Australia: Selected Issues

International Monetary Fund. Asia and Pacific Dept

doi:10.5089/9781475575842.002.A001

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Australia: Selected Issues

Author: International Monetary Fund. Asia and Pacific Dept

Publication Date:: 09 Feb 2017

eISBN:: 9781475575842

Language:: English

Keywords:: ISCR; CR; monetary policy; rate; output gap; inflation expectation; unemployment gap; labor productivity growth; equilibrium unemployment value; interpreting labor market dynamics; employment response; Wages; investment decline

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Australia: Selected Issues

Abstract

Australia: Selected Issues

Labor Market Adjustment to Shocks in Australia¹

Australia’s labor markets were not severely impacted by the global financial crisis, and are adjusting smoothly to the sizeable commodity prices bust and mining investment downturn. However, some labor market indicators suggest persistent weaknesses.

For instance, long-term unemployment rates have risen, and prevail above long-term averages. Underemployment rates are also elevated. Much of the employment growth since the end of the mining investment boom has been driven by part-time employment, and wage growth remains weak.

In this context, we examine in detail the adjustment of Australian labor markets to the recent adverse shocks, and address, in particular, the following questions:

Has the mining investment downturn led to an increase in structural unemployment?
How have cyclical labor market adjustment dynamics changed, and what are the implications for labor market slack and wage growth?
What do recent changes in sectoral allocation of labor imply for aggregate labor productivity growth?
How did States’ labor markets adjust to the commodity boom-bust cycle?

The main findings are:

There does not appear to be a significant increase in structural employment in the wake of the commodity prices bust and mining investment decline.
Increased flexibility in average hours per worker has likely moderated employment reduction in downturns and prevented a larger increase in unemployment in the wake of the mining investment downturn. At the same time, elevated underemployment signals additional slack, and is likely weighing down wage growth.
Reallocation of labor to expanding sectors has proceeded smoothly. Labor productivity growth has slowed, partly as a result of the expansion of services.
States’ labor markets have transitioned smoothly, aided in particular by the response of migration to the commodity boom-bust cycle.

A. Introduction

1. The Australian economy has faced two major shocks in recent years, namely the global financial crisis, followed by the shock to commodity prices. On the latter, commodity prices doubled between 2005Q1 and 2011Q3 driving the terms of trade to an all-time high. With the rapid rise in commodity prices, mining investment rose from an average of around 2 percent of GDP before 2005 to a peak exceeding 7 percent of GDP in 2013. Since 2011Q3, a 40 percent decline in commodity prices has led to an equivalent decline in the terms of trade by 36 percent from the 2011Q3 peak. Mining investment has fallen dramatically as major construction projects have come to an end, falling to 5½ percent of GDP by end-2015, and declining further since.

2. Such large external shocks have often been considered to be catalysts for major displacement in labor markets. Indeed, labor markets in some advanced economies were badly impacted by the global financial crisis, and in some cases the damage has yet to be reversed. Measured against unemployment performance, Australian labor market appears to have avoided major dislocation.

3. In Australia, the deterioration in labor market outcomes has been moderate. Figure 1 summarizes key real sector and labor market developments at the aggregate level. Following the global financial crisis, collapse in the terms of trade and the decline in mining investment, real GDP growth has fallen from average rates of around 3¼ percent pre-GFC to around 2½ percent post-GFC. Driven by slower growth, unemployment has risen, reversing the steady decline that started in the early 1990s. Having reached a trough of 4 percent prior to the global financial crisis, unemployment rose to an average of 5½ percent between 2009Q1 and 2016Q2, and peaked at around 6¼ percent in mid-2015 in the wake of the severe terms-of-trade bust and mining investment decline. Since then, unemployment has declined to below 5¾ percent as of August 2016. The relatively mild unemployment fluctuations – within ¾ of percent of the 2000-16 average of 5½ percent – are attributed to the flexible labor market, which has helped with the ongoing successful rebalancing of the economy.

4. The unemployment gap appears to be consistent with the size of the output gap. As a benchmark for assessing the extent to which current labor markets exhibit slackness or tightness, we have estimated an equilibrium long-term value of unemployment (known as the NAIRU: Non-Accelerating Inflation Rate of Unemployment). Based on a multivariate-filtering approach, estimates of the NAIRU point to an unemployment gap of around 0.6 percent.² Estimates of potential output indicate a negative output gap (excess capacity) in the range of 1.25 to 1.75 percent of potential GDP. The unemployment rate varies relative to the NAIRU in the short term according to an Okun’s law relationship based on the output gap. Given the output and labor market gaps estimated for Australia, we deduce that the unemployment gap is in line with what would have been suggested by estimates of the Okun coefficients for Australia (see Ball and others (2013) who estimate the coefficient at -0.4 in a post-1995 sample, and Tulip and Lancaster (2015) who estimate the coefficient between -0.27 and -0.35).

5. Despite the benign cyclical labor market fluctuations, some labor market indicators seem to suggest persistent weaknesses. For instance, Figure 1 shows that both long-term unemployment and underemployment have been elevated since the global financial crisis, prevailing above their long-term (2000-16) averages since end 2012-early 2013. Elevated long term unemployment may indicate a worsening in labor market efficiency and a skills mismatch since the terms-of-trade bust and mining investment decline. Elevated underemployment suggests there is more slack than indicated by the unemployment gap alone, and may have implications for wage growth. In this context, we conduct a detailed assessment of the labor market impact of and adjustment to the adverse shocks, focusing on the following key questions:

Has the commodity price and mining investment decline led to a worsening in skills mismatch and other frictions, such as those related to location?
Have cyclical labor market adjustment dynamics changed over time? And what are the implications for assessment of labor market slack, and relatedly for wage growth?
Has sectoral composition of labor changed much since the end of the mining boom, and what impact has this had on labor productivity?
How have labor markets at the state level adjusted to the commodity boom-bust cycle, and how important a role has migration played in this adjustment?

6. The main findings of the paper are as follows. While labor markets have adjusted smoothly overall, structural unemployment appears to have increased slightly following the commodity price shock; flexible labor markets have moderated reductions in employment in the 2000s relative to previous downturns, likely due in part to labor market reforms in the early 1990s; but falling average hours per worker and rising part-time employment may have led to elevated underemployment, seen as a contributing factor to weaker wage growth. At the state level, we find that migration has played a key role in labor market adjustment to demand shocks, likely an important contributing factor to the relatively smooth adjustment both during the boom and after. Finally, we find that reallocation of labor to expanding sectors has proceeded smoothly, although aggregate labor productivity growth has slowed partly as a result of expansion in services.

7. The paper is structured as follows. Section B examines the impact of the global financial crisis and the terms-of-trade bust and mining investment decline on structural unemployment. Section C discusses changes in cyclical labor market adjustment dynamics over time, and the implications for assessing labor market slack and relatedly for wage growth. Section D assesses the impact of the reallocation of labor following the mining investment decline on labor productivity. Section E focuses on labor market adjustment at the state level and the role of migration and Section F provides some final conclusions. Detailed descriptions of the methodology and data are provided in Appendices I to III.

B. Has Structural Unemployment Increased?

8. Long-term unemployment has risen since the global financial crisis and again following the mining investment decline. Following the crisis, the share of long-term unemployed in total unemployed persons rose from a trough of around 15 percent in mid-2009 to around 20 percent by 2011, and then to around 25 percent since the mining investment decline. Duration of unemployment on average has increased from the pre-GFC trough of 27 weeks to nearly 46 weeks in mid-2016. The long-term unemployment rate has risen to levels seen in the early 2000s preceding the mining boom, though well below the much higher rates observed in the 1990s (Figure 1-panel 4).

To the extent that long-term unemployment reflects skill mismatches, it provides evidence of increased structural unemployment.
Consistent with rising structural unemployment, a multivariate filter estimate of the NAIRU in Australia indicates an increase, albeit small, beginning in 2011 (Figure 2).
Structural unemployment is often illustrated in terms of shifts in a Beveridge curve. For Australia, the Beveridge curve suggests outward shifts in the 1980s, and again in the mid-1990s. Since then, the curve appears fairly stable, despite what appears to be a small outward shift following the terms-of-trade bust and mining investment downturn, compared to the period over 2000s before the global financial crisis.

9. A fitted Beveridge Curve for Australia suggests unemployment is higher than consistent with labor market equilibrium. To examine whether the Beveridge curve has shifted, we construct a fitted Beveridge curve for Australia following Hobijn and Sahin (2012) (see Appendix I for details), which traces labor market equilibrium unemployment values. We note that observations following the terms-of-trade bust and mining investment decline appear to the right of the curve. For instance, in 2016, unemployment was around ¾ of a percent higher than the rate consistent with the labor market (turnover) equilibrium implied by the fitted Beveridge curve.

10. However, this does not necessarily signal an increase in structural unemployment. While a moderate outward shift of the curve would be consistent with the observed increase in long-term unemployment following the recent shocks, the increase is much smaller than in previous downturns, and should recede as slack in the economy is eliminated. Moreover, the divergence of the latest unemployment-vacancy rate observations from the Beveridge curve are relatively small and in line with historical divergences. Compared with other countries, the divergence from the Beveridge curve in the post-GFC period is also much smaller. Indeed, the deviation of the observations after the terms-of-trade bust are within the range of deviations of the in-sample points. Other advanced economies such as Portugal, Spain, Sweden, U.K. and U.S. have experienced much larger outwards shifts of their Beveridge curves.

C. How Have Cyclical Adjustment Dynamics Changed? And What Are the Implications for Labor Market Slack and Wage Growth?

11. Average hours worked per worker have become more flexible and since the early 2000s seem to have contributed importantly to cyclical labor adjustment. A feature of cyclical labor input (total hours worked) adjustment in Australia since the late 1990s is the relatively greater share of adjustment borne by average hours worked per worker relative to total employment. Figure 3-Panel 1 shows peak-to-trough decline in quarterly detrended total hours worked decomposed into employment and average hours. In the early 1990s, most of the cyclical decline in total hours worked was through reductions in employment, whereas starting from the early 2000s (and through to the present) the adjustment during downturns has been almost equally attributed to reductions in average hours and in employment. It can also be seen that the amplitude of peak-to-trough declines in total hours worked has moderated.

12. The employment response to output shocks seems to have moderated. To illustrate the change in adjustment dynamics in response to aggregate output shocks over time, Figure 3-Panel 2 shows the impulse response functions (IRFs) from a VAR estimation in log detrended values of GDP, average hours, and employment using quarterly data run over two samples, 1984Q4–1997Q4, and 1998Q1–2016Q1. Following Bishop and Plumb (2016), the ordering of the VAR reflects the assumption that output responds only with a lag to labor input shocks, and the assumption about the relative speed of adjustment of the components of aggregate hours worked; i.e., firms would first adjust hours worked in response to these GDP shocks than to employment. The IRFs suggest that the response of employment to a 1 percent GDP shock has indeed moderated in the second sample period (see Appendix II for details).

13. In relation to labor market features, the following factors may also help explain the moderation in the employment response to output shocks. To begin with, Australia embarked on labor market reforms in the early 1990s that made it easier for firms to bargain directly with employees, which may have allowed greater scope for firms to adjust hours worked in response to cyclical conditions.³ Following that, and just before the global financial crisis, tight labor markets may have increased costs related to firms’ decisions on hiring and firing. During the crisis, increased uncertainty among workers regarding employment prospects may have increased their willingness to accept lower hours of work in return for employment security (though there is also evidence that the crisis witnessed destruction of jobs with longer hours and their replacement with jobs with shorter hours rather than adjustment within existing jobs). Furthermore, rising skill requirements along with economic development are likely to have increased the cost of screening and training workers, rendering decisions related to hiring and firing costlier to make. One measure of the increase in skill requirements is the level of educational qualifications of the workforce – for instance, the share of workers in the workforce with educational qualifications up to a Bachelor degree or higher rose from 18 percent in 1993 to 26 percent in 2009 (ABS Survey of Education and Training data). Evidence also suggests that the largest increases in demand for labor have occurred in cognitive, non-routine tasks management and professional activities, and non-cognitive non-routine tasks of personal care, and the largest declines in non-cognitive routine tasks of machinery and plant operation, and cognitive routine tasks such as clerical and secretarial functions (Borland, 2011).

14. At the sectoral level, adjustments of employment to output shocks appear inversely related to education levels. To provide further evidence regarding the role of skills requirements in moderating the impact of cyclical output fluctuations on employment, for selected 2-digit sectors we estimate VARs similar to those implemented at the aggregate level (on log detrended sectoral gross value added, average hours, and employment at quarterly frequency, over a sample period from 1990Q1 to 2016Q1 (see Appendix II for details). We then compare the magnitude of the cumulative 8 quarter change in employment in response to a 1 percent gross value added shock in each sector, to the share of workers with educational qualifications at least up to a Bachelor degree (as measured in 2009) in the sector. We find that the size of employment adjustment in response to an output shock varies across sectors (Figure 4). For instance, in transportation and storage industries, and in miscellaneous services, the peak employment impact of a 1 percent shock to gross value added is larger and more sustained than a similar shock in manufacturing or in health services industries. Further, there appears to be a fairly strong negative relationship in the size of cumulative employment adjustment at eight quarters in a sector and the share of workers with educational attainments at least up to a Bachelor degree in that sector. We do not observe such an association between adjustment in average hours and education levels, however.

15. The level of unemployment alone may understate the extent of labor market slack if employed workers are working fewer hours than desired. Figure 5 shows that, while employment growth has strengthened since 2014, much of the growth pickup has been driven by part-time workers. Even as average hours worked appear to have recovered up to trend levels after the terms-of-trade bust, full time hours worked are on a steadily declining trend. Likely due to rising part-time work and declining full-time hours following the bust, the underemployment rate has risen to around 8¾ percent in 2016Q3, well above the historical average of around 7 percent, indicating there may be more slack in the economy than indicated by the unemployment rate alone.

16. Elevated underemployment has likely contributed to weaker wage growth since the terms-of-trade bust. Indeed, aggregate private sector wage growth appears to be weaker than would be implied by the historical relationship with the unemployment gap, indicating that the wage Phillips curve may have shifted downward since the terms-of-trade bust (Figure 6). It may be analogous to the case of the United States, as found in Blanchflower and Levin (2015), who provide empirical support for the negative impact of underemployment (in addition to unemployment) on wage levels.

17. A wage Phillips curve model for Australia suggests underemployment has a negative impact on wage growth. Building on Jacobs and Rush (2015), we estimate Phillips curves regressing private wage growth on measures of labor market slack that include underemployment and unemployment gap measures, expected inflation inferred from inflation indexed bonds, and the GDP deflator to proxy for output prices, building on Jacobs and Rush (2015) (see Appendix II for details). ⁴ These models provide a fairly close fit to wage growth data, and a somewhat better fit as measured by the adjusted R-squared when underemployment measures are included along with the unemployment gap. Moreover, the coefficients on the underemployment gap and lagged change in the underemployment rate terms are negative and significant, and where included, the coefficient on the lagged change in the underutilization rate is also significant (Model 2, 3 and 5 in Table A2, Appendix II). This suggests that in addition to unemployment, underemployment gaps may also have a negative impact on wage growth, and higher underemployment since the terms-of-trade bust may therefore be partly responsible for the weakness in wage growth since then.

18. The results also show that actual wage growth has been somewhat weaker than predicted by the model in recent quarters. Some explanations for the unexpected weakness in wage growth may be offered. In the 2000s, wage growth tended to often prevail above predicted levels, including the period just before the terms-of-trade bust. Part of the weakness in wage growth may thus reflect adjustment of wage levels to correct for previous high growth. Increased flexibility in labor markets, particularly among sectors exposed to commodity prices such as mining and business services, may have also added to downward wage pressure. Finally, low public sector wage outcomes, to which some private sector wages such as in health and education are benchmarked, may have also contributed to the weakness in private wage growth (Jacobs and Rush, 2015). More recently however, public sector wage growth has exceeded private sector wage growth, even though wage caps remain in effect in some state governments.

19. Broader measures of slack in labor markets and increased flexibility in labor markets are therefore central to interpreting labor market dynamics. The increased flexibility of average hours per worker may have helped moderate the rise in unemployment, but the growing share of part time workers and trend decline in full time hours may indicate more slack in labor markets than unemployment gaps alone would, and may well have been an important source of weakness in wage growth.

D. Have Sectoral Shifts in Labor Impacted Labor Productivity?

20. Aggregate labor productivity can be impacted by shifts in the sectoral allocation of labor. For instance, in the United States, the recent decline in manufacturing and mining share of aggregate hours and the rise in the services share is estimated to have reduced labor productivity growth by ¼ and ½ percentage point respectively relative to a counterfactual (of no change), given known higher productivity levels in the mining and manufacturing sectors relative to services. Moreover, the slower pace of labor productivity growth in services could exert a drag on aggregate productivity growth going forward (Van Zandweghe, 2016).

21. In Australia, changes in the share of aggregate hours worked across sectors reflect long-term trends, changes since the global financial crisis, and more recently since the terms-of-trade bust and mining investment decline. Figure 7 shows trends in the share in aggregate hours across sectors. Among goods producing sectors, manufacturing has shown a steady decline in the share of hours worked over time. The small share of mining in aggregate hours relative to other sectors rose sharply over the boom period but has dropped sharply since the end of the boom. In construction, the share rose steadily during the mining boom and has stabilized at that higher level since. With respect to services, a noticeable increase in the share of healthcare services has been observed, following the global financial crisis, with further acceleration more recently. Retail trade and communication shares declined post-GFC, likely due to increasing adoption of internet enabled retail services and expanded use of ICT technology.

22. Since the terms-of-trade bust, the most noticeable changes in the share of total hours worked have occurred in mining and healthcare services. Since 2011Q3, average hours worked have declined across most sectors, and about equally in healthcare and mining sectors. The change in shares in aggregate hours in the mining and healthcare sectors thus reflects changes in employment – mining was a major contributor to job growth during the boom period, but mining sector employment has fallen since the terms-of-trade peak, whereas healthcare services have contributed a third of the jobs added since the terms-of-trade bust, higher than quarter of the jobs added over the boom (Figure 8).

23. Trends in labor allocation and labor productivity in key sectors have changed post-GFC and following the mining investment downturn. The share in hours worked of goods related sectors (mining, manufacturing, utilities, construction, and domestic trade) has declined by nearly 5 percentage points since the global financial crisis and following the mining investment downturn, while the share of business services (finance, real estate services, professional services, and administrative services) and particularly household services (food and accommodation, education, healthcare, recreational, and other personal services) has risen correspondingly. The level of labor productivity (in real gross value added per hour) is markedly higher in the goods-related sector and in business services compared to household services. Moreover, the growth rate of labor productivity in household services is on average much lower (about 0.7 percent compared to 2 percent in goods and 1.9 percent in business services, over 1986 – 2016).

24. The decline in labor productivity growth in recent quarters is partly due to shifts in allocation of total hours worked – a “between” effect. Labor productivity growth has also slowed within both household and business service sectors in recent periods. On average though, post-GFC aggregate labor productivity growth has sustained its pre-GFC growth rate of around 1½ percent (in terms of GDP per hour worked), and unlike many other advanced economies still exhibiting large labor productivity level gaps relative to their pre-GFC trend, no such gap can be discerned in Australia. However, it remains to be seen whether lower labor productivity growth in services will drag aggregate labor productivity growth down to a lower average rate, beyond the peak of mining output at full capacity.

E. How Did States’ Labor Markets Adjust to the Mining Boom-Bust Cycle?

25. The mining boom produced a strong labor supply response at the state level. The boom led to a strong pickup in private investment growth in the mining states, namely Western Australia (WA) and Queensland (QLD), accompanied by a strong pickup in labor demand (as measured by vacancies as per Figure 10). In New South Wales (NSW), Victoria (VIC), and South Australia (SA), investment growth and the states’ shares in total investment fell quite markedly, but there was strong labor demand growth particularly in NSW, likely linked indirectly to the mining boom. There was a marked supply response – participation rates rose, and unemployment rate declined. Working age population rose above long-term average growth rates, particularly in WA and in QLD. Following the terms-of-trade bust, labor demand in mining states fell sharply, and was accompanied by sharp declines in working age population growth, reflecting migration flows.

26. Migration has played a key role in driving working age population growth in Australia. On aggregate, about 50-60 percent of the increase in population is due to international migration. In terms of the supply response to the mining boom-bust cycle, migration has helped avoid labor shortages on the upswing, and big increases in unemployment on the downswing. At the state level, migration has been a key supply side factor especially over the mining boom period, contributing a larger share of population growth over 2005-11 in all states than in the previous two decades.

27. Econometric evidence suggests that the population (migration) response to demand shocks is a key component of states’ labor market adjustment. In order to illustrate the labor market adjustment dynamics at the state level, in particular how migration behaves in response to state-specific demand shocks, we implement a set of state level VARs in (log changes in) employment, unemployment rate, and labor force participation rate (following Bayoumi and others 2006, and Blanchard and Katz 1992) for six states (NSW, VIC, QLD, SA, WA, and Tasmania (TAS)) over 1979-2015. Following the Blanchard-Katz interpretation, shocks to employment within the year are assumed to be demand shocks. Supply side effects occur through the employment rate and participation rates. The ordering of variables reflects the assumption that employment is impacted by shocks to the unemployment rate and participation rate impacts only with a lag. To focus on state-specific shocks, the aggregate cycle component is removed from each variable (details of the estimation procedure are provided in Appendix III). Based on the IRFs of this system, it is possible to trace out the evolution of the unemployment rate, participation rate, and population growth in response to the state-specific or acyclic employment shocks.

In Figure 11A, we show the IRFs from a 1 percent shock to employment. As employment adjusts over 10 years to its long-term growth rate:
In QLD and VIC, employment and participation rates account for around half of the increase in employment over 10 years. Thus, about half the increase in employment in the long term is supported by rising population (migration). In QLD, the employment rate has a smaller role in adjustment relative to participation compared to VIC. In all states, participation rates seem to do more of the adjusting than employment rates.
In NSW and WA, migration seems to play a somewhat smaller role in response to employment shocks, accounting for between 30-40 percent of the long-term increase in employment over 10 years.
One would expect WA to show a larger migration component given the sizeable swings in working age population growth over the past decade. This likely reflects our focus on state-specific employment shocks that remove the influence of aggregate shocks. It is plausible that there are larger population inflows into WA in response to aggregate shocks such as the commodity price boom rather than in response to state-specific shocks. Indeed, IRFs from VARs that do not remove the aggregate cycle from the data show WA has the largest migration response among all states in the sample (Figure 11B).
In SA and TAS, the role of migration appears to be the smallest, accounting for only about 10-15 percent of the increase in employment in the long term.
Further, conducting an historical shock decomposition exercise, we ask what would employment growth in states have looked like had it been driven by shocks to the employment rate and participation rate (Figure 12). The results show variation over time within states as to how closely observed employment growth is driven by these two shocks. By and large in the four bigger states (NSW, VIC, QLD, and WA), the sum of employment rate and participation rate shocks correspond reasonably well with actual changes in employment, with some exceptions. For instance, in QLD the decline in employment after 2006 exceeded what would be caused by unemployment rate and participation rate shocks alone. Conversely, the increase in employment growth in VIC and NSW in the mid-to-late 2000s is larger than the impact of the two shocks alone. In WA, instances over the mining boom period also point to positive employment shocks not caused by employment rate and participation shocks alone. This is further evidence on the historical role of migration in adjusting to employment shocks, particularly in the mining boom-bust cycle.

Overall, the results indicate that migration is among the key aspects of labor market flexibility that helped moderate the impact of the recent shocks. Over the boom period, migration likely prevented labor shortages and additional wage cost pressures. Over the bust period, the decline of migration into mining states has likely helped prevent unemployment from rising higher, and likely also prevented wage growth from weakening further.

F. Summary and Conclusion

28. Overall, Australia’s labor markets have coped well with the adverse shocks as of late, adjusting smoothly in response to the terms-of-trade bust and mining investment decline, but not without some lingering concerns. Unemployment rose modestly following the global financial crisis and during the bust phase of the commodity prices and mining investment cycle. However, there may have been a slight increase in structural unemployment. The employment impact of cyclical downturns has moderated since the early 2000s and more of the cyclical adjustment in total hours worked has occurred in average hours per worker, likely due to increased labor market flexibility following reforms in the early 1990s, and other events including the global financial crisis. The slowdown in growth since the crisis and commodity price bust and mining investment decline produced significantly smaller reductions in employment than in prior downturns, and unemployment deteriorated only modestly.

29. Underemployment has played a key role in labor market slack and recent wage growth weakness. The trend decline in average hours worked per worker and increased part-time work are likely reflected in elevated underemployment and suggest more slack in labor markets than the unemployment gap alone would indicate. Elevated underemployment may also explain some of the weaknesses in wage growth in recent periods. Though wage growth appears to be unexpectedly low in recent periods, wages are expected to strengthen gradually as non-mining investment picks up and the drag from declining mining investment comes to an end.

30. Finally, migration has played a key role in labor market adjustment at the state level. This is the case particularly in response to state-specific demand shocks. Migration has likely helped avoid labor shortages during the boom phase and a worse unemployment outturn in the bust phase of the commodity price cycle. This has likely contributed to the overall moderate impact on labor markets over the boom-bust cycle.

Appendix I. Fitting a Beveridge Curve for Australia

To start with, the labor market is in its “turnover steady state” when the net hiring rate equals labor force growth. Put differently, employment growth equals labor force growth in a turnover steady state. Employment growth (RHS of equation (1) below) is the difference between hires (H_t) and separations (S_t) over (t, t + 1] (here a 1-year period), as a ratio of employment at the start of the period (E_t):

\begin{matrix} g_{t + 1} = \frac{H_{t} - S_{t}}{E_{t}}, & (1) \end{matrix}

where g_t+1 is labor force growth over period (t, t + 1]. To express the steady state condition (1) in terms of unemployment and vacancies, a Cobb-Douglas matching function (equation 2) and separation rate equation (equation 3) are estimated, where (U) is the level of unemployment and (V) is the number of vacancies:

\begin{matrix} \ln (^{H} /_{V}) = μ_{h} + α_{h} l n (^{U} /_{V}) + ɛ_{h}, & (2) \end{matrix}

and

\begin{matrix} \ln (^{S} /_{E}) = μ_{s} + α_{s} l n (^{U} /_{V}) + ɛ_{s} & (3) \end{matrix}

Combining (2) and (3) with (1), and noting ^U/_E can be expressed in terms of the unemployment rate as $(\frac{u_{t}}{1 - u_{t}})$ , the steady-state condition can be expressed as:

\begin{matrix} g_{t + 1} = e^{μ_{h} + ε_{h, t}} (\frac{u_{t}}{1 - u_{t}})^{α_{h}} ν_{t}^{(1 - α_{h})} - e^{μ_{s} + ε_{s, t}} (\frac{u_{t}}{1 - u_{t}})^{α_{s}} ν_{t}^{- α_{s}}, & (4) \end{matrix}

The implicit function defined above is evaluated at errors ϵ_h,t, ϵ_s,t = 0, v_t (the vacancy rate) at observed rates, and g_t+1 set at its pre-GFC average rate of 1.7 percent growth to solve for the equilibrium unemployment rate. Parameter values μ_h, μ_s, α_h, and α_s are obtained from the regressions of (2) and (3).

To estimate the regressions, data on hires and separations are inferred from job tenure data. The reader is referred to Hobijn and Sahin (2012) for details of the derivation; a brief description is as follows. It is assumed that hires and separations occur at a constant rate through the year t (i.e. over (t, t + 1] ), in proportion to the level of employment, i.e., the number of hires at any point in time is hE_t where h is the hiring rate, and separations are sE_t where s is the separation rate. It can be shown that given E_t,E_t+1, and $E_{t + 1}^{τ > 1}$ which is the number of employed workers at t + 1 who have job tenure in excess of 1 year, the hiring rate is given by $h = \ln (E_{t + 1}) - \ln (E_{t + 1}^{τ > 1})$ , and the separation rate is given by $s = \ln (E_{t}) - \ln (E_{t + 1}^{τ > 1})$ . Time aggregating the continuous time flows yields:

\begin{matrix} H_{t} = (\frac{h}{h - s}) [e^{(h - s)} - 1] E_{t}, & (5) \end{matrix}

and

\begin{matrix} S_{t} = (\frac{s}{h - s}) [e^{(h - s)} - 1] E_{t} & (6) \end{matrix}

ABS data on employment duration in excess of a y ear (avail able for year-ended in February) are combined with data on employment in February of the year, and the year before, to obtain estimates of hires and separations over the year ended in February of a given, as outlined above. Table A1 shows the results of estimates of equations (2) and (3). The results presented in the text are based on the shorter sample up to 2008, and are in line with those obtained by Hobijn and Sahin (2012) for Australia. A longer sample up to the terms-of-trade bust after 2011 yields very similar parameter values. The results for the hiring function (2) show a relatively good fit even with the limited sample size. As expected, hires per vacancy (the vacancy yield) is positively correlated with the U-V ratio: more slack makes filling vacancies easier. In the separations equation (3), the fit is much weaker and the coefficient on the U-V ratio is insignificant in the shorter sample.

Table I.1.

Regression Estimates

Appendix II. Cyclical Features of Labor Market Adjustment

Aggregate GDP, hours worked, and employment: The VAR results shown in Figure 3 (Section III) are derived from a three variable VAR in log detrended GDP, employment, and average hours worked, based on two quarterly samples – from 1983 to 1997, and 1998 to 2016 respectively. Variables are detrended using the H-P filter, with the smoothing parameter lambda=1600. Seasonal dummies are included since hours worked data are available in non-seasonally adjusted form. Consequently, GDP and employment data are also included in non-seasonally adjusted form. In the charts below, the left panels show the response of log employment in the two sub-periods, while the response of average hours per worker are shown in the panels on the right. Error bands show two standard errors above and below the estimate response. It seems quite evident that employment responses were larger, and distinctly above zero in the earlier period, as compared to the later period. On the other hand, the response of hours worked does not appear to differ very much across the two samples; though noise in hours worked data may be a factor as noted in Jacobs and Rush (2015).

Sectoral value added, hours worked, and employment: The VAR framework above is extended to sector level data to examine differences in the employment response across sectors. The data include detrended values of real gross value added, sectoral average hours worked, and employment (all in logs). The sample runs quarterly from 1990 – 2016. Data on educational qualifications are taken from ABS Survey of Education and Training. In Figure 4 above, we show the relationship between the cumulative 8 quarter employment response and education attainments (share of sectoral labor with Bachelor degree or higher) for the year 2009. The survey is available every 5 years starting from 1993, but comparability over time is limited with expanded sectoral classification, with the 2009 survey having the most detailed sector classification. The surveys for 2005 and 2001 have 2 sectors fewer than the 2009 survey. In general, the sectoral rankings in terms of the share of workers with education attainments of at least Bachelor degrees are preserved, and the results shown in the text would not be altered by choosing a different year of the survey.

Wage Phillips curve estimates: The estimated equation is of the form:

\begin{array}{l} Δ \log W P I_{t}^{p r i v a t e} = α + β_{1} U R g a p_{t - 1} + β_{2} U E R g a p_{t - 1} + β_{3} Δ U R_{t - 1} + β_{4} Δ U E R_{t - 1} \\ + β_{5} {Bondinfexp}_{t - 1} + β_{6} {Bondinfexp}_{t - 2} + β_{7} {Bondinfexp}_{t - 3} + β_{8} Δ 4 \log G D P d e f l_{t - 1} + β_{9} Δ \log l a b p r o d_{t - 1} + ɛ_{t} \end{array}

where $Δ \log W P I_{t}^{p r i v a t e}$ is private sector wage (log change), URgap_t–1 is the lagged deviation of unemployment from its sample average (sample runs from 1998Q1 – 2016Q1), UERgap_t–1 is the lagged underemployment rate gap similarly calculated, Bondinfexp_i–1 is the lagged expected inflation term implied by 10-year indexed bonds, Δ4logGDPdefl_t–1 is the lagged year-on-year change in log of the GDP deflator to proxy for changes in output prices, and Δ log labprod_t–1 is the lagged change in log output per worker.

We impose parameter restrictions as shown in Table A2. Model 1 assumes β₂ = β₄ = 0, i.e. underemployment gaps are assumed to not impact wage growth. Model 2 removes this restriction, allowing the underemployment gap (and the change in the underemployment rate) to exert a distinct impact on wage growth in addition to unemployment measures. Model 3 imposes equality constraints on β₁ = β₂ and β₃ = β₄. In Model 4 and 5 we consider variants of Model 1 and 3 including a lagged dependent variable term. The results show that output prices (GDP deflator) have a positive impact on wage growth in all models and appears robustly estimated. Model 1 is closest in specification to RBA (2015) and the fit is very close to the RBA model in terms of the R-squared. While the UGap term is not significant, the change in unemployment rate has a negative and significant effect on wages. In Model 2, the coefficient on the underemployment gap variable UEGap is negative and significant, as is the lagged change in the underemployment rate. Moreover, the introduction of this variable leads to the sign on UGap to be larger (and significant at 15%) compared to Model 1. Overall, this specification has a better fit in terms of adjusted R-squared and shows that the underemployment gap has a sizeable impact on wages. In Model 3, the change in underutilization rate is significant and has a sizeable impact. The R-squared is also larger than that in Model 1. In addition, we also show results from including a lagged wage inflation term in Models 4 and 5, which improves the fit noticeably in Model 5 which includes the overall underutilization rate measure. Overall, these results would suggest that underemployment gaps do matter for wage growth and may be having some impact in relatively weak wage growth outcomes observed since the terms-of-trade bust.

Table II.1.

Wage Phillips Curve Estimates

Notes: dependent variable is quarterly log difference in private wage. Sample 1997:1 -2016:1. PWage = private wage, U = unemployment, UE = underemployment, UU = underutilization, ExpinfB10 = inflation expectations inferred from 10 year inflation indexed bonds, GDPdef = GDP deflator. All RHS variables are included at first lag unless otherwise noted. Bold figures are significant at 10% or higher.

Table II.1.

Wage Phillips Curve Estimates

	Model 1	Model 2	Model 3	Model 4	Model 5
Dlog PWage				0.317	0.306
UGap	−0.010	−0.031		−0.015
UEGap		−0.064
UUGap			−0.025		−0.024
DUrate	−0.234	−0.188		−0.198
DUErate		−0.099
DUUrate			−0.138		−0.122
ExpInfB10	0.001	0.001	0.001	0.001	0.001
ExpInfB10 (-2)	−0.001	−0.001	0.000	−0.001	0.000
ExpInfB10 (-3)	0.001	0.001	0.001	0.001	0.001
D4log GDPdef	0.030	0.023	0.027	0.021	0.017
Constant	0.003	0.005	0.004	0.002	0.003
R-sq (adjusted)	0.60	0.64	0.65	0.65	0.72

Table II.1.

Wage Phillips Curve Estimates

	Model 1	Model 2	Model 3	Model 4	Model 5
Dlog PWage				0.317	0.306
UGap	−0.010	−0.031		−0.015
UEGap		−0.064
UUGap			−0.025		−0.024
DUrate	−0.234	−0.188		−0.198
DUErate		−0.099
DUUrate			−0.138		−0.122
ExpInfB10	0.001	0.001	0.001	0.001	0.001
ExpInfB10 (-2)	−0.001	−0.001	0.000	−0.001	0.000
ExpInfB10 (-3)	0.001	0.001	0.001	0.001	0.001
D4log GDPdef	0.030	0.023	0.027	0.021	0.017
Constant	0.003	0.005	0.004	0.002	0.003
R-sq (adjusted)	0.60	0.64	0.65	0.65	0.72

Appendix III. Role of Migration in States’ Labor Market Adjustment

The exercise follows the methodology in Bayoumi and others (2006), building on Blanchard and Katz (1992). Noting that E_t = (1 – UR_t)*LFPR_t * WAP_t, where E is employment, (1 – UR) is the employment rate, LFPR is the participation rate, and WAP is the working age population, taking logs and rearranging we observe that e – emp – p = wap where e is log employment, emp is log employment rate, and p is log participation rate. Thus, from the impulse-responses to employment shocks in a VAR involving employment, employment rate, and participation rate, one can infer the role played by wap in adjustment to employment shocks (as the identity must hold), essentially capturing the migration response of potential workers in working age groups.

In Blanchard-Katz, the VAR on data for the United States is implemented as $y_{t}^{'} = (Δ e_{t}, e m p_{t}, p_{t})$ and $ɛ_{t}^{'} = (ɛ_{Δ e t}, ɛ_{e m p t}, ɛ_{p t})$ , i.e. employment rate and participation rate are level stationary. However, as in the case of the application to Canada in Bayoumi and others, Australian data cast doubt on the stationarity in levels of the unemployment rate and participation rate, and these are thus entered in first differences. Table A3 summarizes the unit root test results, which show that in individual states’ samples, unemployment rate and participation rate may be non-stationary.

Thus, the specification in this paper includes all variables in log differences: $y_{t}^{'} = (Δ e_{t}, Δ e m p_{t}, Δ p_{t})$ and $ɛ_{t}^{'} = (ɛ_{Δ e t}, ɛ_{Δ e m p t}, ɛ_{Δ p t})$ . An implication of this specification is that unemployment rate and participation rate thus have a role in long term adjustment to employment shocks. The shocks to employment are interpreted as labor demand shocks, and supply responses occur through shocks to the unemployment rate and to participation. Employment shocks are ordered first, and supply responses feed through to employment with a lag. As in Bayoumi and others, two lags of each variable are included in the equations.

Data on employment, unemployment rate, and participation rate at the state level are taken from ABS. The sample runs from 1979 to 2015 at annual frequency. Each variable entering the VAR is “acyclic” to the aggregate economy, i.e., the influence of aggregate shocks is removed to consider only state-specific demand shocks. This is done by obtaining the residuals of a regression of the state level variable on the national variable and a constant. This allows us to focus on labor market responses to local level disturbances.

Table III.1.

Individual Unit Root Test: ADF Test with AIC

Table III.1.

Individual Unit Root Test: ADF Test with AIC

	Employment rate	Change in employment rate	LFPR	Change in LFPR
NSW	X	Y	Y	Y
VIC	X	Y	Y	Y
QLD	X	Y	X	Y
SA	X	Y	X	Y
WA	X	Y	X	Y
TAS	Y	X	X	Y

Rejection of null: unit root at 5%
Sample: 1979 - 2015 (annual)

Table III.1.

Individual Unit Root Test: ADF Test with AIC

	Employment rate	Change in employment rate	LFPR	Change in LFPR
NSW	X	Y	Y	Y
VIC	X	Y	Y	Y
QLD	X	Y	X	Y
SA	X	Y	X	Y
WA	X	Y	X	Y
TAS	Y	X	X	Y

Rejection of null: unit root at 5%
Sample: 1979 - 2015 (annual)

References

Ball, L., D. Leigh, and P. Loungani, 2013, “Okun’s Law: Fit at 50?” International Monetary Fund, IMF Working Paper WP/13/10.
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Bayoumi, T., B. Sutton and A. Swiston, 2006, “Shocking Aspects of Canadian Labor Markets,” International Monetary Fund Working Paper WP/06/83.
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Blagrave, P., R. Garcia-Saltos, D. Laxton, and F. Zhang, 2015, “A Simple Multivariate Filter For Estimating Potential Output,” International Monetary Fund, IMF Working Paper WP/15/79.
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Blanchard, O.and L. Katz, 1992, “Regional Evolutions,” Brookings Papers on Economic Activity, 1:1992.
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Blanchflower, D., and A. Levin, 2015, “Labor Market Slack and Monetary Policy,” NBER Working Paper 21094, April 2015.
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Bishop, J.and M. Plumb, 2016, “Cyclical Labor Market Adjustment in Australia,” Reserve Bank of Australia Bulletin, March Quarter 2016.
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Borland, J., 2011, “The Australian Labour Market in the 2000s: The Quiet Decade,” Reserve Bank of Australia Conference Volume.
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Hobijn, B., and A. Sahin, 2012, “Beveridge Curve Shifts across Countries since the Great Recession,” 13th Jacques Polack Annual Research Conference, International Monetary Fund, 2012.
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Jacobs, D.and A. Rush, 2015, “Why Is Wage Growth So Low?,” Reserve Bank of Australia Bulletin, June Quarter 2015.
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Prepared by Adil Mohommad (APD).

The unemployment gap is jointly estimated with the output gap using a small Bayesian-estimated model in tandem with a Kalman filter. See Blagrave and others (2015) for detail regarding the methodology used.

Australia transitioned from a system of centralized wage determination to individual and enterprise level wagesetting following the reforms in the mid-1990s. At present, 80 percent of employees are covered by individual contracts and enterprise agreements, while 20 percent are set by “awards” mainly determined by the Fair Work Commission – which also influence a significant proportion of employees covered by enterprise agreements and individual contracts by establishing minimum standards (RBA, 2016).

Caution should be taken when inferring a decline in inflation expectations from inflation-indexed bonds, as these measures are also affected by changes in the inflation risk premium. Other measures of longer-term inflation expectations in Australia, such as survey-based measures, have remained around the midpoint of Australia’s inflation target.

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Australia: Selected Issues

Author: International Monetary Fund. Asia and Pacific Dept

Volume/Issue:: Volume 2017: Issue 043

Publisher:: International Monetary Fund

ISBN:: 9781475575842

ISSN:: 1934-7685

Pages:: 104

DOI:: https://doi.org/10.5089/9781475575842.002

Keywords
Labor Market Adjustment to Shocks in Australia1
References

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Selected OECD: Deviation of Unemployment Rate in 2016Q2 from 2000-07 average

(Percent)

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Key Real Sector and Labor Market Developments

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Long-term Unemployment Ratio

(Unemployed in excess of 1 year as share of total unemployed, 4QMA)

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Structural Unemployment

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Fitted Beveridge Curve

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Cyclical Adjustment in Labor Input

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Differences in Sectoral Cyclical Adjustment in Labor Input

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Part-time Work, Hours Worked, and Underemployment

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Wages and Labor Market Slack

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Wage Growth: Actual vs. Fitted Values

(Percent q/q annualized)

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Share in Aggregate Hours Worked (percent)

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Share in Aggregate Hours Worked (percent)

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Sectoral Average Hours and Employment Changes

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Decomposition of Labor Productivity Growth

(Percent y/y)

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Hourly Labour Productivity Gap

(Percent)

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Share in Aggregate Hours and Labor Productivity ($ output per hour worked)

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Labor Market Developments in States

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Share of Net Migration in Working Age Population Growth

(Percent)

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Impulse Responses to 1% State-Specific Employment Shock

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Impulse Responses to 1% Employment Shock

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Historical Decomposition of Employment Growth

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Employment response to 1 percent GDP shock (1985-1997)

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Hours response to 1 percent GDP shock (1985-1997)

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Employment response to 1 percent GDP shock (1998-2016)

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Hours response to 1 percent GDP shock (1998-2016)

Table III.1.

Individual Unit Root Test: ADF Test with AIC

	Employment rate	Change in employment rate	LFPR	Change in LFPR
NSW	X	Y	Y	Y
VIC	X	Y	Y	Y
QLD	X	Y	X	Y
SA	X	Y	X	Y
WA	X	Y	X	Y
TAS	Y	X	X	Y

Rejection of null: unit root at 5%
Sample: 1979 - 2015 (annual)

Sample: 1986-2008
	log H/V	log S/V
Constant	2 1***	−1.5***
log U-V ratio	.58***	0.04
R-sq	0.89	0.09
Sample: 1986 - 2011
	log H/V	log S/V
Constant	2.0***	−1.6***
log U-V ratio	.6***	.06*
R-sq	0.89	0.15

Sample: 1986-2008
	log H/V	log S/V
Constant	2 1***	−1.5***
log U-V ratio	.58***	0.04
R-sq	0.89	0.09
Sample: 1986 - 2011
	log H/V	log S/V
Constant	2.0***	−1.6***
log U-V ratio	.6***	.06*
R-sq	0.89	0.15

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Abstract

Labor Market Adjustment to Shocks in Australia1

A. Introduction

B. Has Structural Unemployment Increased?

C. How Have Cyclical Adjustment Dynamics Changed? And What Are the Implications for Labor Market Slack and Wage Growth?

D. Have Sectoral Shifts in Labor Impacted Labor Productivity?

E. How Did States’ Labor Markets Adjust to the Mining Boom-Bust Cycle?

F. Summary and Conclusion

Appendix I. Fitting a Beveridge Curve for Australia

Appendix II. Cyclical Features of Labor Market Adjustment

Appendix III. Role of Migration in States’ Labor Market Adjustment

References

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Labor Market Adjustment to Shocks in Australia¹