A.3. Data

We use the datasets for 22 countries for which all of the below data are available. The 22 countries are South Africa, China, India, Japan, South Korea, Malaysia, Philippines, Taiwan, Thailand, Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, the United States, Denmark, Spain, France, the United Kingdom, Italy, and the Netherlands. Among them, 11 countries are OECD members.

Numerous data sources support the calculations. Sectoral relative prices and relative labor productivity are calculated using the sectoral nominal GDP, real GDP, and employment data from the 10-Sector Database provided by the Groningen Growth and Development Centre. The available data cover the years 1950–2012; however, depending on the country, the periods are different. The 10 sectors are agriculture, mining, manufacturing, utilities, construction, wholesale and retail trade, transport services, business services, government services, and personal services. In accordance with the sectoral assignment by the World Development Indicators (the World Bank) and Inklaar and Timmer [11], which provides the data of absolute relative valueadded prices (PS=PT), agriculture, mining, manufacturing, utilities, and construction combine to categorize the nonservices sector and the other sectors comprise the services sector.

Inklaar and Timmer [11] provided data of absolute relative value-added prices of 2005 for 42 countries. Other countries' data which are not provided by Inklaar and Timmer [11] are obtained by estimation. Absolute relative price of services sector to that of nonservices sector can be linearly estimated by log-transformed per-capita income for sample countries owing to the Balassa-Samuelson effect.

The macro-based data of labor income share, and capital stock are obtained from Penn World Tables version 9.0 (Groningen Growth and Development Centre) and UNCTAD STAT (United Nations Conference on Trade and Development).

For calibration of the distortion parameter (di), we take per-capita income data (x) from Penn World Tables version 9.0 by Groningen Growth and Development Centre. Per-capita income is calculated by dividing expenditure-side real GDP at chained PPPs (in millions 2011 US\$) by population (in millions).

Finally, we demonstrate the comparison results between the analytically calculated variables from the model and the actual data for relative labor income share θLT=θLS ¼ ð Þ 1 � θ<sup>T</sup> = ð Þ 1 � θ<sup>S</sup> , the

Figure 9. The relative labor income share (left) and the capital stock share of services (right).

The Declining Labor Income Shares Revisited: Intersectoral Production Linkage in Global Value Chains

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The Declining Labor Income Shares Revisited: Intersectoral Production Linkage in Global Value Chains http://dx.doi.org/10.5772/intechopen.81316 39

1 þ dS 1 þ dT

applied to all sample countries.

A.3. Data

38 Globalization

OECD members.

the Balassa-Samuelson effect.

population (in millions).

Nations Conference on Trade and Development).

where α assumes a negative value as the degree of the distortion diminishes along economic development. We calibrate values of α and β to dissipate the difference between the simulated results and the real data of relative labor income share and relative capital deepening. The results are α = �0.0001 and β = 0.4. Eq. (11) is called the "implied distortion index" and is

We use the datasets for 22 countries for which all of the below data are available. The 22 countries are South Africa, China, India, Japan, South Korea, Malaysia, Philippines, Taiwan, Thailand, Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, the United States, Denmark, Spain, France, the United Kingdom, Italy, and the Netherlands. Among them, 11 countries are

Numerous data sources support the calculations. Sectoral relative prices and relative labor productivity are calculated using the sectoral nominal GDP, real GDP, and employment data from the 10-Sector Database provided by the Groningen Growth and Development Centre. The available data cover the years 1950–2012; however, depending on the country, the periods are different. The 10 sectors are agriculture, mining, manufacturing, utilities, construction, wholesale and retail trade, transport services, business services, government services, and personal services. In accordance with the sectoral assignment by the World Development Indicators (the World Bank) and Inklaar and Timmer [11], which provides the data of absolute relative valueadded prices (PS=PT), agriculture, mining, manufacturing, utilities, and construction combine to

Inklaar and Timmer [11] provided data of absolute relative value-added prices of 2005 for 42 countries. Other countries' data which are not provided by Inklaar and Timmer [11] are obtained by estimation. Absolute relative price of services sector to that of nonservices sector can be linearly estimated by log-transformed per-capita income for sample countries owing to

The macro-based data of labor income share, and capital stock are obtained from Penn World Tables version 9.0 (Groningen Growth and Development Centre) and UNCTAD STAT (United

For calibration of the distortion parameter (di), we take per-capita income data (x) from Penn World Tables version 9.0 by Groningen Growth and Development Centre. Per-capita income is calculated by dividing expenditure-side real GDP at chained PPPs (in millions 2011 US\$) by

Finally, we demonstrate the comparison results between the analytically calculated variables from the model and the actual data for relative labor income share θLT=θLS ¼ ð Þ 1 � θ<sup>T</sup> = ð Þ 1 � θ<sup>S</sup> , the

categorize the nonservices sector and the other sectors comprise the services sector.

<sup>¼</sup> exp <sup>α</sup><sup>x</sup> <sup>þ</sup> <sup>β</sup> (11)

Figure 9. The relative labor income share (left) and the capital stock share of services (right).

(2008 release) for South Korea, the CIP database 2015 (RIETI) for China, and the STAN database (OECD) for Mexico. Due to data constraints, we compare actual and model-derived series only of relative labor income share for Mexico. We find that the model with the distortion factor can

The Declining Labor Income Shares Revisited: Intersectoral Production Linkage in Global Value Chains

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41

Department of Economics, School of Political Science and Economics, Tokai University,

[1] ILO and OECD. The labor share in G20 economies. In: Report Prepared for the G20

[2] Brada JC. The distribution of income between labor and capital is not stable: But why is

[3] Young AT, Tackett MY. Globalization and the decline in labor shares: Exploring the relationship beyond trade and financial flows. European Journal of Political Economy.

[4] Acemoglu D. Directed technical change. The Review of Economic Studies. 2002;69:781-809 [5] Alvarez-Cuadrado F, Long NV, Poschke M. Capital-labor substitution, structural change,

[6] Dao MC, Das M, Koczan Z, Lian W. Why is labor receiving a smaller share of global income? Theory and empirical evidence. In: IMF Working Paper WP/17/169. 2017

[7] Lane PR, Milesi-Ferretti GM. The external wealth of nations revisited: International financial integration in the aftermath of the global financial crisis. IMF Economic Review. 2018;

[8] Elsby MWL, Hobijn B, Şahin A. The decline of the U.S. labor share. Brookings Papers on

[9] Takeuchi F. Industrial structural change and productivity growth in the era of global value chains (GVCs). In: Kabe S, Ushiyama R, Kinkyo T, Hamori S, editors. Moving up the Ladder: Development Challenges for Low & Middle Income Asia, Chapter 3. Singapore: World

explain the actual data well in almost all countries.

Address all correspondence to: ftake@tokai.ac.jp

Employment Working Group. Antalya, Turkey; 2015

and growth. Theoretical Economics. 2017;12:1229-1266

that so and why does it matter? Economic Systems. 2013;37:333-344

Author details

Fumihide Takeuchi

References

2018;52:18-35

66:189-222

Economic Activity. 2013;Fall:1-63

Scientific Publishing; 2016. pp. 41-59

Hiratsuka-shi, Kanagawa, Japan

Figure 10. The relative labor income share (left) and the capital stock share of services (right).

capital stock share of services (KS=K) to ascertain the usefulness of the model. Figure 9 indicates the comparison results for the United States, the United Kingdom, France, Italy, and Denmark. Actual data are obtained from EU KLEMS (The Conference Board). The data of these five countries are available in the newest version of EU KLEMS (September 2017 release). Figure 10 makes comparisons in the same manner for Japan, South Korea, China, and Mexico, using different data sources, including the STAN database (OECD), the JIP database (RIETI), and EU KLEMS (2012 release) for Japan, the World KLEMS, the STAN database (OECD), and EU KLEMS (2008 release) for South Korea, the CIP database 2015 (RIETI) for China, and the STAN database (OECD) for Mexico. Due to data constraints, we compare actual and model-derived series only of relative labor income share for Mexico. We find that the model with the distortion factor can explain the actual data well in almost all countries.
