The Balassa-Samuelson Effect andthe Labor Market in Japan：1977-2008
Takao Fujii
Yoichi Matsubayashi

September 2016
Discussion Paper No.1626

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Balassa-Samuelson Eﬀect andthe Labor Market in Japan：1977-2008∗
Takao Fujii† and Yoichi Matsubayashi‡
Abstract
This study examines the BalassaSamuelson eﬀect based on the real exchange
rate by sectors in Japan and the United States. Although it is theoreticallyassumed that the eﬀect holds under identical wages between tradable and nontradable sectors, few empirical studies verify the validity of this assumption.
We attempt to investigate the feasibility of the Balassa-Samuelson eﬀect byfocusing on Japanese labour market dynamics using a panel threshold model.
The empirical results show that the eﬀect may not have held since the end of the
1990s, given the wage discrepancy between tradable and non-tradable sectors,
and that structural changes have been driven by the machinery sectors such as
electricity and general machinery.

∗This is a revised version of the paper presented at Japan Society of Monetary Economics held at

Musashi University on 14 May 2016. We would like to thank Jun Nagayasu (Tohoku University) and
Etsuro Shioji (Hitotsubashi University) for their helpful comments and suggestions. We would also
like to thank Taro Esaka (Kobe City University of Foreign Studies), Shoji Haruna (Fukuyama University), Shinya Horie (Kobe University), Jun Iritani (Kobe University), Takeshi Koba (Kumamoto
Gakuen University), Masanaga Kumakura (Komazawa University), Fumitoshi Moriya (Kobe City
University of Foreign Studies), Satoru Tanaka (Kobe City University of Foreign Studies) and Yasunobu Tomoda (Kobe City University of Foreign Studies) for their useful comments.
†
Kobe City University of Foreign Studies, E-mail: fujii@inst.kobe-cufs.ac.jp‡
Kobe University, E-mail: myoichi@econ.kobe-u.ac.jp

1

Introduction

The role of exchange rate is gaining importance in the current global economy. Itshould be noted that in the long run, the real exchange rate―the exchange ratio ofgoods between two countries―is crucial.
Figure 1 illustrates changes in the real eﬀective exchange rate in Japan. As
described by Yamamoto (2013) and Ito (2015), the yen appreciated until 1995 andhas depreciated since then. This exchange rate turnaround poses an interesting
object for academic analysis.
Balassa (1964) and Samuelson (1964) formulated a theory for long-term real exchange rates, called the BalassaSamuelson eﬀect,1 which was originally intended to
explain why the exchange rate deviates from purchasing power parity (PPP). The
main mechanism is that there is a tendency for the real exchange rate to appreciate
as productivity of traded goods sectors rises relative to that of non-traded goods
sectors. As pointed out by Devereux (1999) and Ito et al. (1999), , this eﬀect was inexistence in Japan until 1995. In fact, productivity of the traded goods sectors washigher than that of non-traded goods and the yen appreciated. Since then, however,
the real exchange rate trend has reversed; thus, if we consider it in the context of theBalassaSamuelson eﬀect, it follows that tradable sector productivity declines while
the non-tradable sector productivity increases. However, the data presented in Figure 2 provide no evidence for a relative increase of productivity in the non-tradable
sectors since the 1990s. This suggests that the movements in the real exchange ratesince the 1990s cannot be explained by the Balassa-Samuelson eﬀect. It is necessary, therefore, to investigate the validity of assuming an identical wage between the
tradable and non-tradable sectors. Only a few attempts have been made to examine
this assumption.2 Strauss (1997) investigated 14 OECD countries from 1970 to 1990
1

As Tica and Družić points out, Harrod (1933) was the seminal work to formulate and goes backfurther to Ricardo (1821). Rogoﬀ (1992) was the first to fully construct the Balassa-Samuelson eﬀect
within the general equilibrium framework. Although it seems that the name of “Harrod-BalassaSamuelson eﬀect” is popular, we call “Balassa-Samuelson eﬀect” in this paper.
2
Tica and Družić (2006) is a comprehensive survey concerning the empirical studies of BalassaSamueslon eﬀect.

2

and showed that the wage diﬀerence between tradable and non-tradable sectors affects the real exchange rate. Konopczak and Torój (2010) and Cardi and Restout(2015) applied the unit root test to examine the wage diﬀerence between two sectors
in Poland from 1999 to 2008 and in 14 OECD countries, respectively.3 In addition, Konopczak (2013) conducted the Granger test to check the wage causality in
tradable and non-tradable sectors in Central and Eastern European countries4 ; however, these time-series tests were under-powered and lack persuasiveness. Yamamoto(2013) applied the cointegration test with regime switch, developed by Gregory andHansen (1996), to the Japanese real exchange rate from 1970 to 2008. He pointed
out that there has been a diminishing mechanism of the BalassaSamuelson eﬀectsince the 1990s. Although lacking empirical evidence, Yamamoto (2013) referred to
the violence of the assumption that there is an identical wage between the tradable
and non-tradable sectors.5
This study oﬀers three analytical diﬀerences from earlier studies. First, we attempt to investigate the feasibility of the Balassa-Samuelson eﬀect by focusing on
the dynamics of the Japanese labour market using a panel threshold model. Hansen(1999) developed the panel threshold estimation method. This method does not
require any specific functional form of parameter nonlinearity and assumes that thethresholds are endogenously determined. From this reasoning, we apply Hansen’spanel threshold estimation method to test whether there is a threshold eﬀect between the real exchange rate and relative wage between tradable and non-tradable
sectors in Japan.
Second, we explicitly investigate the Japanese labour market structure by focusing on the part-time and non-regular workers ratio in the non-manufacturing sector.
It is shown that this ratio has increased remarkably since the 1990s. Yamamoto
3

Cardi and Restout (2015) search much further than that. They describe the importance of thedegree of labor mobility across sectors.
4
They include Poland, Czech republic, Hungary and Slovakia.
5
Nagayasu and Liu (2008) investigate the relationship between wage dynamics and real exchange
rate in China. He constructed the twenty nine areas (22 provinces, three municipalities directly
under the control of central government and four autonomous areas) and focuses on the relative
wage between home and foreign countries.

3

(2013) and Ito (2015) also recognised this point, although there is no quantitative
analysis in their study.
Third, we divide industry into several sectors and examine each sector in detail.
Previous studies mostly set the panel data with countries. However, as pointed
out in Konopczak and Torój (2010), it is diﬃcult to identify the characteristics ofeach country with these panel data..6 Konopczak and Torój (2010) and Konopczak(2013) overcame this problem by dividing the non-manufacturing sector into several
sectors. In Japan, however, manufacturing is a driving force of economic growthand it seems to be essential to divide manufacturing into several sectors. This point
was emphasised by Marston (1986), who investigated the yen-dollar exchange rate
movements since the 1960s.
This empirical study yielded three findings. First, the assumption of identical wages between sectors is relevant and the eﬀect does not work in situations
where the relative wage exceeds the threshold, which was determined endogenously
to be around 1995. Second, the role of the non-regular workers ratio in the nonmanufacturing sectors seems not to be important, although it is a matter of speculation, and further investigation is necessary. Third, the eﬀect may not have held since
the end of the 1990s under wage discrepancy between tradable and non-tradable sectors, and such a structural change has occurred in the machinery sectors (electricity
and general machinery).
The remainder of this study is organised as follows. Section 2 describes the data
and methodology and lays out estimation results. Section 3 discusses additional
empirical evidence. Section 4 summarises the conclusions.

6

Nagayasu’s study was an interesting attempt to analyse the Balassa-Samuelson eect using spatial

panel data by prefectures in Japan.

4

2

Data and Empirical Results

2.1

Data and Panel Unit Root Tests

This study uses annual balanced panel data for 23 manufacturing sub-sectors over the
1977-2008 period.7 In the next subsection, we adopt the panel threshold regression
method proposed by Hansen (1999). As a prerequisite for the methodology,8 a
panel unit root test is conducted for the real exchange rate (rer) and relative labour
productivity (rlp) using the LLC (Levin et al. (2002)) and IPS (Im et al. (2003)) test.
The results are shown in Table 3. All the tests reject the null hypothesis for levels
except relative labour productivity using the IPS (Im et al. (2003)) test. Judging
from these tests results, we decide that the data series is I(0).

2.2

Empirical Results

As pointed out by Chinn and Johnston (1997),9 we perform the analysis by focusing
on the supply side (labour productivity). Specifically, we analyse the relative labour
productivity of Japan’s tradable/non-tradable sectors, considering the wage ratio
in both sectors. Therefore, we use the panel threshold regression model proposed
by Hansen (1999). The Balassa-Samuelson model assumes wage equalisation acrosstradable and non-tradable sectors, which means that the wage ratio in both sectors
is one. We can easily imagine that the Balassa-Samuelson model does not hold as
the wage ratio departs from one. This nonlinearity can be considered using Hansen’s(1999) model and, thereby, we can endogenously determine the threshold value or
the wage ratio.

7

See the data appendix for detailed documentation. The reason why we choose 2008 as the

sample end period is that it is the latest data which we can obtain at this time. Specifically, weonly collect the data up to 2008 in OECD Stat in order to calculate the wage in service industries.
8
The method propsed by Hansen (1999) is designed for balanced panel and stationary data.
9
Chinn and Johnston (1997) classify previous studies into three categories.

5Our estimation model is as follows1011 :ln rerit = αi + β1 ln rlpit I(rwit ≤ γ1 ) + β2 ln rlpit I(γ1 > rwit ) + ϵit .

(1)

where ln rer denotes the log of the real exchange rate, ln rlp the relative labour
productivity and αi is an individual eﬀect. Subscripts i and t denote sectors andtime periods, respectively. Moreover, I(·) denotes the indicator function, rw is therelative wage ratio which serves as the threshold variable and γ is the thresholdparameter.
We use two kinds of relative wage.12 One is the relative wage ratio of total
manufacturing to non-tradable sector (Case 1). In this case, we assume the same
relative wage ratio upon any manufacturing sector, which implies rwt not rwit . The
other is the relative wage ratio of individual manufacturing sectors to non-tradable
sector (Case 2). This means rwit in mathematical presentation, which Hansen (1999)
normally assumed.
Before estimation, we need to conduct a linearity test and determine the numberof thresholds. The results are shown in Table 4. Whichever case (Case 1 or 2) ischosen, the tests reject the null hypothesis of linearity and support that the numberof thresholds is one.
Following the above test results, we proceed to the estimation of Equation (1).
The results are reported in Table 5. In any case, the results show that the relative
labour productivity has a deteriorating eﬀect on the real exchange rate when thethreshold variables, or the relative wage ratio, exceed the threshold values. Specifically, the estimates are from −0.3132 to −0.1477, which implies a 52% decline in theabsolute value in Case 1 and, in Case 2, from −0.2912 to −0.0220 (i.e. insignificant).
These results show a decrease in the Balassa-Samuelson eﬀect.

10

We do not need to test the choice of fixed-eﬀect or random-eﬀect models because the fixed-eﬀectmodel is assumed in Hansen (1999).
11
Non-inclusion of control variables in Equation (1) may produce misspecification bias. On thispoint, we conduct the robustness check later.
12
See the data appendix for detailed documentation.

6

2.3

Robustness Check

As a robustness check of previous results, we employ two methods. First, we reestimate Equation (1) using the sub-sectors except other electrical equipment andprecision equipment. The results are shown in Tables 7 and 8. These support theprevious fact that the Balassa-Samuelson eﬀect decreases when the relative wage
ratio exceeds the threshold values.
Next, we add control variables for other factors which may influence the estimation results. Thus, we estimate the following equation:
JapS
ln rerit = αi + β1 ln rlpit I(rwit ≤ γ1 ) + β2 ln rlpit I(γ1 > rwit ) + β3 ln rlpU
it + β4 gcit + ϵit(2)where rlpU S denotes relative labour productivity in the United States (US) and gc is
the Japanese government consumption to GDP ratio.13 We add the latter variablebecause we follow Rogoﬀ (1992), who considered the eﬀects of the demand side asstated by Tica and Družić (2006) and others. The results are shown in Tables 9 and10. Table 9 shows that the number of thresholds is one as in the previous subsection.
Table 10 supports that while coeﬃcients of relative labour productivity in the US
and Japanese government consumption have the wrong sign,14 the BalassaSamuelson
eﬀect decreases when the relative wage ratio exceeds the threshold values.

13

Note that the subscript is not i but it. The reason is that when we construct the data, both

numerator and denominator are divided by the deflator of each sector.
14
While the coeﬃcient of Japanese government consumption has the wrong sign, this is consistent with Rogoﬀ’s(1992) mention. He confirmed that the correlation between the ratio of Japanese
government consumption to GNP and the real yen/dollar exchange rate has a positive sign, which
supports our results. Moreover, other previous research has the wrong coeﬃcient sign about government consumption as in Chinn (2000). We can think of two reasons why the coeﬃcients of relative
labour productivity in the US have the wrong sign. First, the data are constructed by connecting
the older data set and the newer data set using the growth rate of the older dataset. Second, we
use Y/L as a measure of labour productivity. In the case of the US, however, we use the numberof employees in place of man-hour input as the denominator L. The use of the latter is supposed tomore appropriate.

73Discussion

We scrutinise the relation between the relative wage ratio and the threshold value.
First, we consider Case 1, where the threshold value is 1.0958, as shown in Table 4.
The above in Figure 4 shows the movement from the past of the relative wage ratiowith the threshold value. We recognise that the relative wage ratio has exceeded
the threshold value since 1997. Combined with the results shown in Table 5, theBalassa-Samuelson eﬀect has decreased since then. This is consistent with the resultof Yamamoto (2013), who empirically showed that the structural change occurred in
the mid-1990s, with a view to the Balassa-Samuelson eﬀect.
Next, Figure 4 and Table 12 are for Case 2. From these results, we classify 23 subsectors into three groups: five sub-sectors (pulp, paper, paperboard and coated andglazed paper, final chemical products, general industry machinery, motor vehicles
and other transportation equipment) whose relative wage ratios remain above thethreshold value throughout the estimation period; seven sub-sectors (special industrial machinery, miscellaneous and general machinery, heavy electrical equipment,
electrical equipment and electric measuring, other electrical equipment, motor vehicles, parts and accessories and precision instruments) whose relative wage ratios
cross the threshold value at a certain times; eleven sub-sectors (other than listed
above) whose relative wage ratios remain below the threshold value throughout theestimation period.
Considering the results of Cases 1 and 2 together, we obtain the following interesting results. From the results of Case 1, the Balassa-Samuelson eﬀect seems to havedecreased since the late 1990s due to the increased ratio of relative wages. However,when we examine the relation between the relative wage and the threshold value by
sub-sectors, such an interpretation is supported only by the above seven sub-sectors
crossing the threshold value. We can suggest that the Balassa-Samuelson eﬀect is
not confirmed in the five sub-sectors and is confirmed in the other sub-sectors. Thisjustifies the importance of examination by sector.
Following the above results, we examine another hypothesis that the rise in theratio of relative wages is due to a diﬀerence in the ratio of part-time and non-regular8

employees between the tradable and non-tradable sectors. As shown in Figure 5, thediﬀerence seems to have widened in Japan since the 1990s. If we support the hypothesis, then this diﬀerence is a primary cause of the decrease in the BalassaSamuelson
eﬀect. To test this hypothesis, we examine the linearity of the following Equation (3)by replacing the threshold variable, or rw in Equation (1), with pt, which represents
the part-time and non-regular employees ratio of non-tradable sector over tradable
sector:ln rerit = αi + β1 ln rlpit I(ptit ≤ γ1 ) + β2 ln rlpit I(γ1 > ptit ) + ϵit .

(3)

If the hypothesis is supported, then we expect that the linearity test is rejected andthe boundary of the threshold is approximately the 1990s, as shown in Equation(1). The results are shown in Table 6. We do not support the single thresholdnor double threshold nor triple threshold.15 While we carefully need to handle theresults because of the non-rejection of the null hypothesis, the results here do notstrongly support that the diﬀerence in the ratio of part-time and non-regular employees between the tradable and non-tradable sectors, and this seems not to be theroot cause of the decrease in the Balassa-Samuelson eﬀect. In fact, as Figure 6 shows,the behaviours of the part-time and non-regular employees ratio in the service sector over 19 sub-manufacturing sectors do not exhibit similar tendencies among the
sub-sectors above, below or crossing the threshold.

4

Conclusion

This study empirically investigates the Balassa-Samuelson eﬀect using the Japanesepanel data by sectors from 1977 to 2008. Thise eﬀect is well-known in international
finance theory, and abundant research has been conducted on this topic.
Thise present study is diﬀersent from earlier ones studies in three significant ways.
First, we revisit the validity of the assumption that there is an identical wage between
tradable and non-tradable sectors. Second, we focus on the recent changes in the15

We also conducted a robustness check on this result using the sub-sectors as in the previous

analysis. As Table11 shows, the threshold eﬀects are not detected.

9non-tradable labour market, wherein the ratio of non-regular workers has increased
remarkably. Thirds, we divide industries into several sectors and successfully examine
their diﬀerences.
We succeed in obtaining a deeper understanding of the feasibility of the BalassaSamuelson eﬀect and present the following key findings. First, the assumption of an
identical wage between sectors is relevant and the eﬀect does not hold in situations
wherein the relative wage exceeds the threshold value. Second, we recognised thatthe ratio of non-regular workers in the non-manufacturing sectors seems not to beimportant., even though it is a matter of speculation;, and more careful further
investigation is necessary in this regard. Lastly, the eﬀect may not have held since
the end of the 1990s given the wage discrepancy between tradable and non-tradable
sectors, and such a structural change has been driven by the machinery sectors―
electricity and general machinery.
Finally, there are three directions for further research. The first is the inclusionof other factors into the estimation. The Balassa-Samuelson eﬀect is basically asupply-side phenomenon; thus, demand-side variables that aﬀect movements in thereal exchange rate are not considered. The second direction is the extension of thethreshold panel model to a dynamic version that explicitly considers the inertia of
the real exchange rate. The last direction is the application of this study to othercountries, particularly European countries. Since the 2010s, economic growth inEuropean countries has been decelerated and the number of non-regular workers in
the non-manufacturing sectors has increased. This trend may diminish the validity of
the Balassa-Samuelson eﬀect. Therefore, the empirical technique used in this study
is useful for examining the medium- and long-term movements in the real exchange
rate in these areas.

Data Appendix
We obtain the following data from JIP database 2015, Bureau of Economic Analysis(BEA) and OECD.Stat:

10• Sectoral real exchange rate(rer)16
The real exchange rate is calculated by multiplying nominal exchange rate (inyen per dollar) by the relative prices. When we calculate the relative prices,
we use sectoral price levels. We calculate them by dividing sectoral nominal
output by sectoral real output in Japan and chain-type price indexes for value
added in the US. Due to the diﬀerence in the base year of price indexes in eachcountry, we unify them as year 2000=100.
• Relative labor productivity between tradable and non-tradable sectors (rlp)
Labour productivities are calculated by sectoral real outputs divided by sectoral
man-hour labour inputs.17 Using these, we calculate relative labour productivities as tradable productivities divided by non-tradable productivity. We treat
23 manufacturing sectors as tradable and construction as non-tradable.18• Relative wage ratio between tradable and non-tradable sectors (rw)
We use relative wage ratios as threshold variables. When we use Case 1 in
the text, where we consider in terms of manufacturing as a whole and not bysector, the relative wage ratio is calculated as tradable (manufacturing) wage
divided by non-tradable (service) wage. Specifically, we calculate the relative
wage using labour costs (compensation of employees) and number of persons
engaged (total employment) in OECD.Stat and then measure the ratio. When
we use Case 2 in the text, where we consider in terms of manufacturing bysector, we calculate relative wage ratios using sectoral nominal labour cost
divided by the number of sectoral persons engaged in the JIP database 2015.
• Part-time employees ratio (pt)
We usepart-time employees ratio

in the JIP database 2015. Importantly,

the data are only obtained every five years. Therefore, we convert them toannual data using linear interpolation. The relative part-time employees ratio is
16
17

See Figure 1
As stated in footnote 12, we do not sectoral manhour labor inputs but the number of employee

in US data due to data availability,18
Because there does not exist aggregate service data, we use construction as a substitute.

11

calculated as the part-time employees ratio in the service sector divided by thatin manufacturing. As explained above, although we consider manufacturing as
a whole and not by sector in Case 1, we consider it sectorally.

References
Balassa, Bela (1964) “The Purchasing-Power Parity Doctrine: A Reappraisal,” Journal of Political Economy, Vol. 72, pp. 584-596.
Cardi, Olivier and Romain Restout (2015) “Imperfect mobility of labor across sectors: a reappraisal of the BalassaSamuelson eﬀect,” Journal of International Economics, Vol. 97, pp. 249-265.
Chinn, Menzie (2000) “The Usual Suspects? Productivity and Demand Shocks andAsia-Pacific Real Exchange Rates,” Review of International Economics, Vol. 8,
No. 1, pp. 20-43.
Chinn, Menzie and Louis Johnston (1997) “Real Exchange Rate Levels, Productivity
and Demand Shocks; Evidence from a Panel of 14 Countries,” IMF Working Papers
97/66, International Monetary Fund.
Devereux, Michael (1999) “Real Exchange Rate Trends and Growth: A Model of
East Asia,” Review of International Economics, Vol. 7, No. 3, pp. 509-21.
Gregory, Allan and Bruce Hansen (1996) “Residual-based tests for cointegration inmodels with regime shifts,” Journal of Econometrics, Vol. 70, No. 1, pp. 99-126.
Hansen, Bruce (1999) “Threshold eﬀects in non-dynamic panels: Estimation, testing,and inference,” Journal of Econometrics, Vol. 93, No. 2, pp. 345-368.
Harrod, Roy (1933) International Economics (Cambridge Economic Handbooks):
James Nisbet & Co Ltd.
Im, Kyung So, M Pesaran, and Yongcheol Shin (2003) “Testing for unit roots inheterogeneous panels,” Journal of Econometrics, Vol. 115, No. 1, pp. 53-74.

12

Ito, Takatoshi (2015) “Current Account, Exchange Rate and the Balassa-Samuelson
Eﬀect,” in Japan’s Current Account Balance: Current Situation and its Optimal
Policy, The Japan Society of Monetary Economics 2015 Spring Annual Meeting
Program.
Ito, Takatoshi, Peter Isard, and Steven Symansky (1999) “Economic Growth andReal Exchange Rate: An Overview of the Balassa-Samuelson Hypothesis in Asia,”
in Changes in Exchange Rates in Rapidly Developing Countries: Theory, Practice, and Policy Issues (NBER-EASE volume 7): National Bureau of Economic
Research, Inc, pp. 109-132.
Konopczak, Karolina (2013) “The Balassa-Samuelson eﬀect and the channels of its
absorption in the Central and Eastern European Countries,” National Bank of
Poland Working Papers 163, National Bank of Poland, Economic Institute.
Konopczak, Karolina and Andrzej Torój (2010) “Estimating the Baumol-Bowen andBalassa-Samuelson Eﬀects in the Polish Economy - a Disaggregated Approach,”Central European Journal of Economic Modelling and Econometrics, Vol. 2, No.
2, pp. 117-150.
Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu (2002) “Unit Root Tests inPanel Data: Asymptotic and Finite-Sample Properties,” Journal of Econometrics,
Vol. 108, No. 1, pp. 1-24.
Marston, Richard C. (1986) “Real Exchange Rates and Productivity Growth in the
United States and Japan,” NBER Working Papers 1922, National Bureau of Economic Research, Inc.
Nagayasu, Jun (2016) “Regional inflation, spatial location and the BalassaSamuelson eﬀect,” Urban Studies.
Nagayasu, Jun and Ying Liu (2008) “Relative Prices and Wages in China: Evidence
from a Panel of Provincial Data,” Journal of Economic Integration, Vol. 23, pp.
183-203.

13

Ricardo, David (1821) On the Principles of Political Economy and Taxation: McMaster University Archive for the History of Economic Thought, 3rd edition.
Rogoﬀ, Kenneth (1992) “Traded Goods Consumption Smoothing and the Random
Walk Behavior of the Real Exchange Rate,” Monetary and Economic Studies, Vol.
10, No. 2, pp. 1-29.
Samuelson, Paul (1964) “Theoretical Note on Trade Problems,” The Review of Economics and Statistics, Vol. 46, pp. 145-154.
Strauss, Jack (1997) “The influence of traded and nontraded wages on relative prices
and real exchange rates,” Economics Letters, Vol. 55, No. 3, pp. 391-395.
Tica, Josip and Ivo Družić (2006) “The Harrod-Balassa-Samuelson Eﬀect: A Surveyof Empirical Evidence,” EFZG Working Papers Series 0607, Faculty of Economics
and Business, University of Zagreb.
Yamamoto, Shugo (2013) “The Structural Change of Balassa-Samuelson Eﬀects inJapan in 1990s,” Review of Monetary and Financial Studies, Vol. 35, pp. 1-15.

14

Table 1: Part-time Employees Ratio
Max

Min

Mean

MedianStandard deviation

Tradable good(manufacturing)
1970

10.30

0.50

5.30

5.20

1.70

1975

16.20

0.90

8.60

8.50

2.50

1980

15.60

0.70

7.20

6.90

2.60

1985

17.40

0.70

7.70

7.50

2.80

1990

22.90

2.50

9.80

9.70

3.40

1995

26.80

5.10

11.10

10.90

3.90

2000

28.80

4.30

12.50

11.90

4.50

2005

30.20

6.30

13.40

12.90

4.50

2010

38.90

7.00

15.90

15.20

6.30

Non-tradable good(Service, utilities and construction)
1970

14.60

1.70

7.00

7.00

3.60

1975

18.70

2.30

9.40

9.40

4.60

1980

17.90

2.30

9.50

9.20

4.80

1985

21.00

2.20

10.00

9.40

5.50

1990

30.80

3.20

14.10

14.10

7.40

1995

33.30

2.20

16.70

16.90

7.70

2000

37.70

2.90

20.30

19.90

8.60

2005

45.20

3.20

22.80

22.90

10.40

2010

51.20

5.30

24.70

23.40

12.40

Source: RIETI JIP Database 2015, Share of part-time workers (part-time workers / total workers, %)15

Table 2: Sectoral Correspondence(23 sectors)

Japan

US

1

Livestock products

Food and beverage and tobacco products

2

Seafood products

Food and beverage and tobacco products

3

Beverages

Food and beverage and tobacco products

4

Sawing, planing mills and wood

Wood products

5

Furniture and fixtures

Furniture and related products

6

Pulp, paper, paperboard and coated and glazed paper

Paper products

7

Paper products

Paper products

8

Printing, plate making for printing, and bookbinding

Printing and related support activities

9

Rubber products

Plastics and rubber products

10 Final chemical products

Chemical products

11 Fabricated constructional and architectural metal products Fabricated metal products
12 Miscellaneous fabricated metal products

Fabricated metal products

13 General industry machinery

Machinery

14 Special industrial machinery

Machinery

15 Miscellaneous general machinery

Machinery

16 Heavy electrical equipment

Electrical equipment, appliances, and components

17 Electrical equipment, electric measuring

Electrical equipment, appliances, and components

18 Other electrical equipment

Electrical equipment, appliances, and components

19 Motor vehicles

Motor vehicles, bodies and trailers, and parts

20 Motor vehicles, parts and accessories

Motor vehicles, bodies and trailers, and parts

21 Other transportation equipment

Other transportation equipment

22 Precision instruments

Electrical equipment, appliances, and components

23 Plastics

Plastics and rubber products

16

Table 3: Panel Unit Root Tests
lnrer

lnrlp
level

Statistics

p-value

Statistics

p-value

LLC−1.6150∗(0.0532)−4.6045∗∗∗(0.0000)

IPS−2.8288∗∗∗(0.0023)−1.0708(0.1421)

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. A constant is included for variables in levels and in first diﬀerences. In all these tests, the lag lengthis set to 2.

Table 4: Tests for Threshold Eﬀects Using Wage Ratio as Threshold Value
F valuecase 1
Test for Single Thresholdcase 251.51∗∗ (0.0400)

30.62∗ (0.0967)[41.46, 48.88, 65.02][30.40, 35.38, 42.31]

Test for Double Threshold

5.17(0.8367)

11.04(0.6400)[37.31, 43.04, 73.09][25.36, 29.03, 38.99]

Test for Triple Threshold

16.13(0.9933)

6.01(0.9633)[73.23, 78.20, 90.64][30.95, 33.42, 44.94]

Threshold estimates

1.09581.389895% confidence interval[1.0766, 1.1070][1.3757, 1.3939]

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are bootstrap p-values.
3. Values in bracket are the 10%、5% and 1% critical values, respectively.

Table 5: Regression Estimatescase 1case 2ln rlpit I(rwt ≤ γ1 )−0.3132∗∗∗ (0.0342)−0.2912∗∗∗ (0.0362)

ln rlpit I(γ1 < rwt )−0.1477∗∗∗ (0.0257)−0.0220(0.0321)

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are standard error.

17

Table 6: Tests for Threshold Eﬀects Using Ratio of Part-Time Workers as Threshold Value
F value
Test for Single Thresholdcase 1case 262.15(0.2133)

16.38(0.5367)[79.10, 97.10, 118.51][31.10, 36.78, 41.45]

Test for Double Threshold 54.42(0.4667)

12.31(0.6300)[110.13, 139.50, 173.53] [26.45, 29.48, 39.29]
Test for Triple Threshold

12.71(0.8367)

7.52(0.9267)[51.39, 70.03, 119.18][27.65, 33.63, 40.85]

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are bootstrap p-values.
3. Values in bracket are the 10%、5% and 1% critical values, respectively.

Table 7: Robustness Check of Tests for Threshold Eﬀects
F valuecase 1
Test for Single Threshold

59.15∗∗ (0.0267)

case 239.49∗∗ (0.0267)[47.54, 53.45, 77.64] [30.66, 35.12, 43.36]
Test for Double Threshold 8.05(0.8133)

12.82(0.4300)[41.02, 52.46, 64.27] [22.42, 24.95, 36.93]
Test for Triple Threshold

13.24(0.9767)

5.68(0.9667)[63.93, 69.70, 77.74] [36.26, 43.96, 53.28]
Threshold estimates

1.09581.375795% confidence interal[1.0766, 1.1070][1.3693, 1.3802]

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are bootstrap p-values.
3. Values in bracket are the 10%、5% and 1% critical values, respectively.

18

Table 8: Robustness Check of Regression Estimatescase 1case 2ln rlpit I(rwt ≤ γ1 )−0.3351∗∗∗ (0.0377)−0.3240∗∗∗ (0.0411)

ln rlpit I(γ1 < rwt )−0.1687∗∗∗ (0.0287)−0.0286(0.0349)

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are standard error.

Table 9: Robustness Check of Tests for Threshold Eﬀects
F value
Test for Single Thresholdcase 1case 241.81∗∗∗ (0.0100)

51.64∗∗∗ (0.0000)[24.08, 29.80, 41.46] [26.25, 28.98, 40.81]
Test for Double Threshold 21.32(0.3333)

10.14(0.5667)[36.56, 43.54, 59.19] [21.25, 24.67, 30.10]
Test for Triple Threshold

15.21(0.2367)

6.09(0.9033)[19.06, 22.99, 29.63] [23.92, 26.32, 33.69]
Threshold estimates

1.11951.389895% confidence interal[1.0958, 1.1367][1.3777, 1.3928]

1. Here, the ratio of tradable wage to nontradable wage is used as the threshold value.
2. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
3. Values in parenthesis are bootstrap p-values.
4. Values in bracket are the 10%、5% and 1% critical values, respectively.

Table 10: Robustness Check of Regression Estimatescase 1case 2ln rlpit I(rwt ≤ γ1 )−0.1993∗∗∗ (0.0372)−0.3220∗∗∗ (0.0414)

ln rlpit I(γ1 < rwt )−0.0701∗ (0.0422)−0.0344(0.0419)−0.4279∗∗∗ (0.0430)−0.4160∗∗∗ (0.0420)

0.0473∗∗∗ (0.0060)

0.0608∗∗∗ (0.0054)

S
ln rlpUitAP
rgcratioJit

1. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
2. Values in parenthesis are standard error.

19

Table 11: Robustness Check of Tests for Threshold Eﬀects
F value
Test for Single Thresholdcase 1case 259.15(0.2500)

16.06(0.4833)[76.50, 100.74, 124.67][30.70, 33.58, 40.05]

Test for Double Threshold 47.52(0.4700)

11.19(0.6767)[115.81, 135.66, 167.37] [25.33, 30.51, 36.33]
Test for Triple Threshold

11.95(0.9400)

7.43(0.8800)[73.40, 84.72, 116.46][33.38, 41.84, 62.40]

1. Here, the ratio of part-time workers is used as the threshold value.
2. ∗, ∗ ∗ ∗ indicate 10% and 1% significance, respectively.
3. Values in parenthesis are bootstrap p-values.
4. Values in bracket are the 10%、5% and 1% critical values, respectively.

20

Table 12: Threshold Value and Sectoral Relative Wage Ratio

Japan

US

relationship withthreshold value

1 Livestock products

Food and beverage and tobacco products

below

2 Seafood products

Food and beverage and tobacco products

below

3 Beverages

Food and beverage and tobacco productsbelow

4 Sawing, planing mills and wood

Wood products

below

5 Furniture and fixtures

Furniture and related products

below

6 Pulp, paper, paperboard and coated and glazed paper

Paper products

above

7 Paper products

Paper products

below

8 Printing, plate making for printing, and bookbinding

Printing and related support activities

below

9 Rubber products

Plastics and rubber products

below

10 Final chemical products

Chemical productsabove

11 Fabricated constructional and architectural metal products Fabricated metal products

below

12 Miscellaneous fabricated metal products

Fabricated metal products

below

13 Miscellaneous general machinery

Machineryabove

14 Special industrial machinery

Machineryacross15 Miscellaneous general machinery

Machineryacross16 Heavy electrical equipment

Electrical equipment, appliances, and components

across17 Electrical equipment, electric measuring

Electrical equipment, appliances, and components

across18 Other electrical equipment

Electrical equipment, appliances, and components

across19 Motor vehicles

Motor vehicles, bodies and trailers, and parts

above

20 Motor vehicles, parts and accessories

Motor vehicles, bodies and trailers, and parts

across21 Other transportation equipment

Other transportation equipment

above

22 Precision instruments

Electrical equipment, appliances, and components

across23 Plastics

Plastics and rubber products

below

21

Figure 1: Exchange Rate Movements
Nominal Exchange Rate (yen per U.S. Dollar)
300

250

200

150

100

50

0
75M1

80M1

85M1

90M1

95M1

00M1

05M1

10M1

15M1

05M1

10M1

15M1

Real Eﬀective Exchange Rate
60

80

100

120

140

75M1

80M1

85M1

90M1

95M1

00M1

Real Exchange Rate by Sectors
250

rer̲data5[, 2]

200

150

100

50
1980

1985

1990

1995

2000

2005

Source: Bank of Japan22

Figure 2: Movement of Relative Tradable/Non Tradable Labor Productivity in Japan
120

110

100

90

80

70

60
1980

1985

1990

1995

2000

2005

Source: OECD STAN Database for Structural Analysis。
Note: Labor productivity is calculated as Production (gross output), volumes/Number of persons engaged (total employment). We define the relative labor productivity as the ratio of tradable to nontradable labor productivity .

Figure 3: Movement of Nominal Wages
5.0
tradable
nontradable

4.5

4.0

3.5

3.0

2.5

2.0
80

85

90

95

00

Source: OECD STAN Database for Structural Analysis。
注：Wage is calculated as Labour costs (compensation of employees)/Number of persons engaged (total employment).

23

05

Figure 4: Relative Wage Ratio and Threshold Valuecase 11.25
relative wage ratio
threshold value

1.20

1.15

1.10
threshold value

1.05

1.00

0.95
80

85

90

95

00

05

case 2
below the threshold value through the sample period
over the threshold value through the sample period
cross the threshold value within the sample period

2.0

1.5
threshold value

1.0

0.5
1980

1985

1990

24

1995

2000

2005

Figure 5: Movement of the Ratio of Part-time and Non-Regular Employees to Total Employees
25.0
tradable
nontradable
22.5

20.0

17.5

15.0

12.5

10.0

7.5

5.0

2.5

0.0
75

80

85

90

95

00

05

10

Source: RIETI JIP Database 2015, Share of part-time workers (part-time workers / total workers, %)Figure 6: Movement of the Ratio of Part-time and Non-Regular Employees to Total Employees(bysector)3.0

below the threshold value for the relative wage ratio
over the threshold value for the relative wage ratio
cross the threshold value for the relative wage ratio

2.5

2.0

1.5

1.0

80

85

90

95

00

05

Note: The figure plots the ratio of nontradable to tradable industry.

25