metadata: authors: 0: author: Chong, Alberto
1: author: Lopez-de-Silanes, Florencio
docna: 0: docna: Privatization and labor force restructuring
            around the world
docty: Policy Research Working Paper
subtopic: Public Sector Management and Reform,State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,Gender and Development,Privatization,Energy Privatization,Democratic Government,Economics and Finance of Public
            Institution Development,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Health, Nutrition and Population
abstracts: cdata!: Some critics of privatization argue that
            poor labor force restructuring is a key concern and that
            governments should establish better retrenchment programs.
            Using new data from a sample of 400 companies in the world,
            Chong and Lopez-de-Silanes test competing theories about the
            wisdom of retrenchment programs and their effect on prices
            paid by buyers, and rehiring policies by private owners
            after privatization. The results show that adverse selection
            plagues retrenchment programs carried out by governments
            before privatization. Controlling for endogeneity, several
            labor retrenchment policies yield a negative impact on net
            privatization prices. In confirmation of the adverse
            selection argument, various types of voluntary downsizing
            lead to a higher frequency of rehiring of the same workers
            by the new private owners. Compulsory skill-based programs
            are the only type of program that is marginally associated
            with higher prices and lower rehiring rates after
            privatization, but the political and economic costs of this
            policy may make it somewhat impractical. While a qualified
            non-intervention policy appears to be the safest bet in
            labor retrenchment before privatization, another one might
            be to set up a social safety net or labor reallocation
            program before privatization, and then let the new private
            owners decide who is redundant and who is not. Setting up
            the program before privatization may help with the political
            viability of the process and letting the new owners manage
            the retrenchment may help avoid adverse selection.
repnme: repnme: Privatization and labor force restructuring
            around the world
display_title: Privatization and labor force
            restructuring around the world
listing_relative_url: /research/2002/09/2018944/privatization-labor-force-restructuring-around-world
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Human Resources m1326304 1211,State Owned Enterprises m1326323 721,Privatization m1327236 1283,Gender m1327621 615
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population
origu: Development Research Group (DECRG)
no_of_pages: 48
model_extractions: | Poi icr RESEA1RCH \VWOkKIN(; PAiPHl 2884

# **Abstract**

SoImle critics of privatization argue that poor<sup>laborforce</sup> restructuring is a key concer-ni and th;at<sup>goverinimlelits</sup> should establishi better retrenichliimenit programs.<sup>Using</sup> ncw data from a sample of 400 companies<sup>inthe world,</sup> Chong anid l-6pez-de-Silanes test competinig<sup>thcories</sup> about the wisdom of retrencilhimenlt programs<sup>andtheir</sup> cffect on prices paid by buyers, and rehilrinlg<sup>policies hb</sup> private owners after- privatizatiotn. Thc<sup>restilts showthat</sup> adverse sccctioni plagues retrenchlimlenit<sup>programscarried</sup> **Out** by governments before privatization.<sup>Controllingfor</sup> cndogeneitv, several labor retrnchiclimienit<sup>policiesyielda</sup> negative imiipact oln net privatization<sup>priccs. In</sup> confirimiationi of the adverse selectioni<sup>argUmient,various</sup> types of voluIntary downsizing lead to a<sup>highierfrequelicy</sup> of rehil-inig of the same wvorkcrs by the<sup>newprivate</sup> owners. Compulsorv skill-bascd programs<sup>aretheonlI</sup> type of program that is miarginally associated<sup>withhigher</sup> prices and lower rchirinig rates after privatization,<sup>butthe</sup> political and economilic costs of this policy<sup>mav' makeit</sup> somewhat impractical. While<sup>a</sup> qualified<sup>**non-**</sup> intervention policy appears to be the<sup>safestbetinlabor</sup> retrcichimlenit before privatization, another<sup>one might be</sup> to set up a social safety' net or labor reallocation<sup>program</sup> hefore privatization, and theln let the new<sup>private owners</sup> decidle who is redulindant and who<sup>is</sup> not.<sup>Settingupthe</sup> program before privatization **may** hlelp with<sup>thepolitical</sup> viability of tllc process and letting the<sup>newvowyners</sup> manage the retrenchmienit may help<sup>avoidadverse</sup> selection.

I his paper-a prodtict of PulliC Services,<sup>DevelopmcnitResearchGrouIp-ispartofalargereffortinthegrouptO</sup> understand the labor implicationis of public<sup>sectorreform. Thestudy wasfundedbythieBank'sResearchSupport BLudget</sup> ulider the rescarch project "Ptublic Sector Downsizing"<sup>(Rl'O 683-69). Copics of thc paper are available freefrom the World</sup> 13ank, 18 18 H Street NW, Washinigtoni DCl<sup>20433.Pleasecontact Hedy Sladovicil,roomMC3-3 11,tclephonie 202-473-</sup> 7698, fax 202-522- 1154, email address<sup>lsladovichi(L/gxvorldbanik.org.PolicyResearchWorkingPapersarealsopostedon</sup> th'e Web at http ://ccon .worldhank. org.<sup>Theautlorsm11av</sup> be contacted at albertoch( )iadb. org<sup>or</sup> florcicio.lopczdcsilanesC(iyale.eduL. September<sup>2002.(40pages)</sup>

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Plroduced by the Research Advisory Staff
# Privatization and Labor Force Restructuring

# **around** the World

## Alberto Chong

Inter-American Development Bank and Georgetown University Public Policy Institute Albertoch(Iadb.Org Florencio Lopez -de-Silanes Yale University School of Management and National Bureau of Economic Research florencio.lopezdesilanes@yale.edu We are grateful to Alexander Dyck, Martin Rama, Jaime<sup>Saavedra, and seminar</sup> participants at a conference at Fondazione Eni Enrico Mattei,<sup>and theLatin Americanand</sup> Caribbean Economic Association Meeting for comments<sup>and suggestions.VirgilioGaldo</sup> and Alice Hong provided research assistance and Madison<sup>Boeker providedediting.The</sup> Research Committee of the World Bank supported this project<sup>throughgrant RPO683-69.</sup> The Yale School of Management provided support<sup>throughitsInternationalInstitute of</sup> Corporate Governance.
# **1. Introduction**

In the last two decades many countries have embarked<sup>on major privatizationprograms,</sup> but there are still several countries that have been reluctant<sup>to privatize.This is</sup> particularly true in developing countries, as reflected by<sup>thefact that state-owned</sup> enterprises in these countries still account for more than<sup>10 percentof gross domestic</sup> product, 20 percent of investment, and about 5 percent of<sup>formal employment(Kikeri</sup> 1999). While there is growing evidence of the benefits<sup>of privatization (for example</sup> Megginson and Netter 2001, La Porta and L6pez-de-Silanes<sup>1999), the unwillingnessto</sup> privatize appears to be associated with a rather negative<sup>perceptionof the privatization</sup> process with respect to the labor force. Critics argue that<sup>poor labor forcerestructuring is a</sup> key concern and that governments should establish better<sup>retrenchmentprograms.</sup> On the other hand, opponents of the government's restructuring<sup>of to-be-privatized</sup> firns argue that public unions can influence the future of<sup>politicians, effectivelyreducing</sup> the government's bargaining power (Freeman 1986;<sup>L6pez-de-Silanes,Shleiferand</sup> Vishny 1997). It has also been argued that it is not worth<sup>spending resourcesin</sup> restructuring the labor force before privatization, as<sup>governmentsmay not be able to</sup> distinguish the particular workers that should be retained<sup>(Haltiwangerand Singh1999,</sup> Rama 1999). Govermnents that administer human resources<sup>risk retrenching the wrong,</sup> more productive personnel. This may result in the loss of<sup>know-howthat, at a minimum,</sup> may help solve short-run post-privatization efficiency<sup>problemsand,at worst may be</sup> linked with permanent damage to the productive structure<sup>of thefirm.Dismissing the</sup> workers that the new owners would rather keep may not<sup>add valueto thefirm and</sup> consequently may reduce privatization prices. This<sup>is particularly true in developing</sup> countries where available information is even more<sup>lacking(Rama1999).</sup> Although labor restructuring is one of the most difficult<sup>and sensitive issuesin</sup> privatization, the empirical literature available is quite<sup>scarceasa result of thelack of data</sup> to address these issues. Our paper contributes to this literature<sup>in threeways:by creatinga</sup> new cross-country database with detailed information<sup>about labor retrenchmentpolicies</sup> before privatization and labor rehiring efforts after the<sup>firmsgo intoprivate hands;by</sup> documenting the effects of different labor restructuring<sup>policieson the net privatization</sup> prices paid by buyers; and by analyzing worker rehiring<sup>in privatizedfirms thatwere</sup> subjected to various types of retrenchment programs.

The paper pays particular attention to the quality of various<sup>targetingprocesses.In</sup> fact, not all targeting is created equal. From an empirical<sup>perspective,it may well be the</sup> case that a state-owned enterprise that uses targeting<sup>when restructuringlabor prior to</sup> privatization may be able to fetch a higher privatization<sup>price by applyinga targeting</sup> mechanism that may prove very costly afterwards. An<sup>examplemay be the case of age-</sup> biased retrenchment, where retrenchment is targeted on<sup>olderand typically more</sup> expensive workers. Although privatization prices may<sup>be thought toincrease as a resultof</sup> this policy, as the future operation costs of the firm<sup>are expectedto improve, the net</sup> 1
impact on future efficiency is unclear as some of these workers may also have been the more productive, experienced or better trained.

Unlike the previous literature, which focused on the effects of restructuring on prices for the case of Mexico (L6pez-de-Silanes 1997), the nature of our data allows us to look at two complementary slices of the picture before and after privatization across countries. We are able to analyze the impact of a large set of labor policies before privatization, and observe the private firms' reaction in terms of rehiring of the previously fired workers. Our analysis uses these two types of results to provide an evaluation of the price effect of labor restructuring policies and the managerial quality of the downsizing efforts carried out by the governrment before privatization.

The database was constructed by randomly selecting 400 firms privatized between 1982-2000 around the world. We obtained pre- and post-privatization data by sending a detailed questionnaire to the CEO of the privatized firm and accessing privatization files. We followed up with each of the firms and corroborated their answers with several public sources and data for these firms coming from international financial agencies and privatization ministries. The result is a comprehensive cross-country (see Appendix I for a list of countries) database with firm characteristics, detailed labor restructuring policies before privatization, and labor rehiring policies after the firm entered the private sphere.' The basic thrust of our results is that adverse selection plagues retrenchment programs carried out by governments before privatization. Controlling for endogeneity, 2 labor downsizing does not do much in terms of net privatization prices. This finding may appear somewhat counterintuitive, as, according to the conventional wisdom, sellers will always want the government to downsize prior to privatization. However, this is fairly consistent with the political view on prior restructuring before privatization (L6pez-deSilanes 1997). Results also show that after controlling for endogeneity, pay cuts do not increase prices while employment guarantee programs forced on the buyer do carry a significant discount in prices.

To further study the results in the area of downsizing, we focus on the nature of the retrenchment process prior to privatization and its impact on rehiring. Confirming the adverse selection argument, several kinds of voluntary downsizing lead to a higher frequency of rehiring of the same workers by the new private owners.

The only exception is compulsory skill-biased programs as they are marginally associated with higher prices and lower rehiring rates after privatization in some

1. An additional benefit of this data is that it allows us to consider the issue of failed privatizations on prices, rarely treated in the literature. Research on privatization imnplicitly sets aside failed privatizations since data is typically available only for companies that actually attract bidders. This potentially underestimates the imnpact of labor changes for these companies, which are likely the ones that went through more thorough restructuring efforts prior to privatization. Methodologically, this issue is addressed by using a simple non-linear procedure for the entire, truncated, distribution of privatization prices.

2. As defined by the amount that accrues to the government after all costs are taken into account, adjusted by shares sold and divided by average sales during the three years prior to privatization.

2
specifications. One could argue that the nature of<sup>the program itself, typicallybased on</sup> written exarns or panel reviews, may partly explain<sup>these results. Unfortunately,this</sup> policy is one of the most politically difficult to implement<sup>by thegovernment and requires</sup> a tough stance from the authority. Results show that<sup>the managerialquality of the</sup> government may have an impact in the results, but<sup>asa generalpolicy it appearsthat not</sup> much time and effort should be spent on labor restructuring<sup>before privatization.</sup> The paper is organized as follows. The next<sup>section describes the data collection</sup> process and discusses the empirical methodology. In<sup>section 3, we test whetherlabor</sup> restructuring has an impact on privatization prices<sup>and whether such resultshold when</sup> failed privatization and or potential endogeneity<sup>are taken intoaccount.Section 4 extends</sup> our results on prices for specific types of downsizing<sup>measures. Section5 providesa new</sup> look at the effect on prior downsizing by the government<sup>by analyzing the natureof post-</sup> privatization re-hires by private owners. Section<sup>6 concludes.</sup>

# **2.** Data **and** Methodology

Our sample was formed based on a list compiled by<sup>the authors of about1,500</sup> privatizations around the world covering the period<sup>1982-2000.3The two main sources for</sup> this list are the World Bank Privatization database<sup>andPrivatisationInternational,which</sup> together arguably provide the largest source of privatization<sup>transactionsin the world.</sup> From this original list, we selected a random sample<sup>of 400 bigand small firms to whom</sup> we sent a detailed questionnaire designed by the<sup>authors.The questionnaire wasaddressed</sup> to the CEO with a recommendation to direct it to the chief<sup>financial officerand the</sup> director of human resources of the firm.<sup>4</sup> In order to ensure the quality of our data, we employed<sup>four additionalsources.</sup> First, we took advantage of the fact that in several<sup>developing countriesmany</sup> privatizations have been performed as part of structural<sup>adjustmentor otherlending</sup> programs with the support of the World Bank.<sup>Wewereable toaccess a widerange of</sup> World Bank's internal documents to double check and<sup>in some instances,complement the</sup> information collected in our survey. In particular,<sup>we madeextensive use of the World</sup> Bank's electronic Intranet system called _ImageBank,_<sup>whichallowsfullaccess to such</sup> documents.<sup>5</sup> Second, we also made broad use of _NEXIS_<sup>to search for a number of national</sup> 3. We excluded voucher privatizations. As it has been<sup>discussedelsewhere,there are fundamental</sup> differences between such privatization technique and<sup>others,which wouldhave madecomparisons</sup> particularly difficult (Boycko, Shleifer, and Vishny,<sup>1994).</sup> 4. While directors of human resources answered 71.4<sup>percent of the laborpart of thequestionnaires,</sup> public relations managers answered 16.6 percent of<sup>them.Personnelworking at human resources</sup> departments other than managers answered about<sup>5.5percent, and personnelworking at public relations</sup> answered 6.5 percent, usually on behalf of managers.<sup>Financialinformationwastypically provided by the</sup> office of the chief financial officer or, in the case of<sup>smallfimns,by theofficeof the CEO.</sup> 5. Typical World Bank documents include Country<sup>EconomicReports,Staff Appraisal Reports,</sup> President's Reports, Supervision documents, Project<sup>CompletionReports,AuditReports,Operation</sup> Evaluation Studies, and Sector Reports. We covered<sup>about63.4percent of the sample.</sup> 3
and international publications.<sup>6</sup> Third, whenever possible we interviewed officials from the World Bank, the International Monetary Fund, and the Inter-American Development Bank who were directly associated with the privatization programs in different countries.<sup>7</sup> Finally, when necessary, we directly contacted the privatization offices or corresponding ministries (e.g., finance, industry) of each country to request specific pre-privatization information missing.<sup>8</sup> Whenever we found discrepancies we contacted again the national privatization agencies and the firms themselves to clarify the issues.9 We organized the questionnaire in four areas. The first area covered, preprivatization firm characteristics, and asked about sales, assets, profits, liabilities, management changes, and sector of origin. The second area covered pre-privatization labor characteristics and policies, and asked about number of blue and white collar workers, presence and incidence of unions, number of strikes, political affiliation of unions, labor restructuring measures and targets. The third area focused on the privatization process, and in particular, on privatization prices, transaction methods used, shares sold, and foreign participation. Finally, the fourth area included post-privatization labor re-hiring policies. We tried to get both dummy variables and exact number of workers for every possible category. However, we were not able to achieve this, as most respondents did not provide enough numerical information about the workers involved in various retrenchment and rehiring programs, so we settled for dummy variables that tell us if the policies were undertaken or not.'I Table 1 provides definitions of the specific variables that we collected.

Table 2 shows the results of our efforts to gather data. Out of the 400 cases targeted we ended up collecting data for 308 privatizations comprising 84 countries for the period 1982-2000. Of those, 16 are failed privatizations or operations in which preparation for privatization occurred but in which the sale ended up not materializing. As the table indicates, the complete information for our 308 firms accounts for 97.21 percent of total sales. On the other hand, 25 companies comprising about one percent of total sales supplied quite incomplete information that could not be further completed and thus, ended up not being used. Additionally, 26 firms did not respond to our requests, denied the existence of information, or simply refused to provide it. Twenty-two firms, accounting for 0.78 percent of total sales, could not be included since they were liquidated and no longer exist. Finally, nineteen firms could not be included in our sample as they merged 6. To do this, we mainly employed the Wall Street Journal, the Financial Times, Oxford Analytica, and the Economist Intelligence Unit. We covered around 70.1 percent of the sample.

> 7. We also specifically selected firms totaling about 15 percent of the total sample and double or triplecheck most of the information.

8. Using this approach we covered 73.2 percent of the sample.

9. We found most discrepancies in developing countries, in particular Africa and the Middle East. Whenever data led to significant discrepancies that could not be reconciled the firms were eliminated from the sample. This occurred in 6 cases in total, 4 from Africa, 1 from the Middle East, 1 from Asia. These firms were classified under the category firms that supplied incomplete information in Table 2.

4
and no longer keep separate accounting and financial<sup>statements.The pattern in our</sup> resulting sample in terms of both, region, year of privatization,<sup>and sector fits closely with</sup> the compiled list of privatizations of Privatisation<sup>Internationalandthe WorldBank,</sup> particularly, when excluding voucher privatizations.<sup>This leads us to reasonably conclude</sup> that our sample is unbiased.

Figure 1 shows the distribution of our sample by<sup>regionof the world with respect</sup> to the pattern found in the original privatization lists.<sup>Whereas33 percentof the</sup> privatizations in our sample are from Latin America,<sup>8 percent from Asia,21percent from</sup> Africa and the Middle East, 25 percent from developed<sup>countries,and13percentfrom</sup> Transition Economies, the corresponding percentages<sup>for the originallist are 30,10, 20,</sup> 23, and 18 percent, respectively. Similarly, Figure<sup>2comparesthedistribution of</sup> privatizations in our sample and the original list of<sup>privatizations. In both cases,the bulk</sup> of privatizations are between the mid-1990s<sup>and late 1990s,where more than half of</sup> operations in our sample were carried out. Finally,<sup>Figure 3 comparesthe distributionof</sup> privatizations by broad sector category and, as<sup>before,the resulting patternis remarkably</sup> similar in both cases. In fact, in our sample and<sup>inthe originalcompiledlist, privatizations</sup> occur mostly in services, industry and mining, in<sup>that order. I I</sup> Table 3 presents summary statistics for the variables<sup>usedin this paper.The set of</sup> variables is organized according to firm attributes,<sup>privatizationcharacteristics, labor</sup> characteristics, labor-restructuring policies, and some<sup>basic post-privatization hiring</sup> measures. Along the lines of L6pez-de-Silanes (1997)<sup>the net privatization priceis defined</sup> as the amount that accrues to the government after<sup>all privatizationandrestructuring costs</sup> are taken into account, such as government commitments<sup>at the timeof sale,and other</sup> adjustments are made to the sale contract. This number<sup>is adjusted by the percentageof</sup> company shares sold and divided by the average<sup>net salesduring the three years prior to</sup> privatization. The present value of the resulting number<sup>as of December2000 is the</sup> dependent variable employed which is labeled<sup>Net Privatization Price /Sales.'2</sup> The labor-downsizing variable may be viewed<sup>as a basic summarymeasure of</sup> labor restructuring, as it is typically the most widely<sup>employed and most relevant froma</sup> policy perspective. This variable is defined as<sup>a dummy variablethat equals one if the</sup> firm undertook any reduction in the labor force up<sup>to threeyears prior to privatization,and</sup> zero otherwise. We follow Haltiwanger and Singh<sup>(1999) andclassify this variable by</sup> type, as voluntary and compulsory, and by targeting<sup>nature,asage-biased, skill-biased,</sup> 10. In fact, only 26.3 percent of respondents provided<sup>some numericalinfornation.</sup> 11. The World Bank privatization data, which<sup>arefor developingand transition economies,only also</sup> has information on number of shares sold and<sup>foreign participation. When we compareour developing</sup> country sub-sample (231 observations) to the<sup>onefrom theWorld Bankwe also find avery consistent</sup> pattem between both databases.

12. L6pez de Silanes (1997) also uses the firms'<sup>total assets and total liabilitiesto developa so-called</sup> Privatization Q. In our case, such variable was<sup>not possibleto construct.However,asa rough proxy in our</sup> regressions we include a dummy variable that equals<sup>onewhen totalliabilitiesare greaterthan total assets</sup> (see Table I for variable definition).

5
and female-biased downsizing. Table 4 provides a breakdown of our sample along these lines. Voluntary downsizing is defined as any kind of non-compulsory labor downsizing where the worker chooses to leave, typically as a result of a monetary or non-monetary severance compensation package by the firm. Monetary packages are usually given as a function of two variables, wage and seniority. Typically, the older the worker and the higher the current wage the larger the severance package.'<sup>3</sup> Pension enhancements and similar benefits may also be included under this category. Non-monetary packages include any type of in-kind payment that range from training to any other similar enhancements to the safety net intended to help workers that leave, such as food and clothes.'<sup>4</sup> The type of targeting employed, if any, is another useful classification of labor downsizing. As mentioned above, three are considered in this paper: age-biased downsizing, skill-biased downsizing, and gender-biased downsizing. Age-biased downsizing includes any labor cut that used age as a reference. This type of downsizing may be applied in the context of voluntary or involuntary downsizing programs. In fact, while voluntary age-biased downsizing is found in nearly 34 percent of total downsizing cases (and 82 percent of total voluntary cases), this kind of downsizing is not limited to choice as it may be implemented by force, too. In fact, one-half of all the involuntary downsizing cases are age-biased. This is shown in Table 4. The most common age-biased downsizing programs are voluntary early retirement programs through pension enhancements, which, as it implies, target older workers. _1_<sup>_5_</sup> Mandatory retirement of a specific group of older workers is relatively common in developing countries, too.

Skill-biased downsizing includes any labor cut that uses any written or oral method, test, interview, or certification to measure skills or occupational ability as a retrenchment reference. Thus, skill-biased programs restrict the program along detailed occupational or skill groupings (Haltiwanger and Singh 1999). A typical example is to test workers on general or particular labor skills in order to decide whether or not to keep them. In recent years, the classic example of this is the Peruvian Tax Administration in the early 1 990s, which required all workers to pass a written test for workers to keep their jobs. Those workers that did not pass were fired and the new potential workers brought in to replace the old ones also had to pass a written exam. As a consequence of this, turnover in the Tax Agency reached about 30 percent. Another example is the case of the Central Bank of Ecuador. After a disastrous attempt to downsize using voluntary programs, the Central Bank decided to classify all its personnel in three categories: those who were essential for its functioning, those who were clearly redundant, and those for whom it was difficult to tell. This classification was based on the nature of the worker's unit and on the 13. Recent research proposes using compensation schemes based on additional characteristics of the individual or her household (such as education, gender, and others) and not just wage and seniority (Rama 1999; Chong and Rama 2001).

14. This last is not uncommon in African countries.

15. This program typically improves pension benefits if the worker retires earlier than the legal or agreed upon age. It has been applied widely in developing and developed countries.

6
worker's occupation and educational<sup>attainment.Essential workersdid not havean option</sup> to leave (they were ring fenced),<sup>redundantworkers did not have the option tostay, and</sup> the rest were offered a voluntary<sup>separation program (Rama and MacIsaac1999).</sup> Gender-biased retrenchment refers<sup>to laborcuts basedon any implicit or explicit</sup> gender-based indicators. Practically<sup>allgender-biasedretrenchment is female biased.</sup> Since most developed countries<sup>have lawsagainst gender discriminationand in both,</sup> developing and developed countries<sup>such biasis, at leastexplicitly, considered ethically</sup> wrong, we expect this variable<sup>to be biased downwards. Finally,neutral downsizing refers</sup> to any labor cuts that did not include<sup>any of the three target groupsabove.Though in</sup> theory a firm may apply one,<sup>two,or moretargeted downsizingmechanismsat the same</sup> time, interestingly, the overlap of<sup>retrenchment policiesisrelativelysmall in the case of</sup> our sample of firms, as 87 percent<sup>of firms did not apply more than one singlemechanism.</sup> On the other hand, less than 1<sup>percentof firms in our sample appliedskill,age,and</sup> female-biased retrenchment when<sup>downsizingat thesame time.This is alsoshown in</sup> Table **4.16** Roughly 78 percent of our sample<sup>of firmsdid somelabor forcedownsizing,most</sup> of it compulsory, as shown in Table<sup>5.17Such percentageissimilar when looking at</sup> regions.'<sup>8</sup> However, the data show<sup>great variation in the labor cuts before privatization</sup> both in terms of their nature<sup>aswell asacrossregions. For instance, age-biased</sup> downsizing was the more predominant<sup>in our sample,with 49 percentof firms usingit. On</sup> the other hand, skill-biased retrenchment<sup>is used by only 13percentof state-owned</sup> enterprises worldwide, mostly by<sup>developed countries with15percent. Additionally,</sup> Table 5 also includes two other<sup>labor restructuringpolicies,employmentguarantees(after</sup> privatization) and pay cuts (prior<sup>to privatization). Theseare dummy variablesthat capture</sup> whether a specific firm proceeded<sup>with the mentioned policy inany of the three years that</sup> preceded privatization and, as their<sup>namesindicate, their interpretationisstraightforward.</sup> Table 6 provides some simple<sup>correlationof our labor downsizingmeasures.Two</sup> things stand out. First, the general<sup>downsizing measure is, as expected,correlated withits</sup> components, in particular, voluntary<sup>downsizing.Second,the voluntarydownsizing</sup> measure is significantly correlated<sup>with age-biased downsizing,as it is frequentto find</sup> as a<sup>downsizing mechanism. However,other than that,there is</sup> early retirement programs little significant correlation among<sup>downsizing measures.</sup> 16. Also, while voluntary and compulsory<sup>downsizing may have been used simultaneously,asthe</sup> exarmple in the Central Bank of Ecuador<sup>aboveshows,this wasan extremelyuncommon occurrence</sup> according to our sample. In the three<sup>instanceswherethis occurredwe treated oneastwo separateepisodes,</sup> and in the others we chose the predominant<sup>downsizing method.</sup> 17. This percentage is strikingly<sup>simnilar tothe onefound by HaltiwangerandSingh (1999)for 41</sup> retrenchment programs in 37 countries<sup>using a mainlycivilsector sample.They alsofind thatcompulsory</sup> downsizing is as predominant<sup>as voluntary downsizing(46 percent in our sample).</sup> 18. In Latin America, Africa,<sup>and industrial countries,82,79, and 79 percentof thefirmsdid some</sup> labor force downsizing, respectively.

7
Table 7 provides a first analysis of the data. We divide the sample into two groups according to whether any labor restructuring did or did not take place in a state-owned enterprise. The table shows the value of the mean and median of the share adjusted net privatization prices of the firms, the difference in net price means and medians, and the t- statistic and z-statistics associated with such difference in means and medians, respectively. Most labor restructuring policies yield statistically significant differences in means and medians. Interestingly, this finding does not provide support to the idea that governments should pursue labor restructuring, quite the opposite. Governments that restructured labor in state-owned enterprises before privatization obtained significantly lower privatization prices in relation with those that did not restructure labor. In particular, governments received _lower_ revenues as a result of labor force downsizing, the key measure. This finding, however, does not consider that other prior restructuring policies may be playing a role, and in particular, do not take into account endogeneity problems. In fact, it may be argued that the firms that downsize are the ones that need to do so as they are the worst performers. According to these results, only labor restructuring through pay cuts yield increased prices, and even in this case the difference in means is only weakly statistically significant.

# **3. Downsizing and Privatization Prices**

> In this section we present regression analysis on the link between labor restructuring policies and privatization prices. Net privatization prices are regressed against a set of variables that has been classified in four groups. The first is firm and privatization characteristics. We use a dummy that equals one when net total liabilities are

> greater than zero for the average of the three years prior to privatization. Similarly, we include a set of dummy variables to take into account the economic sector. 1<sup>9</sup> We also include the percentage of shares sold, a variable that takes into account whether foreign participation was allowed, as well the type of privatization sale, in particular, public offerings and direct sales. The second group includes firm labor characteristics, as

> reflected by the presence of unions and the existence of strikes and related physical protests on the last three years before privatization. The third group reflects laborrestructuring policies applied prior to privatization, namely, employment guarantees, pay

> cuts, and labor cuts, including whether such downsizing was compulsory or voluntary, and whether there are any skills, age, or gender bias in the labor downsizing operation. Finally, the last group includes country-specific macroeconomic variables, in particular,

> the gross domestic product, and the rate of inflation.<sup>2 0</sup>

19. These dummies are not reported in the regressions. We considered the following economic sectors: (i) mining (metallic minerals and nonmetallic minerals); (ii) manufacturing (canned fish and seafood; sugar mills; tobacco products; beverages; textiles, clothing, and leather; wood; paper and printing; heavy machinery; transportation equipment); (iii) services (hotels and restaurants; land and sea transportation; communications; and recreation); (iv) others (land; unclassified firms)

> 20. Since the country-specific macroeconornic variables do capture any specific variation among countries in our sample, country dunmmies are excluded when using them and vice versa. Results do not

> change. Also, other macroeconomic variables were considered and results are very similar.

8
The first column in Table 8 presents<sup>our basicresults. We first use a simple</sup> ordinary least squares approach<sup>andassumethat labor-restructuringpolicies are</sup> exogenous. In this case, sixteen<sup>observationsare excluded fromour sampleas they</sup> represent failed operations,<sup>that is, privatizationsof state-ownedenterprisesthatfor one</sup> reason or another did not find<sup>a buyer and consequently have no privatizationprice. With</sup> respect to the first group of variables,<sup>firm andindustry characteristics,we find, as</sup> expected, a negative and statistically<sup>significant coefficientin the case net liabilities.The</sup> result suggests that when net<sup>total liabilitiesare present, the privatization price decrease</sup> by 31 percent (L6pez-de-Silanes<sup>1997).With respectto privatizationcharacteristics,we</sup> find that the coefficient of the<sup>share of thefirm that was privatizedyieldsa negativeand</sup> statistically significant link with<sup>privatization prices.This resultsuggests that an</sup> additional 10 percent of privatized<sup>sharedecreases the privatization price by 3percent.</sup> Additionally, foreign participation<sup>yieldsa positiveand statisticallysignificantsignat1</sup> percent. This result suggests<sup>that allowingforeign participation is associated with a 32</sup> percent increase in privatization<sup>prices. Public offeringsyielda positiveand statistically</sup> significant sign and are associated<sup>with a 19 percent increase in the privatizationprice.</sup> Direct sales yield a negative<sup>and statistically non-significantsign (Dewenterand Malatesta</sup> 1997, L6pez-de-Silanes 1997).<sup>With respectto laborcharacteristicswe find that the</sup> presence of unions up to three<sup>yearsprior to privatizationis associatedwitha privatization</sup> price 25 percent lower, as the<sup>sign of the coefficientis negativeand statistically significant</sup> at 1 percent. Similarly, we<sup>also findthat thestrikesandother formsof physical protestare</sup> negatively linked with privatization<sup>pricesthoughit is not statisticallysignificant.21These</sup> findings are similar to the ones<sup>by L6pez-de-Silanes(1997)for thecase of Mexico.22</sup> When focusing on the set of labor<sup>policy variables,our key set of interest, we find</sup> that the downsizing summary<sup>measureis associatedwith a privatization price8 percent</sup> lower, as the sign of the coefficient<sup>is negativeand statistically significantat 5 percent.</sup> Again this is under the assumption<sup>of exogeneityand when excludingfailed</sup> privatizations. Similarly, we<sup>findthat using employment guaranteesprior to privatization</sup> is linked with a privatization<sup>price16 percentlower,as the correspondingsign is negative</sup> and statistically significant at 1<sup>percent.If maximizing revenuesis the soleobjectiveof</sup> policymakers, applying this<sup>kind of policy contradictssuch anobjective.2 3On the other</sup> hand, pay cuts prior to privatization<sup>yielda negativesign that is not statistically</sup> significant.

21. Since unions and strikes are<sup>relativelyhighly correlated it is nota surprise that the latter yields a</sup> statistically non-significant coefficient.<sup>When excludingthe unionsvariableor constructinga combined</sup> unions-strikes variable the signs<sup>are negativeand statistically significant at1 percent.</sup> 22. They are consistent with the<sup>politicalview of labor restructuringby which unions may try to block</sup> privatizations which are costly<sup>to buyers(Shleiferand Vishny1994;Lopez-de-Silanes, Shleifer and Vishny</sup> 1997; Boycko, Shleifer, and<sup>Vishny1996).</sup> 23. However, governments frequently<sup>havemore than one, and frequently contradictoryobjectives.</sup> The value of this finding from<sup>a policyperspectiveis, perhaps,to make policymakersaware that there</sup> appears to be a trade-off between<sup>objectives andtheir cost.</sup> 9
> The second column in Table 8 provides results when failed privatizations, but not endogeneity, are taken into account. As mentioned above, we use tobits (censored below

> at zero) to include observations from failed privatizations from which we did obtain labor restructuring. Our findings using this technique to account for failed privatizations are similar to our previous results. For instance, we find unions associated with a privatization price 25 percent lower, as the sign of the coefficient is negative and statistically significant at 1 percent. Similarly, the downsizing variable is associated with a 12 percent decrease in privatization prices, as the sign of coefficient is negative and statistically significant at 5

> percent. Finally, employment guarantees are associated with a privatization price 18 percent lower. In general, the results provided so far support the idea that labor force restructuring, and more importantly, labor retrenchment _decrease_ privatization prices. A

> problem with the empirical results above is, however, that they do not take into account potential endogeneity problems. This may arise as governments try to restructure the labor

> force of the state-owned enterprises before the sale in order to raise the privatization price. The negative sign may be simply a reflection that the firms in the worst shape are shedding labor. For instance, if the unobservable characteristics of a firm are positively

> correlated with the presence of strong unions, the government may be particularly interested in dismantle such union.

Using a method by L6pez-de-Silanes (1997) we apply a two-step instrumental variables approach by estimating a non-linear reduced-form equation that describes the probability that a particular labor restructuring policy may be implemented.<sup>2 4</sup> The instruments used are classified in two groups, macroeconomic-level determinants and firm-level determinants. The macroeconomic variables considered are (1) the average growth rate in the three years prior to privatization, (2) the average unemployment rate three years prior to privatization, (3) the average fiscal deficits over gross domestic product three years prior to privatization, (4) the size of the public sector, (5) openness, (6) law origin, and (7) continental dummies. The firm-level variables included are (1) a dummy variable to reflect whether the firm had profits greater than zero in any of the three years prior to privatization, (2) the presence of a leading agent bank in the country, (3) management change, (4) political affiliation of unions, and (5) sector variables. In general, these variables correspond with the variables employed in L6pez-de-Silanes (1997). As required in this procedure, none of these variables is statistically significant when included in the price equation. Also the F-statistic for the excluded instruments is statistically significant at 1 percent. Appendix 2 shows the first stage probit for the case of the key labor downsizing summary measure.<sup>25</sup> The set of instruments used for each laborrestructuring variable is shown in Table 9.

> 24. These variables are excluded instruments, as they are not included in the privatization price

> equation. This instruments have very low statistical power when included directly in the price equation, but

> they are highly correlated with the labor restructuring actions of the firrn, as shown by applying F-statistics

> to test for the joint hypothesis that they are all equal to zero (Lopez-de-Silanes 1997).

25. Because of space considerations, the first stage for all the other labor restructuring measures and for the fn-rm labor conditions are not presented. We would be happy to provide them upon request.

10
The third and fourth columns<sup>in Table8 presentour findings when correctingfor</sup> endogeneity using the method<sup>above.Theformer excludesfailed privatizations, while the</sup> latter includes them. In general,<sup>the results do not differ much with respectto previous</sup> results presented. In terms of<sup>firm characteristics,nettotal liabilitiesare negativeand</sup> statistically significant at 1 percent.<sup>This resultsuggeststhat when net total liabilitiesare</sup> greater than zero, the privatization<sup>pricedecreases by 29 percent.26With respect to</sup> privatization characteristics<sup>theresultsare very similar to the regressionsassuming</sup> exogenous independent variables.<sup>27On the other hand, the coefficientsof the labor</sup> characteristics variables are<sup>always negative and statisticallysignificant in the case of</sup> unions. This result suggests<sup>that unions are associatedwith a privatizationprice 25 percent</sup> lower. Again, these results are<sup>identicalto the non-instrumentedfindings.With respect to</sup> our key group of variables of<sup>interest,labor-restructuring policies, we find that even</sup> though the variables keep the<sup>samesigns asthe non-instrumentedregressions,two out of</sup> the three variables included<sup>(in particular,the summary downsizing measure) yield no</sup> statistical significance. These<sup>findings indicate that when controllingfor endogeneity,</sup> labor retrenchment policies<sup>do not appear tosignificantly increase privatizationprices.</sup> Moreover, employment guarantees,<sup>the only labor restructuringpolicy that has statistical</sup> power is, as expected, and contrary<sup>tothe aimof policymakers,negativelylinked with net</sup> privatization priced as its presence<sup>is associated with a 22 percent decrease in prices.</sup> While the conventional wisdom<sup>has it that prospective buyers will prefergovernmentsto</sup> get rid of labor before privatization,<sup>our resultsso far show that such may not be the most</sup> adequate policy. As seen above,<sup>our findings assuming exogeneity in the explanatory</sup> variables yield a negative link<sup>with privatizationprices,whereas those taking endogeneity</sup> into account also yield negative<sup>signs, which though non-statistically significantat</sup> conventional levels are barely<sup>so.2 8</sup>

# 4. Voluntary and Targeted Downsizing<sup>and AdverseSelection</sup>

Governments sometimes<sup>intervenein thelabor downsizing process by using</sup> voluntary downsizing schemes<sup>as wellas indicators of skills,age,or gender of thefirm's</sup> workforce. The inclusion of<sup>these variablesinour regressionsareshown in columns5-8in</sup> Table 8, when a dummy accounting<sup>for voluntarydownsizing is includedas an</sup> explanatory variable. Voluntary<sup>downsizingschemesusually accountfor a very large</sup> 26. All the coefficients of economic<sup>sectorshave positivesignsand arenot statisticallysignificantin</sup> the ordinary least square regression.<sup>However,they become statisticallysignificant at10 percent when</sup> including failed privatizations.

27. As in the regressions that<sup>donot control for endogeneity,the direct salevariable changessign</sup> although, as before, it yields no<sup>statistical significance.</sup> 28. These results are actually<sup>similar to thoseby Lopez-de-Silanes (1997)regardinglabor restructuring</sup> in Mexico. In fact, he finds that<sup>laborcuts yield a negative sign at 10 percentstatistical significancewhen</sup> not controlling for endogeneity,<sup>and a positive sign alsoat 10 percentstatisticallysignificance.Both results</sup> are not robust.
> percentage of total labor downsizing (Haltiwanger and Singh 1999).29 The reason for their popularity is simple. Such schemes are politically non-costly, are attractive to workers and thus, by-pass the power of unions, and can be relatively easily designed and administered by governments (Rama 1999, Jeon and Laffont 1999). We find that regardless of the econometric method and inclusion of failed privatizations, this variable yields a negative

> and statistically significant sign.<sup>30</sup> Our results suggest that voluntary downsizing is associated with about a 12 percent decrease in privatization prices. It appears that prospective buyers do not favor labor retrenchment. This negative link may be a reflection of adverse selection, as workers with the best outside prospects will leave and those with

> the worst outside perspectives will tend to stay. Human capital of the firm has deteriorated and the privatization price will reflect this. When the government is in charge of downsizing, and uses voluntary schemes it will tend to separate the wrong workers from their jobs at an excessively high cost (Haltiwanger and Singh 1999, Rama 1999). The

> resulting wrong composition of separations may occur, as governments may not be able to make the adequate decisions or apply proper mechanisms to identify whom to retain and whom to lay-off.

Governments also try to manage the downsizing process by focusing on three downsizing categories that are particularly predominant: age-biased, skill-biased, and female-biased downsizing. As described in the data section, age-biased retrenchment focuses on age as the deciding variable, skill-biased downsizing usually focus on workers along measures of skills, for instance, written tests, and female-biased downsizing focuses on gender as the critical retrenchment factor.<sup>31</sup> Table 10 shows our findings using these measures. Controlling for endogeneity we find that downsizing using age as a benchmark results in a decrease of around 10 percent in privatization prices as the sign of the corresponding coefficients are negative and statistically significant at 1 percent (columns 3 and **4).32** On the other hand, our findings suggest that downsizing using skills is barely positively significant with respect to privatization prices (columns 3 and 4 in Table 10). According to this finding, using skills

> 29. In our sample, for instance, voluntary downsizing accounts for about 41 percent of total downsizing as shown in Table 5.

> 30. The statistical significance ranges from I percent in the simple ordinary least squares case, to 10 percent in the tobit instrumental variables case.

31. Notice, regardless of the legal issues that may preclude the use of gender or age to downsize, firms themselves reported such actions. Also, rule enforcement tends to be much less predominant in developing countries (Knack and Keefer 1995).

32. In the case of age-biased downsizing, ordinary least squares coefficients are statistically significant at 1 percent (columns 1 and 2). In the case of admiinistered downsizing by skills, ordinary least squares yield similar signs as in the instrumental variables methods, but the coefficients are not statistically significant in the former. As we argue above, and as L6pez-de-Silanes (1997) shows, endogeneity is a problem.

12
as a benchmark indicator may increase<sup>prices by 22 percent.When controlling for</sup> endogeneity, the female bias variable<sup>isnegativebutstatistically non-significant. 33</sup> The results above suggest that adverse<sup>selection may bean issuesinceboth</sup> voluntary downsizing and age-biased<sup>downsizingappear toreduce privatizationprices.</sup> Workers that are let go using these<sup>methods are not necessarily the least productive or</sup> least skilled as asymmetric information<sup>theoryshows.Furthermore,thefact thatskill-</sup> biased downsizing yields a positive,<sup>though marginally statistical significantlink with</sup> privatization prices, provides some<sup>further evidence towards this,especially if one</sup> believes that skill-biased retrenchment<sup>better identifies productivefrom unproductive</sup> workers.

We further explore the adverse selection<sup>issue.Whiledownsizing may be</sup> classified as voluntary or compulsory,<sup>when introducinggroup targeting,the</sup> corresponding resulting categories<sup>may be labeledasvoluntary-targeted downsizing and</sup> compulsory-targeted downsizing.<sup>Furthermore,as before,voluntaryor compulsory</sup> targeted downsizing may be age-biased,<sup>skilled-biasedor female-biaseddriven which</sup> results in three voluntary targeted<sup>downsizing categoriesand threecompulsory targeted</sup> downsizing categories (see Table<sup>4). Voluntary targetedresults are shownin Table11and</sup> compulsory targeted results are<sup>shown in Table12.The classic example of voluntaryage-</sup> biased downsizing is early retirement<sup>programs.3 4As Table11shows,voluntaryage-</sup> biased downsizing is negative and<sup>statisticallysignificantatone percentwhen using the</sup> uncorrected method, and negative<sup>and statisticallysignificantat 5 percentin thetwo-step</sup> procedure. This result further confirms<sup>the idea that adverse selectionis a problem when</sup> applying administered downsizing<sup>in thepublic sector.Moreover,thevoluntaryskill-</sup> biased downsizing variable is,<sup>as before, positive butit is non-significant.3 5On the other</sup> hand, Table 12 shows results<sup>when usingcompulsory targeted variables.As the name</sup> implies, in this category there is<sup>no choice elementby worker.Firms simply choose the</sup> workers that will stay and those<sup>that willleaveusing age (for example,older workers),</sup> skills (for example, managers<sup>in each division choosethe mostskilled ones), or gender,as</sup> a retrenchment reference. Though<sup>thesigns in the compulsoryage-biasedvariableand</sup> compulsory skill-biased variable<sup>are similar to our previous results, the age coefficient,</sup> unlike the voluntary targeted<sup>case,is now statistically non-significant.There is no impact</sup> on privatization prices. This result<sup>is consistent with thefact that voluntary programs are</sup> 33. In fact, this variable is never statistically<sup>significant and doeschangesignsdependingon the</sup> methodology employed. People<sup>admittingto gender bias werefew.This is clearly not surprising,as people</sup> may not be truthful for fear of<sup>retaliation.Also,legal and particular society considerationsmay be at issue.</sup> 34. In the skill-biased category finding<sup>a clean example is somewhatmoredifficult. After all, who</sup> would want to take a voluntary<sup>test whenshe will knowit may be used to fireher?Somewhat like theFifth</sup> Amendment though, where, for all<sup>practicalpurposes taldng it is frequently associatedwith a presumption</sup> of guilt, not taking a suggested exam<sup>may exposethe worker toretaliation. While onemay argue that this</sup> mechanism is not really voluntary,<sup>from the perspectiveof fn-ms,ministries, and privatizationagencies-</sup> from whom we mainly got the data from-the<sup>explicitmethod is clearly understood asvoluntary.</sup> 35. While high correlation with the voluntary<sup>age-biased variablemay be a problem,as14out of the</sup> 20 observations that are positive<sup>are also voluntary age-biased(Table 4).</sup> 13
theoretically expected to produce somewhat larger adverse selection problems than compulsory ones (Jeon and Laffont 1999, Levy and McLean 1997, Kahn 1985). In fact, while in voluntary age-biased programs it may be expected that the more productive workers may leave rather than the more unproductive ones, in compulsory programs it is reasonable to expect that the good and the bad will leave and thus, the net effect will tend to cancel out. On the other hand, in the case of the compulsory skill-biased downsizing variable we find that the corresponding coefficient yields a positive and marginally statistically significant coefficient in the two-step Tobit procedure that includes failed privatizations, only. Compulsory exams, as a relatively good objective measure of productivity, may help keep the more productive workers, which is reflected in an increase in privatization prices. From a practical perspective, however, this policy prescription is highly controversial, as its applicability will clearly depend on the political climate of the country.<sup>36</sup> In summary, so far we have found that when controlling for endogeneity labor retrenchment and pay cuts do not have any bearing on net privatization prices. Employment guarantee programs affect prices negatively as their imposition lowers the privatization prices by 16 percent, ceteris paribus. Furthermore, our results show that, if anything, administered labor downsizing appears to produce a selection of the wrong group of workers, the less productive ones as voluntary and age-biased downsizing reduce net privatization prices between 10 and 15 percent, ceteris paribus. In fact, while governments resort to a different array of productivity-identification methods in order to select which workers to fire and which to keep, the evidence above shows that their application results in the opposite effect to the one originally desired, that is, lower privatization prices instead of higher ones. In other words, it appears that the labor downsizing process ends up marred with adverse selection problems, which appears to be reflected in penalized prices as potential buyers suspect that govermnents do not have the capacity to collect adequate information on the productive abilities of workers.

# 5. **Re-Hiring** After **Privatization**

> As much as adverse selection appears to be a reasonable factor why buyers may not be willing to bid higher prices for state-owned enterprises on sale, it may also be the

> case that some other unobserved factor may produce a negative link between labor downsizing and privatization prices. The question is whether there is anything close to a silver bullet that can provide further, robust, evidence on the presence of adverse selection

> in the downsizing process prior to privatization. We believe re-hires, defined as workers that were dismissed prior to privatization but were re-hired after privatization, provide a

> quite reasonable proxy that allows us to understand the pervasiveness of adverse selection

36. In fact, countries where skill-biased programs have been used rather successfully did so under notso-democratic regimes. Two examples in our sample are Chile (Pinochet) and Peru (Fujimori).

14
in the labor downsizing process prior to privatization.37 After all, it is by no means obvious that a firm would need to re-hire a worker that was deemed expendable a relatively short time ago.<sup>38</sup> According to our data nearly 70 percent of firms did hire more personnel after privatization. However, this number is misleading for total increases in personnel after privatization are due not only to new hires of workers not previously associated with the firm, but, as explained above, also with re-hires. While the former may be attributed with the natural progression of privatized firms as an on-going concern, the latter may be reasonably linked with the quality of the downsizing prior to privatization An indication that re-hires may be a useful measure of quality in the downsizing managerial process prior to privatization may be illustrated by studying its relationship with voluntary downsizing as an explanatory variable. As the theory and the empirical evidence on net privatization prices above shows, this latter variable is a primary suspect in producing adverse selection. Using re-hires as the dependent variable, we find that the coefficient of the voluntary downsizing variable is positive and statistically significant at 5 percent. That is, voluntary downsizing before privatization increases the probability of rehiring workers after privatization.<sup>39</sup> Results are shown in the upper panel of Table 13, specification 1. In fact, since typical voluntary downsizing mechanisms are theoretically flawed with adverse selection problems, this finding is not surprising (Jeon and Laffont 1999, Kahn 1985). Workers that leave voluntarily are usually those that have the highest chances of obtaining work outside in less time. They are also the ones that are more able to find better-matched jobs to their abilities and skills outside of the public sector and quasi-public sector.

On the other hand, it may be argued that the incidence of re-hires after privatization may not necessarily reflect the presence of voluntary downsizing but the presence of high labor firing costs and related rigidities. Possible firm buyers may want to take advantage of regulations that allow for a clean slate approach so that state-owned enterprises are permitted to have as many workers as possible retrenched before privatization only to be re-employed by the privatized firm. In this way, high labor costs are avoided while the human capital of the firm is preserved. To test for this idea, two labor cost measures are employed. The first is an indicator of labor rigidities as measured by the extent to which the country has signed agreements with the International Labor Organization (ILO) and comes from Rama and Artecona (2001). This variable is defined as the cumulative number of ILO conventions ratified by the country at the time of privatization and is based on legal documents as compiled by Rama and Artecona.

37. While the concept was first introduced by Haltiwanger and Singh (1999), we are the first to apply rigorous econometric methods using re-hires. We work with re-hires up to 18 months after privatization. We tested shorter periods (12 months) and longer ones (24 months) and the empirical results do not vary.

38. Nearly 35 percent of firms did some re-hiring after privatization, of which Latin America was the most active with upwards of 40 percent, and Asia the least active with a little more than 10 percent.

39. This, when controlling for share sold, sectoral dummies (see footnote 18), macroeconomnic controls (rate of growth, rate of inflation, initial gross domestic product), and continental dunmnies.

15
The second variable is an index of labor firing costs constructed from legislation from Heckman and Pages (2001). This measure summarizes the tenure-severance pay profile using a common set of dismissal probabilities across countries and computes the expected future costs, at the time a worker is hired, of dismissing her in the future (also see Table 1).40 We find that the coefficients of both labor rigidity measures are negative but statistically non-significant using re-hires measures. This is also shown in the upper panel in Table 13. It appears that labor rigidities do not change the probabilities of re-hires when controlling for voluntary downsizing.

Re-hiring after privatization occurred not only at the firm level, but also in some instances, from the very same departments or areas from which the workers had been previously fired. When exploring this more restrictive measure of re-hires as the dependent variable we find that the coefficient of the voluntary downsizing variable is not statistically significant. This is shown in the lower panel in Table 13, specification 2. Data are the likely culprit of this result as only 4.7 percent of the sample re-hired in the same department or area, compared to more than 34 percent that simply re-hired. Thus, not too much weight should be attributed to this result.<sup>4</sup> 1 Additionally, and similar to the re-hires variable, labor rigidity measures yield negative and statistically non-significant coefficients with respect to the re-hires-same variable. This is also shown in the lower panel in Table 13.

Voluntary downsizing is only part of the story. In fact re-hires after privatizations are closely linked with the labor-administered process applied before privatization according to skills, age, and gender. This is shown in fist column in the upper panel of Table 14. Using re-hires as the dependent variable, we find that the age-targeted retrenchment variable yields a positive and statistically significant sign at 1 percent. Agetargeted retrenchment prior to privatization increases the probability of re-hiring by a huge 31 percent after privatization. On the other hand, the skill-targeted coefficient is negative and statistically significant at 5 percent. Skill targeting decreases the probability that firms will employ re-hires after privatization by 16 percent. Finally, the female-biased retrenchment is positive but it is not statistically significant. These results are quite consistent with our findings regarding net privatization prices. In fact, this finding suggests that adverse selection may be a problem as voluntary downsizing increases prices but age-biased downsizing reduces prices and increases the probability of re-hiring after privatization, while skill-biased downsizing marginally increases net privatization prices 40. Since the Heckman and Pages (2001) samnple is relatively limited we also use an alternative measure suggested by them, law origin. They show that French law origin is very highly correlated with labor separation costs. In fact, we find very similar results. We would be happy to provide these additional estimations upon request.

41. Another explanation may be related with the fact that enticing the best workers back after having them fired entails an additional cost. It is not easy to lure good workers back, especially given their potential alternatives outside. Additional pay, perks, or higher position may be necessary. The fact that the more restrictive measure of re-hires after privatization is not significantly linked with voluntary downsizing before privatization is not surprising.

16
and reduces the probability of re-hiring after privatization. Even more revealing, the results above hold when using the more restrictive re-hiring measure. These findings are shown in the lower panel of Table 14. Again, the corresponding coefficient for the agebiased variable is positive and statistically significant at 1 percent while the coefficient of **42** the skill-biased variable is negative and statistically significant at 5 percent.

Similar to the case with voluntary downsizing, the incidence of re-hires after privatization may not necessarily reflect poor management, but high labor firing costs and related rigidities. To explore this issue, we run probit regressions using the same two measures of labor costs used above. This is also shown Table 14. When using ILO conventions as an additional explanatory variable we find that such control is negative but statistically non-significant. Excessive labor costs and regulations do not seem to be a determinant on the probability of re-hires after privatization. Moreover, the signs and statistical significance of the age and skills variables do not change. The age-bias variable is always positive and is statistically significant at 1 percent in both re-hires and re-hiressame. Age-biased downsizing prior to privatization increases the probability of re-hires increase by 32 percent and increases the probability of re-hires in the same department by 16 percent. On the other hand, the skill-bias variable is always negative, implying a lower probability of re-employment as it is statistically significant at 5 percent for both re-hires in general and re-hires from the same department or area in the firm. Skill-biased downsizing prior to privatization is linked with a lower probability of re-employment that ranges between 1 percent (re-hires-same) and 17 percent (general re-hires). Very similar results are obtained when using the Heckman-Pages firing costs variable instead. In fact, this variable is negative but statistically non-significant suggesting that high firing costs do not seem to have a bearing in the probability of re-hires. The signs of the skill-bias and age-bias variables are maintained, as well as their corresponding statistical significance. However, the statistical coefficient of the skill-bias variable decreases to 10 percent in the case of general re-hires.<sup>43</sup> Finally, analogous to the analysis perforned with privatization prices, Tables 15 and 16 provide evidence related with voluntary targeting and compulsory targeting, respectively." According to our results in Table 15, voluntary age-biased downsizing increases the probability of re-hiring between 18.2 percent and 20.1 percent, as the corresponding coefficients in the three specifications presented are positive and 42. As before the coefficient of the female-biased variable is positive but it is not statistically significant.

43. We also use data for temporary workers, defined as those workers that were downsized prior to privatization but were re-hired after privatization on a temporary basis, presumably in order to take advantage of lower labor costs. As expected, we find that both the ILO and firing cost variables increase the probability of ternporary hiring. Also, we find that voluntary downsizing increases the probability of temporary hiring which is also somewhat consistent with the idea that adverse selection may be present.

44. Lack of enough observations did no allow us to provide further evidence using re-hires same as the dependent variable for these two tables.

17
statistically significant.<sup>45</sup> This finding further provides evidence of adverse selection. On the other hand, voluntary skill-biased downsizing yields the expected, negative sign, as before but in this case the corresponding coefficients are statistically non-significant. Similarly, Table 16 shows that compulsory age-biased downsizing does imply a higher probability of re-hiring as the corresponding signs are positive and statistically significant at 5 percent or better regardless of the specification. When labor downsizing was done according to age and in a compulsory manner, the probability of being re-hired will increase between 19 and 22 percent after privatization. Similarly, compulsory skill-biased downsizing prior to privatization appears to lower the probability of re-hiring after privatization. The results in this case are, however, not very clean as the skill-biased measure becomes barely significant when including labor costs (Heckmnan-Pages measure) or not significant at all (ILO conventions case).

In summary, our findings with respect to rehiring policies are consistent with the adverse selection hypothesis in labor restructuring by the government before privatization. What governments do before privatization does have a direct bearing on how firms behave after privatization. Voluntary downsizing is associated with a 13-15 percent higher probability of re-hiring workers that were previously fired prior to privatization. Agebiased downsizing yields a higher probability of re-hiring workers, which in the case of voluntary age-biased downsizing reaches around 20 percent, and in the case of compulsory age-biased downsizing reaches around 22 percent, sometimes re-hiring workers even in the same exact departments. The one exception to these results is skillbased downsizing which in some specifications leads to significantly lower rehiring rates by private firms.

# **6. Conclusions**

Despite its importance, labor has probably been the single least addressed issue in privatization (Megginson and Netter, 2001). There is ambivalence with respect to the optimal policy approach to labor restructuring in privatization processes as reflected by the recommendations of development agencies around the world. In fact, such institutions have had a difficult time taking a position on whether or not it is a good idea to restructure a firm and, in particular, how to deal with labor force changes prior to privatization. Early advice called for labor restructuring prior to privatization under the premise that governments are better able to cushion any financial blow to displaced workers mainly through safety nets (Nellis and Kikeri 1989). Subsequent recommendations called for a less meddlesome approach by discriminating between large firms and smaller ones. It was suggested that smaller firms with relatively little overstaffing were sold with essentially no labor restructuring, under the logic that such a decision should be left to the new owners who would be in better position to choose which workers they would like to retain or dismiss (Kikeri, Nellis, and Shirley 1992). A final view came later when prior

> 45. However, statistical significance using the Heckman-Pages labor firing costs reaches only 10 percent.

18
restructuring in privatization, including the labor area, was found to be associated with lower net privatization prices paid by winners (L6pez-de-Silanes 1997).

This paper is the first to provide a formal cross-country analysis of the effects of a detailed list of labor restructuring measures before privatization to provide guidance for those countries still embarking in the privatization process. The lack of information on what happens to workers during the privatization process has exacerbated the fears and concerns of workers and governments, and delayed privatization in several countries (Kikeri, 1999).

In this paper we address some policy concerns above by testing several competing theories that aim to answer the following key question: should governments restructure labor before privatization, as measured by privatization prices? While as a general principle, getting rid of redundant workers should increase the privatization price, in practice, governments have a very difficult time identifying the genuinely redundant workers as asymmetric information problems remain. Firing the wrong workers may even reduce the privatization price.

Our data allows us to analyze the impact of labor restructuring measures not only in prices, but also in the rehiring policies followed by firms after they are privatized. The benefit of such data is that we are able to say something about the management quality of retrenchment policies followed by governments around the world.

We find that labor retrenchmnent does not significantly impact privatization prices, if anything, voluntary retrenchment has a negative impact on net prices, suggesting a potential problem of adverse selection. Through a detailed analysis of various targeting policies, the paper also shows that government administration of the downsizing process, can be subject to political considerations and may also result in adverse selection leading to rehiring of the same workers after privatization. We did find that it may be possible for governments to achieve some positive results through the managing of the process by using a skill-focused retrenchment. This type of policy is associated with lower probability rates of rehiring of the same workers after privatization. This type of policy is one associated with negative political effects as workers may find it too aggressive and may react negatively towards the whole process of privatization. The fact that the political costs of this type of program are higher, suggests that there may be some not easily observable or measurable firm characteristics that could explain the marginally positive results of this policies. Another reason for this result is the possibility that the level of documentation and design of this type of program simply makes it more palatable to buyers. The political difficulties of using such policy and the problems it might cause in terms of the overall objective of achieving privatization should be considered.

The summary of this paper is that governments should think long and hard before they restructure labor force in preparation for privatization. The political consequences may be large, the impact on privatization prices is not there, and the data on rehiring policies shows that firms where retrenchment takes place may end up losing some of its 19
most valuable workers. A qualified do not intervene appears to be the safest bet in labor retrenchment before privatization.

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The word "processed" describes informally reproduced works that may not be commonly available through library systems.

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21
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22
Figure 1. Distribution of Privatizations by Region (percent) <!-- Start of picture text --> Transition Economies I<br>Developed Countries<br>Africa and Middle East<br>Asia<br>Central and South<br>America<br>0 10 20 30 40<br>* Sample * Original List<br><!-- End of picture text --> _Source:_ Data collected by authors. Original list based<sup>on1,500firms.Samplereflects 308firms.</sup> Figure 2. Accumulated Distribution of Privatizations by Year (percent) <!-- Start of picture text --> 100 -<br>90<br>80-<br>70-<br>60-<br>50 -<br>40-<br>30-<br>20-<br>1 0<br>0*<br>-Sample -Original List<br><!-- End of picture text --> _Source:_ Data collected by authors. Original list based on 1,500 firms.<sup>Sample reflects308 firms.</sup> 23
Figure 3. Distribution of Privatizations by Sector (percent) <!-- Start of picture text --> 60- _-____, ,<br>50-<br>40-<br>30 U~~~~~~~~~~~~ Sample<br>U Original List<br>20-<br>10<br>Mining Industry Services Othersl/<br><!-- End of picture text -->

> 1/ Includes land and unclassified firns.

_Source:_ Data collected by authors. Original list based on 1,500 firms. Sample reflects 308 firms.

24
# Table 1. Description of Variables

Vanable 3 .Description ____ vmo.prce/sale Thf etpeen.se o9f teqn psce of sal to SU S. dollar afteralpsvh o annrsiuoo csts wre take itacotladjust.ed1 by th Percentag o _otpsi'aae od n ree yttlalsbfr tvna Sales Th~~~~~~~~'e net present vaLue 2f!the thre _-year_ avraje.frm sa!les befor pevnaoidn ntd toin olr Net total liabilsoes ~~Deensy instabl equal to 1 if act total liabliies ar gete ta ueo p to in years pro to privaucation. ano0there iccsto I Preprivancanon profits Dsioany vanable e~~~~qual. to I ifthe compnymadeanyuroitp ttheyasprotoprivxatiain an terwise Sector Dsinan~~~~~~~~~~~~~L.y vsial eqalt I tO inketot accon for economic sector. The usmssi osdrd ia fr 0<sup>,ssdsy.ad s _eevsc.</sup> Jin respSctively, end 0 otherw-ise (reflecting lan. atw reoreongtutr,orucasid *aagme1 hage Duianry vatableequal tolIif the compay changed CEOupto thee yearsprior toprivatir-atiend 0 otiheris Foreign pararspanon Dunony ~~~vanable equal to I - p earspton wano j5 qprocess. end 0 otherwsse. Shape ofsold DeeUMSagefonsnshaeisdreedattokssa1ccou - - Type of sale Diamny ~~qvuariale equal to I to take into accoutot for mthod~ of p.tv. a on sale. Two dwstrnes arecotdrdoacun forw Pinita pulcofs" and dret (no-compeiie se opcvl.ad0ohrie(elcigothe tnethiods such an Agent bank Diamny variabl~~~~~Eequal toIlaig gn akorga re si ato Procs Leading sgets ban a dfined as bankthat orane ms ptsvazaionsthe cosmaytthotener ofoarreea Agent ialsnea chre of obtamnon informaion on thetawe-own~ed eteeprie ugs etulan esre,adognz h aets UssionsDnm ntbeeult fta a nosot ie es pio to p....rio. and0 etherwise. Poitca.a..iaio oftn. ito ntbeeult sfpo..cal.afititonfutnonisthe smenste dLpolti,cal parthlnked web he dnsg govretoen at th ~tim ofplnatza 'n EAn 0 otherie . Soilces Dra~~~~~~~~~Nnny vasribl-e eqalt !I if there wer a id of pretests. picketing, or stikes upto three years ro to p bioriaso.a Dowassnstg ~~~~~~~~~~~~Durmoy otherwvise. vaiabe Dp!owig.may eulto (sInE-biased~~ I iffirem s,todeetono becasfe dowain.gender s * o mSi oploty.an teLa,f- aose)o mayb penl-iae may targte be a cTcring& to nentl(no p age (agebiasediicular group agtd VobailnY Dowasn `g: Dumanyinst:abluedeua to I if there wa ay kind ofvolintny do-umina toIk labor force three er eot psov natn Oothmwiae VoliatNaY dLownasng ia defind as sa o-compaly worke-bated dcisson dwns nog Typically severac patckege!. peosio ~enhncm nts andoter benth are !re to incentive worskers to laethe fian Age-biased dewensang ~Dusm.isibeeul oIite a n ag-biaed labor downag t he er ro opeaisin tewo Skill-bidew **as** iong ed Dumrmy variable equal to I if ther wa jn kll-biased*labr dwn-,AsUn p t he easpirt p mstrl on0 ohrse. SItl-biased prpram resol the program, along de'Yd cuasoa oranlgoos(aawanger and sw8b. 999 for ensamiple. Female-blase ~~~~~~~thro4. the Pasasi ofFwesCien or oral ema .panel interviews. and so on.Skill-biased pormssay be voharrar or snoints .... d doalig Dnnyvnbeeul oIi hr was any . dIowantong op to tree _years_ peior to p fvsaion. 0 Othcewac. Female-biased progrm ma evltr rivhts ic ytsl hr r oepii polcie onvtIn. thr ted to be r- underrpoenyjs this woulSantcentto tlesi onhgMdo 0iads tob Employment Guarate D-amyv al qalto? I if there was ayp omieo mlyet urne pt treyaspir to pewtiaton 0 therwise pay, Cut D!aemy enmabLe equa to If tlhere was any pa L o [Le _total_ salarv zt ..sg thde welies Icree _ye_ no pn,, so ptIaw_.aLorar.on Re-hoes ~~~~~~~~~~~D-armY vnable equaldto I if the piseased imrkeo-lerd preomounly tE.d we oo (ptothree yersprior topevatin Re-lares same mtoon~~~~~~~~YvarihleeqLo iif th vaid- then r-edpreojoul fired woles(op to three years prio topesan) andP plcdthe wositer inthe same departentorare from which he was orinal firedO."0ohews Law p ostg Gross domestic ... .... prodact .... Infatin Con.inental .tsae _Lga orgne_ o-f! tE- cny From wIc opiyi ej9Ielybsduo iepsil lega ogins cosiere otFc alabcmon Law. French.cad Code Getman1 coe1m1ercial code. Scandiwavian code.,n oshtlw L ot.Lpr Groan.~ domef ~slic pRodct US! c PPP) - inivg.: 1- YAverg ofte-ee year psortostaiztin(WoidBn.20 Aerae f~aon n te eunty tre er f!peter to psvszto (Wosd Bank 200p F Dam.yqualdoIi o ....count for. ollw.. ruonLaaAmerica. Asia. ltddle East, Mfica. Developed Courntes, 0 Sire of the public ector

1.0 Co..n........ ..

Lbr&tig ot unemploytont at Employmenthi th erafra-gove m toh.- " of P5!rsom asAercWNtag of th laborforce. Inldes wOrkers of both aemes C...slT?!" manbe of ntntina Labor 9rgMtiOn (I0 Mcnentions ratifed by the coutry at the time of piairts Meanr tha **esuarnanies** the tonureseverance--L......................_. _ypoeug!~q qse f pAOit!ciscunis Temaur _comuputS_ thie eapectod Oiflure cost, a the time a woelcer is hired. of diasrf g e oteftr u eooal cnmccniin Th idn is consteucted to _include only_ firigcssta fette' dcso' ttemrl.I include the cost of proiigstoe adac oteat everance Pgy ?onMtina oneach psileeveoftotnsa ~ worker cmattsn in **t** he hfutur Mea,ore thus Averageuemlymn rate of thounuy' thee year -I.prior t1o Pr..v.....tio (WodBak20T,R aanAten.201 _25_
TabRe 2. Observations in the Sanmpile

||_NumberPercentagefsales_|
|---|---|
|Firms inourfinalsample|308<br>97.21|
|Firmsthat suppliedincomplete information|25<br>1.04|
|Firmsmergedandkeepnoindependent records|19<br>0.12|
|Firmsthat were liquidatedannolonger exist|22<br>0.78|
|Firmsthat deniedorrefused togiveinformation|26<br>0.85|
|AllPrivatizod Firms(1982-200|400<br>100.00|

_Note:_ This table breaks our world samnple between 1982 and 2000 into two groups. For each group we provide the number of firms and the percentage of pre-privatization sales in the total. Source: Data collected by authors.

26
Table 3. Summary Statistics

|Variable|Obs|Mean|Median|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|_FirmCharacteristics:_|||||||
|NetPrivatizationPrices/sales|308|0.587|0.609|3.228|0.000|1.367|
|Sales|308|1.415|0.140|3.167|0.001|21.991|
|Nettotalliabilities|308|0.432|0.000|0.496|0.000|1.000|
|Preprivatizationprofits<br>|308<br>|0.455<br>|0.000<br>|0.499<br>|0.000<br>|1.000<br>|
|Mining|308|0.143|0.000|0.350|0.000|1.000|
|Industry|308|0.231|0.000|0.422|0.000|1.000|
|Services|308|0.558|1.000|0.497|0.000|1.000|
|Managementchange|308|0.449|0.000|0.498|0.000|1.000|
|_PrivatizationCharacteristics:_|||||||
|Foreign participation|308|0.682|1.000|0.467|0.000|1.000|
|Sharesold|308|0.509|0.506|0.282|0.010|1.000|
|Publicoffering|308|0.653|1.000|0.477|0.000|1.000|
|DirectSale|308|0.198|0.000|0.399|0.000|1.000|
|_LaborCharacteristics:_|||||||
|Unions|308|0.844|1.000|0.363|0.000|1.000|
|Strikes|308|0.474|0.000|0.500|0.000|1.000|
|_LaborPolicies:_|||||||
|Downsizing|308|0.782|1.000|0.413|0.000|1.000|
|Voluntary downsizing|308|0.325|0.000|0.469|0.000|1.000|
|Age-biased downsizing|308|0.497|0.000|0.501|0.000|1.000|
|Skill-biased downsizing|308|0.130|0.000|0.337|0.000|1.000|
|Female-biaseddownsizing|308|0.058|0.000|0.235|0.000|1.000|
|Employmentguarantee|308|0.282|0.000|0.451|0.000|1.000|
|Paycut|308|0.075|0.000|0.263|0.000|1.000|
|Re-hiring|292|0.345|0.000|0.475|0.000|1.000|
|Re-hiringSame|292|0.047|0.000|0.321|0.000|1.000|
|_Country-SpecificVariables:_|||||||
|Englishcommonlaw|308|0.253|0.000|0.436|0.000|1.000|
|Frenchcommercial code|308|0.500|0.500|0.501|0.000|1.000|
|Germancommercialcode|308|0.117|0.000|0.322|0.000|1.000|
|Scandinaviancommercialcode|308|0.019|0.000|0.138|0.000|1.000|
|Socialist/communistlaws|308|0.110|0.000|0.314|0.000|1.000|
|Gross domesticproduct|308|25.398|25.452|1.851|19.448|28.856|
|Inflation|308|109.876|11.485|292.683|0.618|1667.207|
|Openess|308|31.137|28.158|31.953|0.000|314.588|
|Economicgrowth|308|3.028|2.726|3.811|-11.144|21.320|
|Fiscaldeficits|308|-2.580|-2.279|3.475|-14.003|13.629|
|ILOconventions|221|54.164|52.000|28.883|1.000|123.000|
|Laborfiringcost|151|2.526|2.718|1.216|0.443|4.756|

27
# Table 4. Decomposition of Labor Downsizing Measures

<!-- Start of picture text --> Downsizing<br>yes no<br>241 67<br>(78. %) (21.6%)<br>Voluntary Compulsory<br>Downsizing Downsizing<br>100 141<br>(41.5%) (58.5%)<br>Neutral Age-biased(^) Sklill-biased(-) Female-biased(-) Age-biased(**) Skill-biased(-") Femalk-biased(-) Neutral<br>5 82 20 7 71 20 9 62<br>(5.0%/) (82.1%) (20%) (7%) (50.4%) (14.2%) (6.3%) (44.0%)<br>Age-biased and skill-biased 14 (14) Age-biased and skill-biased 12 (8.5)<br>Age-biased and female-biased 3 (3) Age-biased and female-biased 5 (3.5)<br>Skill-biased and female-biased 3 (3) Skill-biased and female-biased 3 (2.1)<br>Age-biased, skill-biased and Age-biased, skill-biased and<br>female-biased 0 (0) female-biased 3 (2.1)<br><!-- End of picture text --> _Note:_ This table shows the decomposition of labor downsizing cases in our sample in terms of cases and percentages (in parenthesis). Downsizing may be voluntary or compulsory (non-voluntary). Additionally, it mnay be classified according to its targeting nature as age-biased, skill-biased, and femalebiased. Thus, voluntary and compulsory downsizing may be targeted. Since one firm may opt to pursue more than one targeted downsizing method, biases do not add up to 100 percent. For instance, one firm may concurrently pursue age-biased downsizing and skill-biased downsizing in its downsizing program. The corresponding numbers for all the possible combinations are shown in (*) and (**).

28
**Table 5. Labor Restructuring Measures around the World**

(percent)

||_Latin_<br>_America_|_Asia_|_Africaand_<br>_MiddleEast_|_Developed_<br>_Countries_|_Transition_<br>_Economies_|_All_|
|---|---|---|---|---|---|---|
|Downsizing|82.2|58.3|79.7|79.2|76.2|78.2|
|Voluntarydownsizing|32.5|12.5|45.3|28.6|14.3|32.5|
|Age-biased downsizing|57.4|29.2|54.7|54.5|26.6|49.7|
|Skill-biaseddownsizing|12.5|13.9|9.4|15.6|11.9|13.0|
|Female-biaseddownsizing|5.0|8.3|14.1|0.0|4.8|5.8|
|Employmentguarantee|8.4|20.1|51.6|13.0|52.4|28.2|
|Paycut|8.9|0.0|1.6|13.0|7.1|7.5|
|Sample|32.8|7.8|20.8|25.0|13.6|100.0|

_Source:_ Data collected by authors.

**Table 6. Simple Correlation of Labor Downsizing Measures**

<!-- Start of picture text --> ..-z Volunamy AWaid ill-biased Fm1e-biased rrploypmit<br>downsang domn7g doiWmng domsizing gatee<br>Dmwsizng<br>VoluitarydofiziZng 0.36568 1<br>A4biaged dwnsizitg 0.5239a 0.4483e 1<br>Skill-biaseddoWMiZiDg 0.20373 0.1447 0.1184 1<br>Fenmle-biadowning 0.0643 0.0342 -0.0261 0.1508 1<br>Enployrnt garantee 0.1036 0.027 0.0113 0.0365 0.2126e 1<br>PNav c -0.0456 -00099 -0.009 -0.0584 -0.0803 -0.0788 1<br><!-- End of picture text -->

**a** Significant at 1 percent, **b** Significant at _5_ percent., **C** Significant at 10 percent.

29
Table 7. Labor Restructuring and Privatization Prices:<sup>Testsof Meansand Medians</sup>

|||**SOEswhere**<br>**measurewas**<br>**taken**<br>**(a)**|**SOEswhere**<br>**measurewas**<br>**nottaken**<br>**(b)**|**Difference**<br>**(a)-(b)**|**T-statisticforchange**<br>**inmean**<br>**Z-staftsticforchange**<br>**inmedian2V**|
|---|---|---|---|---|---|
|Downsizing||||||
||mean|0.5532|0.7085|-0.1552<br>|3.547**a**<br>|
||median|0.5711|0.7070|-0.1360|3.576**a**|
|Voluntarydownsizing||||||
||mean|0.4818|0.6376|-0.1557|4.064**a**|
||median|0.4716|0.6259|-0.1543|3.909**a**|
|Age-biaseddownsizing||||||
||mean|0.5265|0.6467|-0.1202|3.320**a**|
||median|0.5136|0.6320|-0.1184|3.184a|
|Skill-biaseddownsizing||||||
||mean<br>|0.5616<br>|0.5908<br>|-0.0292<br>|0.534<br>|
||median|0.6074|0.6157|-0.0083|0.371|
|Female-biaseddownsizing||||||
||mean|0.3533|0.6015|-0.2482|3.213**a**|
||median|0.3765|0.6150|-0.2385|2.977**a**|
|Employment guarantee||||||
||mean|0.4200|0.6496|-0.2296|5.853**a**|
||median|0.3664|0.6508|-0.2844|6.936**a**|
|Paycut||||||
||mean|0.6893|0.5787|0.1106|-1.585**c**|
||median|0.7424|0.6006|0.1417|-1.725**c**|

_Note:_ Table 7 reports mean and median values of the privatization<sup>price/salesinthe groupof firms both</sup> where the labor restructuring measure was taken compared to<sup>those firmswherethe labor restructuring</sup> measure was not taken. The particular measure taken is indicated<sup>in the lines (downsizing,voluntary</sup> downsizing, age-biased downsizing...) The third column<sup>showsthedifference in meanand medians</sup> between the net privatization price of the group of firms<sup>that took themeasurecompared tothe group that</sup> did not. The fourth column reports the resulting t-statistics<sup>and z-statisticsof the difference inmeans and</sup> medians of the two groups respectively. I/ T-test for<sup>Hoabout differencebetween means.Unequal N's2/ Z-</sup> test for Ho about difference between medians. Unequal<sup>N's.(Wilcoxon rank sum).</sup><sup>**a**significantatIpercent;</sup> **b** significant at 5 percent; ' significant at 10 percent 30
# Table 8. Labor Restructuring and Privatization Prices

||Dependentvar|iableisn|etprivati|zationpr|ice/sales||||
|---|---|---|---|---|---|---|---|---|
|Variables|||Two-Stepp|rocedure|||Two-Step p|rocedure|
||OLS|TOBIT|<br>OLS|<br>TOBIT|OLS|TOJIT|<br>OLS|<br>TO81T|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(S)|
|**_1.-_**_Firmandprivalizationchatac_|_teristics:_||||||||
|Nettotalliabilities|-0.0903|-0.0918b|-0.2113|-0.2142|-0.0887|-0.0897|-0.1455|-0.145|
||(0.043)|(0.044)|(0.072)|(0.062)|(0.041)|(0.043)|(0.057)|(0.053)|
|Share Sold|-0.0040|-0.0037|-0.0021|-0.0028|-0.0039|-0.0036|-0.0036|-0.0033|
||(0,002)|(0.002)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|Foreignparticipation|0.1502|0.1657|0.1229|0.1376|0.1439|0.1606'|0.1413'|0.1558|
||(0.032)|(0.028)|(0.030)|(0.028)|(0.032)|(0.028)|(0.031)|(0.029)|
|Public offering|0.0911|0.1339|0.0855|0.1315|0.0927|0.1364|0.0895|0.1339|
||(0.042)|(0.041)|(0.043)|(0.040)|(0.041)|(0.040)|(0.043)|(0.041)|
|DirectSale|-0.0007|0.0482|-0.0079|0.0441|-0.0018|0.0481|-0.0044|0.0463|
||(0.046)|(0.045)|(0.048)|(0.044)|(0.046)|(0.045)|(0.048)|(0.045)|
|**_2.-_**_LaborCharacteristics:_|||||||||
|Unions|-0.1484|-0.1250|-0.1536|-0.1314|-0.1487|-0.1242|-0.1641|-0.1413|
||(0.035)|(0.038)|(0.034)|(0.036)|(0.035)|(0.038)|(0.035)|(0.037)|
|Strikes|-0.0075|-0.0172|-0.0191|-0.0197|-0.0069|-0.0179|-0.0323|-0.0386|
||(0.028)|(0.026)|(0.043)|(0.044)|(0.027)|(0.026)|(0.038)|(0.040)|
|**_3.-_**_LaborPolicies:_|||||||||
|Downsizing|-0.0619|-0.0600|0.0284|0.0215|||||
||(0.030)|'(0.030)|(0.033)|(0.038)|||||
|Voluntarydownsizing|||||-0.0773|-0.0692|-0.0572|_-0.0561_|
||||||(0.028)|(0.027)|(0.027)|(0.029)|
|Employmentguarantee|-0.0927'|-0.0956'|-0.0862|-0.091|-0.1005|-0.1024*|-0.0996'|-0.103|
||(0.032)|(0.030)|(0.032)|(0.030)|(0.031)|(0.030)|(0.033)|(0.031)|
|Paycut|-0.0650|-0.0723|0.0803|0.0692|-0.0688|-0.0762|0.0306|0.0155|
||(0.045)|(0.044)|(0.091)|(0.104)|(0.044)|(0.044)|(0.093)|(0.110)|
|_4.-MacroeconomicVariables:_|||||||||
|GrossDomestic Product|0.0514|0.0533|0.0525'|0.0553'|0.0512'|0.0533|0.0447|0.0467|
||(0.009)|(0.008)|(0.009)|(0.009)|(0.009)|(0.008)|(0.010)|(0.009)|
|Inflation|0.0001|0.0001|0.0001|0.0001|0.0001|0.0001|0.0001|0.0001|
||(0.000)|(0.000)|(1.128)|(0.728)|(0.000)|(0.000)|(1.841)|(1.353)|
|Constant|-0.7242|-1.1126|-0.8489|-1.2934'|-0.7211|-1.1236|-0.6119|-1.0373|
||(0.310)|(0.238)|(0.332)|(0.283)|(0.299)|(0.237)|(0.330)|(0.270)|
|Observations|292|308|292|308|292|308|292|308|
|R-squared|0.53||0.546||0.536||0.528||
|F|21.99||24.59||23.39||22.97||
|Prob>F|0.000||0.000||0.000||0.000||
|PseudoR2||1.327||1.3471||1.338||1.3184|
|LR chi2||314.26||317.23||316.86||312.24|
|Prob>chi2||0.000||0.000||0.000||0.000|

_Note:_ The dependent variable is net privatization price/sales, defined as the amount that accrues to the government after all privatization and restructuring costs are taken into account, such as government commitments at the time of sale, and other adjustments are made to the sale contract. This number is adjusted by the percentage of company shares sold and is dividesd by the average net sales during the three years prior to privatization. The present value of the resulting number as of December 2000 is used. Columns (1), (2) (5) and (6) consider prior restructuring measures and the rest of variable as exogenous and provide estimates from OLS and TOBIT regressions. Column (3), (4), (7) and (8) show the second stage of the two-step procedure in order to account for endogeneity. All regressions include sectoral controls and **firm** size controls. Robust standard errors are given in parentheses. a significant at **I** percent; b significant at 5 percent; c significant at **10** percent.

31
# **Table 9. Instruments for Potentially Endogenous Variables**

||Agent<br>Bank|Managemrent<br>Change|Political<br>affiliation<br>ofunions|Pre-<br>privatization<br>profits|Law<br>I/<br>ongin|Continental<br>2/<br>dummies|Macro<br>controls|<br>F-statistic<br>3<br>onexluded<br> <br>isrnnts|
|---|---|---|---|---|---|---|---|---|
|Lownsizing|yes||yes|yes|yes|yes|yes|4.32<br>|
|Voluntarydownsizing|yes|yes||yes|yes|yes|yes|3.88|
|Age-biaseddownsiang||yes|yes|yes|yes|yes|yes|3.72|
|Skill-biased downsizing||yes|yes|yes|yes|yes|yes|3.23<br>|
|Fermle-biaseddownsizing||yes|yes|yes|yes|yes|yes|3.09|
|Enploynrntguarantee|yes||yes|yes|yes|yes|yes|3.18<br>|
|Paycut|yes||yes|yes|yes|yes|yes|3.36|

1/ This set includes English ComrDn Law, Genmn Conunercial Law, Scandinavian Comrercial<sup>Law.</sup> 2/ This set includes Latin Armrica, Asia, Africa and Middle East, Developed Countries.

3/ This set includes unemployment rate, fiscal deficit, openness, Rrowth, and sie of public sector.

_Note:_ Table 9 reports the group of instruments used in the first-step regression. The rows give the names of the<sup>dependentvariables in thefirst-step</sup> regression. The columns describe the different groups of instruments used in each regression writing a yes if that group<sup>is usedin the estimationof each</sup> dependent variable. The last column gives the F-statistic on the excluded instruments.

32
Table 10. Labor Targeting and Privatization Prices

# Dependent variable is net privatization price/sales

|Variables||||Two-Step pr|ocedure|
|---|---|---|---|---|---|
||OLS|TOBIT||OLS|TOBIT|
||(fl|(2)||(3)|(4)|
|**_1.-_**_Firm_**_andprivtiazion_**_characterisdes:_||||||
|Nettotalliabilities|-0.0891|-0.0926|i|-0.0932|-0.0952|
||(0.040)|(0.043)||(0.080)|(0.069)|
|ShareSold|-0.004|-0.0038|**b**|-0.0038|**b**<br>-0.0035|
||(0.002)|(0.002)||(0.002)|(0.002)|
|Foreignparticipation|0.1506|0.1652|'|0.142|'<br>0.1564|
||(0.032)|(0.028)||(0.031)|(0.028)|
|Public offering|0.0863|**b**<br>0.1289||0.0971|b<br>0.1407|
||(0.044)|(0.040)||(0.041)|(0.041)|
|DirectSale|-0.0029|0.0462||0.0086|0.0585|
||(0.047)|(0.045)||(0.046)|(0.045)|
|_2.-_**_Labor_**_Characteristics:_||||||
|Unions|-0.1321|-0.109||-0.1784|-0.1537|
||(0.036)|(0.038)||(0.036)|(0.037)|
|Strikes|-0.0041|-0.0128||0.009|0.0001|
||(0.027)|(0.026)||(0.054)|(0.050)|
|**_3.-LaborPolicies:_**||||||
|Age-biaseddownsizing|-0.0832|-0.0843||-0.0833|'<br>-0.0823|
||(0.026)|(0.026)||(0.029)|(0.030)|
|Skill-biased downsizing|0.0161|0.0039||0.1616|0.1532|
||(0.036)|(0.037)||(0.097)|(0.086)|
|Fetrale-biaseddownsicing|0.0082|0.0274||0.0170|0.0115|
||(0.059)|(0.056)||(0.095)|(0.097)|
|Employmentguarantee|-0.0992|-0.1025||-0.1029|'<br>-0.1064|
||(0.031)|(0.030)||(0.034)|(0.031)|
|Paycut|-0.0726|-0.0798||0.0749|0.0564|
||(0.044)|(0.044)||(0.119)|(0.122)|
|**_4.-Macroeconomic_**_Varabla:_||||||
|GrossDomesticProduct|0.0539|0.0565||0.0461|0.0483|
||(0.009)|(0.008)||(0.010)|(0.009)|
|Inflation|0.0001|0.0001||0.0001|0.0001|
||(0.000)|(0.000)||(0.000)|(0.000)|
|Constant|-0.8018|-1.1946||-0.3127|-0.7673|
||(0.291)|(0.237)||(0.425)|(0.352)|
|Observations|292|308||292|308|
|R-squared<br>|0.54<br>|||0.54<br>||
|F|20.05|||19.73||
|Prob>F|**0.000**|||**0.000**||
|PseudoR2||1.357|||1.364|
|LRchi2||321.34|||323.04|
|Prob> chi2||0.000|||**0.000**|

_Note:_ The dependent variable is net privatization price/sales, defined as the amount that accrues to the government after **all** privatization and restructuring costs are taken into account, such as government commitments at the time of sale, and other adjustments are made to the sale contract. This number is adjusted by the percentage of company shares sold and divided by the average net sales during the three years prior to privatization. The present value of the resulting number as of December 2000 is used. Columns (1) and (2) consider prior restructuring measures and the rest of variable as exogenous and provide estimates from an OLS and TOBIT regressions. Columns (3) and (4) show the second stage of the two-step procedure to take account of endogeneity. All regressions include sectoral controls and firm size controls. Robust standard errors are given in parentheses. a significant at I percent; b significant at 5 percent; c significant at 10 percent.

33
Table 11. Voluntary Targeting and Privatization<sup>Prices</sup>

# Dependent variable is net privatization<sup>price/sales</sup>

|Variables|||Two-Step p<br>|rocedure<br>|
|---|---|---|---|---|
||OlS|TORIT<br>|O1LS<br> <br>|TORIT|
||(l)|~~~(2|<br>(1)||
|**_1.-Firmandprivatizationcharacteristics:_**|||||
|Nettotalliabilities|-0.0863<br>|-0.087<br>|-0.1331<br>|-0.1348<br>|
||(0.040)|(0.043)|(0.073)|(0.065)<br>|
|ShareSold|.0.0039<br>|-0.0036<br>|-0.0039<br>|-0.0036<br>|
||(0.002)|(0.002)|(0.002)<br>|(0.002)<br>|
|Foreignparticipation|0.1464<br>|0.1623<br>|'<br>0.1433<br>|0.1572<br>|
||(0.032)|(0.029)|(0.031)|(0.029)|
|Publicoffering|0.0863|0.1308<br>|0.0932<br>|0.1366<br>|
||(0.041)|(0.040)<br>|(0.042)<br>|(0.041)<br>|
|DirectSale|-0.0099<br>|0.0408<br>|0.0045<br>|0.0545<br>|
||(0.046)|(0.045)|(0.047)|(0.046)|
|**_2.-LaborCharacteristics:_**|||||
|Unions|-0.1412<br>|-0.1164<br>|-0.1682<br>|-0.1428<br>|
||(0.035)<br>|(0.038)<br>|(0.036)<br>|(0.037)<br>|
|Strikes|-0.0077<br>|-0.0189<br>|-0.0262<br>|-0.0336<br>|
||(0.027)|(0.026)|(0.040)|(0.041)|
|**_3.-LaborPolicies:_**|||||
|Voluntaryage-biaseddownsizing|-0.0933|-0.0823<br>|-0.0517<br>|-0.0494<br>|
||(0.031)|(0.029)<br>|(0.020)<br>|(0.022)<br>|
|Voluntaryskill-biaseddownsizing|0.0319<br>|0.0252<br>|0.0295<br>|0.0136<br>|
||(0.050)|(0.054)<br>|(0.057)<br>|(0.056)<br>|
|Voluntaryfemale-biaseddownsizing|-0.1348|-0.132|0.0077<br>|0.0134<br>|
||(0.069)|(0.103)|(0.052)|(0.046)<br>|
|Employmentguarantee|-0.099<br>|-0.1005<br>|-0.1037<br>|-0.1071<br>|
||(0.032)|(0.030)|(0.033)<br>|(0.031)<br>|
|Paycut|-0.0644|-0.0723<br>|0.0029<br>|0.0234<br>|
||(0.044)|(0.044)|(0.125)|(0.129)|
|**_4.-MacroeconomicVariables:_**|||||
|GrossDomestic Product|0.0513|0.0533<br>|0.0465<br>|0.049<br>|
||(0.009)|(0.008)<br>|(0.010)<br>|(0.009)<br>|
|Inflation|0.0001|0.0001|0.0001|0.0001|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|-0.7168|-1.1205<br>|-0.5996<br>|-1.0611<br>|
||(0.296)|(0.237)|(0.361)|(0.294)|
|Observations|292<br>|308|292<br>|308|
|R-squared|0.54<br>||0.53<br>||
|F|22.98||21.26||
|Prob>F|0.000||0.000||
|Pseudo R2<br>||1.352<br>||1.3558<br>|
|LRchi2<br>Prnh>chi2||320.13<br>non||313.97<br>**_**<br>**non**|

The dependent variable is net privatization<sup>price/sales, definedas the amountthat accruesto thegovernment</sup> after all privatization and restructuring costs<sup>are takeninto account,such as government COmmitmentsat the</sup> time of sale, and other adjustments are made<sup>tothe salecontract.This numberisadjusted by the percentage</sup> of company shares sold and divided by the<sup>averagenetsales during thethree yearsprior to privatization.</sup> The present value of the resulting number<sup>as of December 2000is used.Columns(1),(2) considerprior</sup> restructuring measures and the rest of variable<sup>as exogenousand provide estimates from an OLS and TOBIT</sup> regressions. Columns (3), (4) show the second<sup>stage of the two-step procedureto takeaccountof</sup> endogeneity. All regressions include sectoral<sup>controlsand firm sizecontrols. Robust standarderrorsare</sup> given in parentheses. a significant at 1 percent;<sup>bsignificantat 5percent;c significantat 10 percent</sup> 34
**Table 12. Compulsory Targeting and Privatization Prices**

**Dependent variable is net privatization price/sales**

|Variables|||Two-Step p|rocedure|
|---|---|---|---|---|
||OLS|TOBIT|OLS|TOBIT|
||(1)|(2)|(3)|(4)|
|**_1.-Firmandprivatizationcharacteristics:_**|||||
|Nettotalliabilities|-0.0888|-0.0925|-0.0641|-0.0697|
||(0.042)|(0.044)|(0.074)|(0.069)|
|ShareSold|-0.0043|-0.0042|-0.0037|-0.0034|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Foreignparticipation|0.1517|0.1659'|0.145*|0.1584|
||(0.032)|(0.029)|(0.031)|(0.028)|
|Public offering|0.095|0.1356'|0.0962|0.139|
||(0.044)|(0.041)|(0.043)|(0.041)|
|DirectSale|-0.0007|0.0461|0.0004|0.0496|
||(0.048)|(0.045)|(0.048)|(0.046)|
|**_2.-LaborCharacteristics:_**|||||
|Unions|-0.147|-0.1231|-0.17099|0.1477|
||(0.036)|(0.038)|(0.036)|(0.037)|
|Strikes|-0.0171|-0.0257|0.0278|0.0197|
||(0.027)|(0.026)|(0.050)|(0.050)|
|**_3.-LaborPolicies:_**|||||
|Compulsoryage-biaseddownsizing|-0.0211|-0.0322|-0.0137|-0.0425|
||(0.033)|(0.031)|(0.100)|(0.104)|
|Compulsoryskill-biased downsizing|0.0266|0,011|0.0624|0.0722|
||(0.046)|(0.051)|(0.038)|(0.042)|
|Compulsoryfemale-biaseddownsizing|0.0709|0.1081|-0.0504|-0.0559|
||(0.071)|(0.068)|(0.065)|(0.069)|
|Employmentguarantee|-0.1003|-0.1021|-0.0989*|-0.1024|
||(0.032)|(0.030)|(0.035)|(0.032)|
|Paycut|-0.0704|-0.0786|0.2176|0.2135|
||(0.046)|(0.045)|(0.132)|(0.142)|
|**_4.-MacroeconomicVariables:_**|||||
|GrossDomesticProduct|0.0517|0.0545|0.0499*|0.0524|
||(0.009)|(0.008)|(0.010)|(0.010)|
|Inflation|0.0001|0.0001|0.0o001|0.0001|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|-0.7866|-1.1795|-0.4776|-0.9101|
||(0.300)|(0.242)|(0.475)|(0.460)|
|Observations|292|308|292|308|
|R-squared<br>|0.53<br>||0.53<br>||
|F|19.49||20.14||
|Prob>F|0.000||0.000||
|PseudoR2||1.3248||1.3182|
|LRchi2<br>Prob>chi2||313.74<br>0.000||312.20<br>0.000|

The dependent variable is net privatization price/sales, defined as the amount that accrues to the government after all privatization and restructuring costs are taken into account, such as government commitments at the time of sale, and other adjustments are made to the sale contract. This number is adjusted by the percentage of company shares sold and divided by the average net sales during the three years prior to privatization. The present value of the resulting number as of December 2000 is used. Columns (1), (2) consider prior restructuring measures and the rest of variable as exogenous and provide estimates from an OLS and TOBIT regressions. Columns (3), (4) show the second stage of the two-step procedure to take account of endogeneity. All regressions include sectoral controls and firm size controls. Robust standard errors are given in parentheses. a significant at I percent; b significant at 5 percent; c significant at 10 percent.

35
**Table 13. Voluntary Downsizing and Re-Hiring**

|||||**Deoenden**|**tVanable**<br>|**:re-hire**<br>||||
|---|---|---|---|---|---|---|---|---|---|
||Probit||**dF/dX**|**Probit**||dF/dX|Probit<br>||dF/dX|
||**(1)**|||**(2)**|||(3)|||
|Voluntarydownsizing|0.3476|(0.170)<br>|[0.1292]|0.3741|(0.198)<br>|[0.1374]|0.4027|(0.240)**c**|[0.1581]|
|Union.|0.7863|(0.270)a|[0.2373]|0.5006|(0.313)<br>|[0.1598]|1.1636|(0.888)<br>|[0.3492]|
|ILOConventons||||-0.0002|(0.004)<br>|[-0.0001]||||
|LaborFinngCost|||||||-0.1199|(0.096)|[-0.0468]|
|Constant|-1.947|(1.334)||-3.9402|**(2.0 52) b **||-1.1906|(0.617)**b**||
|Observations|292|||214|||146|||
|Loglikelihood<br>|-179.87|||-130.22|||-94.92|||
|Waldchi2|16.8|||9.19|||7.92<br>|||
|**Prob> **chi2|0.010|||0.031|||0.054|||
|||||**Dependent V**<br>|**ariable:r **<br>|**e-hiresane**<br>||||
||**Probit**<br>**(lb)**||dF/dX|Probit<br>(2b)||dF/dX|Probit<br>(3b)||dF/dX|
|Voluntarydownsizinw|0.0758|(0.253)|[0.0068]|0.0897|(0.289)<br>|[0.0087]|0.1010|(0.111)|[0.0187]|
|Union|0.2795|(0.458)|[0.0204]|0.1313|(0.486)<br>|[0.0113]|0.1728|(0.346)|[0.0123]|
|ILOConventions||||-0.0030|**(0.004)**<br>|[-0.0003]||||
|LaborFinngCost|||||||**_-0.0730_**|(0.152)|[-0.0072]|
|Constant|-4.9733|(1.680)a||-3.3286|(2.328)||-3.4810|(2.012)'||
|Observations|292|||214|||146|||
|Loglikelihood|-51.89|||-39.81|||-37.12|||
|Waldchi2|7.03|||6.23|||6.03|||
|Prob**>**chi2|0.060|||0.07|||0.08|||

All **Regression** include: **partial** privatzation<sup>**dunmry,**sectoraldummies,cotry</sup><sup>**macro**controls,and</sup><sup>**continental**dumrnes.</sup> Standard emors and nral effects are given in parentheses and brackets respectively. **a** significant at **I** pere **b** significant at **5** percent; **c** sigrificant<sup>at 10 paent</sup>

**36**
Table 14. Labor Targeting **and Re-Hires**

|||||**Denende**|**ntVariab**|**le:re-hire**||||
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)||dF/dX|Probit<br>(3)||dF/dX|
|Age-biaseddownsizing|0.885|(0.170)|[0.3100]|0.9453|(0.198)|[0.3200]|0.8354|(0.242)a|[0.3114]|
|Skill-biased downsizing|-0.512|(0.242)b|[-0.1624]|-0.5894|(0.288)b|[-0.1778]|-0.5827|(0.315)**c**|[-0.2089]|
|Female-biaseddownsizins|0.557|(0.360)|[0.2141]|0.7464|(0.450)|[0.2861]|0.0577|(0.825)|[0.0223]|
|Union|0.633|(0.283)**b**|[0.1936]|0.2859|(0.325)|[0.0931]|0.9826|(0.610)|[0.3090]|
|ILOConventions||||-0.0002|(0.000)|[-0.0001]||||
|LaborFiringCost|||||||-0.0450|(0.118)|[-0.0175]|
|Constant|-1.974|(1.390)||-4.1495|(1.9 7 6)||-3.8461|(2.747)||
|Observations|292|||214|||146|||
|Log likelihood|-164.49|||-118.09|||-88.48|||
|Waldchi2|47.13|||34.17|||20.38|||
|Prob>chi2|0.000|||0.000|||0.000|||
|||||**Dependent **|**Variable:**|**re-hiresame**||||
||Probit<br>(lb)||dF/dX|Probit<br>(2b)||dF/dX|Probit<br>(3ba||dF/dX|
|Age-biaseddownsizing|0.9907|(0.248)|[0.1664]|1.0145|(0.245)|[0.1612]|0.8945|(0.334)|[0.1521]|
|Skill-biaseddownsizing|-0.1389|(0.070)b|[-0.0060]|-0.2741|(0.139)|[-0.0140]|-0.2615|(0.115)|[-0.0131]|
|Female-biaseddownsizint|0.1108|(0.583)|[0.0164]|0.1211|(0.620)|[0.0201]|0.1614|(0.609)|[0.0193]|
|Union|0.2801|(0.522)|[0.0380]|0.4801|(0.642)|[0.0488]|0.3814|(0.542)|[0.0512]|
|ILOConventions||||-0.0017|(0.004)|[-0.0029]||||
|LaborFiringCost|||||||-0.0585|(0.149)|[-0.0138]|
|Constant|-3.0785|(1.596)||-3.7566|(1.901)||4.1594|(2.979)||
|Observations|292|||214|||146|||
|Loglikelihood|-87.91|||-67.11|||-62.51|||
|Wald chi2|20.1|||22.15|||12.21|||
|Prob >chi2|0.001|||0.003|||0.070|||

All Regression include: partial privatization dummy, sectoral dummies,country macro controls, and continental dummies. Standard errors and marginal effects are given in parentheses and brackets respectively. **a** b Significant at I percent: significant at 5 percent: **C** significant at 10 percent 37
**Table 15. Voluntary Targeting and Re-Hiring**

|||||Dependen<br>|tVariabl|e: re-hire<br>dF/dX|Pbit||dF/dX|
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)|||ro<br>(3)|||
|Voluntaryage-biased downsizing|0.486|(0.189)<br>|[0.1825]|0.5438|(0.223)b|[0.20121|0.4696|(0.261)|[0.1850]|
|Voluntaryskill-biaseddownsizing|-0.230|(0.357)<br>|[-0.0793]|-0.6009|(0.433)|[-0.1778]|-0.4238|(0.506)|[-0.1571]|
|Voluntaryfemale-biased downsizing|-0.276|(0.727)<br>|[-0.0932]|-0.1348|(0.779)|[-0.0459]|-0.1241|(0.788)|[0.0323]|
|Union|0.499|(0.259)<br>|[0.1638]|0.1576|(0.292)|[0.0540]|0.8966|(0.495)|[0.2993|
|ILOConventions||||-0.0035|(0.004)|[-0.0012]||||
|LaborFiringCost|||||||-0.0772|(0.120)|[-0.0303]|
|Constant|-0.798|(0.648)||-0.8361|(1.149)||-1.3176|(1.177)||
|Observations|292|||214|||146|||
|Log likelihood|-177.33|||-125.82<br>|||-88.48<br>|||
|Waldchi2|22.8|||20.75<br>|||7.87<br>|||
|Prob>chi2|0.000|||0.000|||0.040|||

All Regression include: parbal privatization<sup>dummy, sectoraldummies,countrymacrocontrols, and continental dummies.</sup> Standard errors and marginal effects<sup>are</sup> given<sup>in parenthesesand brackets respectively.</sup> Significant at I percent; b significant at 5<sup>Dercent;</sup> '<sup>significantat10 percent</sup>

**Table 16. Compulsory Downsizing and**<sup>**Re-Hiring**</sup>

|||||Dependen<br>|tVariabl|e:re-hire<br>||||
|---|---|---|---|---|---|---|---|---|---|
||Probit<br>(1)||dF/dX|Probit<br>(2)||dF/dX|Probit<br>(3)||dF/dX|
|Compulsoryage-biaseddownsizing|0.713|(0.190)'|[0.2699]|0.726|(0.221)|[0.2695]|0.5602|(0.257)b|[0.2201]|
|Compulsoryskill-biased downsizing|-0.862|(0.353)**b**|[-0.242]|-0.583|(0.366)|[-0.1738]|-0.7775|(0.435)'|[-0.2660]|
|Compulsoryfemale-biaseddownsizing|0.738|(0.404)'|[0.2861]|1.008|(0.538)'|[0.3858]|0.3483|(0.869)|[0.1382]|
|Union|0.449|(0.252)|[0.1480]|0.094|(0.301)|[0.0323]|0.8522|(0.490)'|[0.2858]|
|ILOConventions||||-0.0056|(0.004)|[-0.0019]||||
|LaborFifing Cost|||||||-0.0496|(0.120)|[-0.0194]|
|Constant|-0.572|(0.649)||0.0014|(1.120)||-0.8201|(1.164)||
|Observations|292|||214|||146|||
|Loglikelihood|-171.05|||-121.4|||-92.4|||
|Waldchi2<br>Prob>chi2|32.59<br>0°00°0|||24.44<br>0000|||11.66<br>0.040|||

All Regression include: partial privatization<sup>dummy, sectoraldummies,countrymacro controls, and continental dummies.</sup> Standard errors and marginal effects<sup>aregiven in parentheses and bracketsrespectively.</sup> Significant at I percent; b significant at<sup>5oercent;' significant at10Percent</sup> 38
**Appendix 1.** Country **Sample**

|_Africa:_||||||
|---|---|---|---|---|---|
|1.-Benin|(2)|30.-SaintVincent|(1)|57.-Austria|(5)|
|2.-Cape Verde|(1)|31.-Peru|(13)|58.-Belgium|(I)|
|3.-Coted'lvoire|(10)|32.-PuertoRico|(1)|59.-Bulgaria|(6)|
|4.-Egypt|(6)|33.-St.Kitts and<br>Nevis|(2)|60.-Croatia|(3)|
|5.-Gabon|(1)|34.-Trinidadand<br>Tobago|(2)|61.-Czech Republic|(6)|
|6.-Ghana|(8)|35.-Venezuela|(6)|62.-Denmark|(1)|
|7.-Kenya|(7)|36.-Barbados|(2)|63.-Estonia|(4)|
|8.-Lesotho|(1)|||64.-Finland|(2)|
|9.-Senegal|(2)|_Asia:_||65.-France|(7)|
|10.-SouthAfrica|(4)|37.-Bahrain|(1)|66.-Germany|(5)|
|11.-Tanzania|(2)|38.-China|(1)|67.-Hungary|(I)|
|12.-Uganda|(6)|39.-India|(1)|68.-Ireland|(3)|
|13.-Zambia|(3)|40.-Indonesia|(3)|69.-Italy|(7)|
|14.-Madagascar|(3)|41.-Israel|(1)|70.-Latvia|(2)|
|||42.- Japan|(8)|71.-Lithuania|(2)|
|_Americas:_||43.-Jordan|(1)|72.-Netherlands|(3)|
|15.-Argentina|(8)|44.- Korea,Rep.|(5)|73.-Poland|(10)|
|16.-Belize|(2)|45.-Kuwait|(2)|74.-Portugal|(3)|
|17.-Bolivia|(8)|46.-LaoPDR|(1)|75.-Russia|(3)|
|18.-Brazil|(16)|47.- Malaysia|(3)|76.-Serbia|(I)|
|19.-Canada|(4)|48.- Pakistan|(1)|77.-SlovakRepublic|(2)|
|20.-Chile|(4)|49.-Philippines|(3)|78.-Spain|(5)|
|21.-Colombia|(9)|50.-Qatar|(1)|79.-Sweden|(3)|
|22.-DominicanRepublic|(2)|51.-Singapore|(2)|80.-Switzerland|(2)|
|23.-ElSalvador|(2)|52.-SriLanka|(1)|81.-Turkey|(2)|
|24.-Grenada|(1)|53.-Taiwan|(1)|82.-United Kingdom|(13)|
|25.-Guatemala|(1)|54.-Thailand|(1)|||
|26.-Guyana|(3)|55.-Yemen,Rep.|(1)|_Oceania:_||
|27.- Jamaica|(4)|||83.-Australia|(5)|
|28- Panama|(2)|_Europe:_||84.-NewZealand|(4)|
|29.-Mexico|(8)|56.-Albania|(2)|||

39
|Appendix2.FirstStageProbit|
|---|
|Variables|

|Variables|ProbitModel|
|---|---|
|Pre-privatization profits|_-0.7573_a<br>|
||(0.168)|
|Politicalaffiliationofunions|-0.1823b|
||(0.091)|
|LatinAmerica|-0.1941<br>|
||(0.243)|
|Asia|0.1790<br>|
||(0.298)<br>|
|Africaand MiddleEast|0.3652|
||(0.304)<br>|
|DevelopedCountries|0.3681<br>|
||(0.263)<br>|
|EnglishCommonLaw|-0.4983c|
||(0.277)<br>|
|German Commercial Code|-0.3351<br>|
||(0.213)<br>|
|ScandinavianCode|-1.0645b|
||(0.463)<br>|
|Openness|0.0011|
||(0.003)|
|Numberofobservations|308|
|PseudoRSquared|0.205<br>|
|F-statisticsonexcluded instruments|4.32|
|Prob>F|0.000|

_Note:_ This appendix p;esents the first-step<sup>regressionof the two-step procedure for oneof the potentially</sup> endogenous variables. Robust standard errors<sup>are givenin parentheses. Regressionsinclude agentbank</sup> dummy (not reported) a significant at I percent;<sup>bsignificantat 5 percent;</sup><sup>**c**significantat10percent.</sup> 40
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