The document presents a new non-employment index for Ireland, which accounts for various degrees of labor market attachment among non-employed individuals to measure labor market slack more accurately. It demonstrates that this broader measure, which includes the unemployed, underemployed, and marginally attached workers, outperforms traditional metrics in predicting wage inflation. The findings emphasize the importance of considering diverse labor force dynamics when analyzing the country's economic conditions.

1. A Non-Employment Index For Ireland
Stephen Byrne, Thomas Conefrey &
Shayan Zakipour Saber
Central Bank of Ireland
May 22, 2018

2. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Motivation
Another reason why there is some uncertainty over slack is the correct notion of unemployment[...]
Unemployment in the euro area has risen during the crisis, but so too has the number of workers who
are underemployed (meaning that they would like to work more hours) or who have temporary jobs and
want permanent ones.
This has implications for inflation dynamics, since these people might prioritise more hours or job
security over higher wages in employment negotiations.
If one uses a broader measure of labour market slack including the unemployed, underemployed and
those marginally attached to the labour force - the so-called “U6” - that measure currently covers 18%
of the euro area labour force. Phillips curve models that employ this measure appear to be more
successful in predicting inflation.
Mario Draghi, 2017.
Byrne, Conefrey and Zakipour-Saber CBI 1 / 17

3. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Summary
Not in employment = Unemployed.
Restricted definition of “Unemployed” in labour statistics.
We construct a new measure of labour market slack for Ireland which takes account of different
cohorts’ attachment to the labour force.
Use the average transition probability to employment for each cohort of non-employed as weights
to generate index.
“Average transition probability” acts as a proxy for labour force attachment.
Show that this measure outperforms traditional right hand side ‘real activity variables’ in a
standard phillips curve forecast of wage inflation for Ireland.
Byrne, Conefrey and Zakipour-Saber CBI 2 / 17

4. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Restricted Definition of Unemployed
The unemployed comprise all persons above a specified age who during the reference period were:
Without work, that is, were not in paid employment or self employment during the reference period
Available for paid employment within the next two weeks.
seeking work, that is, had taken specific steps in a specified recent period to seek paid employment
or self-employment.
Key Concept: individual seeks work.
Not a clear-cut process.
Individuals “seek work” with varying degrees of intensity.
We account for varying search intensity across the “non-employed”.
Byrne, Conefrey and Zakipour-Saber CBI 3 / 17

5. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Motivation & Previous Literature
Important in the context of analysing the degree of available labour in the Irish labour market.
Implications for wage pressures.
Similar to methodology of Hornstein, Kudylyak and Lange (2015) and Kudlyak (2017) who were
the first to publish a “non-employment index” for the US.
ECB (2017) examined wider measures of labour market slack in comparison with the
unemployment rate. ECB (2017) argues that the high level of underutilisation indicated by the
extended measures is likely to result in a continuation of subdued wage dynamics.
Byrne, Conefrey and Zakipour-Saber CBI 4 / 17

6. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Differences with Established Extended Measures
A number of extended measures of unemployment are established which include some individuals
not usually counted as unemployed.
A key characteristic of these broader measures of unemployment is that they assign the same
weight to all non-employed individuals outside the labour force.
Do not take into account the substantial differences in the degree of labour force attachment of
different individuals.
Unsurprising example, those who state that they are actively looking for work consistently have a
much higher transition rate to employment than individuals who report that they are not engaged
in job search.
Byrne, Conefrey and Zakipour-Saber CBI 5 / 17

7. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Broader measures already published
0
5
10
15
20
25
30
Q102 Q104 Q106 Q108 Q110 Q112 Q114 Q116
%
Unemployed plus PALF plus others not in education or training plus underemployed workers
(PLS4)
Unemployed plus PALF plus others not in education or training (PLS3)
Unemployed plus Potential Additional Labour Force (PALF) (PLS2)
Unemployed plus discouraged workers (PLS1)
Unemployed
Byrne, Conefrey and Zakipour-Saber CBI 6 / 17

8. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Non-Employment Index
Use micro-data from the Quarterly Labour Force Survey which contains harmonised data on labour
market flows for each country in the euro-area.
Rotational Panel Structure individuals remain in survey for 5 quarters.
The detailed information on worker flows allows us to calculate the cohort-level heterogeneity
probability of workers moving between different states, i.e. from unemployment to employment or
from inactivity to unemployment, and these probability weights are used in constructing our
non-employment index.
Byrne, Conefrey and Zakipour-Saber CBI 7 / 17

9. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Stocks
2002Q4 2008Q4 2012Q4 2016Q4
No % WP No % WP No % WP No
Short Term Unemployed 32,784 1.22 130,727 4.22 115,942 3.82 63,309
Long Term Unemployed 34,318 1.28 41,005 1.32 175,904 5.79 79,075
Seeking not immediately Available 1,518 0.06 3,369 0.11 5,611 0.18 3,585
Available Not Seeking, Discouraged 3,708 0.14 6,717 0.22 26,990 0.89 8,296
Passive Job Seekers 3,149 0.12 2,196 0.07 6,782 0.22 4,695
Available Not Seeking, others 12,507 0.47 10,434 0.34 15,799 0.52 11,186
Not Seeking - In Education 34,130 1.27 24,530 0.79 26,994 0.89 16,996
Not Seeking, Illness 19,872 0.74 17,223 0.56 22,615 0.74 13,805
Not Seeking, other reasons 35,664 1.33 20,478 0.66 33,075 1.09 16,505
Don’t Want Job 744,992 27.72 798,768 25.80 803,747 26.47 813,640
Byrne, Conefrey and Zakipour-Saber CBI 8 / 17

10. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Flows
Table 1: Average Transition Probabilities (Ireland)
Average Transition Probability
Short Term Unemployed (<1 Year) 16.3%
Long Term Unemployed (>1 year) 6.6%
Seeking but not immediately Available 10.98%
Available Not Seeking, Discouraged 3.11%
Available Not Seeking, others 8.02%
Not Seeking - In Education 8.7%
Passive Job Seekers 9.6%
Not Seeking, Illness 2.1%
Not Seeking, other reasons 3.7%
Does not want Job 3.4%
Part-time Underemployed 3.7%
Source: CSO and Authors’ calculations
Note: Transition probability of “Part-Time Underemployed” reflects transitions to full time employment.
Byrne, Conefrey and Zakipour-Saber CBI 9 / 17

11. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Non-Employment Index: Calculation
The weights for each cohort are given by that group’s average transition probability to employment
over the period 1998 - 2016 (Table 2).
This index gives a more appropriate measure of the available units of labour in the economy.
We assign a weight of 1 to the short term unemployed, who have the highest transition probability,
and assign each of the other cohorts’ weight relative to this.
Example, persons ‘seeking but not immediately available’ have a transition probability over the
sample of 10.98 per cent. As such, they are given a weight of 10.98
16.3 = 0.67.
9
j=1
θj
Popj
Pop
This yields the non-employment rate as a percentage of the working age population which we can
interpret as the degree of utilisation of labour in the economy.
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12. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
0
2
4
6
8
10
12
14
16
99Q2 01Q1 02Q4 04Q3 06Q2 08Q1 09Q4 11Q3 13Q2 15Q1 16Q4
Unemployment Rate -
per cent of Labour
Non-Employment
Rate - per cent of
Working Age
Population
Unemployment Rate -
per cent of Labour
Unemployment Rate -
per cent of Labour
Byrne, Conefrey and Zakipour-Saber CBI 11 / 17

13. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
0
2
4
6
8
10
12
14
16
18
99Q2 01Q1 02Q4 04Q3 06Q2 08Q1 09Q4 11Q3 13Q2 15Q1 16Q4
Unemployment Rate -
per cent of Labour
Non-Employment
Rate - per cent of
Working Age
Population
NEI + Part Time
Underemployed
Unemployment Rate -
per cent of Labour
Non-Employment
Rate - per cent of
Working Age
Population
NEI + Part Time
Underemployed
Unemployment Rate -
per cent of Labour
Non-Employment
Rate - per cent of
Working Age
Population
NEI + Part Time
Underemployed
Byrne, Conefrey and Zakipour-Saber CBI 12 / 17

14. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Conditional Forecasting
Initial test of our hypothesis (that the NEI explains the “missing wage puzzle”) given by forecast
comparison.
Estimate a two variable BVAR with hourly earnings and one of seven different real activity
measures (unemployment rate; output gap; four “broader measures” and the NEI).
VAR estimated between 1998 and 2012, with conditional forecast (Waggoner and Zha (1999))
from 2012 to 2017.
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15. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
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16. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
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17. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Table 2: Forecast Performance
RMSE Cond Mean Log score
NEI 0.97135734 -1.6232842
U6 3.31929497 -2.7025598
PLS1 2.03126384 -2.1835126
PLS2 1.93342905 -2.153409
PLS3 2.35892631 -2.3250974
OGEC 2.99671904 -3.1569719
URATE 2.94553213 -2.7409013
UGAP 3.15639189 -3.2194193
Byrne, Conefrey and Zakipour-Saber CBI 16 / 17

18. Motivation and Overview Calculating the measure Empirical Strategy: Conditional Forecasting Next Steps
Conclusion and Further Work
Taking account of all non-employed important when assessing cyclical position of the labour
market.
Ongoing work using NEI to explain low wage growth in Ireland.
Do the same for EA-19.
Byrne, Conefrey and Zakipour-Saber CBI 17 / 17