Download The Diffusion of Privatization in the Developing World

THE DIFFUSION OF PRIVATIZATION
IN THE DEVELOPING WORLD
Nancy Brune and Geoffrey Garrett
Yale University
August 2000
Abstract
This paper explores the determinants of privatization using annual panel data for the 19881997 for 148 developing countries. Privatization was more pronounced in countries with
stable macroeconomic conditions but low investment levels. None of the usual suspects in the
comparative political economy of reform (i.e. viewing countries as independent units of
analysis) – democratization, the extent of international market integration, and indebtedness
to the IMF – had any impact on privatization. In marked contrast, however, we found
statistically significant and substantively large diffusion effects. That is, the more prior
privatization in other countries in a nation’s peer group (defined in terms of geographic region
and common legal heritages), the more that nation subsequently privatized. Privatization has
thus been an interdependent policy choice in the developing world. Future research should
seek to determine whether this process has been the , product of competitive emulation, social
emulation, or learning.
Prepared for presentation at the Annual Meetings of the American Political Science
Association, Washington, DC, August 30-September 3, 2000.
1. Introduction
Along with democratization, liberalization, and macroeconomic stabilization, the sale
of state-owned enterprises (SOEs) – privatization – is a defining characteristic of the
contemporary global political economy. There is a voluminous literature in economics on the
efficiency gains from privatization and the optimal design of privatization programs. 1 But
economists have paid much less attention to explaining the patterns of privatization that have
actually taken place, and political scientists and sociologists have not yet filled this void by
exploring the causes of privatization around the world.2
The paper analyzes the extent of privatization in the developing world and test several
competing explanations to address important unanswered questions. Why did the scale and
scope of privatization increase exponentially around the world in the 1990s from the small
and sparse initiatives of the latter 1980s? Why, despite this explosion, has the extent of SOE
sales varied so much, with many countries essentially having missed the privatization
bandwagon? Is privatization more likely under conditions of economic hardship or
prosperity? What are the inter-relationships among democratization, globalization and
privatization? Has the spread of privatization been the product of governments’ trying to keep
up with their competitors by emulating their policy choices (“competitive emulation”), the
result of mimicry based on historical connections unrelated to market competition (“social
emulation”) or simply the rational processing of new information about the privatization
experiences of others ("learning")?
This paper analyzes the determinants of privatization in the developing world using
panel data for 148 countries over a ten-year period from 1988 to 1997.3 We offer two primary
insights into the spread of privatization among the developing economies. The first is
economic and unlikely to be particularly controversial.
Privatization has been more pronounced in developing countries with higher per capita
incomes that are macroeconomically stable (e.g. low rates of inflation and low levels of short
term debt) and that have low investment levels. The investment result accords well with
economists’ fundamental belief in the efficiency of privatization, in this case as a tool for
capital accumulation. Although economically troubled countries (such as poor nations with
high levels of indebtedness and spiraling inflation) arguably have the most to gain from
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privatization, our analysis suggests that the dislocations generated by privatization render it a
luxury that is better afforded by countries in sound economic shape.
The second major finding of this paper is more novel. None of the “usual suspects”
from political economy for the determinants of policy reform – notably democratization,
international market integration and interventions by the International Monetary Fund (IMF) –
had any significant impact on patterns of privatization. In marked contrast, there has been a
powerful diffusion dynamic to the sale of SOEs in the developing world. That is, the scale of
privatization in a given developing country has been heavily influenced by prior privatizations
in its “peer group” of nations. In particular, we find that one country’s privatizations are
heavily influenced by earlier SOE sales in other countries that are either in its geographic
region or with which it shares a similar legal heritage.
Identifying the precise causal mechanisms that drive these patterns of diffusion is
difficult. One could interpret diffusion among geographic regions and countries with similar
legal heritages as competitive emulation or as social emulation or, indeed, as learning.
Countries in the same region tend to be more economically interconnected, as gravity models
of trade attest, but neighbors also tend to share many common linguistic, religious and other
cultural traits. Alternatively, in the framework of learning, informational cascades are more
likely to start among neighbors or members of the same regional group.
The legal heritage finding could be understood in social terms because it closely
correlates with countries’ colonial legacies. But economic interactions also tend to cluster by
colonial, and hence legal, heritage. Moreover, legal heritage has an important impact on the
types of property rights regimes in place in different countries. Potential investors in
privatized enterprises may well understand that there are substantial differences in property
rights regimes between countries with British vs. continental European legal traditions.
Recognizing this, governments could decide that the most relevant competitors for investor
dollars are those that share their traditions. But for the same reasons that investors consider
countries to be similar, the governments of these nation-states are also likely to view the
experiences of their peers as most relevant to them.
We therefore remain agnostic as to the precise micro logic that explains the diffusion
of privatization in the developing world. But the strength of our results does suggest that
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greater attention should be paid to diffusion processes, not only with respect to privatization,
but to other aspects of policy reform as well.4
The remainder of the paper is divided into five sections. Section 2 describes
privatization as it has evolved in the developing world since the late 1980s. Section 3
discusses the competing classes of explanations for privatization. Section 4 derives specific
hypotheses about the determinants of privatization and discusses the data and methods we use
to test them. Section 5 presents the empirical results. Section 6 concludes by sketching the
implications of our paper for the broader issue of the role of diffusion processes in the
political economy of reform.
2. Privatization in the Developing World
Whereas the sale of state-owned enterprises (SOEs) was in full swing in numerous
advanced industrial countries by the late 1980s, 1988 is the first year in which privatizations
were undertaken in the developing world.5 As Figure 1 demonstrates, however, the process
mushroomed in the following decade. In 1988, there were 28 discrete privatization
transactions among developing countries, generating a little over three billion dollars in
revenues for the selling governments.6 The number of privatization transactions skyrocketed
to reach a peak of over 1000 per annum in the mid 1990s, while privatization proceeds in
1997 were almost $70 (US) billion.7 Moreover, the number of developing countries engaging
in privatization also increased markedly from 14 in 1998 to over 60 in each year in the latter
1990s. To date, 102 developing countries have successfully initiated privatization programs.
Insert Figure 1 about here
As Figures 2 and 3 attest, however, there have been considerable regional variations in
the extent of privatization in the developing world. Large-scale privatization began earliest in
Latin America, with proceeds reaching $20 billion in 1991. After a brief hiatus, sales of SOEs
again took off in the second half of the 1990s, totaling over $30 billion in 1997. To date,
seventy-five percent of Latin America and the Caribbean have initiated privatization
programs; only the small economies, including Dominica, the Dominican Republic, St. Kitts,
St. Lucia and St. Vincent, have not yet undertaken a single privatization transaction.
3
The countries of Eastern and Central Europe began to privatize the state-owned sector
as soon as the iron curtain fell. By 1994, annual privatization proceeds had reached four
billion dollars, but the pace of privatizations increased exponentially in the following three
years to over $16 billion in 1997. Eastern and Central Europe boasts the highest participation
rate with 89% of the countries having completed at least one privatization transaction.
Privatization took off in East Asian off in 1991, then averaged around five billion
dollars until 1997 – when revenues almost doubled to ten billion. Although East Asia ranks
as a high privatizing region, it has the lowest participation rate. Only 50% of the region’s
countries have privatized some portion of the state’s assets; China, Thailand, Malaysia, and
Indonesia account for most of the privatization activity.
Insert Figures 2 & 3 about here
Figure 3 presents the aggregate data for the three regions in the developing world
where the absolute scale of privatizations has been much smaller. Privatization proceeds were
approximately $1.5 billion per annum in Africa as early as 1989, but they did not
subsequently exceed this figure until 1997 ($2.5 billion). Sixty-two percent of countries
located in the region have transferred ownership out of the hands of the state.
South East Asian privatizations peeked in 1994, reaching almost three billion dollars.
Almost two-thirds (63%) of the region’s members have privatized some portion of state
owned assets. In contrast, the sale of SOEs increased steadily in the Middle East from 1990
on, with annual proceeds amounting to roughly $1.5 billion by 1997.
Only non-
democratically governed Iraq, Libya, Saudi Arabia and Syria have chosen not to privatize.
Figures 1-3 demonstrate two simple points about patterns of privatization in
developing countries. First, there is considerable variation both in where and when
privatization programs have been most intense. Second, notwithstanding this variation, there
has been a secular trend towards greater sales of SOEs, particularly since 1995.
These aggregate data, however, do not facilitate cross-national comparisons because
they do not take into account the relative size of different countries. We present the data
adjusted for economic size, and other information, for the 102 countries that had undertaken
privatization programs by 1997 in the Appendix. These data clearly demonstrate that
privatization has been widespread in the developing world, but that the scope of this has
varied considerably across countries. For example, there were 916 distinct privatization
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transactions in Hungary between 1988 and 1997, and over 300 in other post-communist states
– Bulgaria, Estonia, Macedonia and the Slovak Republic. At the other end of the spectrum,
there are at least 47 developing countries that did not undertake even a single privatization
over the same period. Eastern Europe and Central Asia had the highest regional average for
the period 1987-97 with 361 transactions; Latin America and the Caribbean followed with
104 transactions.
Lagging behind were East Asia with an average of 33 transactions;
Southeast Asia with 30 and the Middle East and North Africa with a low 19.
Turning to privatization revenues, it is not surprising that the largest dollar values for
SOE sales were in large developing countries with internationally lauded privatization
campaigns. Total privatization revenues over the 1988-1997 period exceeded $25 billion in
each of Latin America’s three largest economies – Argentina, Brazil and Mexico. The
regional average was $14.7 billion. Hungary was the largest privatizer in Eastern Europe in
dollar terms (almost $13 billion). Given that many of the SOEs were distributed to employees
or through vouchers, the regional average is much lower - $5.2 billion. In East Asia, formally
communist China led the way with $17 billion in privatization receipts between 1988 and
1997. The regional average was a mere $3.6 billion.
The best way to think comparatively about the extent of privatization is to compare
revenues from the sale of SOEs to GDP. On this measure, Zambia has been the biggest
privatizer in the developing world (also outstripping the most notable OECD cases such as
Australia, New Zealand and the United Kingdom). Privatization proceeds for the 1988-1997
period constituted fully 45% of 1997 Zambian GDP. Three post-communist countries
privatized roughly one-quarter of their economies over the same period – Hungary,
Kazakhstan and Macedonia. Ghana, Jamaica and Peru were the next largest privatizers, at
almost 13% of GDP, while Bolivia and Armenia came in slightly behind at 11%. But at the
other end of the spectrum, 71% of all countries sold SOEs worth less than 10% of 1997 GDP;
and of those, 31 sold assets worth less than 1% of GDP. And 47 developing countries did not
undertake any privatizations in the period under analysis.
3. Explaining Policy Waves
The data presented in the preceding section demonstrate two things. First, while it is
inappropriate hyperbole to suggest that privatization has “swept the globe” in the past twenty
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years, the rapid emergence of large privatization programs in the developing world is
nonetheless a defining feature of the contemporary era. Second, despite this secular trend,
there have been very large differences among countries and regions in the extent of
privatization. Of course, both points also hold for the three other central policy reforms
advocated by development economists – deregulation, international liberalization, and
macroeconomic stabilization (which, together with privatization, constitute “marketization”).
So how might one explain both the over-time secular trends and the cross-national
variations in marketization? The answer is straightforward from the standpoint of neoclassical
economics (and for Marxist-inspired approaches). For mainstream economists, markets are
both efficient and respect choice. For Marxian critics, marketization is unfair, but accepted as
the inevitable result of the hegemonic power of capital and the United States. For both,
countries that have not marketized are not particularly interesting because, in time, they too
will join the bandwagon.
Most political scientists and sociologists, in contrast, are concerned to look for more
precise causal mechanisms explaining policy changes. In this section we discuss two different
classes of explanation – those that consider countries as independent units of analysis and
those that consider countries as making interdependent decisions.
Countries as Independent Actors
The traditional modus operandi of comparative political economy (CPE) has three
characteristics (beginning with Shonfield [1965]). First, countries are assumed to be
independent units of analysis. Policy choice is thus essentially a “decision theory” problem
(should we expect India to privatize its SOEs given the political and economic conditions in
the country?), not a “game theory” problem (for example, should India privatize if it thinkgs
Pakistan will do so?). Second, exogenous shocks may affect countries in similar ways (does a
global recession or a global boom make it more likely that all countries will privatize?). Third,
domestic factors can affect policy choice either directly (what is the impact of political regime
type on the propensity to privatize?) or by mediating other factors (are business cycle effects
on privatization mitigated or exacerbated in democracies?).
One core analytic move of international political economy in the past fifteen years has
been to augment this traditional CPE framework with attention to the domestic effects of
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position in the international economy [Gourevitch 1986, Rogowski 1989]. This typically takes
two forms. First, international position may affect government policy constraints, again either
directly (are countries with open financial markets more likely to privatize) or by mediating
other factors (does the extent of financial openness affect the impact of the business cycle on
privatization?). Second, position in the international economy may condition the policy
preferences of domestic groups that lobby governments (are mobile financiers more likely to
support privatization?).
In addition to these essentially impersonal structural variables, the behavior of foreign
actors – both private (most importantly, multinational corporations) and public (such as the
IMF and the World Bank) – features prominently in both popular commentaries and academic
analyses of marketization in developing countries [Haggard and Kaufman 1992, Taylor 1993].
The type of question raised by this approach is: are countries more likely to privatize if they
have outstanding credit from the IMF or if have been recipients of World Bank grants and
loans or if they are hosts to MNCs?
All of these frameworks have natural interpretations for the coexistence of secular
trends over time and enduring cross-national variations in marketization. For example, it is
entirely plausible that the trend to marketization could be a function of common exogenous
shocks (such as global recessions or financial crises), domestic factors changing
simultaneously in many countries (democratization), changes in the structure of the
international economy (technologically-induced increases in international market integration),
or changes in the policy views of the international financial institutions (IFIs). At the same
time, variations in how intensely these trends manifest themselves in specific countries would
also explain cross-national differences in the extent of marketization.
But the common assumption linking these perspectives is that countries react
independently to the environment they encounter, irrespective of whether this environment is
characterized by domestic or international factors, or by impersonal structural forces or
specific actors. That is, the behavior of other countries is presumed not to affect their
decisional calculus. Conversely, a game theoretic framework recognizes countries as strategic
actors that are influenced by the actions of other countries. While moving from governments
as independent actors to interdependent ones necessarily complicates the analysis, these
complications may be warranted if the decision theoretic framework misspecifies the problem
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whereas a game theoretic one generates new insights into the phenomena of interest [Tsebelis
1989].
Countries as Interdependent Actors
There are three different ways to conceive of interdependent policy choice – as the
strategic response to the behavior of competitors (“competitive emulation”); as Bayesian
updating based on the revelation of new information about the effects of different policies
(“learning”); and as mimicry based on taking cues from other countries in a given social
network (“social emulation”). Consider the following simple interrelationship between policy
choices in two countries. In period 0, both countries have SOEs; in period 1, country B
chooses to privatize its state-owned assets; in period 2, country A chooses to privatize. What
explains this set of outcomes? Here are three plausible approaches:
1.
Competitive Emulation
a. Country A has independent reasons for not privatizing. Even though there
are efficiency benefits to privatization, these are outweighed by political
costs in terms of the distributive implications of privatization.
b. Country A knows that if B privatizes and it does not, economic activity
will flow from A to B (i.e. they are economic competitors) such that the
efficiency costs of not privatizing would now outweigh the benefits of
keeping SOEs.
c. Country B independently chooses to privatize, for any of the reasons
discussed in the previous subsection. As a result, Country A privatizes.
2.
Learning
a. Country A does not know whether it is desirable to privatize its SOEs.
b. Country B privatizes, for whatever reason.
c. When Country B privatizes, Country A observes Country B’s actions and
their consequences. Thus, information is revealed about the costs and
benefits of privatization.
d. On the basis of this updated information, Country A decides that it is
preferable to privatize.
3.
Social Emulation
a. Country A does not know whether it is desirable to privatize its SOEs.
b. Countries A and B are connected by an array of social ties based on
history, culture, language, etc. As a result of these ties, country A takes
policy cues from country B.
c. When country B privatizes, country A mimics its behavior.
8
Competitive emulation is essentially a (complete information) “follow the leader”
coordination game in which country A’s privatization decision is strategic: it privatizes if B
has privatized; it does not privatize if B has not. The choice between these two equilibria is
thus determined by B’s policy stance (which is not conditioned by A’s).8 This two-player
interaction can be easily extended to a world of many countries that are differentially linked.
Conditional statements replace the absolutes of the simple follow the leader game. Most
importantly, the “follower” country is more likely to privatize the greater the extent of
privatization in countries with which it competes more intensely.
The notion that the effects of privatization are well understood is central to the pure
form of competitive emulation. The follower country knows with certainty that it will be
adversely affected if the leader (or leaders) privatize and it does not. But not everyone accepts
the assumptions required to support this framework. Some economists believe that many
interactions are better characterized as being evolutionary than strategic [Nelson and Winter
1982]. If actors do not know what is optimal behavior, how can they act strategically? And, in
the case of privatization, though the theoretical efficiency benefits of privatization are
unambiguous, empirical studies of the consequences of privatization in practice are much less
definitive [Megginson and Netter 2000]. The situation becomes even less clear when political
consequences such as the distribution of wealth and risk are included in the calculation of
utility.
Uncertainty and incomplete information make the strategic environment one in which
“learning” might be an accurate metaphor for how countries behavior [Bikhchandani,
Hirshleifer and Welch 1998, Banerjee 1992]. In the case of privatization, there is likely a
status quo bias in favor of maintaining SOEs because countries do not know what the
consequences of privatization will be [Fernandez and Rodrik 1991]. However, some countries
– for whatever reasons – decide to privatize. These behaviors then constitute natural
experiments from which other countries can subsequently learn. If these experiments are
successful, others are more likely subsequently to privatize (i.e. Bayesian updating).9
In this framework, more accurate information is gleaned from experiments in similar
countries. That is, countries can more effectively reduce information asymmetries by
observing countries of similar types that share similar preferences, payoff structures, and
relevant degrees of expertise. Hence, if countries that compete economically are also similar
9
in economically germane respects, and if privatization is indeed beneficial, the empirical
pattern of privatization diffusion generated by learning and competitive diffusion would be
the same. More and more countries would privatize over time, with the sequencing ordered by
similarities with early privatizers. Conversely, if privatization is in fact costly, one would
witness no competitive emulation, or if this takes some time to become clear, early patterns of
diffusion would subsequently erode.
But learning assumes that countries can relatively quickly and accurately ascertain the
real effects of privatization from experiments in other countries. Constructivists in political
science believe that this assumption is frequently inappropriate, and argue instead that actors’
causal beliefs about policy consequences significantly condition their behavior for relatively
long periods of time [Goldstein and Keohane 1993, Hall 1989]. In turn, some scholars believe
that such causal beliefs tend to diffuse among groups of countries, with patterns of diffusions
following networks that are characterized by common social traits such as shared history,
culture, language and the like [Meyer et. al. 1997]. This process can be termed “social
emulation”.
Discriminating among these diffusion processes is not easy because of correlations
among the factors that lead countries to compete with each other, learn from each other, and
mimic each other. These correlations, however, are less than 1.0 – a fact we exploit in the
empirical analysis below.
4. Data and Methods
Estimation
This paper attempts to distinguish between explanations for patterns of privatization in
the developing world that treat countries as independent units of analysis and those that
explicitly consider interdependence among countries. The parameters of the data set are
determined by the observations we have on privatizations. For this purpose, we use the World
Bank Privatization Database 2000, which has information on approximately 8,000
privatization transactions in low and middle-income countries during the period 1988-98.10
The data set comprises the 102 developing countries in which some privatizations took place
during the period and the remaining universe of developing nations (46 others). Due to
10
limitations on many of our explanatory variables, we had to exclude 1998 data from the
analysis. Thus the data matrix in theory would be 10 (years) x 148 (countries) = 1480
observations. In practice, however, our baseline regressions had 906 observations for 109
countries.11 When we controlled for the prior size of the state-owned sector, the number of
observations was reduced to 633 for 76 countries.
The basic form of the estimated equation was:
PRIVit  b1DOMEC it 1  b2 DOMPOLit 1  b3 INTECONit 1
 b4 IFI it 1  b5 DIFFUSION it 1  b6TIMEt  it
(1)
We estimated this equation using OLS with panel corrected standard errors (using
xtpcse in Stata 6). Given the trend for the magnitude of privatization proceeds to increase over
the sample period, we included various measures to control for the passage of time – such as a
yearly time counter and a full battery of year dummy variables. After some experimentation,
we found that simply controlling for the last year in the analysis (1997) took into account all
the significant time trends in the data.
Our baseline regression included a lagged dependent variable (LDV) to take into
account privatization dynamics. We also estimated an alternative specification in which serial
correlation was modeled as a first order auto regressive AR(1) process. Equations that
included the size of the state-owned sector before 1988 were also estimated to take into
account the fact that the extent of privatization is constrained by the quantity of nationalized
assets. Finally, we also ran fixed effects regressions (i.e. containing dummy variables for all
countries, with the intercept suppressed). Stability in the results across these specifications
would increase our confidence in the estimated parameters.12
The Dependent Variable
The measure we use to assess the extent to which governments have privatized is
annual privatization revenues/GDP (%). While this is the most sensible measure to compare
the extent of privatization across countries, there is one potential problem with it that should
be mentioned. Some of the post-communist privatizations, Romania and Russia for example,
distributed shares of SOEs directly to workers or through vouchers to citizens. In both cases,
11
the governments did not receive revenue from the transfer of ownership – thereby scoring 0
on our privatization revenues measure.
To check for possible bias against countries that privatized a large share of the state
owned sector through voucher systems or distributions to employees, we compared the log of
the annual number of sales for each region to the log of the annual privatization revenues (in
constant 1995 $US) for each region in the analysis. The correlation between the rate of
change of privatization sales and privatization proceeds for most of the regions was very high
(above .90), though it was somewhat lower for Latin America (.62) and Africa (.78). Given
that our primary concern was whether our revenues measure was biased against formerly
communist countries, these results suggest that unusual nature of voucher privatizations and
distributions to employees did not have any undue influence on the reported results.
We should also note that the World Bank Database records privatization transactions
in the year that the government signed the contract to sell (part of) the enterprise. 13 In doing
so, it avoids dealing with the problematic measurement issues of stalled privatization
programs or changes in the political climate that may delay the government's receipt of
revenues. Also, the privatization database does not document whether purchasers are to pay
government in installments over a period of years.14
Fortunately, neither of these
measurement issues associated with the timing of the government’s receipt of revenues affects
our analysis since we are concerned with the reasons why governments initially choose to
privatize.
Domestic Economic Conditions
We acknowledge that there may be some lapse in time between the causal determinant
and the governments’ decision to privatize. As a result, all independent variables were lagged
by one year.
We included a fairly standard set of variables measuring domestic economic
conditions. Gross Domestic Product Per Capita and Gross Domestic Product reflect the
country’s income level. Economists would certainly predict that privatization should increase
GDP over time, and that poorer countries therefore have greater incentives to privatize.
However, in the short run, we expect lagged national income also to be positively correlated
with privatization because wealthier countries are likely better positioned to offset
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dislocations by compensating those who are adversely affected by privatization programs
(through the public sector or labor market institutions, for example). Poorer countries may
privatize less if market and labor institutions are underdeveloped and if SOEs are the primary
source of employment.
Inflation and GDP Growth capture short-term macroeconomic conditions.15 Shortterm Debt, measured as the share of all external debt owed by the government, provides
information about the immediate budgetary pressures facing the government. Gross Fixed
Investment as a share of GDP captures the scale of capital accumulation. The direction of the
causal relationship between these determinants and the decision to privatize is unclear. On
one hand, economists have argued that countries may privatize when suffering from adverse
economic conditions so as to send signals to the markets about their commitments to market
reform. But on the other hand, macroeconomic crises may impose significant political
constraints on governments’ ability to privatize – fearful of the dislocations selling state
owned assets invariably generates.
Domestic Political Conditions
In keeping with the recent literature on the politics of economic reform, we included a
Democracy score (from the Polity III Database, computed as the democracy minus autocracy
score). But as is always the case, the relationship between democracy and policy outcomes is
unclear [Przeworski & Limongi, 1996]. The fact that democracy increases the accountability
of leaders may promote privatization if voters believe that this is desirable. But democracy
also enlivens distributional coalitions that may be opposed to policies such as privatization
even if they increase national welfare. Moreover, given that costs of privatization are
concentrated and immediate, while the benefits are more dispersed, unknown and realizable in
the long-term, we would expect the losers of privatization to more effectively lobby
governments to oppose privatization.
In addition to political regime, we include a dummy variable French and German
Legal Heritage to take into account the effects of legal institutions – namely civil vs. common
law systems – on privatization outcomes.16 Common law and civil law countries differ
significantly in the state’s role in market activity, notions of ownership, and the protection of
property and investors’ rights. Since common law systems emphasize property and individual
13
rights and a minimal role for government in market activity, whereas this is much more muted
in civil law tradition countries, we expect the French and German Legal Heritage dummy
variable to be negatively associated with privatization [Bortolotti 1998].
Position in the International Economy
We also explore the impact of a country’s position in the international economy on
privatization using two standard measures of globalization. Trade is measured conventionally
as the sum of exports and imports as a share of a country’s GDP. Capital Mobility is an index
(0-9) measuring capital account openness on the basis of the IMF annual publication,
Exchange Arrangements and Exchange Restrictions [Brune, Garrett, Guisinger and Sorens
2000]. Again, the effects of the international economy on domestic politics are ambiguous.
On one hand, open economies may have access to investment and capital thereby postponing
the task of selling inefficient, revenue bloated enterprises. On the other hand, open economies
already entrenched in the process of liberalizing their markets, promoting free trade, and
undertaking structural reforms may find it easier to reduce state ownership and intervention in
the market.
Interventions by IFIs
The International Monetary Fund (IMF) and World Bank Group have long and vocally
supported privatization in the developing world. Both institutions have conditioned grants
and the extension of loans or access to future monies on the extent of privatization efforts.
The variable IMF measures both the country’s use of IMF credit and repurchase obligations to
the IMF as a share of GDP. WB is the sum of International Bank of Reconstruction and
Development loans & International Development Agency credits extended to the country by
the World Bank Group as share of GDP. We would expect the greater the participation of a
country in the programs of these IFIs, the greater the size of privatization programs [Garrett,
Guillen and Kogut 2000, Molano 1997, Ramamurti 1998].
International Diffusion
Finally, we included a battery of variables to capture possible interdependencies in the
privatization choices of developing countries. The basic structure of the diffusion variables
used in our model test whether the scale of privatization is influenced by the independent
14
variables described above and by the extent of privatization in countries with which it shares
some kind of relation. The diffusion variables all take the mean value of privatization/GDP in
the preceding year in a country’s “peer group” of nations. If diffusion is taking place, these
variables should all be positively associated with the country’s subsequent privatization
behavior.
We measured for diffusion effects in four ways. DIFF_REG measures prior
privatizations in a country’s geographic region. The regional categories are: East Asia and the
Pacific, Eastern Europe and Central Asia, Middle East and North Africa, South Asia, SubSaharan Africa, and Latin America and the Caribbean.17 DIFF_INC divides countries into
peer groups based on income level. In low-income countries, per capita income (1997 US$
current) is $785 or less; in middle lower-middle income countries, per capita income ranges
between $786 and $3,125; and in upper-middle income countries, the range is between $3126$9655.18 DIFF_LEG groups countries according to their legal traditions – British, French,
Socialist, German or Scandinavian – to test whether a country’s privatization efforts are
influenced by the scale of privatization in peer nations with shared legal traditions. Finally,
DIFF_REL analyzes the diffusion effects of cultural ties – measured here as shared religious
backgrounds (in terms of a country’s major religion). The categories are Protestant, Catholic,
Eastern Orthodox, Hindu, Buddhist, Muslin, Jewish and Traditional/Local religions.19
5. Results
Lagged Dependent Variable (LDV) and First Order Auto Regressive (1)
Specification
The results of our regression analysis are presented in Table 1. The first two columns
present the two basic types of equations we estimated. In column 1, we took into account
dynamics in privatization by including a lagged dependent variable, which was positive and
statistically significant at the .1 level. Privatization trends were sticky, but not as much as one
might have expected. Countries that privatized one point of GDP in one year were estimated
to privatize just over one-quarter of a point of GDP in the following year. In column 2, we
modeled serial correlation among the error terms as an AR(1) process.
15
Insert Table 1 about here
The fit of the estimated equation is quite good given that the dependent variable –
privatization proceeds/GDP – is in essence a change variable (the decrease in the size of the
state-owned sector, as valued by the buyers of privatized enterprises). Not surprisingly, the R 2
of the LDV equation was considerably larger, twice as large in fact, as the AR(1) model.
However, it is also the case that the rho parameter for the AR(1) equation (.75) was large and
highly significant. Thus, we do not have strong grounds for preferring one specification over
the other. As would have been expected from Figure 1, the passage of time was an important
independent predictor of the amount of privatization in both equations. The only year dummy
variable that was statistically significant, however, was that for 1997. We estimate that
privatization proceeds increased by roughly one-third of a point of GDP in that year (a
dramatic increase given that the sample mean for the whole period was just over two-fifths of
a point of GDP).
The structure of the remaining results for the two estimations is quite similar. Among
our battery of domestic economic conditions, revenues from the sale of SOEs constituted a
significantly larger portion of GDP in countries with lower inflation rates and lower
investment rates. Privatizations were also positively correlated with per capita income and
total GDP and negatively correlated with short term debt in the LDV equation; they were
similarly signed but insignificant in the AR(1) estimation. These results suggest that
privatization is promoted by stable macroeconomic conditions (low inflation rates, low levels
of short term debt and high per capita incomes), on the one hand, and by low investment
levels, on the other. The investment finding is consistent with conventional views on the
economic benefits of privatization, namely that countries privatize in order to spur capital
accumulation. The macroeconomic stability result suggest that governments are more willing
to privatize during good times than bad – probably because privatization destabilizes existing
distributions of income and risk in society.20
Turning to domestic political conditions, the extent to which a country was democratic
was negatively associated with privatization, though this effect was not statistically significant
in either the LDV or AR(1) equations. Countries with French or German legal heritages were
also less likely to privatize, though this effect was only significant in the LDV equation. This
16
negative association may well reflect the fact that these legal systems are not based on the
common law tradition that best promotes stable property rights and contractual arrangements.
Neither of the two conventional measures of market integration – trade as a portion of
GDP or the openness of capital accounts – had any significant impact on privatization. Thus,
these results do not support the view that globalization puts downward pressure on the public
sector (including SOEs) in the developing world, notwithstanding recent evidence of such
effects for government spending [Garrett 2000, Garrett and Nickerson 2000].
We found more evidence for conventional views about the effects of the IMF's and
World Bank's programs. Specifically, the greater the disbursements of these institutions to a
developing country, the more it of its economy it privatized in the following year.
Interestingly, World Bank loans and grants were more “effective” than IMF loans in inducing
countries to privatize (only the World Bank coefficient was statistically significant). This is a
surprising finding given the prominence of privatization reforms in IMF conditionality
agreements, though the World Bank also encourages developing countries to privatize. One
can easily assess the substantive implications of this effect in terms of the estimated effect of
a one standard deviation increase in World Bank grants and loans (13.7%, for the 906
observations in the baseline regression) on privatization revenues/GDP. The average annual
privatization rate in our sample was 0.42% of GDP per annum. A one standard deviation in
the World Bank variable is estimated – in both the LDV and AR(1) equations – to have
increased this number by 13.7*0.006 = 0.082 points of GDP (roughly a 20% increase).
Finally, there is considerable evidence of the international diffusion of privatization in
both the LDV and AR(1) equations. However, not all relational measures were equally
significant in influencing follower countries to privatize. In particular, there was no evidence
of diffusion among countries with similar per capita incomes or shared major religions. In
fact, the religion parameter was negative and significant in the AR(1) equation – the greater
the amount of privatization in the preceding year in other countries with the same major
religion, the less privatization subsequent privatization in the country under analysis.
But both the Region and Legal Heritage variables were positive and highly statistically
significant. Recall that these variables measure the average privatizations (as a % of GDP) by
other members of a given country’s peer group – defined in these cases by geography and
shared legal traditions – in the preceding year. The standard deviation for the Region variable
17
was 0.47; for Legal Heritage it was .38. Thus, a one standard deviation increase in last year’s
privatizations in a country’s region was associated with approximately a 0.84*0.47 = 0.40
points of GDP increase in its privatization in the current year. For Legal Heritage, these
effects were (0.59*.38 = 0.22 GDP points) in the LDV equation and (1.23*.38 = 0.47 points)
in the AR(1) equation.
These are very large effects indeed given that mean annual privatization/GDP in the
sample was 0.42 points of GDP. Before interpreting these effects in terms of the different
theoretical approaches to diffusion we laid out in section 3, we need to explore the robustness
of these findings. We checked for robustness in two ways. First, we included the size of a
country’s state-owned sector at the beginning of the period under analysis (1984-1987
average) to take into account the fact that the extent of a country’s subsequent privatization
was limited by the initial size of the state-owned sector. Second, we estimated fixed effects
regressions in which we allowed the intercepts for each country to vary. This allows for
differences in the “natural” propensity of different countries to privatize.
Initial Size of the State-Owned Sector
The third and fourth columns of Table 1 present equations that add the initial size of
the state-owned sector to the parameters estimated in the first two columns.21 As expected, the
initial size of the state-owned sector was positively associated with positive privatizations, but
this variable was insignificant in both the LDV and AR(1) equations. In the LDV model,
however, the t-statistic for this variable was almost 1.5, and the substantive magnitude of this
effect was not trivial. In this equation (which only had 633 observations for 76 countries), the
standard deviation for the state-owned sector variable was 22.8% of GDP. Thus a one
standard deviation increase in this variable is estimated to have increased privatization
proceeds by 22.8*.0044 = 0.10 points of GDP. In the AR(1) equation, where the standard
error for the SOE variable was four times as large as its coefficient, the estimated effect of a
one standard deviation increase in it was only about half this big.
Many other aspects of the estimated equations remained quite similar once the SOE
variable was added. Privatization was still negatively associated with investment levels and
short-term debt, and positively correlated with GDP per capita. Moreover, the World Bank
18
variable was still positive and significant in the LDV equation, and its substantive magnitudes
were quite similar to those in columns 1 and 2.
The most important facet of columns 3 and 4, however, concern the diffusion
parameter estimates. There was no evidence of privatization diffusion among countries with
similar income levels or sharing common major religions. The Region and Legal Heritage
coefficients, however, were again positive and statistically significant (except for Region in
the AR(1) equation). While the estimated regional diffusion parameters were smaller than in
the equations that did not include the SOE variable, the estimated effects of diffusion among
countries with shared legal traditions were even bigger than they were in columns 1 and 2.
The similarities in the parameters of primary interest to our argument between columns 1 and
2 and columns 3 and 4 increases our confidence in the underlying pattern of results.
Fixed Effects
Columns 5 and 6 included dummy variables for all the countries in the analysis. While
it is certainly always a prudent move to estimate fixed effects regressions on panel data, in our
case, this strategy is liked to have dramatic effects on the results because each of the panel
time series has a maximum length of 10 observations (we had only two years of all data for
some countries). Nor surprisingly, the R2 in the fixed effects regressions was much larger than
in any of columns 1-4. Moreover, only one of our non-diffusion variables had a statistically
significant impact on privatization – countries with higher inflation rates were less likely to
privatize.
The basic structure of the diffusion effects, however, was quite similar in the fixed
effects regressions to those reported earlier. Again, there was no evidence of diffusion either
among countries sharing common major religions or at similar levels of economic
development (though the latter coefficients turned from negative to positive in the fixed
effects models, suggesting a glimmer of evidence for diffusion based on income levels). The
regional diffusion parameters were of similar size to those in columns 1-4, but these were far
from statistical significance in the fixed effects equations.
There was again, however, very strong evidence of privatization diffusion among
countries sharing legal heritages. In fact, the magnitudes of these effects were larger in
columns 5 and 6 than they were in the baseline equations in columns 1 and 2. In the fixed
19
effects LDV equation, a one standard deviation increase in the Legal Heritage variable was
associated with a 1.24*0.38 = 0.48 points of GDP increase in annual privatizations. The
comparable figure in the AR(1) equation was 0.53 GDP points.
6. Conclusion
Privatization is an extremely important phenomenon, a defining feature of the
contemporary global political economy and a key component in economic reform packages in
many developing countries. Having tremendous faith in the economic benefits of
privatization, economists have devoted great attention to designing plans to maximize these
benefits. Much less attention has been paid, however, to explaining how privatization has
spread around the world, and why the magnitude of sales of the state-owned sector has varied
so widely across countries and time.
This paper has sought to generate some explanations for the diffusion of privatization
in developing countries. Economic conditions have, predictably, been important. Privatization
has been more apparent in developing countries with low investment levels that are searching
for ways through which to stimulate capital accumulation and that have relatively stable
macroeconomic environments (characterized by low inflation rates, low levels of short term
debt and higher income levels).
But probing more deeply into the political economy of privatization yields some very
interesting results. On the one hand, two contemporary shibboleths – democratization and
globalization – appear to have had no real impact on patterns of privatization in the
developing world. There is some evidence that IFI programs have had their intended effect of
promoting privatization in the developing world – a finding that should be explored more
broadly with respect to economic reform programs. The most interesting finding of our
analysis, however, is that there are strong patterns of diffusion among “similar” countries.
Specifically, the more privatization has previously taken place in a country’s geographic
region and/or among countries with which it shares a common legal heritage, the more of its
economy the country subsequently privatizes.
This diffusion result clearly shows that the privatization choices of governments in the
developing world have been interdependent. While the kinds of variables conventionally
20
studied in “countries as independent units” analyses are important to our understanding of
policy choices over privatization, our findings suggest the need to think harder about
countries in relation to their peers. Simmons and Elkins [2000] present a similar finding with
respect to the liberalization of foreign economic policies.
We have proposed three distinct mechanisms that might explain such patterns of
diffusion – competitive emulation among countries that are trying to attract the same capital
and markets; learning among countries who update their own beliefs about the costs and
benefits of privatization on the basis of the experiences of other countries; and cultural
emulation among countries who take their policy cues from those with which they have strong
historical connections, without really knowing what the political and economic effects are.
Our results do not allow us precisely to discriminate among these explanations because our
battery of the four diffusion measures is not isomorphic with our theoretical categories. We
found no evidence of privatization diffusion among countries either at the same income level
(a measure designed to capture competitive emulation) or that share the same major religion
(cultural emulation). The two variables with significant results, in contrast, could be
interpreted in different ways.
Consider first the evidence of regional diffusion. This could certainly indicate
competitive emulation since the economic connections among neighbors are invariably dense
and strong. But the finding is also consistent with learning since countries may well believe
that the effects about privatization in neighboring countries is more relevant to them than the
experiences of distant nations. Finally, geographic regions tend also to have similar cultural
histories, thus supporting the cultural emulation perspective.
Identifying the causal story behind the significance of shared legal heritage is no less
difficult. This variable no doubt captures cultural historical similarities based on colonial
legacies. But countries with the same legal heritage may also be direct competitors – either
because economic activity flows along colonial lines or because legal heritages condition
property rights regimes in ways that affect investment decisions. For the same reasons,
countries would be acting rationally if they close to learn about privatization and its effects
from countries with similar legal heritages.
Perhaps the only way to discriminate among these explanations would be to ascertain
the real economic and political effects of privatization. If these effects were positive, this
21
would suggest that some form of competitive emulation, perhaps with Bayesian updating
playing an important role, has been taking place. Conversely, if privatization were an
unmitigated bad for developing countries, in contrast, diffusion would suggest (“irrational”)
herd behavior or some form or ideational emulation. Unfortunately, we do not know enough
about the effects of privatization in the developing world. The theoretical efficiency gains
highlighted by economists have been stubbornly hard to detect in the real world. Moreover,
privatization undoubtedly has political effects – in terms of the distribution of wealth and risk
– that vary in their import from country to country.
Thus, we can only conclude by saying that studying interdependent policy choices is
an important agenda for future research, and one that may call into question many findings
that have been generated by influential studies based on reasoning that assumes countries
should be treated as independent units of analysis.
22
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24
Table 1. The Determinants of Privatization in Developing Countries, 1988-1997
Types of
Explanation
Independent
Variables
LDV
I. Domestic
Conditions
Privatization
Lagged
Size of State
Owned Sector a
GDP PC (log) b
.275*
(.162)
GDP (log) c
Inflation (log) d
GDP Growth e
Short Term Debt f
Investment g
II. Domestic
Political
Conditions
III. Position in
International
Economy
Democracy h
French/German
Legal Heritagei
Trade j
Capital Mobility k
IV. Interventions
by International
Actors
IMF Credit l
V. International
Diffusion
Region n
World Bank m
Income o
Legal Heritage p
Religion q
VI. Time
1997 r
Constant
Observations
R2
# Countries
.212***
(.063)
.063**
(.030)
-.162*
(.086)
-.004
(.007)
-.007**
(.003)
-008***
(.003)
-.005
(.005)
-.173**
(.082)
-.0003
(.0021)
-.014
(.017)
.022
(.015)
.006***
(.001)
.839***
(.241)
-.195
(.233)
.590**
(.249)
-.158
(.110)
.341***
(.081)
-1.76*
(.931)
906
.166
109
AR(1)
.294
(.200)
.068
(.105)
-.134*
(.076)
.006
(.008)
-.007
(.007)
-.018**
(.009)
-.012
(.011)
-.121
(.333)
.002
(.008)
-.063
(.050)
.025
(.033)
.006*
(.003)
.883*
(.466)
-.101
(.287)
1.23***
(.379)
-.247**
(.110)
.314**
(.132)
-2.57
(2.31)
906
.081
109
LDV
with SOE
AR(1)
with SOE
.240
(.166)
.004
(.003)
.348***
(.096)
.068
(.046)
-.209
(.133)
.003
(.012)
-.011**
(.005)
-.023***
(.009)
-.006
(.007)
-.216
(.140)
.0003
(.0030)
-.009
(.022)
.017
(.016)
.009***
(.003)
.540**
(.224)
-.230
(.325)
.983**
(.425)
-.152
(.120)
.431***
(.107)
-2.36*
(1.28)
633
.178
76
.002
(.008)
.451*
(.263)
.053
(.133)
-.126
(.212)
.012
(.014)
-.012
(.011)
-.030*
(.017)
-.018
(.016)
-.165
(.425)
.002
(.010)
-.055
(.062)
.022
(.034)
.007
(.007)
.588
(.512)
-.045
(.352)
1.77***
(.514)
-.304***
(.118)
.397**
(.164)
-3.14
(2.86)
633
.104
76
LDV w/
Fixed
Effects
.065
(.171)
AR(1) w/
Fixed
Effects
-.412
(.587)
-.136
(.532)
-.243**
(.097)
.005
(.007)
-.009
(.006)
-.004
(.008)
-.013
(.012)
5.85
(9.90)
-.0008
(.0065)
-.024
(.034)
.004
(.022)
.001
(.005)
.448
(.349)
.258
(.323)
1.24***
(.321)
-.075
(.118)
-.219
(.758)
-.228
(.593)
-.199**
(.090)
.008
(.008)
-.011
(.007)
-.008
(.010)
-.016
(.014)
8.38
(9.91)
.0002
(.0082)
-.032
(.043)
-.002
(.026)
.002
(.006)
.561
(.440)
.259
(.330)
1.40***
(.349)
-.133
(.098)
906
.378
109
906
.277
109
The dependent variable is annual privatization revenues/GDP (%) for years 1988-1997. All right hand
side variables are lagged one year.
* p < .1; ** p < .05; *** p < .01
25
a Mean of SOE economic activity/GDP during period 1984-1987. Transition economies coded as
maximum value in the sample [Easterly and Yu 1999, WDI 1997-2000, Shirley et al Bureaucrats in
Business].
b GDP Per capita in constant US$ [WDI 2000].
c Annual GDP in constant dollars [WDI 2000}.
d Inflation rate (GDP deflator) [WDI 2000].
e Rate of Growth of Real GDP [WDI 2000].
f Short-term debt as % of total debt [WDI 2000].
g Gross Domestic Investment/GDP (%)[WDI 2000].
h Democracy-Autocracy [Polity III 1998].
i Dummy if French/German legal tradition [Easterly and Yu 1999].
j Exports + Imports/GDP (%) [WDI 2000].
k Index (1-9) of Capital Account Openness [Brune, Garrett, Guisinger and Sorens 2000].
l Total outstanding credit from the IMF/GDP (%) [WDI 2000].
m Total outstanding IBRD loans & IDA credits from the World Bank/ GDP (%) [WDI 2000].
n Mean of dependent variable for all countries in region. Region from Easterly and Yu [1999].
o. Mean of dependent variable for all countries in income group. Income groups from Easterly and Yu
[1999].
p. Mean of dependent variable for all countries with same legal tradition. Legal tradition groups from
Easterly and Yu [1999].
q. Mean of dependent variable for all countries with same major religion. Religion groups from
Simmons and Elkins [2000].
r. Dummy for 1997.
26
1200
70
1000
60
50
800
40
600
30
400
20
200
0
1988
10
1989
1990
1991
1992
Total Sales
1993
1994
Revenues
All data from World Bank Privatization Database [2000].
27
1995
1996
0
1997
revenues ($US billions)
number of transactions
Figure 1. Privatization in the LDCs, 1988-1997
Figure 2. High Privatization Regions
40
35
$US billions
30
25
20
15
10
5
0
1988
1989
1990
1991
1992
LA & Caribbean
1993
1994
EEurope & C. Asia
28
1995
1996
EastAsia
1997
Figure 3 Low Privatization Regions
3
2.5
$US billions
2
1.5
1
0.5
0
1988
1989
1990
1991
1992
Middle East & NAfrica
29
1993
1994
SouthEast Asia
1995
1996
Africa
1997
Appendix
Country
Albania
Algeria
Angola
Argentina
Armenia
Azerbaijan
Bahrain
Bangladesh
Barbados
Belarus
Belize
Benin
Bolivia
Brazil
Bulgaria
Burkina Faso
Burundi
Cameroon
Cape Verde
Chile
China
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Czechoslovakia
Ecuador
Egypt
Estonia
Fiji
Gabon
Ghana
Grenada
Guatemala
Guinea
Guinea-Bissau
Guyana
Honduras
Hungary
India
Indonesia
Total Privatization
Revenues (1988-97)
(US$ current mil)
27.7
9.3
3.8
27921.0
182.1
2.51
10.3
60.3
51.0
10.8
54.3
56.5
884.2
34559.4
875.0
6.4
4.2
41.1
0.26
1484.1
17036.0
5685.1
56.7
476.3
246.3
4277.3
169.4
2777.7
467.4
2.3
12.0
872.6
6.1
43.4
45.0
0.51
44.3
99.1
12785.3
7073.1
5162.8
1988-97 Privatization
Revenues/1997 GDP
(%)
1.21
0.02
0.05
9.53
11.11
0.07
0.19
0.15
2.44
0.05
8.38
2.64
11.10
4.21
8.70
0.27
0.44
0.45
0.05
2.00
1.90
5.23
0.58
4.65
1.21
8.07
0.86
3.67
9.81
0.11
0.23
12.68
1.92
0.24
1.15
0.19
5.91
2.10
27.96
1.68
2.39
30
Number
of
Privatization
Sales (1988-97)
48
1
1
157
66
14
1
28
6
68
5
14
82
101
304
3
8
1
1
26
125
27
6
41
20
83
11
74
404
1
1
89
2
2
1
3
4
40
916
71
18
Country
Iran
Jamaica
Jordan
Kazakhstan
Kenya
Kyrgyz Republic
Lao PDR
Latvia
Lithuania
Macedonia
Malawi
Malaysia
Mali
Mauritania
Mexico
Moldova
Montenegro
Morocco
Mozambique
Nepal
Nicaragua
Nigeria
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Romania
Russia
Rwanda
Sao Tome &
Principe
Senegal
Serbia
Sierra Leone
Slovak Republic
Slovenia
South Africa
Sri Lanka
Total Privatization
Revenues (1988-97)
(US$ current mil)
18.1
532.9
58.7
5797.6
235.1
139.5
32.1
431.1
1021.8
611.1
10.8
10076.3
21.9
1.1
33353.1
16.3
14.7
1846.7
110.6
15.0
130.2
763.5
60.1
1951.0
823.8
223.6
42.0
7477.5
3810.0
5845.6
766.7
6603.5
0.00
0.35
1988-97 Privatization
Revenues/1997 GDP
(%)
0.02
12.89
0.84
26.16
2.22
7.91
1.86
7.65
10.56
23.14
0.43
10.06
0.87
0.10
9.27
0.85
.
5.53
3.22
0.30
6.78
1.92
0.38
3.10
9.52
4.70
0.44
11.71
4.64
4.08
2.20
1.51
.
0.80
Number of
Privatization
Sales (1988-97)
3
42
3
23
93
23
25
15
97
549
6
45
6
2
243
10
1
62
143
11
78
58
7
111
11
1
2
130
97
97
46
63
1
1
191.4
907.0
1.6
1979.4
521.1
3728.5
725.9
4.37
.
0.19
10.18
2.86
2.53
4.81
2
1
1
349
12
12
83
31
Country
Tanzania
Thailand
Togo
Trinidad & Tobago
Tunisia
Turkey
Uganda
Ukraine
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Yugoslavia
Zambia
Zimbabwe
Total Privatization
Revenues (1988-97)
(US$ current mil)
140.8
1378.4
38.8
448.4
171.0
3843.4
151.6
31.5
15.0
212.0
5914.1
2.6
0.8
360.0
1800.0
1080.0
1988-97 Privatization
Revenues/1997 GDP
(%)
2.00
0.92
2.58
7.75
0.90
2.02
2.41
0.06
0.08
0.86
6.69
0.01
0.01
.
45.77
12.58
Number of
Privatization
Sales (1988-97)
61
14
11
17
37
190
56
8
8
1
41
4
6
3
60
6
Regional Averages
Privatization
Revenues (1988-97)
Privatization
Sales (1988-97)
3591.6
14697.5
Privatization
Revenues/1997 GDP
(%)
1.5
3.9
931.8
1074.2
5208.9
1.3
2.2
6.0
30
68
361
549.7
0.8
19
(US$ current mil)
East Asia
Latin America & the
Caribbean
Southeast Asia
Sub-Saharan Africa
Eastern Europe &
Central Asia
Middle East &
North Africa
33
104
The following countries initiated privatization programs in 1998: El Salvador (5 sales), Eritrea
(2 sales), Ethiopia (1 sale) and Lebanon (1 sale). Cuba was omitted from our analysis.
Developing countries that have not yet privatized include:
Afghanistan, Antigua and Barbuda, Bhutan, Bosnia and Herzegovina, Botswana, Cambodia,
Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Djibouti,
Dominica, Dominican Republic, Equatorial Guinea, Gambia, The, Georgia, Haiti, Kiribati,
Korea, Rep., Lesotho, Liberia, Libya, Madagascar, Maldives, Marshall Islands, Mauritius,
Mayotte, Micronesia, Fed. Sts., Mongolia, Myanmar, Namibia, Niger, Samoa, Saudi Arabia,
Seychelles, Solomon Islands, Somalia, St. Kitts and Nevis, St. Lucia, St. Vincent and the
Grenadines, Sudan, Suriname, Swaziland, Syrian Arab Republic, Tajikistan, Tonga,
Turkmenistan and Vanuatu.
32
Notes
1
See Megginson and Netter [2000] and Shleifer [1999] for reviews of the literature.
Boix [1998] and Wright [1994] are important exceptions with respect to Western Europe. See Appel [2000] for
a comparative analysis of privatization in post-communist countries.
3
For an analysis that includes the OECD countries, see Garrett, Guillen and Kogut [2000].
4
See Simmons and Elkins [2000] for an analysis of the diffusion of liberal foreign economic policies.
5 The exception is Chile, which privatized in the 1970s under the Socialist rule of Allende. See section 4 for a
detailed discussion of the nature of the privatization data we use.
6
Note that there is not a one-to-one correlation between privatization transactions and the number of SOEs sold.
This is because individual enterprises, particularly large ones, are often privatized in more than one tranche.
7
Note that while revenues and transactions were highly correlated until 1992, they subsequently diverged – as
countries began to sell more, but smaller companies. This trend was reversed in 1997.
8
See Gruber [2000] for an analysis of competitive emulation with respect to monetary and trade integration.
9
The outcomes of such behavior, however, need not be “rational”. Such “herd behavior” has been associated
with phenomena such as bank runs and currency crises.
10
The data is available on CD- ROM – World Bank, Global Development Finance 2000. The World Bank has
not collected data on privatizations in developed countries. Responding to a World Bank survey, the
governments of approximately 60 low- to middle-income countries submitted information on privatization
transactions. Supplemental information was gathered from additional sources including Privatisation
International, Institutional Investor, International Financing Review, Euromoney, Economic Intelligence Unit,
and Oxford Analytica.
11
The countries were: Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Benin,
Bhutan, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Central African
Republic, Chad, Chile, China, Colombia, Comoros, Congo, Dem. Rep., Congo, Rep., Costa Rica, Cote d'Ivoire,
Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Ethiopia, Fiji, Gabon,
Gambia, The, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, India,
Indonesia, Iran, Jamaica, Jordan, Kazakhstan, Kenya, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho,
Lithuania, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco,
Mozambique, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru,
Philippines, Poland, Romania, Russian Federation, Rwanda, Senegal, Sierra Leone, South Africa, Sri Lanka,
Sudan, Swaziland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia,
Turkey, Uganda, Ukraine, Uruguay, Uzbekistan, Venezuela, Vietnam, Zambia and Zimbabwe.
12
We also used the log of privatization proceeds/GDP as the dependent variable to take into account the long
right-hand tail in this variable. However, this did not affect the results we report. We thus present the results for
privatization/GDP because these offer more straightforward substantive interpretations.
13
The Privatization Database records each privatization transaction individually. So, if a government sells a
telecommunications company in several parcels or tranches, the Privatization Database separately lists each of
the transactions.
14
The World Bank Privatization Database does record cases where privatization was not completed.
15
Other economic variables such as balance of payments crises, budget deficits, levels of bank deposits and
banking crises were tested but excluded because they were found to have no impact on privatization and because
including them significantly reduced our sample size.
16
Political institution variables, such as veto players, rule of law, bureaucratic quality and party of executive,
were tested but excluded because they were found to have no impact on privatization and because including
them significantly reduced our sample size.
17
Regions from Easterly and Yu 1999. There were no observations for the Western Europe and North America
regional categories.
18
Because the time frame of our analysis spans ten years, we do not encounter countries that changed income
groups within this period.
19
We thank Beth Simmons for sharing this data with us.
20
Budget deficits, balance of payments crises and level of bank deposits had no effect on privatization.
21
Data for the variable Size of the State-Owned Sector was compiled using Mary Shirley et al’s Bureaucrats in
Business [1996] and the World Bank Global Development Indicators CD Rom 1997, 1998, 1999, 2000. Data
2
33
was limited and in the final analysis, we only had information for 76 countries. In order to increase the number
of observations, we computed the Size of the State-Owned Sector as the average over 1984-1988 (or in some
cases, the three time periods prior to 1987, the first observation year in our sample.)
34