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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 1 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 2 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 4 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 5 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 6 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 7 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 12 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 References Appel, Hilary. 2000. The Ideological Determinants of Liberal Economic Reform. World Politics 52: 520-49. Banerjee, Abhijit. 1992. A Simple Model of Herd Behavior. The Quarterly Journal of Economics 107: 797-817. Bikhchandani, Sushil, David Hirshleifer and Ivo Welch. 1998. Learning from the Behavior of Others: Conformity, Fads and Informational Cascades. Journal of Economic Perspectives 12: 151-170. Bird, Graham. 1996. The International Monetary Fund and Developing Countries: A Review of the Evidence and Policy Options. International Organization 50: 477-511. Boix, Carles. 1998. Political Parties, Growth and Equality. New York: Cambridge University Press. Bortolotti, Bernado et. al. 1998. Privatizations and Institutions: A Cross-Country Analysis. MS FEEM, Milan. Brune, Nancy, Geoffrey Garrett, Alexandra Gusinger and Jason Sorens. 2000. The Political Economy of Capital Account Liberalization. MS. Yale University. Garrett, Geoffrey. 2000. Globalization and Government Spending Around the World. MS. Yale University. Garrett, Geoffrey and David Nickerson. 2000. Globalization and Government Spending in Middle Income Countries. MS. Yale University. Garrett, Geoffrey, Mauro Guillen and Bruce Kogut. 2000. Privatization Around the World. MS. Yale University. Goldstein, Judith and Robert O. Keohane (eds.). 1993. Ideas and Foreign Policy. Ithaca: Cornell University Press. Gourevitch, Peter Alexis. 1986. Politics in Hard Times. Ithaca: Cornell University Press. Gruber, Lloyd. 2000. Ruling the World. Princeton: Princeton University Press. Haggard, Stephan and Robert R. Kaufman. 1992. The Politics of Economic Adjustment. Princeton: Princeton University Press. Hall, Peter A. (ed.). 1989. The Political Power of Economic Ideas. Princeton: Princeton University Press. Megginson, William L. and Jeffry M. Netter. 2000. From State to Market. MS. University of Oklahoma. Meyer, John W. et. al. 1997. World Society and the Nation State. American Journal of Sociology 103: 144-81. Molano, Walter. 1997. The Logic of Privatization. Westport: Greenwood Press. Nelson, Richard R. and Sidney G. Winter. 1982. Evolutionary Theory of Economic Change. Cambridge: Harvard University Press. Przeworski, Adam and James Vreeland. 2000. The Effect of IMF Programs on Economic Growth. Journal of Development Economics 62: 385-421. Ramamurti, Ravi. (GET CITE). Rogowski, Ronald. 1989. Commerce and Coalitions. Princeton: Princeton University Press. Shirley, Mary et al. 1995. Bureaucrats in Business. Washington, DC: World Bank. Shleifer, Andrei. 1998. State Versus Private Ownership. Journal Of Economic Perspectives 12(4): 133-150. Shonfield, Andrew. 1965. Modern Capitalism. New York: Oxford University Press. 23 Simmons, Beth and Zachary Elkins. 2000. Globalization and Policy Diffusion. APSA, Washington, DC. Taylor, Lance (ed.). 1993. The Rocky Road to Reform: Adjustment, Income Distribution, and Growth in the Developing World. Cambridge: MIT Press. Tsebelis, George. 1989. The Abuse Of Probability In Political Analysis. American Political Science Review 83: 77-91. Wright, Vincent (ed.). 1994. Privatization in Western Europe. London: Pinter. 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