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wiiwThe Vienna Institute
for International Economic Studies
Western BalkansLabor Market Trends 2018
Hermine Vidovic, wiiwIsilda Mara, wiiwStefanie Brodmann, World Bank
wiiwThe Vienna Institute
for International Economic Studies
Motivation
• Provide comparable labor market data for the Western Balkans for national policy makers in the region, national and international institutions/ organisations and the academic community
• produce an annual report on labour market trends in the Western Balkans
• create a repository for high-quality research on labour market issues in the region
Outline
• Western Balkan Labor Market Trends 2018 Report
• SEE Jobs Gateway Database
Western Balkans Labor Market Trends 2018
REPORT
A summary of labor market trends in the Western Balkans and four peer countries (Austria, Bulgaria, Croatia, Hungary) between 2016 Q2 and 2017 Q2
Part I• Population (activity)• Employment • Focus on
- atypical forms of employment- informal sector employment
• Unemployment- NEETs
Part II• Special topic: Improving data on labor mobility in the Western Balkans
Statistical annex
Shrinking working-age population in most Western Balkan countries
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Working-age population (15–64 years), 2016 Q2–2017 Q2, change in %
-6
-5
-4
-3
-2
-1
0
1
2
WB-6 AL BA ME MK RS XK AT BG HR HU
Continued increase in activity rates – but female rates are (very) low compared to peer countries
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Activity rates (15–64 years), in %
Total Gender
30
40
50
60
70
80
2010 2011 2012 2013 2014 2015 2016 2017
WB-6 AL BA ME
MK RS XK AT
BG HR HU
0
10
20
30
40
50
60
70
80
90
WB
-6 AL
BA
ME
MK RS
XK
AT
BG
HR
HU
male female
Gender differences in labor market outcomes
• Traditional roles assigned to women, such as care takingresponsibilities (children and older family members)
• Religious and cultural reasons
• Labor taxation and social benefit systems, due to
potential disincentive effects for those seeking
employment (Koettl, 2012)
• Reliance on remittances
ample amount of human potential unused
Inactivity
• Gender: Primarily a phenomenon of women
• Age: Lack of participation is most acute among the youngpeople (2015: total - 71%; women – 78%, men – 64%)
• Education: more likely for the low educated
• Explanations– Lower growth as for example in the EU-CEE– Early retirement– Rising enrolment rates in universities – young people
longer in eduation– Labour taxation, social benefits, remittances
Employment growth highest for females and the highly educated
Note: BA: no employment data by age groups above 24 years.
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Employment growth, 2016 Q2–2017 Q2, in %Gender Age Education
Total Male Female 15–24 25–54 55–64 Low Medium High
Albania 3.4 5.6 0.8 13.4 3.0 1.4 2.4 1.2 10.8
Bosnia and Herzegovina 1.9 -0.9 6.8 17.2 . . 5.7 2.5 -4.1
Kosovo 9.2 11.0 2.8 18.2 5.7 18.7 3.5 9.4 13.1
Montenegro 3.5 4.8 1.9 4.9 1.9 9.5 20.1 3.2 -1.0
FYR Macedonia 2.7 1.4 4.7 13.2 3.2 0.1 -6.0 3.9 6.5
Serbia 4.3 2.9 6.2 3.0 2.9 8.9 1.2 4.9 5.4
Western Balkans 3.9 3.3 4.7 9.8 . . 1.8 4.1 5.4
Austria 1.2 1.1 1.3 -1.3 0.8 6.5 -2.2 0.1 4.2
Bulgaria 4.5 4.5 4.5 5.1 3.6 5.7 8.8 4.5 3.0
Croatia 1.7 3.4 -0.2 7.2 1.3 2.4 -18.5 5.2 1.4
Hungary 1.8 2.5 0.9 3.0 1.6 0.6 1.2 1.5 2.6
High share of self-employment signaling large informal sector
Note: Data for 2017 refer to the first two quarters. Data for BA refer to the working-age population 15+.
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Self-employment, share in total employment (15–64 years), in %
0
5
10
15
20
25
30
35
40
45
2010 2011 2012 2013 2014 2015 2016 2017
WB-6 AL BA MEMK RS XKAT BG HR HU
0
5
10
15
20
25
30
35
40
45
WB
-6 AL
BA
ME
MK RS
XK
AT
BG
HR
HU
male female
Total Gender
Temporary employment highest among young people
Note: Data for 2017 refer to the first two quarters. Data are not available for Bosnia and Herzegovina.
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Temporary employees, share in total employees (15–64 years), in %
0
10
20
30
40
50
60
70
80
90
AL ME MK RS XK AT BG HR HU
total (15-64) young (15-24)
0
10
20
30
40
50
60
70
80
90
2010 2011 2012 2013 2014 2015 2016 2017
AL ME MK
RS XK AT
BG HR HU
15-64 years
Part-time employment less common in the Western Balkans than in EU countries
Note: Data for 2017 refer to the first two quarters. Data for Bosnia and Herzegovina refer to the population aged 15+.
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Part-time employment, share in total employment (15–64 years), in %
0
10
20
30
40
50
2010 2011 2012 2013 2014 2015 2016 2017
WB-6 AL BA MEMK RS XKAT BG HR HU
0
10
20
30
40
50
WB
-6 AL
BA
ME
MK RS
XK
AT
BG
HR
HU
male female
Total Gender 2017 Q2
Despite declining, informal sector employment remains at high levels
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat. For BA, ME Krstic and Gashi (2016), for XK Cojocaru (2017).
Share of informal employment in total employment, in %
0
10
20
30
40
50
60
2014Q1 2015Q1 2016Q1 2017Q1
Albania FYR Macedonia Serbia
0
5
10
15
20
25
30
35
40
AL BA ME MK RS XK
20162014 Q1 – 2017 Q2
Self-employed in unregistered businesses now represent the majority of informal sector employment
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Structure of informal employment by individual categories, in 1000 persons
Wage workers without contract Unpaid family workers Self-employed
0
25
50
75
100
125
150
2010 2011 2012 2013 2014 2015 2016
FYR Macedonia
0
100
200
300
400
500
600
2010 2011 2012 2013 2014 2015 2016
Serbia
Informal employment – characteristics
• Men are more likely to work in the informal sector than women; the only exception being Serbia
• Young people (young men in particular) account for the highest share of informal employment in Albania, FYR Macedonia and Serbia
• Low-educated constitute the majority of informal workers in Albania and FYR Macedonia, and medium-educated that in Serbia
• About two thirds of informal work is concentrated in agriculture in FYR Macedonia and Serbia (the only countries providing detailed data)
Unemployment decreasing in most countries, but levels remain high
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Unemployment rates, in %
Total
Unemployment rates fell faster than average (2016 Q2 -2017 Q2) for- females - young people (15-24 years)- Low- and medium-educated
Unemployment rates in WB-6 in 2017 Q2were - equal for men and women- double the overall rate for the young- highest for the medium-educated
0
5
10
15
20
25
30
35
40
2010 2011 2012 2013 2014 2015 2016 2017
WB-6 AL BA ME
MK RS XK AT
BG HR HU
Despite declining, long-term unemployment remains a major concern for the Western Balkans
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
Long-term unemployment rate, in %
0
5
10
15
20
25
30
WB
-6 AL
BA
ME
MK RS
XK
AT
HU
Total Low (levels 0-2)
Medium (levels 3-4) High (levels 5-8)
0
5
10
15
20
25
30
201
3 Q
4
20
14 Q
2
20
14 Q
4
20
15 Q
2
201
5 Q
4
20
16 Q
2
20
16 Q
4
20
17 Q
2
AL ME MK RS XK
AT BG HR HU
percentage of labor force (15+ years) Educational attainment, 2016
Probability of young people becoming a NEET (neither in employment nor in education or training) is high
.
Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat.
NEET rates (15–24 years), in % of the respective population
0
5
10
15
20
25
30
35
40
2010 2011 2012 2013 2014 2015 2016
WB-6 AL BA ME
MK RS XK
AT BG HR HU
0
5
10
15
20
25
30
35
40
AL BA ME MK RS XK AT BG HR HU
male female
Gender (2016)Total
Western Balkan countries EU peer countries
Wage levels differ within the region and in comparison to the peer countries
Note: Wage data refer to register-based survey data for the Western Balkans and peer countries, except Austria and EU-28 which are based on gross wages of National Accounts. Albania: methodological break 2013/2014.Source: SEE Jobs Gateway Database, based on data provided by national statistical offices and Eurostat, own calculations.
Average monthly gross wages, Austria=100 (PPP EUR based)
20
30
40
50
60
70
2010 2011 2012 2013 2014 2015 2016
AL BA ME
MK RS XK
20
30
40
50
60
70
2010 2011 2012 2013 2014 2015 2016
BG HR HU
Main findings
• 231,000 jobs created between 2016 Q2 and 2017 Q2, largest share due to increasing self-employment
• Unemployment decreased by 169,000 (from 18.6 to 16.2 percent of the labor force), reaching historical lows in some countries
• Activity remains low especially among women; long-term unemployment is high and persistent
• Youth unemployment fell faster than overall unemployment, but remained high compared to EU countries; NEETs account for almost one quarter
• Improvements in the labor market were not sufficient to prevent young, educated people from continuing to emigrate
• Emigration from WB-6 in figures
• Main implications
• Data and knowledge gap
• Future direction for research and policy intervention
Special topic: Improving data on labor mobility in the Western Balkans
Emigration from the WB-6 in figures
Emigration share in resident population, 1990-2017
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
1990 2000 2010 2017
Albania Bosnia and Herzegovina
FYR Macedonia Montenegro
Serbia*
Note: * 1990–2000 including Kosovo.Source: UN Statistics (2018).
Note: The stock of migrants as a share of resident population does not include intra-regional migration in the Western Balkans. Source: Stocks of migrants and resident population: UN Statistics (2018).
Stock of emigrants from the Western Balkan region, million persons, 1990–2017
0%
10%
20%
30%
40%
50%
Albania Bosnia andHerzegovina
FYRMacedonia
Montenegro Serbia*
1990 2000
2010 2017
AL BA MK ME RS*
ALMKRS*
BAME
Source: UN Statistics (2017).
Main destination countries for Western Balkan emigrants, share in %, 2015
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Albania Bosnia &Herzegovina
Kosovo FYR Macedonia Montenegro Serbia Total
EU-15 USA Intra-region Canada Asia (Turkey) Australia Other CEE Switzerland
Emigration and demography nexus
0 500 1000 1500 2000
0-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+ Population (thousands) 2030
Population (thousands) 2015
Population (thousands) 1990
0 100 200 300
0-4
5 to 9
10 to 14
15 to 19
20 to 24
25 to 29
30 to 34
35 to 39
40 to 44
45 to 49
50 to 54
55 to 59
60 to 64
65 to 69
70 to 74
75 to 79
80 to 84
85+
Source: Eurostat (2017) and United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision.
Age structure of WB-6 emigrantsto EU-28 & EFTA, 2015, in thousands
Age structure of WB-6 population 1990-2015-2030
Emigration and labor market nexus
Source: DIOC Database 2010-2011.
0%10%20%30%40%50%60%70%80%90%
100%
Me
n
Wo
men
Me
n
Wo
men
Me
n
Wo
men
Me
n
Wo
men
Me
n
Wo
men
Me
n
Wo
men
Me
n
Wo
men
Albania BiHFYR MacedoniaKosovoMontenegroSerbia Total
Employed Unemployed
Inactive NA
0%
20%
40%
60%
80%
100%
Albania Bosnia andHerzegovina
FYR Macedonia Kosovo Montenegro Serbia
NAHigh skill occupation (ISCO: 1-3)Medium skill occupation (ISCO:4-8)Low skill occupation (ISCO:9)
Employed emigrants by occupational skill level, share in %, 2011
Labor force status of emigrants in the OECD countries, share in %, 2011
AL BA MK XK ME RS
AL BA MK XK ME RS Total
Emigration and development nexus
Note: Real GDP per capita in US dollar at 2011 prices.
Source: Maddison Project Database (2018).
GDP per capita, Western Balkan countries versus other countries, 1990–2016, in thousand US dollar
0
5
10
15
20
25
30
35
40
45
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Albania Bosnia and Herzegovina FYR of MacedoniaMontenegro Serbia EU-CEEEU-15
• Large gap in income levels still a strong pull factor
• Youth unemployment is a strong push factor for migration
• High level of emigration among highly educated – loss of human capital
• Employment of emigrants below their skill level:
– brain waste
– signal of mismatch between skills acquired at home and those in demand
• Brain gain in the form of return migration and transfer of know-how has been less tangible in the region
• High level of remittances, but channeling into investment – with the goal of generating employment – remains of low significance
Main implications
Main data challenges and gaps
• Recording: different rules for registration on arrival and deregistration on departure
• Reaching of target group: from the perspective of origin, emigrants not directly or easily reachable
• Measurement is a complex task due to the many dimensions of labor migration and the target groups involved
• Internationally agreed definition of “migrant worker” is not available
– Duration of stay – intended or actual – is critical in measuring the scale and type of migration, e.g. permanent or temporary
– Motivation for leaving the country of origin is different from the motivation for moving to a particular destination country
– Residence permit registration statistics are too narrow for capturing the variety of migration reasons, different forms of short-term migration, e.g. circular, seasonal.
• There is a lack of consistent statistics on work experience prior to and during the migration, on the recognition of qualifications, and on the acquisition of new skills
• Limitation of resources: infrastructure, financial, and human resources
• Institutional setting surrounding the collection of migration statistics is work in progress
• Adjusting and harmonizing indicators to meet international standards, so that a wider spectrum of push and pull factors can be measured, is of utmost importance for the study of migration
• Increasing the reliability and quality of data in line with international standards on workforce composition at home and on migrants living abroad is necessary for accurate analysis of labor market dynamics
• A special module on labor migration measurement within existing household labor force surveys in the countries of origin could help tackle challenges associated with the comparability of data at a regional and international level
How to address data gaps
• Migration from the region has not lost momentum, potential for leaving is high
• Governments have largely taken a passive approach in the past, but it is time to step in.
• The potential for highly educated migrants to return, knowledge transfer, involvement of the diaspora should be a top priority in the policy agenda
• Maximizing gains but also minimizing losses from massive emigration requires
– a bottom-up approach – building a bridge of communication with migrants
– a top-down approach: e.g. sound empirical data and knowledge on the relationship between emigration and its impact
– explore new venues, initiatives and programs, offer incentives and consultancy for guiding the diaspora/migrants to become more involved in investment and funding activities at home
Policy response and steps forward
Example: direction for future research and policy
Source: World Bank Open Data (2018).
0
5
10
15
20
25
30
Av.
20
05
-20
10
Av.
20
11
-20
16
Av.
20
05
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10
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16
ALB BIH XKX MKD MNE SRB
Remittances (% of GDP)
FDI, net inflows (% of GDP)
0
5
10
15
20
25
30
Av.
20
05
-20
10
Av.
20
11
-20
16
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20
05
-20
10
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20
11
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16
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20
05
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10
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05
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20
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20
11
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20
05
-20
10
Av.
20
11
-20
16
BGR HRV MDA POL ROU WLD
Remittances (% of GDP)
FDI, net inflows (% of GDP)
• Massive emigration generates high inflow of remittances• Remittances inflows are much higher than FDI inflows in most
of the WB-6• High-skilled migration has contributed to a higher inflow of FDI• Channeling remittances, more into sectors that generate
employment, but also high-quality jobs, would be more beneficial than simply smoothing consumption
Remittances vs. FDI, peer countries2005-2010, 2011-2016, average
Remittances vs. FDI, WB-6 2005-2010, 2011-2016, average
AL BA XK MK ME RS BG HR MD PL RO World
SEE Jobs Gateway Database – Content
• Key economic indicators: GDP, CPI
• Labor market
Population, activity, employment, unemployment, inactivity
Informal employment
Non-standard forms of employment (temporary, part-time, self-employment)
Long-term unemployment
Youth not in employment, education, or training (NEET)
• Labor market – sub-national data
Population, activity, employment, unemployment, inactivity
• Earnings and unit labor costs
Average monthly gross wages, monthly gross minimum wages
Unit labor costs
SEE Jobs Gateway Database – Content
• Basis for labor market data: Labor Force surveys
• Countries covered:
6 Western Balkans: Albania, Bosnia and Herzegovina, FYR Macedonia, Kosovo, Montenegro, Serbia
Western Balkans-6: Aggregate 6 countries (only if data are available for countries)
4 EU peer countries: Austria, Bulgaria, Croatia, Hungary
• Availability:
Annual and quarterly data – 2010 to 2017 (Q2)
About 19350 time series
• Excellent support from the Statistical Offices of the region
• Available at https://www.seejobsgateway.net
wiiwThe Vienna Institute
for International Economic Studies
SEEJobsGateway.worldbank.org
Thank you for your attention!