東京大学 公共政策大学院

ワーキング・ペーパーシリーズ
GraSPP Working Paper Series
The University of Tokyo

GraSPP-DP-E-07-005 and ITPU-DP-E-07-005

International Trade and Air Cargo :An Analysis of US Export and Air Transport Policy

Katsuhiro Yamaguchi
April 2007

GraSPP Discussion Paper E-07-005

ITPU Discussion Paper E-07-005

GRADUATE SCHOOL OF PUBLIC POLICY
THE UNIVERSITY OF TOKYOHONGO, BUNKYO - KU, JAPANGraSPP-DP-E-07-005

International Trade and Air Cargo:
An Analysis of US Export and Air Transport Policy
Katsuhiro Yamaguchi
April 2007

Professor, Graduate School of Public Policy
The University of Tokyo, Japan
7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
Phone: +81-3-5841-1710
Fax: +81-3-5841-1313
yamaguchi@pp.u-tokyo.ac.jp

GraSPP Discussion Papers can be downloaded without charge from:http://www.pp.u-tokyo.ac.jp/

Discussion Papers are a series of manuscripts in their draft form. They are not intended
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Discussion Papers may not be reproduced or distributed without the written consent of
the author.

Abstract: Today, in major economies, air cargo service is intensively used for transport ofinternational merchandise trade. The need for high-speed cross-boarder transport has been
accelerated by strategic emphasis on supply chain management (SCM) in the manufacturingsector. Air cargo service has become an integral part of the linkage system for SCM. Although
there is growing attention on the importance of air transport and international trade, there is a
missing link in empirical analysis between the two. This paper tries to fill the gap from two
aspects by estimating export demand function and air transport supply function with asimultaneous equation model using US export data with twenty-one trading partners for 1998
– 2002. We first find that airfare is more significantly correlated with export than distance.
Second, the level of market concentration computed as Herfindahl-Hirschman Index (HHI)
has significant positive effect on airfare. Assuming Cournot competition, Price-Cost Marginof US export markets with twenty-one nations ranges from 0.05 to 0.75. Strikingly, HHI ishigher for air cargo markets with trading partners that have concluded Open Sky Agreementswith US. We need to go beyond Open Skies if we are to seriously pursue enhancement ofcompetitive forces in air cargo market to facilitate international trade 1 .
Keywords: Merchandise trade, gravity model, air cargo, Open Sky Policy.
JEL Classification: L93, F14, L50
Acknowledgement: Research environment and financial support from International Transport
Policy Research Unit (ITPU), Graduate School of Public Policy, The University of Tokyo arehighly appreciated. Comments at ATRS World Congress 2006 (May 2006), a seminar in
University of British Columbia (August 2006) and a workshops at The University of Tokyo(autumn 2006) were helpful. I am grateful to Shigeo Wakita for his contribution in sorting out
trade data. Remaining error is mine.

1 The original paper was presented at 2006 ATRS World Conference, Nagoya. Comments and suggestions

are welcomed. Not to be quoted without author’s consent.
1Introduction
Manufacturing of modern merchandise is characterized by cross-border investment
and division of labor. Efficient supply chain that links between facilities in different locations
enables strategic marketing that requires feedback from down stream to up stream of value
chain. Air cargo serves as an indispensable mean of logistics in today’s manufacturing. By
expeditious and reliable transport service manufacturing sector could avoid risk of holding
inventory as well as to penetrate into final markets quickly. In major states such as US andJapan about 30% of internationally traded merchandise, in ad-valorem, is transported by air.
By looking at goods traded by air cargo, we could see the forerunners of business segments inthese economies as well as to identify transport policy that would facilitate international trade.
This paper has two sections. First, we briefly look at US merchandise trade andtransport mode in comparison with Japan. Second, by using a gravity model we analyze therelevance of distance and airfare between nations with regards to trade by air transport anddiscuss the effect of public policy such as US Open Sky Policy.
1. US Trade and transport mode: a comparison with Japan
Common perceptionof international merchandise
Air Transporttransport
Allmodemodeofoftransporttransport
All
Air
trade of US and Japan may bedifferent.
Compositionoftransport mode for trade,however, is quite similar.
While US have land-transportand Japan does not forinternational trade, the share of
air transport is about the sameat approximately thirty percent.
Details of merchandise tradetransported by air are
Figure 1 Share of Total Trade (ad-valorem) (2002)
somewhat different. For
U.S.A

tons
4,000,000

JAPAN

20.0%

U.S.A

23.4%

1

JAPAN

11.0%

U.S.A

26.4%

2

MEXICO

12.5%

CHINA

13.5%

2

ENGLAND

7.8%

CHINA

9.5%

3

JAPAN

9.3%

S. KOREA

5.8%

3

GERMANY

6.8%

TAIWAN

8.6%

4

CHINA

7.9%

TAIWAN

5.3%

4

CHINA

5.2%

S. KOREA

6.9%

5

GERMANY

4.8%

HONG KONG

3.6%

5

FRANCE

5.1%

HONG KONG

5.5%

6

ENGLAND

4.0%

GERMANY

3.5%

6

IRELAND

5.0%

GERMANY

4.9%

7

S. KOREA

3.1%

THAILAND

3.1%

7

TAIWAN

4.9%

SINGAPORE

4.7%

8

TAIWAN

2.7%

AUSTRALIA

3.0%

8

S. KOREA

4.9%

PHILIPPINE

3.9%

9

FRANCE

2.6%

MALAYSIA

2.9%

9

MALAYSIA

4.5%

MALAYSIA

3.7%

10

ITALY

1.9%

INDONESIA

2.7%

10

SINGAPORE

4.2%

THAILAND

3.1%

OTHERS

31.2%

OTHERS

33.1%

OTHERS

40.6%

OTHERS

22.8%

Million US＄
1,400,000

air
sea
land

( )：Unit value of cargo

3,500,000

1,200,000

3,000,000
2,500,000

JAPAN

CANADA

air weight(tons)(77US＄/KG)

U.S.A

1

２４％

1,000,000(97US＄/KG)

800,000

2,000,000(66US＄/KG)

1,500,000

400,000

1,000,000

４６％

３２％

３０％

２８％

３０％

200,000

500,000

３０％

４０％

７０％

７０％

exportimport00exportimportUS

Figure 2

600,000(98US＄/KG)

exportexportimportimportUS

Japan

Volume of Trade in weight (2002)

Figure 3
2

Japan

Volume of Trade in ad-valorem(2002)

instance, 40% of machinery in US export is aircraft related whereas in Japan only 5% is in thesame category. Another area of difference is in precision instrument. Nearly half of precision
instrument in US export is medical related, while Japanese export is more diverse. As forimport, US imports large volume of bio-chemicals and medicines from Europe, whereas
Japanese import in this segment is relatively small. However, composition of merchandise at amore aggregate level has similar characteristics. In both countries 80% of export and 60% of
import is machinery. Also, price of the traded goods per weight is similar at 97-98 US$/Kg forexport and 66-77 US$/Kg for import (Figure 1 – Figure 4). In the following section, we lookfurther into US export and relationship between international trade, air cargo and air transport
policy.
Comparison of Japan and US Trade
Machines and instruments

Exportair value（US）

air value（Japan）

0%

food

Importnon-metal

10%fabrics

20%

chemical

30%

metal

40%

50%

machinery

60%

70%

80%

precision instruments

90%

electronics

100%

othersair value (US)air value (Japan)

0%
10%
food
chemicalprecision instruments

20%

30%
40%
non-metal
metal
electronics

50%

60%

70%

80%
fabrics
machineryothers90%

100%

Figure 4 Comparison of US Trade with Japan (2002)

2. Analysis of US Export and the Air Cargo Market
Distance between nations is one of the major factors that affect trade. Disdier et al(2005) reviewed 1,467 results from 103 papers on international trade. From this meta-data,
average distance elasticity on trade was found to be -0.9. Due to recent development intransport, impact of distance may have diminished while cost of transport is gaining relevanceto trade. Until recently, however, there have not been many attempts to consider transport ininternational trade theories. Hummels (1999), Liamo and Venables (2001), Harrigan (2005)
are some of recent analyses that discuss trade and transport cost, but these studies are notfocusing on implications for transport policy.
Transport studies, on the other hand, has considerable work on demand analysis.
Oum et al (1990) sites three studies on price elasticity of air cargo that range from –0.82 to–1.60. Linkage between transport policy and trade, however, has not been fully analyzed intransport research either.
In this section we use a gravity model to integrate assessment of trade flows andtransport. Through this analysis, we compare distance with airfare as an index of transaction
cost of trade in the model and discuss public policy implications.
1) The Modeli) Export Demand
Gravity model is used to express demand function of export. Here we consider US
export by air transport in ad-valorem. Usual gravity model uses product of GDPs of the3country pairs, however, we drop US GDP for simplicity, since we are concentrating on US
export. Note that only trade volume transported by air is taken into account. GDP is broken
down into per capita GDP and population in order to reflect level of development 2 and size ofthe economy. Subsequent equations are expressed in log-linear form.

lairvalueit = c1 + α 1 lg dppercapitait + α 2 lpopulationit + α 3ldis tan cei + ε it …...(1)where,

airvalueit : US export in ad-valorem to country i in year t transported by air.

gdppercapitait : per capita GDP of country i in year t .
populationit : population of country i in year t .
dis tan cei : distance between US and country i .

c : constant.

ε it : μ i (fixed effect) + vt (year dummy) + uit (error term).

In gravity models, distance is considered as an index of spatial transaction cost.
Alternatively, cif/fob is often used to capture aggregate transport cost. Since we are focusing
on transport cost in the narrow sense, instead of taking cif/fob we replace distance with airfare.
Airfare could be regarded as “c” in cif for trade in air transport. Thus, we have the following
equation.

lairvalueit = c 2 + α 4 lg dppercapitait + α 5 lpopulationit + α 6 lyield it + ε it ……..(2)where,

yieldit : average yield, i.e., average airfare (in US$ per Kg), from US to country i inyear t .
ii) Air Transport Supply Function
Since output of air cargo market is captured by weight or product of weight anddistance rather than by value of transported goods, air transport supply function for exported
goods is expressed as follows. Herfindahl- Hirschman Index (HHI) of air cargo market isincorporated to reflect level of competition in the segment.

lyield it = c3 + γ 1ldis tan ceit + γ 2 lairweight it + γ 3lairhhiit + ε it ……..(3)where,

airweightit : US export in weight (Kg) to country i in year t transported by air.
airhhiit : Herfindahl-Hirschman Index, sum of airline’s market share in weigh squared,of air cargo market from US to country i in year t .
2 Per capita GDP could also be regarded as a proxy for consumption patterns or “tastes” of thatnation.
4iii) Market Conditions
Also, we check the relationship between per unit value of exported merchandise withrespect to distance and transport cost. Per unit cost such as transport cost should induce
demand from far away locations to center around relatively expensive merchandise per weight.
Since demand for relatively expensive merchandise is higher in developed economy, we holdper capita GDP constant, and we assume value per Kg of exported merchandise to be afunction of distance or airfare. “Airfare” should be as significant as “distance” since far away
locations would be more expensive to ship the goods. We would, therefore, have thefollowing two functions.

lunitvalueit = c 4 + η1 lg dppercapitait + η 2 dis tan cei + ε it ……..(4)lunitvalueit = c5 + η 3 lg dppercapitait + η 4 yield it + ε it ……..(5)Finally, we look at correlation between HHI and Open Sky Agreement with US.
While holding the trade volume constant, the dummy variable could identify whether an OpenSky Agreement leads to enhanced level of competition or provides a market for oligopoly.

lairhhiit = c6 + δ 1lairvalueit + δ 2 dum1t + ε it ……..(6)lairhhiit = c7 + δ 3lairweight it + δ 4 dum1t + ε it ……..(7)where,

dum1t : Dummy for nation that have Open Sky Agreement with US in year t .
We expect the following for the parameters;

α 1 , α 2 , α 4 , α 5 , β1 , β 2 , β 4 , β 5 > 0 , α 3 , α 6 , β 3 , β 6 < 0
γ 1 > 0 , γ 2 > 0, or , < 0 , γ 3 > 0

δ 1 , δ 3 < 0 , δ 2 , δ 4 > 0, or , < 0 , η1 ,η 2 ,η 3 ,η 4 > 02) Data
Data for twenty-one US export partners 3 were compiled from US Export of
Merchandise 1998 – 2002 (US Census Bureau), Air Cargo Annual 1999-2003 (IATA), World
Economic Indicators (World Bank), Form 41 (T100) of Air Carrier Statistics (Bureau ofTransport Statistics, US DOT). Data for years from 1998 to 2002 were collected. IATA Air
Cargo Annual, which is the source of average fare for air cargo, is terminated since 2003.
Distance is the great circle distance between geographical centers of the nations. In 2002, US
export was 693 billion US$ and 320 million tons 4 . Those transported by air were 225 billion
US$ (32.5%), 2,310 thousand tons (0.7%). Export by air to the twenty-one nations are 126
3 The twenty-one nations are Canada, Mexico, Peru, Venezuela, Brazil, Chile, Argentina, United Kingdom,
Germany, Israel, Saudi Arabia, South Africa, Nigeria, Hong Kong, Singapore, India, Pakistan, Japan, South
Korea, Australia, and New Zealand. They were selected to have balance between continents and data
availability.
4 Weights are total volume of export transported by air and sea. Land transported volume is not reported.

5

billion US$ (55.9% of US export in ad-valorem by air), 1410 thousand tones (61.2% of US
export by air in weight).
Add-on cost of trade from airfare ranges from 0.7% to 6.3% with weighted averageof 1.3%. This average add-on cost of trade from airfare accounts for 12% - 13% of cif/fob
ratio of 8 - 11% that Harrigan (2005) estimated for US imports. Other than airfare, packaging
and delivery cost, customs and insurance compose the total tangible transport cost 5 .
3) Econometric Issues
In analyzing the panel data, we used the random effect model and the fixed effectmodel. Hausman test supported the random effect model. This is plausible since thetwenty-one nations were selected in a random manner and that relationship between trade andtransport should hold in general rather than as a unique correlation for the twenty-one nations.
Considering simultaneity of demand and supply functions, simultaneous equation
models for (2) and (3) are estimated by Two Step Lease Squared Panel Estimation (2SLS
Panel) based on Baltagi (2001). In case where distance, which is one of the fixed effects,
enters the model, in addition to the Random Effect Panel Estimation (RE) we used theMaximum Likelihood Estimation (MLE) to enhance robustness of the regression analysis 6 .
4) Empirical Findings
First, the results of the regressions on distance are summarized in Table 1. Sign of theparameters are as we expected. When we take natural log of ad-valorem US export by airtransport (lairvalue) as dependent variable, coefficients for natural log of distance (ldistance)
were -0.323 (RE) and -0.327 (MLE), although their statistical significance are rather low.
Compared to the mean distance elasticity of -0.9 reported in meta-data analysis by Disdier etal (2005), elasticity is lower. This suggests that there are other variables that better reflect theresistance factor in the gravity model for the trade by air transport.
So we turn to the simultaneous equation models. The results are reported in Table 2.
Elasticity of airfare with respect to export in ad-valorem (airvalue) was -1.227. Holding
export volume constant, elasticity of HHI (airhhi) with respect to airfare (yield) was 0.113 andstatistically significant. Elasticity of airfare with distance was 0.227. 7 Dummy variables foryears 2001 and 2002 were significant reflecting macro-economic shocks from 911 terrorist’s
attack etc.
Table 3 shows that per unit value of merchandise exported by air transport are higher
for distant destinations. Coefficient for airfare was statistically more significant than distance
implying that source of per unit cost that leads to this phenomenon is captured more
appropriately by the actual shipping cost (airfare) rather than by distance.
In Table 4, Pooled OLS for HHI and Open Sky dummy variable is listed. Strikingly,
we can see that there is a positive correlation between them. Random effect model and thefixed effect model of panel data analysis, however, did not result in significant outcome. To
check whether Open Sky dummy had direct impact on the level of airfare, we replaced the
HHI with Open Sky dummy variables in 2SLS Panel equation (3). The result showed positiveand statistically significant correlation between the Open Sky dummy and airfare.
5 There are intangible costs associated with international trade such as time factor and communication
barriers.
6 We also tested the GMM approach, although due to data constraints, there was no statistically significant
finding.
7 This is within the bounds of results achieved by Hummels (1999).

6

5) Public Policy Implications
Assuming Cournot competition, following relationship between HHI and Price-Cost
Margin would stand 8 .

−

HHI

ε

∑s

≡− i

ε

2
i

= ∑ siip−mpwhere, ε is elasticity of demand, si is output share of firm i , p is airfare, m ismarginal cost. Computed from the estimated parameter for ε , Price-Cost Margin oftwenty-one US air cargo markets ranges from 0.05 to 0.75 (Figure 5). There is a generaltendency that price-cost margin decreases as volume increases, although there is varianceparticularly on the lower band of the volume.

0.8

Price-Cost Margin

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5000000
01E+08

1.5E+08

2E+08 2.5E+08
airweight

3E+08

3.5E+08

4E+08

Figure 5 Price-Cost margin and Trade Volume for US export by air with twenty-one
nations (1998-2002)

Non-Open Sky

Singapore
0.7
0.6
0.5
0.4
0.3
South Korea
0.2
0.1
0
40000000 60000000 80000000 10000000 12000000
00
airweight

Figure 6

0.7
0.6
0.5
Hong Kong
0.4
0.3
Australia
0.2
0.1
Brazil0
Mexico40000000 60000000 80000000 10000000 12000000
00
airweight
price-cost margin

price-cost margin

Open Sky

Price-Cost margin and Trade Volume for US export by air with six nations (1998-2002)

We expected that pro-competitive policy such as Open Sky Policy would drive8

See Carlton and Perloff (2005) for details.
7price-cost margin down. This, however, was not the case and strikingly, HHI is higher for air
cargo markets with trading partners that have concluded Open Sky Agreements with US. By
taking six nations in the bound of 40 million to 100 thousand tons per annum, we can clearly
see that non-Open Sky markets have in fact lower price-cost ratio (Figure 6). This could also
be depicted for markets in the same geographical region, such as US-Japan / US-South Korea
and US-UK / US-Germany, although there is difference in trade volume (Figure 7).
Price-Cost Margin (US→Japan, South Korea)

Price-Cost Margin (US→Germany, UK)
0.4

0.3

2000
1998

0.35

0.25

2001

2002

0.25

price-cost margin

price-cost margin

1999
0.3

Germany*

0.2
0.15

UK

0.1
0.05
1.4E+08

0.2

1999
1998
2002
2001

0.15

2000
Japan

South Korea*
1998

0.1

2002

1998

0.05

2001 2000

1999
2002
1.6E+08

1.8E+08

2E+08 2.2E+08* Open Sky Agreement (1996.2 -)airweight2001

2.4E+08

1999

2000
2.6E+08002.8E+08

5E+07

1E+08

1.5E+08

2E+08

2.5E+08

3E+08

3.5E+08

4E+08*Open Sky Agreement (1998.4 -) airweightand Trade Volume for US-Japan / US-South Korea andUK / US-Germany (1998-2002)

3. Concluding Remarks
Through this analysis, we have confirmed the strategic importance of air transport for
the leading manufacturing industries. Today, air cargo is an integral part of SCM for high-end
products. This paper revealed that airfare is more significantly correlated with volume of trade
than distance. Also, competitive force in the international air cargo market is all the more
important for international trade. Direct impact of market concentration on the level of airfare
was manifested. Assuming Cournot competition, Price-Cost Margin in the twenty-one air
cargo markets ranges from 0.05 to 0.75.
The most strikingly finding is that market concentration is higher, thus the level ofairfare is relatively higher, for air cargo markets with trading partners that have concluded
Open Sky Agreements with US. We need to go beyond Open Skies if we are to seriously
pursue enhancement of market forces to facilitate international trade. We point out threecaveats in considering policies beyond Open Skies.
First is the effect of the rigid framework of international air transport. Air transport
has traditionally been provided under a framework in which international and domestic
activities are segregated. In particular, cabotage, which is solely reserved for national air
carriers, could be serving as an anti-competitive factor. Domestic segment, which is not
exposed to international competition, not only serves as an important segment for thedoor-to-door service in international air transport but also as a source of economic rent fromexternalities that could be internalized by these national oligopolies.
Second is collusive relationship among international air cargo carriers. In fact, there
have been cases under investigation by the anti-trust institutions on alleged international cartel.
We need to take uncompromising action against such suspicious incidents and need to
scrutinize activities in international institutions such as IATA and individual international air
cargo alliances.
Third is landing slots at congested airports. From our data, markets such as US-Japanand US-UK with highly saturated Narita and Heathrow had relatively low HHI. We need tokeep an eye on this issue. In the presence of airport congestion new and growing airlines are8forced to refrain for providing competitive services. This, however, should not be an excuse
for not undertaking further steps in liberalization. Ingenuity in economic approach shouldpave the way to cope with congestion.
In sum, enhancement of competitive forces in the transport sector would facilitate
international trade. We still need to go a long way, however, before being able to sit back andbe comfortable with market performance in international trade and air cargo.

9Reference
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Puzzle,” American Economic Review, 93(1): 170-192.
Anderson, J.E., 1979, “A Theoretical Foundation of the Gravity Equation,” American
Economic Review, 69: 106-16.
Baltagi, B.H., 2001, Economic Analysis of Panel Data, 2nd ed., John and Wiley & Sons.
Bergstrand, J.H., 1985, “The Gravity Equation in International Trade: Some Microeconomic
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Bergstrand, J.H., 1989, “The Generalized Gravity Equation, Monopolistic Competition, andthe Factor Proportion Theory in International Trade,” Review of Economics andStatistics, 71(1): 143-53.
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Carlton, D.W., J.M. Perloff, 2005, Modern Industrial Organization, 4th ed., Pearson.
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Trade,” Centro Studi Luca d'Agliano Working Paper 186.
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Harrigan, J., 2005, “Airplanes and Comparative Advantage,” NBER Working Paper W11688.
Hummels, D., 1999, “Have International Transportation Costs Declined?” Purdue University,
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Hummels, D., 2001, “Toward a Geography of Trade Costs,” Purdue University, mimeo.
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10Table 1 Panel (Random Effect (re), Maximum Likelihood (mle))
independent variable
dependent
lgdppercapita t-stats
lpopulation t-stats
variable
1.763
9.47 ***
0.595
4.26
re lairvalue
1.75
10.28 ***
0.588
4.67
mle lairvalue
1.593
9.81 ***
0.601
4.94
re lairweight
1.557
10.61 ***
0.583
5.46
mle lairweight***: significant at 1% level**: siginificant at 5% level
Year : statistically significant (5% level) year dummy.

n=105

ldistance
***
***
***
***

t-stats-0.323-0.327-0.51-0.522-1.08-1.23-1.96 **
-2.34 ***: significant at 10% level

Table 2 Panel (Random Effect 2SLS(re), Fixed Effect (fe))
function 1
independent variable
dependent
lgdppercapita t-stats
lpopulation t-statslyieldt-statsvariable
1.465
6.02 ***
0.511
3.16 ***-1.22664-2.67 ***
re lairvalue #Hausman specification test wrt fe: chi2(7)=2.65 Prob>chi2=0.913
function 2
independent variable
dependent
ldistance
t-stats
lairweight t-stats
lairhhit-statsvariable
0.227
1.78 *-0.103-1.95 *
0.113
1.69 *
re lyield #Hausman specification test wrt fe: chi2(6)=3.98 Prob>chi2=0.679***: significant at 1% level**: siginificant at 5% level*: significant at 10% level# Instrumental variables: lgdppercapita, lpopulation, ldistance, lairhhi, year dummies
Year : statistically significant (5% level) year dummy.
Table 3 Panel (Random Effect (re), Maximum Likelihood (mle))
independent variable
dependent
ldistance
t-statslyieldt-statsvariable
0.193
1.63
re lunitvalue
0.193
1.77 *
mle lunitvalue
0.15
1.98 **
re lunitvalue
0.151
2.05 **
mle lunitvalue***: significant at 1% level**: siginificant at 5% level
Year : statistically significant (5% level) year dummy.

year

R2

02
02
01,02
01,02

0.817
0.837
-

n=105

year

R2

02

0.829
-

n=105

year

R2

01,02

0.503
-

n=105

lgdppercapita t-stats

year

R2

0.192
2.95 ***
0.194
3.2 ***
0.209
3.04 **
0.21
3.26 ****: significant at 10% level

00,01
02
00,01,02
00,01,02

0.398
0.342
-

Table 4 Pooled
independent variable
dependent
variablelairhhilairhhilairvalue-0.228t-statsn=105

lairweightt-stats-4.99 ***

dum1

t-stats

0.375
2.41 **-0.304-6.12 ***
0.315
2.11 *****: significant at 1% level**: siginificant at 5% level*: significant at 10% level
Year : statistically significant (5% level) year dummy.

11

year
-

R2
0.269
0.337