Does Foreign Aid Promote Privatization? Empirical Evidence from Developing Countries

World Development Vol. 31, No. 9, pp. 1527–1548, 2003� 2003 Elsevier Ltd. All rights reserved

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Does Foreign Aid Promote Privatization?

Empirical Evidence from Developing Countries

SUDESHNA GHOSH BANERJEEThe World Bank, Washington, DC, USA

and

DENNIS A. RONDINELLI *University of North Carolina at Chapel Hill, USA

Summary. — International assistance organizations use foreign aid to encourage governments indeveloping countries to enact market reform and privatization policies aimed at acceleratingeconomic growth. Recent research findings, however, question the assumption that foreign aid hasa positive impact on governments� decisions to adopt economic reforms or that it accelerateseconomic growth. What is foreign aid�s impact, then, on the decisions of governments in developingcountries to privatize state-owned enterprises? In an analysis of factors influencing the decision toprivatize with respect to pace, timing and intensity of the process in 35 developing countries, wefind that foreign aid has no systematic impact on the privatization process. Aid, especially technicalassistance, can positively affect the pace and intensity of privatization and the findings point towardincreasing the technical assistance component in aid. We find strong evidence of foreign aid being afacilitating factor in privatization in the presence of complementary institutional capacity.Ultimately, privatization is driven by domestic political and economic factors, and foreign aid canfacilitate implementation only when developing countries have, or are able to create, market-supporting institutions.� 2003 Elsevier Ltd. All rights reserved.

Key words — foreign aid, privatization, governance

*The opinions and conclusions of this paper are entirely

ours and in no way reflect the views of the World Bank

or any other organization. We are grateful to Michael

Munger, Michael Luger, Ronald Johnson, and Daniel

Gitterman for their advice and support. We are espe-

cially thankful to two anonymous referees for their in-

sightful comments and helpful suggestions. Final revision

accepted: 3 March 2003.

1. INTRODUCTION

Bilateral foreign aid programs, internationalassistance organizations––such as the WorldBank, the International Monetary Fund, andthe International Finance Corporation––andregional development banks in Asia, Africa,Europe, and Latin America have, over the pastdecade, focused their lending and technical as-sistance to developing countries on strength-ening market economies, promoting theexpansion of private enterprise, and privatizingstate-owned enterprises (SOEs). Since the early1980s, governments have implemented privati-zation programs in more than 100 countriesthroughout the world (World Bank, 2001a).Privatization policies seek to accelerate thetransition to market economies, promote com-petition, increase economic efficiency, improvepublic service delivery, and reduce public debtburdens from loss-making enterprises.

152

SOEs are government-owned or publicly-managed economic entities that generate reve-nues from selling goods and services (Haggarty& Shirley, 1997). Losses from inefficient SOEs,ineffective public service delivery, and the needto mobilize scarce resources for health, educa-tion, and other social services have all drivengovernments in developing countries to priv-atize SOEs. Economic globalization also seemsto be driving privatization. By the early 1990s,private capital investment exceeded official

7

WORLD DEVELOPMENT1528

development assistance as the primary sourceof financial transfers from richer to poorercountries (World Bank, 2001a). By 2000, pri-vate flows of capital accounted for 87% of thenearly $296 billion transferred from richer topoorer countries while official development as-sistance comprised less than 13%. Privatizationis an important means by which developingcountries acquire foreign capital through directsales to, and mergers and acquisitions with,foreign multinational corporations (Rondinelli& Black, 2000).During 1990–2000, SOEs valued at nearly $1

trillion were transferred from government tothe private sector (Mahboobi, 2001). Membercountries of the Organization for EconomicCooperation and Development (OECD) raisedmore than $650 billion from the sale of SOEs,and the total privatization proceeds in devel-oping countries exceeded $271 billion (WorldBank, 2001a). The worldwide value of publicand private sales of SOEs alone grew fromabout $25 billion annually in 1990 to more than$161 billion in 1997 (Baker, 1998). As a result,the average share of public enterprise in na-tional output declined from 16% of GDP toabout 5% by the end of the 1990s (Megginson& Netter, 2001).International aid organizations argue that

privatization holds immense promise for de-veloping countries to enhance the efficiency ofenterprises and improve service delivery. TheInternational Monetary Fund (IMF) and theWorld Bank often make privatization a condi-tion for loans to developing countries. TheWorld Bank (1995, p. 23) points out that‘‘. . .large and inefficient SOE sectors are costingdeveloping economies dearly, especially thepoorest of them. Yet reform is possible andoffers potentially large benefits. . . all of whichcould contribute to more rapid economicgrowth.’’ The IMF claims that privatization is apowerful instrument ‘‘to redeploy assets fromthe public sector to the private sector, wherethey are expected to be used more efficiently’’(MacKenzie, 1997, p. 1). The InternationalFinance Corporation (IFC) promotes priv-atization as a way of strengthening capitalmarkets, developing competitive enterprises tobetter serve consumers, increasing access ofdeveloping countries to technology, know-how,and international markets, and creating stron-ger corporate governance (Donaldson &Wagle, 1995).The process of privatization is continuing in

both advanced and developing economies. The

World Bank (1995) found that in the mid-1990sthousands of enterprises in developing coun-tries were still under state ownership. Sub-stantial privatization will continue to take placein Central and Eastern Europe, South Asia,Southeast Asia, Central and South America,and Africa over the next decade (Gibbon,2000). International financial institutions andricher countries continue to provide loans,grants, and technical assistance for privatiza-tion in developing countries and to require theirgovernments, through loan conditionality, tosell loss-making SOEs as part of their economicreform policies (Rondinelli & Priebjrivat,2000). But increasingly, questions arise aboutthe impacts of foreign aid on economic reformin general, and on privatization in particular.In this paper, we analyze foreign aid as a

determinant of privatization. Although the lit-erature on the effects of foreign aid on eco-nomic growth and development is large andgrowing, analysis of its impacts on economicreforms in general, and on specific reform pol-icies such as privatization, is still nascent. Ourwork aims to contribute to that literature byaddressing the following questions: what is theeffect of foreign aid on governments� decisionsto adopt and implement privatization policy?Does foreign aid have any impact on stimu-lating governments to privatize SOEs, and if so,what types of aid are most effective? Over theyears, foreign aid has supported unproductivepublic enterprises (Mason & Asher, 1973). Howhas it now impacted their reform? If foreigneconomic and technical assistance affect gov-ernment decisions to privatize, what if any in-fluence does it have on three attributes of theprivatization process––timing, pace, and in-tensity?We first review the debate on the relation-

ships between foreign aid, economic develop-ment and reform policies, then construct ananalytical model for examining the net politicalbenefits to governments from privatizing anddiscuss data and present descriptive statisticson the variables used in analysis. We presentempirical models for testing the relationshipsbetween foreign aid and privatization and ex-plore results and implications.

2. THE IMPACTS OF FOREIGN AID ONECONOMIC DEVELOPMENT

Determining the impact of foreign aid oneconomic development and policy reform has

DOES FOREIGN AID PROMOTE PRIVATIZATION? 1529

always been a controversial issue. Continuingcriticisms of the impacts of foreign aid havebrought sweeping changes in its goals and ob-jectives over the past 50 years (Rondinelli,1993). In the 1950s and 1960s, richer countriesused foreign aid to fill gaps in resource con-strained economies, encouraging national in-vestment and industrial development throughcentral government planning (Waterston,1965). The belief that foreign aid could helpdeveloping countries to accelerate the ‘‘takeoff’’into self-sustained growth by generating newinvestment permeated international assistanceprograms (Rostow, 1960).In the following decade, criticisms of the

failure of foreign aid to address issues of socialequity led international aid organizations tofund projects aimed at creating physical andsocial infrastructure, transforming subsistenceagriculture, integrating rural development, ex-panding small- and medium-sized industries,and meeting basic human needs (Adelman &Morris, 1973; Streeten & Burki, 1978). But thefocus changed in late 1980s and early 1990s asinternational financial organizations began toplay a more influential role as channels forforeign aid and as globalization made privatecapital flows a stronger element in finan-cial transfers from richer to poorer countries.The international financial organizations usedstructural adjustment loans to pressure gov-ernments to undertake policy reforms in whichprivate sector development and privatizationbecame major components of the reformpackage (Gwin & Nelson, 1997; Rondinelli &Montgomery, 1990).Recent evaluations of the effects of foreign

aid on economic growth and policy reformcome to mixed conclusions. In his study of 96developing countries, Boone (1994, 1996) foundthat foreign aid had no effect on investment andgrowth––the marginal propensity to consumefrom foreign aid was insignificant and themarginal propensity to invest was zero. Eas-terly�s (2001) analysis of crosscountry data alsofound no empirical relationship between for-eign aid and economic growth or between aidand investment and concluded that aid basedon filling the finance gap had not delivered theexpected results and may have created thewrong economic incentives. Using an aid-fun-gibility model, Feyzioglu, Swaroop, and Zhu(1996) found that out of each dollar of devel-opmentassistance,75centsgoestocurrentspend-ing and 25 cents to capital expenditure, and theratio is far more skewed in favor of current

consumption in the case of concessionary loans.Their results vary from Boone�s in finding thataid significantly affects public investment.Burnside and Dollar (2000) found that for-

eign aid has positive impacts on economicgrowth only in the presence of ‘‘good policy’’––strong fiscal, monetary, and trade policies––and that aid has no positive effect on growth incountries that have weak or poor policies.Without economic reform policies, foreign aidwas converted to unproductive governmentconsumption with no effect on growth. AsCollier (1997) notes, most African countrieshad poor policy environments, in which aid hadexactly the opposite effect of what was in-tended. Further, Collier and Dollar (2002) re-visited the relationship between aid and qualityof policies using a larger dataset of countriesand more comprehensive indicators of policyenvironments. They confirm that aid efficiencydoes depend on the quality of economic policiesand is subject to diminishing marginal returns.On the other hand, Hansen and Tarp (2001)conclude that aid increases growth via capitalaccumulation, and it does not depend on goodpolicy. They note that growth regressions ofthis kind are sensitive to choice of controlvariables and choice of estimator and thatmuch more theoretical work is needed beforedrawing policy insights.With respect to aid�s effect on development,

Burnside and Dollar (1998) note that aid has nopositive systematic impact on development incountries with weak incentive regimes. Theyevaluate the effect of aid by using the infantmortality rate as a proxy for development, afterobserving significant decreases in infant mor-tality rates as countries grow economically.They found a relationship that was weaker thanthat between foreign aid and economic growth.Boone (1994) and Feyzioglu et al. (1996) foundno significant effect of foreign aid on infantmortality rates. Svensson (1999) concluded thatforeign aid has a positive long-term impact indemocratic countries; but in countries withauthoritarian regimes, aid is often dissipated onunproductive activities. Burnside and Dollar(1998) argued that aid does not effect policychange in recipient countries and Collier (1997)confirmed that although foreign aid disturbsthe local political equilibrium, it is unlikely tobring about lasting reforms. Ultimately, thecountry�s development path is determined bydomestic political and economic factors.Foreign aid has not fulfilled its expectations

for many reasons. Corruption or rent-seeking is


often identified as a primary cause (Krueger,1993). If the donor–recipient relationship ismodeled as a noncooperative game, then aidhas the effect of softening budget constraints,allowing governments to fund nonproductiveactivities. Svensson (2000) argues that an in-crease in government revenues through foreignaid can lead to a lower provision of publicgoods, and consequently, lower welfare. Hefound a positive association between foreignaid and corruption in countries with dividedpolicy processes and no systematic evidencethat aid is withheld from corrupt countries.Foreign aid may also have little or no impacton economic policy reforms because of differ-ences in the political objectives of recipients anddonors. In an ideal world, the objectives ofrecipient governments should be to optimizesocial conditions and the goals of donorsshould be benevolence (Burnside & Dollar,2000). In reality, differences in objectives canskew the distribution of aid funds––either di-rectly or by allowing governments to substituteaid for other resources––toward activities thatdonors had not intended.How does foreign aid affect the distributional

struggles consequent to economic reforms? Thedifference in the ultimate reform outcome de-pends on the conflicts among stakeholders andhow they are willing to negotiate and compro-mise. Alesina and Drazen (1991) argue that anyefficient policy change, such as privatization,with significant distributional consequences canbe delayed by a ‘‘war of attrition,’’ i.e. eachgroup attempts to wait the others out. Policychanges occur only when one group concedesand bears a disproportionate share of the bur-den. They argue that even if privatization wasnot politically popular ex-ante, it can eventu-ally become successful. In the war of attrition,the passage of time, political consultations withdifferent interest groups, and the accumulationof costs lead one group to concede and acceptdefeat. In this model, information asymmetryresults in each group knowing only its owndistribution costs. Cassella and Eichengeen(1996) add foreign aid to this model. Theyargue that the longer the period between theannouncement and the disbursal of aid, thehigher the probability that opposing factionswill just await the actual arrival of aid. Foreignaid announced at the onset of a period of deficitor inflation can hasten stabilization, but gov-ernments that are aware of the forthcoming aidcan also continue an unsustainable policylonger. The distributional costs of adjustment

provide governments with the ‘‘price of re-form’’ (Collier, 1997). Structural adjustmentloans are designed to help countries recoverfrom the short-term costs––such as job lossesand price increases––of economic reform. Butthe price the countries place on reforms can behigher than expected and governments can goback on their word.Evidence of the effect of foreign aid on priv-

atization remains ambiguous. The level of aiddependence of a country may be the most sig-nificant factor affecting a government�s assess-ment of whether or not privatization isdesirable. On the one hand, countries dependenton foreign aid must give the impression ofheeding the desires of donors. Governmentsmay undertake privatization if the internationalaid donors insist that they adopt the policy as acondition for loans or grants. Then privatiza-tion becomes imperative for an aid-dependentcountry. On the one hand, higher levels of aiddependence may delay the privatization deci-sion because the additional financial resourcesallow unsustainable public policies to continueindefinitely. Foreign aid has been a potentialsource of rent that has contributed to the dete-rioration of the quality of SOEs by subsidizingemployment and allowing them to make un-profitable investments (Vernon, 1988). Largedeficits in public enterprises were sustainedthrough foreign aid especially in sub-SaharanAfrican economies (Knack, 2000). If the pro-portion of aid to GDP is high, then the countrymay be able to delay privatization. Foreign aidrepresents additional sources of funds to reducethe fiscal deficit without undertaking publicsector reform. Because privatization imposesshort-term sacrifices on large portions of thepopulation, certain groups (such as labor) aremore adversely affected. Governments can useforeign aid to delay privatization by postponingthe sacrifices of politically volatile social groups.Recently, foreign aid has included a labor

adjustment component and funding for socialprograms to reduce the short-term negativeimpact of policy reforms on poorer groups(World Bank, 2002). Thus, the net effect offoreign aid on the political desirability ofprivatization is indeterminate.

3. AN ANALYTIC MODEL OF THEDECISION TO PRIVATIZE

A substantial literature on policy reformpoints out that it is fundamentally a political

Determinants... Macroeconomic Political Institutional

DECISION

TO PRIVATIZE

Net Political Benefits

Privatization Attributes

Timing — year of first privatization Pace— annual privatization transactions Intensity— annual proceeds

...

Figure 1. The net political benefits model of the governmental decision to privatize.


decision (Nelson, 1984). In many countries,governments make decisions about policy re-form on the basis of a political calculationabout the probability that the benefits ofchange will outweigh the costs. In many ways,the decision to privatize can be assessed in thesame way that Rodrik (1994) assessed govern-ments� decisions to enact trade reforms––apolitical cost–benefit ratio (PCBR). He arguesthat since redistributing income is politicallycostly, this cost is weighed against the benefitsarising from economic efficiency of trade re-forms. He defines PCBR as the ratio of redis-tribution generated by reform to its efficiencybenefits. We apply the theoretical concept ofpolitical net benefits––the difference between theperceived political benefits and costs to in-cumbent regimes of making changes––to thedecision of governments to privatize. In thisapproach, costs are immediate and the benefitsaccrue in the future. We argue that privatiza-tion is most likely to occur when a governmentperceives the expected political benefits fromefficiency gains to be higher than the politicalcosts of redistribution. The PCBR concept isdifficult to make operational because the deci-sion to privatize is based on ex-ante efficiencygains and political costs of redistribution. Inour analytical framework, governments� per-ception of political net benefits affects the tim-ing, pace, and intensity of privatization(Ghosh, 2001). The politician�s decision vari-ables are factors that enter into the politicalcost–benefit analysis framework affecting thevalue of the political net benefits. These factorscan be categorized as macroeconomic, political,and institutional variables. The justification forusing these variables comes mainly from thepolitical economy of reform literature, which isreferenced below. The net political benefitsmodel is summarized in Figure 1.

4. DATA AND DESCRIPTIVE STATISTICS

Annual data on 35 countries are used toconstruct a panel dataset with 630 observa-

tions. The countries in the sample are: fromLatin America––Argentina, Mexico, Colombia,Bolivia, Venezuela, Ecuador, Peru, Brazil,Honduras, Guatemala, El Salvador, Paraguay,Panama, Uruguay, Trinidad and Tobago, andCosta Rica; from Africa––Coote d�Ivoire, Egypt,Tunisia, Ghana, Nigeria, Morocco, Zambia,Kenya, Zimbabwe, and South Africa; and fromAsia––India, Indonesia, Bangladesh, Pakistan,Sri Lanka, Malaysia, Philippines, Thailand,and Turkey. The dataset covers 1982–99, whichis the most dynamic phase of privatization ac-tivity. ‘‘Country’’ provides the cross-sectionvariation while ‘‘year’’ provides the time-seriesvariation. Therefore, the analysis can identifyboth the cross-section and temporal dynamicsof privatization.The dependent variables have been con-

structed from the privatization database of theWorld Bank that covers 1988–99 for all thedeveloping countries that have privatized. Thisnew database is included in the World Bank�sGlobal Development Finance, 2001a report. Thedatabase records more than 8,000 individualtransactions. For 1982–87, the data are avail-able from the Candoy-Sekse (1988) report. TheCandoy-Sekse report is not comprehensive(year and privatization proceeds are not clear)but it is the only credible source of data for theearly years of privatization. As a result, ourdataset covers only 1988–99 for the intensitymeasure.Three dependent variables are used to mea-

sure the timing, pace, and intensity of privati-zation. For the timing measure, the dependentvariable is 0 or 1, with 0 assigned for the yearsof no adoption and 1 assigned in the year inwhich privatization began and subsequentyears following the year of adoption. For thepace or sequencing measure, we have calculatedthe ‘‘number of privatizations undertaken ineach year’’ from the privatization database,that is, annual count data of the number ofprivatization transactions. For the intensityor value measure, the dependent variable islog privatization revenues at constant USD(1996¼ 100). In addition, we used the variable


‘‘privatization revenues as share of GDP,’’ butthe variance is very low, consequently, weproceed with only log privatization revenues asthe primary indicator.With respect to foreign aid as an explanatory

variable, we use four measures: (a) aid as apercentage of gross national income, (b) aid as apercentage of central government expenditures,(c) effective development assistance (EDA) as apercentage of gross domestic product and (d)technical assistance (TA) as a percentage ofgross domestic product. The use of EDA isrelatively new in the literature; it is measuredas the sum of grants and grant equivalentsof official loans (Chang, Fernandez-Arias, &Serven, 1998). We have included technical as-sistance separately in order to evaluate the roleof this component in facilitating the privatiza-tion process.In addition to aid, we include variables to

represent a country�s political economy andinstitutional structure. They can be categorizedinto economic, political, and institutional vari-ables:

––Economic variables––budget surplus as apercentage of GDP (BUDSUR), inflationrate (INFLA), growth rate as a percent-age of GDP (GRORATE) agriculture as apercentage of GDP (AGRGDP), degree ofinequality (INEQ), initial SOEs as a percent-age of GDP (INITSOEGDP).––Political variables––years in office of thechief executive (YRSOFFC), ideology(RIGHTEXEC) and political cohesion ofthe government (COHESION), democracy(DEMOC).––Institutional variables––market capitaliza-tion as a percentage of GDP (MARKET),quality of governance (QUALGOV), ethnictension (ETHNTEN).The sources and description of the data are

presented in Table 1.The macro-economic variables––‘‘budget

surplus as a percentage of GDP,’’ ‘‘inflationrate,’’ and ‘‘growth rate’’––have been laggedone period because it is unlikely that a crisiswould bring about policy change in the sameyear. In addition, lagged value of budget deficithas been included to control for possible end-ogeneity as privatization revenues are oftenused for fiscal stability. Agriculture as a shareof GDP is included as a structural indicator. Incases of nonagrarian economies, the publicenterprises and the rest of the economy areinterlinked and potential gains from privatiza-tion would be higher (Campos & Esfahani,

1996). Degree of inequality is an indicator ofdistributional struggles associated with priv-atization. Given the sparse nature of ‘‘highquality’’ observations in the Deininger andSquire (1996) inequality database, it is difficultto create a time series. So we used the averagegini coefficient of the entire period as the in-equality variable.Among the political variables, we test for the

‘‘honeymoon hypothesis’’––that is, the as-sumption that new governments are the mostlikely to undertake potentially contentious re-forms (Alesina & Drazen, 1991). Shleifer andVishny (1994) claim that ideology is a signifi-cant determinant of economic reforms, andthat privatization occurs when governmentsfavored by tax payers come to power instead ofgovernments favored by labor unions. Othersposit that more cohesive governments are moreconducive to undertaking privatization becausefewer interests need to be satisfied, a processthat can delay the implementation of privati-zation (Alesina & Drazen, 1991). Knack andKeefer (1995) found that lack of cohesion in-creases the uncertainty related to policy out-comes.The democracy variable is included to test

whether freer political and civil conditions aremore conducive to privatization. This variableincludes the ‘‘political rights’’ and ‘‘civil rights’’values. The effect of democracy on economicreforms is ambiguous. Roubini and Sachs(1989) argue that to agree on reforms is diffi-cult, especially in crisis conditions. The ex-anteuncertainty associated with privatization makesa majority of voters turn against privatization,although they know that ex-post they wouldbe better off. As a result, in a democracy, wel-fare-increasing policies are less likely to beundertaken. On the other hand, democracy ismore suited to dealing with adverse shocks.Empirical analysis on the transition econo-mies has found that democracy facilitates theadoption of market-oriented reforms. Pres-ence of democracy is the meta-institutionfor the existence of other nonmarket insti-tutions (Rodrik, 2000). The checks andbalances that are implicit in the democraticsystem help lock in privatization reforms(Dethier, Ghanem, & Zoli, 1999). Democracyalso changes the incentives for rent-seeking.The checks and balances penalize self-inter-ested politicians (by reducing their chancesof re-election) and hence limit rent-seekingopportunities (Aslund, Boone, & Johnson,1996).

Table 1. Source and description of data

Explanatory variables Definition, description, and sources

Macroeconomic variables

BUDSUR––budget balance as a percentage of Gross Domestic Product

(excluding grants); lagged one period

IMF International Finance statistics

(2000)

INFLA––annual inflation rate; lagged one period World development indicators (2000)a

GRORATE––growth rate; lagged one period World development indicators (2000)a

AIDGNI––aid as a percentage of Gross National Income World development indicators (2000)a

AIDEXP––aid as a percentage of central government expenditures World development indicators (2000)a

EDAGDP, TAGDP––effective development assistance, technical

assistance as percentage of GDP

Chang et al. (1998)

AGRGDP––agriculture value added as a percentage of GDP World development indicators (2000)a

INEQ––degree of inequality in the economy Deininger and Squire (1996)

INITSOEGDP––initial size of the public sector as a percentage of GDP Haggarty and Shirley (1997)

Political variables

YRSOFFICE––number of years the chief-executive has been in office Beck, Clarke, Groff, Keefer, and

Walsh (2001)

RIGHTEXEC––dummy for ‘‘right’’ ideology of the chief executive�sparty

Beck et al. (2001)

COHESION––index of political cohesion Beck et al. (2001)

DOMOC¼Pol_rights + civ_lib

POLRTS––political rights available to citizens Freedom House (2001)

CIVLIB––civil liberties available to citizens Freedom House (2001)

Institutional variables

CORRUPTION––corruption in government; degree to which business

transactions involve corruption or questionable payments; varies from

0 to 6

ICRG (IRIS3)b

RULELAW––rule of law; degree to which the citizens of a country are

willing to accept the established institutions to make and implement

laws and adjudicate disputes; varies from 0 to 6

ICRG (IRIS3)b

BURQLTY––quality of bureaucracy; measures the regulatory environ-

ment the domestic and foreign firms must face when seeking approvals

and permits. ranges from 0 to 6

REPUCON––risk of repudiation of contracts by government; measures

the ‘‘possibility that foreign businesses, contractors and consultants

face the risk of contract modification, postponement or scaling down

as a result of change in government, income drop, budget cutbacks,

indigenization pressure or change in government priorities;’’ the score

ranges from 0 to 10

ICRG (IRIS3)b

EXPRO––risk of expropriation of private investment; measures the risk

of ‘‘outright confiscation and forced nationalization’’ of property;

ranges from 0 to 10

ICRG (IRIS3)b

MARKET––stock market capitalization as a percentage of GDP; lagged

one period

Beck, Demirguc-Kunt, and Levine

(2000)

ETHNTEN––ethnic tension; measures ‘‘degree of tension within a

country attributable to racial, nationality and language divisions;’’

from 0 to 6

ICRG (IRIS3)b

Control variables

D_AFRICA, D_LAC––dummy for the continents World development indicators (2000)a

TRADE––trade openness World development indicators (2000)a

ILLITERACY––adult illiteracy (age 15 and above) World development indicators (2000)a

INITGDPCAP––initial GDP per capita World development indicators (2000)a

LPOP––(log) population World development indicators (2000)a

a Political Services Group (2000).bWorld Bank (2001b).



Among the institutional variables, we haveincluded institutional quality created by ag-gregating rule of law, bureaucratic quality,corruption, risk of expropriation, and repudi-ation of property. Knack and Keefer (1995)and Hall and Jones (1999) have previously usedsuch an aggregation. Doubts can be raisedabout the ordinal nature of the governancevariable, so we created a dummy variable thattook the value 1 if the quality of governancewas higher than the mean value of 3.5, or else ittook the value 0. The results did not change, sowe decided to use the ordinal variable, as adummy variable specification would mean lossof information. In addition, we have includedethnic tension to analyze the effect of ethnicstrife in the economy. We include market cap-italization as a percentage of GDP to accountfor domestic financial market development.Because of concerns about endogeneity––mar-ket development is often an objective of priv-atization policy––we have lagged this variableby one period. Finally, we include the initialsize of the SOE sector in the model. Empiricalevidence indicates that the larger the size of thepublic sector, the more likely it would includeactivities that are in competitive sectors. Di-vesting them would involve higher potentialefficiency gains (Campos & Esfahani, 1996). Onthe other hand, a large public sector wouldmean a more powerful voice of its stakeholdersin the decision-making process. Consequently,it would be more difficult to overcome interest-group opposition. In addition, we include anumber of control variables: dummies for Af-rica and Latin America to account for regional

Table 2. Summary of

Variable Observations Mean S

Timinga 630 0.5825

Latin America 289 0.55

Africa 180 0.55

Asia 161 0.67

Paceb 630 4.02


Africa 180 3.7

Asia 161 4.81

Intensityc 420 577.44


Africa 120 149.34

Asia 107 386.43

a Timing of first privatization.bNumber of privatization transactions each year.c Privatization proceeds in millions (in constant USD, 1996

effects, initial GDP per capita and adult illit-eracy to account for level of development, tradeopenness to account for previous reform expe-rience, and finally (log) population to controlfor scale effects.There exists a possibility of an endogeneity

problem. Aid affects privatization policy and isaffected by it. There can be reverse causalityhere because privatization policy can affect aidallocations; especially in the case of sustainedreformers. But the Durbin–Wu–Hausmanendogeneity test showed that aid is not endo-geneous (v2 is insignificant for all the four in-dicators of aid) in our sample.The descriptive statistics of the dependent

variable are reported in Table 2. The number oftransactions varies from a minimum of 0 to 57,with the mean being four transactions. Thestandard deviation is 8.27. We measure inten-sity as privatization proceeds in constant USD(1996¼ 100). It ranges from 0 to $31421.12(Brazil in 1998). Table 2 also presents the di-saggregated (by continent) statistics of the de-pendent variables––timing, pace and intensity.The mean number of transactions was highestin Asia, followed by Latin America and Africa,but the maximum number of transactions ishighest in Latin America. The mean and stan-dard deviation of privatization proceeds is leastfor Africa.Table 3 presents the descriptive statistics of

the explanatory variables.In our sample, the mean contribution of aid

to gross national income is approximately 4%,to gross capital formation is 19%, and to gov-ernment expenditure is 12%. For example, aid

dependent variables

tandard deviation Min. Max.

0.4935 0 1

0.49 0 1

0.49 0 1

0.47 0 1

8.27 0 57

8.77 0 57

6.82 0 40

8.82 0 51

2218 0 31421.12

3189.01 0 31421.12

376.74 0 2905.42

610.36 0 3140.96

¼ 100).

Table 3. Summary of explanatory variables

Variable Observations Mean S.D. Min. Max.

BUDSUR (budget deficit or surplus as a percentage

of GDP)

566 )3.88 4.47 )45.08 5.08

INFLA (annual inflation rate) 629 88.35 617.32 )13.44 12339.27

GRORATE (annual growth rate) 629 3.24 4.22 )13.38 13.28

AIDGNI (foreign aid as a percentage of GNI) 619 3.39 5.18 )0.47 62.99

AIDEXP (foreign aid as a percentage of central

government expenditures)

475 12.08 17.38 )2.1 98.61

EDAGDP (effective development assistance,

percentage of GDP)

476 2.31 3.69 )0.14 47.78

TAGDP (technical assistance, percentage of GDP) 476 0.81 0.93 0.30 6.06

AGRGDP (agriculture as a percentage of GDP) 621 18.50 9.78 1.44 59.73

INEQ (degree of inequality) 630 45.24 8.02 31.42 62.3

INITSOEGDP (inital size of SOEs as percentage

of GDP)

630 10.96 7.58 1.64 32.8

YRSOFFC (number of years in office of the

government)

630 7.06 8.20 1 38

RIGHTEXEC (‘‘Right’’ ideology of the executive) 630 0.339 0.473 0 1

COHESION (degree of political cohesion) 630 0.536 0.777 0 4

DEMOC (democracy) 630 8.86 2.74 2 14

MARKET (market capitalization as a percentage

of GDP)

355 26.42 43.39 0.28 329.36

QUALGOV (quality of governance) 630 3.53 1.00 1.11 6.33

ETHNTEN (ethnic tension) 630 3.84 1.72 0 9

INITGDPCAP (initial GDP per capita) 630 1848.74 1530.76 235.92 6572.09

D_AFRICA (dummy for Africa) 630 0.28 0.45 0 1

D_LAC (dummy for Latin America) 630 0.45 0.49 0 1

ILLITERACY (adult illiteracy) 630 26.15 19.29 2.3 74.4

TRADE (trade openness) 623 84.62 46.05 16.13 333.71

LPOP 630 16.85 1.41 13.93 20.72


constituted 98% of total central expenditure inGhana in 1989. As expected, aid as a share ofgross national income (GNI) was much higherthan average in Africa (6%) and lower in AsiaandLatinAmerica (approximately 2%). Foreignaid varies on average (as share of GNI) from)0.47% inMalaysia in 1996 to 62% in Zambia in1995. When measured in terms of EDA and TA,Asia received more on average compared toAfrica and Latin America. India, Bangladesh,andEgypt are the top aid receivers in our sample.

5. EMPIRICAL APPROACH

We now turn attention to methods of esti-mating coefficients. The empirical methods arediscussed in the context of timing, pace, andintensity of privatization. We use the Coxproportional hazard model to test the timing,random effects negative binomial model for thepace, and random effects model for the inten-sity of privatization decisions.

(a) Timing model

To understand why some countries are moresuitable to adopting privatization than others,we use a hazard model. In the hazard analysis,the time until the event (in this case, privati-zation) and the event itself are combined in thesame dependent variable. Once a country entersinto the privatization state, it stays there till theend of the analysis period although it might bea deep or shallow privatizer. In our sample,none of the countries have reversed their priv-atization policies or completed their transac-tions by 1999, so such an assumption is notinappropriate. The hazard model is appropriateto our problem because it includes time-varyingcovariates. The speed at which privatizationoccurs depends on the rate at which the differ-ent independent variables change. Using ordi-nary least squares (OLS) to estimate thetransition results in biased estimates because itignores country-specific effects and censoredcases.


The most general of all continuous hazardmodels is the proportional hazard model that issemi-parametric in nature. It is parametric be-cause it specifies a specific functional form inthe regression model, but it is nonparametricbecause it does not specify the exact form of thedistribution of event times (Allison, 1985).Therefore, the exit to privatization from a

no-privatization state can be modeled as afunction of the explanatory covariates:

hitðt;X Þ ¼ b1 ðbudget surplus as share of GDPÞit�1

þ b2 ðinflationÞit�1

þ b3 ðgrowth rateÞit�1

þ b4 ðforeign aidÞitþ b5 ðagriculture as share of GDPÞitþ b6 ðdegree of inequalityÞitþ b7 ðinitial contribution of

state-owned enterprises as share

of GDPÞit¼0

þ b8 ðyears in officeÞitþ b9 ðideology of the executiveÞitþ b10 ðgovernment cohesivenessÞitþ b11 ðdemocracyÞitþ b12 ðmarket capitalization as share

of GDPÞit�1

þ b13 ðquality of governmentÞitþ b14 ðethnic tensionÞit:

As is true for all regression models, a positivesign of the covariate implies that it positivelyaffects the probability of privatization. Theopposite is true if the coefficient is negative.Following Box-Steffensmeier and Jones (1997),the interpretation of dichotomous explanatoryvariable, the percentage change in the hazard ofexperiencing privatization is: 100½eðbk�1Þ �eðbk� 0Þ�=eðbk� 0Þ, if the coefficient is negative.Consequently, eðbk�1Þ < 1 and the hazard ofexperiencing privatization as a result of thatcovariate is negative. For continuous variable,100½eðbk� ðxþ dÞÞ � eðbk� xÞ�=eðbk� xÞ, the percentagechange in the hazard rate as a result of d changein the independent variable x suggests the effectof the variable.

(b) Pace/frequency model

The model of pace and frequency of privati-zation can be specified as:

Annual privatization transactions

¼ b1 ðbudget surplus as share of GDPÞit�1

þ b2 ðinflationÞit�1 þ b3 ðgrowth rateÞit�1

þ b4 ðforeign aidÞitþ b5 ðagriculture as share of GDPÞitþ b6 ðdegree of inequalityÞitþ b7 ðinitial share of state-owned

enterprises to GDPÞit¼0

þ b8 ðyears in officeÞitþ b9 ðideology of the executiveÞitþ b10 ðgovernment cohesivenessÞitþ b11 ðdemocracyÞitþ b12 ðmarket capitalization as share of

GDPÞit�1

þ b13 ðquality of governmentÞitþ b14 ðethnic tensionÞit þ eit:

In this model, we analyze the determinants ofprivatization event counts. Event counts are‘‘variables that have for observations i ði ¼1; . . . ;NÞ the number of occurrences of an eventin a fixed domain.’’ In our analysis, the domainfor each observation is ‘‘year’’ (King, 1988).The Poisson assumption is appropriate as theevents occur randomly in time. In a Markovsense, random means that the expected rate ofoccurrence of the next event either remainsconstant (¼ h) or is uncorrelated with thenumber of observed events. In addition, therandom error around h in one instant of time isindependent and uncorrelated with randomerror in the next instant of time (King, 1988).Previous empirical research has proved that thePoisson specification is well suited to handleinteger properties of count data directly andaccommodate counts that are aggregated overtime periods. The ‘‘number of transactions’’comes from the same event (privatization). Thedependent variable has non-negligible proba-bilities of zero. It can also include non-negativeintegers and zero as a natural outcome of theprocess. In addition, the random effects modelassumes an equi-correlated covariance matrix,so it takes care of the serial correlation.A restrictive assumption of the Poisson

model is the equality between mean and vari-ance of the distribution. It does not take intoaccount over-dispersion that variance is higherthan the mean. Consequently, the estimatedcovariance matrix is biased downward, result-


ing in overstated significance levels (Liao,1994). In this case, the alternative hypothesisconsiders the local alternatives to the Poissondistribution of yt––such as more generalizednegative binomial model. In these generalizedmodels, Poisson parameter is also a randomvariable and not a deterministic function ofindependent covariates. If a c distribution isassumed, then the model reduces to a negativebinomial distribution. In negative binomialmodel, the Poisson parameter kit is distributedrandomly across countries and across time,according to a gamma distribution with shapeparameters (r; s) (Hausman, Hall, & Griliches,1984). c can be assumed to be an exponentialfunction of the explanatory variables, cit ¼expðXitbÞ. We compared the Poisson with neg-ative binomial estimates; though the coefficientsare similar, but z-values of latter model wereconsistently smaller. Therefore, we used thenegative binomial specification. In Table 5,with a random effects negative binomial model,the likelihood ratio test of panel versus pooleddata suggested that panel data are an appro-priate specification (results not reported here).

(c) Intensity/value model

This econometric model includes alternativemeasures of privatization intensity. In the thirdmodel of privatization decision-making, the log(privatization proceeds) is a function of theexplanatory variables:

log ðprivatization proceedsÞ¼ b1 ðbudget surplus as share of GDPÞit�1

þ b2 ðinflationÞit�1 þ b3 ðgrowth rateÞit�1

þ b4 ðforeign aidÞitþ b5 ðagriculture as share of GDPÞitþ b6 ðdegree of inequalityÞitþ b7 ðinitial share of state-owned

enterprises to GDPÞit¼0

þ b8 ðyears in officeÞitþ b9 ðideology of the executiveÞitþ b10 ðgovernment cohesivenessÞitþ b11 ðdemocracyÞitþ b12 ðmarket capitalization as share

of GDPÞit�1

þ b13 ðquality of governmentÞitþ b14 ðethnic tensionÞit þ eit:

The panel design enables us to use a numberof specifications that control for heterogeneitybias. It controls for the time-invariant country-specific effects that may be omitted from theregression model. In a panel dataset, thecountry-specific effects are included in thecountry-specific intercept that may be fixed orrandom (Nielson & Gaddy, 1999). The modelwe estimate is:

Yit ¼ ai þ X 0itbþ eit

where ai capture the country-specific time-in-variant factors omitted from the model. Fol-lowing Nielson and Gaddy (1999), the country-specific errors are now distributed randomly inspace and time. The random coefficients modelis estimated using the GLS estimator. In caseswhere the design is cross-section dominant andthere are significant unit-specific effects, a ran-dom effects model is most useful. Unlike thefixed-effects specification, it removes only afraction of the country-specific means andpermits the use of time-invariant regressors.Random effects model is appropriate as thesample group of countries is drawn from thelarge population of countries where country-specific constant terms are randomly distrib-uted across units. In addition, the randomeffects model is asymptotically efficient com-pared to fixed-effects model (Tuma & Hannan,1979). One of the problems of random effectsmodels is the lack of concern for autocorrela-tion. Since our dataset is cross-section domi-nant, autocorrelation is less of a problem(Stimson, 1985). The consistency of the speci-fications is tested using the Hausman test. Inrandom effects specification, this consistencydepends on zero correlation between the errorterm and the regressors. We tested for the ap-propriateness of the random effects specifica-tion against a fixed-effects model in ourintensity model. The Hausman test results, re-ported Table 6 (v2 is insignificant) suggest thatthe random effects model is an appropriatespecification.

6. RESULTS AND DISCUSSION

In Tables 4–6, we present the results of ahazard model, negative binomial, and randomeffects model, respectively.In all three tables, models (1), (2) and (3) are

estimated with aid as share of gross nationalincome, (4), (5) and (6) are with EDA as share

Table 4. Timing modela

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Dependent variable: timing of first privatization transaction

BUDSUR 0.041 0.06 0.058 0.015 0.027 0.07 0.011 0.032 0.041 0.065 0.069 0.063

)0.035 )0.039 )0.039 )0.034 )0.05 )0.051 )0.034 )0.049 )0.046 (0.038)� )0.044 )0.044INFLA 0.187 0.191 0.194 0.11 0.088 0.136 0.061 0.1 0.081 0.217 0.097 0.115

(0.068)�� (0.081)�� (0.078)�� )0.106 )0.209 )0.277 )0.115 )0.196 )0.196 (0.082)�� )0.122 )0.125GRORATE 0.02 0.024 0.024 0.024 0.035 0.078 0.018 0.033 0.043 0.016 0.037 0.037

)0.021 )0.022 )0.022 )0.029 )0.038 (0.040)�� )0.029 )0.036 )0.033 )0.02 )0.026 )0.026AIDGNI 0.013 0.03 0.319

)0.023 )0.038 )0.21EDAGDP )0.103 )0.004 0.012

(0.042)�� )0.059 )0.09TAGDP )0.513 )0.205 0.001

(0.133)�� )0.127 )0.148AIDEXP 0.01 0.03 0.106

)0.01 (0.010)�� (0.025)��

AGRGDP 0.015 0.012 0.021 0.039 0.063 0.068 0.05 0.066 0.056 0.024 0.031 0.039

)0.01 )0.023 )0.025 (0.013)�� (0.018)�� (0.020)�� (0.011)�� (0.017)�� (0.016)�� (0.014)� )0.03 )0.033INEQ 0.01 0.035 0.029 0.052 0.077 0.1 0.074 0.093 0.091 0.019 0.075 0.069

)0.011 (0.017)�� )0.018 (0.014)�� (0.026)�� (0.025)�� (0.018)�� (0.029)�� (0.027)�� )0.013 (0.028)�� (0.030)��

YRSOFFC )0.01 )0.006 )0.004 0.025 0.025 0.013 0.035 0.03 0.028 )0.015 0 )0.001)0.012 )0.01 )0.01 (0.014)� (0.014)� )0.02 (0.016)�� (0.013)�� (0.014)�� )0.017 )0.015 )0.016

RIGHTEXEC 0.392 0.369 0.312 0.36 0.395 0.369 0.39 0.383 0.336 0.431 0.235 0.284

(0.220)� )0.291 )0.304 (0.207)� )0.321 )0.262 (0.187)�� )0.301 )0.272 (0.260)� )0.375 )0.382COHESION )0.001 )0.111 )0.075 0.168 0.041 0.135 0.165 0.027 0.015 0. )0.107 )0.076

)0.095 )0.092 )0.09 (0.102)� )0.095 )0.096 (0.097)� )0.095 )0.087 )0.118 )0.128 )0.125INITSOEGDP 0.021 0.03 0.027 0.048 0.06 0.074 0.055 0.067 0.054 0.023 0.039 0.04

(0.011)� (0.014)�� (0.014)� (0.014)�� (0.017)�� (0.026)�� (0.015)�� (0.018)�� (0.023)�� (0.012)� (0.019)�� (0.018)��

MARKET )0.001 )0.003 )0.004 )0.004 )0.013 )0.014 )0.005 )0.012 )0.012 )0.001 )0.006 )0.006)0.002 )0.003 )0.003 (0.002)� (0.005)�� (0.008)� (0.002)�� (0.005)�� (0.005)�� )0.002 (0.002)�� (0.002)��

QUALGOV 0.188 0.178 0.278 )0.561 )0.83 )0.788 )0.591 )0.819 )0.339 0.191 0.224 0.323

)0.125 )0.167 (0.143)� (0.182)�� (0.179)�� (0.327)�� (0.186)�� (0.179)�� )0.417 )0.132 )0.173 (0.160)��

ETHNTEN )0.134 )0.135 )0.145 )0.277 )0.153 )0.282 )0.293 )0.165 )0.19 )0.106 )0.037 )0.03(0.072)� )0.085 (0.087)� (0.090)�� )0.11 (0.141)�� (0.090)�� )0.115 )0.122 )0.077 )0.099 )0.103

DEMOC )0.004 0.061 0.054 0.08 0.177 0.13 0.099 0.19 0.176 )0.012 0.048 0.037

)0.033 )0.043 )0.045 (0.046)� (0.048)�� (0.077)� (0.045)�� (0.042)�� (0.042)�� )0.04 )0.047 )0.049LPOP 0.104 0.148 0.803 0.69 0.662 0.784 0.561 0.51

)0.336 )0.34 (0.275)�� (0.366)� (0.277)�� (0.298)�� )0.369 )0.39

WORLD

DEVELOPMENT

1538

INITGDPCAP )0.663 )0.416 )0.383 )0.515 )0.462 )0.433 )1.58 )1.267)0.424 )0.411 )0.623 )0.668 )0.5 )0.557 (0.588)�� (0.648)�

D_AFRICA )0.583 )0.458 )1.363 )1.594 )1.4 )1.657 )1.117 )0.951)0.402 )0.391 (0.560)�� (0.704)�� (0.5 )�� (0.465)�� (0.333)�� (0.369)��

D_LAC 0.017 0.012 0.006 0.01 0.0 0.01 0.026 0.019

(0.009)� )0.01 )0.01 )0.009 )0.0 )0.008 (0.014)� )0.016ILLITERACY 0 0 0.004 0.003 0.0 0.002 )0.002 )0.001

)0.003 )0.003 )0.004 )0.005 )0.0 )0.004 )0.004 )0.004TRADE )0.002 0.006 0.35 0.305 0.2 0.42 )0.012 0.007

)0.078 )0.078 (0.158)�� )0.219 (0.1 )� (0.203)�� )0.101 )0.108AIDQUAL )0.076

)0.052EDAQUAL )0.016

)0.045TAQUAL )0.092

)0.062EXPQUAL )0.02

(0.006)��

Wald test

v2 43.82 91.64 101.63 145.15 312.85 1067.82 129.46 469.9 304.13 60.11 102.52 316.66

Prob> v2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.00 0.00 0.00 0.00

Observations 291 288 288 187 187 157 187 187 187 246 243 243

aRobust standard errors in parentheses.* Significant at 10%.** Significant at 5%.*** Significant at 1%.

DOESFOREIG

NAID

PROMOTEPRIV

ATIZ

ATIO

N?

1539

85

75

38

07

1

03

04

59

52

5

0

Table 5. Pace modela

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Dependent variable: number of annual privatization transactions

BUDSUR 0.055 0.039 0.036 0.09 0.076 0.077 0.089 0.067 0.068 0.093 0. 065 0.065

(0.021)�� (0.022)� )0.022 (0.027)�� (0.028)�� (0.042)� (0.027)�� (0.029)�� (0.029)�� (0.024)�� (0.023)�� (0.024)��

INFLA 0.061 0.028 0.039 0.126 0.117 0.15 0.127 0.108 0.114 0.072 0.063 0.055

)0.054 )0.057 )0.058 (0.073)� )0.085 )0.123 (0.074)� )0.083 )0.085 )0.059 )0.063 )0.063GRORATE 0.013 0.012 0.019 0.027 0.032 0.012 0.028 0.034 0.036 0.015 0.019 0.02

)0.018 )0.018 )0.018 )0.02 (0.019)� )0.024 )0.02 (0.019)� (0.020)� )0.019 )0.018 )0.018AIDGNI 0.033 0.057 )0.413

)0.029 (0.032)� (0.147)��

EDAGDP 0.067 0.152 0.119

)0.059 (0.064)�� )0.077TAGDP 0.379 0.538 0.569

(0.177)�� (0.169)�� (0.196)��

AIDEXP 0.014 0.027 )0.052)0.01 (0.011)�� )0.044

AGRGDP )0.017 )0.007 )0.012 0.003 0.028 0.037 0.002 0.034 0.032 )0.007 0.02 0.009

)0.017 )0.023 )0.024 )0.023 )0.034 )0.036 )0.023 )0.034 )0.034 )0.022 )0.029 )0.03INEQ )0.01 0 0.009 )0.033 )0.033 )0.039 )0.037 )0.046 )0.047 0.008 0.035 0.038

)0.017 )0.023 )0.023 )0.023 )0.037 )0.043 )0.025 )0.039 )0.039 )0.021 )0.027 )0.027YRSOFFC 0.009 0.023 0.021 )0.012 0.032 0.035 )0.014 0.027 0.027 )0.019 0 )0.003

)0.015 )0.017 )0.017 )0.02 )0.027 )0.031 )0.021 )0.027 )0.027 )0.018 )0.019 )0.019RIGHTEXEC 0.286 0.344 0.409 1.015 1.464 1.663 1.017 1.43 1.42 0.311 0.365 0.355

)0.22 )0.226 (0.224)� (0.348)�� (0.401)�� (0.544)�� (0.347)�� (0.389)�� (0.391)�� )0.233 )0.242 )0.238COHESION 0.088 0.104 0.076 0.013 0.117 0.164 0.018 0.107 0.106 0.096 0.069 0.053

)0.1 )0.107 )0.105 )0.129 )0.136 )0.145 )0.129 )0.135 )0.135 )0.103 )0.113 )0.111INITSOEGDP )0.02 0.015 0.02 )0.017 0.029 0.022 )0.021 0.031 0.029 )0.033 0.022 0.024

)0.017 )0.021 )0.021 )0.024 )0.033 )0.046 )0.024 )0.034 )0.034 (0.017)� )0.021 )0.021MARKET 0.001 0.001 0.002 )0.001 )0.008 )0.008 0 )0.006 )0.005 )0.001 )0.003 )0.002

)0.002 )0.002 )0.002 )0.002 (0.004)�� (0.004)�� )0.002 )0.004 )0.004 )0.002 )0.002 )0.002QUALGOV 0.484 0.389 0.148 0.732 0.517 0.369 0.736 0.479 0.618 0.436 0.361 0.204

(0.099)�� (0.107)�� )0.123 (0.129)�� (0.138)�� )0.266 (0.129)�� (0.138)�� )0.468 (0.107)�� (0.122)�� )0.143ETHNTEN )0.026 0.073 0.033 0.078 0.239 0.235 0.088 0.212 0.2 0.095 0.197 0.154

)0.067 )0.079 )0.077 )0.086 (0.106)�� (0.126)� )0.084 (0.098)�� (0.106)� )0.078 (0.087)�� (0.087)�

DEMOC )0.069 )0.114 )0.118 )0.149 )0.203 )0.172 )0.15 )0.229 )0.232 )0.145 )0.221 )0.217(0.041)� (0.048)�� (0.047)�� (0.055)�� (0.070)�� (0.081)�� (0.054)�� (0.072)�� (0.072)�� (0.047)�� (0.053)�� (0.053)��

LPOP )0.185 )0.188 )0.154 )0.356 0.019 0.029 0.01 0.096

)0.276 )0.291 )0.402 )0.461 )0.416 )0.416 )0.33 )0.337

WORLD

DEVELOPMENT

1540

INITGDPCAP )0.541 )0.859 )0.996 )0.985 )0.924 )0.927 )1.358 )1.691)0.499 (0.510)� )0.746 )0.869 )0.738 )0.737 (0.653)�� (0.685)��

D_AFRICA 0.001 )0.104 )0.454 )0.636 )0.357 )0.384 )0.087 )0.255)0.458 )0.46 )0.716 )0.961 )0.71 )0.717 )0.523 )0.526

D_LAC )0.026 )0.022 )0.044 )0.043 )0.046 )0.045 )0.021 )0.013(0.010)�� (0.011)�� (0.016)�� (0.019)�� (0.017)�� (0.017)�� )0.013 )0.014

ILLITERACY )0.003 )0.003 0.004 0.009 0.002 0.002 )0.001 )0.001)0.003 )0.003 )0.005 )0.006 )0.005 )0.005 )0.003 )0.003

TRADE 0.161 0.133 0.412 0.319 0.387 0.419 0.179 0.168

)0.123 )0.125 (0.194)�� )0.286 (0.187)�� (0.213)�� )0.143 )0.143AIDQUAL 0.123

(0.037)��

EDAQUAL 0.02

)0.039TAQUAL )0.025

)0.079EXPQUAL 0.022

(0.011)�

CONSTANT )0.747 )1.879 )0.65 )0.657 )6.129 )3.599 )0.541 )5.874 )6.454 )0.711 )4.986 )4.604)1.287 )3.487 )3.637 )1.735 )5.1 )7.132 )1.784 )4.874 )5.204 )1.46 )4.017 )4.035

Wald test

v2 55.04 68.18 72.96 80.91 94.12 74.14 83.31 103.58 103.28 66.19 86.09 88.15

p > v2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Observations 291 288 288 187 187 157 187 187 187 246 243 243

Number of

countries

35 35 35 34 34 32 34 34 34 32 32 32

a Standard errors in parentheses.* Significant at 10%.** Significant at 5%.*** Significant at 1%.

DOESFOREIG

NAID

PROMOTEPRIV

ATIZ

ATIO

N?

1541

Table 6. Intensity modela

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Dependent variable: log of real privatization proceeds (1996¼ 100)

BUDSUR 0.072 0.075 0.072 0.127 0.133 )0.075 0.13 0.131 0.14 0.103 0.102 0.107

)0.046 )0.047 )0.046 (0.057)�� (0.059)�� )0.075 (0.056)�� (0.059)�� (0.060)�� (0.049)�� (0.050)�� (0.049)��

INFLA )0.088 )0.145 )0.155 0.138 )0.001 )0.323 0.132 0.01 0.014 0.039 )0.04 )0.096)0.119 )0.122 )0.122 )0.201 )0.226 )0.271 )0.201 )0.226 )0.227 )0.145 )0.155 )0.15

GRORATE 0.007 0.01 0.011 0.012 0.002 )0.043 0.011 0.001 0.005 0.001 0.004 0.002

)0.034 )0.034 )0.034 )0.046 )0.047 )0.061 )0.046 )0.047 )0.048 )0.036 )0.037 )0.037AIDGNI )0.095 )0.027 )0.528

)0.064 )0.069 (0.292)�

EDAGDP )0.068 0.106 )0.083)0.147 )0.174 )0.152

TAGDP )0.28 0.039 0.183

)0.395 )0.445 )0.55EXPGDP )0.04 )0.026 )0.165

(0.022)� )0.025 (0.062)��

AGRGDP )0.055 )0.037 )0.043 )0.039 )0.055 0.011 )0.035 )0.05 )0.053 )0.025 0.009 )0.011)0.042 )0.055 )0.051 )0.052 )0.069 )0.05 )0.051 )0.068 )0.07 )0.05 )0.071 )0.062

INEQ 0.047 0.045 0.036 )0.031 )0.004 )0.029 )0.023 0.006 0.004 0.043 0.067 0.059

)0.041 )0.047 )0.042 )0.061 )0.083 )0.059 )0.063 )0.083 )0.086 )0.046 )0.057 )0.05YRSOFFC 0.026 0.024 0.014 0.07 0.069 0.026 0.076 0.066 0.068 0.035 0.045 0.033

)0.038 )0.037 )0.033 )0.05 )0.053 )0.037 )0.051 )0.054 )0.055 )0.043 )0.045 )0.037RIGHTEXEC )0.454 )0.491 )0.402 0.245 0.392 0.01 0.25 0.32 0.375 )0.558 )0.681 )0.688

)0.492 )0.502 )0.484 )0.694 )0.726 )0.702 )0.692 )0.719 )0.728 )0.519 )0.541 )0.518COHESION 0.056 0.013 )0.019 0.166 0.125 )0.051 0.165 0.111 0.115 0.016 )0.032 )0.057

)0.196 )0.204 )0.201 )0.243 )0.251 )0.265 )0.242 )0.251 )0.251 )0.201 )0.212 )0.205INITSOEGDP )0.014 )0.008 )0.009 0.002 0.022 )0.063 0.01 0.018 0.02 )0.022 )0.009 )0.023

)0.048 )0.05 )0.042 )0.058 )0.069 )0.052 )0.059 )0.069 )0.072 )0.053 )0.057 )0.044MARKET 0.003 0.003 0.006 0.004 0.003 0.009 0.004 0.004 0.004 0.002 0.002 0.005

)0.005 )0.005 )0.005 )0.005 )0.007 )0.007 )0.005 )0.007 )0.007 )0.005 )0.006 )0.005QUALGOV 0.278 0.26 )0.007 1.024 0.902 )0.002 1.037 0.901 1.42 0.393 0.462 0.113

)0.203 )0.217 )0.252 (0.328)�� (0.358)�� )0.502 (0.326)�� (0.358)�� )1.097 (0.229)� (0.254)� )0.277ETHNTEN )0.079 )0.005 )0.014 )0.222 )0.058 )0.111 )0.22 )0.083 )0.104 )0.146 )0.089 )0.092

)0.143 )0.16 )0.15 )0.178 )0.207 )0.185 )0.178 )0.203 )0.212 )0.156 )0.179 )0.162DEMOC )0.137 )0.12 )0.096 0.037 0.096 0.129 0.055 0.101 0.073 )0.083 )0.092 )0.079

)0.111 )0.116 )0.107 )0.138 )0.159 )0.141 )0.141 )0.161 )0.171 )0.124 )0.134 )0.116LPOP 0.44 0.58 0.244 1.495 0.146 0.183 0.798 1.032

)0.632 )0.551 )0.866 (0.646)�� )0.886 )0.931 )0.8 )0.642

WORLD

DEVELOPMENT

1542

INITGDPCAP 0.152 )0.372 )0.163 1.425 )0.12 )0.192 )0.804 )1.631)1.2 )1.082 )1.798 )1.202 )1.7 7 )1.838 )1.535 )1.349

D_AFRICA 0.821 0.533 )0.021 1.696 )0.0 9 )0.211 0.708 0.18

)1.166 )0.995 )1.665 )1.278 )1.6 3 )1.723 )1.417 )1.129D_LAC 0 0.01 )0.002 0.005 0.0 3 0.003 0.024 0.043

)0.025 )0.022 )0.038 )0.023 )0.0 7 )0.038 )0.032 )0.027ILLITERACY )0.003 )0.006 )0.003 0.008 )0.0 4 )0.005 )0.002 )0.006

)0.007 )0.006 )0.011 )0.009 )0.0 )0.011 )0.007 )0.007TRADE 0.7 0.666 0.842 1.168 0.7 7 0.86 0.517 0.49

(0.328)�� (0.286)�� (0.505)� (0.429)�� )0.4 9 )0.563 )0.405 )0.333AIDQUAL 0.136

(0.076)�

EDAQUAL 0.102

)0.07TAQUAL )0.089

)0.182EXPQUAL 0.039

(0.017)��

CONSTANT 4.847 )11.011 )9.811 2.371 )15.052 )28.142 1.739 )12.8 3 )15.043 3.834 )13.364 )11.994(2.924)� )8.552 )7.604 )3.953 )11.969 (10.046)�� )4.122 )11.5 2 )13.008 )3.138 )10.062 )8.173

R2 (overall) 0.16 0.34 0.38 0.25 0.38 0.51 0.25 0.3 0.38 0.21 0.38 0.44

Wald test

v2 26.63 35.92 45.74 30.49 37.57 78.08 30.89 37.2 36.80 25.58 32.92 49.61

p > v2 0.02 0.01 0.00 0.00 0.01 0.00 0.00 0.0 0.01 0.02 0.03 0.00

Hausman test

v2 6.68 7.93 8.9 4.41

p > v2 0.98 0.93 0.8 0.99

Observations 205 202 202 124 124 98 124 124 124 177 174 174

Number of

countries

35 35 35 31 31 27 31 31 31 30 30 30

a Standard errors in parentheses.* Significant at 10%.** Significant at 5%.*** Significant at 1%.

DOESFOREIG

NAID

PROMOTEPRIV

ATIZ

ATIO

N?

1543

7

5

4

0

3

0

1

3

7

7

1

9

9

1

1

8


of GDP, models (7), (8) and (9) are with TA asshare of GDP and finally, models (10), (11),and (12) are estimated with aid as share ofcentral government expenditures. Models (1),(4), (7) and (10) are without control variables,model (2), (5), (8) and (11) are with controls,and model (3), (6), (9) and (12) include the in-teraction term of AIDGNI, EDAGDP andTAGDP, AIDEXP with quality of governance,respectively. Our analysis yields the followingresults.

(a) Foreign aid has a differential impacton timing, pace, and intensity decisions

of privatization

Though there is no systematic evidence of aidaffecting timing of privatization, we find thathazard rate is positively affected by AIDEXP.For example, one unit increase in AIDEXPincreases the hazard by 1.03 times, while higherproportion of TA has the effect of delayingprivatization. Interestingly, countries with highAIDEXP in the presence of strong governancestructure undertake privatization later. It ap-pears that in aid dependent countries that havegood quality institutions, the risk of privati-zation reduces by 0.98 times. There is strongevidence of aid facilitating the implementationprocess and positively affecting the pace ofprivatization. One percentage point increase inAIDGNI, EDAGDP and AIDEXP and in-creases the annual transactions by 6%, 15% and3%, respectively (models (2), (5) and (11), Table5), holding other factors constant. The resultsstrongly suggest a constructive role of technicalassistance. For instance, with one percentageincrease in TAGDP, a country�s predicted an-nual transactions increases by a factor of 1.71,holding other variables constant (model (8),Table 5).An explanation for the indefinite result can

be found in the ambiguous role of foreign aidin economic development and reform policies.As Rodrik (1996) argued, aid decreases thecost of doing nothing and reduces the cost ofdoing something. It is counterproductive be-cause it delays privatization. But its role isconstructive in the implementation stage be-cause a severe exogenously induced financialcrisis makes it imperative to undertake eco-nomic reforms and structural adjustment, andforeign assistance facilitates the transition.Our results regarding aid and privatizationare similar to those of the World Bank�s re-cent research on foreign aid and economic

reform in Africa (Devarajan, Dollar, &Holmgren, 2001). That study based on 10cases concluded that large amounts of foreignaid to African countries with poor fiscalmanagement actually helps sustain adversepractices and policies. But foreign aid hasplayed a significant role in implementation ofreform programs in the case of sustained re-formers. As Collier (1997) argues, many Af-rican governments accepted aid-for-reformpackages but did not implement the reforms.They mainly used foreign aid to tide them-selves over economic crises and to fund publicexpenditures.Since institutions are so critical to the success

of privatization, we were interested in exploringwhether foreign aid makes a difference incountries with good governance. To estimatethe impact of aid in the presence of goodinstitutions, we included an interaction termof ‘‘AIDGNI and quality of governance(AIDQUAL),’’ ‘‘AIDEXP and quality of gov-ernance (EXPQUAL),’’ ‘‘EDAGDP and qual-ity of governance (EDAQUAL)’’ and‘‘TAGDP and quality of governance (TA-QUAL).’’ For both the implementation attri-butes, AIDQUAL and EXPQUAL have apositive impact (model (7), Table 5; model(7), Table 6). This finding has significant pol-icy relevance for aid donors. The effects of aidon privatization outcomes are strengthenedif the country has well-functioning institu-tional infrastructure that would facilitate theprocess.

(b) Although crisis drives divestiture,it does not sustain it

This result is consistent with Dollar andSvensson�s (2000) argument that reforms pro-posed during an economic recession involvehigher political costs and will be more diffi-cult to implement. Macroeconomic stability issignificant in implementing the privatiza-tion process. The stability provided by fiscalmanagement facilitates the privatization pro-cess––the stock markets are more receptive topublic divestitures––and increases the shareholding by common people. Raising the bud-get surplus as a share of GDP by one percent-age point would increase the number oftransactions by 8%, holding other variablesconstant (model (5), Table 5), and raises the(log) privatization proceeds by 13% (model (5),Table 6).


(c) Right-wing ideology facilitates theprivatization process

Among the political variables, we do not findany systematic evidence for the honeymoonhypothesis as proxied by the ‘‘years in office ofthe executive.’’ There is robust evidence thatsuggests older, more established governmentsprivatize sooner. For example, a year�s increasein power brings privatization event closer by0.03 years (model (8), Table 4). We find asubstantial effect of ideology of the govern-ment on the privatization process. The dummyvariable RIGHTEXEC (right-wing ideology)captures the ideological and political charac-teristics of the government. The ‘‘market ori-ented’’ ideology of conservatives makes it easierto negotiate with stakeholders and convinceinvestors that the process is irreversible. In fact,the incidence rate of undertaking larger numberof privatization transactions is higher by 1.06times or 5.7% in countries where right wingpoliticians control the executive (model (2),Table 5). The effect is stronger if we estimatewith EDAGDP and TAGDP; frequency in-creases by 1.16 and 1.71 times, respectively(models (5) and (8), Table 5), holding othervariables constant.

(d) The size of the SOE sector influences thetiming of privatization

The timing of privatization policy is hastenedby the initial size of the public sector. For in-stance, one percentage point increase in theinitial size of public sector increases the haz-ard by 1.03 times (model (2), Table 4). Thepossibility of using privatization proceeds tostrengthen fiscal conditions in the country ishigh. With respect to implementation, there issome evidence that higher public sector con-centration delays the process. It appears thatinstitutional interests associated with publicproduction are locked in a suboptimal solution,thus adversely affecting the pace of privatiza-tion.

(e) Inadequate institutional structure can propeldivestiture decisions

Countries with superior governance, ethnichomogeneity, and market supporting institu-tions will delay their privatization. One unitincrease in the ‘‘quality of governance’’ indexand ‘‘ethnic tension’’ index decreases the haz-ard by 0.44 times and 0.75 times, respectively

(models (5), and (6), Table 4). Similarly, a 1%rise in ‘‘market capitalization as share of GDP’’decreases the hazard by 0.99 times. We findstrong evidence of institutions playing a facili-tating role in the implementation stage. Forexample, a one-unit increase in ‘‘quality ofgovernance’’ increases the expected number ofprivatization transactions by 52%, holding allother variables constant (model (5), Table 5)and one unit increase in the indicator raises the(log) privatization proceeds by 0.9 units (model(5), Table 6). The ex-ante expectation about thebenefits is high, but the implementation of theprocess requires an adequate social infrastruc-ture and makes the privatization program morecredible and feasible. The results empiricallysupport Nellis�s (1999) argument that privati-zation can lead to stagnation and decapital-ization rather than financial efficiency when it isundertaken in an institutional vacuum. Priv-atization alone does not deliver positive resultsif the country does not have well-functioninglegal and administrative institutions, such asthose that enforce property rights and regulatecapital markets. Interestingly, existence ofstronger market-supporting institutions in theprevious year puts less pressure on govern-ments to privatize in the present year, althoughthe effect is negligible. For example, for everypercentage point increase in lagged value ofmarket capitalization as a share of GDP, acountry�s predicted number of annual transac-tions decreases by less than 1%, holding allother variables constant.

(f) Social inequality and agrarian structurehasten privatization

It is possible that governments in countrieswith more unequal societies use privatization asa way to raise resources to spend on other basicservices, such as health and education. On theother hand, it may be that power is concen-trated in a few hands in unequal societies andthat these dominant groups undertake privati-zation transactions without much opposition.A one unit increase in the gini coefficient in-creases the hazard by 1.08 times (model (5),Table 4). More agrarian economies privatizesooner, but receive less for their transactions.One standard deviation increase in agricultureas a share of GDP, or 9.7%, results in a 0.6years decrease in the time until privatization(model (5), Table 4). Since agriculture is usuallylabor-intensive in developing countries, the like-lihood of the existence of supporting-market


institutions to raise debt and equity capital islow. Consequently, large numbers of transac-tions do not translate into large privatizationproceeds. A one percentage point increase inagriculture as a share of GDP reduces the pri-vatization intensity negligibly by 0.1% (model(1), Table 6).

(g) Democracy is an ambiguous variable in theprivatization process

Recent research points to inconclusive evi-dence about the relationship between formaldemocratic institutions and good economicpolicy (Devarajan et al., 2001). Successful re-formers encourage a broad consultative processto arrive at a consensus on economic reforms,but this can happen irrespective of the type ofpolitical structure. Authoritarian regimes inGhana and Uganda adopted privatizationwhile those in Nigeria and Zaire did not. Sim-ilarly, democratic governments in Zambia andIndia have struggled with economic reforms.We find strong evidence of democratic gov-ernments privatizing sooner. Hazard increases1.19 times for each unit increase in the de-mocracy indicator or one standard deviationincrease in the indicator hastens privatizationby 0.48 years (model (5), Table 4). But de-mocracy seems to impact adversely the numberof privatization transactions and the value re-ceived from them. The burden of involvingstakeholders delays the pace of the process. Forinstance, a one-unit increase in the demo-cracy indicator reduces the expected fre-quency of transactions by 0.82 times or 18%,holding other variables constant (model (5),Table 5).

(h) Education and geographical location impactprivatization decisions

Consistent with expectations, education as adevelopment indicator has significant impacton the privatization process. Although coun-tries with larger illiterate populations privatizesooner, they experience more delays in imple-mentation. Divestiture requires the servicesof investment bankers and lawyers, amongothers, who are less likely to be available ineconomies with a large proportion of illiter-ate population. We find interesting evidenceof the regional effects on privatization. Coun-tries located in Asia have privatized soonerthan those in Latin America and Africa––Tur-key, Malaysia, and Bangladesh, for example,

were early pioneers. Asia also appears tohave privatized more frequently compared toAfrica.

7. CONCLUSION

International assistance organizations con-tinue to see privatization as an important in-strument, along with programs promotingprivate enterprise development, to help devel-oping countries accelerate economic growth(World Bank, 2002). Governments in Asia,Africa, and Latin America are likely to con-tinue to privatize SOEs in the future. The im-pact that foreign aid donors can have onprivatization, however, seems to be limited andcan only be increased by carefully targetingtechnical assistance based on an understandingof the conditions under which governments arewilling and able to privatize.Our analysis confirms that the announcement

of a privatization policy and its implementationare two distinct processes. There is no consis-tent evidence of foreign aid being a significantdeterminant in the decision of governments inthe 35 countries we studied to privatize, buttechnical assistance plays a significant positiverole in the implementation of privatizationprocess. TA has an unobtrusive but critical rolein the countries� economic reform program;most countries need technical support in exe-cuting the transactions. Technical assistancecan have an influence on the privatizationprocess in countries that have, or are willing tocreate, strong institutional infrastructure tosupport market systems. We also found robustevidence of foreign aid playing a constructiverole in privatization in the presence of superiorgovernance structures.Privatization is, fundamentally, a political

decision based on government leaders� assess-ments of the political net benefits of makingpolicy changes. Our results indicate that gov-ernments are most likely to adopt and imple-ment privatization policies immediately after asevere economic or financial crisis and incountries where the public sector is a large partof the national economy. Using foreign aid topressure developing countries to adopt demo-cratic political processes may have little or noeffect on the government�s decision to privatize.Although governments in countries with highlevels of social inequality may be prone toprivatize when the political costs of not doingso become high enough, and in those where


institutions are still weak, the success of aid inhelping them to implement the process dependson providing resources that help offset the im-

mediate negative impacts of change on the poorand on quickly building strong market-sup-porting institutions.

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Does Foreign Aid Promote Privatization? Empirical Evidence from Developing Countries

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