Workers' Heterogeneity and Risk Aversion: A Segmentation Model of the Russian Labor Market

Journal of Comparative Economics 29, 230–256 (2001) doi:10.1006/jcec.2001.1711, available online at http://www.idealibrary.com on

Workers’ Heterogeneity and Risk Aversion: A Segmentation Model of the Russian Labor Market1 Irena Grosfeld DELTA, CNRS, 48, Boulevard Jourdan, 75014 Paris, France

Claudia Senik-Leygonie DELTA and University Paris-IV Sorbonne, 48, Boulevard Jourdan, 75014 Paris, France

Thierry Verdier DELTA, CERAS, 48, Boulevard Jourdan, 75014 Paris, France

Stanislav Kolenikov University of North Carolina, Department of Statistics, Chapel Hill, North Carolina 27599

and Elena Paltseva Centre for Economic and Financial Research, Nakhimovsky Prospekt 47, 117418 Moscow, Russia Received July 5, 2000; revised February 28, 2001

1 We thank David Brown for his crucial contribution to the construction of a consistent data base, Guido Friebel, Serguei Guriev, and Jean-Olivier Hairault for helpful discussions, and participants in various seminars and conferences (DELTA, University of Lille-I, the 54th ESEM conference, the IVth Annual International Conference on Transition Economics, the Second Annual International RECEP– EERC Conference), two anonymous referees, and the Editor of this journal for their comments. We gratefully acknowledge financial support of the Maison des Sciences de l’Homme. The MacArthur Foundation and the European Commission (ACE-TACIS contract T 95-4115-R) supported the purchase of the Enterprise Registry data base. This work was realized in the framework of the Russian European Centre for Economic Policy in Moscow.

0147-5967/01 $35.00 C 2001 by Academic Press Copyright ° All rights of reproduction in any form reserved.

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Grosfeld, Irena, Senik-Leygonie, Claudia, Verdier, Thierry, Kolenikov, Stanislav, and Paltseva, Elena—Workers’ Heterogeneity and Risk Aversion: A Segmentation Model of the Russian Labor Market This paper proposes an explanation of the puzzling coexistence of elements of both inertia and dynamism in the Russian labor market. In an environment of high uncertainty, risk averse and heterogeneous workers face a trade-off between wages and insurance against risk. The firm proposes a contract that includes a low monetary wage and access to social services. Workers choose to accept the contract or to leave the firm. Their choice gives rise to ex post segmentation: the most productive workers leave the firm and concentrate in the most productive firms; the less adaptive workers remain in their initial firm, where they accept lower wages as a price for security. In this framework, wage arrears can be viewed as an element of the implicit contract between firms and less productive workers. We test the relevance of this model using a panel data set containing 9,233 Russian firms for 1993 to 1998 as well as a subsample for which we have information on firms’ wage arrears. J. Comp. Econ., June 2001, 29(2), pp. 230–256. DELTA, CNRS, 48, Boulevard Jourdan, 75014 Paris, France; DELTA and University Paris-IV Sorbonne, 48, Boulevard Jourdan, 75014 Paris, France; DELTA, CERAS, 48, Boulevard Jourdan, 75014 Paris, France; University of North Carolina, Department of Statistics, Chapel Hill, North Carolina 27599; and Centre for Eonomic and Financial Research, Nakhimovsky Prospekt 47, 117418 Moscow, Russia. ° C

2001 Academic Press

Key Words: labor market; segmentation; uncertainty; implicit contracts; social assets; wage arrears; transition; Russia. Journal of Economic Literature Classification Numbers: D21, H32, P31.

1. INTRODUCTION The labor market is part of the often alleged Russian difference. Compared to other countries such as Poland, the Czech Republic, Hungary, Slovakia, and Slovenia, Russia exhibits a relative inelasticity of employment to the level of activity. In Central Europe, a substantial decline in employment has generally followed the decline of output. In Russia, the aggregate level of employment has fallen by only 15% while output was cut by 45% between 1991 and 1998 (EBRD, 1999).2 This characteristic has been interpreted in two radically opposite ways. From one perspective, the apparent irresponsiveness of labor demand has been attributed to the lack of enterprise restructuring, because labor layoffs are considered as a typical restructuring measure of firms’ early adjustment. This interpretation points to the drawbacks of the privatization strategy, which was dominated by political objectives and transferred the control of enterprises to insiders.3 Insiders have poor incentives to restructure, and they discourage external investors whose property 2 For an analysis of the output fall, see Blanchard and Kramer (1997), Roland and Verdier (1999), and Calvo and Coricelli (1993). Official figures do not take the shadow economy into account. According to Johnson et al. (1997), the share of the shadow economy in Russia increased from 12% of GDP in 1989 to 41.6% in 1995. By contrast, the shadow sector declined in Poland, according to their estimations, from 15.7 to 12.6% of GDP in the same period of time. 3 For the description of the Russian privatization strategy, see Boycko et al. (1995).

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rights are poorly protected (Blanchard and Aghion, 1996; Frydman et al., 1996). Institutional features of the labor market, e.g., high separation and hiring costs, are also taken as responsible for the weak incentives to shed labor (Garibaldi and Brixiova, 1998). The unwillingness of local governments to accept high levels of regional unemployment and the related ongoing subsidies and soft budget constraints also support the status quo in terms of labor relations.4 Finally, Friebel and Guriev (1999) argue that regional mobility, and hence restructuring, are reduced by the strategy of firms that use social assets to retain the most productive workers. From the other perspective, some authors have interpreted the relative stability of employment as a sign that the Russian labor market is highly competitive and flexible (Kapeliushnikov, 1997) or is a neoclassical dream (Layard and Richter, 1994). If labor contracts are adjusted and labor reallocated with no delay in response to various shocks of the transition, the aggregate level of employment will remain fairly stable. Is the Russian economy rigid or highly flexible? Has restructuring not started yet in Russia, or is it already happening at a fast pace? We believe that each of these two perspectives is relevant to a certain extent, but neither captures the entire picture of the Russian labor market. To date, there is no theoretical explanation of this contrasted picture. The objective of this paper is to propose a model of the Russian labor market that encompasses elements of both inertia and dynamism. The model is based on the presence of uncertainty, workers’ heterogeneity, and risk aversion. At the beginning of the period, firms offer their workers a contract that includes a low monetary wage and access to social services. Workers choose to accept the contract or to leave the firm, depending on their outside opportunities, which in turn depend on their productive characteristics. The contract plays the role of insurance against the risk faced by workers on the labor market. Because firing workers is difficult, firms can simply induce employees to leave by offering a more or less attractive wage.5 It is also in the interest of the firm to keep workers with this contract rather than hire new workers on the competitive market because of the ex ante uncertainty about future shocks and the productivity of employees. The choice of the workers gives rise to an ex post segmentation of the market whereby the most productive workers leave their initial firm, contract on the spot labor market, and concentrate in the best performing firms. However, less productive workers remain in their initial firm. This notion of segmentation, or duality, differs from the usual two-sector models of transition in which firms are contrasted according to their type of ownership, i.e., whether they are privatized or still state owned (Blanchard, 1997). Moreover, both segments of our model exist jointly within the same firm since 4 Gaddy and Ikes (1998) suggest that managers’ relational capital can be used as a substitute for restructuring. 5 This description corresponds to the state of the Russian economy from 1992 to 1998. Since 1998, layoffs have happened more frequently, as reported by the IMF (1999). Unemployment, which had stayed relatively low, reached 14% in 1999.

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good firms employ both types of workers. This aspect is in contrast to the intersectoral segmentation observed in developing countries (Harris and Todaro, 1970). On the other hand, our framework is related to the literature on labor tying contracts (Bardhan, 1984) that describes rural relations between landowners and peasants in developing economies. In such models, landowners can insure themselves against climatic and seasonal variations by proposing low-wage, long-term, labor-tying contracts to peasants. During peak seasons or high harvest times, they occasionally satisfy their additional labor needs by hiring more workers, with short-term contracts, on the spot market. This duality is also related to the segmented labor markets approach (Taubman and Wachter, 1986), in the sense that our model puts important emphasis on institutional differences between a primary (internal) labor market and a secondary (external) labor market. However, traditional segmentation models (Doeringer and Piore, 1971) contrast the primary segment, wherein workers are stable and receive contractual compensation, and the secondary segment, composed of mobile workers paid a competitive wage. In our setting, workers of the primary segment have less demanded qualifications and receive lower wages than workers of the secondary segment. The next section presents the major stylized facts of the Russian labor market, illustrating more precisely its ambivalence. In Section 3, we capture the duality of the Russian labor market in a model of segmentation. Section 4 illustrates this vision using the Russian Enterprise Registry data base,6 from which we extract a panel of 9,233 firms observed from 1993 to 1998. To the best of our knowledge, this is the largest and most extensive data set available on Russian enterprises. In this section, we assess the segmentation model of the Russian labor market developed in Section 3 relying on firm-level observations. We also confront an alternative interpretation of the Russian labor market based on turnover costs and the opposition between insiders and outsiders with the data. Section 5 concludes. 2. SALIENT FEATURES OF THE RUSSIAN LABOR MARKET From the rich empirical literature devoted to the Russian labor market, four major features emerge, pervasive labor hoarding, the importance of social assets, mounting wage arrears, and the important mobility of some workers. The first two features can be interpreted as elements of inertia, the last two as elements of flexibility. Pervasive labor hoarding, acknowledged by most Russian firms7 (Aukutsionek and Kapeliushnikov, 1996), is obviously associated with the relative inelasticity of employment to the variation in output. Garibaldi and Brixiova (1998) relate the pervasive labor hoarding in Russian firms to labor market institutions such as high search and hiring costs and legislation that limits social plans. In the same spirit, 6 The information is collected by the Russian Statistical Committee (Goskomstat) on a compulsory and regular basis. 7 In the survey of enterprises by the authors, the average level of labor hoarding acknowledged by the firms is 20%.

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Aukutsionek and Kapeliushnikov (1996) and Commander et al. (1996) underline the political obstacles to layoffs, such as the reluctance of managers to create conflicts with local governments unwilling to allow large open unemployment,8 as well as with the employees. Brown (1998) points at information asymmetries and coordination problems that make it rational for a given firm to hoard labor when all other firms do and thereby increase hiring costs. The reluctance of firms to divest their social assets can also be viewed as an element of rigidity. In the Soviet period, a large number of social services were provided by firms to their employees. These included housing, catering, kindergartens, clinics, sanatoriums, transportation, sport and vacation resorts, and retirement benefits. These services were provided at highly subsidized prices by firms that, in turn, received transfers from local government to cover the costs9 (Commander and Schankerman, 1997). As the system of social protection has not been reformed, employees are still dependent on their firm for the provision of social services. They can also benefit from access, inside firms’ buildings, to subsidized sales of foodstuff and basic consumption goods. Even though firms have been allowed and encouraged to divest social assets, most, especially the larger ones, have not done so (Commander et al., 1996). The subsidization of food and other integrated services continues, probably because firms have been able to preserve their relations with the former suppliers without having to pay the increased costs of the new distribution chains. In the context of uncertain outside opportunities, these assets certainly provide incentives to workers to remain in the firm. In spite of these factors of rigidity, elements of flexibility are also evident. For instance, the high variability of wages compensates for the relative stability of employment levels. On average, Russian wages lost 70% of their purchasing power between 1989 and 1996, not taking arrears into account. In the same period, real wages fell by no more than 30% in Central European countries (Garibaldi and Brixiova, 1998). The variability of real wages takes two specific forms in Russia. First, firms frequently adjust the number of working hours, and accordingly the wages, of their staff. For instance, they often impose compulsory, unpaid leaves on their employees. According to Gimpelson and Lippoldt (1997), unpaid leaves affected 12% of the employed labor force in 1997, with an average duration of 40 days. Second, wage arrears constitute an important method of real wage reduction. Increasing spectacularly since 1994, accumulated wage arrears are reported by Earle and Sabirianova (1998) to have reached 8 billion dollars in 1997. Wage arrears amount to 3 months of wages, on average, in the concerned firms and affect 62% of the households surveyed by the Russian Longitudinal Monitoring Survey 8 The law allows local governments and trade unions to suspend massive redundancies. The recent bankruptcy law also foresees that any liquidation plan that can be expected to provoke massive layoffs can be delayed for six months. 9 The authors report that about 25% of housing, health, and education services to the population were provided by firms.

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(RLMS).10 Wage arrears, due from both the government and enterprises, have decreased substantially in the last months of 1998, after the devaluation of the ruble, but are still common throughout the country (Russian Economic Trends, 1998). Various studies show that the allocation of wage arrears is differentiated across employees and that they tend to fall systematically on the same groups of people. Desai and Idson (1998) consider that firms use wage arrears as a device to differentiate real wages effectively paid to their employees in order to preserve the real wage of the best employees. Hence, each employee gets paid exactly the amount necessary to retain him in the firm. In the same spirit, Earle and Sabirianova (1998) suggest that firms use wage arrears in a discriminating way against the employees with the most specific human capital. Lehman et al. (1999) claim that firms allocate wage arrears to the most stable employees. All these analyses share the idea that wage arrears are used as a differentiation device. In addition to the adjustment of real wages, recent studies also reveal a significant quantity adjustment on the Russian labor market. In spite of labor hoarding and the attraction of social assets, gross flows of hirings and separations seem to be important. As documented by Gimpelson and Lippoldt (1997), the turnover rate of labor11 reached 50% in 1997. This figure is exceptionally high for an economy in transition, e.g., the turnover rate was 42% in Poland and 24% in Romania in the same year, and it is close to levels typical of OECD countries, e.g., the average turnover rate is 60% in the OECD and 67% in the United States. Labor turnover is dominated by job-to-job changes and by voluntary separations rather than layoffs, which represent less than 8% of the cases of separations (Gimpelson and Lippoldt, 1997). This last feature is specific to Russia; in other transition economies, layoffs have been much more frequent. Importantly, this high mobility is uneven across employees. Empirical studies show a contrast between behavior toward very mobile blue-collar workers and that toward more immobile white-collar workers.12 Other factors, such as qualifications, age, and local labor market conditions, also play a differentiating role. More qualified, younger, urban workers are much more mobile (Gimpelson and Lippoldt, 1997). Studies based on RLMS data confirm that blue-collar workers are more mobile. Labor mobility is also differentiated across firms. Smaller firms, and those in a better financial situation, have a more active employment policy. They use shortterm contracts more often and hire younger, more qualified workers (Denisova et al., 1998). By contrast, firms that experience financial difficulties keep their 10 The Russian Longitudinal Monitoring Survey is a periodic, nationally conducted household survey, launched in 1992, that samples about 9000 individuals from randomly selected households. 11 The turnover rate is calculated as the ratio of hirings and separations over the total stock of employment in a given period. 12 The turnover and replacement rates of blue-collar workers are twice as high as those of white-collar workers.

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employees for longer periods of time and employ persons over the retirement age. The next section proposes an analytical framework to encompass these differentiated labor relations. 3. A SEGMENTATION MODEL To the best of our knowledge, there is no theoretical explanation of this puzzling coexistence of both dynamism and inertia in the Russian labor market, even though it has been documented empirically, e.g., Gimpelson and Lippoldt (1997), Lippoldt and Grey (1997) and Clarke (1999). Most of the existing theoretical literature sheds light on workers’ immobility by analyzing wage arrears, pervasive labor hoarding, and the role of social assets. We propose a model of the Russian labor market that encompasses both the immobility of certain employees and the high mobility of others. In our setting, agents are heterogenous and face a high degree of uncertainty. Firms have to take employment decisions before they observe the productivity shock that will hit them and before they can observe the adaptability of their initial pool of workers. This last piece of information is private to employees, who are themselves confronted with another type of uncertainty concerning the cost of finding a new provider of social services in case they leave their initial firm. Before shocks occur, the firm proposes a labor contract containing a contractual wage and the continued provision of social services. Workers can accept the contract, remain in their initial firm, receive the contractual wage, and keep their access to the social assets of the firm, or they can leave the firm and find a job on the labor market, where they will be paid according to their marginal product. Because the contract proposed by the firm suppresses the risk associated with the cost of moving to another firm, it constitutes a form of insurance. Workers choose whether to remain in their firm or not by comparing the expected utility in the two cases. We show that it is possible to define a threshold level for workers’ marginal productivity above which workers leave the firm, whereas employees whose productivity is lower than the threshold accept the contract. This generates a segmentation of the labor market. We show that, in particular circumstances, it is possible that some employees choose to accept the contract and remain in the firm even if the contractual wage is set at zero by the firm. 3.1. Transition, Shocks, and Uncertainty The transition can be perceived as a series of exogenous shocks. Some of these shocks are macroeconomic, e.g., a credit crunch or aggregate output fluctuations, others are idiosyncratic. Firm-specific demand shocks result from change in trade relations, e.g., the collapse of the CMEA and the disruption of state orders in 1991, competition of imported goods on the domestic market, and change in consumers’ demand. Idiosyncratic supply shocks relate to change in the cost of the inputs used by the firm, e.g., large swings in energy prices. Worker-specific shocks follow from the fit between their skills and the new requirements of the market.

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The pervasiveness of idiosyncratic shocks implies that agents live in a world of uncertainty. For instance, firms ignore whether their managers, employees, organization, installed capital, and production techniques will be adapted to the new competitive environment after the shocks occur. Concerning the quality of their employees, uncertainty comes from the fact that the skills developed by workers in a context of administrative coordination can prove useless in a decentralized market. Employees themselves are confronted with uncertainty, which mainly concerns the availability and the value of job opportunities on the newly emerging labor market. Another important element of uncertainty is related to the social protection system. When leaving his firm, an employee does not know exactly what level or kind of social services he will be able to find in other firms and at what cost. From the perspective of the employee, a certain level of risk is associated with quitting the firm. We consider the transition process to be divided into many subperiods, each of which is dominated by a set of shocks. For instance, the aftermath of liberalization is dominated by radical change in relative prices; the next period is dominated by the reallocation of resources resulting from mass privatization. Thus, the entire transition process can be viewed as a series of shocks that trigger a change in the market valuation of the characteristics of the agents. The crucial point is that agents have to make decisions and start actions in the beginning of the period, before knowing exactly the nature of the shocks that will affect their characteristics. Consider an economy with N initially identical firms. Each firm has a stock of physical capital K and employs l0 workers so that the total labor force is L = Nl0 . Each worker supplies inelastically one unit of time. Firms face two types of uncertainty. The first concerns supply and demand idiosyncratic shocks that we represent as affecting the production technology. More precisely, we suppose that, at the beginning of the period, each firm faces a technology production function Q = Min{AK , l}, where A is a productivity parameter subject to an idiosyncratic ¯ according to a cumulative H (·), with H (A) = 0 shock and distributed over [A, A] ¯ ¯ ¯ = 1. and H ( A) The second type of uncertainty faced by firms relates to the productivity of their employees. We assume that workers learn their potential productivity, or the units of efficient labor they are able to provide, denoted a, at the beginning of the period. However, this remains private information and the firm only knows that ¯ according to a cumulative F(·) the skills of its workers are distributed over [0, a] ¯ = l0 . A particular case that we will consider is a uniform distribution with F(a) on [0, l0 ]. It is important to stress that a is not the technical productivity of the employee but rather a measure of his relative adaptation to the new requirements of the market. The informational asymmetry concerning this parameter stems from the fact that firms have been able to observe their employees only in the task that they performed before the shock. Firms do not know how employees will function in the new environment with new tasks. The routine organization of work under central planning did not require employee initiative and swift reactions to

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unexpected situations; hence, there was no revelation of this information. However, the employee knows his capability either from self-assessment or from the observation of job opportunities of his peers and is thus able to anticipate his performance in the new environment. As observed in the previous section, socialist firms provided various social services to their employees out of social assets they had at their disposal. Any nonpecuniary benefits provided by firms to their workers can be considered as a part of an employment package. We assume that all firms have a stock, S, of social assets built up before the transition that can still be used at little cost during the transition process.13 We assume that there is a cost to workers, denoted by σ , of shifting from their initial firm to another firm. The shifting cost can be interpreted, in a narrow sense, as the search cost that an employee who leaves his firm must assume in order to find a new provider of the social services he needs, or, in a more general sense, as the cost of changing workplace. This cost is related to the weakness of the institutional environment, in particular the high level of uncertainty perceived by workers and firms. The weakness of both the state and the rule of law and the slow pace of institution building in Russia shorten the time horizon of agents and weaken the quality of their expectations. Thus, the shifting cost σ reflects the adjustment cost of moving from one location to another, the cost of adapting to a new environment, and the possible mismatch between the social services provided by another firm and the needs of a particular worker. The last element implies that the cost will depend on the relative worker-specificity of the social assets provided by the firm. For social provision of housing, health care, child care, education, training, and leisure goods, this specificity can be quite high and probably depends on the length of service of the worker in his initial firm.14 During the transition, workers lack information about the nature and the magnitude of these costs. Therefore, we consider σ to be a random variable distributed over [0, σ¯ ] according to a cumulative G(·) with G(σ¯ ) = 1. For simplicity, we assume that each worker consumes one unit of social services, which can be seen as a minimum consumption or social protection level, and that the uncertainty he faces relates to the effort needed to access this unit consumption level. Agents make certain decisions at the beginning of the period. The timing of the sequential process of revelation of information is as follows. First, workers learn their own productivity level, a. Second, before observing their shock and without knowing the exact productivity of each individual, firms propose to their

13

The cost of providing social services is low because the fixed cost of social assets has already been paid in the past and because the firm continues to receive subsidies from various levels of the government. For a discussion of the reasons that these assets are not under the control of the government itself, see Commander and Schankerman (1997). 14 For simplicity, we assume that, when he quits, an employee loses access to the social assets of his firm. In reality, some workers retain access to some of the social assets of their initial firm. This is the case, in particular, for housing (Commander and Schankerman, 1997).

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initial workers15 a more or less implicit contract (w, s) composed of a fixed wage w and the provision of the social services s. In conformity with the stylized facts of Section 2, we assume that some institutional constraints hinder layoffs and that voluntary separations dominate workers’ outflows. Thus, the firm can simply induce workers to leave or to stay by offering them a more or less attractive contract through the level of the contractual wage w. Workers can accept or reject the offer. If they accept the contract, they stay in the firm and enjoy the social assets of the firm. If they reject the offer, they must find a job on the competitive labor market. At a third stage, the productivity of all workers is revealed to all agents in the economy and becomes common knowledge. Firms also learn their own idiosyncratic productivity shock, A. After the shock, each firm can hire more workers to maximize its profits on the spot market in which all characteristics have been revealed. Hence, firms offer a wage per efficiency unit of labor ω so that an individual with productivity a is paid a total wage of ωa. Each worker has a utility function u(c, s) defined on the two types of goods, the consumption good c, taken as the numeraire, and the social services s. As already noted, we consider the consumption of the social services to be essential and social services to be supplied inelastically to each individual at an amount s = 1. Therefore, the relevant utility function can be rewritten as u(c) = u(c, 1). Workers are assumed to be risk averse, and u(c) is a von Neumann–Morgenstern utility function with u 0 (c) > 0 and u 00 (c) < 0. 3.2. Labor Contracts Denote by ωe the expectation of the wage paid per efficiency unit of labor on the spot market. Then, a worker will accept the contract at the first stage if and only if Z σ¯ u(w) ≥ u(ωe a − σ ) dG(σ ). 0

This means that the utility level he receives under the contract is higher than the expected utility he would receive by leaving the firm and facing the uncertainty related to the provision of social services. As workers are risk averse, they are ready to pay an insurance premium, i.e., accept a low wage w, in order to avoid the uncertainty associated with the shifting cost σ . We define the insurance premium I (y) that a worker with income y is willing to pay to avoid the uncertainty associated with shifting from his initial firm to another one as Z σ¯ u(y − I (y)) = u(y − σ ) dG(σ ). 0

Consequently, φ(y) = y − I (y) is the certain equivalent income that makes the worker indifferent between going to the competitive labor market and experiencing 15 In reality, the wages of the workers who decide to remain in the firm are heterogenous. This reflects the legacy of the past and also the possibility for firms to discriminate according to characteristics that are already observable.

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the risky adjustment cost σ or staying with the initial firm and not facing this uncertainty. Clearly, φ 0 (y) > 0 and φ(y) < y. Given our assumption about the distribution of a, all workers with a productivity level a smaller than the threshold a ∗ (w, ωe ) =

φ −1 (w) ωe

accept the contract of their initial firm. Hence, the number of workers who want to remain in their initial firm is l1 = l1 (w, ωe ) and is given by l1 = l1 (w, ωe ) = Min{F(a ∗ (w, ωe )), l0 }. After revelation of the workers’ productivity to the firm, each firm, through a contract w, disposes of an amount of efficient contractual labor L˜ 1 given by Z Min(a ∗ , A) ¯ ad F(a) = Min{8(a ∗ (w, ωe )), l0 E(a)}, L˜ 1 = L˜ 1 (w, ωe ) = 0

R A¯

R a∗ ∗ ¯ where E(a) = (1/F( A) 0 ad F(a) and 8(a ) = 0 ad F(a). After realization of its idiosyncratic productivity shock, each firm decides whether to hire more labor on the spot market. Given the Leontieff technology of production Q = Min{AK , l}, only those firms with a labor requirement AK larger than L˜ 1 will want to do so. Thus, the ex post profit 5(A, ωe , L˜ 1 ) of a given firm depends on the realization of the shock A in the following way16 : 5(A, ω, L˜ 1 ) = AK − wl1

L˜ 1 ; K L˜ 1 when A > . K

when A ≤

= AK − wl1 − ω (AK − L˜ 1 ) e

(1)

Low-productivity firms, i.e., firms with A < ( L˜ 1 /K ), are content with their pool of stable workers l1 . In contrast, high-productivity firms, i.e., firms with A ≥ ( L˜ 1 /K ), hire at the anticipated market wage, ωe , the additional units of labor (AK − L˜ 1 ) needed to maximize profits after the realization of their productivity shock A. From Eq. (1), we compute the expected profits of a firm as a function of the labor-tying contract w it proposes and the expectation ωe of the equilibrium wage on the spot labor market. This expectation is Z L˜ 1 /K e [AK − wl1 ] d H (A) E5(w, ω ) = A

¯Z

+ 16

A¯ L˜ 1 /K

[(1 − ωe )AK − wl1 + ωe L˜ 1 )] d H (A),

The output good is also the numeraire consumption good, and its price is fixed at 1.

(2)

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where17 l1 = l1 (w, ωe )

and

L˜ 1 = L˜ 1 (w, ωe ).

Equation (2) can be rewritten as Z A¯ Z e e AK (1 − ω ) d H (A) + E5(w, ω ) = A

( L˜ 1 /K )

ωe [AK − L˜ 1 ] d H (A)

A

¯ ¯ + (ωe − w) L˜ 1 + w( L˜ 1 − l1 ).

(3)

The first term of Eq. (3) reflects the expected profit flows of the firm if there were only spot market contracts. The second term is negative and reflects the excess cost, evaluated at the spot market wage, of hiring L˜ 1 efficient units of labor under the labor contract when the firm faces a low productivity shock and consequently ex post, it needs only AK units of labor. This term accounts for the labor hoarding discussed in Section 2. The third term (ωe − w) L˜ 1 is positive and reflects the savings in labor costs for the firm that has hired L˜ 1 units of labor at the contractual wage w rather than at the higher anticipated spot market wage ωe . Finally, the last term w( L˜ 1 − l1 ) can be either positive or negative and depends on the shape of the distribution of workers’ skills. This term reflects the fact that, under the labor-tying contract, the firm pays the same wage w to all workers who accept the initial contract regardless of their productivity, which cannot be observed at this stage. Hence, the firm may pay a wage rate in excess of productivity to some of the less-productive workers. Alternatively, the firm may underpay those relatively productive workers who have chosen to remain in the firm. In aggregate terms, the effect on the wage bill will depend on how the productivity level of workers who remain in the firm is actually distributed. It is a simple matter to derive the following: µ µ ˜ ¶¶ L1 ∂ E5 = ωe 1 − H ; ˜ K ∂ L1

∂ E5 = −w; ∂l1

∂ E5 = −l1 . ∂w

The problem for the firm is to choose w in order to maximize its expected profits E5(w, ωe ). The solution of this problem gives the optimal labor-tying wage w(ωe ) as a function of the expected spot market wage ωe . It is actually more convenient to rewrite the profit function in terms of the threshold level a ∗ = a ∗ (w, ωe ), taking When writting the expression of the expected profit E5(w, ωe ), we implicitly take into account ¯ Obviously, it is never in the interest of a firm to have the fact that L˜ 1 /K takes a value between A and A. ¯ since this would mean that the¯ firm hires useless units of labor even under the best outcome L˜ 1 /K > A, ¯ However, it could be the case that L˜ 1 /K < A. Hence, the upper (resp. for the productivity shock A. ¯ lower) bound of the first (resp. second) integral on the left-hand side of Eq. (2) would be replaced by ˜ Min{ L 1 /K , A} For notational simplicity, we focus on the most relevant case in which L˜ 1 /K is larger ¯ than A. ¯ 17

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into account w = φ(ωe a ∗ ) ≥ 0, and to consider the firm’s choice in terms of a ∗ . The first-order condition for an interior solution is µ µ ˜ ¶¶ L1 d E5 e a ∗ F 0 (a ∗ ) − φ(ωe a ∗ )F 0 (a ∗ ) − ωe F(a ∗ )φ 0 (ωe a ∗ ) = 0 = ω 1− H da ∗ K (4) for a ∗ such that φ(ωe a ∗ ) ≥ 0 and L˜ 1 = 8(a ∗ ).18 Solving (4), we obtain an equilibrium threshold a ∗ = a ∗ (ωe ) and correspondingly a labor-tying contract wage w = w(ωe ) = Max{φ(ωe a ∗ (ωe ), 0}.19 3.3. Spot Labor Market Equilibrium The total labor demand on this market is simply the total demand emanating from the firms that have experienced a good productivity shock, i.e., a shock A higher than L˜ 1 /K , µ ˜ ¶¸ · µ˜ ¶ L˜ 1 L˜ 1 L1 L1 ˜ − + H , [AK − L 1 )] d H (A) = K E(A) − 9 L = K K K K ˜L 1 /K Z

A¯

d

R A¯ Rx where E(A) = A Ad H (A) and 9(x) = A Ad H (A). The total labor supply is ¯ efficient units of labor provided ¯ the total number of by workers who have rejected the initial contract. Hence, it is written as Z Ls =

a¯

a∗

ad F(a) = l0 E(a) − L˜ 1 ,

R A¯ ¯ 0 ad F(a). where E(a) = (1/F(a)) The spot labor market equilibrium requires L d = L s , which imposes the following condition on L˜ 1 : µ ˜ ¶¸ · µ˜ ¶ L1 L1 L˜ 1 + H . l0 E(a) = K E(A) − 9 K K K

(5)

The right-hand side of Eq. (5) is an increasing function of L˜ 1 /K . Under the ¯ it provides a unique solution L˜ ∗ ∈ (A K , assumption that K E(A) < l0 E(a) < K A, 1 ¯ ¯ ˜ AK ). The labor market equilibrium { L ∗1 , a ∗ , ω∗ , w∗ } is characterized recursively Note that this equation also holds for the case in which L˜ 1 /K < A by simply including H ( L˜ 1 /K ) = ¯ 0 in the expression. 19 We assume that second-order conditions are satisfied. 18

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by (5) and

ω

∗

µ

L˜ ∗1 = 8(a ∗ )

¶¶ µ ˜∗ L1 − A a ∗ F 0 (a ∗ ) − φ(ω∗ a ∗ )F 0 (a ∗ ) − ω∗ F(a ∗ )φ 0 (ω∗ a ∗ ) = 0 1− H ¯ K w ∗ = Max{φ(a ∗ ω∗ ), 0}.

As it is difficult to characterize the solution completely with general distribution functions, we consider a simple example with uniform distributions for workers’ skills and firms’ productivity and constant absolute risk aversion for workers. Let F(a) = a and a¯ = l0 . Also H (A) = (A − A)/1A with 1A = A¯ − A, and assume ¯ ¯ that K

A¯ A¯ + A ¯ ¯ < l0 < K A. 2 2

In this case, l1 = a ∗ (w, ωe );

(a ∗ (w, ωe ))2 L˜ 1 = . 2

The equilibrium { L˜ ∗1 , a ∗ , ω∗ , w∗ } is characterized by · ¯ µ ˜ ∗ ¶2 A +A L1 a¯ 1 A2 =K + ¯ − ¯ + 2 2 21A K 21A ¶ µ ∗ L˜ 1 a ∗ − φ(ω∗ a ∗ ) − ω∗ a ∗ φ 0 (ω∗ a ∗ ) = 0 ω∗ 1 − 1AK l0

¸ A L˜ ∗1 ¯ 1A K with φ(ω∗ a ∗ ) ≥ 0

and w∗ = Max{φ(ω∗ a ∗ )}, 0). One interesting particular case involves a constant absolute risk aversion utility function for workers, i.e., u(C) = u 0 − e−γ C . It is easy to see that · Z σ¯ ¸ 1 γσ φ(y) = y − Log e dG(σ ) = y − I. (6) γ 0 Hence, the insurance premium I (y) = I is independent from y. From Eq. (6), it is easy to see that the first-order condition can be written as ¶ µ L˜ ∗1 a ∗ − (ω∗ a ∗ − I ) − ω∗ a ∗ = 0 with ω∗ a ∗ ≥ I. (7) ω∗ 1 − 1AK

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As is clear from Eq. (7), the first-order condition cannot be satisfied at an interior solution. Therefore, ω∗ a ∗ = I and w ∗ = 0. In this extreme case, firms offer a zero wage to the workers who choose the labor contract. The spot market wage ω∗ is established by the following equations: · ¯ µ ˜ ∗ ¶2 ¸ a¯ A2 A +A 1 L1 A L˜ ∗1 l0 = K + ¯ − ¯ , ¯ − 2 2 21A K 21A 1A K · Z σ¯ ¸ (a ∗ )2 1 ∗ ∗ γσ ˜ Log e dG(σ ) . and ω = with L 1 = 2 γ a∗ 0 This extreme example shows that, in some cases, the firm proposes a zero contractual wage, which is the minimum it can set. Even at this zero wage, some employees will accept the contract and remain in the firm because, given their low productivity or low adaptation to the labor demand, they are better off retaining access to the social assets of the firm than receiving the positive competitive wage ω∗ a on the spot market but facing the risk associated with the shifting cost σ . According to the model, we expect to observe an ex post segmentation pattern for workers and firms. Bad firms, i.e., firms having a productivity shock A smaller than A∗ = L˜ ∗1 /K , would retain only low-productivity workers, i.e., those who would accept the labor contract. These workers would be paid w∗ , which can equal 0, and would enjoy the social assets at no cost. In good firms, i.e., firms that have a productivity shock A higher than A∗ = L˜ ∗1 /K , there would be two types of workers. The first type are low-productivity workers having a < a ∗ , who accept the labor contract w ∗ and the provision of social assets with no uncertainty. The second group20 are high-productivity workers having a ≥ a ∗ , who are hired by the firm on the spot labor market and are paid according to their market productivity the higher wage w ∗ a. In the next section, we confront this hypothesis of Russian labor market segmentation with the available empirical evidence. More specifically, we check whether we find signs of the type of adjustment predicted by the model in a large sample of Russian industrial firms. We also test the alternative theory, whether firms’ wage-setting behavior is consistent with a model of insider control. 20 De novo private firms may employ only high-productivity workers since their employment policy is not determined by the inherited pool of less productive workers. We consider only the pool of firms and employees who are present throughout the period. Workers who leave their firm for the newly created private sector are excluded from this picture. In terms of our model, they can be viewed as ¯ Moreover, we do not consider the possibility that workers can having a productivity a greater than a. have a second job in the hidden economy, although this is an important part of the Russian reality. In the framework of our model, the only difference between a job on the spot market and on the shadow labor market is that, in the latter, the worker can both keep his job in his original state firm and use its social assets and get a better wage in the shadow economy. We simply assume that there is a limit to the amount of time that the firm would let the worker spend in the shadow market, and we take this limit to be zero.

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4. EMPIRICAL ANALYSIS To test our model, we would ideally like to have information on individual labor supply and firm labor demand.21 We would also like to observe gross flows of hirings and separations. Unfortunately, the available data do not allow us to do so. In spite of some attempts to match individual information on labor supply and labor demand (Earle and Sabirianova, 1998), it is impossible to build an extensive data set of this type. Furthermore, there is no information available to date on gross employment flows at the level of enterprises. In the absence of an ideal data set, we use the Russian Enterprise Registry data base, collected by the Russian Statistical Committee (Goskomstat) on a compulsory and regular basis. These data include information on firms’ economic performance and labor dynamics. From this data set, we extract a balanced panel of 9,233 firms observed between 1993 and 1998. Among other indicators, the panel contains information about the value of output, the number of employees, the wage fund, the type of property, the region, and the product codes of each firm.22 We eliminate observations with inconsistent data and constrain the sample to those firms for which we have nonzero and nonmissing output, employment, and wage bill information. We also eliminate parts of consolidated firms, mergers, subsidiaries, spinoffs, and new entries. The descriptive statistics for the sample are presented in Table A1 in the Appendix. We wish to confront our model of the labor market with the information contained in the Registry. In particular, we want to check whether firms adjust to their productivity shocks by changing the employment of high-productivity workers. This would lead to the formation of a stagnant pool of low-productivity workers in firms with adverse productivity shocks and the concentration of high-productivity workers in firms with favorable productivity shocks. In the following regressions, we take the variation in firms’ real output as a proxy for their idiosyncratic total factor productivity shock. The rationale is that a firm whose production expands (decreases) has certainly faced a favorable (unfavorable) shock. Although the productivity of the employees follows a continuous distribution in the model, the ex post segmentation contains two categories of workers, the 21 Empirical studies of the Russian labor market usually hinge on household surveys such as the RLMS. On the labor demand side, data on hirings and separations are collected by the Goskomstat, through mandatory regular reporting by medium-sized and large enterprises which cover over 75% of total employment. However, in addition to other drawbacks (see Gimpelson and Lippoldt, 1997), the declarations do not include any economic information about the firms. More fragmented information is also available. A number of case studies have been prepared by Russian institutions (the Institute of Comparative Labour Studies and the IMEMO: Institute of International Economic Relations), the OECD, and the World Bank (see Denisova et al., 1998). Surveys of many enterprises are also conducted on a regular basis. The “Russian Economic Barometer,” TACIS-ACE program of the European Commission, managed by a team of the IMEMO, polls the managers of a panel of 251 firms regularly. More occasionally, the VCIOM (All Russian Public Opinion Center), the ILO (International Labour Office), and the Russian Labour Flexibility Survey conduct surveys of enterprises. 22 For the description of the registry database, see Brown and Brown (1997).

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high-productivity group, whose parameter a is above the threshold a ∗ , and the low-productivity group, under a ∗ (where a ∗ is the threshold that triggers the decision to quit or to stay with the firm). In the Registry data, the distinction is made between the number and wage fund of blue-collar workers (production related) and white-collar workers (nonproduction related) in each firm. To be sure, such a distinction does not encompass all aspects of high-productivity versus lowproductivity workers. However, based on the existing empirical evidence, we take this distinction to be a rough approximation of the differences between employees in terms of mobility and external opportunities. Thus, we assume that blue-collar workers are in higher demand, while white-collar workers are less adaptable to the new market environment. It may seem counterintuitive that blue-collar workers are demanded more than white-collar workers. In a Western economy, white-collar workers would be considered more qualified and more adaptable. However, in postsocialist economies, white-collar workers probably have the wrong skills. These people specialized in administration and management tasks based on the needs of the former system, e.g., they might have a comparative advantage in relations with the planning organs and the administration of the branch ministry, and are poorly suited to the new tasks of a market environment (Boeri et al., 1998). There may be some complementarity between installed capital and the blue-collar workers who are trained to use it, which creates excess demand for qualified workers. Gimpelson and Lippoldt (1997) provide evidence that white-collar workers in the former state sector are older than blue-collar workers, which may also explain their lesser adaptability. In our the model, all types of workers are perfect substitutes whereas blue- and white-collar workers are usually not considered as such. However, in a period of profound organizational change, we assume that the initial skills and specialization of workers lose part of their significance and that large productivity gaps between different types of workers emerge. Furthermore, the model assumes that employers cannot observe ex ante the productivity or adaptability of their workers. If the firm could observe ex ante the exact productivity of each worker, it would offer him the exact contractual wage necessary to retain him in the firm. How is this consistent with the distinction between blue- and white-collar workers, two eminently observable categories? One possibility is that the firm does not know a priori which is the most adaptable category. Moreover, within each category, the firm cannot observe the exact productivity of a given worker. Thus, the firm is faced with a certain degree of uncertainty concerning the adaptability of its workers. With all these reservations in mind, and without a better alternative, we look for segmentation of the market between blue- and white-collar workers, assuming that the former are more productive and the latter less productive, in the following regressions. We first verify that the employment of blue-collar workers is more elastic than that of white-collar workers relative to output shocks. We then discuss alternative explanations. In particular, the higher mobility of blue-collar workers may come from the fact that they are a divisible resource whereas white-collar

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workers may be considered as a fix cost. Alternatively, this higher mobility of blue-collar workers may come from their lower negotiation power inside the firm. We show that these hypotheses are not consistent with the data. Table 1 compares the sensitivity of the change in employment of white-collar versus blue-collar workers to shocks. In order to control for sectoral and regional effects, we include, as explanatory variables, the average growth rate of employment TABLE 1 Employment and Output Shock, 1993 to 1998

All firms Change in the number of workers:

Output growth Intercept R2 N Test of the null hypothesis: the coefficients of the output growth are the same for blueand white-collarworkers. Output growth Intercept R2 N Test of the null hypothesis: the coefficients of the output growth are the same for blueand white-collar workers.

Bluecollar

Whitecollar

Firms with increasing employment Bluecollar

Whitecollar

Firms with decreasing employment Bluecollar

Whitecollar

Panel A: OLS 0.086∗∗∗ 0.038∗∗∗ −0.002 0.132∗∗∗ (0.002) (0.002) (0.002) (0.006) 0.118∗∗∗ 0.114∗∗∗ 0.066∗∗∗ 0.136∗∗∗ (0.004) (0.004) (0.004) (0.007) 0.250 0.248 0.061 0.200 46165 46165 11297 11297 Rejected Rejected F(1,92310) = 282.15∗∗∗ F(1,22574) = 41.45∗∗∗

0.106∗∗∗ 0.067∗∗∗ (0.002) (0.002) 0.060∗∗∗ 0.061∗∗∗ (0.004) (0.004) 0.195 0.175 34868 34868 Rejected F(1,69716) = 170.88∗∗∗

Panel B: Fixed effects 0.098∗∗∗ 0.059∗∗∗ 0.030∗∗∗ −0.004 (0.002) (0.003) (0.003) (0.008) 0.199∗∗∗ 0.129∗∗∗ 0.254∗∗∗ 0.176∗∗∗ (0.007) (0.009) (0.013) (0.030) 0.158 0.205 0.013 0.255 46165 46165 11297 11297 Rejected Rejected F(1,73846) = 143.64∗∗∗ F(1,11156) = 14.68∗∗∗

0.076∗∗∗ 0.047∗∗∗ (0.002) (0.003) 0.098∗∗∗ 0.109∗∗∗ (0.008) (0.010) 0.129 0.145 34868 34868 Rejected F(1,51628) = 71.28∗∗∗

Note. Estimations include average employment growth in the region and in the five-digit industry level, time, and size dummies. Three size levels were used, with two thresholds at 200 and 1000 employees. Standard errors are in parentheses. ∗∗∗ Significant at the 1% level.

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in the firm’s industry and region23 as well as time and size dummies. The regressions pooling all firms, i.e., columns 1 and 2, show that the response of the growth rate of employment of blue-collar workers to shocks is significantly stronger than that of white-collar workers. The same result is obtained whether regressions are estimated by OLS (panel A) or fixed effects (panel B). Firms do seem to be adjusting to shocks by changing their stock of blue-collar workers more. Ideally, we would like to find that the variation in the number of low-productivity workers (white-collar workers) is independent from the shocks of the firm, i.e., that the parameter is not statistically significant. The fact that the coefficient is significant may be due to the imperfect measure, white- or blue-collar workers, used to account for the productivity or adaptation of workers. To capture the asymmetry between good and bad firms, i.e., those having favorable or negative shocks, we divide the sample into two parts, firms with increasing or decreasing employment. According to the model, firms hire new workers only if their total factor productivity increases beyond a certain threshold, i.e., A > L˜ ∗1 /K . This asymmetry is best captured in the data by distinguishing between firms whose employment rises and firms whose employment falls. The results in columns 3 to 6 show that the employment of blue-collar workers is more dependent on output shocks than that of white-collar workers. It turns out that in the case of firms whose shock is favorable enough to trigger net hirings, the variation in the number of white-collar workers is independent from the shock, as the coefficient is not significant. In the case of a negative shock, the employment of blue-collar workers decreases almost twice as strongly as the employment of white-collar workers. These observations support the hypothesis that, in the case of a positive shock, firms hire blue-collar workers rather than white-collar workers. Hence, production-related workers leave bad firms and are hired by good firms. Nonproduction-related workers are significantly less responsive to shocks, in particular good shocks. Of course, our hypothesis would be more precisely tested if we could observe gross flows of hirings and separations. In the absence of such data, we consider net hirings to be a proxy for gross hirings. The higher responsiveness of production-related workers to output variation is not a surprising result as it is a common observation in the literature on labor markets. This is usually attributed to the fact that production-related workers are a more easily divisible resource, whereas accountants and clerks perform tasks that are much less dependent on the level of activity of the firm. Hence, the employment of these workers has a lower elasticity to output. However, in the case of Russia, such an explanation would not be consistent with the empirical evidence presented in Section 2. This evidence shows that labor layoffs are extremely rare and separations are essentially voluntary. The observed lower mobility of white-collar workers might be due to higher dynamic adjustment costs, which is a frequent motive for labor hoarding. Higher 23 The average growth rates are calculated on the basis of the whole sample, without the restrictions imposed for the construction of the panel.

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TABLE 2 Wages and Productivity Gains, 1993 to 1998 OLS Wage growth Productivity growth

Intercept N R2 Test of the null hypothesis: the coefficients of the productivity growth are the same for blueand white-collar workers.

Fixed effects

Blue-collar workers

White-collar workers

0.295∗∗∗ (0.003) 0.154∗∗∗ (0.008) 46163 0.576

0.267∗∗∗ (0.004) 0.110∗∗∗ (0.011) 46163 0.363

Rejected F(1,92304) = 30.75∗∗∗

Bule-collar workers

White-collar workers

0.298∗∗∗ 0.269∗∗∗ (0.003) (0.005) 0.127∗∗∗ 0.046∗∗ (0.014) (0.020) 46163 46163 0.573 0.357 Rejected F(1,73840) = 24.34∗∗∗

Note. Estimations include average wage growth in the region and in the five-digit industry level, regional unemployment, time, and size dummies. Three size levels were used, with two thresholds at 200 and 1000 employees. Standard errors are in parentheses. ∗∗∗ Significant at the 1% level. ∗∗ Significant at the 5% level.

turnover costs might be associated with white-collar workers because their capital is more firm-specific, whereas production-related workers are more easily replaced. Hence, white-collar workers should be able to use their greater bargaining power to capture a larger share of firm productivity gains. Empirically, we should observe that the wages of white-collar workers are more sensitive to firm productivity shocks than those of blue-collar workers. To test this conjecture, we estimate a wage equation for blue- and white-collar workers, by regressing the variation of wages on labor productivity growth, controlling for regional and industry-specific wage increases. As shown in Table 2, the wages of blue-collar workers are significantly more sensitive to productivity shocks than those of white-collar workers. These results suggest that the position of white-collar workers is not stronger than that of blue-collar workers inside the firm. As we will see in the next section, this result is supported by the allocation of wage arrears. Hence, we consider the most obvious alternative explanations for the higher mobility of blue-collar workers to be rejected by the data. 5. AN INTERPRETATION OF WAGE ARREARS In the framework of our model, pervasive wage arrears can be interpreted as an element of the contract between firms and employees. Russian firms are constrained by two types of institutional limits; the first is the difficulty of firing workers while

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the second is the influence of the official wage schedule (razriad). In the public sector, individual wages are based on the official minimum wage multiplied by a coefficient reflecting the formal qualification of the employee. In the private sector, wage setting is free from this constraint in principle. In fact, wages are highly influenced by the wage schedule prevailing in the public sector (Denisova et al., 1998). Hence, it is difficult for a firm to pay different nominal wages to formally identical employees openly. In such a context, firms may use wage arrears as a device to differentiate the real effective wages paid to their employees, i.e., in terms of the model, to differentiate contractual wages from market competitive wages. Low-productivity workers tend to receive low wages and are discriminated against compared to productive workers.24 A low level of the effective wage, set in the framework of an implicit contract, can result in systematic and recurrent wage arrears for low-productivity workers. From this perspective, wage arrears are anticipated fully. In our opinion, wage arrears cannot be a surprise to employees when they affect the same individuals, period after period. Rather, wage arrears are more than a simple reflection of firms’ financial situation.25 They are not an ex post adjustment of wages to an observed performance; on the contrary, they are partly determined ex ante by the firm together with the nominal wage proposed in the contract. From this perspective, the term ω∗ L˜ ∗1 − wl1 in Eq. (3) can be interpreted as the total wage arrears in a given firm. This term reflects the difference between the total wage bill ω∗ L˜ ∗1 that would have to be paid on the spot market for the l1 unproductive workers inside the firm and the actual wage bill wl1 paid to these workers. Hence, wage arrears should be higher in firms where low-productivity workers are more numerous since these workers are the main recipients of arrears. Do the enterprise data support this interpretation? To examine this issue, we use a subsample of the Goskomstat Registry for which we have balance sheets of the enterprises, including information on cumulated wage arrears, i.e., overdue wage payments, for 6,370 enterprises in 1996, 2,928 firms in 1997, and 2,569 firms in 1998. The descriptive statistics of the sample are presented in Table A2 in the Appendix. As far as we know, wage arrears have not yet been analyzed using such a large firm-level data base. In order to use this cross section information fully, we pool the observations of the various years to create an unbalanced panel. We regress the ratio of wage arrears to the wage fund on the share of white-collar workers in total firms’ employment, controlling for firms’ profitability and size, year of observation, and regional unemployment. The results are presented in Table 3. Table 3 shows that wage arrears depend not only on a firm’s performance measured by profitability, defined as the ratio of profits to output, but also on the weight of white-collar workers in total employment. Arrears are significantly higher in 24 It is simple to see that, at the threshold level, the initial contractual wage is inferior to the competitive market wage (w ∗ < ω∗ a ∗ ) because of the shifting cost. 25 Alfandari and Schaffer (1996) find that wage arrears are not correlated with a firm’s performance. They interpret wage arrears as a device used by managers to extract subsidies from the government. On the other hand, Earle and Sabirianova (1998) argue that wage arrears reflect a firm’s bad performance; i.e., firms survive by accumulating payment arrears instead of going bankrupt.

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TABLE 3 Wage Arrears, OLS, 1996–1998

Ratio of wage arrears to wage fund Profitability Share of white-collar workers in total employment Small Medium Regional unemployment Intercept R2 N Test of the null hypothesis: the share of white-collar workers is the same in firms with increasing and decreasing productivity.

All firms

Firms with increasing employment

Firms with decreasing employment

−0.245∗∗∗ (0.007) 0.110∗∗∗ (0.015) −0.024∗∗∗ (0.004) −0.013∗∗∗ (0.003) 0.004∗∗∗ (0.000) 0.067∗∗∗ (0.018) 0.238 11867

−0.192∗∗∗ (0.012) 0.063∗∗∗ (0.020) −0.008 (0.006) −0.004 (0.004) 0.003∗∗∗ (0.001) 0.096∗∗∗ (0.024) 0.213 4054

−0.255∗∗∗ (0.009) 0.151∗∗∗ (0.021) −0.034∗∗∗ (0.005) −0.016∗∗∗ (0.003) 0.004∗∗∗ (0.001) −0.050** (0.024) 0.239 7813

Rejected F(1,11813) = 8.48∗∗∗

Note. Region, industry, and time dummies included. Profitability is defined as the ratio of profit to output. Standard errors are in parentheses. ∗∗∗ Significant at the 1% level. ∗∗ Significant at the 5% level.

firms that employ more white-collar workers, which is consistent with our view that wage arrears are allocated predominantly to the less-productive employees, proxied here by white-collar workers. The positive and significant coefficient of regional unemployment shows that the labor contract is influenced by local labor market conditions. Employees accept lower wages, of which wage arrears are an integral part, if the prospects outside the firm are bad. The table also indicates that large firms have more wage arrears than small and medium-sized ones (column 1). This result can be explained by the fact that large firms have more social assets; hence, more white-collar workers are willing to accept the implicit contract. Another way of interpreting wage arrears is to suppose that, in case of difficulties, they will be imposed on low-productivity workers. Empirically, this implies that wage arrears imposed on white-collar workers should increase more when the firm’s performance is low. To check this conjecture, we rerun the regression dividing the sample in two groups, i.e., firms with increasing or decreasing employment. According to the model, a positive (negative) shock triggers a rise (a fall) in employment. Table 3 (columns 2 and 3) confirms that wage arrears do increase more with the share of white-collar workers when firms are affected by an adverse shock. For the subsample of firms with increasing employment,

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the size variables are not significant anymore; this is consistent with the result that the relation between wage arrears and the weight of white-collar workers is much stronger in firms with an adverse shock (decreasing employment). The result that wage arrears increase with the number of white-collar workers could be interpreted, alternatively, as a sign that white-collar workers are a dominant group inside the firm and collude in order to impose wage arrears on blue-collar workers. In this respect, the more numerous they are, the more powerful they are. However, this interpretation is inconsistent with the conclusion of the previous section that white-collar workers do not seem to have insider power because of the lower response of their wages to productivity gains. 6. CONCLUSION This study demonstrates that the Russian labor market is divided into a stagnant pool of less-productive people and a dynamic segment of mobile, more-productive workers employed in firms with relatively good performance. More-productive workers contract in the competitive labor market, whereas less-productive workers engage in contractual relationships that are similar to risk insurance contracts in which employees exchange security for reduced real wages. The implications of this segmentation in terms of restructuring are ambiguous. On the one hand, it reflects some adjustment of the industrial sector, namely, a dynamic employment policy of some firms and efficient reallocation of the mostproductive workers. On the other hand, if restructuring means reducing employment, then the process is far from being completed in Russia. The stagnant segment may exert an eviction effect on the dynamic sector, in particular, if it receives subsidies from the local or state government for social protection. The presence of social assets in former state firms also increases the entry cost of de novo firms. In fact, many newly created private firms do offer social services to their employees (Estrin et al., 1995). Such provision certainly slows down the development of the new private sector. This peculiar segmentation of the labor market is due to the weakness of the institutional environment in Russia in that a high level of uncertainty affects the decisions of workers and firms. In terms of our model, the particular Russian characteristic, compared to other transition countries or to market economies, is the uncertainty associated with the shifting cost σ . This uncertainty is more important in Russia because of the weaker enforcement of the law and the weaker credibility of the regime change. The firm’s budget constraint is not as hard in Russia as it is in Central European countries. This is evidenced by the uninterrupted government subsidies and, above all, by the ubiquitous payments arrears that characterize the Russian economy, i.e., interenterprise arrears, bank arrears, tax arrears, and wage arrears, as reported by Perotti (1998), Earle and Sabirianova (1998), and Ivanova and Wyplosz (1998). The weakness of both the state and of the rule of law and the slow pace of institution building in Russia shorten the time horizon of agents and lower the quality of their expectations.

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Reducing this uncertainty is a key condition for overcoming the segmentation of the labor market. This requires the creation of a social protection system independent of firms and the provision of reliable social services in terms of quantity and quality. Alleviating the irreversibility and uncertainty attached to employees’ decisions would improve the situation. The risk attached to separations would be reduced if, as in China (Fan et al., 1998), employees of the state sector who find new jobs in the private sector were allowed to return to their initial job during a certain period. Training and qualification programs can also improve the opportunities of employees on the labor market and facilitate separations. Currently, blue-collar workers enjoy better job opportunities than white-collar workers in Russia because the peculiar skills of white-collar workers are better suited to the old system of bureaucratic coordination than to the new market environment, and because resilient technical complementarities between installed physical capital and qualified workers put blue-collar workers in high demand. This situation might not endure. It could indeed be reversed as soon as firms start replacing their old capital and investing in new technologies, and when white-collar workers are trained adequately. APPENDIX TABLE A1 Descriptive Statistics, Panel 1993 to 1997, 9233 Observations: Mean Values (Standard Deviations)

Growth rate of employment Growth rate of employment of productive workers (blue-collar) Growth rate of employment of unproductive workers (white-collar) Growth rate of real output Growth rate of real wages Growth rate of real wage for blue-collar workers Growth rate of real wage for white-collar workers Number of firms with increasing real output Number of firms with increasing employment Number of firms with increasing productivity Total number of firms

1994

1995

1996

1997

1998

−0.097 (0.130) −0.105 (0.142) −0.062 (0.120) −0.591 (0.436) −0.409 (0.559) −0.424 (0.329) −0.378 (0.345) 634

−0.081 (0.133) −0.086 (0.146) −0.061 (0.125) −0.314 (0.382) −0.438 (0.278) −0.454 (0.292) −0.413 (0.319) 1712

−0.063 (0.136) −0.090 (0.136) 0.030 (0.246) −0.335 (0.399) −0.025 (0.280) −0.032 (0.309) −0.054 (0.381) 1594

−0.106 (0.147) −0.094 (0.161) −0.140 (0.295) −0.174 (0.414) 0.045 (0.221) 0.048 (0.260) 0.047 (0.395) 3081

−0.091 (0.188) 0.107 (0.231) −0.060 (0.330) −0.230 (0.559) −0.015 (0.262) −0.024 (0.304) −0.011 (0.453) 3038

1870 866 9233

2378 2080 9233

2670 1803 9233

1805 4081 9233

2574 3521 9233

Note. Growth rates are defined as first differences of logarithms. Nominal output is deflated by monthly industry-specific (five-digit) producer price index.

254

GROSFELD ET AL. TABLE A2 Descriptive Statistics, Sample with Wage Arrears: Mean Values (Standard Deviations)

Ratio of wage arrears to wage bill Profitability (profit-to-output ratio) Share of white-collar workers in total employment Proportion of small firms in the sample Proportion of medium firms in the sample Growth rate of employment Growth rate of real output Growth rate of real productivity Number of firms with increasing real output Number of firms with increasing productivity Number of firms with increasing employment Total number of firms

1996

1997

1998

Whole sample

0.144 (0.124) 0.045 (0.165) 0.225 (0.084) 0.200 (0.400) 0.566 (0.496) −0.019 (0.172) −0.242 (0.410) −0.222 (0.396) 1370 1359 2532 6370

0.166 (0.151) 0.013 (0.164) 0.233 (0.089) 0.090 (0.286) 0.484 (0.500) −0.090 (0.177) −0.071 (0.422) −0.019 (0.406) 1259 1580 676 2928

0.170 (0.169) 0.011 (0.181) 0.234 (0.091) 0.105 (0.307) 0.505 (0.500) −0.055 (0.201) −0.126 (0.474) −0.017 (0.453) 887 991 846 2569

0.155 (0.142) 0.030 (0.169) 0.229 (0.087) 0.152 (0.359) 0.532 (0.499) −0.044 (0.182) −0.175 (0.434) −0.130 (0.424) 3516 3930 4054 11867

Note. Growth rate are defined as first differences of logarithms. Nominal output is deflated by monthly industry-specific (five-digit) producer price index.

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