WELFARE EFFECTS OF PROTECTION AND ECONOMIES OF SCALE-THE CASE
OF THE AUSTRALIAN AUTOMOTIVE INDUSTRY
by
MoonJoong Tcha
and
Takashi Kuriyama
DISCUSSION PAPER 02.11
DEPARTMENT OF ECONOMICS
THE UNIVERSITY OF WESTERN AUSTRALIA
35 STIRLING HIGHWAY
CRAWLEY, WA 6009
AUSTRALIA
Welfare Effects of Protection and Economies of Scale - The Case of
The Australian Automotive Industry
MoonJoong Tcha*
and
Takashi Kuriyama
The existence of economies of scale requires different interpretations of the welfare effect of
protection on the industry from conventional analyses. This paper finds that the Australian
automotive industry has economies of scale, and carries out relevant welfare analysis of tariff.
Using the fully modified Phillips-Hansen method, the paper estimates long-run elasticities and
changes in consumer and producer surplus by tariffs. With the presence of economies of scale, it
is reported that the net deadweight effect of tariffs is relatively small, while the redistribution
effects of tariffs are substantial. Also, it is found that the argument that tariffs protect domestic
employment is not plausible in the Australian automotive case, due to the existence of economies
of scale.
Key Words: Australian automobile indust1y, protection, economies of scale, welfare effects
JEL Classification: F 12, F 13, F 14
* Main correspondence: Department of Economics, The University of Western Australia,
Crawley, WA 6009, Australia. (email) mtcha@ecel.uwa.edu.au. The authors appreciate Ken
Clements and Paul Miller for their constructive comments on the earlier version of this paper.
Assistance from Patricia Wang and editorial assistance from K. Andrew Semmens is gratefully
aclmowledged.
Introduction
Despite the global trend towards free trade, a few favored sectors in each country have shielded
themselves from import competition. In Australia, since the first successful Australian car was
built in 1901, the automotive industry has been regarded as important for the economy (Stubbs,
1972). Accordingly, the Passenger Motor Vehicle (PMV) industry has been the most heavily
protected sector, together with Textile, Clothes and Footwear (TCF), by the Federal Government.
While trade barriers for the industry have been reduced, partly due to the globalisation of the
world automotive market from the late 1980s, the rate of tariffs on the PMV industry still
remained relatively high (15%) in 2001. This study aims to analyze the welfare cost incurred by
tariff imposition in the automotive industry for the past two decades.
While the PMV industry has been heavily protected, in Australia, the trade deficit in the
industry, has been increasing over time. The deficit reached $11.7 billion in 1999, which is a
76% increase from the deficit level in 1990 as shown in table 1. Although exports have been
growing consistently over time thanks to the Export Facilitation Scheme (EFS), an increase in
the demand for small cars and a reduction in the tariff rate are believed in to result in a more than
proportionate increase in imports.
Table 2 shows the local and import split of new PMV' s by sectors. In the small car
sector, the share of imported cars, which was 36.6% in 1990, has grown dramatically reaching
92. 7% in 1999. This is explained by the withdrawal of local manufacturers such as Holden,
Ford, Mitsubishi, Nissan and lately Toyota from small car production and the shift to the
production of medium cars where they are more competitive (Capling and Galligan, 1992). No
company produced small cars in Australia after Toyota's withdrawal in 1999. The small car
sector has been dominated by Japanese manufacturers (with about 30% market share in 1999) as
well as South Korean manufacturers. While locally produced vehicles dominated the medium car
sector, Japanese car manufacturers still played an important role as Toyota and Mitsubishi
operated locally. Table 2 also shows that the share of imported vehicles in the medium car
market has been decreasing as domestic manufacturers have specialized in the production of
medium sized cars. The crucial difference between the small and medium car sectors is that the
total number of vehicles sold has been increasing in the small car sector together with the
number of vehicles imported. On the other hand, total sales in the medium car sector have been
2
relatively stable but the proportion of imported vehicles sold. Imported cars outnumber
domestically produced cars in the luxury car sector (this includes large cars and sports cars) as
purchasers in this class preferred differentiated imported models.
Protection and Welfare Cost
The effects of protection of the automotive industry have been analysed by many researchers.
Takacs (1994) assessed the net impact of the protection regime in the Philippine's motor vehicle
industry and Okamoto and Sjoholm (2000) examined the productivity of the Indonesian
automotive industry under high levels of tariff protection; however, they did not investigate and
measure the impact of tariff changes on national welfare. The welfare effect on consumers and
producers of tariffs or voluntary export restraints was estimated or simulated (for example, Goto
(1992) and Hafbauer and Elliot (1994) for the U.S. automotive industry) and the implications of
tariff structure for the motor vehicle industry was also explored (for example, Van Zyl and Kotze
(1994) for the South African automotive industry). However, research on the welfare cost of
protection of the automotive sector has rarely been carried out in Australia. For Australia, a
general examination of the welfare cost of protection or trade policy was conducted (for
example, Corden (1997) and Snape (1997)), and the welfare effect of tariff reform and the
removal of monopoly was analyzed for some other industries (for example, Simmons and Smith
(1994) for the sugar industry). Dixon (1978) developed theoretical concepts on how to measure
the economic impact of tariffs (or the removal of tariffs) and Chand (1999) assessed the
relationship between industry assistance and economic efficiency for various industries.
Nonetheless, bearing in mind that the Federal Government of Australia regarded the PMV
industry as crucial to the welfare of the economy and protected it intensively, it is surprising that
a rigorous examination of the welfare cost of this protection has rarely been carried out.
This paper analyzes the impact of trade barriers on the automotive industry on Australia's
welfare by using a partial equilibrium model. The partial equilibrium model is used, as it enables
relatively less complex and more transparent policy analysis by focusing on a few variables such
as price and income (Francois & Reinert, 1997). Hafbauer and Elliot (1994) also used the partial
equilibrium approach, arguing that it is simpler as the data requirements are far more modest
3
than those required for general equilibrium modelling, while still maintaining transparency.
Completeness and complexity of the analyses might be lost by the partial equilibrium approach,
however, the loss is not considered to be significant as the automotive sector does not explain a
large portion of the Australian economy, regardless of the government's emphasis. While a
variety of protection measures has been adopted in Australia over the last two decades, such as
import quotas and local content requirements, tariffs have been the most prevailing policy tool
used by the Federal Government. Therefore, this paper focuses on and measures the effect of
tariffs on Australia's welfare. The demand for motor vehicles consists of two sources: new and
second-hand motor vehicles. Australia has a very active second-hand car market. Maxcy and
Silverston (1959) described the market for cars as 'a set of interrelated markets for a series of
close substitutes'. As this study concentrates on the new car market only, changes in consumer's
welfare due to fluctuations in the second-hand market prices are not investigated, although they
are affected by the new car prices.
It is widely accepted that tariffs have two effects: static and dynamic. Furthermore, static
effects can be divided into direct and indirect effects (Productivity Commission, 2000). While
direct effect refers to the change in consumer and producer surplus due to tariffs, indirect static
effect, derived from direct effect, refers to the impact on job opportunities. That is, the higher
domestic price caused by the imposition of tariffs will decrease the profitability of the consumer
and user industry, and as a result decrease investment in that industry. Lower levels of
investment will in turn adversely affect employment. On the other hand, the protected industry
will enjoy greater profits and might spend more on investment, which will create more job
opportunities. Dynamic effect includes a firm's opportunity to gain competitiveness in the world
market or to accelerate the specialization facing larger markets over time. Dynamic effects are
not easily observed and fall outside the scope of this study. Direct static effects, in particular
consumer and producer surplus, will be computed in this paper, with some discussion on the
indirect effects.
Modelling The Automotive Industry and Data
It is necessary to measure relevant elasticities of both demand and supply to estimate the direct
welfare cost of tariffs using the conventional method. The Automotive Industry Council (1988)
4
claimed that the main factors influencing demand are vehicle prices, income and interest rates.
However, many studies of demand analysis identified income and price as the most important
determinants of demand. A few studies have concentrated on the supply equation for motor
vehicles because there are a number of factors that affect the production of vehicles such as
wages, input prices, technology and so on. Nevertheless, in this paper, we regard vehicle price to
be the most influential factor, and hence, it is chosen as a single variable to avoid unnecessary
complication.
This study assumes that demand and supply curves have constant elasticities since
isoelastic demand curves are convenient to work with (Pindick and Rubinfeld, 1997). More
specifically, hyperbolic demand and supply curves, which are one of the most commonly used
constant-elasticity-non-linear curves, are used:
Demand:
NR= a1x(PC)b'x(Rl)b2
Supply:
CA= a2x(PC) 1
(1)
0
(2)
where NR: the number of new car registrations,
PC: the weighted average of the real price of cars,
Rl: real national income, and
CA: the number of cars produced in Australia.
From equations (1) and (2), the price elasticity of demand is b1, the income elasticity of demand
is b 2 and the price elasticity of supply is c 1, which are the coefficients for the variables reported
from log-linear regression of the equation as
+ b1 In (PC1) + b 2 In (Rlt) + E
Demand:
In (NR1) = a1
Supply:
In (CA,)= a2 + c1 In (PC 1)
+ v,
(3)
(4)
Quarterly data was used in this study for 1984 to 1999. Data for CA was obtained from the
Australian Bureau of Statistics (ABS) catalogues number 8363.0 and number 8301.0. NR and Rl
data were taken directly from the ABS database, which are seasonally adjusted. ABS adopts
SEASABS (Seasonal analysis to ABS standards) method, which is based on the X-11 ARlMA
5
package from Statistics Canada, to eliminate seasonal data problems (ABS, 2001). Real national
income is calculated using the consumer price index (where 1989-1990 is the standard year). It
is difficult to decide one representative price for automobiles, since automobiles are
differentiated to a high degree. Even the same model has different prices depending on various
options and characteristics. This study first selects models of automobiles that are frequently sold
in Australia. Where some popular models are replaced with new models of vehicles, such as
Hyundai Accent for Excel, the price of new model is used as the characteristics of those models
are almost identical. Reflecting market shares, 15 models of small cars, 12 models of medium
cars, and 19 models oflarge and luxury cars are selected 1• After the average price of each model
(standard) is obtained (which are, as of 2001, $16,682 for small, $26,776 for medium, $44,444
for large), the average real price of cars for each year is calculated using the market share of each
model, and the consumer price index.
Estimating Demand and Supply
Stationarity Tests for Variables
Variables are called non-stationary or unit-root variables iftheir mean and variance change over
time. Regression using non-stationary variables produces spurious results. Therefore, the unit
root test ofrelevant variables is undertaken before proceeding with the regression analysis.2 The
Augmented Dickey Fuller test (ADF) is adopted to test stationarity (Dickey and Fuller, 1981).
ADF regression takes the form of
k
LiY, = $Yt-I +a +~t + :E 9j ilYt-j +Et,
J=l
where LiY 1 = Y1 - Y1_1, t is time trend, j is lag length, and s 1 is a sequence of independently and
identically distributed random variables. The null hypothesis of the test is Ho: $ = 0. If the null
hypothesis cannot be rejected, variable Y is non-stationary. The test results are summarized in
table 3.
Variables NR, CA and RI in logarithm (noted as LNR, LCA and LRl) are non-
1
The full list of models included in this study is available on request from the authors.
We accept that the data used in this study, with 64 quarterly observations, are not sufficient to conduct unit root
tests, however, believe that they would provide more reliable results.
2
6
stationary, while PC in logarithm ( = LPC) is stationary. Further tests are carried out for the three
variables that have a unit root by taking the first difference. Table 4 exhibits the results. The two
tests show that, after taking logaritlun, all the variables used, NR, CA, Rl and PC, are integrated
of order 1 (I(l)).
This study applies cointegration to resolve the unit root problem. Some pairs of nonstationary variables are said to be cointegrated if they wander closely due to the disequilibrium
forces (Kennedy, 1992), which means that variables are non-stationary individually, but some
linear combination of them is stationary.
Error Correction Model of Automotive Demand and Supply
As the variables with a unit root are identified, the order of the vector autoregressive (VAR)
model needs to be determined. Firstly the estimation of demand curve is run using the logaritlun.
Both Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) indicate that
the order of VAR model should be four (table 5). The next step is to choose the number of
cointegrating vector, r. Assuming that cointegration has unrestricted intercepts and unrestricted
trends in the VAR, this study carried out the cointegration log-likelihood ratio (LR) test. The
tests based on both the maximal eigenvalue of the stochastic matrix and the trace of the
stochastic matrix reject the null hypothesis Ho: r = 0 but cannot reject Ho: r :> 1. Therefore,
there is only one cointegration relationship between LNR and LRl. The demand relationship
estimated by error correction model (ECM) is
3
3
dLNR, = a10 +all t + L);dLNR,_; + LY;dLRl,_; + 15, LPC, + $1EC (-1),
i=I
(5)
i=l
where EC is the error correction term. The results are reported in table 6.
The coefficients for lagged variables dLNR t-i (= LNR,.; - LNR,.;.1, for i = 1,2, and 3) are
all statistically significant and positive while those for lagged income variable dLRl
t-i
are
insignificant. The direct relationship between the demand for motor vehicles and real income is
not statistically proved. However, the error correction term, which includes the income variable
(LRI) is statistically significant, which indicates that there exists a certain relationship between
demand and income.
The coefficients for price variables are also significant and negative,
7
implying that the change in prices adversely affects the demand for automobiles. This estimation
reports conclusively that an increase in income and an increase in price respectively, affects
demand for automobile positively and negatively, where the impact on demand disappears over
time.
The supply equation was estimated by using the same method. The order of VAR should
be 1 based on the AIC and SBC. The number of cointegrating vectors is inconclusive: While the
cointegration LR test based on both the maximal eigenvalue of the stochastic matrix and the
trace of the stochastic matrix suggested the number of cointegrating vectors should be zero, the
same test based on model selection criteria indicates that it should be one. This study presents
the results from the both cases. When r is zero, as there is no integration term, ordinary least
squared estimation is used for dependent variable dLCA on price variable LPC (as LCP is I(O)),
that is,
dLCA1 = a20 + 02 LPC1.
(6)'
The result is reported in table 7. The coefficient for price variable is positive, however,
statistically insignificant. Table 8 reports ECM when the number of cointegrating vectors is 1,
that is,
dLCAt = U30 + Cl31t + 03 LPC1 + ~3EC (-1).
(6)"
In this case, the coefficient for price variables is reported as being negative, however, it is not
statistically significant.
The result from the ECM indicates that the quantity demanded is related to price and
income significantly in the short-run. However, it fails to show the relationship between the
quantity supplied locally and prices. Also, while the demand equation was estimated, the results
are not easily applied to the welfare analysis. Therefore, this study alternatively estimates longrun relationships among relevant variables to explore whether welfare analysis is possible based
on long-run elasticities.
8
Estimating Long-Run Elasticities Using Phillips-Hansen Method
The fully modified Phillips-Hansen OLS model is particularly appropriate for estimation and
inference when a single cointegration relation between a set ofl(I) variables exists (Pesaran and
Pesaran, 1997). The variables used in this study are all I(l), except LPC which was found to be
I(O). However, as discussed earlier, we have a limited number of observations, which might
mislead the result of the unit root test. 3 Considering that the time series automobile price variable
could be I(l), the fully modified Phillips-Hansen OLS method is applied to measure demand and
supply elasticities. Parzen lag window is chosen where lag length is 1.
As shown in table 9, price elasticity of demand is estimated as - 0.43 and income
elasticity of demand as 1.27. These results are consistent with general observations of the
relationship between the quantity demanded and prices and incomes. The figures are also
consistent with other studies on automobile markets. For example, Hymans (1971) reported that
for 20 years up to 1970, the elasticity of demand with respect to price was - 0.40 and that with
respect to income was 1.00. However, the elasticity of supply with respect to price estimated by
the Phillips-Hansen method is -0.38, which is substantially different from what might have been
expected as it indicates that manufacturers produce more vehicles as the price (cost) of vehicles
decreases. The result, that the Australian automobile industry has a long-run supply curve with
negative slope, possibly indicates that existence of economies of scale is prevailing in the
industry. Bloomfield (1978) explained that from an early stage in the history of the motor
vehicle, economies of scale in both production and marketing have been significant in shaping
the structure of the industry. The automotive industry in Australia has experienced dramatic
restructuring since the mid-1980s, affected by government policy that aimed to improve the
efficiency of the sector. Senator Button announced the requirement for the low volume producers
in December 1986, to either stop production or to increase output (Snape, Gropp and Luttrell,
1998). As a result, the factories were automated and average costs decreased as more cars (but
fewer models) were produced. These facts imply that it is highly probably that the Australian
automotive industry could reduce average or marginal costs as it increased the number of
automobiles produced.
' An investigation ofthe automobile price in Australia over time shows that it had increased with a clear trend until
1998, and then started to decrease. In addition, the test statistic for LPC is very close to the critical value of the ADF
statistic as reported in table 3. These indicate that the automobile price is possibly 1(1 ).
9
Since the automotive industry is extremely capital intensive, an increase in investment on
capital goods leads to the greater production efficiency. Empirical evidence indicates that
investment expenditure for capacity increased from $53.4 million (1991-94) to $161.3 million
(1995-98) as well as an increase in investment for efficiency from $292.8 million (1987-90) to
$565.9 million (1991-94). It is doubtful however, whether the benefits derived from economies
of scale is fully realized by this increase in investment. It was frequently observed that as the
elasticity of supply with respect to price is negative, the tariff on automobiles actually reduced
domestic production, contrary to the case of the decreasing returns to scale.
Another valid argument for the downward supply curve can be found from the gradual
reduction of tariff on imports of both completely assembled motor vehicles and motor vehicle
parts. The abolition, in 1998, of local content requirements contributed to reducing the price of
car parts. Lower prices of motor vehicle parts in turn facilitated the production of automobiles
with lower costs, which contributed to shaping the long-run supply curve downward sloped. This
effect might be more clearly explored once protection on related sectors, through tariff and nontariff protection measures, is considered as a whole.
The welfare analysis of tariffs with the presence of economies of scale was intensively
examined by Dixon (1978) in the context of general equilibrium. While traditional analyses
reported relatively minor consumption and production effects of protection, Dixon (1978)
pointed out that economies of scale, together with intra-industry specialization, would
dramatically increase the effect. However, his measures of the production and consumption
effects, arising from the reallocation of expenditures on goods and factors, are different from the
welfare analysis conducted in this paper that concentrates on surplus in partial equilibrium
models.
Measuring Welfare Effects of Tariff - Discussion
When a supply curve has a negative slope, the stability of the equilibrium is not necessarily
guaranteed. In order to have a stable equilibrium, an increase in price should reduce excess
d(Qo -Qs) 0 h
demand, and vice versa. In other words, - - - - - < , w ere
dP
JO
d(Qo -Qs)
dP
Tlo x Qo -ris x Qs
p
-0.43 X Q 0 +0.38 X QS
"'---~~--~~
p
where
(5)
rio= dQo ...!'..._ (the price elasticity of demand), and
dP Qo
dQs p
T]s = - - - - (the price elasticity of supply).
dP Qs
Data used in this study shows that the number of locally produced automobiles (Qs) always
failed to meet demand (Qo) throughout the period (1984-1999). Consequently, the value from
(5) is always negative (i.e. d(Qo -Qs) < 0 ), which indicates that the Australian automobile
dP
market has a stable equilibrium and our welfare analysis is valid. Using elasticities estimated and
data on quantity demanded, local production and supply and domestic and international prices,
the net welfare effect of tariffs on automobiles can be calculated. This section will measure three
kinds of welfare effects: changes in consumer surplus, changes in producer surplus and tariff
revenue. Net welfare effects and deadweight Joss will be also calculated.
Changes in consumer surplus over time are graphically shown as area [PAbcPw] in figure
1. Using the information obtained from regression and collected data, changes (Joss) in consumer
surplus by tariffs in each year t can be obtained as
which is, using the integration of the inverse demand curve for prices for each year t,
4.95 x
f PA
Pt -0.43x(RI,/27 dP,
PW
II
where PA is 1he autarky price and Pw is the world (or free trade) price. As Australia is a small
open economy, 1here would not be any terms of trade effect, and 1he world price and 1he
domestic price have such relationship as PA = Pw + T where T are tariffs on automobiles, or
txPw with t being the (ad valorem) tariff rate. The changes in consumer surplus measured are
reported in the second column in table 10.
As the elasticity of supply wi1h respect to price is found to be negative, the supply curve
has a negative slope, which is steeper 1han the demand curve. Where economies of scale exist, an
increase in domestic price due to tariffs reduces domestic production, which in turn decreases
'negative' producer surplus. This change in producer surplus is area [PAgePw] in figure 1. Area
[PAgePw], which is the gain in producer surplus in each year t is computed as
SrA [PA- Pw] +
f,
Sew
SPA
SdQ - [SrA- Srw] Pw,
which is, using the integration of the inverse supply curve for prices for each year t,
13.0lx
t
P1 -o.is dP,
and the results are reported in the third column in table 10.
Government tariff revenue is the product of the total number of automobiles imported
and the tariff on each automobile, which is (DrA-SrA)x(PA-Pw) as shown in figure 1 as area
[gbdt] and reported in the fourth column in table 10.
Area [get] in figure 1 is the negative deadweight loss due to the reduction of production.
As area [gbdt] is total tariff collection, the net welfare loss is the difference between the
deadweight loss of consumers and this deadweight gain (or negative deadweight loss) of
producers, which is revealed in the last column in table 10. Total net deadweight loss for the 16
years (1984-1999) was calculated as $1.2 billion, which is on average $75 million each year and
considered small (e.g. Hufbauer and Elliott, 19944). This 'small' amount of net deadweight loss
is largely due to the 'deadweight gain' (or the negative deadweight loss) of producers. If the
industry did not have economies of scale, the two deadweight losses should be added rather than
4
The size of the markets in Australia in this study and the US in Hufbauer and Elliott (1994) should be certainly
considered in this comparison.
12
subtracted, and the total deadweight loss would be larger. As tariffs have been relatively low
throughout the 1990s, this amount has been decreasing, reaching only $33 million in 1999.
While tariffs prohibited the industry from benefiting from economies of scale, they also
contributed to reducing negative producer surplus. In fact, consumer surplus has shrunk as much
as $47 billion for the same period, which is on average about $3 billion per year. Therefore, if we
consider consumer surplus only, this is a significant distortion. 5
The total consumer welfare loss due to tariffin 1999 was measured as $1.7 billion. This
loss consists of two parts: area [PAbdPw} and area {bed]. The former area represents consumers'
loss of surplus as they reduced their consumption of automobiles due to higher prices, which is
about $1.1 billion. In 1999, as the number of new car registrations in Australia reached 552,575,
each consumer who purchased a car paid about $2,000 ("' $1.1 billion/552,575) more than they
would without tariffs. The latter area reflects consumers' loss by reducing their consumption of
automobiles, which reached about $600 million. This $1.7 billion total loss (of consumers) in
1999, is equivalent to the total annual salary for 43,400 employees in the automobile and parts
industry in Australia. In the same year, the total number of employees in that industry was only
51,694.
The loss that consumers experienced is partly compensated for by the increase in
producer surplus, which is about $2 billion per year. Taking into account that the net welfare loss
due to protection is about $7 5 million per year, it should be pointed out that the protection policy
for the automobile industry in Australia brought about more serious problems in terms of the
redistribution of wealth than net efficiency loss.
One of the arguments most frequently used to advocate protection policy is that tariffs
can increase domestic employment. It is observed that the number of workers in the automobile
industry in Australia has been decreasing since 1981, as the Federal Government demolished its
protection policies. However, in order to make the argument valid that this decrease in
employment is due to the removal of tariff protection, the industry supply curve should be
positively sloped. If the industry has economies of scale as found in this study, a reduction in
tariffs would decrease domestic prices, which in turn should increase employment. A close
observation of data indicated that the total number of employees in the motor vehicle and related
industries rapidly decreased until 1990, and then stagnated until 2000. Tariffs on passenger
5
As reported in table I, Australia had never exported automobiles more than$ 3 billion each year until 1999.
13
motor vehicles, components and replacement parts also rapidly decreased from 40% in 1990 to
15% in 2000. The total number of employees was not significantly affected by tariff-cuts. In
addition, the motor vehicle manufacturing sector was the only sector which experienced a
decrease of employment for the same period: other related sectors such as motor vehicle body
manufacturing, automotive electrical and instrument manufacturing and automotive component
manufacturing actually expanded or maintained existing levels of employment over the same
period. Therefore, the reduction in employment in the motor vehicle industry should be regarded
as the result of productivity increases (there has been an approximately 7% increase in labor
productivity in this industry from 1992 to 1999) or capital-labor substitution, rather than the
removal of tariffs.
Conclusion
The analysis of the welfare effects of protection policies for the Australian automobile industry
resulted in some interesting findings. The industry' supply curve for 1984-1999 was found to be
negatively sloped, which indicated the existence of economies of scale in the industry. When
welfare effects of tariffs were measured through three parts as conventionally dealt with consumer surplus, producer surplus and government's tariff collection - it was reported that net
welfare loss was extremely small. This can be expected once the industry demonstrates
economies of scale, since the higher domestic price due to protection reduces negative surplus of
producers, which in tum offsets part of the reduction in consumer surplus. Negative effects on
consumers were in fact fairly large. In 1999, the loss of consumer surplus was equivalent to the
total annual salary or wage for approximately 80% of workers then employed in the motor
vehicle and parts industry in Australia. Also, there was no significant clue to conclude that the
reduction of tariff progressed in Australia contributed to decreasing employment in the industry,
at least for the last ten years. In fact, the reverse should be true: as the industry showed
economies of scale, employment is supposed to increase as the domestic price falls due to
liberalization. In summary, for the Australian economy, the efficiency loss due to tariff
protection was relatively small. The distribution effect was however, much more serious. The
amount of producers' gain and consumers' loss are both extremely high.
14
As the globalization of world automotive markets continues, the price of motor vehicles
is expected to decrease as a result of active mergers and acquisitions in this industry and the
trend of free trade. If the automobile industry in a country also has economies of scale, the
combination of both lower prices and lower trade barriers will result in similar welfare effects as
they produced in Australia. While the total net welfare gain is not as high as expected, there will
be significant improvement in the distribution effect; sufficient to compensate consumers for
their losses incurred as a result of protectionism. The exact magnitude will be, of course,
dependent on the extent of each country's elasticities and the particular variables such as tariff
rates and domestic automobile manufacturing costs.
15
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17
Table 1. Australia's Balance of Trade for Automobiles
Year Export (A$m) Import (A$m) Total deficit % change
1990
1,038.3
5,451.5
4,413.2
1991
1,162.2
5,023.2
3,861.0
-12.5
1992
1,248.7
6,398.8
5,150.1
33.4
1993
1,474.3
7,631.9
6,157.6
19.6
1994
1,537.6
8,881.1
7,343.5
19.3
1995
1,775.9
9,225.9
7,450.0
1.5
1996
2,263.5
10,690
8,426.5
13.1
1997
2,717.1
12,052
9,334.9
10.8
1998
2,574.3
14,494
11,919.7
27.7
1999
3,252.2
14,972
11,719.8
-1.7
Source:
Ke~
Automotive Statistics 1999, JSR (2000)
Table 2. New PMV Sales - Local and Import Split
Small car
Medium Car
Large Car
Local
Import
S.o.M.*
1990 95,883
55,390
36.6
209,314 53,562
20.4
14,179 35,089
71.2
76,949
64,402
45.6
178,664 22,785
11.3
7,900
38,222
82.9
1992 61,392
76,551
55.5
191,006 26,869
12.3
8,022
42,587
84.1
45,295
89,923
66.5
209,326 20,885
9.1
7,614
41,382
84.5
1994 36,320
109,186
75.0
235,991
21,542
8.4
9,275
48,384
83.9
1995 26,020
145,112
84.8
237,574 21,439
8.3
11,161
47,066
80.8
1996 24,452
158,925
86.7
230,765
20,066
8.0
9,729
48,121
83.2
1997 22,348 205,830
90.2
222,415 26,018
10.5
8,906
54,836
86.0
1998 21,889 229,500
91.3
243,705 24,900
9.3
7,767
56,599
87.9
16,870 212,849
92.7
239,266
7.4
9,716
54,751
84.9
Year
1991
1993
1999
Local
Import S.o.M.*
19,123
• S.o.M. stands for "Share of Import" in percentage.
Source: Ke~ Automotive Statistics 1999, !SR (2000)
18
Local
Import S.o.M.*
Table 3. Summary of ADF Test (in Logarithm)
Test statistic*
Variable
ADF statistic**
Unit root
Order
LNR
-0.4233
-2.9006
Yes
;eI(O)
LCA
-2.1069
-2.9241
Yes
;tI(O)
LRI
-0.9693
-2.9006
Yes
;eI(O)
LPC
-3.0153
-2.9006
Yes
=I(O)
•Value for ADF (!)
•• 95% critical value for ADF statistic
Table 4. Summary of ADF Test (First difference, in Logarithm)
Variables
Test statistic*
ADF statistic**
Unit root
Order
DLNR
-4.8482
-2.9012
No
=I(l)
DLCA
-5.0893
-2.9256
No
=I(l)
DLRI
-9.3701
-2.9012
No
=I(l)
"Value for ADF (I)
""95% critical value for ADF statistic
Table 5. Choice Criteria for Selecting the Order of the VAR Model
LL
AIC
SBC
Adjusted LR test
4
515.9146
476.9146
431.7236
----------
3
442.7441
412.7441
377.9818
120.9752 [.000)
2
436.2800
418.2280
404.3230
131.6626 [.000]
1
430.2280
418.2280
404.3230
141.6686 [.000]
0
121.4224
118.4224
114.9462
652.2271 [.000]
Order
19
Table 6. ECM for Demand Based on Cointegrating VAR (4): 1984-1999
(dependent variable= dLNR)
T-Ratio [Prob]
Coefficient
Standard Error
7.3202
1.3706
5.4309 [.000]
Trend
.0064
.0012
5.1441 [.000]
dLNRl
.0321
.1140
.2826 [.000]
dLRll
-.0191
.1081
-.1766 [.860]
dLNR2
.4961
.1335
3.7152 [.000]
-.1148
.1230
-.9334 [.354]
.5793
.1371
4.2243 [.000]
dLR13
-.0643
.1049
-.6128 [.542]
EC (-1)
-.5250
.0976
-5.3805 [.000]
LPC
-.3453
.0780
-4.4246 [.000]
Regress or
Intercept
dLR12
dLNR3
EC(-1) = 1.0000*LNR-0.042l*LR1
·-------------------------------------------------------------------------------------------------------------------------R-Squared
.4147
R-Bar-Squared
.3337
S. E. of Regression
.0555
F-Stat. F( 9, 65)
5.1171 [.000]
Mean of Dependent Variable
.0055
S. D. of Dependent Variable
Residual Sum of Square
.2002
Equation Log-Likelihood
Akaike Information Criterion 105.7942
Schwarz Bayesian Criterion
DW-Statistic
System Log-Likelihood
1.9917
20
.0678
115.7942
94.2068
297.5374
Table 7. Ordinary Least Squares Estimation for Supply: 1984-1999
(dependent variable= dLCA)
Regress or
Intercept
dLPC
Coefficient
Standard Error
-.1380
.2288
-.6030 [.549]
.0295
.0503
.5859 [.561]
R-Squared
.0070
R-Bar-Squared
S. E. ofRegression
.0801
F-Stat. F( 1, 49)
Mean of Dependent Variable
-.0041
Residual Sum of Square
.3146
Akaike Information Criterion 55.3884
DW-statistic
T-Ratio [Prob]
-.0133
.3433 [.561]
S. D. of Dependent Variable
.0796
Equation Log-Likelihood
57.3884
Schwarz Bayesian Criterion
53.4566
2.4659
Table 8. ECM for Supply Based on Cointegrating VAR (1): 1984-1999
(dependent variable= dLCA)
Regressor
Coefficient
Standard Error
T-Ratio [Prob]
3.4482
1.4900
2.3142 [.025]
-.8535E-3
.0021
-.4015 [.690]
EC (-1)
-.2096
.0758
-2. 7663 [.008]
dLPC
-.0178
.1445
-.1230 [.903]
Intercept
Trend
EC(-1) = 1.4154*LCA
R-Squared
.1482
R-Bar-Squared
S. E. of Regression
.0758
F-Stat. F( 3, 47)
.0938
2.7255 [.055]
Mean of Dependent Variable
-.0041
S. D. of Dependent Variable
Residual Sum of Square
.2698
Equation Log-Likelihood
61.3033
Schwarz Bayesian Criterion
53.4367
System Log-Likelihood
61.3003
Akaike Information Criterion
DW-Statistic
57.3003
2.2077
21
.0796
Table 9. Fully Modified Phillips-Hansen Estimates for Demand and Supply: 19841999
Regressor
Coefficient
Standard Error
T-Ratio [Prob]
Demand (Dependent variable= LNR)
Intercept
4.9481
.5456
9.0687 [.000]
LPC
-.4292
.0590
-7.2750 [.000]
LRI
1.2731
.1058
12.0307 [.000]
13.0087
.3198
40.6762 [.000]
-.3790
.0702
-5.3953 [.000]
Supply (Dependent variable= LCA)
Intercept
LPC
22
Figure 1. Welfare Effect of Tariff with Economies of Scale
p
a
h
c
Pw
Spw
DrA
Dpw
23
D
Q
Table 10. Estimated Welfare Effects of Tariff ($million)
Year
Changes in
Consumer Surplus
Changes in
Producer Surplus
1984
-3,851
3,057
693
-100
1985
-4,475
3,370
975
-130
1986
-3,734
2,983
656
-96
1987
-3,649
3,049
518
-83
1988
-3,487
2,782
630
-75
1989
-3,572
2,679
809
-84
1990
-3,442
2,352
999
-91
1991
-2,630
1,768
764
-67
1992
-2,693
1,713
909
-71
1993
-2,686
1,688
931
-67
1994
-2,868
1,742
1,056
-70
1995
-2,763
1,545
1,149
-69
1996
-2,489
1,333
1,096
-59
1997
-2,302
1,075
1,170
-56
1998
-2,111
983
1,081
-47
1999
-1,701
807
861
-33
Total
-47,452
32,927
14,328
-1,197
Tariff Revenue
Net Effect
NOTE: Tariff rates between 1984 and 1987 are treated as being 57.5% although various policies such as tariff
quotas and trigger tariffs were implemented.
24
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