Wage Flexibility and the Great Recession: The Response of the Irish
Labour Market
Aedín Doris*, Donal O’Neill** & Olive Sweetman*
19 June 2014
Preliminary: Please Do Not Quote
Abstract
There is considerable debate about the role of wage rigidity in explaining unemployment.
Despite a large body of empirical work, no consensus has emerged on the extent of wage
rigidity. Previous attempts to empirically examine wage rigidity have been hampered by
small samples and measurement error. In this paper we examine nominal wage flexibility in
Ireland both in the build up to, and during the Great Recession. The Irish case is particularly
interesting because it has been one of the countries most affected by the crisis. Our main
analysis is based on earnings data for the entire population of workers in Ireland taken from
tax returns, which are free of reporting error. We find a substantial degree of downward wage
flexibility in the pre-crisis period. We also observe a significant change in wage dynamics
since the crisis began; the proportion of workers receiving wage cuts more than doubled and
the proportion receiving wage freezes increased substantially. However, there is considerable
heterogeneity in wage changes, with a significant proportion of workers continuing to receive
pay rises at the same time as other were receiving pay cuts.
JEL Classification: J31, J38, D31
Keywords: Wage Flexibility, Great Recession
**
Corresponding author; National University of Ireland Maynooth and IZA, Bonn. E-mail:
[email protected]; tel.: 353-1-7083555; fax: 353-1-7083934; address: Rhetoric House, NUI Maynooth,
Maynooth, Co. Kildare, Ireland.
*
National University of Ireland Maynooth.
We would like to thank Paul Devereux, Uwe Sunde, Frank Walsh and participants at the NERI Labour Market
Conference, Dublin, May 2013 and the Irish Economic Association, Limerick, May 2014 for helpful
discussions. We would like to thank Ben Berstock, Berni Dunne, John Dunne and Mervyn Ó Luing for help in
accessing the Job Churn data and Marion McCann for help with the EU-SILC data.
1
1. Introduction
The issue of whether wages are rigid or flexible is one that has been central to
macroeconomics for many years. Wage stickiness in either direction is of concern as it can
explain why nominal shocks translate into real effects.1 However there is no consensus on the
impact of wage rigidity on unemployment. Downward inflexible wages could prevent labour
markets from clearing causing unemployment to persist. However there is also a literature
that argues that under depression type conditions, with interest rates close to zero, wage
flexibility will have little impact on unemployment and may even exacerbate the problem.2
This paper examines the flexibility of wages in the Irish labour market before and during the
Great Recession.
The Irish economy provides a very useful setting for examining the flexibility of
wages. Firstly, the Irish labour market is generally held to be flexible with relatively low
levels of job protection legislation, above average working time flexibility and above average
functional flexibility, measured by the ease with which employers can change the content of
work (Andranik 2008). It is therefore interesting to see if flexibility along these dimensions
translates into flexibility in wage setting. In the only analyses of Irish microeconometric wage
data published to date, nominal rigidity was found to be low by international standards
(Dickens et al. (2007); Knoppik and Beissinger (2009)). However Dickens et al. expressed
misgivings about the quality of the data used and suggested that the Irish results might be due
to measurement error. In this paper, we use two data sources for which measurement error is
likely to be much less of an issue; the data we use are also more recent.
1
Such general wage stickiness is usually explained by menu costs such as the management time costs of
performance reviews and the administrative costs of adjusting payslip details.
2
For a discussion of the role of wage flexibility in alternative economic models see Gali (2012).
2
A second reason for interest in wage flexibility in Ireland is that the Irish economy
has sustained a serious downturn in recent years. After a period of very rapid growth from
1994 to 2007, when the average annual GDP growth rate was over 7%, the economy
collapsed and the average growth rate over 2008-2011 was -1.75%. There are similar patterns
for unemployment; having been relatively stable at 4%-5% for most of the early 2000s, the
unemployment rate rose from 4.5% in 2007 to 12% in 2009 and continued to rise further to
14.6% in 2011. Inflation averaged 2.53% in the period from 1994 to 2011 but was negative in
2009 (-4.5%) and 2010 (-1%). Given these substantial changes in the macroeconomic
environment, it is very interesting to examine the extent to which wages responded during
this period.
The predominant explanation for why wages might be downwardly rigid is that
employers avoid reducing wages because of the effect on morale.3 Bewley (1999) examined
the downward rigidity of wages in the US during the recession of 1991-1992, and found that
managers used wage cuts only in circumstances where the firm faced serious problems. Since
the economic crisis in Ireland caused serious problems for many firms, it is plausible that
downward nominal wage rigidity would be lessened in these years. In addition, Gordon
(1996), in his comment on Akerlof et al.’s paper on the impact of wage rigidity in a lowinflation environment, suggests that nominal wage reductions would no longer be seen as
unfair. The fact that inflation dropped and then turned negative during the crisis might also
lead us to expect lesser downward nominal wage rigidity.
3
Note however that Smith (2002) found, using British data, that there was no difference in the effect on
morale between wage freezes and wage cuts, which suggests that the morale theory does not explain
downward nominal wage rigidity. A more recent (2013) version of this paper using a longer panel and a more
elaborate methodology finds that the significant reference point at which a drop in job satisfaction occurs is at
the median pay change rather than at a nominal zero pay change, which again fails to support the morale
theory for nominal rigidity.
3
In this paper we look at nominal wage changes over the pre-crisis and post-crisis
periods using a newly-available administrative panel dataset covering the population of Irish
workers, known as the ‘Job Churn’ data. The very large number of observations (700,000800,000 in the subset of the data that we study) allows for the examination of the wage
change distribution at a level of detail not previously possible. In addition, because these data
are derived from employee records returned to the tax authorities by employers, they are free
from the reporting errors that plague analyses of survey-based data. We also use the Irish
component of the EU-SILC to carry out some supplementary analyses; although based on
survey data, the EU-SILC includes additional controls that allow us to examine some possible
explanations for the patterns we observe in the Job Churn data.
We find a significant degree of downward wage flexibility in the pre-crisis period in
both annual earnings and hourly wages. We also observe a significant response in wage
change behaviour since the crisis began; the proportion of workers receiving wage cuts more
than doubled and the proportion receiving wage freezes increased substantially. However,
there is considerable heterogeneity in wage changes, with a significant proportion of workers
continuing to receive pay rises at the same time as other were receiving pay cuts. While one
might expect the industrial sectors most intimately associated with the crisis, namely
construction and finance, to exhibit the largest responses during the crisis our analysis shows
that this has not necessarily been the case. As expected construction workers were hardest hit
by the crisis, not only in terms of job losses but also in terms earnings reductions. However,
the same does not seem to be true of finance. Workers in this sector experienced the largest
earnings growth pre-crisis but have been largely insulated from earnings reduction through
the time frame of our analysis.
4
2. Literature Review
There is a substantial body of research that uses microdata to examine the extent of wage
stickiness. However, as of yet no general consensus has emerged. Much of this work has
focused on the US and UK. McLaughlin (1994) analysed PSID data and concluded that
wages in the US were flexible; 43% of household heads who did not change employers faced
real wage cuts annually, while approximately 17% of the sample faced nominal wage cuts.
However, these results have been challenged by a number of authors who argue that the
extent of wage cuts in these data may be exaggerated by measurement error. Altonji and
Devereux (2000) using both firm level personnel files and household survey data conclude
that nominal wage cuts are rare once one accounts for measurement error. More recently
Barattieri et al. (2010), using an alternative identification strategy, reach a similar conclusion.
Looking at quarterly SIPP data they find that in a typical quarter in 1996, 48.1% of the survey
report a different wage than in previous quarter. However, when adjusted for measurement
error this falls to 17.8%.
For the UK, Smith (2000) uses the 1991-1996 British Household Panel Survey
(BHPS) to examine wage rigidity. Her initial results indicate that 9% of job stayers
experienced zero nominal wage changes from year to year, and that 23% experienced
nominal wage reductions. To examine the consequences of measurement error, she uses the
fact that the BHPS records whether respondents consulted their pay slips when answering the
wage question. On the assumption that measurement error should be lower for those who
consult their pay slips than for those who do not, a comparison of these two groups can be
used to identify the impact of measurement on wage changes. In contrast to the results for
Altonji and Devereux (2000) and Barattieri et al. (2010), she finds that measurement error in
household surveys leads to an understatement of the extent of wage flexibility. The
5
proportion of workers reporting no wage change falls from 9% to 5.6% when the sample is
restricted to those who consult their payslip. She attributes this difference to rounding error
and notes that in contrast to classical measurement error, rounding errors may lead
researchers to understate the degree of wage flexibility; for example, a worker whose wage
was €10.75 last year and €11.25 this year may round to €11 in both years.
Evidence of wage changes for other countries is more limited. Dickens et al. (2007)
report the results of the International Wage Flexibility Project, which analyses individual
earnings in 31 different data sets from 16 countries. They find that on average, 8% of workers
receive nominal wage freezes, and in many countries wage cuts are rare so that wage change
distributions are typically asymmetric. Ireland is unusual in that there is a lower incidence of
wage freezes, and almost as many wage cuts are reported as would be if the wage cut
distribution were symmetric. They argue that the data used for the Irish analysis, the
European Community Household Panel (ECHP) may explain the unusual Irish results, as it
contains fewer observations and more reporting errors than the datasets available for other
countries. Knoppik and Beissinger (2009) develop an econometric multi-country model based
on the histogram-location approach to estimate a more formal model of wage rigidity. In
keeping with Dickens et al. (2007) they find wage flexibility to be high in Ireland. However,
like Dickens et al. (2007) their results are based on survey data from the ECHP.
Barwell and Schweitizer (2007), Bauer et al. (2007) and Devicienti et al. (2007) use a
common methodology that corrects for measurement error in order to identify real and
nominal wage rigidities in the U.K., Germany and Italy respectively. 4 They find far less
downward nominal wage rigidity than earlier studies that corrected for measurement error.
Beissinger and Knoppik (2001) use administrative data for Germany to compare alternative
4
A non-technical discussion of the estimator used in these papers is given in Goette et al. (2007).
6
econometrics approaches for estimating nominal wage rigidity. They find substantial wage
rigidity in the German labour market, with weaker rigidity in times of rising unemployment.
Recently researchers have begun to examine wage adjustment in the Great Recession.
Blundell et al. (2013) examine payroll data from the National Employment Survey (NES)
data for the UK and find that wages have responded much more to the current recession than
to previous recessions. The number of workers experiencing wage freezes has increased from
approximately 5% in 1990 to 12% in 2011. However, in line with Smith’s (2000) results
based on survey data, they find a significant degree of downward wage flexibility in their
payroll data; they find that throughout the 1990s and 2000s, almost 20% of stayers report a
nominal wage cut.
Daly et al. (2012) argue that downward nominal wage rigidity has been a key reason
for the limited extent of real wage reductions in the U.S. in recent years. Elsby et al. (2013)
examine wage adjustments in the US in more detail and caution against relying on nominal
wage stickiness to explain the high unemployment rates observed during the Great Recession.
They report several key features of the wage adjustment process. First, there is always a
significant spike at zero in the wage change distribution – between 6% and 20% of workers
report exactly the same nominal wage in both years. Secondly, there is always a non-trivial
fraction of workers (between 10% and 20%) who report nominal wage reductions. Thirdly,
while the zero-spike increased during the Great Recession, the increase was not substantial,
and layoffs were not significantly more prevalent than in earlier severe recessions. Based on
their analysis of US data, they suggest that the high unemployment of the Great Recession
would have been nearly as high in a world with completely flexible wages. They also analyse
UK NES data and find a smaller spike at zero than in the US data, which they attribute to the
7
greater accuracy of the UK payroll-based data. Like Blundell et al. (2013), they find that
nominal wage cuts are frequent in the UK.
To our knowledge, apart from the Dickens et al. (2007) and Knoppik and Beissinger
(2009) papers cited above that used ECHP data from 1994-2001, there has been no study of
wage changes in Ireland using microdata. Several recent papers have used a 2007/2008
survey of European firms undertaken for the Wage Dynamics Network to investigate the
extent to which wages show downward rigidity and the reasons for this. Du Caju et al. (2013)
find that only 2% of firms report having cut wages over the previous five years; the figure for
Ireland was just 1%. Babecký et al. (2010) report that 9.6% of firms froze base wages, with a
corresponding figure for Ireland of 9%.
Walsh (2012) uses the Earnings, Hours and Employment Costs Survey (EHECS) to
examine wage changes during the recession. The EHECS is an employer-based survey that
collects information on employment and the firm’s total wage bill, and so allows the
calculation of average wages in a firm. In addition, it has surveyed firms about the nature of
their responses to the recession. Walsh finds that 23% of establishments report cuts in
average hourly earnings between 2008 and 2009, rising to 31% between 2009 and 2010.
Bergin et al. (2012) use repeated cross-sectional data from the EHECS and Irish NES5 and
find that in the private sector, average earnings and average labour costs increased marginally
between 2006 and 2009, while there was no change between 2009 and 2011.
3. Irish Policy Response to the Crisis.
As noted earlier, Ireland was one of the countries worst affected by the Great Recession, with
output falling by over 10% in real terms between 2008 and 2010. The effects of the global
5
Despite having the same name as the UK dataset, the Irish version does not allow individual workers to be
followed over time.
8
recession felt elsewhere were compounded in Ireland by the bursting of a property bubble
that had inflated during the early 2000s and the subsequent collapse of output and
employment in the construction industries. Because bank lending was so highly concentrated
in construction activities, Irish banks experienced huge losses following this collapse. On foot
of this banking crisis, the Irish government took the decision to guarantee all liabilities of
Irish banks in September 2008. However, continued falling tax revenue and exposure to
growing bank liabilities resulted in the Irish government deficit going from almost zero in
2008 to 7.4% in 2009, 13.9% in 2010 and a remarkable 30.8% in 2011, when banking losses
crystallized. As a result of the outlook for government finances, yields in Irish bonds reached
unsustainable levels in 2010, and the government sought and accepted a rescue package from
the EU, ECB and IMF.
The crisis resulted in the government undertaking a severe programme of austerity
measures, combining tax increases and expenditure cuts. In addition, the government
abandoned the national wage setting process that had been in place since 1987, in which
unions and participating employers bargained at a national level over wage increases, with
tax cuts being offered by the government to encourage wage moderation.6 The immediate aim
of these measures was to reduce the government deficit. A longer-term aim was to effect an
internal devaluation; as a member of the euro area, a nominal exchange rate devaluation was
not possible for Ireland, and so only a substantial increase in competitiveness through cuts in
labour and other costs could reduce real exchange rates and return the economy to its longrun equilibrium level of output.
6
There is evidence that the nationally-negotiated wage increases came to be regarded as a floor in the private
sector during the boom years. As a result, there was a perception that public-sector pay had fallen behind that in
the private sector, which the government dealt with by commissioning a ‘benchmarking’ report that was
intended to redress this gap. In the benchmarking exercise, public sector employees were given an average pay
rise of 8.9%, paid between 2003 and 2005.
9
Tax increases began in 2009, when the government introduced a new income levy of
1% on incomes up to €100,100 and 2% on income above that. These rates were doubled soon
afterwards7. 2011 also saw the abolition of the income ceiling on social contributions and a
reduction in the upper threshold of the standard tax rate, so that the highest tax rate now
applied to income above €32,800 as opposed to €36,400. The standard VAT rate was
increased from 21% to 23% in 2012. In addition, a range of new taxes such as a household
property tax, a tax on second homes, charges for water usage and a new carbon tax were
introduced in an attempt to raise revenue.
At the same time as taxes were increasing, 2010 and 2011 saw cuts in the rate of
support provided to most social welfare recipients, especially the young unemployed. In
addition the rate of universal child benefit was reduced over successive years, particularly for
larger families.
The government also set out to cut payroll costs in the public sector substantially by
reducing staff and directly cutting pay. Employment in the public sector was reduced through
a major programme of early retirement along with a hiring ban. The number employed fell
from 417,600 in the second quarter of 2009 to 377,300 in the second quarter of 2013, a fall of
almost 10%. Pay rates in the public sector were initially reduced via a Pension Levy
introduced in 2009 ranging from 5% on incomes of €15,000-€20,000 to 10.5% on earnings
above €60,000. Further pay cuts were implemented in 2010,
8
ranging from 5% on the first
€43,000 to 10% on income above €70,000. Most recently, the Haddington Road Agreement
(2013) introduced additional wage cuts on higher paid workers, ranging from 5.5% on those
earning from €65,000 to €80,000 to 10% on earnings over €185,000. In addition, there were
In 2011 both the income levy and the existing health levy were combined into a new tax called the ‘Universal
Social Charge’.
8
These cuts were implemented as part of the Financial Emergency Measures in the Public Interest (No 2) Act
2009, which came into effect on 1 January, 2010.
7
10
increases in hours worked by all public sector workers, the reduction or elimination of
overtime rates, and lower pay scales for new entrants into professions such as teaching.
Throughout this period, there has also been severe curtailment of promotions. However, it
should be noted that incremental pay increases, laid down in public sector contracts of
employment, continued to be paid until 2013, when they were delayed.
Surprisingly given these government implemented pay cuts and the abandonment of
national wage bargaining, aggregate data indicate only modest falls in hourly pay rates
among public sector workers and stability in the wages of private sector workers since the
onset of the crisis (Barrett and McGuinness, 2012). This would suggest that Ireland has been
unable to achieve the necessary internal devaluation through wage reductions, perhaps
indicating a substantial degree of wage rigidity. However, as acknowledged by the authors,
these aggregate data suffer from a number of drawbacks. First it is difficult to control for
compositional changes in the workforce that have taken place during the crisis. If workers
who have lost their jobs differ from those who continue to be employed then basing average
wages only on the population of workers will be misleading. Secondly even if the aggregate
wage change is relatively small this may be hiding substantial differences in wage
adjustments across individuals. By following the earnings of individuals over time we
address both these issues.
4. Data
Two datasets are used in the analysis. Our main analysis is based on data taken from the Job
Churn (JC) dataset, which is an administrative dataset covering the years 2005-2011 that has
been compiled by the Central Statistics Office (CSO). The data combines three elements: first
data on annual income and weeks worked are provided by the tax authorities (the Revenue
Commissioners); the data come from P35 returns, which must be submitted each year by each
11
employer in respect of every worker who was an employee during that year. To these data are
added information on workers’ age, sex and social welfare class from the Department of
Social Welfare. Finally, data from the CSO’s own Central Business Register is added to
provide information on the sector in which the firms operate and the enterprise’s legal form.
There are several significant advantages to using the JC data to examine changes in
earnings over time. Firstly, because they are administrative data, based on tax returns, they
are largely free from measurement error; it is a criminal offence to misreport workers’
earnings in these returns. Secondly, the data comprise the entire population of employees in
Ireland and so the number of observations is large enough to allow very detailed analysis of
earnings changes; there are up to three million employment records in any year. Thirdly,
since employers are obliged to file these returns for every worker, problems associated with
non-response and attrition are absent from the data. Additionally, the data covers both the
period before the crisis (2005-2008) and the period since (2009-2011).
The nature of the earnings variable available in the JC data is also a significant
advantage of the data. Earnings are defined as annual ‘reckonable’ income for the calendar
year; this is gross income from all sources including bonuses and taxable benefits-in-kind,
after pension contributions have been deducted (as pension contributions are not taxable up to
a limit on the contribution that increases with age). The fact that irregular earnings sources
are included is important because firms can adjust labour costs through these sources as well
as basic pay. In addition, the fact that the income measure is net of pension contributions
allows us to take into account the Public Sector Pension Levy, mentioned in Section 3 above.
Since this levy reduced earnings and entailed no compensating increase in pension
entitlements it had the same effect as a reduction in gross pay, but it does not register as such
in household surveys that record gross earnings; hence, commentaries on the extent of wage
12
flexibility in Ireland, particularly in the public sector, routinely include a disclaimer that the
analysis cannot take into account the Public Sector Pension Levy and therefore understates
the true extent of pay cuts. A disadvantage of a measure of annual income that is net of
pension contributions is that changes in contributions by individuals will lead to
overstatements of the degree of wage changes, both positive and negative.
One complication that arises from the use of an annual earnings measure is that the
number of pay days can vary for weekly paid workers because of the structure of the
calendar, leading to observed changes in annual earnings which are not reflective of any true
changes. For instance workers who are paid weekly would have received 53 pay cheques in
2010 if paid on Fridays but only 52 in 2009 or 2011 – resulting in a recorded earnings rise of
1.9% between 2009 and 2010 and a cut of 1.9% between 2010 and 2011 for workers whose
weekly earnings were unchanged. We return to this issue later in the paper.
Because firms can react to labour market shocks by changing either hourly pay or
hours of work, data on earnings are the most appropriate for capturing the flexibility firms
have in adjusting costs. While it is of interest to decompose earnings changes into these two
components, the JC data contain no information on hours worked, so it is not possible to
make this distinction. For this reason, we supplement the analysis of the JC data with an
analysis of another data source, the Irish component of the EU-Survey of Income and Living
Conditions (EU-SILC).
EU-SILC data is collected to satisfy a requirement that all members of the European
Union collect cross-sectional and longitudinal information on income and living conditions;
in Ireland, this requirement is implemented using a dedicated survey. About 5,000
households are interviewed annually. To satisfy the longitudinal requirements, most of the
households are re-interviewed for four successive years, with one quarter of the panel being
13
dropped on a rotating basis in any given year, and replaced with new interview households.
This means that in the data for any given year, up to three-quarters can be traced back to the
previous year. The period covered by the data is the years 2004-2011.
In the Irish EU-SILC data, the ‘income reference year’ for the annual income variable
is the 12 months prior to interview, and interviews are carried out on a rolling basis
throughout the year. This means that, for example, the annual income of someone
interviewed in July 2012 will in fact refer to seven months of 2012 and five months of 2011;
exact matching to a given calendar year is not possible. We have adopted the convention of
taking annual income recorded in a given year as referring to that calendar year.
A separate income variable is also recorded in the EU-SILC data that refers to current
income; to be precise, it records the income received in the last pay cheque. For this variable,
there is no ambiguity about the year referred to. Other important details are also recorded,
such as the period covered by the pay cheque, whether it was the usual pay, whether it
included overtime payments and weeks, days and hours worked. Respondents were
encouraged to consult their pay slips, and whether they did or not was also recorded. Based
on this current income variable, an hourly wage variable is also included in the dataset. This
is constructed using the information provided on hours worked.
All income variables in the EU-SILC were subject to careful cleaning by the CSO.
This initially involved checking for consistency with the occupation variables provided, but
also checking the information on pay and weeks worked and any job changes against the P35
information provided to the Revenue Commissioners by all employers. In a few cases where
other documentation was missing, information from P60 forms, which are provided to all
employees by employers, was used. Because respondents were encouraged to check their
payslips before responding and because the data were subsequently extensively cleaned prior
14
to public release, we expect that reporting error is likely to be less important in the EU-SILC
than in the ECHP used by Dickens et al. (2007), even though both are survey data sources.
For both JC and EU-SILC datasets, we focus on job stayers, those who remain with
the same employer in successive years.9 In the JC data, this was based on matching worker
identifiers and firm identifiers across pairs of years. Any worker working for the same firm
across two calendar years is therefore identified as a stayer. It should be noted, however, that
workers may have changed roles within the firm, and so they may have changed jobs even if
they did not change employer. In the EU-SILC data, respondents were asked if they had
changed job, and if so, when. Job changes were defined to include promotions at work, so in
this case it is possible to identify individuals whose responsibilities had not changed.
For both datasets, we also restrict our samples to workers who had worked for the full
year in each pair of years. In the JC data, the weeks worked for the employer is recorded, so
we exclude workers who worked for less than 52 weeks in either year. We also exclude all
workers who had multiple jobs and all self-employed workers. In the EU-SILC data,
respondents were asked about their employment status – including whether any employment
was on a part-time or full-time basis – in each of the 12 months prior to the interview, and
from this, it was possible to identify individuals who had worked in each month in the
income reference year. As noted earlier, there are no hours data available in the JC data and
so for ease of comparison, we include both part-time and full-time workers in the EU-SILC
sample. The possibility that workers changed hours of work during the observation period is
an issue that we return to later.
9
Summary statistics on the proportion of workers who are job stayers by sector and year are given in Table A1
of the Appendix.
15
Because of the different wage-setting mechanisms that pertain in the public and
private sectors, we supplement our overall analysis with separate examinations of these
sectors. There is no public sector identifier in the JC data. However, the enterprise’s NACE
code and its legal form can be combined to give a good indication of which sector an
individual works in. When defining public sector workers, we omit workers in commercial
state enterprises (‘semi-state bodies’) to the extent possible.
After imposing these restrictions, the number of observations remaining lies between
700,000 and 800,000 in the JC data, and between 800 and 1,700 in the EU-SILC data.
5. Results
a. All Workers
To analyze wage dynamics, we first look at annual earnings changes in the JC data for each
pair of years between 2005 and 2011. Following Ziliak et al. (2011), we calculate percentage
earnings changes using the arc percent change method. In particular the percentage change in
earnings is measured as
𝑦𝑖𝑡 −𝑦𝑖𝑡−1
𝑦̅𝑡
, where yit is earnings for person i in time t and 𝑦̅𝑡 =
𝑦𝑖𝑡 +𝑦𝑖𝑡−1
2
.
The key advantage of this method is that it is symmetric in gains and losses.
Table 1 presents descriptive statistics of the annual earnings changes from 2005/2006
to 2010/2011. Column 2 shows the median earnings changes for each pair of years. The
growth in earnings in the pre-crisis period is evident in the numbers reported for 2005/2006
and 2007/2008, with median growth rates of between 4.5% and 6.1%. The impact of the
crisis is clearly observed in the later period, with median wage reductions of about 1% in
2008/2009 and 2009/2010. These wage changes are consistent with the relatively small
changes in average earnings reported by Barrett and McGuinness (2012). However, as noted
16
earlier, aggregate measures may hide important differences across the distribution. Columns
3-5 report the proportion of workers receiving an earnings freeze, an earnings cut and an
earnings rise. Similar to Blundell et al. (2013), we classify a change of less than 0.1% as an
earnings freeze. These data reveal substantial flexibility. In the pre-crisis period, we find that
between 17% and 23% of workers experience earnings cuts. These findings are similar to the
UK findings of Blundell et al. (2013) who found, using payroll data, that during the 1990s
and 2000s almost 20% of job stayers report a nominal wage cut.
Not surprisingly, during this period in Ireland between 74% and 80% of employees
received earnings increases. However, this pattern changed dramatically with the onset of the
crisis. The proportion of workers experiencing earnings cuts increased to more than 50% in
both 2008/2009 and 2009/2010. This figure fell slightly in 2010/2011, but was still high at
39%. While these figures illustrate a significant wage response to the crisis, it is important to
note that while many workers were having their earnings cut in recent years, a substantial
proportion of workers experienced earnings increases. In fact, the proportion of workers who
experienced increased earnings never fell below 40% over this period.
To look at these earnings changes in more detail, Figure 1 shows the histograms of
annual earnings changes in each of the years. The very large sample sizes in the JC data allow
us to describe the distribution of earnings dynamics in great detail. The red line in each
histogram indicates an earnings freeze, defined as a change in annual earnings of less than
0.1%. These histograms display many of the features of wage dynamics noted by Elsby et al.
(2013) in their discussion of US wages. Firstly, in each year we note a significant spike in the
nominal earnings change distribution at zero. Secondly, there is always a non-trivial fraction
of workers who report nominal earnings reductions. This could reflect changes to overtime
rates or reductions in hours worked as well as changes in hourly rates; we will return to this
17
issue later in the paper. Thirdly, the spike at zero increased during the Great Recession.
However, in contrast to Elsby et al. (2013), the increase in the spike at zero for Irish workers
during the Great Recession is much more dramatic than in the US, with the height of the
spike doubling in Ireland between 2007/2008 and 2010/2011. Furthermore, the main increase
in the spike occurred in 2010/2011, following two years of substantial earnings cuts.10
Another notable feature of these histograms is that the increased spike at zero was
also accompanied by a substantial increase in the proportion of workers receiving pay cuts.
Interestingly, for those affected by pay cuts, the median cut in each of the years from
2005/2006 to 2009/2010 was relatively stable at 5%-6%, falling somewhat in 2010/2011 to
3.7%.
As well as the incidence of pay cuts and pay rises it is also informative to examine the
magnitude of these changes. These are reported in Table 2. The second column of table 2
shows the size of the median earnings cut for those receiving cuts over the period. Although
the number of workers who had their earnings cut increased dramatically over this period,
there was relatively little change in the magnitude of these cuts. While table 1 shows that
there was still a sizeable proportion of the workforce receiving earnings increases during the
crisis, the final column of table 2 shows that the size of these increases fell over this period
While Table 1 shows year on year earnings changes for the entire sample, it is not
possible to determine whether the same individuals were having their earnings cuts in
successive years, or whether those receiving pay cuts in one year had their earnings restored
in later years. To do this we divide the study period into the years before and during the
crisis. The median pay change between 2005 and 2008 was an increase of 15.5%, and from
10
There are also notable spikes at roughly -2% and +2% in the later years; this reflects the distribution of pay
days for weekly paid workers and the complication this causes for annual pay discussed in Section 4. We will
return to this issue in Section 7.
18
2008 to 2011, a fall of 2.7%. Table 3 shows the patterns of year on year earnings changes in
these two periods in more detail. For example the pattern considered in the first row involves
earnings increases in all years within a period. The fourth column shows that this was the
dominant pattern pre-crisis, with over 50% of workers in this category. Looking at all
patterns in the pre-crisis period we see that over 85% of workers received pay rises in two of
the three years during this time. The situation reversed dramatically during the crisis. The two
most common patterns after 2008 were three successive cuts and two cuts followed by a rise.
Over 13% of workers received pay cuts in every year during the crisis and over 50% of job
stayers received cuts in at least two of the three years. However, in keeping with our earlier
results it is noteworthy that a substantial proportion of job stayers, approximately 40%,
received pay rises in at least two of the three years during the crisis.
Finally in this section we consider how the earnings changes varied by initial earnings
levels. For both the pre-crisis and crisis period we group individuals into earnings terciles at
the start of the period and then consider earnings changes across these terciles. The results are
given in Table 4. Looking at the pre-crisis period we see only small differences in earnings
growth across the distribution, with earnings growth slightly higher for lower paid workers.
The third column of Table 4 shows that the earnings cuts during the crisis were highly
progressive. The median pay change for those in the bottom tercile in 2008 who stayed in the
same jobs until 2011 was close to zero. However, the upper two terciles experienced earnings
cuts over this period; the median pay changes were -3.1% and -7.1% for the middle and
higher terciles respectively.
b. Sectoral Analysis
A major focus of policy discussion during the crisis centred on the relative wage
adjustments in the public and private sectors. Table 5 presents earnings dynamics separately
19
for public and private sector workers. Looking at the pre-crisis years, we see that the earnings
behaviour was relatively similar in both sectors; between 15% and 25% of workers in both
sectors received pay cuts, with 71%-85% receiving pay increases. A comparison of the
experiences of workers in both sectors during the crisis is of particular interest. A key
government response to the crisis involved reform of the public sector, including the
imposition of a series of direct pay cuts on public sector workers. These cuts are evident in
2008/2009 and 2009/2010. As noted earlier, the income measure in these data allows us to
see the effect of the Public Sector Pension Levy in 2009; 59% of public sector workers
received a pay-cut in the 2008/2009 period. In 2009/2010 the number of public sector
workers experiencing a pay cut increased to 81%, reflecting direct pay cuts. The median
worker in the public sector experienced a 6% reduction in earnings. Although there were no
legislated pay cuts in 2010/2011, 36% of public sector workers received a reduction in annual
earnings.
The earnings change distributions for private sector workers also reveal a significant
response to the crisis. The proportion experiencing pay cuts increased from 25% in the year
immediately preceding the crisis to between 46 and 50% in 2008/2009 and 2009/2010. The
figure for 2010/2011 remained high at 40%. In 2009/2010, the median earnings change in the
private sector was zero. However, this masks the fact that 46% of private sector workers
received a pay cut in this period, while about the same proportion received pay increases.
This analysis clearly reveals the dynamic and heterogeneous response of earnings in the Irish
labour market during the crisis, a response that can be seen only by looking at this type of
data. We will return to this issue later when we look at a more detailed sectoral breakdown.
The histograms reported in Figures 2 and 3 for public and private sector workers
respectively show these earnings dynamics in more detail. Looking at Figure 2, we see the
20
clear shift to the left of the earnings change distribution for public sector workers,
representing the substantial cuts in earnings for workers in this sector over the crisis period.
We also see the emergence of a strong spike at zero for public sector workers in 2010/2011
which was entirely absent for these workers prior to the crisis.11 The histograms for private
sector workers are in keeping with the discussion for all workers; a persistent spike at zero
that increased dramatically during the crisis, combined with a substantial increase in the
proportion of workers receiving pay cuts at the height of the crisis.
Since we know the legislated public sector cuts were progressive it is interesting to
return to the issue of progressivity by sector. Table 6 repeats the distribution analysis in Table
4 by sector. The results show that the progressivity of pay cuts during the crisis period cannot
be explained by the pattern of pay cuts in the private sector. Across sectors, progressivity of
earnings cuts was much more evident in the public sector.
The very large sample size available in the JC data permit more detailed breakdown
of the earnings changes, classifying workers by broad industrial sector. We begin by looking
at the incidence of year on year pay cuts, increases and freezes. The results are given in
Figure 4. As similar patterns were evident in each of the pre-crisis year pairs, we only present
the graph for the first year of the pre-crisis period. Looking at the results for 2005-2006 we
see that pay rises dominate in every sector. Approximately 80% of workers in each sector
received earnings increases, which is consistent with the aggregate number reported in Table
1. The graphs for the crisis period reveal an increase in the incidence of earnings cuts in all
sectors. However, substantial sectoral differences are evident. Construction stands out in all
three graphs; a substantial proportion of job stayers in this industry experienced pay cuts
throughout the crisis. In other years, public administration and education and health stand out
11
There are, however, substantial spikes away from zero; there was strict adherence to the national wage
agreements in the public sector, so these spikes are likely to correspond to the wage rises from these agreements.
21
for the extensive pay cuts in 2009/10, whereas in 2010/11, pay cuts were particularly
widespread in Utilities – the gas, electricity and water sectors.
To understand how this pattern of cuts, freezes and rises manifests itself in cumulative
pay changes, we calculate the cumulative pay change for the pre-crisis and crisis period for
each of these sectors. The results are given in Figure 5. Looking at the pre-crisis period we
see substantial pay growth from 2005-2008 across all sectors, with cumulative increases for
job stayers ranging from a median of 12.9% in Real Estate to 22.5% in Finance. During the
crisis, the median pay change was negative in most sectors (the exceptions being IT,
manufacturing and mining). However there was substantial heterogeneity in the magnitudes
of cuts, ranging from falls of 9-10% in Construction and Public Administration & Education
to rises of 2.3% in Manufacturing. It is notable that the sectors experiencing biggest increases
pre-crisis are not necessarily those experiencing the largest reductions during the crisis.
6. Further Analysis using EU-SILC Data
As mentioned earlier, the earnings variable in the JC data reflects changes in hours worked as
well as changes in the rates of pay. To examine this distinction in more detail, we look at the
EU-SILC data. These data are survey-based and have much smaller sample sizes, but they do
contain information on hours worked, allowing the construction of an hourly pay rate. A
comparison of wage changes using EU-SILC and JC will be useful in helping understand the
dynamics presented earlier.
We begin by comparing the dynamics for annual earnings presented for the JC data
reported above with similar summary measures based on the EU-SILC data. As discussed
above, the restrictions on the EU-SILC sample are similar to those used for the JC analysis.
We examine gross annual earnings from the EU-SILC data.
22
Table 8 shows the results from EU-SILC, alongside the results from JC, reproduced
from Table 1. Looking first at the median changes, we see that with the exception of
2008/2009, both data sets give very similar aggregate results. In both data sets, earnings
growth fell from about 6% in 2005/2006 to about 4.5% in 2007/2008. Furthermore, both data
sets indicate negative earnings growth in 2009/2010 and very small positive earnings growth
in 2010/2011. The only difference between the two series arises in 2008/2009, where the EUSILC earnings data shows that the median rise in earnings was 5% in 2008/2009, while the
JC data reports a median fall of 0.6%. This can be explained by the fact that, as mentioned
above, 2009 was the year in which the Public Sector Pension Levy took effect. This levy did
not reduce the measure of earnings used in the EU-SILC (gross earnings), but did reduce the
measure of earnings used in the JC data (taxable earnings).
The trends in the proportions receiving earnings freezes, cuts or increases are also
similar across the two data sets. Both data sets show a rise in the proportion receiving
earnings cuts and a fall in the proportion receiving earnings increases during the crisis. In
both datasets, about 6% of workers receive a pay freeze in 2010/2011 compared to 2.5% in
2005/2006. As well as the difference in the 2008/2009 figures previously discussed, other
year pairs do show differences in the percentages of workers facing pay cuts and pay rises.
However, in many years, the differences are small. The heterogeneity in earnings responses
revealed above in the JC data is also evident in the EU-SILC data; while many workers were
receiving earnings cuts during the crisis, a substantial proportion (over 40%) continued to
receive earnings increases.
It has been well documented (e.g. Walsh, 2012) that firms in Ireland responded to the
crisis in part by adjusting hours of work, which would be reflected in changes in annual
earnings with no corresponding change in hourly pay. The primary advantage of using the
23
EU-SILC data is that it provides data on hours worked and therefore allows us to examine
dynamics in hourly pay. The results are given in Table 9; to allow comparison, we reproduce
the EU-SILC results on annual earnings from Table 8 in the first five columns. The most
notable difference between the two series is the significant increase in workers who reported
a pay freeze when using hourly pay as opposed to annual pay; over 12% of workers reported
hourly pay freezes in 2010/2011 compared to 6% with annual freezes. Despite this difference,
the major features of wage dynamics reported earlier for annual earnings using both the JC
and EU-SILC data are still very evident when we use hourly pay. The proportion of workers
receiving a cut in hourly pay increased from below 30% in 2004/2005 to almost 50% in
2009/2010, as the labour market reacted significantly to the crisis. As before the cuts
experienced by many workers are hidden in aggregate data by the fact that at the same time,
at the height of the crisis, a significant proportion of workers continued to receive increases in
hourly pay.
Since the EU-SILC data are survey-based, they are more likely to suffer from
measurement error than the JC data.12 To examine the likely impact of measurement error on
the EU-SILC findings, we follow Smith (2000). She examined the impact of measurement
error on wage dynamics in the BHPS using the fact that respondents in the BHPS were told
that they could consult their pay slips when answering the wage question. On the assumption
that measurement error should be lower for those who consult their pay slips, a comparison of
the two groups illustrates the impact of measurement error on wage changes. The EU-SILC
data allows us to conduct a similar comparison. The results are given in Table 10. Our
findings are similar to Smith (2000) in that we find that the proportion of workers reporting
wage freezes is smaller when the sample is restricted to those workers who consulted their
pay slip. As noted earlier, this is not consistent with classical measurement error but is
12
As noted earlier, the EU-SILC earnings data is subject to extensive cleaning prior to public release.
24
consistent with rounding of reported earnings. While there are some differences in the levels
of freezes, cuts and increases across the two samples, the broad features of wage dynamics
highlighted throughout this analysis remain evident when we restrict our attention to workers
who consulted their wage slips. These results suggest that measurement error is not the
driving force behind our findings.
7. Measurement of Wage Rigidity
So far, we have examined the proportion of freezes and cuts in the wage change distribution
as indicators of wage flexibility. However, these statistics alone are not useful, as they are not
informative about how the observed wage change distribution relates to the counterfactual
distribution that would be observed in the absence of rigidity. We now turn to the
construction of a measure of wage rigidity.
Many measures of rigidity have been proposed in the literature. Beissinger and
Knoppik (2001) provide a summary of many of these measures. These include the ‘skewness
location approach’ (McLaughlin, 1994), the ‘histogram location approach’ (Kahn, 1997) and
the ‘symmetry approach’ (Card and Hyslop, 1997; Montes and Ehrlich, 2013). These
alternatives identify wage rigidity either through shifts in the location of wage change
histograms, or by assumptions on the form of the counterfactual distribution. However, all
these measures require that the highest wage change affected by rigidity is smaller than the
median of the counterfactual distribution. The economic crisis in Ireland was so severe that in
a number of years the median observed pay change was negative, thus violating this
requirement.13 In addition, the location-based approaches require sufficient variation in the
13
Yet another approach is the earnings function approach of Altonji and Devereux (2000). This approach uses a
range of human capital, industry and aggregate measures, as well as wage rigidity, to model wage changes but
is relies on parametric assumptions for identification.
25
location of the wage change histogram to identify rigidity. Again, this is problematic in our
data.
Dickens et al. (2007) propose a simple measure of downward nominal wage rigidity
that is not dependent on wage change medians being positive or on large variation in the
location of the distribution. This measure is based on the assumption that everyone who had a
nominal freeze would have had a wage cut in the absence of wage rigidity; equivalently, there
is no excess mass in the counterfactual distribution. The measure is defined simply as the
ratio of the proportion of workers receiving freezes to the proportion receiving either cuts or
freezes. This measure will provide a consistent measure of wage rigidity provided all desired
wage cuts that are not enacted are recorded as wage freezes and that wage rigidity does not
affect firms wishing to offer pay increases.14
In Dickens et al.’s cross country comparison they found that the average degree of
downward rigidity across years and datasets was 28%. Their average for Ireland for the
period 1993 to 2001 was 4%, the lowest of all countries covered, but as noted earlier, they
expressed reservations about the Irish data. We have calculated this measure at a two digit
NACE sector level for the period 2006 to 2011 using the JC data.15 The results, averaged
across NACE codes, are reported in the second column of Table 11. These figures show that
rigidity was about 18-21% in the pre-crisis period; although these figures are substantially
higher than those reported by Dickens et al. for Ireland, they are still low relative to many of
the other countries reported in their analysis. These estimates are comparable to Dickens et
al.’s results for Belgium and the U.K. and only slightly higher than those for Denmark and
14
See Elsby (2009) for a model where this latter restriction may not hold.
When calculating the rigidity measure we take account of the fact that the different distribution of pays days
across years may result in some freezes being recorded as increases or decreases of approximately 2%. In
particular we use the observed frequency of workers in small bins either side of -1.9% and +1.9% to impute the
predicted frequency in the cells containing +/-2%. The difference in the recorded and predicted frequency is
added to the pay freezes when calculating the rigidity measure.
15
26
France, which are the two most flexible countries in their analysis after Ireland. At the onset
of the crisis, measured downward rigidity fell substantially to 12.6% in 2008/2009 before
rising again to 22% by 2010/2011.
The finding that wage rigidity fell with the onset of the crisis is consistent with
previous work that found that cyclical downturns relax nominal wage rigidity (Beissinger and
Knoppik 2001). However, our findings for the latter part of the crisis suggest that this
reduction in rigidity may not be permanent. By the end of our sample period, at which time
Ireland was still experiencing significant economic difficulties, estimated rigidity had
returned to its pre-crisis level. From this it would seem that firms in Ireland had little problem
implementing desired pay cuts with the immediate onset of the crisis but may have found
further pay cuts difficult to implement after two consecutive years of cuts. An alternative
possibility is that the rigidity measure is rising because of reluctance by firms to give pay
rises because of continuing economic uncertainty (Elsby, 2009), in which case the increased
spike at zero would be coming from the right side of the distribution. To obtain a better
understanding of the rise in rigidity between 2009 and 2011, Figure 6 plots the actual
distribution of the percentage earnings cuts for these two years. Comparing the two
distributions, it is apparent that the right side of the distribution is similar in both years and
that the increase in rigidity in later years is being drawn largely from the left side of the
distribution, with some counterfactual wage cuts being swept up to zero.
We have also estimated these wage rigidity measures separately for 65 two digit
NACE sectors for each year, giving a total of 390 (65x6) observations16. In the second and
fourth columns of Table 12 we report rigidity measures separately by NACE code averaged
over the years of the pre-crisis and crisis period. The results reveal substantial heterogeneity
16
There are fewer than the usual 88 two digit sectors because, as mentioned previously, we restrict our attention
to NACE categories in the private sector with over 500 job stayers.
27
across sectors in estimated rigidity. 17 of our 65 sectors have estimated rigidity measures in
excess of 25% in the pre-crisis period. These sectors are mostly in Wholesale and Retail,
Accommodation and Food Services, Real Estate, Legal and Other Professional Activities and
the Arts, Entertainment and Recreation Sectors. In addition for most of these sectors,
estimated rigidity declined during the crisis.
For the pre-crisis years, wage change medians were positive, and so for these years,
we have also implemented the Montes and Ehrlich symmetry approach. For each 2-digit
NACE code,17 we calculate the frequency in each 1% bin of the wage change histogram and
use the frequencies in the positive bins to predict the frequencies in the corresponding
negative bins, using the symmetry assumption about the counterfactual distribution. The
difference between the actual and predicted frequencies in the negative bins is then used to
construct a measure of the proportion of wage cuts that were prevented from occurring.
We report the annual values of this symmetry-based rigidity measure in the third
column of Table 11. Averaged over NACE codes, both measures give similar results in the
pre-crisis years. However, substantial differences emerge during the crisis years. Based on
Monte-Carlo simulations and observations of the patterns in the data, we believe that these
differences arise because the symmetry-based measure is invalid when the median of the
wage change distribution is negative, which arises in many industries in the later years.
The agreement between the two rigidity measures when averaged masks differences
between the values obtained at the sectoral level. This can be seen by comparing the two
measures by NACE code, reported in Table 12. At this level, the measures are different, even
for the pre-crisis years when the negative medians are not an issue. This may reflect
17
We use only private sector job stayers in sectors with more than 500 observations to calculate these
measures.
28
deviations from symmetry in the counterfactual distribution. Unsurprisingly, the two
measures are particularly different in sectors with negative wage change medians; there is a
strong negative correlation of -0.53 between the two measures for these sectors. However,
even in sectors where the medians are positive, the correlation between the two measures is
disappointingly weak, at 0.23.
A long-term aim of this project is to examine the impact of wage rigidity on
employment dynamics. Preliminary results using the sectoral and temporal variation in
rigidity to identify this relationship are presented in Table 13. We regress a measure of annual
employment growth (the percentage change in person weeks in each year pair and each
NACE sector) on our preferred rigidity measure, based on the proportion of wage freezes and
dummy variable for the crisis period. As expected the results indicate that employment
growth is lower during the crisis. However, contrary to our expectations, the estimated
relationship between wage rigidity and employment is positive. Other specifications using the
alternative symmetry-based rigidity measure show similarly counter-intuitive results. We
intend to explore these results in future work. In doing this, we will have to consider the best
measure of rigidity, possible endogeneity of the measure and potential survivor bias resulting
from the use of job stayers.
8. Conclusion
To be added
29
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31
Table 1: Earnings Dynamics, Job Churn Data
All Job Stayers
2005/2006
2006/2007
2007/2008
2008/2009
2009/2010
2010/2011
Median
Change
(2)
%
Freezes
(3)
%
Cuts
(4)
%
Increases
(5)
0.060
.061
.045
-.006
-.011
.006
.025
.025
.028
.033
.044
.068
.172
.176
.229
.527
.552
.393
.804
.799
.742
.440
.403
.539
Table 2: Sizes of Pay Cuts (Increases) for those Receiving Cuts (Increases)
2005/2006
2006/2007
2007/2008
2008/2009
2009/2010
2010/2011
Median
Cut
-.050
-.051
-.053
-.060
-.060
-.037
Median
Increase
.078
.079
.066
.046
.051
.044
32
Table 3: Relative Frequencies of Combinations of Pay Changes (Rankings in Brackets)
Pay Changes over Year Pairs
Pair 1
↑
Pair 2
↑
Pair 3
↑
↑
↑
↓
↑
↓
↑
↓
↑
↑
↑
↓
↓
↓
↑
↓
↓
↓
↑
↑
↑
↔
↓
↓
↓
↓
↓
↔
↑
↓
↔
Pre-Crisis: 2005/06,
2006/07. 2007,08
Crisis: 2008/09,
2009/10, 2010/11
51.42
(1)
14.84
(2)
9.54
(3)
8.30
(4)
3.81
(5)
3.50
(6)
2.32
(7)
1.11
(8)
0.89
(9)
0.09
(23)
0.27
(15)
3.91
8.27
(7)
7.79
(8)
13.26
(3)
9.18
(5)
10.02
(4)
8.47
(6)
17.72
(1)
0.61
(14)
13.39
(2)
2.49
(9)
1.79
(10)
7.01
All other combinations
Table 4: Median Cumulative Pay Change by Initial Pay
Bottom Tercile
Middle Tercile
Upper Tercile
All
4 years
2005/08
2008/11
.183
.166
.153
.165
.009
-.023
-.064
-.020
2005/11
(3 year)
.187
.128
.086
.129
33
Table 5: Earnings Dynamics, Job Churn Data, Public and Private Job Stayers
All
2005/
2006
2006/
2007
2007/
2008
2008/
2009
2009/
2010
2010/
2011
Public Sector
Private Sector
Median
Change
%
Freezes
%
Cuts
%
Increases
Median
Change
%
Freezes
%
Cuts
%
Increases
Median
Change
%
Freezes
%
Cuts
%
Increa
ses
0.060
.025
.172
.804
.063
.006
.148
.846
.058
.034
.183
.782
.061
.025
.176
.799
.067
.006
.136
.858
.058
.034
.189
.777
.045
.028
.229
.742
.050
.007
.185
.808
.042
.039
.249
.713
-.006
.033
.527
.440
-.015
.014
.593
.393
-.002
.043
.504
.453
-.011
.044
.552
.403
-.060
.012
.810
.178
0
.057
.464
.479
.006
.068
.393
.539
.010
.048
.364
.588
.004
.078
.399
.522
Table 6: Median Cumulative Pay Change by Initial Pay and Sector
05/08
Bottom
Tercile
Middle
Tercile
Upper
Tercile
All
08/11
05/11
Public
Private
Public Private
Public
Private
.205
.173
-.009
.010
.176
.180
.181
.153
-.074
-.003
.088
.147
.154
.145
-.116
-.016
.037
.131
.174
.156
-.116
-.016
.091
3
years
.151
3 years
34
Table 7: Median Cumulative Pay Change by Sector
NACE Sector
05/08
08/11
Agriculture/Mining
Manufacturing
Utilities
Construction
Wholesale Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Admin/Support
Public Admin and
Education
Health
Arts/Recreation
Other Services
.149
.138
.186
.133
.147
.162
.140
.160
.225
.144
.193
.161
.176
.007
.029
-.008
-.095
-.003
0
-.018
.010
.001
-.038
-.017
-.002
-.075
05/11
(3 years)
.168
.165
.171
.041
.124
.162
.111
.181
.197
.070
.139
.142
.088
.169
.161
.169
-.044
-.023
0
.113
.133
.164
35
Table 8: Earnings Dynamics, EU-SILC and Job Churn Data, All Job Stayers
Nominal Annual Earnings EU SILC
N
%
Freezes
.026
%
Cuts
.278
%
Increases
.696
Nominal Annual Earnings Job Churn
(Taken from Table 1)
Median
%
%
%
Change Freezes Cuts Increases
20042005
1599
Median
Change
.067
20052006
20062007
20072008
20082009
20092010
20102011
1705
.063
.025
.273
.703
0.060
.025
.172
.804
1567
.069
.028
.234
.738
.061
.025
.176
.799
1490
.047
.036
.283
.680
.045
.028
.229
.742
1294
.051
.009
.342
.650
-.006
.033
.527
.440
1254
-.003
.013
.504
.482
-.011
.044
.552
.403
863
.009
.058
.394
.548
.006
.068
.393
.539
Table 9: Earnings and Hourly Wage Dynamics, EU-SILC, All Job Stayers
Nominal Annual Earnings
N
Nominal Hourly Wages
Median
%
%
%
Change Freezes Cuts Increases
N
Median
%
%
%
change Freezes Cuts Increases
20042005
1599
.067
.026
.278
.696
1558
.061
.051
.283
.676
20052006
20062007
20072008
20082009
20092010
20102011
1705
.063
.025
.273
.703
1668
.061
.046
.277
.678
1567
.069
.028
.234
.738
1540
.060
.037
.275
.688
1490
.047
.036
.283
.680
1484
.049
.051
.290
.659
1294
.051
.009
.342
.650
1267
.025
.069
.361
.570
1254
-.003
.013
.504
.482
1214
0
.086
.479
.436
863
.009
.058
.394
.548
835
0
.122
.456
.423
36
Table 10: Earnings Dynamics, EU-SILC, Job Stayers with Pay Slips and All Job Stayers
Pay Slips Available
N
20062007
20072008
20082009
20092010
20102011
All (Taken from Table 3)
Median
Change
352 .064
%
Freezes
.026
%
%
N
Median %
%
Cuts Increases
Change Freezes Cuts
.028
.234
.216 .759
1567 .069
830 .049
.016
.270 .714
1490 .047
.036
.283
.680
690 .052
.006
.343 .651
1294 .051
.009
.342
.650
651 -.015
.005
.539 .456
1254 -.003
.013
.504
.482
434 .011
.039
.387 .573
863
.058
.394
.548
.009
%
Increases
.738
Table 11: Time Pattern of Wage Rigidity Measures, Averaged Across All Sectors
2005/2006
2006/2007
2007/2008
2008/2009
2009/2010
2010/2011
Freeze-Based
Measure
0.211
0.202
0.185
0.126
0.182
0.227
Symmetry-Based
Measure
0.224
0.209
0.192
0.164
0.048
0.099
37
Table 12: Summary of Wage Rigidity across Sectors
Nace2 digit
Crop and animal
production,hunting and
related service activities
Mining of metal ores
Other mining and
quarrying
Manufacture of food
products
Manufacture of
beverages
Manufacture of textiles
Manufacture of wood
and products of wood
and cork;except
furniture;manufacture of
articles of straw and
plaiting materials
Manufacture of paper
and paper products
Printing of reproduction
of recorded media
Manufacture of
chemicals and chemical
products
Manufacture of basic
pharmaceutical products
and pharmaceutical
preparations
Manufacture of rubber
and plastic products
Manufacture of other
non-metallic mineral
products
Manufacture of basic
metals
Manufacture of
fabricated metal
products,except
machinery and
equipment
Freeze Wage
Rigidity
2006-2009
.43
Symmetry
Measure
2006-2009
.33
Freeze Wage
Rigidity
2009-2011
.34
Symmetry
Measure
2009-2011
.21
.09
.29
.07
-.07
.13
,12
.12
.25
.16
.25
.19
.12
.10
.17
.10
.01
.19
.19
.17
.15
.22
.25
.14
.21
.16
.23
.13
.17
.19
.21
.17
.13
.12
.24
.13
.17
.08
.15
.09
.10
.13
.15
.14
.14
.16
.16
.14
.17
.14
.11
.14
.10
.16
.09
.14
.17
38
Manufacture of
computer,electronic and
optical products
Manufacture of electrical
equipment
Manufacture of
machinery and
equipment n.e.c.
Manufacture of motor
vehicles,trailers and
semi-trailers
Manufacture of other
transport equipment
Manufacture of furniture
Other manufacturing
Repair and installation of
machinery and
equipment
Waste
collection,treatment and
disposal
activities;materials
recovery
.10
.16
.10
.00
.13
.19
.14
.16
.14
.22
.14
.20
.11
.09
.13
.07
.10
.18
.12
.14
.24
.19
.13
.17
.08
.22
.18
.23
.10
.16
.14
.17
.20
.13
.15
.00
.20
.02
.17
.08
.15
-.05
.15
-.001
.19
.01
.16
.07
.28
.23
.23
.06
.21
.26
.19
.12
.27
.27
.19
.11
.31
.13
.26
-.003
.08
.15
.12
.21
.08
.16
.15
.12
.27
.38
.23
.21
.18
.27
.05
.09
.13
.28
.23
.29
.15
.25
.04
.17
Construction of buildings
Civil engineering
Specialised construction
activities
Wholesale and retail
trade and repair of motor
vehicles and motorcycles
Wholesale trade,except
of motor vehicles and
motorcycles
Retail trade,except of
motor vehicles and
motorcycles
Land transport and
transport via pipelines
Air transport
Warehousing and
support activities for
transportation
Accommodation
Food and beverage
service activities
Publishing activities
Motion picture, video
39
and television
programme production,
sound recording and
music publishing
activities
Telecommunications
Computer programming,
consultancy and related
activities
Information service
activities
Financial service
activities, except
insurance and pension
funding
Insurance, reinsurance
and pension funding,
except compulsory social
security
Activities auxiliary to
financial services and
insurance activities
Real estate activities
Legal and accounting
activities
Activities of head offices;
management consultancy
activities
Architectural and
engineering
activities;technical
testing and analysis
Scientific research and
development
Advertising and market
research
Other professional,
scientific and technical
activities
Veterinary activities
Rental and leasing
activities
Employment activities
Travel agency, tour
operator and other
reservation service and
related activities
Security and investigation
activities
Services to buildings and
landscape activities
.09
.14
.22
.22
.09
.17
.01
.19
.08
.38
.13
.12
.09
.14
.13
.06
.11
.25
.10
.12
.17
.21
.16
.08
.26
.31
.20
.43
.28
.28
.09
.12
.24
.33
.22
.21
.18
.25
.14
.23
.17
.27
.18
.13
.17
.29
.16
.14
.30
.22
.22
.15
.40
.23
.35
.09
.43
.17
.14
.07
.15
.24
.19
.26
.17
.19
.09
.13
.13
.16
.11
.08
.16
.19
.12
.09
40
Office administrative,
office support and other
business support
activities
Residential care activities
Social work activities
without accommodation
Creative, arts and
entertainment activities
Gambling and betting
activities
Sports activities and
amusement and
recreation activities
Activities of membership
organisations
Repair of computers and
personal and household
goods
Other personal service
activities
.14
.13
.14
.06
.12
.13
.27
.35
.10
.22
.07
.11
.34
.31
.36
.02
.26
.15
.15
.01
.27
.27
.24
.04
.35
.49
.37
.19
.31
.25
.32
.16
.38
.26
.26
.10
Table 13: Preliminary Regression Results of Employment Change on Wage Rigidity
(Regression includes sectoral fixed effects and is weighted by the inverse of the square
root of sector size). Standard Errors in parentheses
Dependent
Variable :
Coefficient
Freeze
Rigidity
Crisis
Dummy
Constant
.70 (.09)
-.11(.01)
-.09 (.02)
41
Figure 1: Earnings Dynamics, Job Churn Data: All Job Stayers
8
6
4
2
0
0
2
4
%
6
8
10
2006 to 2007, All Workers
10
2005 to 2006, All Workers
-.5
-.4
-.3
-.2
-.1
0
.1
.2
.3
.4
.5
-.5
-.4
-.3
-.2
0
.1
.2
.3
.4
.5
.2
.3
.4
.5
.2
.3
.4
.5
8
6
4
2
0
0
2
4
%
6
8
10
2008 to 2009, All Workers
10
2007 to 2008, All Workers
-.1
-.5
-.4
-.3
-.2
-.1
0
.1
.2
.3
.4
.5
-.5
-.4
-.3
-.2
-.1
0
.1
8
6
4
2
0
0
2
4
%
6
8
10
2010 to 2011, All Workers
10
2009 to 2010, All Workers
-.5
-.4
-.3
-.2
-.1
0
.1
.2
Proportionate Change in Annual Pay
.3
.4
.5
-.5
-.4
-.3
-.2
-.1
0
.1
Proportionate Change in Annual Pay
42
Figure 2: Earnings Dynamics, Job Churn Data: Public Job Stayers
10
8
2
0
4
6
Percent
2
0
4
6
Percent
2
0
4
6
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
2008 to 2009, Public Sector
2009 to 2010, Public Sector
2010 to 2011, Public Sector
10
8
8
6
2
0
4
6
2
0
4
6
2
0
4
Percent
8
10
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
10
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
Percent
Percent
Percent
2007 to 2008, Public Sector
8
10
2006 to 2007, Public Sector
8
10
2005 to 2006, Public Sector
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
Figure 3: Earnings Dynamics, Job Churn Data: Private Job Stayers
10
6
4
2
0
Percent
8
10
8
6
4
2
0
Percent
6
2
0
4
2007 to 2008, Private Sector
2008 to 2009, Private Sector
2009 to 2010, Private Sector
2010 to 2011, Private Sector
8
6
4
2
0
Percent
8
6
4
2
0
Percent
8
6
2
4
10
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
10
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
0
Percent
2006 to 2007, Private Sector
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
10
Percent
8
10
2005 to 2006, Private Sector
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Proportionate Change in Annual Pay
43
Figure 4: Incidence of Pay changes by NACE Sector
Pay Changes by Sector 2005-2006
Pay Changes by Sector 2008-2009
Agriculture & Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Administrative/Support
Public Admin & Education
Health
Arts/Recreation
Personal Services
Agriculture & Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Administrative/Support
Public Admin & Education
Health
Arts/Recreation
Personal Services
0
20
40
Cuts
60
percent
80
Freezes
100
0
20
Rises
40
Cuts
Pay Changes by Sector 2009-2010
60
percent
80
Freezes
100
Rises
Pay Changes by Sector 2010-2011
Agriculture & Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Administrative/Support
Public Admin & Education
Health
Arts/Recreation
Personal Services
Agriculture & Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Administrative/Support
Public Admin & Education
Health
Arts/Recreation
Personal Services
0
20
Cuts
40
60
percent
Freezes
80
100
Rises
0
20
Cuts
40
60
percent
Freezes
80
100
Rises
44
Agriculture & Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Personal/Scientific
Administrative/Support
Public Admin & Education
Health
Arts/Recreation
Other Services
-.1
0
.1
Median Cumulative Pay Change
2005-2008
.2
2008-2011
.1
.15
Figure 6: Distribution of Nominal Wage changes 2008-2009 and 2010-2011
0
.05
Relative Frequency
Figure 5: Cumulative pay changes 2005-2008 and 2008-2011 by NACE Sector
-.4
-.2
0
binvalues
%Earnings Changes 2008-2009
.2
.4
%Earnings changes 2010-2011
45
Appendix
Table A1: Proportion of Workers who are Job Stayers by Sector and Year
NACE Sector
2005/ 2006/ 2007/ 2008/ 2009/ 2010/
06
07
08
09
10
11
Agriculture/Mining
Manufacturing
Utilities
Construction
Wholesale/Retail
Transport/Storage
Accommodation/Food
Information/Communication
Finance/Insurance
Real Estate
Professional/Scientific
Admin/Support
Public Admin and
Education
Health
Arts/Recreation
Other Services
All
0.351
0.449
0.602
0.192
0.289
0.468
0.144
0.442
0.518
0.336
0.386
0.171
0.651
0.347
0.434
0.592
0.177
0.295
0.423
0.135
0.439
0.484
0.309
0.374
0.161
0.664
0.347
0.446
0.563
0.157
0.304
0.445
0.141
0.445
0.560
0.298
0.369
0.152
0.665
0.304
0.421
0.572
0.138
0.322
0.455
0.164
0.457
0.614
0.296
0.363
0.183
0.652
0.338
0.479
0.603
0.153
0.409
0.483
0.191
0.500
0.647
0.337
0.399
0.222
0.493
0.363
0.507
0.596
0.174
0.410
0.507
0.195
0.491
0.561
0.373
0.385
0.244
0.625
0.465
0.259
0.330
0.360
0.477
0.257
0.304
0.358
0.503
0.261
0.308
0.365
0.518
0.284
0.314
0.377
0.547
0.314
0.344
0.408
0.528
0.314
0.342
0.434
Note: workers included in the denominator are the total number of primary job records associated
with a sector in the first year of the year-pair, where primary job records are the employment in which
the worker spent the most weeks in that year. Workers included in the numerator are workers who
worked for 52 weeks in each year of the relevant year pair and who worked for only one firm during
the whole seven years of observation; workers are allowed to enter the panel of job-stayers after the
first year as long as they continue to work for the same firm for the remaining years and as long as
they have not worked for another firm previously and similarly, workers are allowed to leave the
panel as long as they have worked for the same firm for each year prior to exit and do not work for
another firm subsequently.
46