How Skills Do and Could Determine Labour Incomes in the Public Sector in Bulgaria

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How Skills Do and Could Determine Labour Incomes in the Public Sector: the Case of Bulgaria

Ralitsa Simeonova-Ganeva,

Sofia University “St. Kliment Ohridski”

Nataliya Panayotova,

AEAF, Ministry of Finance

9 October 2009

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Outline

Stylized facts

Research questions

Brief review of literature

Data

Logical framework

Results and conclusions

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Stylized facts

The Government is actively involved in setting the

wage levels

Official statistics – NSI, Annual Data from the

Regular Enterprises’ Survey

Economic Activity Share of employees in the economy

2002, 2006

growth 2002-2006

Average wage growth

2002-2006

Public administration 5.0%, 5.9%

38.7%

34.5%

Education 10.3%, 8.4%

-3.4%

40.7%

Health 6.9%, 5.6%

-3.8%

53.2%

Total economy growth for the period 2002-2006

Employment growth

17.7%

Wage growth

40%

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Stylized facts (2)

More detailed official statistics – NSI, Data from

the Structure of Earnings Survey in 2002 and 2006

The educational status of the employees in the public

sector is relatively higher than the total-economy average in 2002 and even more in 2006.

However, the average wage of the most educated employees in the public sector increases slower than the total-economy average for the period 2002 - 2006.

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Average wage and share of employees holding Bachelor, Master or PhD degree by economic activity, 2002 and 2006

0

10

20

30

40

50

60

70

80

Tota

l econom

y

Min

ing

Manufa

ctu

ring

Ele

ctr

icity

Constr

uction

Whole

sale

and

reta

il t

rade

Hote

ls a

nd

resta

ura

nts

Tra

nsport

and

com

unic

ation

Fin

ancia

l

inte

rmedia

tion

Real esta

te

Public

adm

inis

tration

Education

Health

Oth

ers

0

200

400

600

800

1000

1200

Average wage (BGN), 2002, right scale

Average wage (BGN), 2006, right scale

Share of employees holding Bachelor, Master, or PhD degree (%), 2002

Share of employees holding Bachelor, Master, or PhD degree (%), 2006

6

Deviation of average years of education and accumulated human capital (measured on the basis of labour incomes) from total-economy average by economic activity (%)

7

Deviation of average years of education and accumulated human capital (measured on the basis of labour incomes) from total-economy average by economic activity (%)

2006

-60

-30

0

30

60

90

120

150

Fin

ance

Min

nin

g

Public

adm

inis

tration

Health

Tra

nsport

and

com

munic

ations

Real esta

te

Education

Tra

de

Energ

y

Manufa

ctu

ring

Constr

uction

Hote

ls

-10

-5

0

5

10

15

20

25

Accumulated HC Years of education

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Research questions

The variance of wages among workers with

the same level of education is significant

If qualified labour in public sector is

underestimated, then why not public

employees change their employer?

Do public wages conform prevailing wage

standard or do they differ from wages for

private sector jobs?

How Do and How Could Skills Determine

Labour Incomes in the Public Sector?

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Brief review of literature

Rise in earnings disparity: Wheeler(2005),Blau and Kahn(1996)

Cross-sectional wage variance across skill groups: Vinay and Robin(2002)

Determinants of wage differentials: Acemoglu(2002), Freeman(2002), Abowd, Kramarz and Margolis(1999), Krueger and Summers(1988), Gosling and Lemieux(2001), Brown and Medoff(2003)

Public sector employment and wage inequality: Fuller(2005)

Labour mobility and mobility costs: Lee and Wolpin(2006)

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Data:

The present study utilizes data from the Structure of Earnings Survey held in 2002 and 2006 by the National Statistical Institute, covering 148 942 and 176 276 employees respectively

Income determinants explored: age, gender, occupation, education, experience, wage, size of enterprise

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Logical framework

1. Identification of the most competitive economic activity in terms of nexus between incomes and education

this is done by the preliminary statistical analysis

the analysis led to identification of the economic activity Finance as the most competitive

2. Estimation of returns to skills in each economic activity using Mincerian equations

3. Calculation of potential labour incomes in the public sector using the most competitive activity as a benchmark for returns to skills

using estimated Mincerian equation for the benchmark activity

4. Calculation of wage gaps by level of education and occupation

calculating what the income of each employee in the public sector would have been if he/she was working in the Finance sector considering his/her educational attainment, occupational level and years of experience

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Wage gaps by educational and occupational level in

public administration sector

Wage gaps by level of education, 2002

-26%12%

43%41%

76%

83%

49%

0

200

400

600

800

1000

Primary Basic Secondary Vocational 2-years of

colleage

Bachelor or

Master

PhD degree

Actual wage (BGN) Potential wage (BGN)

Wage gaps by level of occupation, 2002

77%

66%

57%

78%24%

6%5%

13%

0

200

400

600

800

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers

C lerks P rofess ionals Legis lators ,

senior offic ials

and managers



10%

93%

124%

83%

77%60%

0

300

600

900

1200

1500

1800

Primary Basic Secondary 2-years of

colleage

Bachelor or

Master

PhD degree



77%

2%

-5%14%

42%

159%

73%

138%

93%

0

300

600

900

1200

1500

1800

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers

C lerks Technic ians

and

assoc iated

profess ionals

P rofess ionals Legis lators ,

senior

offic ials and

managers


13


education sector


-44% 6%23%

32%40%

22%

17%

0

200

400

600

800


colleage

Bachelor or

Master

PhD degree



22%

38%

31%

45%1%

-5%-7%

-10%

0

150

300

450

600

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers


senior offic ials

and managers



21%

93%

79%

43%

-4%-32%

0

300

600

900

1200


colleage

Bachelor or

Master

PhD degree



68%

-20%-7%

8%34%

69%

72%

72%

88%

0

300

600

900

1200

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers


and

assoc iated

profess ionals


senior

offic ials and

managers


14


health sector


-18% 23%

60%76%

113%

99%

50%

0

200

400

600

800


colleage

Bachelor or

Master

PhD degree



102%

91%

77%

88%32%

26%11%

24%

0

150

300

450

600

750

900

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers


senior offic ials

and managers



16%

26%

55%

31%

20%-21%

0

300

600

900

1200


colleage

Bachelor or

Master

PhD degree



32%

-26%-12%

2% 32%41%

36%

53%

23%

0

300

600

900

1200

E lementary

occupations

P lant and

machine

operators and

assemblers

C raft and

related trade

workers

Skilled

agricultural

and fishing

workers

Service

workers , shop

and market

sales workers


and

assoc iated

profess ionals


senior

offic ials and

managers


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If qualified labour in public sector is significantly

underestimated, then would public employees change

their employer?

Conclusions

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Conclusions (2)

What could happen in the public sector if no major changes occur on the labour market:

Scenario 1: Policy makers decide to decrease labour incomes (by quitting payment of regular bonuses)

The wage gaps will increase and most qualified employees in the public sector will have strong incentive to leave their current jobs

Scenario 2: Policy makers choose to freeze labour incomes (by not changing both wages and bonuses)

Highly qualified employees may move to private sector, if mobility costs are reasonable and expected income in the private sector is substantially higher

Scenario 3: Policy makers decide to increase labour incomes (by efficient reforms in the public sector and restructuring)

Highly qualified employees will stay in the public sector and new qualified labour could be attracted from the private sector, or from abroad

This could lead to possible increase in gender pay gaps of not considered in advance in policy measures!

What could happen in the public sector if major changes occur on the labour market:

Labour decisions of public sector employees will be driven by the dynamics of the wage gaps as well as future expansion or decline in employment in other sectors or the economy as a whole

Outcome in this case could not be determined as of today but they will definitely depend on these wage gaps

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Thank you!

Whole paper is available upon request.

Documents

How Skills Do and Could Determine Labour Incomes in the Public Sector in Bulgaria