The document discusses future work by the OECD on measuring job quality. It will include expanding the job quality framework to emerging economies, shifting the focus from jobs to worker careers, and improving data collection. Specific areas of future work are assessing job quality in informal employment contexts and analyzing career outcomes using a lifetime perspective. The OECD also plans to develop an inventory of international job quality indicators and fill gaps in measurement.

1. EC/OECD seminar, Brussels,
27-28 November 2014
FUTURE OECD WORK ON JOB QUALITY

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Will include:
• Extending the OECD framework to emerging
economies
• Shifting the focus from jobs to careers
• Filling up the gaps in the data
Future OECD work

3. Future OECD work
1. Job quality in emerging economies
– Some preliminary results

4. • The framework needs to be adapted
– To account for the risk of low-pay
– To deal with data limitations (especially w.r.t. QWE)
• Informality plays a crucial role
– Is job quality lower in informal jobs? If lower in all dimensions of
job quality cannot be a voluntary choice
– Is informality a stepping stone or a trap?
• Country coverage restricted to emerging economies with a
link to the OECD or G20 for which suitable data are available
– Asia (CHN, IDN, IND), Africa and Middle East (TUR, ZAF), Latin
America (ARG, BRA, CHL, COL, CTR, MEX) – not there yet
Job quality in emerging economies

5. Earnings quality in EEs
Source: OECD calculations based on national surveys: EPH (Argentina), PNAD (Brazil), CASEN (Chile), ESLF (Colombia), ENHAO (Costa Rica), ENOE (Mexico), SILC
(Turkey), NIDS (South Africa)
* The earnings measures used are net and hourly
2010 $ (PPP), high inequality aversion (alpha= - 3)
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1.0
0
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ZAF COL BRA MEX TUR CRI ARG CHL
2010 US dollars, PPP
Earnings quality Average earnings Earnings inequality (right axis)

6. The risk of low-pay in EEs
Low-pay threshold: 2.5 $ (PPP)
Source: OECD calculations based on national surveys: EPH (Argentina), PNAD (Brazil), CASEN (Chile), ESLF (Colombia), ENHAO (Costa Rica), ENOE (Mexico), SILC
(Turkey), NIDS (South Africa)
*Mobility threshold is 2.5 $ PPP per hour
Mobility rates are calculated using a pseudo-panel approach where individual mobility is approximated by cohort mobility (see appendix for more details and validation).
A cohort is defined on the basis of gender, education and year of birth.
Only individuals between the age of 25 and 55 at t=0 are considered for this calculation ( approximately one cohort)
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COL MEX ZAF TUR BRA CHL CRI ARG
Steady-State incidence of low pay Upward Downward

7. • To what extent is the job-demands job-resources model appropriate
in EEs?
– Not designed to deal with self-employment even though this trend to be s
widespread in EEs
• To what extent does it need to be calibrated differently? And how
can this be done?
– Should the same demands and resources be considered? Can the same
cutoff be used to define strained jobs?
• How to measure QWE with the data available?
– Focus on physical work accidents as reduced-form alternative to job strain?
– But even data on work accidents is patchy and subject to various quality
concerns
7
Quality of the working environment

8. • Informality is measured either by social security coverage or having a
formal contract
• Job quality tends to be lower on average among informal workers which
point at exclusion
– Average earnings gap between 30 to 50%; earnings dispersion similar -> overlap
modest
– Informal workers much more likely to earn less than 2.5$
– Reflect higher probability of becoming low paid and lower probability of leaving
low pay
• However, analysis falls short of full assessment of informality, in part due to
difficulty of measuring QWE in a comprehensive way
– To what extent could job satisfaction data provide a way out?
• Some preliminary evidence that informality harms career prospects
– Takes a long time to find good formal job for informal workers; most transitions
from informal to formal result in temporary jobs
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Job quality and informality

9. Informality and non-standard work
0%
2%
4%
6%
8%
10%
12%
14%
I--> Permament (Formal) I--> Temporary (Formal)
0%
2%
4%
6%
8%
10%
12%
14%
Permanent (Formal)-->I Temporary (Formal) --> I
Transitions out of
informality
Transitions into
informality
Source: OECD calculations based on Encuesta Social Longitudinal de Fedesarrollo (ESLF) for Colombia . Informal employees only. Informality: Does not pay any
social contributions
Transition rates over consecutive survey waves
Colombia

10. Future OECD work
2. Shifting the focus from jobs to
careers – Initial thoughts

11. • Shift focus from jobs to worker careers over the life cycle
– Individual well-being not only depends on current job but also the prospects it provides
– Also matters for assessment of social welfare because of its implications for risk and
inequality (see next slide)
-> Main in interest is understanding what drives poor outcomes and suggest policy policy
instruments
• Life-cycle approach provides alternative approach for assessing labour
market performance in terms of job quantity & quality
– Captures earnings (potential) and labour market security
– But also non-employment spells and working time
• For the moment no intention to also look at quality of the working
environment in detail
The quality of working lives

12. • Quality of working lives can be summarised in terms of life-time
earnings and incomes
– Since long panel data are not available on a cross-country basis simulation
techniques may need to be used
• Has potentially important implications for international comparisons
of inequality and the incidence of low-paid work
– Life-time measures of earnings take account of differences in earnings-experience
profiles and exclude transitory shocks
– e.g. countries with steeper earnings profiles and higher incidences of transitory
shocks are likely to look less unequal from a life-time perspective
• Also has important implications for the assessment of social welfare
– High mobility may alleviate concerns over inequality in the short-term but raise
others over risk -> reduce calls for redistribution but increase those for insurance
– e.g. US traditionally accepted high inequality as mobility was also high; recently
rising concerns that mobility is stalling despite rising inequality
Life-time earnings, mobility
and earnings inequality

13. • What is the role of job quantity (unemployment and working
time) and job quality (earnings) for inequality in life-time
earnings?
• To what extent are low life-time earnings associated with higher
labour market risk?
• What is the role of taxes-and-benefits systems for insurance and
redistribution over the life course?
• To what extent does having a temporary contract have long-lasting
effects for worker careers?
– EmO 2014 shows that many temporary workers do not manage to find
permanent jobs even after three years
What explains low life-time earnings?

14. • What is the role of learning?
– How do the returns to potential labour market experience differ across
countries?
– And how do these compare across socio-economic groups (gender and
education)?
• What is the role of job mobility?
– Job mobility is a potentially important channel for advancement for workers
– Governments may also be interested in promoting job mobility for both social
(to reduce low-quality work) and economic reasons (improve optimal allocation)
• What is the role of firm productivity, work organisation and worker
representation?
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What explains upward mobility?

15. Future OECD work
3. An ambitious statistical agenda

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• Inventory of information on the quality of the working environment
(international sources)
– identify questions, underlying concepts, data sources and gaps
– document key insights on job quality
• OECD database on job quality
– based on quality assessment and covering indicators for all three
dimensions of Job Quality
– with currently available information by country and eventually also by
socio-economic group
– gradually extend country coverage to non-OECD members
– to become available via OECD.Stat
• Further work with OECD countries to fill in methodological and
statistical gaps and improve harmonisation
Ambitious statistical agenda

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Motivation: To take stock of available international data sources and
identify gaps
 7 international surveys were identified that :
 Have a focus on working lives
 Collect information specifically on individuals’ own job
 In total cover 25 years and 160+ countries
 EWCS, ESS, ISSP, EULFS AHMs, Gallup World Poll, EQLS, Eurobarometer
 18 sub-dimensions of QWE shown to have an impact on well-being in
the literature were identified
 E.g. Work intensity, Physical risk factors, Task discretion and autonomy, organisational participation
 Relevant questions in each survey were classified and documented in
the Inventory
 Analytical work on comparability of indicators across surveys underway
OECD Inventory of Indicators on
Quality of Working Environment

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OECD Inventory of Indicators on
Quality of Working Environment
Identifying gaps:
Example: Task discretion and autonomy
The inventory soon downloadable from the OECD database.
EWCS ISSP ESS ESS, ISSP
EWCS, EQLS, Eurobar EWCS, ESS, EQLS
EWCS, ESS, ISSP
EWCS, ESS, EQLS, ISSP
EWCS, EQLS, ISSP, Eurobar EWCS, EQLS, ESS, Eurobar ALL 5 SURVEYS

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Assessing the relevance and statistical
quality of Job Quality indicators (I)
Criteria used:
Relevance
• Face validity (capacity to measure
what is intended to be measured)
• Unambiguous interpretation (whether
the indicator is good or bad for well-being)
• Policy relevance (amenable to
concrete policy actions)
• Can be disaggregated (by socio-economic
groups in order to assess
distributional aspect of job quality)
Statistical Quality
• Well-established instruments collected
(relying on statistical instruments
developed within the official statistical
system or academic community)
• Comparable definition (via using
internationally accepted standards and
surveys)
• Country Coverage (ensuring maximum
coverage for the OECD and other
major economies where possible)
• Recurrent data collection (in order to
monitor progress)

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Assessing the relevance and statistical
quality of Job Quality indicators (II)
Sub-indicator
Properties
Relevance to measure and monitor JQ Statistical Quality
Face Validity
Unambiguous
interpretation
Policy
relevance
Can be
disaggregated
Well-established
instruments
collected
Comparable
definition
Country
Coverage
Recurrent
data
collection
Earnings quality
Average Earnings VV VV VV VV VV VV VV VV
Earnings Inequality X
Labour Market Security
Unemployment risk
U probability (monthly U inflows)
Expected U duration
(inverse of U outflows)
Effective insurance
Coverage rate of unemployment
compensation (insurance/assistance)
Average net replacement rate of
insurance/ assistance
V
Quality of the working environment
Work stressors
Time pressure at work
Physical Health risk factors
Workplace intimidation
Workplace resources
Work autonomy & learning opportunities
Workplace relationships
Good management practices
VV: Largely meets criterion
V: Meets criterion to a large extent
X: Does not meet the criterion or meets it only to a limited extent

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• For further information on OECD work on job quality
please contact:
– Sandrine Cazes (STD): sandrine.cazes@oecd.org
– Alexander Hijzen (ELS): alexander.hijzen@oecd.org
Thank you!