod-Domar models became extraordinarily influential
in policy circles despite the fact that Domar (1957) later argued that he did not intend his
work to be used in development.

1Policy has always been important, even in the models revolving around physical andhuman capital, but the focus of work such as Barro (1991) has been on the potential ofpolicy to do harm. In particular, he finds that government distortions, failure to maintain
openness to foreign trade and investment, and excessive government spending (and, he
speculates, the taxes needed to fund it) destroy growth.
But Barro’s work and the literature it has spawned focuses mostly on standard,ceteris paribus analysis. Without question, this has been important, in that it hasestablished what Yusuf and Stiglitz (2001) term a consensus up to a point about whatsorts of policies promote growth. But there has been little attempt to quantify the effects
of policy in explaining the wealth and poverty of nations. Countries that manage
economic policy well (poorly) can promote (squelch) growth, but how much? This paper
attempts to answer that question. Using standard growth regressions, it is able to make(necessarily speculative) estimates of how much countries have in the last several
decades suffered and benefited from their government policies. The results are not onlyof theoretical interest, in that they suggest the magnitude of the income created or
destroyed by policy, but raise issues of importance in terms of promoting economic
reform. There is controversy over the virtues of radical versus gradual reform in
dysfunctional economies. The findings allow some inferences about the virtues of thetwo approaches. In addition, if political constraints are assumed to rule out radical,“shock therapy” reform, the results are of interest in suggesting which reforms canprovide the most immediate benefit for struggling nations. Section 1 lays out the extent
of development failure, Section 2 presents the basic empirical approach, Section 32extends the analysis by controlling for different effects at different levels of development,
and Section 4 lays out some implications for improved economic policy.

1. The nature of the problem
The best place to begin the discussion is perhaps to note how difficult the struggle
to modernize has been for a disappointingly large fraction of the world’s poorest
countries. Table 1 depicts two measures of the absence of progress over the last thirtyfive years. The first list consists of those countries whose per capita incomes were in thebottom half of the by-country distribution in 1960 but had by 1995 moved past the global
median in 1985 – US$2463.2 The right-hand portion lists the 32 nations that haveachieved at least two-percent average annual growth in per capita income between 1960
and 1995. Note that this latter standard is none too exacting – it means that the standardof living roughly doubles in 35 years. In light of the prevalence poverty throughout muchof the world, the brevity of these lists has to be counted as a substantial failure. Whether
it is a failure of the advice rendered to poor countries or their failure to heed it is an open
question, but the widespread criticism in recent years in development organizations suchas the World Bank of decades of mistakes, combined with references to lost years andeven lost decades in countries throughout the developing world, suggests the former. A
compelling question naturally arises: what does it take to get economic policy right?
The stylized facts of the most prosperous economies are an extremely complex
division of labor, the use of substantial amounts of machinery that enhances the marginal
productivity of work, and a high endowment of human capital. The latter variable is
2. All dollar figures in the paper are expressed in 1985 prices.

3

greater both in absolute quantity and, by at least one measure, in quality as well in
wealthier nations. Table 2 lists the average capital stock per worker, the population’s
average years of schooling and the student-teacher ratio in primary school for each thirdof the distribution of national per capita gross domestic product. Workers in the richest
countries have the most capital to work with and have the most and the highest-quality
schooling. While there is some controversy over whether high educational levels are aneffect or a cause of prosperity (Bils and Klenow, 2000), it seems hard to imagine that apoorly educated society has brighter prospects than a well-educated one, other things
equal. As noted in the introduction, in the case of both types of capital, the theoreticalbasis for growth arising from accumulation of these factors is high.
Indeed, another implication of the Romer (1986) school of thought is that theconvergence prediction of the neoclassical model – that, due to a common production
technology, nations should as they accumulate capital see their standards of living
converge roughly to the production frontier – is false. Wealthy nations with significant
stocks of human capital and flows of research activity can generate ever-greater
prosperity that allows them to further distance themselves from the vast bulk of nations
not so characterized. To make the leap from stagnation to modern, technologicallysophisticated growth in this view may in fact be quite difficult. Exhibit A in thisargument is generally held to be the failure of the bulk of the world’ countries toconverge as predicted. Of the countries that were not developed at the end of World War
II, only a handful – concentrated in East Asia and Europe with a few other bright spots inplaces like Mauritius and Chile – have managed to escape the poverty trap.

4

But it is possible that ordinary policy mistakes have been of such a magnitude thatthey explain a significant portion of the failure to converge. Why would we expect anation plagued by macroeconomic chaos, crippling rent-seeking and other problems ofwhat I will call malgovernance to be converging toward anything but abject misery?
Given that the advice handed out by economists differs from what it once was withrespect to the balance between markets and planning, and that governments may besubject to incentives far removed from maximizing the growth of per capita income, there
is no reason to suppose that countries are immune to long periods of bad economic policy.
The extent of such activities is of course, an empirical question.

2. The Basic Empirical Model
The analysis begins by noting that the standard approach in growth regressions
has been to treat all countries the same regardless of their current standard of living. The
effect of a marginal increase in human capital or inflation is assumed to be the same for acountry with a per capita of $1000 as $10,000. This is done by specifying growth as thedependent variable and the various state variables of the economy as independent
variables. But there may be diminishing returns to physical or human capital, and policyor the other state variables may affect countries differently depending on where they arein the global income distribution. To capture the full effect for countries at different
levels of income it will be necessary later to test for these effects.
The data for the regressions are the updated appendix to the widely used BarroLee (1994) cross-country data sets. The data contain national-level observations over
five-year intervals dating from 1960 to 1995. Empirically, the first task is to establish the5overall relation between economic growth and its theoretical determinants. The approach
is somewhat similar to that of Barro (2000), who takes the neoclassical growth model as
his starting point and argues that at any moment in time an economy has potential as wellas actual output. While the similarity in terminology to traditional Keynesian analysis of
the business cycle is obvious, the words mean something different here. Potential output
is defined by the available production technology as well as the choices that governments
have made, for both good and ill, in economic policy and the country’s ability to engage
in exchange with other countries. A government that provides the most productive purepublic goods, enforces property rights and controls externalities while avoiding
distortions and avoiding the costly disincentives of excessive or inappropriate taxation
will have a higher level of potential output, as will a country that can trade with othercountries on favorable terms. All of these things determine the economy’s steady-state
output once the neoclassical capital-accumulation process has ended. Actual output, on
the other hand, is a function of the amount of physical and human capital an economy
possesses relative to the Solow steady-state equilibrium. Countries should over time
converge to their potential output, and the speed at which they do so will depend on therate of growth of capital.
With that framework in mind, the following equation is first estimated for theentire Barro-Lee sample:

GROWTH = a0 + a1 PCGDP + a2 INVGDP + a3 PREMIUM + a4 GCGDP +a5 TRADESHOCK + a6 OPENNESS + a7 HUMCAP + a8 INSTABILITY +a9 INFLATION + a10 DEMOCRACY

6(1)

GROWTH is the five-year average growth rate of per capita gross domestic
product. INVGDP is the five-year average of investment as a percentage of GDP, and isa direct measure of the addition of capital stock. The variables determining potential
output are PREMIUM, GCGDP, TRADESHOCK, OPENNESS, INSTABILITY and
INFLATION. PREMIUM is a measure of the black-market premium charged to thecountry’s official exchange rate. It is log(Black-market rate/official rate). Barro (1991)
argues that it is a proxy for the level of governmental distortions, e.g. subsidies, taxes,
monopoly privileges, foreign-exchange and import restrictions, in the economy. A
greater number of such distortions is assumed to lower the economy’s potential output.
GCGDP is government consumption spending other than on defense and education.
TRADESHOCK is the growth rate of the country’s export prices minus that of its importprices. OPENNESS is the measure of openness to foreign trade and investment used bySachs and Warner (1995).
Political choices, structures and events can also affect growth. INSTABILITY is
the Barro/Lee formulation of political instability during the period, defined as one-halftimes the combined number of assassinations and revolutions during the five-year
interval. DEMOCRACY is the Barro (2000) index of democracy, which ranges from 0 to
1. The effects of greater electoral competition on economic growth are not clear.
Wittman (1989) argues that greater democracy amounts to more competitive politicalmarkets which, like more competitive product markets, ought to yield greater efficiency.
But Cheung (1998) believes that democracy in poor countries is a recipe for disaster, inthat it allows pressure groups to more easily mobilize to engage in predatory activities.

7

INFLATION is the average inflation rate during the five-year period. Inflation might
harm a country’s growth potential for all sorts of reasons: the noise it introduces into theprice system, hindering its ability to effectively coordinate economic activity; theintroduction of a distortionary tax on holding currency; the increased difficulty of longrange forecasting and hence the introduction of a bias against long-term contracting; thetransaction costs of inflation management; and a host of others. A summary of these
theoretical arguments can be found in Heymann and Leijonhufvud (1995).
The final variable is HUMCAP, a measure of the country’s human capital. It isthe country’s average schooling divided by its primary student-teacher ratio, with thisfigure then multiplied by life expectancy. There is a significant modeling issue withrespect to how to incorporate human capital into the analysis. On the one hand, it can beseen as just another production factor, like physical capital and labor. If so, theappropriate measure is the rate of change of human capital, analogous to investment as
the rate of change of physical capital, either in the current period or lagged. The
alternative is more appropriate to the analysis of the literature descended from Romer(1986), in which human capital generates knowledge, which then becomes a public goodraising productivity of other inputs throughout the economy. In that case a higher level ofhuman capital should generate more knowledge and hence faster growth. Bothestimation methods were tried. With respect to the growth of human capital, both oneperiod and two-period growth rates were used in an attempt to proxy for the rate of
growth of the productive factor. In none of the cases was this variable significant. On
the other hand, using the contemporaneous stock of human capital is sometimes

8significant in ways detailed below. Thus throughout the stock instead of the growth rateof HUMCAP is used.
The results using OLS are presented in Table 2.3 All of the variables are
statistically significant in the expected direction. The negative sign on PREMIUM
indicates that a greater level of distortions retards growth. Interestingly, the sign on
OPENNESS is independently significant. If the static distortionary effects of trade
restrictions are incorporated in PREMIUM, then some other effect is being captured by
the significance of OPENNESS. Some candidates, many of which have been emphasizedin recent years in the international-trade literature, include economies of scale fromexporting to wealthier and larger markets, learning by doing through foreign economic
interaction, and the ability to tap foreign capital markets and advanced technology
through foreign investment. The simple measure of openness used here does not allow
discrimination among these (not necessarily exclusive) hypotheses. But in any case open
economies seem to grow faster even for a given level of other distortions. DEMOCRACY
is not a significant predictor of growth.
In addition, the change in a country’s relative export prices positively influences
growth, as expected. Both the addition to physical capital and the level of human capital
positively influence it. Political instability, which might disrupt the stability of property
rights, negatively affects growth. Government consumption, which has ambiguous
effects on growth in theory, appears to negatively affect it in practice. And consistent
3. The data are in cross-sectional time-series form. A generalized random-effects
regression was conducted, and the results were almost indiscernible from those reported
here.

9

with years of macroeconomic thinking, inflation is toxic to growth. Finally, theneoclassical growth model finds support, as in the past, with the negative sign of PCGDP.
After standardizing for other considerations, poorer countries grow faster. Convergence
begins with a period of rapid growth (other things equal), which slows down as the
production frontier is approached, a result consistent with intuition about the once rapidly
growing economies of northeast Asia.

3. Differences by Per Capita Income
To test whether the effects of changes in the state variables are the same forpoorer and richer countries, interaction variables are used, where the interaction is withdummy variables representing, respectively, the top and bottom fifths of the total
distribution over the 35-year sample of per capita GDP. The geographic and temporal
distribution of observations included in the regression is presented in Table 5. The
poorest fifth is unsurprisingly dominated by sub-Saharan African nations, while therichest fifth is dominated by nations in Europe. The interaction variables INTER1PREM,
INTER1GC, INTER1INFL, INTER1INST, INTER1INV, INTER1HC, and INTER1OPEN
represent the interaction of being in the top fifth of per capita income withLOGPREMIUM, GCGDP, INFLATION, INSTABILITY, INVGDP, HUMCAP and
OPENNESS respectively. The INTER2 variables represent interaction with a presence inthe bottom fifth of the distribution. The results were also tested with dummy-variableinteractions for the top and bottom third, respectively, and the results are similar.
The results for the various models are broadly similar, with none of the interaction
terms significant, with two exceptions. For the poorest countries, INTER2PREM is10significant with a positive sign, suggesting that governmental distortions are lessdamaging to the poorest countries. And INTER1INV has a negative sign, suggesting thatthe richest countries get less of a GDP payoff for a given level of physical capital and theother independent variables, which suggests diminishing returns to capital. Other thanthat, the basic model is quite resilient in explaining the sources of economic growth. The
two significant interaction terms will be incorporated in the calculation of policy-related
losses below. Table 6 lists the re-estimation of (1) incorporating these two interaction
terms.

4. Evaluating economic policy
The regression results provide an opportunity to measure the effect of economic
mismanagement, as well as economic misfortune. Several of the variables – PREMIUM,
GCGDP, and OPENNESS – are unequivocally results of conscious policy choices, andone more, INFLATION, is certainly arguably such a result. One other, TOFTSHOCK, isa variable that is largely out of the hands of government officials, except insofar as thegovernment can encourage a change in the country’s trade patterns. It will be useful tocontrast the effects of this relatively independent variable with those of the policy
variables.
The value in expectation of the loss imposed by a particular form of inefficiency
is simply the regression coefficient times the value of that variable for that country.
Table 6 lists the ten worst performers with respect to each of the policy variables over theperiod 1990-1995, expressed as annual growth in gross domestic product foregone. The

11

final column lists the total sums of the losses incurred from mistaken policy. The losses
are expressed as deviations from the mean.
The first thing to note is that the losses from bad government policy are hardly
negligible for the poorest-performing countries, in excess of five percentage points per
year for Zambia, which is the worst-governed country for the period 1990-1995. The
costs of bad government policy are substantial. It is worth noting that the figures aredefined for a given level of human capital and investment. Those variables, particularly
the latter, are subject to government policy choices as well, and the data do not lendthemselves to analyzing why countries choose to build more or less human capital, or tocourt or punish investment. But presumably malgovernance with respect to these factoraccumulation variables would make the problem worse.
The second finding of interest is that there does not seem to be one component
that dominates in terms of contributing to poor growth performance. Figure 1 contains
the depiction of the components of losses from poor economic policy contained in Table
7. Inspection reveals that there is no overall dominant component; each of thecomponents in some cases does significant damage. In addition, no particular componentof bad governance necessitates the others. Table 8 is the correlation matrix for the four
components of the four components listed in Table 7. The top portion of the table is forthe entire sample, and the bottom portion is for the subset containing all observations inwhich nations had negative per capita growth during a five-year period. In neither case is
the correlation overwhelming, with the possible exception of OPENNESS and PREMIUM.
Many nations are not overwhelmed by many policy problems simultaneously. This result
can perhaps be interpreted as making the transition to more sound policy somewhat easier.

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Another interesting exercise is to talk about the unluckiest countries, in the senseof having suffered because of adverse developments in their terms of trade. The losses
from terms of trade shocks during 1990-95 are contained in Table 9. There is of course
no reason why a country cannot be both unlucky and badly governed, and so Nigeria ison the lists both of countries that suffered from poor governance and from adverse
international economic developments from 1990 to 1995. Two other countries, Syria and
Gabon, suffered unusually poor terms of trade shocks during this period as well as one
component of bad governance.
These experiments can also be carried out for the entire sample period. Table 10
depicts the same figures as in Table 7, only for the full 1960-1995 period. It again
reveals the extraordinary costs of malgovernance for many of the countries that havesuffered from it. Even a loss of one percentage point a year amounts, over thirty-fiveyears, to a per capita gross domestic product that is forty percent lower. Those pondering
the mystery of why seemingly promising countries like Argentina (itself once one of thewealthiest countries in the world) and India have struggled so mightily in the postwar
period have a promising explanation in poor government policies.
To again assess who which countries have benefited from terms-of-tradeadjustments presumably out of their control, Table 11 depicts the average annual gains or
penalties to growth from adverse terms-of-trade developments from 1960-1995. The
most noteworthy finding is how small the losses other relative to those from bad policy.
Each of the individual components of the costs of malgovernance has had more dramaticeffects than terms of trade shocks, and there is not a single country that has lost more than
three tenths of a percentage point of average annual per capita growth between 1960 and

131995. This is a significant finding in light of the argument that one still frequently hears
that developing countries are systematically handicapped by their dependence oncommodities, which allegedly are subject to systematically adverse developments inrelative prices. In fact, the list of countries that have benefited most from changes ininternational relative prices over the full sample period is dominated by developing
countries.
As before, the correlation matrix for events associated with policy (i.e., between
PREMIUM, INFLATION, GCGP and OPENNESS) is calculated, and presented in Table
12. Other than the relation between PREMIUM and INFLATION, there is no
overwhelming sense in the full sample of coincidence among the various components.
The lesson that emerges is compelling: bad policy is costly. In that sense, theincreasing emphasis, at least in their public statements, that multilateral organizations andOECD countries are placing on sound policy as a condition for substantially enhanced
development assistance is undoubtedly a step forward form past practices. Some
evidence (Alesina and Weder, 2002) indicates that not only was sound policy not a
condition in the past for substantial development assistance, there was even a negative
correlation between corruption and aid, and conditioning aid on policy may be effective
more as a payoff than as a further contribution to development, in that development
assistance may contribute negatively to growth (Osborne, 2003).

5. Lessons for economic reform
The last ten to fifteen years have demonstrated that the process of economic
reform is surprisingly complex. It is true that the Washington Consensus, substantial

14

market reform as a condition for IMF and other multilateral assistance, has been
substantially accepted among the most influential development economists, as well as
many government officials in developing countries. From Carlos Salinas in Mexico to
the nominally Peronist Carlos Menem in Argentina to Yoweri Museveni in Uganda, thelist of leaders who embraced market-based reforms, whether out of original conviction orconversion, is lengthy and until recently was growing. However, acceptance by the
public of such measures, particularly in Latin America, is another matter. Recent election
results in Venezuela, Ecuador and Brazil suggest public impatience with the inability ofreform, or at least the appearance of reform, to quickly deliver increased prosperity. In
Argentina the timeline is somewhat different, in that there were several years of rapid
growth followed by a spectacular collapse, but the public sentiment for expanding oreven maintaining economic reform is quite possibly fading.
If good policy pays off (as the above evidence suggests), but not quickly enoughgiven political constraints, this is a significant problem for those in charge of making
policy. Indeed, if one assumes that a government wants to enact the reform program thatwill be most effective, this amounts to maximizing economic growth over the long termagainst the constraint of political resistance. But as we have seen different componentsof reform contribute to reform in different amounts. Each component – inflation,
distortions, openness, government spending – can be thought of as generating marginal
benefits in the form of higher growth as well as marginal costs in the form of both
political resistance and the political transaction costs of implementation. To conquer
hyperinflation will harm some constituencies, but probably not many – those who benefit
from inflation management, perhaps, but few others. Untangling the web of government

15

distortions, each of which benefits one or more constituencies in obvious ways while
imposing costs on other disparate, poorly organized groups that may well be unknown tothem, is arguably a far more difficult task. The same logic holds for lower government
spending. Increasing openness is a more complex case, as each trade or investment
barrier benefits some domestic constituency, but if most or all such barriers can beeliminated at a stroke the benefits may flow immediately and obviously to many groups
as well. The optimal path of reform clearly must take these considerations into account.
Theory has a fair amount to say about which steps should be taken when. There
are two independent issues. First, should reform even be gradual, or instead of the “bigbang” type? Only if gradual reform is recommended does sequencing even become anissue. With respect to the first question, Fernandez and Rodrik (1991) specifically model
political resistance by sectors of the population, and find that one-shot reform is preferred
when losers are substantial and who the winners and losers are is difficult to predict.
Gradualism in such an environment invites mobilization by the losers and hence potential
failure. Dewatripont and Roland (1995) find that gradual reforms are generally more
likely to survive politically, particularly if winners can be generated and politicallycultivated in the early stages.
But such work is of course only helpful if the existence or absence of thoseconditions can be established before reform begins. Given the difficulty of identifying
such conditions, and then identifying the constituencies likely to support reform, theempirical relevance of such work is somewhat muted. But other work specifically tackles
the proper ordering of reform steps. In general, it finds that macroeconomic stabilization,by which is meant control of budget deficits and inflation and the establishment of a more16

realistic exchange rate, ought to come first along with trade liberalization. Financialmarket liberalization should come later. This literature is surveyed in (World
Development, 1997) and Krueger (2000, “Second stage”). In any event, political
opposition may compel a partial liberalization in the early stages, and when that is
combined with the theoretical arguments in favor of gradualism the question arises, what
first?
This amounts to asking, of the four measured components of malgovernancetackled here, which one would contribute the most good? The analysis above suggeststhat there is no ironclad recipe for choosing among tackling inflation, government
distortions, the closed nature of the economy and excessive government spending. In
different countries different problems do different amounts of damage. Thus, eachcountry must examine its own costs from malgovernance to see which policies exact thegreatest costs in terms of foregone growth. For example Zambia, the country with thegreatest total losses during the 1990-1995 period in Table 7, lost .014896 points ofgrowth (relative to the mean) to excessive spending, .016545 points to inflation, 0071508
points to closure of the economy to foreign trade and investment, and .0142833 points togovernment distortions. If one reform must be chosen, and assuming (perhaps heroically)no feedback effects to other costs, control of inflation would seem to be the most urgent
task from the point of view of benefits. (The political costs of implementing various
types of reform are another matter.)Some interesting results come from investigating the path of those countries thathave actually engaged in the greatest amount of reform. One way to do that is to examine
the countries whose total losses from malgovernance have decreased by the greatest

17

amount between the 1960-1965 and 1990-1995 periods. Table 13 lists the values for
LOSSTOTAL for each five-year period between 1960 and 1995 for the ten nations withthe greatest positive change in that variable between 1960-65 and 1990-95. Among thenations listed are several exemplars for either dramatic economic reform or successfulpolicy, which is an indication that the measurements of distortion and reform used in thispaper are consistent with the general understanding of these concepts. In particular,
Indonesia can be seen to have begin dramatic reform after 1970, Korea in roughly 1965
or 1970, Chile in the 1975-1980 period, Israel after 1985, Ireland after 1970, New
Zealand after 1985 and Uganda after 1990. In addition two countries, the Central African
Republic and Tunisia, have reform patterns that are less sudden and will be discussed in
more detail below.
The details of reform are somewhat different in various countries. Figs. 2-11
depict the contribution, positive or negative, of each policy component to growth relative
to the mean. In Indonesia, where the net contribution of policy changed by the greatest
amount between 1960 and 1995, the most significant contributions were made byconquering inflation after 1970 and opening up to the world economy after 1975. Korea
made modest cuts in government distortions after 1965 and opening up the economy after
1970. Inflation was never a significant contributor to poor policy there. After 1985,
government consumption actually went from being a modest boost to growth to a modest
negative. This might be because as it became wealthier Korea underwent the well-known
tendency of wealthier countries to expand the reach of government (Peltzman, 1980).
In Chile, interestingly, the obviously radical reforms after 1975 in fact at firstsimply returned the country to the status quo before the Allende government took power18

in 1970. But the reform was broad-based, with inflation, distortions and openness
contributing far more substantially to growth after 1975 than before. Only government
consumption worsened its negative impact on growth. In Israel, it was inflation andopenness that were doing the most damage by 1985, and whose repair was the mostdramatic change in policy. As in Korea and Chile, government consumption rose after
the dramatic reform period, and it is possible that this involved extending the socialspending safety net to make reform more palatable, a possibility that has been suggested
for Chile in particular (Valdés, 1995).
Ireland made dramatic improvements in openness after 1970, and steady but
modest improvements in government consumption. New Zealand’s post-1985 reform, so
obvious in Fig. 9, again revolved mostly around opening the economy. Openness is also
important in Uganda after 1990, with OPENNESS changing from a small drain to a major
boost to the economy. Inflation is also a big part of the story there, changing from amajor to a tiny drag on economic performance. In the Philippines openness is again amajor part of the reform after 1985, with a significant improvement in each subsequent
five-year period. There was major improvement in the cost of government distortions
between 1985 and 1990, followed by a slight retrenchment in the next period.
Several implications emerge from this pattern. First, openness is a part of each of
the successful reform efforts. While the imprecision of the measurement of OPENNESS
merits caution as to the exact size of the effects from opening up in each case, it is clear,that if a reform is substantial it generally includes this step. In most cases, opening up is
in fact one of the first things that is done. The same is true of inflation – if it is asignificant problem, tackling it seems to be a necessary first step. In each case where19reform took hold and inflation was a problem, its elimination occurred early in theprocess. In one case, Indonesia, major reforms in openness occurred after inflation was
conquered.
The importance of inflation conquest in successful reform is not surprising.
Inflation is costly in a very visible way – all segments of the population are affected by it,
it is not invisible to the average citizen in the way that government distortions or tradebarriers might be, and many people are largely defenseless against it. To slay the
inflation dragon is to chalk up a substantial success in economic reform, which may give
governments that achieve it the credibility to engage in further reforms.
The tackling of government distortions also reveals interesting problems. In
several countries (Indonesia and Israel) distortions appear to have been addressed after
other problems – a sequential approach. In the Central African Republic and Korea, it
appears that distortions were never seriously addressed. In one – Tunisia – they wereaddressed before other problems, but even in 1995 distortions were in that country
relative to the global mean a drag on economic growth. By the measure used, twocountries – New Zealand and Ireland – had no distortions to tackle. In only two countries– Chile and the Philippines – was a simultaneous solution to distortions and inflation
and/or openness successfully achieved.
That inflation-fighting is such a big part of successful reform is unsurprising. In
addition to the aforementioned immediate political benefits, there are a number ofrelatively simple remedies available on the shelf for purging inflation, especially
hyperinflation. Adopting significantly tighter monetary policies, more exotic hardcurrency systems such as the currency board, and in several Latin American countries

20

outright dollarization have all had significant success in curbing hyperinflation.
Admittedly, in at least on case – Argentina – the currency-board option has been blamedfor exacerbating if not outright causing the economic collapse in that country in late 2001
and early 2002. On the other hand, such systems have been argued to be successful in
Lithuania, Estonia and Hong Kong, among other places. The verdict on dollarization isstill out – the preliminary evidence from Latin America is that they are overwhelmingly
successful in slaying hyperinflation, but their long-term effects are unknown.
The relatively low transaction costs of inflation fighting, combined with the
payoffs in higher growth and the immediate political approval from same, surely explain
why gradualist nations emphasize it in the early stages. In addition, the relative rarenessof distortion removal in the early stages of gradual reform provide some support for theframework sketched here of the growth and political benefits versus political- andtransaction-costs framework. This is buttressed by the relative unimportance in earlystages of reform of lower government consumption spending. In Indonesia, government
spending declined in the period after other reforms, before rising again after 1985. In
Chile it actually expanded during the first five-year period, but eventually declined after
1985. Similar initial deteriorations occurred in Israel and Uganda, if reform is dated as
beginning after 1985 and 1990 respectively in those countries. In the Philippines
government consumption does not appear to have been a part of the reform process at all,while in Tunisia, Ireland and New Zealand it was not a big part of the problem. Only in
the Central African Republic was government consumption tackled early in the reform
process, and there the reform was quite gradual. That government consumption is such asmall part of these reforms is of interest in light of the emphasis it often receives in IMF

21

reform programs. Of course, to be fair often the demands to slash government spending
are not because spending per se is high but because deficits are huge. However, absent
chronic deficits of that sort, substantial cuts in spending may rank relatively low in termsof immediate importance in economic reform.

Gradual vs. total

Is radical reform practical? The feasibility of empirical analysis suffers from an
inability to differentiate it from gradual reform in the data. The analysis here does allow
at least an attempt. Reform must be defined by a change from policies that are hostile tothose that are friendly to growth. The calculated policy loss LOSSTOTAL can serve as aproxy for that variable. I will define the scope of reform as any change in LOSSTOTAL
from one five-year period to the next of at least 0.01, i.e. a change of at least one
percentage point in the growth-friendliness of policies. The list of countries who haveengaged in it by that measure is found in Table 14. There are three such changes between
1965 and 1970 (Venezuela, Brazil and Taiwan), four between 1970 and 1975 (Indonesia,
Egypt, Senegal and Korea) and between 1975 and 1980 (Chile, Cameroon, Indonesia and
Sri Lanka), two between 1980 and 1985 (Chile and Mauritania), five between 1985 and1990 (Bolivia, Israel, Ghana, Costa Rica and the Central African Republic) and 14
between 1990 and 1995 (Argentina, Poland, Uganda, Mexico, Venezuela, Paraguay,
Bolivia, Israel, Turkey, Ghana, Uruguay, Tunisia, Gambia and the Philippines). That themeasure is a useful proxy for the phenomenon is supported by the huge increase in such
reforms in the 1990s, which is generally judged to be the time when the pressures of22

globalization, the desires of multilateral agencies and ODA donor countries and thedesires of the body politic (particularly in the former Soviet bloc) are generally held tohave created a great deal of momentum for market-friendly policies.
It is possible to (roughly) distinguish between gradual reform and shock therapy
by looking at the changes in the components of LOSSTOTAL. In so doing, two questions
present themselves: is each type of reform sustained, and does it generate higher
economic growth? Nine of the 17 reforms involved only two of the four components,
while eight involved three or four. First, it was worth noting that ten years out, in all 17
cases the growth penalty was less than before reforms began, suggesting that in eithercase reform can hold. But the average growth penalty in the modest-reform countries had
improved by 1.29980 percentage points during this period, while in the more dramaticallyreformed economies it had improved by 3.25151 points. Of course, the proof of thepudding is in growth, not the estimated growth penalty. During the five-year period
beginning ten years after the onset of reform, the gradual-reform countries by the above
criteria grew an average of 3.00 percent per year, versus 2.42 percent in the five-year
period prior to reform while the shock-therapy countries grew an average of 3.68 percent,versus prior growth of 2.86 percent. Of course, the small number of observations in thisrespect make firm conclusions hazardous, but both methods do seem to provide somepayouts down the road, with a modest edge to radical reform.

6. Conclusion
If the findings are to be believed, policy matters – a lot. The notion that a selectgroup of nations might, because of their knowledge and technological infrastructure, leap

23

irretrievably beyond the ability of poorer nations to catch up at least to a first
approximation is an appealing one in light of the results of fifty years or more ofsurprisingly difficult efforts to develop. But the notion of an impassable barrier between
rich nations growing ever more prosperous and a large group nations mired in hopelesspoverty would, if true, argue against any nations ever advancing from poverty toprosperity. That significant numbers of nations have made some progress, and that a few
have completely made the transition from underdeveloped to developed is a sign that all
is not lost. The prime implication of the findings here is that good policy, however it is tobe achieved, is effective in making substantial improvements to growth rates. Thisappears to be true whether reform is gradual or sudden, although there does seem to beslight room in the data for preferring sudden reform. In any case, the importance ofsound policy ought to be elevated to a dominant level in the discussion of improving the
lot of the world’s poor.

24

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Solow, Robert M. “A Contribution to the Theory of economic Growth.” The
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27

Table 1
Growth in the developing world, 1960-1995
Below-median climbers

Most rapid growth

Per capita Y 1960

1995

Annual per capita growth rate

Seychelles
Swaziland
Tunisia
Indonesia
JordanS. Korea
Sri Lanka
Thailand

4260
2629
3160
2499
3197
9145
2536
4869

S. Korea
Singapore
TaiwanHong Kong
Malta
Thailand
Malaysia
Botswana
PortugalIndonesia
Lesotho
China
Seychelles
GreeceSyria
Tunisia
JordanBarbados
MoroccoTurkeyEgyptCape Verde
Brazil
Mauritius
Pakistan
Colombia
PanamaGabon
Swaziland
MexicoSri Lanka
Dom. Rep.

1253
1240
1095
641
1158
898
1253
940

28

6.86
6.72
6.29
6.15
5.36
4.81
4.64
4.48
4.31
3.96
3.76
3.75
3.56
3.51
3.19
3.07
2.94
2.80
2.71
2.60
2.60
2.57
2.56
2.54
2.40
2.32
2.30
2.20
2.17
2.13
2.03
2.03

Table 2
Physical and human capital, by national per capita income

Bottom third

Middle third

Top third

Non-residential
capital stock,1990

1627.80(n = 10)8408.68(n = 19)

30,289.23(n = 31)

Average yearsof schooling,
1995

3.05
(n = 30)

5.73(n = 33)

7.89(n = 48)

Primary studentteacher ratio,
1995

44.03
(n = 30)

28.55(n = 33)

21.41(n = 48)

Sources: Penn World Tables 5.6 (physical capital), Barro and Lee (1994) (human capital)

29

Table 3
OLS results, entire sample
Variable

CoefficientStandard Error

CONSTANT.0241222.0067432

PCGDP***-6.48e-06

1.10e-06

INVGDP***.1110481.0240248

PREMIUM*-.0018652.0010576

GCGDP***-.1143939.0252939

TRADESHOCK***.0832733.0257677

OPENNESS***.0138895.0040703

HUMCAP**

1.98e-06

6.75e-07

INSTABILITY*-.0141174.007702

INFLATION***-.0281259.0071934

DEMOCRACY-.0004643.0052111

F: 17.98***
R2: 0.3493
N: 346
Note: * denotes statistical significance at the ten-percent level.
** denotes statistical significance at the one-percent level.
*** denotes statistical significance at the 0.1-percent level.

30

Table 4
Geographical and temporal distribution of per capita income
Poorest fifth (below $816.60)
Middle Sub-Saharan
S. Asia East
AfricaEast Asia

Latin Amer.

Eur./N.
Amer. Total

1960

1

0

4

1

1

0

7

1965

1

0

8

1

0

0

10

1970

1

0

7

1

0

0

9

1975

0

0

8

0

0

0

8

1980

0

0

8

0

0

0

8

1985

0

0

13

0

0

0

13

1990

0

0

7

0

0

0

7

Total

3

0

55

3

1

0

62

EuropeTotal

Top fifth (above 6807.2)
Middle Sub-Saharan
S. Asia East
AfricaEast Asia

Latin Amer.

1960

0

0

0

0

3

3

3

1965

0

2

0

0

2

4

4

1970

0

3

0

0

3

7

7

1975

0

4

0

1

5

12

12

1980

0

4

1

2

6

16

16

1985

0

2

1

3

4

16

16

1990

0

2

1

2

5

15

15

Total

0

17

3

8

28

0

73

31

Table 5
Regression Results
Top fifth

32

Variable

Coefficients

PREMIUM-.0013522*-.0013636*-.0012824*-.0012493*-.0015188*-.0013196*-.0012861*-.0012591*

GCGDP-.1095196***-.1052964***-.1119875***-.1116001***-.1047702***-.1075777***-.1102927***-.1119689***

INFLATION-.0299949***-.0304655***-.0292873***-.0299961***-.0302654***-.0308095***-.030057***-.029743***

INSTABILITY -.0140906*-.0138494*-.0143462*-.0138278*-.0142824*-.01408*-.0141127*-.0142784*

INVGDP.0930784***.0924124***.0933907***.0927042***.0981478***.0899006***.0926308***.0933675***

HUMCAP.00000139**.00000148**.00000154**.00000143**.00000139**.00000175**.00000142**.00000140**

OPENNESS.015067***.0149436***.0147649***.0151598***.0144974***.0148969***.0153538***.0149408***TOFTSHOCK.0691863***.069556***.0699264***.0692126***.0609432***.0695487***.0689953***.074587***

PCGDP-.00000459***-.00000445***-.00000483***-.00000483-.00000400***-.00000456***-.00000470***-.00000484***

CONSTANT.0237669***.0229764***.0243904***.0246634***..0212466***.0233844***.0241665***.024788***

INTER1PRE.0004215

-

-

-

-

-

-

-

INTER1GC--.0705123

-

-

-

-

-

-

INTER1INFL---.0178962

-

-

-

-

-

INTER1INST

-

-

-

-.0084581

-

-

-

-

INTER1INV

-

-

-

-

-.0392014*

-

-

-

Table 5 (continued)
Top fifth

33

Variable

Coefficients

INTER1HC

-

-

-

-

-

INTER1OPEN

-

-

-

-

INTER1TT

-

-

-

N

487

487

R2

.3577

F

26.51***-.00000551

-

-

-

-.0018991

-

-

-

-

-.0700875

487

487

487

487

487

487

.3591

.3583

.3575

.3626

.3584

.3575

.3449

26.67***

26.58

26.48

27.08***

26.59***

26.49***

26.59***

Bottom fifth
PREMIUM-.0018659**-.0013168*-.0012426*-.0012801*-.0012559*-.0012423*-.0012323*-.0012224*

GCGDP-.1022232***-.1057736***-.1119918***-.1111923***-.1110792***-.1096271***-.1114181***-.1121667***

INFLATION-.0296984***-.0296478***-.0300588***-.0290463***-.0298885***-.0295879***-.0297585***-.0300572***

INSTABILITY -.0150719*-.0146726***-.01414*-.0131193*-.0142314*-.0147174*-.0143447*-.0142904*

INVGGDP.0877031***.0913523***.0933768***.0903788***.0931822***.0911466***.0923011***.093261***

HUMCAP.00000141**.00000140**.00000142**.00000138**.00000141**.00000141**.00000141**.00000144**

OPENNESS.013868***.0150124***.0151217***.0151371***.0150959***.0152334***.0154135***.0152491***TOFTSHOCK.0684828***.0690617***.0693247***.0680788**.069182***.0697726***.0696151***.0782199***

Table 5 (continued)

34

Variable

Coefficients

PCGDP-.00000508***-.00000487***-.00000485***-.00000485***-.00000486***-.00000487***-.00000486***-.00000486***

CONSTANT.0249556***.024754***.0247058***.0251914***.0247206***.0252052***.0248409***.0247126***

INTER2PRE.0026683*

-

-

-

-

-

-

-

INTER2GC--.0146201

-

-

-

-

-

-

INTER2INFL--.0007989

-

-

-

-

-

INTER2INST

-

-

-

-.0239171

-

-

-

-

INTER2INV

-

-

-

-

-.0046387---

INTER2HC

-

-

-

-

-

-.00000249--

INTER2OPEN

-

-

-

-

-

-

-.0037755-

INTER2TT

-

-

-

-

-

-

-

-.0450959

N

487

487

487

487

487

487

487

487

R2

.3577

.3580

.3574

.3589

.3574

.3579

.3575

.3583

F

26.51***

26.55***

26.47***

26.65***

26.47***

26.53***

26.48***

26.58***

Table 6
Regression results, with interaction terms

35

Variable

CoefficientStandard Error

CONSTANT*.0127393.0050774

PCGDP***-3.35e-06

7.20e-07

INVGDP***.1279158.0182063

PREMIUM**-0.0020454

0.0007124

GCGDP***-0.0552653

0.0162408

TRADESHOCK***

0.075532

0.02177

OPENNESS*** .

0.0122968

0.0032949

HUMCAP*

1.10e-06

5.03e-07

INSTABILITY*-0.0135358

0.0065842

INFLATION***-0.0217672

0.0052823

INTER1INV**-0.0522926

0.0200165

INTER2PREM*

0.0022576

0.011876

F: 23.74***
R2: 0.3472
N: 503
Note: * denotes statistical significance at the ten-percent level.
** denotes statistical significance at the one-percent level.
*** denotes statistical significance at the 0.1-percent level.

Table 7
Policy losses, 1990-1995
PREMIUM
1. Sudan
2. Iraq
3. Zambia
4. Iran
5. Rwanda
6. Afghanistan
7. Burundi
8. Tanzania
9. Syria
10. Haiti-.018012-.0157129-.014283-.0139607-.0123634-.0116902-.0101165-.0100075-.0097141-.0094832

GOV. CONSUMPTIONINFLATION

Bangladesh
Congo
Seychelles
ZambiaGambiaMalawiIndia
Egypt
Cameroon
GuyanaD.R. Congo
BrazilNicaragua
Suriname
ZambiaPeru
TurkeyUruguay
MozambiqueCongo-.0288824-.0264371-.0153241-.014896-.0145059-.0129835-.011956-.011014-.0090921-.008683

TOTAL (including OPENNESS)-.0881611-.0602273-.0192669-.0176086-.016545-.0130841-.0127837-.0098094-.0076283-.0075382

ZambiaBangladesh
Congo
Nicaragua
Malawi
Nigeria
Algeria
Zimbabwe
Egypt
India

-.0528751-.0412315-.0406117-.0333811-.0333646-.0278548-.0235717-.023245-.0216799-.0206507

36Countries with maximum losses from lack of openness (.0071508): Iraq, Congo, Senegal, Myanmar, Chad, Mauritania, Zimbabwe, Cote D’Ivoire, Papua New
Guinea, Kenya, Trinidad and Tobago, Nigeria, Algeria, Cameroon, Gabon, Zambia, Haiti, Madagascar, Malawi, Burundi, D.R. Congo, Sierra Leone, Somalia,
Mozambique, Bangladesh, Iran, India, Pakistan

Table 8
Correlation of components of malgovernance, 1990-95
Full sample
PREMIUM

INFLATION

INFLATION

0.3666OPENNESS-0.5728-0.1457

GCGDP

0.2806

0.0586OPENNESS-0.4522

Negative per capita growth
PREMIUM

INFLATION

INFLATION

0.6109OPENNESS-0.5329-0.2466

GCGDP

0.2447

0.4819OPENNESS-0.3147

37

Table 9
Losses from terms of trade shocks, 1990-1995
1. Yemen
2. Trinidad and Tobago
3. Syria
4. Comoros
5. Nigeria
6. Mozambique
7. Angola
8. Guinea-Bissau
9. Guinea
10. Gabon-.0128522-.0089113-.0077276-.00705-.0063427-.0063083-.0048-.0046544-.0042862-.0034569

38

Table 10
Policy losses, entire sample period
PREMIUM39

1. D.R. Congo
2. Tanzania
3. Ethiopia
4. Poland
5. Sudan
6. Malawi
7. Zambia
8. Rwanda
9. Bangladesh
10. Nepal
11. Ghana
12. Hungary
13. Algeria
14. Nigeria
15. Burundi
16. Iraq
17. Iran
18. Pakistan
19. Sri Lanka
20. Zimbabwe
21. Syria
22. Chile
23. El Salvador
24. Dom. Rep.
25. Argentina
26. Yugoslavia
27. Kenya
28. Botswana
29. Brazil
30. Paraguay
31. Jamaica-.0122629-.0106803-.00973-.0092194-.0090739-.0088354-.0084939-.0078205-.0075058-.0074826-.0074071-.0073461-.0072049-.0069961-.0067901-.006649-.0061675-.0056121-.0054044-.0051847-.0051125-.0050871-.0050643-.0045662-.0043291-.0042262-.0041586-.0041427-.0041158-.0039924-.0037313

GOV. CONSUMPTIONINFLATION

Bangladesh
ZambiaGuyanaCent. Afr. Rep.
India
PanamaMalawiTogo
Nigeria
Cameroon
Sri Lanka
Egypt
Kenya
Costa Rica
UgandaChile
Bolivia
Burkina Faso
Ghana
Philippines
Algeria
EcuadorSwedenMauritius
Nicaragua
Honduras
DenmarkMadagascar
Paraguay
Tunisia
PakistanArgentina
D.R. Congo
Peru
Nicaragua
Uruguay
Bolivia
Chile
UgandaIndonesia
IsraelTurkeyGhana
ZambiaSierra Leone
MexicoIcelandMozambique
EcuadorColombia
Suriname
Nigeria
Tanzania
Jamaica
Venezuela
Costa Rica
Paraguay
Iran
MalawiDom. Republic
Portugal
Greece-.0196369-.0152793-.0127932-.0111499-.0107055-.0083839-.0083092-.007987-.0064362-.0055758-.0045808-.0044096-.0041309-.0038931-.0032936-.0030952-.0028805-.0027812-.0025502-.0010618-.0008865-.000756-.0007465-.0006513-.000635-.0004637-.0004298-.0003237-.0002802-.0001063.0005149

TOTAL (including OPENNESS)-.0198353-.0181369-.0138244-.0103602-.0091125-.0082233-.0070161-.0056179-.0055085-.0052582-.004231-.0034466-.0031905-.0030815-.0023561-.0017781-.0014082-.0011974-.0010542-.0010542-.0005407-.000205-.0001461.0001497.0004618.000541.0005945.0006956.0007439.0008047.0008985

ZambiaUgandaArgentina
India
Nigeria
Ghana
Uruguay
Chile
AlgeriaCent. Afr. Rep.
Sri Lanka
Kenya
Bolivia
Pakistan
BurundiCosta Rica
Togo
Paraguay
Dom. Republic
Cameroon
IsraelPhilippines
TurkeyTunisia
Indonesia
Iran
Colombia
Burkina Faso
EcuadorHonduras
Venezuela-.031146-.0259174-.0245935-.0223809-.0211238-.0171532-.0152309-.0151685-.0136571-.0136571-.0123975-.011222-.0105097-.0102893-.010203-.0095969-.0091251-.0089928-.0088553-.0082892-.0079563-.0071179-.0066875-.005536-.0051368-.0050051-.0043421-.0038689-.0028875-.0017107-.0005113

Table 10 (continued)
Policy losses, entire sample period

40

PREMIUM

GOV. CONSUMPTIONINFLATION

32. Lesotho-.0037192
33. Ecuador-.0034773
34. Turkey-.0033647
35. Indonesia-.0033255
36. Tunisia-.0033255
37. Israel-.0029391
38. Bolivia-.0028373
39. Costa Rica -.0027941
40. Colombia -.0024809
41. South Africa -.002416
42. Philippines -.0022117
43. Korea-.0019039
44. Afghanistan -.0018385
45. Morocco-.001818
46. Uruguay-.0015518
47. Cyprus-.0006754
48. Greece-.0003031
49. Venezuela -.0002153
50. Jordan-.0001181
51. Portugal.0015044
52. Congo.0017881
53. Senegal.0017881
54. Cote D’Ivoire.0017881
55. Benin.0017963
56. Mexico.0019827
57. Cameroon .0023342
58. Honduras.0025404
59. Guatemala .002625
60. Malaysia.002854
61. Haiti.0030167
62. Niger.0033434

Cyprus
JordanUruguay
Portugal
Zimbabwe
U.K.
Indonesia
Peru
BurundiThailandAustria
Dom. Rep.
Ireland
New ZealandFinland
Colombia
Malaysia
FranceJamaica
NorwayIsraelItaly
Spain
Venezuela
GreeceMexicoArgentina
Australia
CanadaTrin. & Tobago
TurkeyPhilippines
Madagascar
Korea
Syria
Kenya
Congo
MyanmarZimbabwe
El Salvador
South Africa
Egypt
Bangladesh
LesothoSwaziland
Spain
Algeria
Nepal
GambiaGuatemalaHaiti
Trin. & Tobago
Italy
Botswana
Sri Lanka
BurundiIndia
Pakistan
Mauritius
New ZealandHonduras
Ireland.0008778.0011225.0013182.0013263.0013794.0015656.0017776.0018034.0018116.0019013.002063.0021201.0025293.0026407.0029071.0029425.0031164.0036493.0037186.0037281.0039904.0041861.0042052.0048155.0049405.0051376.0054557.005684.0059382.00607.0061693

TOTAL (including OPENNESS).0010388.0010563.0010737.0011797.0011949.0011979.001245.0012955.0012966.0013178.0013549.0013698.0014509.0015276.0015803.0015851.0016521.0016656.0016681.0017638.0017714.0017944.0018046.0018474.0019263.001957.0019588.0019674.0019838.001984.002023

Jamaica
Syria
MexicoNew Zealand
Korea
Cyprus
JordanPortugal
GreeceMalaysia
SwedenDenmark
ThailandIrelandU.K.
Austria
Spain
Finland
Italy
FranceNorwayAustralia
Belgium
CanadaNetherlands
U.S.
Switzerland-.0001289.0002564.0006368.0080982.0085963.0092031.0094892.0097122.0116128.0153013.0153017.0156429.0158535.0159626.0173859.0181553.0184582.0188725.0196585.0198412.0198748.0198962.0223842.022394.0233248.0243614.0245606

Table 10 (continued)
Policy losses, entire sample period

41

PREMIUM

GOV. CONSUMPTIONINFLATION

63. Cen. Afr. Rep..0033473
64. Burkina Faso .0033473
65. Togo.0033473
66. Chad.0040476
67. Thailand.0051632
68. Oman.0055031
69. Spain.006596
70. Hong Kong .006785
71. Belgium.0069053
72. Japan.0069903
73. Austria.0070068
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.

Belgium
Netherlands
Korea
Iran
Switzerland
U.S.
Singapore
Syria

Mauritania
Hungary
Cameroon
U.K.
Barbados
Cote D’Ivoire
Mali
Finland
Gabon
Hong Kong
Ethiopia
SwedenFiji
Denmark
JordanAustralia
Cent. Afr. Rep.
NorwaySenegalFranceMorocco
KuwaitTunisia
Papua N.G.
Chad
Togo
Benin
TaiwanThailand
China
Burkina Faso

.0064832.0066627.006751.0077188.0077392.0078275.0085003.0090726

TOTAL (including OPENNESS).0020695.0020851.0021032.0021423.0022061.002217.0022776.0022875.0022902.002292.0023104.0023703.0023898.0023948.002408.0024239.0024316.0024687.0024851.0025139.002527.0025408.0026902.0026171.0026583.0026656.0026889.0027102.0027121.0027155.0027159

Table 10 (continued)
Policy losses, entire sample period
PREMIUM

42

94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.

GOV. CONSUMPTIONINFLATION
Niger
CanadaJapan
U.S.
Belgium
CyprusNetherlands
Austria
Luxemburg
Saudi Arabia
Switzerland
W. Germany
Malta
Malaysia
Singapore
PanamaTOTAL (including OPENNESS).0027542.0027779.0028114.002856.0029188.0029239.0029843.0030086.0030094.003046.0031435.0032239.0032274.003254.0032775.0033705

Note: Australia, Canada, Denmark, Finland, France, Ireland, Italy, Netherlands, New Zealand, Norway, Switzerland, U.K., U.S. and W. Germany had equal
estimated bonus from PREMIUM, .0076011.
Note: OPENNESSTable 11(ttloss or losstt?)
1. Ghana
2. Sri Lanka
3. Nicaragua
4. Malawi
5. India
6. Mozambique
7. Uganda
8. D.R. Congo
9. Japan
10. Mauritania
11. Chile
12. Zambia
13. Madagascar
14. Cote D’Ivoire
15. South Africa
16. Pakistan
17. Niger
18. Brazil
19. Taiwan
20. Cameroon
21. Thailand
22. Sierra Leone
23. Peru
24. Egypt
25. Mauritius
26. Papua New Guinea
27. Argentina
28. Bangladesh
29. Cent. Afr. Rep.
30. Italy
31. Togo
32. U.S.
33. Benin
34. Australia
35. Costa Rica
36. Philippines
37. Ethiopia
38. Jamaica
39. Dom. Republic
40. Gambia
41. Trin. & Tobago
42. Canada
43. Guatemala
44. Ireland
45. Belgium
46. Malaysia
47. Spain
48. Netherlands
49. Denmark
50. Sweden
51. Senegal
52. Austria

Terms of trade losses, 1960-1995-.0027388-.0021854-.0019535-.0018178-.0017208-.0016464-.0015207-.0015109-.0015015-.0013217-.0012711-.001258-.0012034-.0011165-.0011071-.0010054-.0009912-.000983-.0008842-.0008494-.0008333-.0008267-.0008257-.0007956-.0007643-.000747-.0006731-.0006689-.0006579-.0005702-.0005658-.0005317-.0005181-.0005132-.0004919-.0004432-.0003988-.0003966-.000368-.0003083-.0002961-.0002779-.0002383-.000201-.0001941-.0001529-.0001515-.0001025-.0000928-.0000881-.0000331-.0000255

43

Table 11 (continued)
53. Chad
54. Mali
55. Israel
56. Uruguay
57. El Salvador
58. U.K.
59. Norway
60. Korea
61. Greece
62. France
63. Colombia
64. Finland
65. Jordan
66. Kenya
67. Morocco
68. New Zealand
69. Indonesia
70. Paraguay
71. Switzerland
72. Algeria
73. Honduras
74. Panama
75. Mexico
76. Angola
77. Nigeria
78. Rwanda
79. Venezuela
80. Iceland
81. Ecuador
82. Tunisia
83. Burkina Faso
84. Syria
85. Congo
86. Iran
87. Gabon
88. Bolivia.0000687.0000996.0000997.0001097.000115.0001285.0001343.0001864.000215.000231.0002367.0003122.0003866.000468.0004759.0005161.0007275.0007363.0007987.0008011.0008217.0008687.0008838.0009083.0009897.0010205.001264.0013467.0014354.0016663.0017934.0018756.0020704.0022896.0023834.0028734

44

Table 12
Correlation of components of malgovernance, 1960-1995 [redo 90-5 with components, not losses]
Full sample
PREMIUM

INFLATION

INFLATION

0.1677-OPENNESS-0.2268-0.1651

GCGDP

0.07900.0161OPENNESS-0.3064

Negative per capita growth
PREMIUM

INFLATION

INFLATION

0.1779OPENNESS-0.1327-0.1115

GCGDP

0.0106-0.0686

OPENNESS-0.1844

45

Table 13
Successful reform: Value of LOSSTOT in each five-year interval
1. Indonesia
2. Korea
3. Chile
4. Israel
5. Cent. Afr. Rep.
6. Tunisia
7. Ireland
8. New Zealand
9. Uganda
10. Philippines

1965-70-.030431-.007256-.018092-.010399-.032787-.009538.006428.00741-.017973-.007432

1970-75-.0309901-.000848-.018434-.002959-.029149-.009543.017759.007028-.01579-.004313

1975-80-.000881.010531-.049918-.002688-.034761-.006078.017748.006858-.018534-.009758

1980-85.009903.012981-.016838-.011932-.025594-.003984.017691.004588-.029676-.008946

1985-90.0092.016904.003626-.027665-.021843-.004327.018437.005584-.023176-.010773

1990-95.006098.017369-.000316-.00025-.009438-.001425.022174.014794-.025364-.002747

1990-5.00601.023761.007058.013466-.011672.00941.024768.023693-.002997.007412

Change.036441.031017.02515.023865.021115.018948.01834.016283.014976.014844

46

Table 14
Gradual and radical reform
1965

1970

1975

1980
1985

1990

TaiwanBrazilVenezuela

Government, Premium
Government, Inflation
Government, Premium, Openness

Indonesia
Egypt
Senegal
Korea

Premium, Inflation, Openness
Government, Premium
Government, Premium
Government, Premium, Openness

Chile
Cameroon
Indonesia
Sri Lanka

Premium, Inflation, Openness
Premium
Premium, Inflation, Openness
Government, Premium, Openness

Chile
Mauritania

Government, Openness
Government, Premium

Bolivia
IsraelGhana
Costa Rica
Cent. Afr. Rep.

Government, Premium, Inflation, Openness
Premium, Inflation, Openness
Government, Premium, Inflation, Openness
Government, Inflation, Openness
Government, Inflation

Argentina
PolandUgandaMexicoVenezuela
Paraguay
Bulgaria
IsraelTurkeyGhana
Uruguay
Tunisia
GambiaPhilippines

Government, Premium, Inflation, Openness
Government, Premium, Inflation, Openness
Inflation, Openness
Government, Premium, Inflation, Openness
Premium, Openness
Premium, Inflation, Openness
Government, Premium, Inflation
Government, Premium, Inflation, Openness
Government, Premium, Openness
Government, Premium, Inflation, Openness
Government, Inflation, Openness
Premium, Inflation, Openness
Government, Premium, Inflation, Openness
Government, Premium, Openness47

Fig. 1 - Components of lost growth20%0%
ZambiaBangladesh

Congo

Nicaragua

Malawi

Nigeria

Algeria

Zimbabwe

Egypt

India

-20%

48

Openness-40%

Inflation
Gov. Cons.
Premium

-60%

-80%

-100%

Indonesia
0.015
0.01
0.005
0

19

19

19

19

90

85

80

75

70
-7

-8

-8

-9

-9

5

0

5

0

5

-0.04

0

-0.035

-7

-0.03

65

-0.025

5

-0.02

-6

49

-0.015

60

-0.01

19

19

19

-0.005Govt. Cons.
Inflation
Premium
Openness

Korea
0.02

0.015

0.01

0.005

Openness
Inflation

50

Premium
Govt. Cons.

-0.01-0.015

90
19

85
19

80
19

-9

0
-9

5
-8

0
75
19

70
19

65
19

-8

5
-7

0
-7

5
-6
60
19

-0.00550Chile
0.02

0.01

0
1960-65

1965-70

1970-75

1975-80

1980-85

1985-90

1990-95-0.01
Openness

51

-0.02

Inflation
Premium
Govt. Cons.

-0.03

-0.04

-0.05

-0.06

Israel
0.02

0.01

0
19

19

19

19

19

19

19
60

65

70

75

80

85

90

-6

-7

-7

-8

-8

-9

-9

5

0

5

0

5

0

5

52

-0.01

Openness
Inflation
Premium
Govt. Cons.

-0.02

-0.03

-0.04

Central African Republic
0.01

0.005

0
1960-65

1965-70

1970-75

1975-80

1980-85

1985-90

1990-95-0.005-0.01
Openness

53-0.015

Inflation
Premium
Govt. Cons.

-0.02-0.025-0.03-0.035-0.04

Tunisia
0.015

0.01

0.005

Openness
Inflation

54

0Premium

19

19

19

19

90

85

80

75

70

-6

-7

-7

-8

-8

-9

-9

5

0

5

0

5

0

5

-0.015

65

60

-0.01

19

19

19

-0.005Govt. Cons.

Ireland
0.03

0.025

0.02

0.015
Openness
Inflation

55

0.01

Premium
Govt. Cons.

0.005

0
1960-65-0.005-0.01

1965-70

1970-75

1975-80

1980-85

1985-90

1990-95

New Zealand
0.03

0.025

0.02

0.015
Openness
Inflation

0.01

Premium
Govt. Cons.

56

0.005

0
19

19

19

19

90

85

80

75

70

65

60
-6

-7

-7

-8

-8

-9

-9

5

0

5

0

5

0

5

-0.01

19

19

19

-0.005

Uganda
0.01

0.005

0
19

19

19

19

90

85

80

75

70

65

60
-6

-7

-7

-8

-8

-9

-9

5

0

5

0

5

0

5

-0.01

19

19

19

-0.005Openness

57

Inflation
Premium-0.015-0.02-0.025-0.03-0.035Govt. Cons.

Philippines
0.01

0.005

0
19

19

19

19

19

19

19

Openness

90

85

80

75

70

65

60

-7

-7

-8

-8

-9

-9

5

0

5

0

5

0

5

58

-6

Inflation
Premium
Govt. Cons.

-0.005-0.01-0.015