Determinants of unemployment duration in Ukraine

Journal of Comparative Economics 34 (2006) 228–247 www.elsevier.com/locate/jce

Determinants of unemployment duration in Ukraine Olga Kupets a,b a Labor Group, Economics Research and Outreach Center, National University “Kyiv-Mohyla Academy”,

Voloshskaya Street, bld. 10, office 214, 04070 Kiev, Ukraine b IZA, Bonn, Germany Received 2 February 2006 Available online 29 March 2006

Kupets, Olga—Determinants of unemployment duration in Ukraine There are few studies of unemployment duration in transition economies, including members of the CIS. This paper presents the first evidence of the determinants of unemployment duration in Ukraine. We examine the effects of various individual characteristics and local demand conditions on the hazards to employment or inactivity using multiple unemployment spell data from the Ukrainian Longitudinal Monitoring Survey (ULMS) for the years 1998–2002 and estimating the Cox proportional hazards model with two competing risks. The main estimated results are generally similar to those obtained in developed and other transition countries. The individual’s age, marital status, level of education, income while unemployed (including income from casual work activities and subsistence farming), and local demand constraints are significantly related to the total time in unemployment. The estimates of the baseline hazard to employment suggest marked negative duration dependence after 14 months in unemployment. Journal of Comparative Economics 34 (2) (2006) 228–247. Labor Group, Economics Research and Outreach Center, National University “Kyiv-Mohyla Academy”, Voloshskaya Street, bld. 10, office 214, 04070 Kiev, Ukraine; IZA, Bonn, Germany. © 2006 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved. JEL classification: J64; J68 Keywords: Duration analysis; Unemployment benefits; Labor markets in transition; Ukraine

E-mail address: [email protected]. 0147-5967/$ – see front matter © 2006 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jce.2006.02.006

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

229

1. Introduction Ukraine is one of the transition countries that have been lagging behind in reforms in view of considerable adjustment costs and social unrest usually associated with radical reforms. However, delay in reforms created an environment of rent-seeking, state capture, and freezing of transition (Havrylyshyn, 2005). Prolonged recession coupled with postponed enterprise restructuring has brought about a sclerotic labor market in which losing a job might be relatively rare, but once lost, finding a new job has been increasingly difficult. The weak demand for workers and competition with those still employed but looking for another job have combined to make it difficult for the displaced workers and new entrants to find jobs. The immediate result has been a build-up of a stagnant pool of unemployment and a surge of long-term unemployment at the levels similar to those of the less dynamic OECD countries. In 2003, for example, the fraction of the unemployed who have been looking for a job for more than a year (the measure of the incidence of long-term unemployment) amounted to 50.3% in Ukraine and to 42.3% on average in the OECD European countries (see Table 1 for Ukraine). Despite the extensive literature that examines the causes and consequences of long-term unemployment in developed economies (e.g. Machin and Manning, 1999; OECD, 1993, 2002), the determinants of exits from unemployment and the impact of unemployment benefits on unemployment dynamics in these countries (Devine and Kiefer, 1991; Atkinson and Micklewright, 1991), relatively little has been written about unemployment duration and its determinants in transition countries with lagged reforms. Although there are many potential reasons for the emergence of long-term unemployment including demand and supply shocks, institutional features and outside labor markets, the Western literature focuses primarily on various characteristics of labor market institutions as the main factor behind long-term unemployment in Europe. These include strict employment protection legislation, powerful trade unions, wide use of permanent contracts, generous unemployment and welfare benefits, high labor taxes and minimum wages. However, the functioning of the labor market in a transition economy is less likely to be driven by the same institutional factors as in developed countries. According to Ham et al. (1998), the level of unemployment compensation has a moderate negative effect on the duration of unemployment in the Czech and Slovak republics, compared with the corresponding estimates in western countries, while the principal factors underlying the differences in exit rates from unemployment include growth rate of the new service sector, speed of privatization and restructuring, amount of foreign direct investment, enforcement of labor legislation, and alternatives for the working-age population. A similar argument can be used to explain differences between Russia and Ukraine in terms of unemployment dynamics and long-term unemployment. Brown and Earle (2006) show that increases in job reallocation and in the productivity-enhancing consequences of the labor reallocation process appear to have been somewhat slower in Ukraine than in Russia. They conclude that a more aggressive reform strategy implemented in Russia has produced greater job reallocation, faster job creation, and less net employment decline. This, in turn, has resulted in higher intensity of flows into and out of unemployment and shorter unemployment duration. Therefore, low outflows from unemployment and long unemployment spells in many transition countries could be blamed on unsuccessful transition reforms leading to insufficient job creation and job reallocation rather than on labor market rigidities and generous unemployment compensation. On the other hand, we should not forget that supply-side determinants can be also important in a transition context (Boeri, 2001). Some categories of the unemployed with unattractive work-related characteristics may encounter much greater difficulty in finding regu-

230

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

lar jobs than their more competitive counterparts. Also, depreciation of human capital, erosion of work habits, discouragement, loss of motivation, and other consequences of long-lasting spells of joblessness (so-called duration dependence effect) may result in ever-declining chances of reemployment (Machin and Manning, 1999). In this paper, we examine the factors which may affect the probability of reemployment or withdrawing from the labor force after the period of unemployment in Ukraine. The only study directly related to our research has been done by Stetsenko (2003). The author examines the determinants of duration of the registered unemployment in Kiev using the registered data from the city employment center over 2001–2003 and employing the Cox proportional hazards and the piece-wise constant exponential models. The author finds significant positive effect for the level of unemployment benefits on the duration of registered unemployment. The other findings are that younger workers and males are more likely to leave the register to both competing destinations (to job and for other reasons out of the register); that married females tend to have significantly lower probability of transition to employment; that individuals with less than general secondary education have higher probability of transition to employment relative to individuals with higher level of education; that having profession increases chances to get a job; and that unobserved heterogeneity is insignificant. Our paper makes several contributions. It provides evidence on the duration and demographic structure of general unemployment (not only registered unemployment) in Ukraine. We use individual-level data from the first wave of the Ukrainian Longitudinal Monitoring Survey (ULMS), a nationally representative survey of individuals aged from 15 to 72. Therefore, our results refer to Ukraine as a whole and not only to its capital city, which is often considered an outlier in terms of the labor market conditions. We use the sample of unemployment spells that started between January 1998 and December 2002. Thus, we analyze distribution of unemployment spells over the period which covers the years before and after 2000—the year of economic reversal in Ukraine—as well as the years before and after the 2001 reform of the unemployment benefit system into unemployment insurance system. Finally, we test our hypothesis about the disincentive effect of income from casual activities and subsistence farming during a non-employment period and the negligible effect of unemployment benefits with respect to exits to employment together with some basic hypotheses suggested by a job search model.1 The issue of casual work activities and subsistence farming is very important in a transition economy like Ukraine given the high share of individuals involved in various informal activities and weak monitoring capacity. Although occurrence of casual work activities during unemployment is potentially endogenous, the estimated effect on the conditional probability of exit from unemployment may inform policymakers about important policy direction. Our findings confirm broadly the results of studies for developed and transition countries. Married, younger and educated individuals living in large cities are more likely to leave unemployment to employment. The higher the regional unemployment rate is at the start of unemployment the lower the probability of reemployment (controlling for oblast and calendar time dummies). Those who have alternative sources of subsistence during unemployment including income from casual work activities or subsistence farming, household income, or pensions tend to stay in unemployment before exiting to a job significantly longer. The effect of unemployment benefits with respect to exits to a regular job is found to be insignificant in the total sample of unemployed (i.e. with and without income from casual activities or subsistence farming), while 1 Discussion of the definition of unemployment adopted in our study is offered in Section 4 on Data.

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

231

it is significant and negative in the subsample of “standard” unemployed without any income from casual work or farming activities. The baseline employment hazard is non-monotonic: it increases with duration of unemployment during the first 14 months and decreases afterwards. Analysis of the determinants of unemployment duration before withdrawing from the labor force suggests that individuals over the age of 55, those who rely on household income, previously economically inactive persons, and the residents of rural area or large cities are more likely to leave unemployment for inactivity. The paper is set up as follows. Section 2 provides an overview of the unemployment insurance system and the Public Employment Service in Ukraine and the consequences of their failure to provide real assistance to the unemployed. Section 3 presents the econometric model used in the analysis. Section 4 provides the details of the data and variables used. Section 5 offers the estimation results and Section 6 concludes the paper. 2. Challenge for an unemployed person in Ukraine: unemployment insurance or alternative sources of subsistence? The Public Employment Service (PES) and the unemployment compensation system were established in Ukraine according to the Law on Employment in March 1991. The unemployment compensation system was relatively liberal in terms of eligibility, entitlement and replacement ratio until the new Law on Compulsory State Social Unemployment Insurance went into effect in 2001. In general, the PES is supposed to perform two major functions: to assist unemployed workers in their job search and to provide income support during a period of unemployment. However, it is widely believed that it is not very successful at either of these tasks in Ukraine. First, although firms are obliged to register all vacancies with the local employment center and to use the center during recruitment, many firms fail to do so, preferring other recruitment methods. Also, the PES sometimes provides training or retraining for skills that are already in surplus in the local labor markets, and public works are usually of low skill level (Kupets, 2000). As a result, the probability of finding a good job with the help of the public employment center is likely to be small, while the transaction costs of registration and staying on the register may be relatively high. One such transaction cost of staying on the register is a necessary visit of the unemployed to the local employment center located in the administrative center of his/her civil registration (new name of the old system of propiska) at least once a month. Since many people live far from administrative centers of their registration, the above requirement demands heavy expenses in terms of time and money in some cases. Second, the level of unemployment benefits is too low both in nominal and real terms. The ratio of the average unemployment benefit to the official average wage in the economy is about 25–28%, while its ratio to the nationally established level of minimum wage fluctuates between 50 and 70% (Table 1). Moreover, because of the strict unemployment benefit eligibility criteria and high incidence of long-term unemployment, the coverage ratio (the ratio of those receiving unemployment benefits or unemployment assistance to the total number of registered unemployed) has been less than 70% for all years. Although with respect to incentives/disincentives to work the unemployment insurance system in Ukraine may seem better than in CEE countries, it is certainly worse in terms of income support and poverty prevention. A study of the sources of subsistence during the period of unemployment confirms this statement. Only 4.6% of our sample of the unemployed reported that unemployment benefits were

232

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

Table 1 Unemployment dynamics and unemployment benefits in Ukraine

Registered unemployment Registered unemployment rate, % of workingage able-bodied population Fraction of registered unemployed receiving unemployment benefits, % ILO-defined unemployment Unemployment rate according to LFS, % of labor force aged from 15 to 70 Incidence of long-term unemployment (fraction of the unemployed who have been looking for job for more than 1 year), % UB Ratio of average UB to minimum wage, % Ratio of average UB to average wage, %

1998

1999

2000

2001

2002

2003

3.69

4.3

4.22

3.68

3.80

3.6

53.1

52.8

54.3

62

66.7

69.1

11.3

11.9

11.7

11.1

10.1

9.1

37

46.3

50.5

54.8

53.5

50.3

70 25.1

67.3 28

50.3 25.8

72.2 27.4

64.2 28.1

57.7 25.6

Note: Registered unemployment characteristics correspond to the end of years, while characteristics according to the Labor Force Survey (LFS) are presented for the fourth quarter in 1998 (yearly survey) and on average for 1999–2003 (quarterly surveys). Source: Derzhkomstat.

the main source of their support.2 The dominant role in support of jobless is played by household income, i.e. income of parents, spouses or other relatives (68.3%). Income from various casual activities or subsistence farming constitutes the second largest group among the main sources of subsistence (13.9%). It may serve as the only source of subsistence or operate in conjunction with household income, unemployment benefits, pensions, state transfers, or savings. Markedly, only 27.5% of those who receive unemployment benefits, along with other sources of subsistence, consider it to be their primary source of income during the period of unemployment. Most of them rely on household income. As a consequence of ineffective public employment policy and the unemployment insurance system, less than half of the actual unemployed (defined according to the ILO unemployment criteria) bother to register as unemployed in the public employment centers. An analysis of job search methods among the unemployed in our sample indicates that people rely on the help of friends and relatives (29.2%), direct contacts with employers (16.4%), job advertisements in the newspapers or Internet (37.6%) rather than on the assistance of the public employment service (10.8%). Following from the above argument, it is unlikely that the unemployment insurance system is behind the low outflows from general unemployment (as opposed to registered unemployment) in Ukraine, given how low the benefits are and how few unemployed register to receive them.3 However, another inference based on the weak enforcement of legislation and high payroll taxes could have more explanatory power. Because of very low unemployment benefits accompanied 2 Information about the main source of subsistence is taken from the answers of respondents for the direct question

about the main source of subsistence during a period of joblessness. Most frequent or noteworthy compositions of the sources of subsistence are additionally reported in Kupets (2005) but not presented here for brevity. 3 Typically, economists have seen an unemployment benefit system as having a negative effect on unemployment duration, with high benefits and long entitlement periods causing the unemployed to be less willing to accept jobs. Extensive discussion of this topic is offered in Atkinson and Micklewright (1991).

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

233

with still relatively low labor demand, many jobless people leave the formal labor market, preferring to find an informal working activity or to rely on subsistence farming. Others start some sort of self-employment (usually in a low-productivity sector) just to survive. And some discouraged persons withdraw from the labor market to look for additional sources of income such as stipends, pensions, child allowance, etc. Hence, prolonged joblessness may force some persons to intensify casual work activities or engage more actively in subsistence agriculture. On the other hand, those usually unemployed persons who are occasionally engaged in unreported activities or subsistence farming tend to search for regular jobs less intensively and, therefore, they are less likely to receive a job offer. For such individuals, alternative income may raise their reservation wage, lowering the probability of accepting a job offer and thus the probability of reemployment as a whole. Various kinds of non-labor income during an unemployment spell, including household income, pensions, other state and private transfers are likely to have the same effect on the duration of unemployment as casual labor income. Thus, we hypothesize that the involvement of usually unemployed individuals in informal activities or subsistence farming in view of labor demand constraints in the formal sector are of much greater importance than unemployment benefits in explaining the stagnancy of unemployment in Ukraine during the late period of transition. 3. Econometric model We analyze duration of unemployment spells in Ukraine using a duration model.4 This model is preferable to the usual regression model because of its ability to handle time-dependent covariates and right-censoring in the data. The focus in modeling durations of unemployment is usually on the conditional probability of leaving unemployment, the hazard function. The hazard model used for this study is the Cox proportional hazards model (Cox, 1972) of the following general form:   λi (t) = λ0 (t) exp xi (t)β , where xi is the set of explanatory variables for individual i, β is the vector of parameters to be estimated, and λ0 (t) is the baseline hazard at time t , which is allowed to be nonparametric.5 In this study, most variables are taken as time-invariant (except the year and quarter dummies) due to the limited data on time-varying characteristics of the unemployment benefit system at our disposal and the potential endogeneity of certain characteristics, which vary with time in unemployment (e.g. marital status, number of kids). One of the key assumptions of hazard models is that all inter-individual heterogeneity is due to observed factors. However, it is possible that unobserved variables are also a source of heterogeneity. Uncontrolled heterogeneity in duration models can lead to misleading inferences about duration dependence, and can also bias the estimated effects of the included explanatory variables (Lancaster, 1990). However, in certain cases, this may not be particularly serious. The empirical work of Meyer (1990) and of others suggests that failure to model distribution of unobserved heterogeneity explicitly does not seriously bias results if the baseline hazard is allowed to be 4 See Kiefer (1988) or Lancaster (1990) for more details on duration models and hazard functions. 5 In the general case, explanatory variables may vary with unemployment duration t (classic examples are time-varying

unemployment benefits and the time remaining until their expiration), with calendar time (e.g. local labor market conditions or characteristics of the unemployment insurance system which varies with policy changes), or may remain fixed over time (as most personal characteristics).

234

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

nonparametric. Furthermore, Narendranathan and Stewart (1993) argue that there is no reason to expect any distortions imposed by the use of techniques to allow for unobserved heterogeneity to be less serious than those caused by ignoring unobserved heterogeneity in the first place. For this reason, we have chosen to restrict attention in this paper to the models without accounting for unobserved heterogeneity.6 Taking into account that an unemployment spell may end with the individual either starting a job (exit to employment) or leaving the labor force (exit to inactivity), we estimate an independent competing risks model.7 This assumes that the hazard rate for exit to any destination is the sum of the two destination-specific hazard rates. We estimate the two risks separately: spells ending with transition to inactivity are considered censored when estimating hazard to employment, and vice versa. 4. Data and variables Our data are taken from the first wave of the Ukrainian Longitudinal Monitoring Survey (ULMS-2003), a nationally representative random sample of households consisting of 4056 households and 8641 individuals aged 15 to 72. The ULMS data set is unique in Ukraine, since it is the richest individual-level data set available, and it allows for the analysis of more than five years of labor market flows and unemployment duration owing to its retrospective nature.8 We use the inflow sample of unemployed including everyone who started with an unemployment spell between January 1998 and December 2002 and who provided complete responses to the questions about their period of job search. The spells starting after December 2002 are not used, and ongoing spells starting before December 2002 are censored at the date of December 31, 2002, in order to avoid the possible effects of changing criteria for employment/unemployment status when moving from the retrospective part of the questionnaire to the section referring to the reference week. We have not restricted the age of individuals in the sample according to the usual age of retirement. We think that the low retirement age (55 for women and 60 for men) and very low level of pensions lead older Ukrainians to have almost the same work incentives as those in younger age groups. Moreover, according to the ILO guidelines, pensioners, students and other individuals mainly engaged in non-economic activities who satisfy the basic criteria of unemployment should be classified as unemployed.9 Finally, we have not separated men from women in our analysis because there is no objective reason to expect significant differences in unemployment dynamics between men and women in a country like Ukraine. The total sample 6 We tested for gamma-distributed heterogeneity in our previous models with various specifications of the baseline hazard and different sets of explanatory variables. The variance of heterogeneity (gamma-distributed) was found to be not significantly different from zero. Insignificant unobserved heterogeneity has been also found in Stetsenko (2003) for Ukraine and in Grogan and van den Berg (2001) and Foley (1997) (for exits to employment) for Russia. 7 We restrict our choice to these two main destination states because retrospective data with a very long recall period, used in our study, does not allow using more alternative destination states like in many other studies (e.g. Narendranathan and Stewart, 1993). 8 It should be stressed also that the ULMS is a unique data set in the CIS area because it allows for the hazard analysis in a continuous-time framework without many simplifying assumptions. Widely used Russian Longitudinal Monitoring Survey (RLMS) and other individual-level panel data sets have serious shortcomings as sources of information on unemployment durations. According to Kiefer (1988), the two main problems of such data sets are right-censoring (exclusive sampling of the current unemployed for information on unemployment duration) and length-biased sampling (underrepresentation of short spells). Problems connected with the retrospective ULMS data are discussed below. 9 We control for those who receive any kind of pension (not only for years of service or retirement age but also for disability and loss of provider) by including a separate dummy for such persons.

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

235

according to the definition of unemployment adopted in our study (see below) comprises 1536 unemployment spells experienced by 1293 individuals. The subsample of unemployment spells without income from casual work or farming activities according to the standard ILO criteria (call them “standard” unemployed) includes 1102 unemployment spells, and the subsample of spells with any income from casual work or subsistence farming (call them “casual workers”) consists of 434 spells. The unemployment duration measure refers to the length of an unemployment spell, which is defined as the number of full months between the date of the beginning of job search (month and year only) to the date of its end (or to December 31, 2002 in the case of right-censored spells). In order to isolate the net effect of time out of work on the hazard of leaving unemployment, we introduced a set of control variables based on theoretical grounds and previous empirical findings in developed and transition countries (see Devine and Kiefer, 1991 for review of some of them). The choice of variables was constrained by the data available in the retrospective sections of the ULMS. Controls are included for gender, age, marital status and number of children under 15 years old (and their interaction with gender), education level, previous labor market status, etc. The values of the characteristics are determined at the starting date of the unemployment spell to ensure their exogeneity. Additionally, we use six dummy variables representing the categories of sources of subsistence. These dummy variables reflect the presence or absence of a certain type of income during a non-employment period. Unfortunately, the ULMS does not include retrospective information on the level of income received from various sources. Also, there is no direct information about calendar time and the length of receipt of unemployment benefits or other alternative income, as well as the remaining time for benefits to lapse. Due to the lack of this information, we are unable to analyze the effect of specific features of the unemployment benefit system on reemployment probabilities, an analysis which would provide valuable policy implications. However, given the relatively small variation in the level of unemployment benefits and length of payment as well as the low coverage of the total unemployed population (as opposed to only the registered unemployed), a dummy representing benefit receipt should be sufficient to capture the expected effect on the duration of general unemployment in Ukraine.10 In addition to individual characteristics, we use variables to account for differences in local labor demand conditions. Differences in the local labor markets are proxied by the oblastlevel quarterly registered unemployment rate at time of starting unemployment (accounting for between-region differences) and the type of settlement (accounting for within-region differences).11 In our final model we include also oblast fixed effects to take into account possible omitted regional characteristics which may affect unemployment duration and labor market conditions. Finally, we add calendar time dummies (year and quarter) which are allowed to change with time in unemployment to control for changes in the macroeconomic environment and possible seasonal effects. 10 Hunt (1995) finds for Germany that the dummy on receipt of UI is significant while the level of benefit receipt is insignificant. Addison and Portugal (2003) use a dummy on access to unemployment benefits and find it highly significant in Portugal, but they group individuals by age (seven elements of age regressor) so as to “mimic the stepped increases in benefit entitlement with age.” 11 Regional unemployment rate is the most popular measure of the local labor demand conditions (inter alia Narendranathan and Stewart, 1993 for the UK; Meyer, 1990 for the US; Foley, 1997 for Russia). The alternative measures are local unemployment and vacancy rates for the individual’s education group, real value of regional per capita industrial production, and regional agricultural/industrial employment ratio (Ham et al., 1998) or Herfindahl-Hirschman Index of employment concentration in the local labor market (Denisova, 2002).

236

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

The definition of all variables is provided in Appendix Table 1. Table 2 provides some descriptive statistics for the variables used in the unemployment duration analysis for the three samples. The mean duration of censored spells is almost twice as high as completed unemployment spells, and the maximum duration is 60 months. Distribution of the unemployed with incomplete spells by actual duration of their unemployment at the end of 2002 shows that the incidence of longterm unemployed was 58%.12 Table 2 shows that the majority of spells are experienced by the unemployed without income from casual work (72%) implying that the total sample gives a disproportionate weighting to such job-seekers. As expected, the mean unemployment durations of completed and censored spells among “casual workers” are about 2–3.5 months higher than among “standard” unemployed, and the corresponding difference in the incidence of long-term unemployment is about 6 percentage points. There are much more previously employed males and married people with general secondary/vocational or lower level of education in the subsample of “casual workers” than in the total sample or in the subsample of “standard” unemployed. It is worth noting also that in contrast to the total sample of unemployed or the subsample without income from casual work, the proportion of unemployment spells experienced by the unemployed with income from casual work increases with age up to age 55. Not surprisingly, most of those involved in some kind of casual work or subsistence farming live in villages or very small towns (about 62%). Although the samples are very similar across geographic regions, Western Ukraine has fewer “casual workers” and Southern Ukraine has more.13 Overall, the data suggest that jobless individuals participate in casual work activities because they have fewer employment opportunities, greater financial pressures, and generally worse job prospects. In other words, they have been forced to take these unconventional measures just to survive. Before turning to the discussion of results, several important methodological issues should be stressed. The main problem is that the definition and measurement of unemployment differs across sources, making comparisons difficult. Although we follow the ILO guidelines on defining the unemployed as people without work and currently looking and available for work during a given period of time (ILO, 2004), the definition of unemployment accepted in our study differs from the standard ILO definition due to the retrospective nature of the data with a long recall period. First, since labor market states are measured in relation to a long reference period such as several years rather than to a short period such as one week or one day as in most longitudinal surveys, the definition of the three labor market states employed in our study refers to the “usually” employed, unemployed or economically inactive rather than to the “currently” employed, unemployed or economically inactive individuals. Second, according to the standard ILO unemployment criteria, individuals who engage in casual work or casual business activities can not be classified as unemployed. In our study, however, we do not exclude individuals on the basis of their engagement in irregular activities from the sample of unemployed if: 12 For comparison, the share of unemployed with duration of non-employment of more than 12 months according to the official LFS data (fourth quarter in 2002) was 58.6% of all unemployed previously employed, and the share of unemployed with duration of job search of more than 12 months was 52% of all unemployed who were looking for job during the preceding four weeks. 13 Both Western and Southern parts of Ukraine are considered to be less industrially developed than the Eastern or Central parts. The West is predominantly agricultural, while the Southern oblasts have relatively diversified economies with developed service sectors.

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

237

Table 2 Descriptive statistics Total sample

Mean duration of completed spells, in months Mean duration of censored spells, in months Type of unemployment spell by destination state % Exit to employment % Exit to inactivity % Right-censored Duration group (completed spells) % <3 months % 4–6 months % 7–9 months % 10–12 months % >12 months Duration group (censored spells) % <3 months % 4–6 months % 7–9 months % 10–12 months % >12 months % Female % Married Age group % 24 % 25–39 % 40–54 % 55 Education % Primary or unfinished secondary % General secondary or vocational % Professional secondary or unfinished higher % Higher Sources of subsistence % Unemployment benefits or stipend during training % Casual work % Household income % Pension % Other state transfers % Other sources of subsistence % Previously employed Geographic location % West % Center and North % East % South Type of settlement % Village or small town % Town % Large city Number of spells (observations)

Unemployed with some income from casual work

Unemployed without any income from casual work

12.31 24.83

13.57 27.24

11.87 23.72

46.09 11.39 42.51

40.09 12.21 47.70

48.46 11.07 40.47

23.90 16.76 11.21 6.00 42.13

17.62 18.50 10.57 6.17 47.14

26.07 16.16 11.43 5.95 40.40

16.08 11.18 11.18 3.52 58.04 50.07 54.62

14.01 8.21 12.08 3.38 62.32 33.64 59.22

17.04 12.56 10.76 3.59 56.05 56.53 52.81

34.44 33.27 28.45 3.84

24.42 35.25 37.79 2.53

38.38 32.49 24.77 4.36

12.43 52.67 21.81 13.09

12.44 60.60 18.43 8.53

12.43 49.55 23.14 14.88

19.40 28.26 80.60 9.18 11.33 6.05 67.64

20.28 100 64.75 6.91 8.53 4.61 78.34

19.06 0 86.84 10.07 12.43 6.62 63.43

22.46 29.49 32.94 15.10

19.59 29.72 33.64 17.05

23.59 29.40 32.67 14.34

45.44 32.81 21.74

62.21 22.35 15.44

38.84 36.93 24.23

1536

434

1102

Note: Variables are measured at the beginning of an unemployment spell. Definition of variables is offered in Appendix Table 1. Sources of subsistence refer to all sources of subsistence (not only the main source) reported by the unemployed. Source: ULMS, author’s calculations.

238

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

(1) a person answered that he/she didn’t have a job (including entrepreneurship, business activities, individual work, work in a family enterprise or on a farm, and freelance work) at some time period in the past; (2) a person gave the reason of not having a job and answered that he/she was seeking and available for work for any time during that period; (3) there is no overlap between the period of employment and the period of non-employment according to respondent’s answers (if there was such overlapping we reclassified a person as employed); and (4) a person reported about the income from casual work or business activities, production and sale of products from own land plot, or subsistence farming for his/her own needs as one of the sources of subsistence at that time of non-employment.14 Unfortunately, the ULMS does not allow us to capture accurately the extent and the nature of such irregular, usually short-term, activities within a long period of non-employment. It is impossible to know for sure whether casual work or business activities in this case are really short-term and sporadic or whether they are regular; whether persons without a regular job in the formal sector have chosen these informal activities in light of unattractive formal sector opportunities, or whether they have been forced to engage in casual work activities or subsistence farming just to survive. It is also difficult to say whether engagement in such activities results in prolonging an unemployment spell, or whether the long-term unemployment intensifies the search for any kind of economic activity including casual activities or subsistence farming. The last issue raises the problem of the potential endogeneity of casual activities and subsistence farming, which is extremely difficult to address in duration models. Finally, although some categories of individuals classified as “out of the labor force” are conceptually distinct from the “unemployed” (e.g. disabled or retired in the US), a substantial portion of those reporting themselves as economically inactive may be reclassified as unemployed, and vice versa (Poterba and Summers, 1995). Therefore, some allowance for spurious events that result from classification error should be made when analyzing unemployment duration and dynamics. Furthermore, we might expect that the problem of classification error may become worse as respondents must recall details of events that occurred a long time ago.15 Our analysis based on the retrospective data over more than five years is certainly subject to reliability problems and recall bias (see Paull, 2002, among many others). Nevertheless, we believe that the relatively low labor market mobility of the majority of Ukrainians, the salience and social undesirability of unemployment for most individuals, and the careful design of the questionnaire have minimized this problem. 5. Estimation results Results of fitting the Cox proportional hazards model in a competing risks framework to the three samples of unemployment spells are given in Table 3.16 The figures reported are the esti14 Grogan and van den Berg (2001), who analyze determinants of unemployment duration in Russia, also do not exclude

individuals on the basis of informal sector activity from the sample of unemployed. 15 Paull (2002) argues that time in unemployment is less likely to be recalled correctly than periods of employment and inactivity, and so the spell of unemployment may be reclassified as the spell of inactivity rather than forgotten at all. 16 At the first stage of our empirical work we experimented also with parametric continuous-time models with Weibull, log-normal and log-logistic specifications of the baseline hazard as well as with discrete-time semiparametric model of

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

239

Table 3 Estimation results—duration analysis of exits from unemployment Variable

Female Married Female ∗ Married Number of children Female ∗ Children Age group 25–39 40–54 55 Education General secondary or vocational Professional secondary or unfinished higher Higher Sources of Subsistence Unemployment Benefits Casual work Household income Pension Other state transfers Other sources Previously employed Regional registered unemployment rate Type of settlement Town

Exits to employment

Exits to inactivity

Total sample (1a)

Unemployed with some income from casual work (2a)

Unemployed without any income from casual work (3a)

Total sample (1b)

Unemployed with some income from casual work (2b)

Unemployed without any income from casual work (3b)

−0.226 (0.121) 0.344* (0.136) −0.264 (0.175) −0.141 (0.084) 0.058 (0.109)

0.404 (0.293) 0.113 (0.262) −0.615 (0.378) 0.096 (0.134) −0.266 (0.201)

−0.301* (0.137) 0.562** (0.175) −0.328 (0.211) −0.274* (0.115) 0.170 (0.141)

0.194 (0.255) −0.554 (0.334) 0.389 (0.372) −0.617* (0.283) 0.600* (0.298)

0.148 (0.595) −0.485 (0.547) 0.904 (0.712) −0.551 (0.362) −0.022 (0.456)

−0.024 (0.306) −0.974 (0.523) 0.733 (0.553) −1.265 (0.648) 1.530* (0.659)

−0.325** (0.118) −0.564** (0.128) −0.892** (0.294)

−0.320 (0.259) −0.307 (0.273) −0.534 (0.817)

−0.438** (0.139) −0.752** (0.156) −1.145** (0.327)

−0.134 (0.266) 0.478 (0.254) 2.132** (0.438)

0.070 (0.128) 0.199 (0.142) 0.469** (0.153)

0.199 (0.275) 0.254 (0.322) 0.536 (0.389)

0.006 (0.147) 0.169 (0.162) 0.415* (0.171)

−0.376 (0.236) −0.044 (0.255) −0.636 (0.332)

−0.319 (0.472) −0.296 (0.557) −1.752 (1.119)

−0.782** (0.297) −0.242 (0.305) −0.642 (0.374)

−0.161 (0.108) −0.432** (0.103) −0.367** (0.116) −0.847** (0.191) −0.046 (0.126) 0.229 (0.151) −0.167 (0.095) −0.132* (0.060)

0.136 (0.228)

−0.301* (0.130)

0.185 (0.449)

0.374 (0.268)

−0.086 (0.185) −1.067* (0.500) −0.082 (0.311) 0.263 (0.347) −0.415* (0.211) −0.197 (0.132)

−0.642** (0.158) −0.955** (0.224) −0.100 (0.143) 0.109 (0.178) −0.091 (0.110) −0.128 (0.070)

0.162 (0.209) 0.030 (0.196) 0.683* (0.270) −0.267 (0.292) −0.015 (0.255) −0.515 (0.478) −0.642** (0.180) −0.009 (0.138)

0.801 (0.416) −0.967 (0.699) 0.352 (0.531) 0.004 (0.792) −0.203 (0.371) −0.247 (0.263)

0.707 (0.433) −0.148 (0.373) −0.224 (0.311) −0.788 (0.627) −0.783** (0.226) 0.126 (0.186)

0.007 (0.101)

0.035 (0.230)

0.031 (0.118)

−0.616** (0.223)

−0.563 (0.518)

−0.631* (0.264)

0.776 (0.530) 1.125* (0.554) 2.567** (0.971)

−0.587 (0.349) 0.245 (0.321) 2.262** (0.553)

(continued on next page)

240

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

Table 3 (continued) Variable

Large city N (unemployment spells) Number of failures Log-likelihood

Exits to employment

Exits to inactivity

Total sample (1a)

Unemployed with some income from casual work (2a)

Unemployed without any income from casual work (3a)

Total sample (1b)

Unemployed with some income from casual work (2b)

Unemployed without any income from casual work (3b)

0.243 (0.136) 1536 708 −4582.82

0.007 (0.302) 434 174 −887.52

0.307 (0.158) 1102 534 −3275.51

−0.326 (0.243) 1536 175 −1026.30

0.072 (0.509) 434 53 −232.85

−0.392 (0.292) 1102 122 −651.78

Notes. Estimation uses the Cox proportional hazards model. Figures reported are the estimated coefficients. Standard errors are in parentheses. All models include oblast, year and quarter dummies. Exits to inactivity are considered censored when estimating exits to employment, and vice versa. * Significance at the 5% level. ** Idem, 1%.

mated coefficients implying that the proportionate impact of each variable on the state-specific hazard can be calculated by taking the exponent of the corresponding coefficient. The splitsample estimation (columns (2a), (2b) and (3a), (3b)) points to the striking differences between “standard” unemployed (without any income from casual work) and those with some income from casual work. The estimation results for the total sample and for the sample of “standard unemployed” confirm broadly the results of studies for developed and transition countries.17 Marriage in the case of males is associated with more rapid job finding after a period of unemployment. The simplest explanation of this result is that, as household heads, married man are under greater financial pressure to return to work; they may have higher opportunity costs for staying unemployed and search more intensively for a new job. Surprisingly, the number of small children has no significant effect on the duration of unemployment for either females or males. This finding may be partly attributed to the cheap and well functioning childcare system emerged under the Soviet era with the aim of promoting female labor force participation. These findings for Ukraine are consistent with those obtained by Foley for Russia (Foley, 1997). Age is negatively associated with the probability of reemployment, implying that older workers are at a disadvantage in Ukraine’s rapidly changing economic environment. Generally, the difficulties which older workers face in finding work could be attributed to the restrictive hiring standards of employers (especially in the emerging private sector) due to objective and discriminatory factors, such as obsolete skills, health problems (which from the employer’s viewpoint the form suggested by Meyer (1990). We have also estimated the specifications including variables on religion, nationality, health status, the number of dependants younger than 15 or older than 75 in the household, previous employment status, sector of previous employment, last wage, and last occupation before moving to unemployment, number of previous unemployment spells, and time-changing national unemployment rate, but these factors appear to be not significant. Our main results are robust to their inclusion. Models analyzing only individuals with one unemployment spell show no discernible difference from those analyzing individuals who experienced more than one spell, implying that serial correlation is not a problem. 17 Review of the studies on the determinants of unemployment duration and labor market transitions in the CEE countries can be found in Svejnar (1999). Devine and Kiefer (1991) offer the literature review with detailed discussion for developed countries.

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

241

are usually associated not only with a lower productivity of workers but also with a heavy burden of sickness benefits), loss of motivation and discouragement. All these factors may in turn lead to fewer job offers. These results are consistent with job search theory and empirical evidence for many developed and transition countries (e.g. Foley, 1997; Nivorozhkina et al., 2002; Stetsenko, 2003) but they are in contrast to the findings of many studies for the early period of transition, which found longer periods of unemployment for young people (e.g. Earle and Pauna, 1996). Individuals who have completed higher education have significantly higher hazards to employment than individuals with a lower level of education, ceteris paribus. Higher exit rates among educated people can be explained by their more efficient ability to search for a job due to better access to information, higher opportunity costs of unemployment, greater flexibility and wider range of alternatives for future employment. Whereas higher educated persons are able to compete for jobs that require fewer years of schooling, the reverse is not generally the case. This issue is extremely important during the economic transition of former centrally-planned economies. In her study of occupational mobility in Russia, Sabirianova (2002) found that when the transition period was accompanied by negative demand shocks, more downward unconventional switches occurred on the career ladder (or downward occupational mobility) with greater losses taking place among those with more education.18 However, our finding of the positive effect of education on the re-employment probability is in conflict with Stetsenko’s (2003) findings regarding the effect of education on exits from registered unemployment. We attribute this discrepancy to the difference in the composition of vacancies registered at public employment service offices and those advertised in newspapers and private employment agencies in Ukraine, with the former heavily represented by vacancies for less educated persons with lower skills (Kupets, 2000). As has been shown before, our data favor people finding jobs through direct contacts with employers, the help of relatives or friends, advertisements in newspapers or private employment agencies. These jobs usually attract more highly educated people. Registered vacancies available for registered unemployed at the public employment centers, on the contrary, mainly attract less educated people with low skills level. The estimate of the variable on receipt of unemployment benefits fails to reject our hypothesis of insignificant effect of unemployment benefits on reemployment probability in the case of the total sample of unemployed. However, the effect of unemployment benefits is found to be significant and negative if we take only “standard” unemployed without any income from casual work. This implies that the existing unemployment benefit system may contribute to longer unemployment spells in some cases but it should not be considered the primary determinant of stagnant unemployment in Ukraine. The existence of other sources of subsistence during a period of unemployment, including income from casual work activities and subsistence farming, household income and pension, significantly lowers the probability of reemployment. This effect is consistent with job search theory, with a longer search duration implied by the higher reservation wages and lower job search intensity caused by alternative sources of subsistence. The local labor demand variables proxied in our model by the regional unemployment rate at start of unemployment and the type of settlement have the expected signs. The residents of regions with higher unemployment rates, all else equal, tend to have longer unemployment spells before re-employment. The residents of large cities (more than 500 thousand of inhabitants) are 18 Classic examples of such downward occupational mobility in Ukraine for males include the transition from engineer, technician, and discharged armed forces serviceman to taxi driver, builder, loader or guard. For females, transitions occur from any profession requiring a higher level of education to street salesperson, babysitter or housekeeper.

242

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

likely to have higher exits to jobs than those living in the rural area or very small towns (at the 10% confidence level). These effects presumably reflect the poorer opportunities facing people in depressed areas with relatively low labor market activity and a less diversified economy. In addition to these variables, oblast-level fixed effects were included in the model. A comparison of models with and without oblast dummies (on the basis of the likelihood ratio test) has supported the presence of oblast fixed effects which are assumed to be constant over the observation period. When we turn to the multivariate analysis of the factors affecting exits from unemployment to economic inactivity (column (1b)), several primary results emerge. Individuals with more small children appear to search for a job longer before becoming discouraged or deciding to focus on non-market activities. As expected, workers older 55 have significantly higher exit rates to inactivity than prime-age or younger individuals. This age effect captures the stronger effect of discouragement and loss of motivation among older individuals, higher possibility of retirement and stopping the job search process, health problems and some other attributes. Persons relying on household income during unemployment are more likely to leave the labor force than persons without alternative income support. Significant effect of presence of income from casual work activities with respect to the exit to employment accompanied with its insignificant impact with respect to the exit to inactivity probably indicates that various casual work activities and subsistence farming can be considered as simply survival measures taken by those who would prefer the stability of a formal regular job but with a reasonable remuneration. Previously employed individuals appear to search longer before withdrawing from the labor force than those who came from inactivity. This finding presumably reflects higher importance of work and more negative attributes associated with not having work and being idle for those previously employed. Also they may anticipate their relative advantage in finding a job and are not willing to leave the labor market. Finally, we observe significantly longer unemployment durations before withdrawing from the labor force for the residents of small to medium towns compared to the residents of rural areas or very small towns, and no significant difference in unemployment durations between residents of cities and the latter. One of the possible explanations is that the residents of rural areas can move to self-employment (primarily in subsidiary agriculture) as a last resort or withdraw from the labor market in the case of unsuccessful search of paid employment, whereas residents of small to medium towns stay unemployed longer hoping to find a regular job subject to the limited number of alternative activities. Figure 1 presents the baseline hazard functions for employment and inactivity (panels (a) and (b), correspondingly). Both functions are well behaved in terms of theoretical predictions. The employment hazard is non-monotonic: it first increases with duration until about 14 months and then falls gradually approaching zero in the end. This pattern is fairly close to the one found by Stetsenko (2003) for exits from registered unemployed in Kiev. The inactivity hazard has two peaks at 14 and 37 months, suggesting that the probability of withdrawing from the labor force is increasing during the first and third years of unemployment and is decreasing during the second year and over the last portion of the analysis time scale after the second peak. The spike in the outflow rates at 14 months is around the time of the expiration of benefits (12 months after the date of registration with the local employment center) but this explanation does not seem appropriate given the composition of our sample (with recipients of benefits comprising only one fifth of the sample). We would suggest that unemployed individuals do have some control over when they start work or withdraw from the labor force and that there are other factors inducing changes in their reservation wage. Further research is thus required to ascertain the

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

243

(a)

(b) Fig. 1. Estimated baseline hazard functions by destination state. (a) Exits to employment Note: Baseline hazard function obtained from Table 3, column (1a). (b) Exits to inactivity. Note: Baseline hazard function obtained from Table 3, column (1b).

more fundamental factors at work which could explain the pattern of duration dependence and the functioning of the labor market in Ukraine.

244

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

6. Conclusion This paper analyzes determinants of individual unemployment durations in Ukraine, using a sample of individuals entering unemployment during January 1998–December 2002 from a new, rich, nationally representative data set (Ukrainian Longitudinal Monitoring Survey) and estimating the Cox proportional hazards models and competing risks of exits to employment and to inactivity. Given the absence of an effective system of public employment services and unemployment insurance in Ukraine, this study tries to identify other potential determinants of unemployment duration. Our analysis has shown that there is huge heterogeneity among unemployed in the sample. The estimation results report evidence for existence of the disadvantaged groups of unemployed with respect to the probability of reemployment. They include older, single, less educated individuals, living in small towns or rural areas and relying on household income, pension or income from casual work or subsistence farming during unemployment period. The negative effect of casual work activities is so strong that despite the relatively small share of casual workers in the full sample it has been captured in the general model when a dummy variable on casual work is used. Recipients of unemployment benefits do not have significantly different unemployment durations in the total sample but they tend to remain unemployed considerably longer if a possibility of having income from casual work or farming activities during unemployment is excluded (the subsample of “standard” unemployed). After controlling for oblast fixed effects and changing macroeconomic environment (calendar time dummies), local demand constraints, measured by the oblast-level registered unemployment rate at start of unemployment, are found to have a significant negative effect on the exit probability. As far as duration dependence is concerned, our results show positive duration dependence of the hazard to employment until 14 months and negative duration dependence afterwards. Demand shocks, technological changes in the early 1990s and delayed policy responses have brought about persistent and stagnant unemployment in Ukraine in the late 1990s. In this study we show that the possibility of different casual work activities or subsistence farming can be viewed as one of the potential contributors to stagnant unemployment during the late period of transition. It should be noted, however, that a reverse-causality interpretation of this phenomenon is also possible. Taking into account insufficient labor demand in the formal sector and inadequate assistance in retraining and job matching by public employment centers, many unemployed people, especially from disadvantaged groups at the labor market, may be forced to engage in informal casual work activities or subsistence farming just to survive. This in turn leads to ever-diminishing chances of their reemployment especially when a certain unemployment duration threshold is passed. Thus, forced long-term unemployment accompanied with various casual work activities is likely to be a trap for those who for any reason lost a chance to get a regular job. In this situation, a good choice of policies and reforms in a number of areas is crucial to alleviate the problem of long-term unemployment and to boost the outflows from unemployment. Acknowledgments The author is grateful to the two anonymous referees, Hartmut Lehmann, Rostislav Kapelyushnikov, Irina Denisova, Jonathan Wadsworth, Michael Beenstock, John Earle, Christian Belzil, Atanas Christev, Alexander Skiba, Anna Lukyanova, Inna Maltseva for valuable com-

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

245

ments and suggestions. I benefited also from comments by the participants of the IER (Kiev) International Conference “Labor Market Reforms and Economic Growth in Ukraine: Linkages and Policies” in Kiev (March, 2004), of the 8th IZA European Summer School in Labor Economics in Buch (April, 2005), and of the IZA-EBRD International Conference “Labor Market Dynamics, the Role of Institutions and Internal Labor Markets in Transition and Emerging Market Economies” in Bologna (May, 2005). Financial assistance from the EERC (Russia) on grant R02-237, from INTAS (Belgium) on grant YS 2002-249/F7, and from the Economics Research and Outreach Center (Ukraine) is gratefully acknowledged. Special thanks to the Institute for the Study of Labor (IZA, Bonn) as the INTAS host institution for support and hospitality. Data for this study are taken from the first wave of the Ukrainian Longitudinal Monitoring Survey (ULMS) which has been carried out by the Kiev International Institute of Sociology on behalf of the international consortium of sponsors led by the Institute for the Study of Labor (IZA, Bonn, Germany). Appendix Table 1 Definition of variables Variable

Definition

Duration of unemployment (in months) Observation period Female Married

The length of time elapsed between the dates of inflow into and outflow from unemployment (or censoring date defined as December 31, 2002) January 1998–December 2002 = 1 if Female = 1 if legally married or cohabiting (i.e. in non-registered marriage), = 0 otherwise (never married, divorced, widowed or separated) =Integer number from 0 to 4, number of small children aged 15 or less Three dummy variables for the corresponding age group: from 25 to 39, from 40 to 54, and 55 or older; reference age group is full 24 years or under Three dummy variables for the corresponding level of education: general secondary or vocational (diploma of high school or PTU); professional secondary or unfinished higher (diploma of college or at least 3 years of study at the institute/university); higher (diploma of institute/university, any degree); reference education group is primary or unfinished secondary = 1 if received unemployment benefits or training allowance during an unemployment spell = 1 if received income from casual work, production and sale of products from own land plot, from casual business activities or engaged in subsistence farming for own needs = 1 if lived on income of spouse or parents or support from relatives during a period of unemployment = 1 if lived on pension during a period of unemployment = 1 if lived stipend or study loan, child allowance, alimony, social benefits, subsistence allowance, or support by state or municipal institution = 1 if lived on income from sale of property or rent, dividends, loans or savings = 1 if employed prior to the start of unemployment, = 0 if previously inactive for more than 1 month Registered oblast-level unemployment rate (24 oblasts, Kyiv City and Crimean Republic) corresponding to the starting quarter of an unemployment spell Two dummy variables for the corresponding type of settlement where an individual lived at the beginning of an unemployment spell: town (from 20 to 500 thds. inhabitants), large city (more than 500 thds. inhabitants); reference group is village or very small town (up to 20 thds. inhabitants) (continued on next page)

Number of children Age Education

Unemployment benefits Casual work

Household income Pension Other state transfers Other sources of subsistence Previously employed Regional registered unemployment rate Type of settlement

246

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

Appendix Table 1 (continued) Variable

Definition

Macroregion for geographic location in Table 2

West stands for Chernivets’ka, Ivano-Frankivs’ka, Khmel’nyts’ka, L’vivs’ka, Rivnens’ka, Ternopil’s’ka, Volyns’ka, Zakarpats’ka oblasts, Center and North consists of Kiev City, Vinnyts’ka, Zhytomyrs’ka, Kyivs’ka, Kirovohrads’ka, Poltavs’ka, Sums’ka, Cherkas’ka and Chernihivs’ka oblasts, East includes Dnipropetrovs’ka, Donets’ka, Zaporiz’ka, Luhans’ka and Kharkivs’ka oblasts, and South consists of Crimean AR and Sevastopol’, Mykolayivs’ka, Odes’ka and Khersons’ka oblasts

References Addison, John T., Portugal, Pedro, 2003. Unemployment duration: Competing and defective risks. Journal of Human Resources 38, 156–191. Atkinson, Anthony, Micklewright, John, 1991. Unemployment compensation and labor market transitions: A critical review. Journal of Economic Literature 29, 1679–1727. Boeri, Tito, 2001. Transition with labour supply. Discussion paper No. 257. IZA, Bonn. Brown, David, Earle, John S., 2006. Job reallocation and productivity growth in the Ukrainian transition. In: Handrich, Lars, Burakovsky, Ihor, Betliy, Olexandra, (Eds.), Labor Market Reforms and Economic Growth in Ukraine: Linkages and Policies. Alpha-Print Publishing House, Kiev (in Ukrainian and English). Cox, D.R., 1972. Regression models and life-tables. Journal of the Royal Statistical Society B 34, 187–220. Denisova, Irina, 2002. Staying longer in unemployment registry in Russia: Lack of education, bad luck or something else? Working paper No. 17. Center for Economic and Financial Research, Moscow. Devine, Theresa J., Kiefer, Nicholas M., 1991. Empirical Labor Economics: The Search Approach. Oxford Univ. Press, Oxford. Earle, John S., Pauna, Catalin, 1996. Incidence and duration of unemployment in Romania. European Economic Review 40, 829–837. Foley, Mark C., 1997. Determinants of unemployment duration in Russia. Discussion paper No. 779. Economic Growth Center, Yale University. Grogan, Louise, van den Berg, Gerard J., 2001. The duration of unemployment in Russia. Journal of Population Economics 14, 549–568. Ham, John C., Svejnar, Jan, Terrell, Katherine, 1998. Unemployment and social safety net during transitions to a market economy: Evidence from the Czech and Slovak Republics. American Economic Review 88, 1117–1142. Havrylyshyn, Oleh, 2005. Divergent Paths in Post-Communist Transformation: Capitalism for All or Capitalism for the Few? Palgrave Macmillan. Hunt, Jennifer, 1995. The effect of unemployment compensation on unemployment duration in Germany. Journal of Labour Economics 13, 88–120. International Labour Organisation (ILO), 2004. Sources and Methods: Labour Statistics, vol. 3: Economically Active Population, Employment, Unemployment and Hours of Work (household surveys). ILO, Geneva. Kiefer, Nicholas M., 1988. Economic duration data and hazard functions. Journal of Economic Literature 26, 646–679. Kupets, Olga, 2000. The impact of active labor market policies on the outflows from unemployment to regular jobs in Ukraine. Master of Arts Thesis. EERC, Kiev. Kupets, Olga, 2005. Determinants of unemployment duration in Ukraine. Working paper No. 05-01. EERC, Moscow. Lancaster, Tony, 1990. The Econometric Analysis of Transition Data. Cambridge Univ. Press, Cambridge. Machin, Stephen, Manning, Alan, 1999. The causes and consequences of long-term unemployment in Europe. In: Ashenfelter, Orley, Card, David (Eds.), Handbook for Labor Economics, vol. 3C. Elsevier, North-Holland, pp. 3085–3139. Meyer, Bruce D., 1990. Unemployment insurance and unemployment spells. Econometrica 58, 757–782. Narendranathan, Wiji, Stewart, Mark B., 1993. Modeling the probability models of leaving unemployment: Competing risks models with flexible baseline hazards. Journal of the Royal Statistical Society C 42, 63–83. Nivorozhkina, Lyudmila, Nivorozhkin, Evgenii, Shukhmin, Andrei, 2002. Modeling labour market behaviour of the population of a large industrial city: Duration of registered unemployment. Working paper No. 01-08. EERC, Moscow. Organisation for Economic Cooperation and Development (OECD), 1993. Long-term unemployment: Selected causes and remedies. In: OECD Employment Outlook. OECD, Paris, pp. 83–117. Organisation for Economic Cooperation and Development (OECD), 2002. The ins and outs of long-term unemployment. In: OECD Employment Outlook. OECD, Paris, pp. 187–243.

O. Kupets / Journal of Comparative Economics 34 (2006) 228–247

247

Paull, Gillian, 2002. Biases in the reporting of labour market dynamics. Working paper No. 02/10. Institute for Fiscal Studies, London. Poterba, James M., Summers, Lawrence H., 1995. Unemployment benefits and labour market transitions: A multinomial logit model with errors in classification. Review of Economics and Statistics 77, 207–216. Sabirianova, Klara, 2002. The great human capital reallocation: A study of occupational mobility in transitional Russia. Journal of Comparative Economics 30, 191–217. Stetsenko, Serhiy, 2003. On the duration and the determinants of Ukrainian registered unemployment: A case study of Kyiv. Master of Arts Thesis. EERC, Kiev. Svejnar, Jan, 1999. Labor markets in the transitional Central and Eastern European economies. In: Ashenfelter, Orley, Card, David (Eds.), Handbook for Labor Economics, vol. 3B. Elsevier, North-Holland, pp. 1809–2858.