TITLE : The Nature and Determinants o fLabor Market Transitions inEmerging Market Economies :Evidence from Bulgaria

AUTHOR: Derek C . JonesTakao Kato

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The Nature and the Determinants of Labor Market Transitions i nEmerqinq Market Economies : Evidence from Bulgari a

by

Derek C . Jones and Takao Kato *

April 199 3

Abstract

A new panel (three wave) data set for a sample of the registeredunemployed is used to generate stylized facts (transitionmatrices) on transitions between different labor market states .We find that 60% of the unemployed are long term (more than ayear) unemployed and that only 3% of the unemployed leave th elabor force . Logit regressions are estimated to test th edeterminants of these transitions . Of particular interest w efind, for men, that the probability of being re-employed i ssignificantly higher for : the better educated ; those who hadpartici p ated in a training program ; who worked last i nconstruction or transportation ; and those who belonged to apolitical party . For women, the probability of re-employment i ssignificantly lower when : she had received unemployment benefits ;her last job had a longer tenure ; her overall labor marke texperience is shorter ; and she was not educated beyond primar ylevel . For both men and women the labor market agency does no tappear to assist in successful job search .

* Department of Economics, Hamilton College, Clinton, N .Y . 1332 3and Department of Economics, Colgate University, Hamilton, N . Y .respectively .

The authors gratefully acknowledge support from the Nationa lCouncil for Soviet and East European Research . The p aper ha sbenefitted from comments by Jeffrey Pliskin .

CONTENTS

Introduction 1

Policy and Aggregate Unemployment in Bulgaria 3

The Scope and Nature of Transitions Between Differen tLabor Market States 7

The Determinants of Different Transition Paths 1 0

Conclusions and Implications 1 3

Definitions of Variables 1 6

Tables 1 8

Notes 3 1

Appendix : The Bulgarian Unemployment Survey 3 3

References 35

I Introduction

As market economies emerge in the previously centrally administere d

economies, profound changes have begun to occur in the nature an d

functioning of their labor markets . For example, during the era of

administered socialism, soft budget constraints led to labor markets i n

Eastern and Central European economies being characterized by chronic labo r

shortages (Kornai, 1990) with low or no open unemployment and, reflecting

the social obligation to work and an official commitment to ful l

employment, very high participation rates for all adults . Under the ne w

conditions, with more flexibility and greater diversity in labor markets ,

open unemployment has rapidly emerged and fundamental changes i n

participation rates in labor markets are also underway . Consequently, som e

of the key questions confronting policy-makers in Eastern and Centra l

Europe concern the changing dynamics in labor markets . Of particular

interest are questions relating to the duration of unemployment, and th e

speed at which the unemployed become re-employed (and on what terms) . Ar e

the determinants of transitions between different labor market states (e .g .

from unemployment to employment or from unemployment to unemployment )

essentially the same for all major groups (e .g . for men and women) and how

successful are different policies (e .g . unemployment compensation and

training programs)? Analagously, are the nature and determinants of

changes in participation rates (or the rate at which individuals are

exiting the labor force) the same for all groups in the labor force? I n

this paper we address such questions using a new panel data set for a

sample of the unemployed in Bulgaria collected during three waves in 199 1

and 1992 . 1 Potentially such information may help to inform current policy

making . The organization of the paper is as follows .

2

In the next section, to provide context for our survey and analysis ,

we briefly review key aspects of labor-market policy and aggregate data on

unemployment in Bulgaria during the period of our study . The heart of th e

paper is in parts III and IV in which we address issues concerning labo r

market transitions in two ways . In Part III, we provide stylizedfacts on

key details of the transformation of Bulgarian labor markets and

institutions . While from recent work we know that already there have bee n

enormous changes in labor markets and institutions within these economies 2 ,

at the same time the level of precision on many matters is limited . Fo r

example we know little from the official aggregate data about the duration

of unemployment or even gross flows between different labor market states ,

such as from unemployment to employment or from unemployment to

not-in-the-labor-force . Moreover, the severe fiscal crisis confrontin g

many of these economies has been accompanied by a marked reduction in the

scope and nature of official data collection activities . In thes e

circumstances the sort of survey data we use to record the scope and natur e

of the different transitions (and how they vary by important

characteristics such as education or gender) provide one of the few ways o f

marshalling reliable facts on changes in labor markets .

In Part IV we respond to our second objective, which is to test divers e

hypotheses concerning crucial aspects of dynamic adjustment in Bulgarian

labor markets and institutions . In particular we focus on the determinant s

of the transition between different labor market states, especially fro m

unemployment to employment . Importantly, the data will permit th e

effectiveness of various policies to be evaluated . In particular we attempt

to examine the ways in which the mix of active and passive labor marke t

policies can help to alleviate unemployment .

Throughout we recognize that the Bulgarian situation continues to be

3

quite dynamic and that there have been important changes in some of th e

issues examined in this paper, e .g . unemployment continued to grow

unrelentingly during most of the period since the last wave of dat a

collection . But while there exists a clear need for replicating this an d

similar analyses in the future, nevertheless we hope that even the present

information may help to facilitate a more informed debate amon g

policy-makers, managers and trade unionists on the different ways that

policy-makers might help to address the problems posed by the persistenc e

of large-scale unemployment in Bulgaria . Indeed, we hope to show ho w

findings derived from these kinds of surveys can contribute to th e

identification of those labor market policies which are most effectiv e

during transition .

II Policy andAgg regateUnemp loyment in Bul gari a

To provide context for the period of the survey in this section we

present and discuss those key features of and outcomes in the Bulgarian

labor market that are most relevant to the subsequent analysis ; naturally ,

our focus is on unemployment . Some of the key facts are presented in Table s

1 and 2 . 3 Also, some of the principal features of relevant policies are

briefly discussed . (For accounts that focus on other dimensions of labo r

markets see, for example : Jones 1991 ; Jones 1992 ; Szirackski and Wendel l

(1992) . Also, several broader discussions and evaluations, that do no t

concentrate on the labor market are available . See, for example : Wyzan

(1992), Planecon(1992), Rock (1992) and Bogetic and Fox (1993) .

After Bulgaria joined the World bank and the IMF in September 1990

a stabilization package was introduced which included a program of pric e

liberalization . As is clear from Table 1 the subsequent price reforms led

4

to enormous increases in prices during early 1991 . Another feature was th e

introduction of an incomes policy . By limiting enterprise wage bills and

providing for flat wage supplements to nominal wages that only partiall y

compensated for the consequences of price liberalization, real wages were

cut sharply in early 1991 . (From Table 1 we see that in April 1991, th e

time of the first wave of data collection, real wages on average were onl y

about 37% of what they had been in December 1990) . Thereafter, through

various controls and mechanisms, such as various tripartite structure s

(Jones, 1992), the rate of growth of real wages was to be moderated . As

Table 1 shows, this was effectively accomplished . By August (the second

wave) real wages had increased a little to 41% . and by April 1992 were u p

to 52% of their December 1990 levels . (Note, however, that by the end o f

1991 average real wages had risen to a level equal to 63% of their Decembe r

1990 level .) But during 1992 there was some further erosion and for mos t

of the year wages averaged about half of their levels in late 1990 .

The intent of the new policies was, of course, to encourag e

industrial restructuring . It was expected that part of this process woul d

involve contraction of manufacturing production in the state sector . Since

it was widely believed that enterprises had hoarded labor in the past, i t

was also expected that the new environment, with harder budget constraint s

and more autonomous managers, would be accompanied by a contraction o f

employment . Consequently, unless there was a swift expansion of employmen t

by new firms in the private sector, this would also lead to an increase in

unemployment . The outcomes in fact for production, employment an d

unemployment are seen in Tables 1 and 2 .

Following the shock therapy, production in state firms, especially i n

the manufacturing sector, fell dramatically-- by more than 22% in July 199 1

(compared to July 1990) and by another 25% by July 1992 (compared to July

5

1991) . As such this was probably much more than policy makers had expecte d

(Wyzan, 1992) . In addition this has been accompanied by considerabl e

reduction in employment . (Thus from Table 2 we see that by the third

quarter of 1991, manufacturing employment was almost 20% less than i n

1989 .)

These policies have also produced a steady increase in the level o f

unemployment, from 4 .3% at the time of the first wave, to 7 .8% during the

second wave and 11 .6% at the time of the third wave . Moreover, the

aggregate data show that the incidence of unemployment has varie d

considerably according to factors such as region, gender and level o f

education (Jones, 1991) . For example, unemployment rates have been highe r

for women and regional unemployment has been relatively higher in some

areas, e .g . including Plovdiv . However, while the rate of change o f

unemployment has fluctuated quite a lot, in general this has fallen over

time . The tightening of the labor market is also shown in the fall in th e

number of vacancies and in the dramatic rise in the ratio of unemployment

to vacancies . Clearly there was profound disequilibrium in the labo r

markets during the period when these surveys were being administered .

In response to this situation there has been a mix of active and

passive labor market policies . The principal passive policy has been the

social safety net and the introduction of unemployment benefits .

As part of a specialized fund to provide for "Professional Training an d

Retraining" a program of unemployment compensation was introduced i n

Bulgaria during late 1990 . For those eligible for benefits, for the firs t

month unemployment compensation exactly replaced the gross wage during the

previous month . But the replacement rate fell sharply--by 10% each month

to 50% in the sixth month . Thereafter, and for a maximum of only 3 months ,

an unemployed person was entitled only to benefits equal to the minimum

6

wage . Only employees who were involuntarily dismissed and who have worke d

for a certain period are entitled for compensation . As such, initially tha t

policy provided for a (relatively) generous replacement rate--in late 199 1

this was 76% of the average previous compensation . 4 Hence, soon after the

introduction of price liberalization (and an incomes policy which led t o

real wages falling drastically), and when most of the unemployed had no t

been receiving benefits for long, average UI did not differ much from the

average wage . (See Table 1 for comparisons of the average real unemploymen t

insurance benefit with the average real wage) . Thus in April 1991, (th e

time of the first wave) there was little difference in rate at which th e

average UI benefit for the unemployed and the average earnings of those i n

employment had fallen-- each was about one-third of its December 199 0

levels . Thereafter, however, the value of UI benefits has fallen in rea l

terms whereas the average wage has increased . Hence by the time of the

third wave, the average wage was now about 50% of the December 1990 level ,

the value of the average UI benefits was only one quarter of its real leve l

in December 1990 . In terms of the numbers of people covered, from Table 1

we see that coverage has been pretty constant . (Essentially what has bee n

happening is that those whose eligibility benefits was becoming exhausted

were being replaced by the newly unemployed . In turn, reflecting the fac t

that the number of unemployed was increasing, this means that the ratio of

those receiving benefits to the number of unemployed has been fallin g

--e .g . from 59% in September 1991 (= 202984/342345) to 41% (=214136/524300 )

in August 1992 .

Also, there have been some elements of an active manpower policy . Fo r

those officially unemployed, the labor market exchanges offer informatio n

on vacancies and also administer some retraining schemes . 5

7

III. The Scope and Nature of Transitions Between Different Labor Market States

In this section, (and partly as prelude to hypothesis testing in the

next section), we describe the key "stylized facts" concerning the scope

and nature of transitions between different labor market states . The

principal method is to report diverse "transition matrices" which show, fo r

particular groups and particular periods, the transitions between the thre e

possible states for individuals all of whom begin as unemployed --i .e . from

unemployment to employment ; from unemployment to "not-in-the-labor-force" ;

and those unemployed who remain unemployed . It would, however, be a tediou s

exercise to report all, transition matrices (i .e . using all the informatio n

on the questionnaires and for all three waves) . Hence we focus on thos e

variables which are most likely to be of interest to policy makers (e .g .

those showing the effects of age, gender, education, training an d

unemployment compensation).8 Also since it is the long term nature o f

unemployment that potentially is of most concern to policy makers, we focu s

on transitions between the first and the third waves .

In Table 3 we show data for the transitions that took place between

the first and the second waves (April 1991- August 1991) and the first an d

third waves (April 1991 and March 1992) . A major finding is that, for the

sample as a whole, 60% continued to be unemployed between the first an d

third waves (i .e . during a period of a year) . Were this sample to be

reflective of the total volume of unemployment, this suggests that 60% o f

the unemployed are "long term unemployed ." However the data also indicate

that there is considerable movement in and out of the unemployment pool ,

probably more than the steady climb in the growth of unemployment in th e

aggregate might indicate . Almost 37% of the initial sample were

8

successful in finding re-employment during the sample period . Moreover ,

some of the unemployed have continued to find new jobs even as the

recession has intensified . That is, whereas only 13 .5% of the sample had

found re-employment at the time of the second wave of data collection, thi s

has almost tripled (to 36 .9%) by the time of the third wave . When the

effects of gender are considered, there is some slight evidence that men

were able to find re-employment more easily than were women (a differenc e

of 2 .5%) . The final element in the transition matrix shows the extent to

which those who were unemployed quit the labor force . We see that, overall ,

only 3 .1% of the initial sample had left the labor force after a year .

(Compare this with an annual figure of 7% observed for the UK (Wadsworth ,

1989) . Moreover, the rate of exit was basically unchanged --between Augus t

1991 and April 1992 the exit rate increased marginally, from 3 .1% to 3 .6% .

However, it is also true that men were less likely than women to quit th e

labor force (a difference of 1 .8%) .

In Table 4 we report transition matrices that concentrate on the

effects of age and gender on the different transition rates between th e

first and the third waves . From Table 4 we see that, compared to younge r

men, older men typically have a more difficult time in securing

re-employment and also are more likely to leave the labor force . The

pattern is similar for women except that, relative to middle-aged women ,

younger women also have greater rates of exit and more difficulty i n

finding new jobs .

In Table 5 the effects of education on the different transition s

between April 1991 and April 1992 are investigated . In general, the more

educated are more apt to find new jobs . For both men and women, this i s

strongly apparent for those who have either semi-higher or higher

education . Also, in general, the more educated are more likely to remain in

9

the labor force . As such these findings tends to mirror similar finding s

for western economies in recession (e .g . Ehrenberg, 19-- )

In Table 6 the effects of training on the different transitions are

shown . In the interval between the time of the first two waves ,

participation in training programs does not seem to have had a positive

impact on the probability of either unemployed men or women findin g

re-employment . But the picture seems to have changed since then . Of the men

who had participated in training programs, 68 .8% had found a new job withi n

a year (compared to only 10% between the first two waves) . For women the

corresponding figures are 40 .3% and 10 .2% . This suggests that active labo r

market policies such as training do pay off, particularly after a lag .

In Table 7 the effects of whether or not individuals were receiving

unemployment compensation on the different transitions are shown . In the

interval between the first two waves, the existence of unemployment

insurance seems to have had a ne gative impact on the probability o f

unemployed individuals becoming re-employed . Thus in August 1991, 10% o f

men who had received unemployment compensation reported that they ha d

subsequently found a job, whereas 16% of those who had not received

unemployment compensation reported that they had found a job . For women the

corresponding figures in August 1991 were 13% and 16% . In looking at data

for April 1992, at first glance the neagtive impact of unemployment on the

probability of finding a job seems to have sustantially increased since the

earlier interval . Thus in April 1992, 33% of women who had received

unemployment compensation reported that they had found a job (since Apri l

1991), while 47% of those who had not received unemployment compensation

reported that they had found a job .

10

IV The Determinants of Different Transition Paths

A . Emp irical Strategy

We follow Toikka (1976), Clark and Summers (1979) among others i n

assuming that movements between states are governed by a Markov process .

That is, the probability of transition depends only on the current stat e

occupied . Since in all cases the initial state is unemployment there ar e

three states 7 that we examine, namely UEi, UUi and UNi where, UNi for

example represents the probability that an individual is observed to hav e

left the labor force at time t conditional upon being unemployed in tim e

t-l . The gross probability of transition from state i to state j is give n

by :

Pij = Fij/Si

i,j = E,U .N .

where Fij is the number of individuals in state i in April 199 1

and in state j is

March 1992, while Si is the original stock i n

April 1991 . Assuming a Markov process, the transition probabilit y

is exponentially distributed .

We follow the literature and estimate individual transitio n

equations by standard empirical methods, namely by the applicatio n

of binomial and multinomial logit regression analysis .

1 1

In this procedure, to test competing hypotheses, the Markov probability o f

moving between states of the labor force is considered as a function of vectors

of

variables8 representing : (i) personal characteristics such as

education, labor market experience, age, household size, tenure, wage, rank an d

industry of the last job ; (ii) policy variables such as participation in a

training program, the use of the labor agency for job search, and the receipt o f

UI benefits ;

(iii) non—policy variables that potentially influence the pro-

bability of transition such as union membership, political affiliation .; and (iv)labor

regional variables, reflecting the potential influence of diffferent local/markets .

C : Findings

In Tables 8a and 8b, we report results from the multinomial logit regres-

sion analysis for women and men . Nearly all estimated coefficients for tran-

sition from unemployment to not—in—the—labor—force (UN) for women and al l

coefficients for UN for men are insignificant, suggesting that transition from

unemployment to not—in—the—labor—force may not be a outcome distinct fro m

thattransition from unemployment to unemployment . Considering that less than 5% of the

sample leaves - the labor force, this is hardly surprising . Based on this

result, we proceed to estimate binomial logit models witht two outcomes : (i) having

found a job (UE) and (ii) not having found a job (UU) .

We report results from the binomial logit regression analysis for wome n

and men in Tables 9a and 9b . In Table 9a we see that the probability of th e

unemployed becoming re—employed within a year is significantly lower for wome n

when : (i) she has not attained education levels higher than primary ; (ii) her

last job had a longer tenure (or she has accumulated more specific human capital

in her last job) ; (iii) her overall labor market experience is shorter (or sh e

has accumulated less general human capital) ; (vi) her last job was not in com-

munication ; and (v) she has received UI benefits . For men (Table 9b) the

9

12

transition probabilities from unemployment to employment are significantl y

higher when : (1) he has attained a semi-high level of education ; (ii) his las t

job was in construction or transportation ; (iii) he belongs to a political party

(informal job search channel) ; and (vi) he has participated in a retraining

program .

To measure the size of the effects on the probability of the unemploye d

being re-employed of several policy-relevant variables, we calculate th e

predicted change in the probability caused by changes in each of these varia-

bles . First, consider the average unemployed man who has not participated in a

retraining program . Substituting zero for TRAIN and the mean value for al l

other variables and using estimated coefficients of Table 9b, we calculate hi s

probability of being re-employed within a year to be 36 .2% . Now suppose that he

participates in a retraining program . Substituting one for TRAIN and the mea n

value for all other variables and using estimated coefficients of Table 9b leads

to his probability of being re-employed within a year being 77 .7% . The increas e

in the probability of being re-employed within a year from 36 .2% to 77 .7% gauge s

the impact of the participation in a retraining program .

Second, as shown in Table 9b, the impact of belonging to a political part y

for men is likewise measured by a predicted rise in the probability of re-

employment from 34 .5% to 71 .5% . Third, as shown in Table 9a, for women having

attained a general secondary level of education will increase the re-employment

probability from 22 .3% to 69 .3% whereas having completed a special secondary

level will result in a rise in the re-employment probability from 14 .8% t o

61 .1% . Last, as shown in the same table, for women receiving UI benefits wil l

lead to a fall in the re-employment probability from 52 .7% to 26 .6% .

1 3

Conclusions and Imp lication s

A new panel (three wave) data set for 550 registered unemployed is use d

in part to generate stylized facts (transition matrices) on transition s

between different labor market states . The key findings are that : 60% o f

the unemployed are long term (more than a year) unemployed -- they remai n

unemployed for at least a year ; only 3% of the unemployed leave the labo r

force ; 37% of the unemployed do find jobs within a year .

We also test theories on the determinants of these transitions ,

especially from unemployment to employment . We find significant difference s

in these determinants for men and women . For men the transitio n

probabilities from unemployment to employment are significantly higher fo r

those who : are educated to at least a semi-high level ; have participated i n

a retraining program ; were previously employed in construction or

transportation ; and were amember of a political party . For men many othe r

factors do not seem to significantly effect the transition probabilities .

Such factors that do not seem to matter one way or the other include :

tenure at the last job ; overall labor market experience ; and the receipt of

unemployment benefits . For women, however, these last three factors are

important . The probability of becoming re-employed within a year is

significantly lower for women who : has relatively little overall labor

market experience ; received unemployment benefits ; and had relatively long

tenure in her last job . In addition, for women, the probability of being

re-employed within a year was found to be significantly lower if th e

highest level of education attained was primary . For women, unlike men, two

factors that do not matter one-way-or-the-other are participation in a

training program and membership in a political party . Finally, for bot h

men and women, there are several potentially important variables that do

1 4

not significantly affect the transition probabilities . These include : the

assistance individuals report having received from the Labor Agency ;

membership in a trade union ; the region within which respondant s ' live ; and

age . 10

Some of the particular labor market policy implications that follow

from this analysis apparently are straightforward . 11 In addition, the

magnitude of the expected benefits (in terms of increasing the rate a t

which groups of the unemployed might be expected to find jobs because o f

policy changes) are indicated by the simulations reported in the paper .

Thus the benefical effects (on the probability of male re-employment), fro m

men particpating in training programs, suggests that this type of activ e

labor market policy be expanded in scope . Analagously for women the policy

implication is to expand particular educational programs (thru the level o f

special secondary) . Labor agencies, as constituted during the period of thi s

study, clearly did not help the unemployed much in their job search .

(Informal channels, e .g . the role played by membership in a politica l

party, were more important .) Our findings also point to the detrimental

effect on the unemployment rate because of the payment of unemploymen t

compensation to women . At the same time however, while the payment of U I

may delay the timing of re-employment, it may be justified on othe r

grounds, possibly including other economic efficiency grounds such as i n

facilitiating better labor market matches . Indeed, in

all instances w e

would hope that the importance of the design of feasible and effective

labor market policies would be recognised insofar as they contribute t o

successful overall economic reform and equitable sharing of the costs o f

transition (Freeman, 1991) .

Nonetheless, we are aware that our findings are preliminary and tha t

more work is needed . In particular, in future research it would be very

1 5

useful to undertake a parallel panel survey study that is representative o f

the total working population . Such a procedure would more accuratel y

capture the true extent of initial unemployment as well as allow fo r

analysis of the determinants of more transitions other than those beginnin g

form a state of unemployment .

AppendixVariable Definition *

DEPENDENT VARIABLE (Y )Y-0 if one reported to be unemployed in April 1992 .Y-1 if one reported to be employed in April 1992 .Y-2 if one reported to be out of the labor force in April 1992 .

EDUCATION :

GENERAL2-1 if one reported general secondaryas level of completed education .

SPECIAL2-1 if one reported special secondaryas level of completed education .

SEMIHIGH-1 if one reported semi-highas level of completed education .

HIGHER-1

if one reported higheras level of completed education .

All the above dummy variables are zero if one reported elementary or primar yas level of completed education .

EXPERIENCE :

TENURE1-tenure (years) of last job .OUTYEAR1-general labor market experience (years) .

CHARACTERISTICS OF LAST JOB :OLDWAGE1-wage (lv .) of last job .

EXECUTIV-1 if one reported high-ranking executives as position of last job ,0 otherwise .

CONS-1 if one reported construction as sector of last job .AGRI-1 if one reported agriculture or forestry as sector of last job .TRAN-1 if one reported transport as sector of last job .COMM-1 if one reported communications as sector of last job .SERV-1 if one reported none of the above and manufacturin g

as sector of last job .

All the above dummy variables are zero if one reported manufacturing as secto rof last job .

INFORMAL CHANNEL :UNION-1 if one reported to belong to a trade union, zero otherwise .POLITIC-1 if one reported to belong to a political party, zero otherwise .

POLICY :

TRAIN-1 if one reported to have participated in retraining course sduring 4/91-8/91, zero otherwise .

UI-1 if one reported to have received unemployment benefit sduring 4/91-8/91, zero otherwise .

AGENCY1-1 if one reported'to reply much on the Labour Agencyin order to find a new job .

-2 if one reported to rep ly little on the Labour Agenc yin order to find a new job .

-3 if one reported to almost do not re p ly on the Labour Agencyin order to find a new job .

16

Variable Definitions Continue d

FAMILY :FAMSIZE1-size of family .

FAMUN1-1 if one reported to have other unemployed in family, zero otherwise.

AGE :

AGE20-1 if one reported to be 20 or younger .AGE2130-1 if one reported to be between 21 and 30 .AGE4150-1 if one reported to be between 41 and 50 .

AGE51-1 if one reported to be 51 or older .

All the above dummy variables are zero if one reported to b ebetween 31 and 40 .

17

Table 1 : KeyLabor MarketDataforBulgaria: Feb. 1991 - Sept. 199 2

Unemploy . U/L (U/U . 1

) i

(V) U/V Receive Ave . Ave . Retai l Industrial(U ) (%) (%) Vacancies UI

(i) Real Real Prices Productio n1991 Wage U I

Feb

103184 2 .5 39 .2 47 35 122 .9 79 . 6

Mar

134797 3 .2 30 .6 32 34 50 .5 84 . 2

Apr

176939 4 .3 31 .3 37 38 2 .5 76 . 5

May

205950 5 .0 16 .4 42 37 0 .8 72 . 2

June 233724 5 .7 13 .5 43 44 5 .9 69 . 8

Jul)? 280075 8 .9 19 .8 43 53 8 .4 77 . 5

Aug

316277 7 .8 12 .9 41 49 7 .5 78 . 1

Sept 342345 8 .5 8 .2 18486 18 .5 202984 50 52 3 .8 77 . 1

Oct

375922 9 .2 9 .8 14136 26 .6 216521 52 38 3 .3 76 . 7

Nov

400812 10 .1 6 .6 11741 34 .1 220266 58 36 5 .0 78 . 3

Dec

419123 10 .7 4 .6 9994 41 .9 216728 63 33 4 .9 74 . 6

199 2

Jan

426017 10 .9 1 .7 11299 37 .7 200345 52 33 4 .8 61 . 5

Feb

435550 11 .2 2 .2 10396 41 .9 182198 49 27 5 .9 104 . 8

Mar

452564 11 .6 3 .9 11955 37 .9 177566 54 28 3 .9 80 . 7

Apr

467044 12 .0 3 .2 10824 43 .1 181284 52 28 3 .2 81 . 3

May

470688 12 .1 0 .8 10439 45 .0 177953 47 26 11 .9 76 . 5

June 475822 12 .2 1 .1 10725 44 .4 176069 50 23 5 .8 79 . 6

July.

506025 13 .0 6 .3 10307 49 .1 206280 49 22 2 .8 75 . 4

Aug

524300 13 .4 3 .6 11904 44 .0 214136 50 24 1 . 2

Sept 533117 13 .8 1 .7 10746 49 .6 218128 53 24 3 .4

Notes :

1. Retail price figures are percentage growth, relative to previous month .

2. For Industrial production series the corresponding month in 1991= 100 .

3. Average wage and average UI benefits are real with Dec 1990=10 0

.4 . UI - unemployment insurance (compensation) .

Sources,

Wyzan (1992) ; Planecon (1992) ; AECD (1993) ; authors ' calculations .

18

Table 2

Emp loyment by Sector in Bul garia ('000s )

Total Manufac Construc Health/Ed . Pub Admin .

1989 4365 1496 333 491 6 1

1990 4097 1347 312 494 5 5

1991 Q1 3404 1300 239 484 4 7

1991 Q2 3348 1259 230 481 4 6

1991 Q3 3272 1219 215 450 n .a .

Notes

The data exclude the private sector which did not exist (officially) i n

1989 and has grown storngly since then .

1 9

Table 3 : Gross transition Flows : April 1991 - March 1992 (%)

April

1991-August 1991 April

1991-March

199 2

Total Male Female Total Male Femal eUnemployment to 13 .5 12 .9 13 .7 36 .9 38 .7 36 . 2

EmploymentUnemployment to 2 .7 1 .8 3 .1 3 .1 1 .8 3 . 6

out of Labor Forc eUnemployment to 83 .8 85 .5 83 .2 60 .0 59 .5 60 .2

Unemployment

20

Table 4 : Transition Matrices and the Role of Age:April 1921 - March 1992

Table 5 : Transition Matrices: Role of Education : April '91 - March '92

22

Table 6 : Transition Matrices : Role of TrainingApril '91-March '92

Me nYes No Total

WomenYes No Total

UE

U N

U U

TOTAL

1 1

6 .7917 .4 668 .7 50.000 .000 .000 .005

3 .0 95 .2 131 .2 51 69.88

Men

Yes

5 232 . 182 .54

35.623 .001 .85100.002 .0 59 1

56 .1 7

94.79

62.3314690.12

6 338.89

3.001 .8 5

9 6

59 .2 6

16 2

100

2 56 .4817.8640.321 .000.267.1 4

1 .6 13 69 .3315.5258.066 2

16.06

11 529 .79

82 .1 435:4913.003.3792.864 .0 119 650 .7884 .48

60 .49

32483.94

14036.27

14.00

3.63

23 260 . 1

386100

Total

April '91 - August '91

No

Women

N oTotal YesUE 1 .00 19.00 20 .00 4.00 48.00 52.00

0.65 12.26 12 .90 1 .08 12.94 14.02

5.00 95.00 7.69 92.3 1

10.00 13.10 10.26 14.46U N 0 .00 3 .00 3.00 0.00 12.00 12.00

0 .00 1 .94 1 .94 0.00 3.23 3.230 .00 100.00 0.00 100 .00

0 .00 2.07 0.00 3.6 1UU 9.00 123.00 132.00 35.00 272 .00 307.00

5.81 79 .35 85 .16 9.43 73.32 82.756.82 93.18 11 .40 88.6090.00 84.83 89.74 81 .93

Total 10 .00 145.00 155 .00 39.00 332.00 371 .00

6 .45 93.55 100 .00 10.51 89.49 100 .00

23

Table 7 : Transition Matrices : Effect of Unemployment Insurance

A. April '91 - March '9 2

Men

Wome n

24

Table 8a Multinomial Logit Estimates of Unemployment Transition sApril 1991-March- 1992 (Women) ,

UE(Y-1) UN(Y-2 )IndependentVariable(X)

SampleMean Coefficient t value Coefficient t value

Constant 1 -3 .1382 -1 .957 -19 .806 -0 .0 3EDUCATION :

GENERAL2 0 .21818 2 .1004 1 .84 11 .535 0 .01 7SPECIAL2 0 .43333 . 2 .2471 2 .006 12 .053 0 .01 8SEMIHIGH 0 .042424 2 .5126 2 .015 0 .34979 0HIGHER 0 .26061 3 .2288 2 .828 12 .289 0 .01 9

EXPERIENCE :TENURE1 7 .4336 -0 .040151 -1 .542 0 .21061 2 .09 7OUTYEAR1 13 .952 0 .062235 1 .692 -0 .16259 -1 .14

CHARACTERISTICSOLDWAGE1

OF LAST JOB :

291 .6 -0 .0004255 -0 .345 -0 .0024974 -0 .65 2EXECUTIV 0 .1303 0 .38527 0 .997 1 .1592 1 .059SERV 0 .25455 0 .36548 1 .08 -0 .92872 -1 .01 9CONS 0 .11212 0 .63612 1 .482 -12 .763 -0 .03 7AGRI 0 .039394 0 .99821 1 .419 -11 .756 -0 .01 8IRAN 0 .051515 0 .52265 0 .862 -12 .764 -0 .02 3COMM 0 .18182 1 .1762 3 .002 -0 .56413 -0 .509

INFORMAL CHANNEL :UNION

0 .38485 0 .41068 1 .486 0 .60071 0 .74 8POLITIC

0 .10303 0 .030578 0 .072 -11 .988 -0 .03 2POLICY :TRAIN

0 .16667 0 .034984 0 .103 -1 .1231 -0 .95 1UI

0 .80606 -1 .1107 -3 .257 1 .0888 0 .899AGENCY1

2 .4606 -0 .072486 -0 .416 1 .965 1 .82 5FAMILY :

FAMSIZE1

3 .4273 0 .13482 1 .012 0 .0070002 0 .01 9FAMUN1

1 .8697 -0 .066428 -0 .167 0 .16249 0 .13 8AGE :

AGE20

0 .015152 -14 .985 -0 .011 -11 .048 -0 .00 9AGE2130

0 .24545 0 .043424 0 .104 -0 .0069915 -0 .006AGE4150

0 .29091 -0 .62756 -1 .46 -1 .8934 -1 .379AGE51

0 .033333 -0 .29553 -0 .343 1 .9735 . 0 .8

Log-Likelihood -221 .61Restricted (Slopes-0) Log-L .

-262 .80Chi-Squared (48) 82 .373

25

Table 8bMultinomial Logit Estimates of Unemployment Transition s

April 1991-March 1992 (Men )

UE(Y-1) UN(Y-2 )Independent

Variable(X)SampleMean Coefficient t value Coefficient t value

Constant 1 -2 .6352 -1 .221 4 .6691 0 .001EDUCATION :

GENERAL2 0 .17241 0 .94841 0 .809 -24 .962 -0 .003SPECIAL2 0 .33103 1 .413 1 .248 -47 .288 -0 .008

SEMIHIGH 0 .034483 3 .1917 1 .906 -16 .864 -0 .003HIGHER 0 .38621 1 .6322 1 .376 5 .2302 0 .001TENURE1 7 .3048 0 .045647 1 .239 0 .5395 0 .005

EXPERIENCE :OUTYEAR1 15 .926 -0 .037347 -0 .701 -3 .936 -0 .02 5

OLDWAGE1 387 .08 0 .0015537 1 .04 -0 .092031 -0 .012CHARACTERISTICS

EXECUTIVOF LAST JOB :

0 .29655 0 .87968 1 .507 17 .865 0 .009SERV 0 .23448 -0 .37309 -0 .586 -7 .7441 -0 .005CONS 0 .13793 1 .2941 1 .867 14 .241 0 .00 5ACRI 0 .048276 1 .2165 1 .176 -5 .3038 -0 .00 1IRAN 0 .096552 2 .7397 2 .685 28 .184 0 .006COMM 0 .10345 -0 .55401 -0 .696 -21 .312 -0 .01 1

INFORMAL CHANNEL :

UNION 0 .4069 -0 .78091 -1 .543 42 .659 0 .03 2POLITIC

0 .15172 1 .5393 2 .213 12 .03 0 .00 6POLICY :

TRAIN

0 .089655 1 .7625 2 .177 -4 .3686 -0 .001

UI

0 .75172 -0 .58443 -1 .057 9 .069 0 .00 2AGENCY1

2 .3172 0 .038766 0 .12 1 .1002 0 .001FAMILY :

FAMSIZE1

3 .4 -0 .20881 -1 .083 -0 .28882 -0 .001

FAMUN1

1 .8138 0 .67384 1 .105 -6 .2856 -0 .001AGE :

AGE20

0 .013793 0 .39482 0 .234 52 .727 0 .009

AGE2130

0 .22069 -0 .31981 -0 .431 13 .526 0 .005AGE4150

0 .23448 -0 .48766 -0 .67 -2 .8964 -0 .002ACE51

0 .16552 -0 .79337 -0 .669 106 .75 0 .041

Log-Likelihood -74 .718

Restricted (Slopes-0) Log-L .

-107 .82Chi-Squared (48) 66 .19 5

26

UE(Y-1 )Independent SampleVariable(X)

Mean Coefficient t value Pro(Y-1)

i f

X-0 X- 1

Constant

1 -3 .0876 -1 .93 6EDUCATION :

GENERAL2

0 .21818 2 .0624 1 .81 0 .223 0 .69 3SPECIAL2

0 .43333 2 .2032 1 .971 0 .148 0 .61 1SEMIHIGH

0 .042424 2 .5155 2 .01 9HIGHER

0 .26061 3 .1173 2 .742EXPERIENCE :TENURE1 7 .4336 -0 .05046 -1 .97 8OUTYEAR1

13 .952 0 .063581 1 .73 8CHARACTERISTICS OF LAST JOB :

-0 .0003903 -0 .31 8OLDWAGE1

291 . 6EXECUTIV

0 .1303 0 .31997 0 .8 5SERV

0 .25455 0 .42887 1 .28 1CONS

0 .11212 0 .72455 1 .697AGRI

0 .039394 1 .0941 1 .566TRAN

0 .051515 0 .63624 1 .05 3COMM

0 .18182 1 .1849 3 .082INFORMAL CHANNEL :UNION

0 .38485 0 .37889 1 .38 8POLITIC

0 .10303 0 .13676 0 .32 5POLICY :

TRAIN

0 .16667 0 .069453 0 .20 7UI

0 .80606 -1 .1245 -3 .326 0 .527 0 .26 6AGENCY1

2 .4606 -0 .11554 -0 .66 6FAMILY :

FAMSIZE1

3 .4273 0 .1479 1 .12 8FAMUN1 1 .8697 -0 .058313 -0 .14 8

AGE :AGE20

0 .015152 -11 .105 '-0 .05 6AGE2130

0 .24545 0 .018203 0 .044AGE4150

0 .29091 -0 .53352 -1 .25 6AGE51

0 .033333 -0 .43058 -0 .516

Log-Likelihood -192 .36Restricted (Slopes-0) Log-L .

-216 .86Chi-Squared (24) 49 .01 3

27

Table 9b BinomialLogit Estimates of Unemployment Transition sApril 1991-March 1992 (Men )

UE(Y-1 )Independen tVariable(X)

SampleMean Coefficient t value Pro(Y-1)

i f

X-0 X- 1

Constant 1 -2 .5015 -1 .16 1EDUCATION :

GENERAL2 0 .17241 0 .95411 0 .814SPECIAL2 0 .33103 1 .443 1 .27 7

SEMIHIGH 0 .034483 3 .2316 1 .93 9HIGHER 0 .38621 1 .6146 1 .362

EXPERIENCE :

TENURE1 7 .3048 0 .046443 1 .26 1OUTYEAR1 15 .926 -0 .040068 -0 .75 2

CHARACTERISTICS O F

OLDWAGE1

LAST J08 :387 .08 0 .0016385 1 .099

EXECUTIV 0 .29655 0 .83295 1 .429SERV 0 .23448 -0 .37399 -0 .59 1CONS 0 .13793 1 .3132 1 .889AGRI 0 .048276 1 .2877 1 .244TRAN 0 .096552 2 .7702 2 .71 2

COMM 0 .10345 -0 .5388 -0 .674INFORMAL CHANNEL :

UNION '

0 .4069 -0 .87019 -1 .74POLITIC 0 .15172 1 .5585 2 .237 0 .345 0 .715

POLICY :TRAIN 0 .089655 1 .8102 2 .219 0 .362 0 .77 7UI 0 .75172 -0 .58259 -1 .05 2ACENCY1 2 .3172 0 .023722 0 .07 3

FAMILY :

FAMSIZE1 3 .4 -0 .21414 -1 .12FAMUN1 1 .8138 0 .6413 1 .05 1

AGE :AGE20 0 .013793 0 .338 0 .20 1AGE2130 0 .22069 -0 .36339 -0 .49 2AGE4150 0 .23448 -0 .44418 -0 .6 1AGE51 0 .16552 -0 .8752 -0 .737

Log-Likelihood -75 .27 8

Restricted (Slopes-0) Log-L .

-98 .340Chi-Squared (24) 46 .12 5

28

Table 10 : Portrait of the Sample of Unemployed In Bulgaria

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .CHARACTERISTIC

% AS OF APRIL 199 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70.4%29.6%

3.0%25.6%

37.4%

27.1 %

6.8%

0.2%

5 .1 %

21 .3%39.7%

4 .4%

29.0%

0.2%

2.8%

14.0%78.6%

4.4%

38.6%

12.1%

4.0%

0.4%

5.8%

0.8%

15 .5%

3.4%

3.8%

3.8%

12%

7.4%5.4%

96.8%0.2%

0.6%

0.8%1 .7%

SexFemal eMale

Age0 . 20

21 -30

31 -40

41 . 50

51 - 60

over 60

Completed Educatio nElementaryPrimaryGeneral SecondarySpecial Secondary

Semi - HigherHigher

Level o fPrevious PositionHigh ranking executiv eMiddle level executiveLower level executivePerformersNo prior employment

Sector of Previous EmploymentIndustryConstructionAgricultur eForestryTransportCommunicationsCommerce

ScienceEducationArtPublic HealthManagementOthers

Ethnicit yBulgaria nBulgarian Musli mTurkGypsyOther

Size of FamilyOne

TwoThreeFourFiveSlxSevenNine

Other Unemployed Family Member s

YesPossessors of :

A carA TVA Washing Machine

4 .4%

14 .4%

29.0%41 .6%

8.3%

1 .7%

0.4%

0.2%

14.6%

59.9%79.5%53 .2%

29

Table 11 : Status of the Unemployed In BulgariaApril 1991 - March 199 2

April

1991 August

1991 March

1992

52.4% 73.1% 2 .4%

41 .7% 45.6% 45.1%

5.8% 5.6% 6 .1%

8.1% 18 .4% 16.7%

19 .4% 18.9% 15.4%

33.3% 29.4% 31 .2%

6 .8% 10.5% 3 .2%17 .3% 15.4% 13.4%

43 .2% 38.1% 42.9%

9.7% 1 1 .1 % 17 .9%

Membership in a politica lparty

12 .6%

10 .2%

Membership in a trad eunion

38 .1%

—

19 .5%

Receiving unemploymen tBenefit s

Readily willing to changequalification s

Readily willing to chang eplace o f residence

Readily willing to accept ajob of lower statu s

Readily willing to emigrat e

Readily willing to start aself owned business

Supporting a political partyB .A .P .B .S .P .U .D .F .

Willing to participate i na retraining course

30

Note s

1 The new panel data set, the Bulgarian Unemployment Survey (BUS) i sdescribed in the Appendix. The BUS contains a large range of variables fo rthose factors usually considered by economists as relevant to the analysi sof unemployment and labor market institutions . As such the data represent swhat is one of the largest and most extensive databases on key aspects oflabor market dynamics for any restructuring socialist economy . The onl ymicro data sets that are available so far are for the German case . (SeeKruger et al .1992 ; Bellman et al .1992 ; Katz, 1992) . However, for obviousreasons, the German data constitute a special case that are unlikely t ohave much broader applicability in other transitional cases .

2 See Jones, 1991 for the case of Bulgarian labor markets and Jones an dMeurs, 1991 and Jones, 1992 for the system of labor relations in Bulgaria .

3 Often there is disagreement on the precise figures . (Compare, fo rexample, Planecon (1992), and AECD (1992) . Yet there is much les sdisagreement on the direction of the changes and the key turning points .

4 However even then the ratio declined quite rapidly as the beneficiaryreceives benefits, until after 6/12 months eligibility for benefit sexpired . (See Eurostat) .

5 Unfortunately we were unable to find more precise details on the scop eand nature of these schemes .

5 Many other variables for which we have information, for example ,membership of political parties and ownership of particular assets (such a sautomobiles), do not appear to be associated with different transitio nrates for different groups .

6 Our contribution is empirical --we do not formally model the transitio nprocess . Implicitly, however, we test hypotheses that are developed fro mseveral perspectives . We adopt this eclectic approach, in part because w enote that economic theory, even when well-developed, often does not yiel dunambiguous predictions on specific economic outcomes of the determinantsof the transition between different labor market states (see Devine andKiefer, 1991) . This ambiguity is likely to be especially relevant whenmainstream economic theory is attempted to be applied to former socialis teconomies that are in transition and where labor markets are undergoingprofound changes and clearly are not in equlibrium .

7 All variables are defined in an appendix .

$ No attempt is made to model the instantaneous probabiity of transition ,i .e . the hazard rate, since the data do not permit it .

9 When regional dummies were included in either multinomial or binomia lestimates, none was ever significant and other coefficients wereunaffected .

10 As such we believe that our findings are some of the first on thi smatter for an economy in transition . The only other study of which we ar e

3 1

aware is for the special case of the former East Germany . Perhapsreflecting the specific nature of that experience, Bellman et al . foundthat the key influence behind the outflow from the labor market (U-N) wa sthe level of UI received . In addition, the higher the level of unemploymentcompensation the greater the probability of getting a job .

11 However, some might argue that, because of the changes in the causes o fcurrent (and impending?) unemployment, compared to the situation prevailin gduring our study, our-conclusions may not apply today . In other wordssince 1991 arguably it is principally shock therapy that has led both to ageneralised drop in demand for output as well as huge shifts in th erelative demands for labor across industries . But in the future, no mor emajor adverse demand shocks are to be expected . Additional employmen tlossess will result mainly from the effects of ownership changes during th eimpending era of mass privatisation, in which the state owned sectorshrinks even more . In addition, since the employment needs of the privat esector will be so different, the mismatching between the supply and deman dfor labor arguably will increase . But our sense is that the implications o four findings, e .g . the promotion of polices that favor the formation o fdifferent forms of human capital through training and education, appl yirrespective of the causes or employment .

Appendix : The Bul garianUnemp loyment Survey (BUS )

The new data were gathered by a team of Bulgarian and American socia lscientists . The principal member of the Bulgarian team was Dokhomir Minev ;Jones was the main member on the American side . The authors of this paperwish to recognize the unstinting efforts of their Bulgarian colleagues .

A sample of 550 individuals was drawn from those registered a sunemployed in March 1991 in five large towns . At that time the sampl erepresented 4 .1% of the total stock of registered unemployed, 134,797 . (SeeTable 1) . The regional distribution of the sample is : Varna = 118 ; Sofia =143 ; Plovdiv = 106 ; Pleven = 90 ; and Shoumen = 93 . Three waves of data havesince been collected, with the first wave gathered from April 1-20 1991 an dsubsequent waves from August 1-20 1991 and March 1-20 1992 . Everyonein the sample provided some information during at least one of the waves ;in that sense the response rate was 100% . However, there were alway smissing values for some variables for some respondants . For example, only527/550 provided information on gender (a response rate of about 96%) . Andthe panel containing information on all the variables used in the logitestimates was available for only 475/550, a response rate of 86% . Howeve reven this rate (a maximum cumulative attririon rate of 14%) compare svery favorably with comparable rates for panel data sets (compare fo rexample with the first waves of the PSID) .

The characterisitcs of respondants and summary information for th esample are given in Tables 10 and 11 . This shows that the sample of th eunemployed was disproportionatley female (in March 1991, 70 .4% werefemale) . (This refelcted an early judgement that the unemployment problemwas likely to be more acute for women and a deliberate decision tooversample women .) Most were experienced workers (rather than new entrant sto the labor force, such as school leavers, who could not find jobs . (Thi sconnects, of course, with the incentives for registering as unemployed) .About 38% were members of trade unions . As such this is well below therates for the employed (Jones, 1993 finds unionisation rates for thos eemployed in manufacturing in 1992 in excess of 75%,) . Throughout theperiod, presumably reflecting family ties as well as labor immobilit yresulting from a poorly developed housing market, few respondants indicatedthat they were willing to relocate to find jobs . (Also, our high respons erates thoughout the three waves is indicative of very limite dinter-regional mobility in practice . )

As far as the distribution of the repondants were concerned . Thesample represnted people who had worked in many industries, cut across man yage groups and also across unemployed individuals from diverse edcuationa lbackgrounds . Since it is estimated that 10-15% of the Bulgarian populatio nare from ethnic minorities, the sample does not seem to be representativeof the extent of ethnical minority unemployment . However, the extent ofethnic groups in the urban areas under examonation is probably well belo wthe 10-15% national average .

The content of the instrument was modified after the first wave i norder to : (i) ask a new block of questions to those whose labor marke tstate no longer was "unemployed" ; (ii) to avoid repetition of much of th einitial detail (e .g . concerning characteristics of the previous job an ddemographic characteristics of the respondent .) Variables include :

33

demographic characteristics (such as age, gender, marital status ,education) ; work experience ; characteristics of previous job includin gwage, sector, hours worked) ; wage offers ; reservation wages and, wher erelevant, the duration of completed spells of unemployment .

In general, we believe that the overall quality of these data i salways quite good and often very good . A considerable effort was made t oensure that scientific procedures were followed and good quality data wer ecollected . For example when administering the instruments, a traine dinterviewer was present to conduct the interview or to answer questions .(This contrasts with the frequent use of telephone interviews in othe rpanel surveys .) Training was done either by principals associated with th eproject . In addition, we have tried to provide for a high degree o freliability for some of the more sensitive information that was collected ,such as on incomes . In particular this was done by asking similar question swhen information was collected on different occasions from the sam eindividuals . However, we were unable to undertake independent validatio nstudies .

34

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Toikka, R . (1976) "A Markovian Model of Labor Market Decisions byWorkers' A .E .R . vol 66, # 5 . 821-83 4

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