Publications by Type: Book Chapter

2019
Jorgenson, Dale W., Mun S. Ho, and Jon D. Samuels. “Educational Attainment and the Revival of U.S. Economic Growth.” In Education, Skills, and Technical Change: Implications for Future U.S. GDP Growth, edited by Charles R. Hulten and Valerie A. Ramey, 23-60. Chicago: University of Chicago Press, 2019. Publisher's VersionAbstract

Labor quality growth captures the upgrading of the labor force through higher educational attainment and greater experience. We find that average levels of educational attainment of new entrants remain high, but will no longer continue to rise. Growing educational attainment will gradually disappear as a source of U.S. economic growth. Our second finding is that the investment boom of 1995-2000 drew many younger and less-educated workers into employment. Employment rates for these workers declined during the recovery of 2000-2007 and dropped further during the Great Recession of 2007-2009. Our third finding is employment rates for less-educated workers are unlikely to recover the peak levels that followed the investment boom of 1995-2000. In order to assess the prospects for recovery of employment rates as a potential source U.S. economic growth, we project these rates of each age-gender-education group. JEL No. E01,E24,O4,O47

2018
Jorgenson, Dale W.The Growth of the World Economy.” In Productivity Dynamics in Emerging and Industrialized Countries, edited by Deb Kusum Das, 37-57. London: Routledge, Taylor and Francis, 2018.Abstract

Introduction

The World KLEMS Initiative was established at the First World KLEMS Conference, held at Harvard University in August 2010[i]. The purpose of the Initiative is to generate industry-level datasets, consisting of outputs and inputs of capital (K) and labor (L), together with inputs of energy (E), materials (M), and services (S). Productivity for each industry is defined as output per unit of all inputs. These datasets provide a new framework for analyzing the sources of economic growth at the industry and aggregate levels for countries around the world. This framework has closed a critical gap in systems of national accounts.

Growth of output, inputs, and productivity at the industry level is important for understanding changes in the structure of an economy and the contributions of different industries to economic growth. International comparisons of differences in productivity levels based on purchasing power parities of outputs and inputs at the industry level provide a second focus for industry-level productivity research. These comparisons are essential in assessing changes in comparative advantage and formulating strategies for economic growth.

The EU (European Union) KLEMS study provides industry-level datasets on the sources of growth for 25 of the 27 EU member countries[ii]. These datasets are essential for analyzing the slowdown in European economic growth that preceded the current financial and fiscal crisis. The datasets and results were presented at the Final EU KLEMS Conference in Groningen, The Netherlands, in June 2008[iii]. Marcel P. Timmer, Robert Inklaar, Mary O’Mahony, and Bart van Ark (2010) describe the datasets and analyze the sources of economic growth in Europe at the industry level.

The EU KLEMS project also included datasets for Australia, Canada, Japan, Korea, and the United States. Matilde Mas and Robert Stehrer (2012) present international comparisons within Europe and between Europe and the advanced economies in Asia and North America. As European policy-makers focus on removing barriers to the revival of economic growth, international differences in the sources of growth have become central in understanding the impacts of changes in economic policy.

The EU KLEMS project identified the failure to develop a knowledge economy as the most important source of the slowdown in European economic growth. Development of a knowledge economy will require investments in human capital, information technology, and intellectual property. An important policy implication is that extension of the single market to the services industries, which are particularly intensive in the use of information technology, will be essential for the removal of barriers to the knowledge economy.

The Second World KLEMS Conference was held at Harvard University on August 2012[iv]. The conference included reports on recent progress in the development of industry-level datasets, as well as extensions and applications.[v] Regional organizations in Asia and Latin America have now joined the European Union in supporting research on industry-level datasets. Due to the growing recognition of the importance of these datasets, an effort is underway to extend the new framework to emerging and transition economies, such as Brazil, China, India, and Russia. 

LA-KLEMS, the Latin American Chapter of the World-KLEMS Initiative, was established in December 2009 at a conference at ECLAC, the Economic Commission for Latin America and the Caribbean, in Santiago, Chile[vi]. This Chapter is coordinated by ECLAC and includes seven research organizations in four leading Latin American countries – Argentina, Brazil, Chile, and Mexico.[vii] Mario Cimoli, Andre Hofman, and Nanno Mulder (2010) have summarized the results of the initial phase of the LA-KLEMS project.

A detailed report on Mexico KLEMS was published in 2013 by INEGI, the National Institute of Statistics and Geography.[viii] This was presented at an international seminar at the Instituto Techologico Autonoma de Mexico (ITAM) in Mexico City on October 2013[ix]. Mexico KLEMS includes a complete industry-level productivity database for 1990-2011 that is integrated with the Mexican national accounts. This database will be updated annually. A very important finding is that productivity has not grown in Mexico since 1990. Periods of positive economic growth have been offset by the negative impacts of the Mexican sovereign debt crisis of 1995, the U.S. dot-com crash in 2000, and the U.S. financial and economic crisis of 2007-2009.

Asia KLEMS, the Asian Chapter of the World KLEMS Initiative, was founded in December 2010 and the first Asia KLEMS Conference was held at the Asian Development Bank Institute in Tokyo in July 2011[x]. Asia KLEMS includes the Japan Industrial Productivity database[xi], the Korean Industrial Productivity database[xii], and the China Industrial Productivity database[xiii]. Industry-level databases have been constructed for Taiwan and work is underway to develop a similar database for Malaysia. These databases were discussed at the Second Asia KLEMS Conference, held at the Bank of Korea in Seoul in August 2013[xiv].

Kyoji Fukao (2012, 2013) has employed the JIP data base in analyzing the slowdown in productivity growth in Japan after 1991, now extending into the Two Lost Decades. The initial downturn in productivity growth followed the collapse of the “bubble” in Japanese real estate prices in 1991. A brief revival of productivity growth after 2000 ended with the sharp decline in Japanese exports in 2008-2009. This followed the rapid appreciation of the Japanese yen, relative to the U.S. dollar, after the adoption of a monetary policy of quantitative easing by the Federal Reserve, the U.S. central bank. When the Bank of Japan failed to respond, Japan experienced a much more severe downturn in productivity growth and a larger decline in output than the U.S. in the aftermath of the financial and economic crisis of 2007-2009.

The Third World KLEMS Conference was held in Tokyo in May 2014[xv]. This conference, discussed industry-level datasets for more than 40 countries, including those that participate in the three regional organizations that make up the World KLEMS Initiative – EU KLEMS in Europe, LA KLEMS in Latin America, and Asia KLEMS in Asia. In addition, the conference considered research on linking datasets for the 40 countries through the World Input-Output Database (WIOD)[xvi]. Another important theme of the conference was the extension of the measurement of capital inputs to include intangibles, such as human capital and intellectual property, as well as the familiar tangible assets – plant, equipment, and inventories.

Linked data sets are especially valuable in analyzing the development of global value chains in Asia, North America, and Europe. For this purpose international trade can be decomposed by tasks performed at each link of the value chain. Trade in tasks can be compared with trade in commodities, which involves “double-counting” of intermediate goods as products pass through the value chain. A central finding is that regional value chains are now merging into global value chains involving all the major countries in the world. The World Input-Output Database is now undergoing a substantial expansion at the OECD with support from the World Trade Organization[xvii].

The Third World KLEMS Conference included reports on new industry-level data sets for India and Russia. Russia KLEMS was released in July 2013 by the Laboratory for Research in Inflation and Growth at the Higher School of Economics in Moscow[xviii]. Russia’s recovery from the sharp economic downturn that followed the dissolution of the Soviet Union and the transition to a market economy has been very impressive. Surprisingly, increases in productivity growth widely anticipated by observers inside and outside Russia have characterized only the service industries, which were underdeveloped under central planning. Mining industries have attracted large investments, but these have not been accompanied by gains in efficiency. The recent collapse in world oil prices poses an important challenge for the future growth of the Russian economy.

The India KLEMS database was released in July 2014 by the Reserve Bank of India[xix], shortly after the Third World KLEMS Conference in Tokyo. This database covers 26 industries for the period 1980-2011. Beginning in the 1980’s, liberalization of the Indian economy has resulted in a gradual and sustained acceleration in economic growth and a transfer of resources from agriculture and manufacturing to the service industries. The most surprising feature of the acceleration in Indian economic growth has been the stagnant share of manufacturing and the rapid growth in the share of services. Given the shrinking share of agriculture and the size of the Indian agricultural labor force, another surprise is that growth of capital input has been the most important source of growth in manufacturing and services, as well as more recently in agriculture.

 

[i] For the program and participants see: http://www.worldklems.net/conference1.htm

[ii] Updated data are available for the EU countries are posted on the EU KLEMS website: http://www.euklems.net/eukNACE2.shtml

[iii] For the program and participants see: http://www.euklems.net/conference.html

[iv] For the program and participants see: http://www.worldklems.net/conference2.htm

[v] The conference program and presentations are available at: http://www.worldklems.net/conference2.htm

[vi] For the program and participants see: http://www.cepal.org/de/agenda/8/38158/Agenda.pdf

[x] For the program and participants see: http://asiaklems.net/conferences/conferences.asp Asia KLEMS was preceded by International Comparison of Productivity among Asian Countries (ICPAC). The results were reported by Jorgenson, Kuroda, and Motohashi (2007).

[xi] http://www.rieti.go.jp/en/database/JIP2014/index.html Data are available for 108 industries covering the period

1070-2011.

[xiv] For the program and participants see: http://asiaklems.net/conferences/conferences.asp

chapter_1-routledge_taylor_francis_india.pdf
Jorgenson, Dale W., and Khuong M. Vu. “Total Factor Productivity and the Sources of Singapore’s Economic Growth: Measurement, Insights, and Consequences.” In Productivity Dynamics in Emerging and Industrialized Countries, edited by Deb Kusum Das, 275-312. London: Routledge, Taylor and Francis, 2018.Abstract
Singapore has been a focal point in the debate on the East Asian growth model, in which total factor productivity growth (TFPG) is unusually low relative to remarkable output growth. We use a rigorous growth accounting framework to decompose the sources of Singapore’s growth, including information technology and labor quality. Our results show that TFPG in Singapore for long periods was as low as 0.5-0.6 per cent, which verifies Singapore’s low TFPG. However, we found that Singapore’s low TFPG was caused not by a steady low TFPG pattern but by its acute vulnerability to external shocks, which causes TFPG to plummet in periods of turmoil. Singapore’s vulnerability to external shocks is due to its large export-reliant manufacturing sector and small domestic market. Our results help to further advance our understanding about Singapore’s growth model from previous studies, such as Young (1992, 1995), Kim and Lau (1994), and Hsieh (2002). We predict that, based on our base-case projection results, Singapore’s growth over the next decade will be at 2.35 per cent for labor productivity and 3.10 per cent for GDP relative to the respective rates of 2.52 per cent and 5.45 per cent, for the period of 1998-2008.
Jorgenson, Dale W.The World KLEMS Initiative: Measuring Productivity at the Industry Level.” In The Oxford Handbook of Productivity Analysis, edited by Emili Grifell-Tatje, C.A.Knox Lovell, and Robin C. Sickles, 1:663-698. New York: The Oxford University Press, 2018.Abstract

The World KLEMS Initiative was established at the First World KLEMS Conference at Harvard University in August 2010[i]. The purpose of this Initiative is to generate industry-level data on outputs, inputs, and productivity. Productivity is defined as output per unit of all inputs. The inputs consist of capital (K) and labor (L), the primary factors of production, and intermediate inputs of energy (E), materials (M), and services (S). The acronym KLEMS describes these inputs.  Industry-level data have been proved to be indispensable for analyzing the sources of economic growth for countries around the world.

International productivity comparisons are the second focus of industry-level productivity research. Productivity gaps between two countries are defined in terms of differences in productivity levels. These differences are measured by linking the productivity levels for each country by purchasing power parities for inputs and outputs. As an example, the purchasing power parity for Japan and the U.S. is defined as the price in Japan, expressed in yen, relative to the price in the U.S., expressed in dollars. Purchasing power parities can be defined in this way for commodities, industries, or aggregates like the GDP. Productivity gaps are essential for assessing competitive advantage and designing strategies for economic growth.

We review productivity measurement at the industry level in Section 2. The landmark EU (European Union) KLEMS study was initiated in 20003 and completed in 2008. This study  provided industry-level data sets for the countries of the European Union. These data have proved to be invaluable for analyzing the slowdown in European economic growth. The EU KLEMS study also included data for Australia, Canada, Japan, Korea, and the United States. These data have been widely used for international comparisons between European countries and the leading industrialized countries of Asia and North America.

Regional organizations – LA KLEMS in Latin America and Asia KLEMS in Asia – have joined the European Union in supporting industry-level research on productivity. The Latin American affiliate of the World KLEMS Initiative, LA KLEMS, was established in 2009 at the Economic Commission for Latin American and the Caribbean (ECLAC) in Santiago, Chile. The Asian affiliate, Asia KLEMS, was founded at the Asian Development Bank Institute (ADBI) in Tokyo in 2010. The regional organizations have stimulated the development of industry-level productivity measures for the emerging economies of Asia and Latin America, such as Brazil, China, and India, as well as measures for the advanced economies of Asia, Europe, and North America.

In Section 3 we present the KLEMS framework for productivity measurement for a single country. Development of this framework within the national accounts has the important advantage that official measures can be generated at regular intervals in a standardized format.[ii] The production account in current prices contains nominal outputs and incomes, while the production account in constant prices provides real outputs and inputs, as well as productivity. Paul Schreyer’s (2001) OECD Productivity Manual provided methods for productivity measurement within the national accounts.

A key feature of the KLEMS framework is a constant quality index of labor input that combines hours worked for different types of labor inputs by using labor compensation per hour as weights. Similarly, a constant quality index of capital input deals with the heterogeneity among capital services by using rental prices of these services as weights. Schreyer’s (2009) OECD Manual, Measuring Capital, presented methods for measuring capital services. Finally, inputs of energy, materials and services are generated from a time series of input-output tables in current and constant prices.

In 2008 the Advisory Committee on Measuring Innovation in the 21st Century to the U.S. Secretary of Commerce recommended that productivity data be incorporated into the U.S. national accounts. This was successfully completed by the Bureau of Economic Analysis (BEA), the agency responsible for the U.S. national accounts, and the Bureau of Labor Statistics (BLS), the agency that produces industry-level measures of productivity for the U.S. Susan Fleck, Steven Rosenthal, Matthew Russell, Erich Strassner, and Lisa Usher (2014) published an integrated BEA/BLS industry-level production account for the U.S. for 1998-2009 in Jorgenson, Landefeld, and Schreyer (2014).

In Section 4 we illustrate the KLEMS methodology for a single country by summarizing the industry-level productivity data for the United States for the period 1947-2012 compiled by Jorgenson, Ho, and Samuels (2016). We analyze the sources of U.S. economic growth for three broad periods: the Postwar Recovery of 1947-1973, the Big Slump of 1973-1995, following the energy crisis of 1973, and the period of Growth and Recession, 1995-2012. To provide more detail on the period of Growth and Recession, we analyze the sources of growth for the sub-periods 1995-2000, 2000-2007, and 2007-2012 – the Investment Boom, the Jobless Recovery, and the Great Recession.

In Section 5 we introduce the KLEMS framework for international comparisons by presenting price level indices and productivity gaps. The price level index is an indicator of international competitiveness, often expressed as over- or undervaluation of currencies. A specific example is the over- or undervaluation of the Japanese yen relative to the U.S. dollar.
The price level index for Japan and the United States compares market exchange rates with purchasing power parities for the GDP.

The productivity gaps between Japan and the U.S. are indicators of the relative efficiency of two countries in transforming inputs into outputs. To measure these productivity gaps we first construct comparable measures of productivity. We then link the U.S. and Japanese outputs and inputs at the industry level by means of purchasing power parities. As an illustration, the U.S. productivity data presented in Section 4 for 1947-2012 have been linked to comparable Japanese productivity data for 1955-2012 by Jorgenson, Nomura, and Samuels (2016).           

The international comparisons between Japan and the U.S. presented in Section 6 are based on industry-level purchasing power parities. These comparisons provide important information on the valuation of the Japanese yen relative to the U.S. dollar. The yen was under-valued from 1955 until the Plaza Accord of 1985. This enabled Japan to achieve a high level of international competitiveness, despite a large productivity gap with the United States. Since 1985 the yen has been over-valued, relative to the dollar, reaching a peak in 1995 that greatly undermined Japanese competitiveness. The yen finally achieved purchasing power parity with the dollar only in 2015, restoring Japanese international competitiveness after several years of monetary policies based on quantitative easing by the Bank of Japan.

The large productivity gap between Japan and the United States that existed in 1955 gradually closed until the end of the “bubble economy” in Japanese real estate in 1991. Since that time Japanese productivity has been stagnant, while productivity in the U.S. has continued to rise. The widening productivity gap can be traced to a relatively small number of industrial sectors in Japan, mainly in trade and services, but also including agriculture. Productivity gaps for Japanese manufacturing industries have remained relatively small. This has created opportunities for formulating a Japanese growth strategy based on stimulating productivity growth in the lagging industrial sectors. Section 7 presents our conclusions.

 

[i] For the program and participants see: http://www.worldklems.net/conference1.htm

[ii] The World KLEMS website presents data sets in a common format for many of the participating countries. See: http://www.worldklems.net/data.htm

oxford_chapter_21_klems_16_07_20_with_figures_tables_1.pdf
2016
Jorgenson, Dale W., Koji Nomura, and Jon D. Samuels. “A Half Century of Trans-Pacific Competition: Price Level Indices and Productivity Gaps for Japanese and US Industries, 1955-2012.” In The World Economy: Growth or Stagnation? 469-507. Cambridge, UK: Cambridge University Press, 2016. Publisher's Version
Jorgenson, Dale W., Mun S. Ho, and Jon D. Samuels. “US Economic Growth -- Retrospect and Prospect: Lessons from a Prototype Industry-Level Production Account for the US, 1947-2012.” In The World Economy: Growth or Stagnation, 34-69. Cambridge, UK: Cambridge University Press, 2016. Publisher's Version
Jorgenson, Dale W.The New World Order.” In The World Economy: Growth or Stagnation? 1-33. Cambridge, UK: Cambridge University Press, 2016.
2015
Jorgenson, Dale W., Mun S. Ho, and Jon D. Samuels. “The Outlook for U.S. Economic Growth.” In Understanding the Growth Slowdown, edited by Brink Lindsey, 11-30. Washington, DC: The Cato Institute Press, 2015.
2013
Jorgenson, Dale W, Jing Cao, and Mun S Ho. “The Economics of Environmental Policies in China.” In Clearer Skies over China, 329-372. Cambridge, MA: The MIT Press, 2013. Publisher's Version
Jorgenson, Dale W, Jing Cao, and Mun S Ho. “Economic-Environmental Model of China, Appendix A.” In Clearer Skies over China, 375-392. Cambridge, MA: The MIT Press, 2013. Publisher's Version
2012
Jorgenson, Dale W., Jing Cao, and Mun S. Ho. “An Integrated Assessment of the Economic Costs and Environmental Benefits of Pollution and Carbon Control.” In The Chinese Economy: A New Transition, 231-258. New York: Palgrave Macmillan, 2012. Publisher's VersionAbstract

Concerns over energy security and domestic air quality have led the Chinese government to reduce the country’s overwhelming dependence on fossil fuels and to shift to a more energy- and resource-efficient development trajectory. Considering the international climate negotiations, this goal now has added emphasis on carbon intensity. The 11th Five-year Plan (FYP) set explicit targets for energy efficiency and pollutant emissions and this has led to a number of ambitious implementing measures. The government recently also set a carbon intensity target for 2020: reducing it by 40–45 per cent compared with the 2005 carbon emissions:GDP ratio. Despite the current global economic slowdown, and partly due to the strong fiscal stimulus in 2009, the growth of the Chinese economy and its resource demands are so swift that they are overwhelming many of these efforts, most notably in emissions of carbon dioxide (CO2), the leading greenhouse gas (GHG).

This research is supported by the Energy Foundation, Harvard China Fund, National Science Foundation of China (Project Code: 70803026 and 71173130) and Tsinghua University Initiative Scientific Research Program. The research is part of a larger study developed and conducted under the China Project, School of Engineering and Applied Sciences, Harvard University, in partnership with Tsinghua University researchers. This paper includes material from Yu Lei, Chris Nielsen, Yuxuan Wang and Yu Zhao, and we also thank Nielsen for comments on this paper.

Jorgenson, Dale W. “Introduction.” In Handbook of Computable General Equilibrium Modeling, edited by Peter B Dixon and Dale W Jorgenson, 1-22. Amsterdam: Elsevier, 2012.
Jorgenson, Dale W, Richard J Goettle, Mun S Ho, and Peter J Wilcoxen. “Energy, The Environment, and U.S. Economic Growth.” In Handbook of Computable General Equilibrium Modeling, edited by Peter B Dixon and Dale W Jorgenson, 477-552. Amsterdam: Elsevier, 2012. PDF
Jorgenson, Dale W. “An Economic Approach to General Equilibrium Modeling.” In Handbook of Computable General Equilibrium Modeling, edited by Peter B Dixon and Dale W Jorgenson, 1133-1212. Amsterdam: Elsevier, 2012. Website
Jorgenson, Dale W, Mun S Ho, and Jon D Samuels. “Information Technology and U.S. Productivity Growth.” In Industrial Productivity in Europe, edited by Matilde Mas and Robert Stehrer, 35-64. Northampton MA: Edward Elgar, 2012.
Jorgenson, Dale W, and Kun-Young Yun. “Taxation, Efficiency, and Economic Growth.” In Handbook of Computable General Equilibrium Modeling, edited by Peter B Dixon and Dale W Jorgenson, 659-742. Amsterdam: Elsevier, 2012. Publisher's Version jorgenson_yun_tax_policy_and_u_s_economic_growth.pdf
2010
Jorgenson, Dale W, and Khuong Vu. “Latin America and The World Economy.” In Innovation and Economic Development, edited by Mario Cimoli, Andre A Hofman, and Nanno Mulder, 19-53. Northampton MA: Edward Elgar, 2010. PDF
2009
Jorgenson, Dale. “Introduction.” In The Economics of Productivity, edited by DW Jorgenson, ix-xxviii. Northampton MA: Edward Elgar, 2009.
Jorgenson, Dale, and Mark Blaug. “Economics of Productivity.” In The International Library of Critical Writings in Economics. Northampton: Edward Elgar, 2009. PDF
2007
Jorgenson, Dale, and Mun S Ho. “Policies to Control Air Pollution Damages.” In Clearing the Air: The Health and Economic Damages of Air Pollution in China, Cambridge, edited by Mun S Ho and Chris P Nielsen, 331-372. Cambridge: The MIT Press, 2007.

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