Discussion Paper No. 801

HOW AN EXPORT BOOM
AFFECTS UNEMPLOYMENTNoel Gaston
Gulasekaran Rajaguru

January 2011

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

This version: 17‐Jan‐11

How an Export Boom affects Unemployment

Noel Gaston and Gulasekaran Rajaguru*

Abstract
Does trade affect the equilibrium rate of unemployment? To theoretically examine thisquestion, we incorporate firm‐union bargaining considerations into a model with a booming
external sector and a stagnating manufacturing sector. In the model, a sustained
improvement in the terms of trade lowers unemployment. To empirically investigate thepredicted determinants of the unemployment rate, we use data for Australia, a country
whose prosperity has always depended on the value of its exports. We find strong evidence
that higher export prices, capital accumulation in tradeable goods industries and a lower
unemployment benefit replacement rate each reduce the equilibrium unemployment rate.
Keywords: equilibrium unemployment rate, specific factors model, bargaining, mining
boom, Dutch disease.
JEL Classification: E24, F12, J51.

* Globalisation and Development Centre and School of Business, Bond University, Gold Coast,
Queensland 4229, Australia. Correspondence: ngaston@bond.edu.au. We thank Tilak Abeysinghe,
Fabrice Collard, Xavier de Vanssay, Rod Falvey, Lance Fisher, Shravan Luckraz and Nic Sim as well
as seminar participants at the University of Adelaide, University of Göttingen, Kansai Institute forSocial and Economic Research, Keio University, Macquarie University, Monash University and the
National Graduate Institute for Policy Studies for their comments. The first‐named author would
like to acknowledge the support and collegial atmosphere provided by the Institute for Social andEconomic Research at Osaka University. The usual disclaimers apply.

1.

Introduction

Understanding the way in which external economic conditions and globalisation affectdomestic labour markets has been a major research endeavour for economists for decades.
More recently, the slow pace of recovery from the effects of the Global Financial Crisis (GFC)
has further stimulated research on how trade affects unemployment. Two years after the
onset of the Crisis, the rate of unemployment in many OECD countries remains stubbornly
high. Those countries with more resilient economies seem to be those which are “exporting
their way to recovery”. In particular, the resilience seems to have a lot to do with high trade
exposure to a robust Chinese economy.
As a small open economy, Australian prosperity has long depended on the strength of its
exports. After peaking at over ten per cent in 1993, Australia’s unemployment rate steadilyfell until the onset of the GFC. The drop in the unemployment rate was particularly sharp
during the years of Australia’s mining boom. During the 2007 election campaign, the then(Coalition) government claimed that the falling unemployment rate was due to its superior
economic management and deregulation of the labour market. The then (Labor) Opposition
claimed that the good news on the unemployment front was primarily due to the booming
demand for mining resources (and certainly not to any labour market reforms). It seems thatbeing in government changes one’s perspective. In response to the threat posed by the GFC,
the Labor government embarked on a massive fiscal spending programme. Unemployment
increased during the GFC, but only moderately. Compared to many of its OECD
counterparts, Australia escaped relatively unscathed. Labor has been quick to claim credit
for its stimulus spending. The Coalition opposition now argues that the strength of theChinese economy has been the key.
This paper examines how a country’s unemployment rate is affected by the demand for its
exports. We use a specific factors model to examine the effect of changing terms of trade on
the unemployment rate. This model of a small open economy has a perfectly (domestically)
mobile factor and an immobile factor. Assuming the former to be labour implies that wages
are equalised across sectors of the economy. Unemployment is generated in our model due
to union‐firm bargaining. Incorporating bargaining considerations into a specific factors
model helps identify the effects of increases in the terms of trade as well as the capital stockin the export sector on the unemployment rate. In the model, these factors raise workers’
wages (i.e., in all sectors of the economy) but, as we shall see, they also lower equilibrium
unemployment. The theory is in the mould of models by Davidson et al. (1999), Hoon (2001a,
2001b), Kee and Hoon (2005), Moore and Ranjan (2005) and Helpman and Itskhoki (2010).
These papers incorporate equilibrium unemployment into general equilibrium trade models
to show how the unemployment rate is affected by international factors, including the termsof trade.1
1

In their review of research on trade and labour markets, Davidson and Matusz (2011) discuss how avariety of labour market frictions are introduced into general equilibrium settings to generate
unemployment. The prominent approaches are: implicit contract models, efficiency wage models,
bargaining models and search or matching models. These models yield a wide variety ofrelationships between relative commodity prices and relative factor prices and between trade andunemployment. Unfortunately, the proliferation of models has not yielded a clearer picture as towhether greater trade openness is beneficial or detrimental to the equilibrium rate ofunemployment.

1

Moore and Ranjan (2005) show that increased trade with developing countries, as manifested
by higher relative prices for skill‐intensive goods, raises both unemployment and income
inequality. In the case of Australia, exports are not skill‐intensive and this may explain whyhigher terms of trade have lead to less income inequality (see Gaston and Rajaguru, 2009).
One explanation for this finding is based on the fact that improved terms of trade raise
producer prices (PPI) more than consumer prices (CPI) (see EEAG, 2008, p.86). If
employment is determined by the real product wage (the nominal wage deflated by the PPI),
then employment and nominal wages increase with improved terms of trade. We build on
this insight by examining external trading conditions and the differential development of key
sectors of the economy.
Our paper is also related to the substantial ‘Dutch disease’ literature. In particular, Cordon
and Neary (1982) model the co‐existence of a booming export sector and a declining or
lagging import sector (taken to be manufacturing industry for most developed economies).
Here the traditional concern is that de‐industrialisation occurs due to the loss of mobile
factors of production to the booming sector. In this paper, we present a two‐sector model,
which seems to be the approach adopted in the most recent literature, as well as athree‐sector model, more in keeping with the older Dutch disease literature.
According to Davidson and Matusz (2011), the empirical evidence on how trade affects theunemployment rate is virtually non‐existent. In addition, what little empirical research does
exist has contradictory findings. This paper aims to at least partially address this deficiency.
Our empirical contribution is to estimate the effect of trade on unemployment using both
reduced form and structural approaches. We use data for Australia for the period 1960‐2008.
The estimates provide strong evidence that rising demand for exports and higher terms oftrade reduce the unemployment rate. Capital accumulation in tradeable goods industries
also reduces unemployment. The other major factor found to worsen unemployment is thelevel of unemployment benefits. In the next section, we outline the theory. Section 3
discusses the data, the econometric methodology and presents the model estimates. The lastsection concludes.
2.

Theory of an economy with a booming export sector

2.1. The basic two‐sector model. Assume that there are two‐sectors, X and Y. The factors of
production are labour and capital, L and K. X is assumed to be the export sector and Y is theimport‐competing sector. We assume that there are a large number of firms in both sectors of
the economy and that bargaining takes place with firm‐level unions. Nevertheless, workers
are free to move between the sectors. On the other hand, capital is assumed to be
sector‐specific, we denote this fact by using an overscore, i.e., K X and K Y .
The production function of a firm in sector X is x = x( k x , l x ) and for a sector Y firm is

y = y( k y , l y ) . All firms are profit maximisers, with profit for a representative firm in eachsector given by

π x = px − wl x − rx k x and π y = y − wl y − ry k y ,(1)where w is the wage and r is the price of capital. Note that good Y is the numeraire and thatthe price of good Y is normalised to one; hereafter p is referred to as the terms of trade.
We consider an industry in which there is no strategic interaction between firms. Anothercritical assumption is that the industry market structure generates rents that are shared2between firms and unions. In the following, we consider the case of a representative firm insector X which bargains with a firm‐level union over wage‐employment contracts. Ifbargaining is efficient, the choice from the set of efficient contracts is the one that maximises
the Nash product, i.e.,2()ϕ

S( w , l) = (π ( w , l; p))1−ϕ U ( w , l) − U ,(2)where U(.) is the union’s utility function and U denotes the disagreement point for theunion. (The subscript on l is dropped). The parameter ϕ ∈ (0 ,1) indexes collective bargaining
strength.
We assume that the solution lies in the interior of the choice set and that S is strictly concave sothat the solution is unique and may be characterised by the first‐order conditions. Suppressing
arguments and using subscripts to denote partial derivatives, the equation for the contract
curve, which equates the slope of the union’s indifference curve and the firm’s iso‐profit
curve is−Ulπ=− l .
Uw
πw(3)

Further headway is made by investigating the implications of some commonly considered
functional forms for union preferences. Consider the popular specification used by
McDonald and Solow (1981). Here the union comprises m homogeneous workers, eachendowed with one unit of labour time. Prior to actual wage and employment negotiations, aworker’s expected utility is given by⎧ m − l⎫EU = U ( w ) + (U (ω ) − U ( w)) max ⎨0 ,
⎬,
m ⎭
⎩

(4)

where U(.) is increasing and concave, w is the wage rate if employed and the reservation
alternative is denoted by ω, i.e., the union’s disagreement payoff is U = U (ω ) .
An obvious implication of equation (4) is that if all union members are employed (i.e., l ≥ m ),then a union representing inside workers becomes completely ‘wage‐oriented’ when it
negotiates with the firm. Specifically, unlike the case in which m is so high that all workers
have a probability of being unemployed equal to ( m − l) m , when m is low so that all union

members are employed, the union is completely wage‐oriented and maximises mU(w). We
consider this particular case in part A of the Appendix.
When l < m, equation (3) yields

U ( w) − U (ω )= −π l .
Uw(5)Since U (ω ) ≅ U ( w) + (ω − w)U w ( w) , then px l = ω and labour is hired until its marginal
revenue product equals the reservation wage. It is straightforward to show that thenegotiated wage equals

2

It is debatable whether firms and unions negotiate both wages and employment or just wages alone.
Empirical evidence in favour of efficient bargaining is mixed (Farber, 1986, p.1067). On the other
hand, negotiation over work rules may ensure that bargains are efficient.

3w =ω +

ϕπ

(1 − ϕ ) l.

(6)Further, w ω > 0 , lω < 0 , π ω < 0 and U ω > 0 . The impact of higher reservation wages shifts
the threat point in the union’s favour, raising their welfare and lowering the firm’s. Equation (6)can be rewritten as⎛
ϕ (1 − η ) ⎞⎟⎟ω ,w = ⎜⎜ 1 +
η
⎝
⎠

(7)

where η = lπ~l π~ > 0 is the elasticity of ‘net profit’.3 Most importantly, the negotiated wage
is higher than the reservation wage. In fact, regardless of whether bargaining is efficient, or
whether unions negotiate over wages alone, the wage is simply a multiple of the reservation
wage.4 That is,

w = λω , λ > 1.

(8)

The crucial issue is what determines the reservation wage. Blanchflower et al. (1996, p.243)
argue that it can be thought of as a function: ω = ω( w o , B , u) , where w o is the going wage inthe other sector(s) of the economy, B (< w o ) is the level of income when unemployed (e.g.,
publicly‐provided unemployment benefits) and u is the unemployment rate of the type ofworker employed by the firm. According to Nickell and Layard (1999, pp.3048‐9), a
reasonable case to consider isω = uB + (1 − u)w .

(9)That is, the reservation wage depends on the wage that can be earned at other firms and theunemployment benefit. These amounts are weighted by the probability of remaining
unemployed and the probability of finding another job. Without any worker heterogeneity,these are the economy‐wide probabilities of unemployment and employment, respectively.
Note that the rate of unemployment is u = ( L − L) / L , with L the total supply of labour and
L the total demand for labour in sectors X and Y.
2.2. The wage‐setting curve. At a micro level, u is regarded as exogenous by the worker (andhis union). Given that firms within each sector are identical and that every firm‐specificunion has the same objective function, the equilibrium of this economy entails w x = w y = w .
Therefore, in equilibrium, the aggregate wage‐setting curve (WSC) of the economy isobtained by combining (8) and (9), i.e.,

3

Combining the first‐order conditions yields: w − ϕ (π~ / l) − (1 − ϕ )ω = 0 , where π~ = π + wl. Defining
η = lπ~ π~ and noting that π~ = ω yields equation (7). Second‐order conditions require η < 1.
l4lThis is not a particularly novel insight, e.g., see McDonald (2002); see also part A of the Appendix. In
addition, this characteristic is not peculiar to bargaining models. For example, in efficiency wage
models firms pay workers a wage above the reservation wage in order to reduce turnover costs or to
motivate worker effort (see Blanchard and Katz, 1997). In matching models, the wage is also
determined by bilateral Nash bargaining and splitting the total surplus from a job match.
Specifically, workers receive a wage over and above the asset value of being unemployed(Mortensen and Pissarides, 1994).

4

w=

B
1−

θ

,

(10)

u

where θ = 1 − λ−1 ∈ (0 ,1) . Equation (10) implies that the wage and the unemployment rate areinversely related and that the wage and total labour demand are positively related. In eﬀect,
the WSC replaces the perfectly inelastic labour supply schedule; an increase in labourdemand leads to higher wages and employment, with a fall in the equilibrium rate ofunemployment.5
2.3. The equilibrium unemployment rate. The aggregate labour demand curve is derived as thehorizontal summation of the sectoral labour demand curves. The equilibrium wage, labourdemand and unemployment are determined by the interaction of the WSC and aggregate
labour demand. The relevant results are collected in the following Proposition.
Proposition 1 (two‐sector model)a)u = u( p , K X , K Y , L , B, λ ) ;b) Unemployment falls with improved terms of trade, u p < 0 ;c) Unemployment falls with greater capital accumulation in either sector, i.e., u K X < 0 and

uKY < 0 ;d) Unemployment rises with a greater labour supply, u L > 0 ;e) Unemployment rises in the unemployment benefit and union bargaining power, i.e., u B > 0

and uλ > 0 .
Proof: See part B of the Appendix.

As long as we are willing to countenance the WSC construct, none of these results seemparticularly surprising.6 The main result from our viewpoint is that higher terms of trade
reduce unemployment. We now turn to the three‐sector model in order to see whether this or
any of the other key results are affected when a non‐tradeables or services sector is explicitly
introduced.
2.4. The three‐sector model and Dutch disease. There is a large literature studying the effects of abooming sector on the rest of the domestic economy. A traditional concern has been thepossibility of deindustrialisation, whereby a booming sector draws factors from a lagging
sector. This phenomenon for some countries is referred to as the “resource curse” (e.g.,
Mehlum et al., 2006), but is more generally referred to as “Dutch disease” (see Cordon and
Neary, 1982 and Cordon, 1984). For obvious reasons, the phenomenon has been long‐studiedin Australia (e.g., Gregory, 1976 and Snape, 1977). Within Australian policy circles there is a5

It is worth noting that if the unemployment benefit replacement rate, b = B/w, were an immutable
constant fixed by policy, then the unemployment rate is also constant, i.e., u = θ(1 ‐ b)‐1. However, inmost countries, the level of B is capped, so that b’(w) < 0 for some critical wage level. The condition
that b’(w) < 0 is sufficient to guarantee that wages and unemployment are inversely related.

6

Blanchflower and Oswald (1995) find strong evidence of a wage curve for twelve countries,
including Australia. The authors conclude that the hypothesis of an elasticity of wages with respect
to unemployment of ‐0.1 cannot be rejected.

5

concern expressed about a “two speed economy”, with a rapidly advancing sector (mining)
and another sector under severe competitive pressure from import competition(manufacturing). While surging raw materials prices are an undoubted windfall forAustralia, the dependence on low value‐added products and the non‐diversification of theeconomy’s industrial structure are viewed as potentially problematic.
The extension of the model to three sectors is relatively straightforward.7 Assume that there
are three sectors – X, Y and N – respectively, the export, import‐competing and non‐tradeables sectors. Once again, the tradeable goods sectors face given world prices. The
economy still has the same Ricardo‐Viner structure, as above. First, under the assumptionthat labour is mobile across all sectors, the WSC is trivially the same. Secondly, the labourdemand side of the economy is readily extended to encompass three sectors rather than two.
The primary concern of the Dutch disease literature is the extent to which a booming sector
drains the lagging sector. The issue is somewhat complicated by the presence of a non‐tradeables sector. Cordon and Neary (1982) note that an improvement in the terms of trade
has two effects: a resource movement effect as well as a spending effect. The former effect is theusual reallocation of labour along the economy’s transformation frontier. As the price ofsector X output rises, this draws labour out of sectors Y and N. The latter effect arises
because higher terms of trade raise real income. This effect raises the demand for non‐tradeables. The price of N relative to tradeable goods rises and this draws labour out of X
and Y. Regardless of the impact on the non‐tradeables sector, the importables sector shrinks.
As mentioned, this deindustrialisation has been the focus of the literature; although as
Cordon (1984, p.363) notes, in the case of Australia this could equally mean‘de‐agriculturalisation’.
The comparative static results for unemployment arising from the extension of the model tothree sectors are given in Proposition 2. The equilibrium wage, labour demand andunemployment are still given by the intersection of the WSC and aggregate labour demand.
The main ‘novelty’ is that the price of non‐tradeables is endogenous.
Proposition 2 (three‐sector model)a)u = u( p , K X , K Y , K N , L , B , λ ) and p n = p n ( p , K X , K Y , K N , B, λ ) , where p = p X p Y
and p n = p N p Y ;b) Unemployment falls with improved terms‐of‐trade, u p < 0 ;c) Unemployment may rises with a greater labour supply, higher unemployment benefits orunion bargaining power., i.e., u L > 0 , u B > 0 and uλ > 0 ;7What is far from straightforward is deciding which industries belong to which sector. For example,
Australia both exports and imports automobiles. Rather than export and import‐competing sectors,
Cordon (1984) refers to booming and lagging sectors. But, obviously this classification has problemsof its own with the lagging sector producing both non‐boom exportables as well as importable
goods. In addition, some outputs are both inputs as well as final goods. Finally, to the extent thatimported goods are not perfect substitutes for domestically‐produced import‐competing goods,
then importables have a non‐tradeable goods component (and therefore may benefit from a boom).
We ignore these issues, but attempt to address these categorisation issues in the empirical work.

6d) Unemployment falls with greater capital accumulation in either of the tradeable goods sectors,i.e., u K i < 0 , i = X, Y;e) Unemployment may rise or fall with greater capital accumulation in the non‐tradeables
sector.

Proof: See part C of the Appendix.

Once again, improvements in the terms of trade lower the equilibrium unemployment rate.
Increases in either export prices or the capital stock in the tradeable goods industries result inhigher total employment in the economy, i.e., the equilibrium rate of unemployment is lower.
Given a positively‐sloped WSC, an increase in aggregate labour demand, not only causes theequilibrium wage rate to be higher, but more importantly, the equilibrium unemployment
rate to be lower. Interestingly, an increase in the capital stock in the non‐tradeables sector
does not necessarily reduce unemployment. The expansion of employment in the
non‐tradeables sector comes at the expense of employment in the tradeable goods sectors.
2.5. Other considerations. In this sub‐section, we briefly consider two issues related to theabove discussion. First, we discuss the relationship between the Phillips curve literature andthe model presented here. Secondly, we consider the longer‐term implications of the model,
in particular, the consequences of weakening the capital‐specificity assumption.
2.5.1. Relation to the Phillips curve literature. The empirical literature on the Phillips curve isenormous. Estimation normally involves regressing, with varying degrees of statisticalsophistication, a measure of wage or price inflation on the unemployment rate. The overall
focus of the studies has essentially been two‐fold. Foremost has been to determine whether
there is any evidence of a unique equilibrium rate of unemployment or whether
unemployment displays evidence of hysteresis, i.e., whether (or not) demand‐side influences,such as government spending, can affect the unemployment rate more than temporarily.
Secondly, has been to ascertain whether supply‐side and institutional factors, such as
unemployment benefits or the benefit replacement rate, affect the equilibrium
unemployment rate.
To our knowledge, there are no empirical studies of the effects of the terms of trade on
unemployment.8 While the concern in the present paper is not with the Phillips curve per se,
it is related. Interestingly, in Australia the steady decline in the rate of unemployment rate in
the past decade or so has coincided with a period of relatively steady rates of price inflation.
The latter development has largely been the result of the Reserve Bank of Australia keeping
inflation within its fairly tight two to three per cent target range for inflation. Taken by itself,
this suggests that a Phillips curve with a constant natural rate does not exist. This provides
further justification for the model developed in this paper.
2.5.2. The long‐run and capital mobility. In our model, improved terms of trade increase labourdemand, raise the nominal wage and lower unemployment. In the Heckscher‐Ohlin model,changes in the terms of trade only affect factor prices. In the model here, there isunemployed labour. Hence, higher export prices affect unemployment and the returns tofactors in the long‐run.

8

Nickell et al. (2005) find that higher real import prices raise unemployment in a panel study ofOECD countries. The authors do not study the impact of export prices.

7

Regardless of the bargaining protocol considered, capital owners are the residual claimants.
Higher terms of trade increase the return to capital owners in sector X and lower the returnto capital in sector Y. In the long‐run, capital flows from sector Y to sector X. Therefore, there
is a secondary or indirect impact on unemployment due to higher export prices. The
movement of capital serves to boost the marginal productivity of labour in sector X evenfurther, while the marginal productivity of sector Y labour falls. The demand for labour rises
in sector X and falls in sector Y. The impact on total labour demand of this movement incapital in the long‐run is therefore indeterminate and depends among other things on theelasticity of labour demand and the factor intensities in the two sectors.9
Moreover, in the case of international capital mobility, capital flows into the export sector in
response to the higher real return on capital. According to the Rybczynski theorem, theoutput of the relatively capital‐intensive sector (in Australia’s case, sector X) increases evenmore, but the output of the labour‐intensive manufacturing sector falls even further.
However, the impact on wages and on unemployment, of this effect is also unknown. At thisjuncture, it seems appropriate to turn to the empirical work.
3.

Econometric methodology and results

3.1. Data description. Our focus in this section is on the examining the determinants of thelong‐run rate of unemployment, as suggested by the three‐sector model. All estimation iscarried out using annual data for Australia for the period from 1960 to 2008. The data sources
are described in detail in Appendix table A1 and the descriptive statistics appear in table 1.
All data are publicly available. The choice of variables is motivated by the theory describedin the previous section. As for the definitions of the sectoral capital stocks, K X is mining,
K Y is manufacturing plus agriculture and K N is the capital stock for all other sectors; thisroughly accords with Cordon’s (1984) booming versus lagging sector categories.
Alternatively, we also define K X as mining plus agriculture, K Y for manufacturing and
K N for the capital stock in all other sectors, which accords with a more ‘usual’ export versus
import sector distinction.
‐ Table 1 here ‐Immediately apparent is the enormous expansion of the mining sector in the last half century;
the investment and subsequent growth of the capital stock easily dwarfs the growth in allother sectors of the economy. A visual impression of some of the other key data is given in
figure 1. The terms of trade, ToT, measured as the ratio of the implicit price deflator ofexports of goods and services to the implicit price deflator of imports of goods and services,
captures the dependence on overseas economic developments and the economic dimensionof growing integration with countries such as China. While industrial country protectionists
fret about China’s impact on the world prices of manufactures, even more pronounced is
China’s impact on the price of energy and resources. For countries, like Australia, the9Matusz (1985) shows that while the factor price equalisation theorem and the Rybczynski theorem
both hold in a Heckscher‐Ohlin model with implicit contracts and unemployment, that the
Stolper‐Samuelson theorem is weakened. Of course, this is further complicated by international
immigration which is stimulated by higher wages. Cordon (1984) shows that immigration increases
spending on both non‐tradeables and importables, although not by enough to reverse
de‐industrialisation.

8

consequence is extraordinarily high terms of trade. The rising terms of trade are predicted toresult in a movement of labour from manufacturing to primary industries.
‐ Figure 1 here ‐We use Union as our proxy for union bargaining power, λ, and BRR as our measure ofunemployment benefits generosity. There has been an enormous surge in the terms of tradesince the year 2000 and this period also coincided with falling unionisation. However,unionisation peaked at nearly one half of the labour force in 1963 and has fallen since;
deunionisation accelerated after 1986. In addition, the unemployment benefit replacement
ratio, after peaking in 1993, has been steadily declining. Coincidentally perhaps, 1993 has thehighest unemployment rate in our data set; the unemployment rate (denoted as UR) has
fallen almost every year since that date. There is an obvious up‐tick in the unemployment
rate just prior to the onset of the GFC in 2008. We also include Inflation in order to examine
the presence of a Phillips curve relationship and to gauge the robustness of estimates of thebaseline model specification.
We now turn to the econometric estimates of the models of unemployment described in
section 2.
3.2. Unit roots. It is well known that the data generating process for most macroeconomic
time series are characterised by unit roots, which rules out the use of standard econometricmethods. Therefore, it is important to analyse the time series properties of the data in order
to avoid spurious results generated by unbounded variances of parameter estimates due tothe presence of unit roots. The three most commonly used unit root tests are applied here, i.e.,
the Augmented Dickey‐Fuller (ADF), Phillips‐Perron (PP) and Kwiatkowski‐Phillips‐
Schmidt‐Shin (KPSS) unit root tests, on the relevant variables in order to ensure therobustness of the test results. What distinguishes the tests is that the null hypothesis for the
ADF and PP is the alternative hypothesis for the KPSS. That is, while the former (ADF and
PP) are derived under the null hypothesis of unit roots the latter (KPSS) is obtained under
the stationary null hypothesis. The results reported in table 2 show that all variables except
Inflation are non‐stationary at the five per cent level of significance. The non‐rejection of theunit root hypothesis leads to testing for the second unit root, i.e., a unit root in firstdifferences. The test results in first differences are also reported in table 2. The results
indicate that Inflation is I(0) and all the other series are I(1). The results based on thestationary alternative (ADF and PP) and non‐stationary alternative (KPSS) in our exercise
ensure that the results are robust and are unaffected by the weak power of standard unit root
test procedures.
‐ Table 2 here –3.3. Co‐integration: In order to examine the existence of a long‐run relationship between thevariables in the theory discussed in section 2, we test for co‐integration among them. For thethree‐sector model, recall that u = u( p , p n , K X , K Y , K N , L , B, λ ) , we test for co‐integration
between the variables in z t = (URt , ln K X ,t , ln K Y ,t , ln K N ,t , ToTt , PN ,t / PY ,t , BRRt , ln LFt , Uniont ,Inflationt ) .

Since all the variables (other than Inflation) are non‐stationary, the linear combination of one
or more of these series may have a long‐run relationship. The multivariate co‐integration test
based on the Johansen and Juselius (1990) method is used to test for these relationships.

9Accordingly, we estimate the following n‐variate, pth‐order Gaussian vector autoregression(VAR) process
zt = μ +

p

∑Π z
i =1

i t − i + ΨDt +ε t , t = 1,2 ,..., T ,(11)where μ is a vector of constants, D t is an intercept dummy and ε t is a normally andindependently distributed n‐dimensional vector of innovations with zero mean andnon‐singular covariance matrix. The optimal lag length p (= 2) is determined by the Schwartz
criterion. As noted above, we consider different specifications for z t to examine therobustness of our results.
Since each component of z t (except Inflation) is I(1), we rewrite equation (11) in vector error
correction (VEC) form

vt = μ + Γ1vt −1 + Πzt −1 + ΨDt + ε t ,(12)where ν t = ( ΔURt , Δ ln K X ,t , Δ ln K Y ,t , Δ ln K N ,t , ΔToTt , Δ( PN ,t / PY ,t ), ΔBRRt , Δ ln LFt , ΔUnion t ,Inflation t ) . In addition, Γ1 = −Π 2 and Π = Π 1 + Π 2 − I . The long‐run n×n matrix Π = αβ ʹdetermines how many independent linear combinations of the elements of z t are stationary.
Here α and β are n × r matrices with rank r (0 < r < n). The rank of Π gives the number of
independent co‐integrating vectors and can be formally tested using the trace test and the
maximum eigen value test.
The trace statistic tests the null hypothesis that H o : r = q against the alternative that r > q
and is given byn

λtrace (q) = −T ∑ ln(1 − λi ),(13)

i = q +1

where the λi ʹ s are the eigen values of Π , such that λ1 > λ2 > ... > λn . The λtrace statistic
sequentially tests the null hypothesis that the number of co‐integrating vectors is at most q
against the alternative that the number of co‐integrating vectors is more than q, whereq = 1,2 ,..., n .
The maximum eigen value statistic tests the null hypothesis that H o : r = q against thealternative that r = q + 1 and is given by

λ max (q) = λtrace (q) − λtrace (q + 1).

(14)

The λ max statistic tests the null hypothesis that the number of co‐integrating vectors is equal
to q against the alternative that the number of co‐integrating vectors is q + 1.
The results of the trace test and the maximum eigen value test are reported in table 3.10 The
results based on the Johansen‐Juselius procedure for the models with Inflation indicate thatthe null of r = 0 (i.e., no co‐integrating relationship) and r = 1 (i.e., one co‐integrating
relationship) are rejected at the five per cent level of significance. The sequential testing fails
to reject the null hypothesis that the number of co‐integrating vectors is at most two at thefive per cent level of significance. This indicates that the stationary variable Inflation alone
10

The critical values for both the trace and the maximum eigen value test statistics are obtained
through Monte Carlo simulations with 10,000 replications.

10forms a trivial co‐integrating vector while all the other variables form another co‐integrating
vector. On the other hand, for the models without Inflation the null of r = 0 (i.e., noco‐integrating relationship) is rejected at the five per cent level of significance. Sequential
testing fails to reject the null hypothesis that the number of co‐integrating vectors is at mostone at the five per cent level of significance. The results strongly suggest the existence of along‐run relationship between the variables of interest.
‐ Table 3 here –3.4.1. The reduced form model: The following vector error correction (VEC) model is estimatedto establish the long‐run and short‐run relationships between the variables.

ν t = μ + α 1 e 1,t −1 + α 2 e 2 ,t −1 + Γν 1,t −1 + ΨD t + ε t ,(15)

where ν t = ( ΔUR t , ΔToTt , Δ( PN ,t / PY ,t ), ΔK X ,t , ΔK Y ,t , ΔK N ,t , ΔUnion t , ΔBRR t , Δ ln LFt , Inflationt )and ν 1,t −1 = ( ΔURt −1 , ΔToTt −1 , Δ( PN ,t −1 / PY ,t −1 ), ΔK X ,t −1 , ΔK Y ,t −1 , ΔK N ,t −1 , ΔUnion t −1 , ΔBRRt −1 ,Δ ln LFt −1 ) are vectors of endogenous and lagged endogenous variables, respectively.

Furthermore, e 1t = URt − β 2ToTt − β 3 ( PN ,t / PY ,t ) − β 4 ln K X ,t − β 5 ln K Y ,t − β 6 ln K N ,t − β 7 Uniont− β 8 BRRt − β 9 ln LFt − β 10 Left t and e2t = Inflationt are the error processes from the long‐run static equation.11 Left is a dummy variable equal to one if there is a Labor government attime t, and zero otherwise. α ij denotes the speed of adjustment parameter for the ith
co‐integrating vector and the jth equation, i.e., it explains the speed at which the process
approaches the long‐run through the jth equation. The models without Inflation have only one
error correction term, e1t . The 10x9 matrix Γ captures the short‐run dynamic relationshipbetween the variables of interest and Ψ is a 10x1 vector of parameters corresponding to theintercept dummies. CUSUM tests are used to examine the validity of other structuralchanges in the model in addition to the Left dummy. The results show the absence ofstructural breaks other than for Left.12
The long‐run relationship between UR and the other variables is established through thestatistical significance of the β coefficients along with the statistical significance of α11 . For
example, the effect of ToT on UR is established through the statistical significance of β 2 . In
order to establish the genuine long‐run causal relationship from any variable to UR requires
the speed of adjustment α11 to be negative in the ΔUR equation. On the other hand, theshort‐run relationship between the variables is established through the components γ of Γ .
Since our concern is with the determinants of the unemployment rate, we only report theresults for the UR equation.13

3.4.2. The reduced form results. The results are reported in table 4. We find that the parameters
representing the short‐run dynamic effects, other than the own lag and the error correction
terms, are statistically insignificant (even at the ten per cent level). Hence, we present the11

The long‐run relationship, or static equation, is represented by a contemporaneous relationshipbetween the variables of interest rather than a relationship with lags.

12

For the sake of brevity, we do not present the results. They can be obtained from the authors byrequest.

13

The complete set of results is available from the authors by request.

11

results for the β’s (i.e., the long‐run relationships), error correction terms, α 11 and α 21 , andthe coefficient corresponding to ΔURt −1 .
‐ Table 4 here ‐The results show that the variables of interest affect the unemployment rate in the long‐run,but not in the short‐run. It is possible that a failure to find any short‐run relationship fromany of the variables to unemployment could be due to an aggregation problem. Marcellino(1999) and Rajaguru and Abeysinghe (2008) also show that temporally aggregated data (e.g.,such as those involving annual frequencies) in a co‐integrated system may lead tomisleading short‐run causal information between the variables. In particular, the short‐run
information disappears due to temporal aggregation and any information is funnelled
through the long‐run contemporaneous relationship between the variables. In turn, this may
create misleading long‐run relationships between the variables. However, Rajaguru and
Abeysinghe (2008) show that the genuine long‐run causal relationship between the variables
can be established, even with highly aggregated data, when the error correction term
appears with the correct sign; in our case, negative for the unemployment rate equation. We
find negative and significant estimates for the error correction term in the unemployment
rate equation. Hence, the long‐run results are unaffected by any spurious causal relationshiparising from temporally aggregated or systematically sampled data.
In table 4, the baseline model suggested by the theory appears in column (1). Column (2)contains the results from the same model which is re‐estimated by including Inflation. We
make this inclusion in order to examine the robustness of the baseline specification in theprevious column. The estimates using the alternative capital stock classification is reported
in column (3). In column (4), we present the results of re‐estimating the models when the logof the labour force (LLF) is excluded. This exclusion is made due to a concern that thisvariable could be spuriously related to the denominator of the unemployment rate and could
therefore be affecting the results. Finally, in column (5) we use the disaggregated price series(i.e., by replacing ToT with the separate prices of exports and imports in the regression
model).14 The multivariate normality test shows that the errors are normally distributed at afive per cent level of significance. In addition, there is no statistically significant evidence of
any autocorrelation, at either one or two lags.
To understand the specifics of what the results show, consider the baseline specification
reported in column (1) of table 4. Presented here are the estimates of equation (15), i.e., therelationship between the unemployment rate, the (natural logarithm of) the labour force andcapital stock in sectors X and Y. It also includes the terms of trade, ToT, and our proxy for λ,Union, as well as the unemployment benefit replacement ratio, BRR. What the results
indicate is that the terms of trade are very important. A ten per cent improvement in ToT
reduces the unemployment rate by just over one full percentage point (i.e., ‐0.15*6.94). This is14

In addition, we estimate equation (15) for a variety of other models to examine the sensitivity of ourresults. Once again, we include and then exclude inflation, use two different capital stockclassifications and include and exclude the labour force variable. Also, we run a variety ofregression specifications in which prices are strictly relative prices and others in which prices areleft unrestricted. These results are in a separate Appendix available from the authors upon request.

12

obviously a large effect. 15 The other key, and highly statistically significant, variable is BRR.
In this case, a ten per cent increase over the most recent value of the BRR raises theunemployment rate by just over 0.7 percentage points (i.e., 0.36*2.02). The effect ofunionisation is significant but economically smaller; a further ten per cent fall in the mostrecent value of the Union would lower the unemployment rate by less than 0.06 percentage
point (i.e., 0.03*1.89). The other statistically significant variables have generally unsurprisingand expected signs.16 The positive sign on the political dummy reveals that the Labor
government has presided during periods of higher unemployment.
An interesting feature of the results is that capital accumulation in the tradeable goods sector
is, as predicted by the three‐sector model, far more important than capital accumulation in
the non‐tradeable goods sector for lowering the rate of unemployment. In other words,
capital accumulation in the services sector (which includes the government sector) is notparticularly useful in lowering unemployment. As for the labour force, the immigration
intake due to the mining boom soared. Together with the native‐born increase, the growth in
the labour force has been about two per cent annually since the year 2000. We find that thisgrowth has lowered the unemployment rate by just less than 0.6 percentage points (i.e.,‐29.81*0.02).
In column (2) we further control for the inflation rate in order to allow for a potential Phillips
curve trade‐off relationship. If there is in fact such a relationship, the estimated coefficient of
Inflation “should” be negative. The coefficient is negative, but statistically insignificant, i.e.,there is no evidence that inflation lowers unemployment. As mentioned, given the targetingof the inflation rate within a narrow band by the Reserve Bank of Australia and the large falls
in the unemployment rate in the last decade and a half, this result was expected. Column (3)contains estimates of the baseline model, with an alternative definition of the capital stock for
sectors X and Y. Once again, the results are robust. The result for ToT is somewhat smaller,that for BRR is identical and that for the labour force is still negative, but statisticallyinsignificant. Likewise, in column (4) the results are clearly not driven by any lock‐stepcorrelation between the log of the labour force and the denominator of the unemployment
rate statistic.17 Finally, the results using the unrestricted prices in column (5) are extremelyinteresting. The ToT result is driven by driven by both falling prices of imports and rising
15

While this particular estimate may seem ‘large’, it should be noted that Felbermayr et al. (2009)
show a ten per cent increase in total trade openness reduces long‐run unemployment by about one
percentage point for a large panel of countries.

16

The standardised beta coefficients for the significant variables are: ToT ‐0.49; LK_mining ‐0.32;
LK_manuf_agr ‐0.44; Union 0.11; BRR 0.53; and LLF ‐0.30.

17

We also estimated VEC models for labour force participation (LFP). One possible caveat concerning
the “good news” about a falling unemployment rate is that this may conceal the fact that LFP is also
falling. For example, while lower unemployment benefits may lower the unemployment rate, they
may also encourage labour force withdrawal and a lower LFP. In fact, LFP tracked steadily
upwards from 1993 (a recession year) until 2008. With LFP defined as the number of workers in thelabour force (i.e., either employed or unemployed) divided by the number of persons aged between
16 and 65 years, it was 65.8 per cent in 1960 (it’s lowest value), 72.9 per cent in 1993 and 77.8 percent in 2008 (it’s highest value). The VEC results reveal that the effects of ToT are positive (albeit
only significant in one model specification) and BRR is negative, but always statisticallyinsignificant. The set of results for LFP are available from the authors upon request.

13

prices for exports. However, the magnitude of the rise in export prices needs to be borne inmind. From 2000 to 2008, import prices fell by 5.1 per cent, while export prices rose 49.1 percent. Over this period then, this suggests that the terms of trade effect reduced the rate ofunemployment by 0.6 percentage points (i.e., (0.01*49.1)+(0.02*5.1)). With China now being
Australia’s number one export market and its second ranked source of imports, it is tempting
to conclude that this effect captures the “China effect” on the rate of unemployment.

3.5.1. The structural model: The following empirical approach addresses the theoretical model
developed in section 2 more explicitly. In particular, the intersection of the WSC (i.e.,equation (10)) and the aggregate labour demand schedule determines the equilibrium
unemployment rate. We estimate the following model (derived as equations (A16) and (A23)in part C of the Appendix),
Δ UR t = κ B Δ B t + κ p Δ p t + κ λ Δ λ t + κ L Δ ln L t + κ K X Δ K X ,t + κ K Y Δ K Y ,t + κ K N Δ K N ,t + κ p n Δ p n ,tΔp n ,t = τ B ΔBt + τ p Δp t + τ λ Δλt + τ K X Δ K X ,t + τ K Y Δ K Y ,t + τ K N Δ K N ,t + τ u ΔURt .

(16)

As we show in part D of the Appendix, many of the coefficients (the κ i and τ i ) are related
to one another, i.e., there are cross‐coefficient restrictions. For example, κ B = δ 2 A B δ 1 andκ p = G 1 BA B p δ 1 , where δ 1 = (1 − A Lδ 2 ) L and δ 2 = (G1 + pH 1 + pJ 1 p n ) are each functionsof A B and A L . The expressions are directly estimated using data, i.e., A B =( L − L) ( L − λL) p and A L = (λ − 1) LBA B ( L − L)( L − λL) . (Note that aggregate labour demandis the sum of the labour demand in each sector, i.e., L( w , K X , K Y ) = G( w , K X ) + H ( w , K Y )+ J ( w , K N ) , where G1 < 0 , G 2 > 0 , H 1 < 0 , H 2 > 0 , J 1 < 0 and J 2 > 0 ). A similar approachis adopted to estimate the remaining parameters.
While A B can be directly estimated using the data, some of the other components arefunctions of labour and capital in the respective sectors, i.e., X = X( L X , K X ) , Y = Y ( LY , K Y )and N = N ( L N , K N ) . X 1 , X 2 , Y1 , Y2 , N 1 and N 2 are the derivatives with respect to the firstand second elements of X, Y and N, respectively. We assume functional forms to estimate
these functions, rather than treating them as parameters. For production in sector X:
X = K X L1X− β X , where β X ∈ (0 ,1) is the inverse of the elasticity of labour demand. Clearly,

X 1 = (1 − β X )K X L−Xβ X and X 2 = L1X− β X . Combining equations (5) and (10) implies: w λ = pX 1
= Y1 = p n N 1 . (See also equation (A12) in part C of the Appendix). It follows that LβXX =−1−1(1 − β X )K X pλw −1 and L X = [(1 − β X )K X pλ ] β X w − β X = G( w , K X ) . Hence, G1 =−1

− β X−1 [(1 − β X )K X pλ ] β X w 1− β X

−1

−1

−1

−1

and G 2 = β X−1 [(1 − β X ) pλ ] β X K Xβ X −1 w − β X . In a similar

fashion, expressions for Y1 , Y2 , N 1 and N 2 ; as well as H 1 , H 2 , J 1 and J 2 are derived.18
Since there are no explicit data for sectoral employment levels, we estimate thesecomponents subject to the constraint that L X + LY + L N = L. In addition, most of the τ icoefficients are functions of Φ , where Φ 1 + Φ represents the share of consumption
expenditure on non‐tradeables; this parameter is estimated as well.
In summary, the parameters that we estimate in our empirical analysis are β X , β Y , β N and18

Simple functional forms are assumed in order to preserve degrees of freedom. In the specific factors
model, the only theoretical requirement is that production is concave in labour.

14Φ . The model specified by the structural equations does not violate any time series
properties as all the variables are I(1) and these variables are considered in differenced formin both equations of (16). Since A B > 0 , we require that L − λL > 0 . This is guaranteed byrescaling the data for Union such that λ ∈ (1, (1 − UR) −1 ) .19 When estimating β X , β Y , β N andΦ , we do not impose any other restrictions other than that for λ and L X + LY + L N = L.
Moreover, we do not impose any sign restrictions as we examine the expected signs of eachcomponent of the models based solely on the data.
The parameters β X , β Y , β N and Φ are estimated using FIML.20 The estimates aresubsequently substituted into each function to calibrate the effect of each variable on
unemployment rate for each period. For example, in order to establish the effect of p on UR,
we calibrate the estimates of κ p for each period. That is,∧d UR t=dpˆ t

Gˆ 1,t Bt A B ,tp
(1 − A L ,t (Gˆ 1,t + p t Hˆ 1,t + t Jˆ 1,t ) L t
p n ,t,(17)

where A B ,t = ( L t − Lt ) ( L t − λt Lt ) p t , A L ,t = (λt − 1) L t Bt A B ,t ( L t − Lt )( L t − λt Lt ) ,
−1
−1
ˆ −1
ˆ −1
Gˆ 1 = − βˆ X−1 [(1 − βˆ X )K X ,t p t λt ] β X wˆ 1− β X , Hˆ 1 = − βˆY−1 [(1 − βˆY )K Y ,t p t λt ] βY wˆ 1− βY and−1ˆ −1
Jˆ1 = − βˆ N−1 [(1 − βˆ N )K N ,t p t λt ] β N wˆ 1− β N .

∧The average effect of p on UR is found by taking the average of d UR t / dpˆ t . The
corresponding test statistic for the null that the effect of p on UR is zero is∧⎛ T d UR
⎞
t ⎟
⎜
T
⎜⎜
ˆ ⎟
t = 1 dp t ⎟⎠,Z=⎝σˆ / T∑(18)∧

where σ̂ denotes the standard deviation of d UR t / dpˆ t . In order to examine the sensitivity∧of our calibrated results, we also report the minimum and maximum values of d UR t / dpˆ t
for the entire time period. A similar approach is used to calibrate the effect of all othervariables on the unemployment rate.
3.5.2. The structural estimates: The results are reported in table 5. Recall that the estimatedparameters of the production functions are substituted into each function to calibrate theeffect of each variable on the unemployment rate for each period. The FIML estimates of
β X , β Y , β N and Φ appear in panel A. While none of the parameters were restricted in theestimation, all the β’s are on the unit interval as required by theory. The estimate of Φ
implies that about 57 per cent of consumption expenditure is on non‐tradeables.
19

The precise transformation used is λ = x*0.01*Union + y, where x =

( (

)

)

(

)

(

)

max (1 − UR) −1 − min (1 − UR) −1
max(0.01 * Union) − min (0.01 * Union)and y = min (1 − UR) −1 − min(0.01 * Union) * x . As required by the theory, for the structural estimation
we use the unlogged sectoral capital stocks as well as the level of unemployment benefits in lieu of
the unemployment benefit replacement rate.
20

Due to the recursive structure implied by equation (16), we also used non‐linear least squares
techniques using maximum likelihood procedures. The results were unchanged.

15Panel B reveals that G1 < 0 , G 2 > 0 , H1 < 0 , H 2 > 0 , J 1 < 0 and J 2 > 0 , also as required bytheory. The impact of greater capital accumulation on employment is biggest in sector Y andsmallest in sector N. Panel C gives the estimates of equation (16), i.e., the κ̂ and τˆ . For theκ̂ , all the coefficient signs are as predicted (see equation (A16) in the Appendix). This
includes the finding that increases in the labour supply raise the unemployment rate (unlike
what was found for the reduced form results). For the τˆ , the coefficients for ToT and thecapital stocks in the three sectors also as predicted. The predicted signs of the other threevariables were ambiguous (see equation (A22) in the Appendix).

Most important are the set of results appearing in panel D, these results capture the total
effect of each of the variables on the unemployment rate. Since UR is a function of p n , thefinal impact of any variables is given by the relevant τˆ as well as κ̂ pn . The estimates shownin the final panel are:dUR κ i + τ i κ pn=
, i = B, ToT , λ , L , K X , K Y , K N .
di1 − κ pn τ u(19)With the exception of the results for the labour supply and the capital stock in the
non‐tradeables sector, the sign patterns are concordant with the reduced form results. They
also completely conform to the theoretical predictions summarised in Proposition 2.
Note that the results for B are exactly that, i.e., for average unemployment benefits, rather
than the BRR, the unemployment benefit replacement rate. Focussing on the mean estimatesin the second column, a ten per cent increase in the latest value of B raises UR byapproximately 0.67 percentage points (i.e., 3.96*0.017).21 More directly comparable is theresult for ToT. A ten per cent improvement in ToT reduces the unemployment rate byapproximately 0.74 percentage points (i.e., 0.0694*‐0.106). Surprisingly, despite the very
different estimation procedures, this estimated effect is similar to that implied by the
reduced form estimates. In addition, the effect of a further ten per cent fall in the most recent
value unionisation (λ) on UR is a statistically significant, albeit economically small 0.04
percentage points (cf. 0.06 percentage points for the reduced form estimates). The effects of aten per cent increase in the most recent values of the capital stock in sectors X and Y are
respectively, ‐0.3 and ‐0.5 percentage points. The effect of capital accumulation in the
non‐tradeables sector is positive, but is not a statistically robust effect. One advantage of thestructural approach is that we can see that the latter effect occurs due to the fact that J 2 isextremely small (see Panel B), i.e., capital accumulation in the non‐tradeables sector barely
boosts labour demand.
Overall, the effects are entirely consistent with the reduced form findings. One caveat is thatwere we to use the median, rather than the mean, values of the coefficient estimates that theestimated effects of the variables on the unemployment rate are marginally smaller (with the
exception of λ). Notwithstanding, the effects are still economically large. The results in theminimum and maximum columns of panel D also reveal that the coefficient signs are robust,with the exception of that for capital accumulation in the non‐tradeables sector – but that waspredicted by the theory as well.
21

Note that all of the raw data were rescaled for the purpose of the structural estimation. One reason,
already mentioned, was the necessity to rescale Union so that it conformed to the definition of λ inthe model. Another reason is that the sectoral capital stocks appear in the model in unlogged form.

16

3.6. Overview. The finding on the importance of external trading conditions for
unemployment is one of the two most prominent and robust findings in this paper. The
result is also consistent with Nickell et al. (2005), who emphasise the importance of labourdemand as well as labour market institutions (particularly, unionisation and theunemployment benefits system) in mediating the effects of shocks to the equilibrium
unemployment rate. In terms of more liberalised trade policy and trade openness, Davidson
and Matusz (2011) note that not only has the theoretical literature yet to reach any firm
consensus on how trade impacts the unemployment rate, but that what little empiricalevidence exists is limited and contradictory.22 Hopefully, the findings in this paper start toaddress this deficit of empirical research. The other key finding is that reductions inunemployment benefits and the unemployment benefit replacement rate were alsosignificant and robust drivers of the lower unemployment rate.
4.

Conclusion

What determines the equilibrium rate of unemployment? The narrowest interpretation of thenatural rate hypothesis suggests that only supply‐side influences should be important. In
Australia at least, the unemployment rate fell dramatically while its trade relationship with
China grew in the decade preceding the GFC. Outwardly, this suggests that the demand forresources and trade have been important drivers of the falling rate of unemployment. We
have examined this proposition by developing a model with a booming external sector and alagging manufacturing sector. This has a precedent in the large Dutch disease literature forcountries with a dominant and growing exporting sector and a shrinking manufacturingsector. These features are evident for Australia.
The focus in this paper has been squarely on unemployment. Traditional trade models, aswell as the Dutch disease literature, assume away unemployment (or in the latter case, if it istreated at all, it is done so in an ad hoc manner by assuming that wages are fixed). In thispaper, unemployment results because of firm‐union bargaining. In fact, all of the mostfamiliar models of bargaining imply that wages are simply a mark‐up over reservation
wages. While this is a far from novel insight, we show how this is an important building
block for understanding why wages and unemployment are inversely related. It also makes
it transparent how the demand‐side, and international trade, can influence the equilibrium
rate of unemployment. As Nickell et al. (2005, p.22) note “unemployment is always determinedby (long‐run shifts in) aggregate demand”. Our model predicts that a sustained improvement in
the terms of trade will lower the equilibrium rate of unemployment.
Using data for Australia for the period 1960‐2008, vector error correction model estimatesindicated that a lower rate of unionisation, a lower unemployment benefit replacement rateand capital accumulation, particularly in the tradeables industries, reduce unemployment.
Requiring more future research was the finding that capital accumulation in tradeable goods

22

One published study is Dutt et al. (2009) which uses cross‐country panel data on unemployment
and trade policy and finds that greater openness leads to more unemployment in the short run, but
to a reduction in steady‐state unemployment. Their latter finding is certainly consistent with ourfindings. On the other hand, we find no short‐run deleterious effect of changes in the terms of tradeon equilibrium unemployment. Our results are consistent with those of Felbermayr et al. (2009)
which presents “overwhelming evidence” that higher trade openness is causally associated with alower structural rate of unemployment.

17

industries had a far stronger beneficial impact on reducing unemployment than did capital
accumulation in the non‐tradeable and services sector of the economy.
Most importantly, we found strong evidence that higher terms of trade lower theequilibrium rate of unemployment. Specifically, rising export prices have been an importantfactor driving reductions in the rate of unemployment. Interestingly, so too have falling
import prices. These effects underscore the importance of Australia’s growing trade
exposure to China for improvements in its domestic labour market and for falls in its rate ofunemployment.
All of the reduced form findings were validated by our structural equation estimates. The
structural model used a combination of maximum likelihood estimation and calibration.
Once again, the findings for the terms of trade and unemployment benefits were thestrongest of the findings. These two factors have been the most important drivers of changes
in the long‐run rate of unemployment.

18

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do we know? Economic Journal 115(500), 1‐27.
Rajaguru, G.; Abeysinghe, T., 2008. Temporal aggregation, cointegration and causality
inference. Economics Letters 101(3), 223‐6.
Snape, R.H., 1977. Effects of mineral development on the economy. Australian Journal of
Agricultural Economics 21(3), 147‐56.

20Appendix
A. Alternative bargaining models

There are two cases of equation (4). In the ‘normal’ case, m is assumed to be sufficiently highso that all workers have a probability of being unemployed equal to (m – l)/m. When l ≥ m ,however, all union members are employed. In this case, the union is completely wage‐
oriented and maximises mU(w). Noting that U l = 0 , equation (3) yields

π l = 0 or px l = w ,(A1)i.e., labour is employed until its marginal revenue product equals the wage. The wage equalsw=η (1 − ϕ )ω
.
η −ϕ

(A2)

Since η > ϕ , the negotiated wage is some multiple of the reservation wage (albeit, a larger
multiple compared to the case when l < m).23
In fact, the wage is a multiple of the reservation wage regardless of whether bargaining isefficient or whether unions negotiate over wages alone. For example, in the right‐to‐manage
model, the union maximises its utility subject to π l = w . It is straightforward to show thatw = ω (1 + ξ −1 ) −1 , where ξ (< −1) is the elasticity of labour demand. Hence, the wage is amark‐up over the reservation wage.
B. Proof of Proposition 1

Assuming I identical firms in sector X and I’ identical firms in sector Y, the sectoral
employment of the factors as well as production of each good is
I

∑k

KX =

x

and K Y =

i =1

X=

Iʹ

Iʹ

I

∑k ; L = ∑l , L = ∑lyIi ʹ =1Iʹ

i =1i ʹ =1

X

i =1

x

Y

i ʹ =1∑ x = Ix and Y = ∑ y = Iʹ y ,yand L X + L X = L ≤ L ;(A3)

where an over‐score denotes a variable being in fixed supply. Note that equation (10) can be
rewritten asw=

λ ( L − L) B.
L − λL(A4)

From equation (5), we havew

λ= pX 1 ( L X , K X ) = Y1 ( LY , K Y ).

(A5)

Now define the following inverse functions that are used to isolate sectoral labour demands:
G ≡ X 1−1 and H ≡ Y1−1 , where G1 < 0 , G 2 > 0 , H1 < 0 and H 2 > 0 (and the subscripts denote
derivatives with respect to the first and second arguments).

23

Once again, w − ϕ (π~ / l) − (1 − ϕ )ω = 0 (†). Since π~l = w , equation (A2) follows. Second‐orderconditions in this case also require η < 1. Moreover, (†) can be written as η − ϕ − (1 − ϕ )wl(π~) −1 = 0
which implies that η > ϕ.

21

dZ( w ,.)dG( w ,.)dH ( w ,.)< 0 , since< 0 and
<0.
dw
dw
dw
dw
< 0 . Then, since Z( L ,.) = G( L ,.) + H ( L ,.) ,Moreover, we know (from equation (A4)) thatdLdZ( L ,.) dZ( w ,.) dw
we have by the chain rule=×
< 0 . Also, since the production function isdL
dw
dL
assumed to satisfy the Inada conditions we have lim Z( L ,.) = 0 and lim Z( L ,.) → +∞ . Now

Let Z( w ,.) = G( w ,.) + H ( w ,.) . Then

L → +∞

L →0

consider the following equation

L = Z( L ,.)(A6)

The graph of the function on the left‐hand side is the 45 degree line whereas the function on
the right‐hand side is always positive, monotonically decreasing and converges to zero. Thisguarantees the existence of an interior solution L* that solves equation (A6). Therefore, we
can write total labour demand as

⎛ w
⎞
⎞
⎛w
L = L X + LY = G⎜⎜, K X ⎟⎟ + H ⎜ , K Y ⎟ .
⎠
⎝λ
⎝ pλ
⎠

(A7)

Using equation (A4) we have⎞
⎛ ( L − L) B
⎛ ( L − L) B⎞
L = G⎜⎜, K X ⎟⎟ + H ⎜⎜, K Y ⎟⎟ .
⎝ ( L − λ L)⎠⎠
⎝ p( L − λL)(A8)Totally differentiate equation (A8) to obtaindL = G 1 ( A B dB + A p dp + A λ dλ + A L d L + A L dL) + G 2 dK X
+ H 1 (C B dB + C λ dλ + C L d L + C L dL) + H 2 d K Y ,where A B =
AL =

(A9)

A B
A B BL( L − L)( L − L) B
( L − L)BL
> 0 ; Ap = −= − B < 0 ; Aλ =
=
>0;
2
2
p
( L − λ L) p
( L − λ L) p
( L − λ L) p ( L − λ L)A L
A B (1 − λ )BLA B (λ − 1)B L(1 − λ )BL(λ − 1)B L=< 0 ; AL ==− L >0;=2
2
L( L − λL) p ( L − L)( L − λL)( L − λL) p ( L − L)( L − λL)

C B = pA B > 0 ; C λ = pAλ > 0 ; C L = pA L < 0 and C L = pA L > 0 .
Ld L − LdL
L−L, so that dL = (1 − u)d L − Ldu . Hence, equation (A9) can, then du =2LL
be rewritten as

Since u =(1 − G1 A L − H 1C L ) Ldu = (1 − G1 A L − H 1C L )(1 − u)d L − G 1 ( A B dB + A p dp + A λ dλ + A L d L)
− G 2 d K X − H 1 (C B dB + C λ dλ + C L d L) − H 2 d K Y .

(A10)This can be simplified as24

24

We ignore here the indirect impact of p on λ. While the impact of higher product prices is to raise
employment, there is a well‐known wage indeterminacy with respect to higher product prices (seeGaston and Trefler, 1995, e.g.). However, the wage is higher the greater is union bargaining
strength and the better is the reservation alternative wage. Moreover, higher prices unambiguously
benefit the union and the firm. Finally, note that in the case of an iso‐elastic labour demandschedule (e.g., when the underlying technology is Cobb‐Douglas), there is no impact on the22

(1 − (G1 + pH 1 ) A L ) Ldu = (1 − u)d L − G1 A p dp − (G1 + pH 1 )( A B dB + A λ dλ )− G 2 dK X − H 2 dK Y .

(A11)Since (1 − (G 1 + pH 1 ) A L ) L > 0 , it follows that: u B > 0 , u p < 0 , uλ > 0 , u L > 0 , u K X < 0 and
uKY < 0 . 

C. Proof of Proposition 2

Proceed as for the proof of Proposition 1. First, we havew

λ= pX 1 ( L X , K X ) = Y1 ( LY , K Y ) = p n N 1 ( L N , K N ),(A12)

where p = p X p Y and p n = p N p Y . Next define the inverse function for the non‐tradeables
sector: J ≡ N 1−1 , where J1 < 0 and J 2 > 0 . Total labour demand is⎞⎞⎛ ( L − L) B
⎛ ( L − L) B
⎞ ⎛ ( L − L) B
L = G⎜⎜, K X ⎟⎟ + H ⎜⎜, K Y ⎟⎟ + J ⎜⎜, K N ⎟⎟ .
⎝ ( L − λ L)
⎠ ⎝ p n ( L − λ L)⎠⎠
⎝ p( L − λL)(A13)Totally differentiate to obtaindL = G1 ( A B dB + A p dp + A λ dλ + A L d L + A L dL) + G 2 dK X
+ H 1 (C B dB + C λ dλ + C L d L + C L dL) + H 2 d K Y+ J 1 ( D B dB + D pn dp n + D λ dλ + D L d L + D L dL) + J 2 d K N ,

(A14)

2

where D B =

p Ap
pA L
pA B
pA λ
pA L
> 0 ; D pn =< 0 ; Dλ => 0 ; DL =< 0 and D L =
>0.
2
pn
pn
pn
pn
pn

Rearranging equation (A14) gives
E L dL = E B dB + E p dp + Eλ dλ + E L d L + G 2 d K X + H 2 d K Y + J 2 d K N + E pn dp n .

(A15)Since dL = (1 − u)d L − Ldu , this can be rewritten as
E L ( Ldu) = (1 − u)d L − E B dB − E p dp − Eλ dλ − G 2 d K X − H 2 d K Y − J 2 d K N − E pn dp n . (A16)
Defining δ 2 = (G1 + pH 1 +p
J 1 ) < 0 , E L = (1 − δ 2 A L ) > 0 ; E B = δ 2 A B < 0 ; E p = G1 A p > 0 ;pnEλ = δ 2 A λ < 0 and E pn = J 1 D pn > 0 .
Consumers derive utility from non‐tradeable services and tradeable goods. Utility is
assumed to take the Cobb‐Douglas form
U (c n , c t ) = c φn c 1t −φ ,(A17)where the tradeable good is a composite commodity. For a small open economy, exportables
and importables represent a group of goods whose relative prices are given and can thus beelasticity of labour demand and no effect on λ in the right‐to‐manage model. Noteworthy from anempirical viewpoint is the fact that in the VEC model results, the long‐run impact of ToT on Union
is statistically insignificant. This is in line with findings that globalisation, broadly defined, has notaffected union membership in a panel of OECD countries (Dreher and Gaston, 2007).

23

treated as one commodity, i.e., t = pX + Y (Carter, 1995). The relative demand fornon‐tradeables is

cn Φ
,
=
ct pn

(A18)

where Φ = φ /(1 − φ ) . The relative supply of non‐tradeables isqn
N( L N , K N ).
=q t pX( L X , K X ) + Y ( LY , K Y )(A19)The equilibrium relative price of non‐tradeables is determined by the equality of relative
demand and relative supply, i.e.,

pn =

(

Φ pX( L X , K X ) + Y ( LY , K Y )N( L N , K N )

).

(A20)

Totally differentiate to obtain[ ()

Ndp n + p n N 1 J 1 ( D B dB + D pn dp n + D λ dλ + D L d L + D L dL) + J 2 d K N + N 2 d K N

[ (

)

]

= ΦXdp + Φp X 1 G1 ( A B dB + A p dp + A λ dλ + A L d L + A L dL) + G 2 d K X + X 2 d K X

[ (

]

)

+ Φ Y1 H 1 (C B dB + C λ dλ + C L d L + C L dL) + H 2 d K Y + Y2 d K Y .

]

(A21)

Collecting terms, we have
R pn dp n = R B dB + R p dp + Rλ dλ + R L d L + R L dL + R K X d K X + R K Y d K Y + R K N d K N ,(A22)

where Rp n = N + pn N 1 J1 D p n > 0 , and by defining δ 3 = Φ( X 1G1 + Y1 H 1 ) − N 1 J 1 , R B = δ 3 pA B ;R p = Φ( X + pX 1G 1 A p ) > 0 ; Rλ = δ 3 pA λ ; R L = δ 3 pA L ; R L = δ 3 pA L ;R K X = Φp( X 1G 2 + X 2 ) > 0 ; R K Y = Φ(Y1 H 2 + Y2 ) > 0 and R K N = − p n ( N 1 J 2 + N 2 ) < 0 .
⎛ LdL − Ld L ⎞⎟⎟ = −δ 3 p LA L du = Ru du . Hence,
Note that R L d L + R L dL = δ 3 p( A L d L + A L dL) = δ 3 pA L ⎜⎜L⎝⎠equation (A22) can be rewritten as
R pn dp n = R B dB + R p dp + Rλ dλ + R K X dK X + R K Y d K Y + R K N d K N + Ru du ,(A23)

Stacking equations (A16) and (A23) in matrix form:⎡ EL L⎢⎣⎢− Ru[E pn ⎤ ⎡ du ⎤ ⎡ − E B=⎢⎥
R pn ⎦⎥ ⎢⎣dp n ⎥⎦ ⎣ R B

where dM = dB

dp

dλ

− Ep
Rp
dL

dK X

− Eλ
Rλ

(1 − u)
0

− G2
RK X

− H2
RKY

dK Y

dK N . The determinant is− J2 ⎤dM’,
R K N ⎥⎦]

LE L R pn + E pn Ru = N L(1 − δ 1 A L ) + J 1 D pn L[ p n N 1 − A L δ 5 ] > 0 , where δ 5 = ( p n N 1δ 1 + δ 3 p) .
Using Cramer’s rule it can be shown that: u B > 0 , u p < 0 , uλ > 0 , u L > 0 , u K X < 0 andu K Y < 0 . However, an increase in K N has an indeterminate effect on unemployment, i.e.,part e) of Proposition 2. 

24

D. Coefficients to be estimated in equation (16)First, define the following: δ 1 = EL L = (1 − ALδ 2 ) L > 0 , δ 2 = (G 1 + pH 1 +pJ1 ) < 0 ,pnδ 3 = Φ( X 1G1 + Y1 H 1 ) − N 1 J 1 and δ 4 = R pn = N + p n N 1 J 1 D pn > 0 . Secondly, note thatAB =( L − L),( L − λ L) p

AL =

pBA B
(λ − 1) LBA B
and D pn = −< 0 . The marginal products are allp n2( L − L)( L − λL)

positive, i.e., X 1 > 0 , X 2 > 0 , Y1 > 0 , Y2 > 0 , N 1 > 0 and N 2 > 0 ; in addition, note that
G1 < 0 , G 2 > 0 , H1 < 0 , H 2 > 0 , J1 < 0 and J 2 > 0 .

Coefficients in Unemployment equation

κB

−

κp

−

κλ

−

κL

EB

δ1

Ep

δ1
Eλ

δ1

, where EB = δ 2 AB < 0, where E p = G 1 A p > 0 and Ap = −, where Eλ = δ 2 Aλ < 0 and Aλ =

BAB
<0
p

BLAB
>0
( L − λ L)

(1 − u)

δ1

κKX

−

G2

κKY

−

H2

κKN

−

J2

κ pn

−

δ1

δ1

δ1
Ep n

δ1

, where Ep n =J1 p 2 Ap
pn225> 0 and Ap = −

BAB
<0
p

Coefficients in Price of Non‐tradeables equation

τB

RB

τp

Rp

τλ

Rλ

τ KX

RK X

τ KY

RKY

τ KN

RK N

τu

Ru

, where R B = δ 3 pA B

δ4

δ4

, where R p = Φ( X + pX 1G1 A p ) > 0 and Ap = −, where Rλ = δ 3 pA λ and Aλ =

δ4
δ4
δ4
δ4
δ4

BLAB
>0
( L − λ L)

, where R K X = Φp( X 1G 2 + X 2 ) > 0, where R K Y = Φ(Y1 H 2 + Y2 ) > 0, where R K N = − p n ( N 1 J 2 + N 2 ) < 0, where Ru = −δ 3 p LA L

26

BAB
<0
p

Tables and Figures

Table 1. Descriptive statistics
Change1960 ‐ 2008

Label

Mean

Std.Dev.

MedianMinimum

Maximum

UR

5.3

2.7

5.6

1.1

10.3

192%

ToT

69.4

8.9

67.9

55.3

100.0

40%

PX

45.7

26.8

48.7

11.8

100.0

716%

PY

67.1

38.5

74.5

17.0

116.1

485%

P NT

51.7

34.8

49.8

9.2

110.1

1091%

Union

38.9

9.6

44.0

18.9

47.9

‐60%

*

21.9

4.0

22.9

13.6

27.2

16%

7364.52060.77131.54143.011257.6

172%

5.4

4.0

4.0

‐0.7

16.9

19%

86356.4

60647.3

84241.04149.0241764.0

5727%

65596.010656.1

70075.043325.085135.097%75936.028349.3

73148.026970.0139697.0418%703325.7

343720.5673526.0

189132.0

1442937.0663%931214.1

442233.7901809.0

263576.0

1909533.0

624%

BRR
LF

‡

Inflation
K_mining
K_agr‡

K_manufK_other
K_total‡‡‡Note: See Appendix table A1 for variable labels and data sources. * Data are for 1960‐2008, except for the BRR
data which are for 1961‐2007. ‡ Logged in econometric work. The capital stock data are in millions of $AU andthe labour force data are in thousands.

27

Table 2. Unit root tests
Levels

UR
ToT
PX
PY
PN
P N /P Y
LK_mining
LK_mining_agr
LK_manuf
LK_manuf_agr
LK_less_mining
LK_less_mining_agr
LK_others
Union
BRR
LLF
Inflation

First Differences

ADF

PP

KPSS

ADF

PP

KPSS

‐1.45
‐0.01
‐0.82
‐0.92
‐3.08
‐1.48
‐2.84
‐3.10
‐3.13
‐2.75
‐2.22
‐2.31
‐2.41
‐0.56
‐2.28
‐1.96
‐4.16***

‐1.49
‐0.41
‐0.83
‐1.24
‐2.44
‐1.49
‐2.56
‐1.98
‐2.99
‐2.51
‐2.41
‐2.64
‐2.42
‐0.74
‐1.23
‐1.96
‐4.45***

0.29***
0.28***
0.28***
0.26***
0.24***
0.29***
0.23***
0.23***
0.24***
0.27***
0.23***
0.24***
0.23***
0.23***
0.24***
0.25***
0.13‐5.48***‐3.69***‐3.03**‐4.82***‐3.78***‐5.34***‐3.84***‐4.08***‐3.98***
3.90***‐3.94***‐3.99***‐3.52**‐4.91***‐3.67***‐6.15***‐5.26***‐4.61***
3.06**‐4.82***‐3.83***‐5.48***‐3.73***‐3.98***‐3.98***‐3.90***
3.96***‐4.00***‐3.81***‐4.99***‐3.85***‐6.14***

0.18
0.17
0.15
0.19
0.20
0.13
0.19
0.21
0.20
0.19
0.19
0.19
0.20
0.15
0.17
0.16

Notes:(i) PP test and ADF test: ***, **, * indicates rejection of null of non‐stationarity at 1%, 5% and 10%
level of significance, respectively.
(ii) KPSS test: ***, **, * indicates the rejection of null of stationarity at 1%, 5% and 10% level ofsignificance, respectively.

Table 3. Johansen‐Juselius procedure for testing co‐integration
Trace Statistic
Maximum Eigen Values Statistic
UR Æ
r=0
r≤1
r≤2
r≤3
r≤4
r≤5
r≤6
r≤7
r≤8
r≤9

(1)
253.4**
156.7
125.0
95.3
67.4
45.2
28.3
14.4
2.8

(2)
432.7**
237.8**
154.1
120.2
89.8
66.3
43.2
28.6
15.5
2.7

(3)
235.1**
158.1
123.0
93.0
65.1
40.2
19.1
6.2
2.5

(4)
209.8**
125.4
94.4
67.5
42.9
22.5
10.6
2.4

(5)
403.8**
256.3
156.7
115.1
79.3
51.8
28.6
10.1
2.4
0.1

(1)
96.7**
31.7
29.7
27.8
22.2
16.9
13.9
11.6
2.8

(2)
194.9**
83.7**
33.9
30.3
23.5
23.1
14.5
13.1
12.8
2.7

(3)
77.0**
35.1
30.0
28.0
24.9
21.1
12.9
3.7
2.5

(4)
84.3**
31.0
26.9
24.6
20.3
11.9
8.2
2.4

(5)
147.6**
99.6
41.6
35.8
27.6
23.2
18.5
7.7
2.3
0.1

Notes: * and ** denotes the rejection of null at 5% and 1%, respectively.
The baseline model is in Column (1), i.e., UR , ToT , P N /P Y , LK_mining , LK_manuf_agr , LK_others , Union , BRR , LLF , Left andan intercept. Column (2) includes Inflation. Column (3) uses alternative capital stock measures. Column (4): LLF is excluded.
Column (5): replaces ToT with disaggregated price series.

28

Table 4. VEC results for Equation (15)
ΔUR t = α 11 e 1t −1 + α 21 e 2 t −1 + γ 11 ΔUR t −1 + γ 12 ΔToT t + γ 13 Δ ( PN ,t / PY ,t ) + γ 14 Δ ln K X ,t
+ γ 15 Δ ln K Y ,t + γ 16 Δ ln K N ,t + γ 17 ΔUnion t + γ 18 ΔBRR t + γ 19 Δ ln LFt + ψ 1 Left twhere e 1t = UR t − β 2 ToT t − β 3 ( PN ,t / PY ,t ) − β 4 Δ ln K X ,t − β 5 ln K Y ,t − β 6 ln K N ,t
− β 7 Union t − β 8 BRR t − β 9 ln LFt + β 10 Left t and e 2 t = Inflation t
ToT
P N /P Y(1)‐0.15***(2)‐0.12***(3)‐0.11***(4)‐0.09***(0.04)(0.03)(0.04)(0.03)

0.06

0.002

0.05

0.02

(0.05)(0.05)(0.05)(0.05)

PX(5)‐0.01*(0.005)PY

0.02*(0.01)

PN‐0.35***

Long‐run results (β )(0.09)

LK_mining‐3.15**(1.57)(1.02)(0.63)(2.89)

LK_manuf_agr‐15.48**‐9.55***‐13.08**‐18.31*(7.99)(2.99)(6.72)(10.27)

13.51

14.99

‐1.82

15.71*(10.17)(9.72)(4.48)(8.54)

LK_others‐2.22**

LK_mining_agr‐1.25**‐8.98***‐6.27*(3.58)

LK_manuf‐6.33*(3.41)

LK_others

8.92(8.28)

Union
BRR
LLF
Left
Intercept

Short‐run*e 1 (‐1) (α 11 )

0.03*

0.03*

0.03*

0.09**

0.06*(0.02)(0.02)(0.02)(0.03)(0.04)

0.36***

0.36***

0.36***

0.31***

0.35***(0.11)(0.11)(0.08)(0.07)‐29.81*‐29.07*‐21.03(15.98)(16.23)(19.01)

1.11**

1.09*

0.99*

0.64*

0.47

0.52

0.56

0.56

0.38

0.39

650.73***(27.49)‐59.65‐26.3468.43‐115.49**(55.61)(64.16)(78.19)(51.48)(198.45)‐0.54**‐0.58**‐0.65**‐0.59**‐0.91***(0.21)(0.25)(0.26)(0.20)(0.23)e 2 (‐1) (α 21 )Δ UR(‐1) ( γ 11)(0.09)‐85.89***

‐0.07
0.09

0.43**

0.32

0.49**

0.45***

0.48***(0.19)(0.20)(0.22)(0.17)(0.19)

20.53

Multivariate Residual Diagnostics
Normality

22.11

25.76

17.81

19.09

(Jarque‐Bera test)(0.23)(0.11)(0.45)(0.39)(0.30)

Lag 1 Autocorrelation

51.44

62.79

79.36

61.39

98.56

(LM test)(0.87)(0.99)(0.53)(0.57)(0.52)

Lag 2 Autocorrelation

67.39

85.66

88.69

65.47

100.03(LM test)(0.36)(0.86)(0.26)(0.43)(0.48)

Note: * insignificant coefficient estimates (γ and ψ ) for the short‐run component of the model not reported.

29

Table 5. Structural model results (equation (16))
Panel A: FIML estimates of parameters of thesectoral production functions
Coeff.

Std. err.

Z‐stat.

βX

0.88

0.43

2.06

βY

0.85

0.42

2.02

βN

0.96

0.48

2.01

Φ

1.31

0.53

2.49

Panel B: Calibrated estimates of the derivatives ofsectoral labour demands
Mean

Std. dev.

Z‐stat

G1

‐0.89

0.83

‐7.38

G2

0.25

0.23

7.25

H1

‐3.28

1.05

‐21.45

H2

0.58

0.53

7.60

J1

‐2.23

1.50

‐10.20

J2

0.07

0.06

8.16

Panel C: Calibrated (mean) structural equation
estimates (indirect effects)

κB

κp
κλ

κL
κKX
κKY
κKN

κ pn

Mean

Std. Dev.

Mean

Std. Dev.

0.02

0.02

τB

0.00

0.00

‐0.07

0.07

τp

0.15

0.06

4.44

2.16

τλ

‐0.68

1.08

0.04

0.02

0.02

0.01

‐0.01

0.02

0.03

0.02

‐0.01

0.01

0.35

1.11

‐0.02

0.03

0.00

0.00

‐0.18

0.14

τ KX
τ KY

τ KN

τu

Panel D: Calibrated structural equation estimates (direct effects)
Mean

MedianMax.

Min.

Std. dev.

Z‐stat.

B

0.01700.01010.07940.00010.01796.511

ToT‐0.1061‐0.09850.0000‐0.3011

0.0923‐7.885λ4.46514.86657.14851.01132.173014.087

L

0.03750.03150.08700.00060.0236

10.886KX‐0.0112‐0.0060‐3.40E‐04‐0.09080.0159‐4.833KY‐0.0232‐0.0142‐9.01E‐04‐0.14190.0262‐6.067KN‐0.0014‐0.0001

0.0024‐2.10E‐02

0.0043‐2.21030

Figure 1. Time series behaviour of UR, ToT, BRR and Union
UR

TOT

12

110

10

100

8

90

6

80

4

70

2

60

0

50
60

65

70

75

80

85

90

95

00

05

60

65

70

75

BRR

80

85

90

95

00

05

90

95

00

05

UNION

28

50
45

24

40
35

20
30
25

16

20
12

15
60

65

70

75

80

85

90

95

00

05

60

31

65

70

75

80

85

Table A1. Data sources and notes
Label

Source and notes

Period*UR

From ABS cat. no. 6202.0 ‐ Labour Force, Australia. June figures. See Table 03 (Labour force status by Sex). Data forthe labour force (LF) are also from this source.

FiscalPrices†From ABS cat. No. 5204.0 ‐ Australian System of National Accounts, 2008‐09. Table 2 (Expenditure on Gross
Domestic Product) has data for gross national expenditure (domestic absorption), exports and imports both atcurrent prices as well as chain volume measures. The deflators are calculated to obtain prices for non‐tradeable(PN), export and import goods and services (PX and PY), respectively. The implicit price deflators are indexed witha reference year June 2008 = 100. ToT is the ratio of export prices to import prices.

FiscalCapital stock‡

From ABS cat. no. 5204.0 ‐ Australian System of National Accounts, 2008‐09. Table 58 (Capital Stock, by Industry)
contains data for net capital stock (chain volume measures).

FiscalUnion

Up to 1996, trade union membership was reported by unions and published by the ABS in its annual Labour
Reports publications, and then in cat. no. 6323.0 Trade Union Statistics, Australia. Unionisation rates measured as
membership divided by the labour force. From 1990, unionisation rates are available on a survey basis from ABS
cat. no. 6310.0 Employee Earnings, Benefits and Trade Union Membership, Australia (Trade Union MembershipTime Series).

Fiscal (June 30
up to 1989;
August figure
from 1990)

BRR§

The measure is defined as the average of the gross unemployment benefit replacement rates for two earnings levels,
three family situations and three durations of unemployment (see, Martin, 1996). Pre‐2003 data have been revised.

CalendarNotes: ABS = Australian Bureau of Statistics. * Fiscal data are for financial years (e.g., data for 1991 means 1 July, 1991 to 30 June, 1992). Calendar data are for calendar
years (e.g., data for 1991 means 1 January, 1991 to 31 December, 1991), converted to fiscal by averaging two adjacent years (e.g., 1991 is the average of 1991 and 1992
figures). The advice of Katrina Watts (†) and Stephen Waugh (‡) of the ABS is gratefully acknowledged. § Herwig Immervoll, Head of Employment‐Oriented Social
Policies at the OECD generously provided these data. ‡ Industries: agriculture, forestry and fishing; mining; manufacturing; electricity, gas and water supply;
construction; wholesale trade; retail trade; accommodation, cafes and restaurants; transport and storage; communication services; finance and insurance; property
and business services; government administration and defence; education; health and community services; cultural and recreational services; personal and otherservices.

32