20 September 2018
labour market, wages
Photo: Sarote Pruksachat
Abstract
Australia has a detailed system of ‘awards’ that specify different minimum
wages depending on the industry, location and skill of an employee. I find
that legislated adjustments to award wages in Australia between 1998 and
2008 were almost fully passed on to wages in award-reliant jobs. There is
no evidence that modest, incremental increases in award wages had an adverse
effect on hours worked or the job destruction rate.
The effects of minimum wages on employment have been widely debated internationally.
Economic theory makes no clear predictions: although the traditional competitive
model of the labour market suggests that a minimum wage rise will reduce employment
– if set above the market clearing wage – other theoretical models
can predict the opposite. For example, an increase in the minimum wage could
increase employment in a labour market where firms have some degree
of market power that allows them to set wages (e.g. Card and Krueger (1994)).
Empirical evidence for other countries has been varied, depending on the methodologies
and datasets used but, on balance, suggests that modest and incremental increases
in minimum wages do not have significant adverse effects on hours worked and
job loss. There has been limited empirical evidence for Australia, partly
because of data constraints. This article contributes to the debate by using
an approach and a job-level dataset that are uniquely suited to studying the
effects of minimum wage increases in Australia.
The analysis focuses on the minimum wage changes most common in Australia: annual
adjustments by the Fair Work Commission
(FWC).
These adjustments have typically been modest, incremental and, to some extent, predictable.
The analysis finds that these changes are almost fully passed on to wages
in award-reliant jobs, and appear to have had little adverse effect on hours
worked or job loss. These findings are consistent with the international evidence
and the FWC’s (2018) current assessment of that evidence. However, the
findings do not necessarily generalise to large, unanticipated changes in
minimum wages.
Minimum Wages in Australia
The National Minimum Wage (NMW) sets a legal floor on wages. As of 1 July 2018, this
was equal to $18.93 per hour for an adult employee. Australia also has a detailed
system of award wages that are layered on top of the NMW. These ‘award
wages’ can depend on the industry, age, skill level and qualifications
of an employee, among other factors. For example, an employee who supervises
lift operators at a ski resort is entitled to a wage of $24.63 per hour, 30 per cent
above the NMW. As such, while many ‘minimum wage’ employees are
paid the NMW – particularly lower-skilled workers and those not covered
by an award or enterprise agreement – many others are entitled to a
higher wage.
The NMW and all award wages are adjusted at the same time every year, usually in
early July. The size of these adjustments is decided at a national level by
the FWC. Any adjustments are applied consistently across awards; historically
the FWC has either added a flat dollar amount to all award wages (1993–2010)
or raised all award wages by the same percentage amount (2011–18). Adjustments
to individual awards are less common. In its recent Annual Wage Review, the
FWC announced a 3.5 per cent increase in the NMW and minimum wages
across all awards, which took effect on 1 July 2018 (Graph 1).
Graph 1
Australia’s system for setting minimum wages is more complicated than in most
other countries. Unlike other countries that set a single minimum wage (e.g.
the United Kingdom, Germany and New Zealand) or a minimum wage that varies
by state (e.g. the United States), the Australian system has thousands of
award wages that currently range from $18.93 per hour to as high as $171.00
per hour (Graph 2). Because award wages are often set above the NMW, the share
of Australian employees whose pay is set according to a minimum wage is relatively
high by world standards. Nearly one-quarter of all employees in Australia
have their wage set according to a minimum or award wage, and this share has
risen over recent years (RBA (Reserve Bank of Australia) 2017). The large
share of employees affected by FWC decisions also reflects that the NMW in
Australia is high relative to other nations, both in absolute terms and as
a share of median earnings (Productivity Commission 2015a).
Graph 2
The Effect of Minimum Wages on Employment
Australia’s unique institutions for setting minimum wages mean that the findings
of empirical studies for other countries do not necessarily apply in the Australian
context. The complexity in Australia’s wage-setting institutions also
makes estimating the effects of minimum wages using Australian data a difficult
task.
The main challenge in estimating the effect of minimum wages on employment is that
it is hard to disentangle the effects of minimum wages from the effects of
everything else that is going on in the labour market. For example, if minimum
wages are adjusted at the same time as a change in income tax rates, then
any change in employment that occurs around the change in minimum wages may
in fact be due to the tax changes rather than the FWC decision. To isolate
the effects of the minimum wage changes on employment, we need to consider
a group of workers who are affected by the change and another group that are
not (i.e. a control group), but are otherwise subject to the same forces affecting
the economy and the labour market. The challenge for researchers is that it
can be difficult to find a suitable control group of individuals who are not
affected – or less affected – by the change.
I overcome this challenge by recognising that, while all jobs have their minimum
wages adjusted at the same time each year, historically, some jobs have had
larger adjustments than others. This means that we can compare the labour
market outcomes of those receiving a relatively large adjustment to those
receiving a smaller adjustment.
The decade leading up to the late 2000s is particularly useful for studying the effects
of award wages on labour market outcomes. During this period, the FWC routinely
granted flat dollar increases to all awards each year, irrespective of the
existing wage rates contained in those awards. For example, in May 2000, the
FWC increased all award wages by 39 cents per hour. This meant that employees
on relatively low award wages received larger wage increases in percentage
terms than those on higher award wages: a worker earning $10.14 per hour
(the NMW at the time) was entitled to a wage rise of 3.9 per cent,
while a worker on $18 per hour received an increase of only around 2 per cent
(Graph 3). On some occasions, the FWC also granted smaller flat dollar increases
to higher award wage ranges, rather than a single flat dollar increase. For
example, in 2003, the FWC announced an increase of 45 cents per hour for all
award wages up to $19.26 per hour, and 39 cents per hour for all wages above
this level. This created a discontinuity in the profile of percentage wage
increases at $19.26 per hour (Graph 3). These discontinuities tended to exacerbate
the differences in the size of the award wage increase between low- and high-award-wage
employees.
This article uses these differences in the size of minimum wage adjustments across
different jobs to disentangle the separate effect of minimum wages from the
effect of other changes in the labour market. The idea is to simply compare
the change in each variable of interest – wages, hours worked or job
loss – around each FWC decision between jobs that experienced a relatively
large percentage adjustment and those that only had a small adjustment.
Graph 3
Data
The approach described above requires detailed data on the wages and employment outcomes
of individual employees or individual jobs before and after each FWC decision.
This article uses a source of data not previously used in Australian research
on minimum wages: job-level data from the Australian Bureau of Statistics
(ABS) Wage Price Index (WPI) survey. This survey includes 3,000 firms per
quarter. Each firm is being surveyed every quarter for five years, before
being rotated out of the sample (roughly one-fifth of the sample is replaced
each year). After being selected into the sample, a firm is asked to randomly
select a certain number of jobs from their payroll records. The firm then
reports information on each of these jobs over time. Around 18,000 jobs are
included in the survey every quarter. Approximately 15–20 per cent
of these jobs have their pay set exactly according to an award.
A key advantage of this dataset is that it includes both a precise measure of actual
wages and an indicator for whether a job’s wage is set according to a
minimum or award wage. This allows one to focus on jobs directly affected
by minimum wage decisions. This approach differs from most previous studies
(particularly for the United States), which, due to data limitations, tend
to focus on low-wage industries, such as the restaurant sector, or on lower-productivity
employees, such as teenagers. Using low-wage groups as a proxy for employees
reliant on minimum wages may lead to biased estimates of the effect of minimum
wages on employment (Jardim et al 2017). The WPI data also allow one to study the effect
of changes to minimum wages on hourly wages themselves, which is rarely possible
with more traditional data. The pass-through of award wages to hourly wages
is a key consideration for the FWC in terms of assessing whether firms are
complying with their legal obligations. The elasticity of hourly wages with
respect to minimum wages is also one of the key parameters in assessing the
effect of minimum wage increases on income inequality (Leigh 2007).
The WPI survey follows jobs, rather than employees (for example, a graduate economist
at the Reserve Bank of Australia). If the occupant of the job leaves the firm
or moves to a different job within the firm, the ABS substitutes the job leaver
with the employee who replaced her or an existing employee with the same job
title. For this reason, the results in this study pertain to jobs, rather
than individual employees. The estimation sample includes all private sector
jobs filled by an adult on an award rate. Juniors, apprentices and trainees
are excluded from the analysis, as their award wage adjustment cannot be inferred. This is unfortunate
because these groups may be particularly vulnerable to job loss following
an increase in award
wages. This leaves a sample
of 32,174 job-period observations spanning the 11 FWC decisions over the period
between 1998 and 2008.
Empirical Strategy
A difference-in-differences (DD) model is used to estimate the effects of minimum
wage changes on wages, hours worked and the job destruction rate of award-reliant
jobs. The intuition for this approach was discussed above; the DD model compares
the change in each outcome variable – wages, hours worked or job destruction
– around each FWC decision between jobs that experienced a relatively
large percentage increase in their award wage and those that experienced a
relatively small increase. This means jobs that experienced a relatively small
adjustment to their award wages fulfil the role of a ‘control group’
for those jobs that had a larger increase. The size of the award wage adjustment
a particular job received is inferred from its wage level immediately before
the decision. Given that attention is restricted to jobs paid exactly an award
wage before each FWC decision, the results provide direct estimates of the
effects of minimum wages on award-reliant jobs.
Changes in wages, hours worked or job destruction around each FWC decision are measured
from the WPI survey immediately before the decision to the survey six months
later. This gives valid estimates of the effect of the minimum wage on wages,
hours worked and job loss to the extent that employers’ adjustments to
award changes happen within six months (Borland 2018). During the period between
1998 and 2008, there were 11 minimum wage decisions. The approach taken here
is to pool the 11 decisions together and use a single DD estimator that constrains
the coefficient of interest to be constant across the decisions. This maximises
the available sample to estimate the effects of interest. The model includes
controls for any macroeconomic shocks that affect all wage groups and controls
for any characteristics of each wage group that do not change over
time.
Details of the model are provided in Appendix A.
The following outcome variables are considered:
Wages: the log of the job’s hourly wage, excluding any
wage changes due to changes in the job occupant’s grade or performance.
Hours worked: the log of the ordinary-time hours paid for
during the most recent pay period. This includes hours of paid leave (e.g.
annual leave and sick leave) but excludes overtime hours.
Job destruction rate: a binary variable that equals one
if the job had ceased by the survey date in question, conditional on the
job existing six months earlier, and zero otherwise. This captures both redundancies
and firm failure, but does not include cases where the job incumbent leaves
the firm (either voluntarily or involuntarily) or if the firm only makes
some of the employees in a given position redundant.
The estimates for wages and hours worked are conditional on the job being in the
sample in the survey before and after a given FWC decision. If a job is vacant
or made redundant in either period, it is dropped from the sample for estimating
the wage and hours effect. This is not the case for the job destruction rate
estimate.
Results
The estimates for wages, hours worked and the job destruction rate are shown in Table
1. Separate estimates are presented using the full sample and a sample that
omits the first four FWC decisions (1998–2001). Although the sample
size is larger for the full sample, there is a data issue prior to 2002 that
could potentially distort the regression
results.
Table 1: Effect of Award Wages on Wages, Hours Worked and the Job Destruction Rate(a)
Effect of a 1 per cent increase in award wages
Wages
Hours worked
Job destruction rate
(%)
(%)
(ppt)
1998–2008 sample
0.84***
0.26
−0.22
(0.03)
(0.37)
(0.44)
2002–08 sample
0.93***
0.13
−0.37
(0.04)
(0.47)
(0.55)
(a) Standard errors (in parentheses) are clustered at the individual job level; ***,
** and * denote statistical significance at the 1, 5 and 10 per cent
levels, respectively
Sources: ABS; RBA
There is strong evidence that award adjustments are passed on to wages. Using the
full sample, the elasticity of wages with respect to the award wage is estimated
to be 0.84 and highly statistically significant (Table 1). The estimate is
larger in the shorter sample period (0.93) which suggests that measurement
issues in the first few years of data may be attenuating the size of the overall
elasticity in the full sample. A wage elasticity of 0.93 implies that a 1 per cent
increase in award wages leads to a 0.93 per cent increase in actual
wages for award-reliant workers. Full pass-through of minimum wage changes
into wages would require this elasticity be equal to one. Given the estimate
is slightly less than one, it suggests less-than-full pass-through of FWC
decisions to wages. This may reflect
some degree of noncompliance by firms in their legal obligations (see Productivity
Commission (2015b, p 12) for a discussion), or simply measurement error. In
any case, the elasticity is sufficiently close to one to characterise this
as near-complete pass-through.
Having established that award wage increases are passed through to wages, the next
step is to see if this leads to any reduction in hours worked or job loss;
this would be the case if the DD estimates are negative for hours worked and
positive for the job destruction rate. There is no evidence that award wage
changes have an adverse effect on hours worked or the job destruction rate:
in both cases the DD estimate is not significantly different from zero (Table
1). (And the point estimates were in fact the opposite signs to an adverse
effect.)
Robustness tests
Like any analysis using a DD approach, these results are sensitive to violations
of the so called ‘parallel trends’ assumption. The assumption
is that, in the absence of a change in award wages, the wages, hours worked
and probability of job loss of low-award-wage workers would have followed
the same trajectory as high-award-wage workers. This would be violated if,
say, there is a change in tax policy that affects low-wage earners relatively
more than high-wage earners that occurs at the same time as the change in
award wages. If this happens, we may incorrectly attribute the effect of the
tax change to the award wage. Bishop (2018) discusses several ways of testing
the validity of the parallel trends assumption. These include placebo tests,
using additional control groups, controlling for firm-specific shocks, and
accounting for ‘pre trends’ in the outcome variables. The results
of these tests suggest that the baseline estimates are likely to be robust
to potential violations of the parallel trends assumption.
Conclusion
There is widespread interest in understanding the effects of minimum wage increases.
This article adds to the evidence base by using an approach uniquely suited
to Australia and a dataset that provides several advantages over those used
previously in the literature. It finds that small, incremental adjustments
to awards are mostly passed on to wages in award-reliant jobs. These adjustments
appear to have no discernible adverse effect on hours worked or job loss.
There are several things to keep in mind when interpreting these findings. Firstly,
as discussed, these results are for adult employees only and do not include
juniors. Secondly, the results do not necessarily generalise to large, unanticipated
changes in award wages. There will always be some point at which a minimum
wage adjustment will begin to reduce employment significantly. Thirdly, the
approach considers six month windows around FWC decisions; it is possible
that firms take longer to respond to changes in minimum wages. Finally, although
this article finds no evidence of an effect of award adjustments on job destruction,
this does not rule out an adverse effect on employment. It is possible that
the adverse consequences of higher wage floors may be borne by job seekers,
rather than current job holders.
Appendix A The Difference-in-differences Model
The DD equation is,
where yikt is the dependent variable of interest for job i
in wage group k at time t. λt
is a full set of time dummies that control for any macroeconomic shocks
that affect all wage groups in any of the 22 time periods (there are 11
different FWC decisions each with their own ‘before’ and ‘after’
period).
FWCk is the log change in award wages for
wage group k
due to the FWC decision. There are also a set of interactions between
FWCk and dt, the latter being a
dummy that takes the value of zero in the ‘before’ period immediately
prior to an award increase and a value of one in the ‘after’
period six months later. The coefficient of interest is β3.
When the dependent variable is the log hourly wage (or log hours worked),
this DD coefficient is the elasticity of wages (or hours) with respect to
the award wage, a parameter of key interest to policymakers.
References
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