There are many different productivity measures for different purposes and policy makers and other users are not always aware of the conceptual and empirical reasons for differences between them. Productivity is a key indicator in the assessment of economic performance and a growing number of statistical offi ces in the OECD area have recently become engaged in the measurement of productivity. This work is raising many new questions for measurement, including the possible approaches to developing measures of aggregate productivity performance, as well as issues related to productivity measurement in specifi c sectors of the economy. Some of these measurement issues, especially those related to the measure of capital services, have been taking into account in the current process of revision of the System of National Accounts (SNA).
OECD Workshops on Productivity Analysis and Measurement
In the section below, we discuss the role of economic theory in providing solutions to some of the diffi cult problems that arise in the measurement of productivity.6 In the third section, we list some 12 measurement problems where further research is required in order to form a consensus on how to "best" solve these problems. The last section concludes with 5 recommendations for the OECD on the way forward.
Productivity Growth and Innovation in OECD
There has been renewed divergence of GDP per capita among OECD countries over the past decade: Whereas the relatively less advanced countries tended to catch up with the leader, the US, from the late 1940s to the late 1980s, the situation has reversed since the mid-1990s. While GDP growth was accelerating in the US, it was just slowing down in most countries of Europe and in Japan. It tended to slow down again in the 2000s in the US, but also in Europe.
The Role of ICT on the Spanish Productivity Slowdown
Spain and most of the rest of the European Union (EU) countries have experienced a productivity slowdown since the middle of the nineties. During the same period, the United States (US) showed an upsurge of productivity that lasted until now. Information and Communication Technologies (ICT) were soon identifi ed as a major force in the reversal of the productivity slowdown in the US63. In contrast, no strong evidence in this direction is still available for most of the EU countries. Many studies concentrate on the aggregate behaviour –referring either to total output or to business sector output. However, it became soon evident that at least a distinction should be drawn between ICT producing sectors and the rest of the economy. Particularly, for those countries without a strong ICT production sector, the classifi cation of the different industries according to the intensity of their ICT use was a great step forward.
Multi-Factor Productivity Measurement
During the past 15 years, the Swiss economy faced sluggish growth and a rise of its unemployment rate. While still low compared to other countries the rise of unemployment triggered political discussions about the relative competitiveness of the Swiss economy. Much attention was then devoted to measurement issues of labor productivity. This focus on labor productivity partly resulted from a lack of data on capital stocks and multifactor productivity (MFP). Another factor was the lack of experience of countries like Switzerland regarding measurement issues and interpretation of results of capital stocks and MFP. In this context, the Organisation for Co-operation and Development (OECD) was a key driver when it published two manuals64 describing the concept and measurement of capital services and their relation to the measures of gross capital stock.
Innovation and Labour Productivity Growth in Switzerland
This study investigates the determinants of labour productivity growth of Swiss fi rms in the period 1994–2002 particularly emphasizing the role of innovation activities. Thus, the main research question pursued is: to what extent do different types of fi rm-level innovations affect labour productivity of fi rms in Switzerland? This is a question of particular interest for Swiss policy-makers in the light of the unsatisfactory growth performance of the Swiss economy in the 1990s (see Federal Department for Economic Affairs 2002). Most observers consider the low growth of labour productivity as the main single factor for explaining this unfavourable performance as measured by GDP growth. Labour productivity depends on physical and human capital as main production factors as well as on new knowledge and innovation. Economies that develop more and more in the direction of a "knowledge-based economy" are relying increasingly on technological innovation. Hence, it is important to gain some insights with respect to the (quantitative) relationship between innovation and economic performance. A better understanding of the relative importance of the factors determining productivity growth could contribute to an explanation of the low productivity growth of the Swiss economy in the 1990s.
On the Importance of Using Comparable Labour Input to Make International Comparison of Productivity Levels
In 2005, Statistics Canada’s Canadian Productivity Accounts released two studies that, for the fi rst time, examined the comparability of labour productivity levels between Canada and the United States.83 Previously, Statistics Canada limited comparisons to productivity growth rates. Using analogous sources, concepts and methods to obtain the most comparable measure possible of productivity levels, these new studies found that the Canada–U.S. productivity level difference was lower than normally described.
Labour Productivity Based on Integrated Labour Accounts
In recent years more attention has been focused on empirical analyses of economic performance. As a consequence of this compilation of productivity growth and pro ductivity levels has been common. These estimations are conducted by a number of different organisations, agencies, institutions, et cetera, but productivity estimates are often based on different data materials, depending on the researcher’s choice.
Are those Who Bring Work Home Really Working Longer Hours?
Advancements in information technology have increased workers’ abilities to conduct their jobs in multiple locations. An ongoing debate surrounding U.S. Bureau of Labor Statistics (BLS) productivity data is that offi cial productivity numbers may be overstated because of an increase in unmeasured hours worked outside the traditional workplace. To shed light on this debate, this paper examines two recent data sources for information on U.S. workers who bring work home from their primary workplace – the 2003 – 2006 American Time Use Survey (ATUS) and the 1997, 2001, and 2004 May Current Population Survey Work Schedules and Work at Home Supplements (CPS Supplement). The ATUS provides detailed information on time spent on work, work-related activities, and non-work activities on one diary day, as well as locations for these activities. The CPS Supplements provide information on the number of hours worked at home each week, whether or not workers had a formal arrangement to be paid for work at home, and reasons for working at home.
Main Sources of Quarterly Labour Productivity Data for the Euro Area
Labour productivity and its measurement is an important issue for the European Central Bank (ECB). Growth in productivity is key for non-infl ationary growth. In addition to structural (annual) data, the ECB requires relatively highly aggregated and timely data on productivity growth for short-term economic analysis. The ECB has for several years calculated euro area productivity estimates and published them in its Monthly Bulletin. The calculation used is GDP per person employed, taken from the European System of Accounts 1995 (ESA95) national accounts. While this calculation is acknowledged to be a less than perfect measurement, there is a scarcity of other suitable data for the euro area, especially data fulfi lling the timeliness requirement. The ECB also uses a number of supplementary euro area productivity indicators from both quantitative and qualitative surveys, which are explained in this note.
U.S. Quarterly Productivity Measures
Since 1967, the Bureau of Labor Statistics (BLS) has regularly published quarterly data on the change in labor productivity. Data on labor productivity and unit labor costs, together with related measures, are published on a very timely basis eight times per year in the form of a "Productivity and Costs" press release.157 The initial data for a quarter are released shortly after publication of the advance gross domestic product (GDP) data by the Bureau of Economic Analysis (BEA) at the end of the month following the close of the quarter. Revised productivity and costs measures are released the following month after BEA’s publication of the "preliminary" GDP data. There is no release in the third month because changes in the data usually are minimal. Historical data are made available on the BLS website and in other formats upon request.
Labour Input Productivity
In the second half of the 1990s, Italy has had a relevant increase of the labour utilisation but the intensity of the growth rates differ in relation to the labour input measure chosen. In particular, the growth rates of the persons employed go faster that those ones of the full-time equivalent units, that represent a proxy of the amount of hours worked. At the same time, data shows that production trend follows the employment time profi le only in some years.
Changes in Human Capital
Productivity growth is the main source of increases in economic welfare, as measured by real output per capita, in the long run. In this respect, the recent evolution of euro area productivity growth has been disappointing. In particular, the euro area has experienced a sustained decline in labour productivity growth since the 1980s. Existing analysis of the causes of this decline suggests that lower productivity growth has been due to both a decline in capital deepening and lower total factor productivity (TFP) growth over this time period (see for example Gomez-Salvador et al., 2006). However, the same analysis suggests that over the last ten years, the observed slowdown in capital deepening appears to be linked mainly to stronger employment growth. Robust euro area employment growth in the late 1990’s together with economic policies aimed at encouraging employment of lower skilled workers in many euro area countries may also have resulted in a shift in the composition of the workforce towards workers with lower human capital. If this were the case, the sustained decline in euro area labour productivity growth could, in part, also refl ect a lower contribution of labour quality growth to labour productivity growth. Standard unadjusted measures of labour input used so far in analysing euro area productivity growth ignore changes in human capital – changes in average labour quality – leading to an underestimation of the contribution of the labour input to economic growth. Best practise in the area of productivity measurement suggests instead that changes in labour quality should be taken into account by using a quality-adjusted number of hours actually worked as a measure of labour input (OECD, 2001).
International Comparisons of Levels of Capital Input and Multi-Factor Productivity
International comparisons of levels of labour and capital inputs, outputs and productivity tend to receive a great deal of attention because they respond directly to policy-makers’ and analysts’ interest in measuring competitiveness, economic well-being of countries’ inhabitants and the intensity by which resources are used. Generally, such level comparisons are more diffi cult to put in place than comparisons of growth rates: data sources are more susceptible to problems of international comparability (Ahmad et al. 2004), and spatial price indices are required to account for differences in the levels of input or output prices.
Research and Development as a Value Creating Asset
In the current environment of rapid technological change, research and development (R&D) has proven to be an important element of economic growth. R&D is considered one of a number of measures of innovation performance and various studies have shown that investment in R&D is an important source of productivity growth (for example Griliches, 1981). R&D investment reduces production costs, as inputs are more effectively transformed in to outputs, and it alters output characteristics, thereby providing new products to the marketplace (Bernstein and Mamuneas, 2004). As a result, the promotion of investment in R&D has become a priority within the EU.
Empirical Analysis of the Effects of R&D on Productivity
There is little, if any, dispute that R&D is a major source of long-term productivity growth. But there is empirical uncertainty about the magnitude of the productivity gains from R&D. This quantitative uncertainty was again highlighted in a study by two colleagues at the Productivity Commission (Shanks and Zheng 2006).224 They set out to update and extend previous time-series analysis of the effects of R&D on Australia’s productivity performance.225 Previous studies had generated estimates of returns to Australian R&D that seemed implausibly high – a result that is not uncommon in this type of analysis, irrespective of country of investigation (Diewert 2005). With the possibility that limitations on degrees of freedom had been an issue in the previous studies, it was judged that new analysis based on a further 10 years or so of data, plus developments in quantitative tests and techniques, could provide a clearer fi x on the effects of domestic and foreign R&D on Australian productivity performance. As it turned out, the modelling results were fragile – and more so than expected. Estimates of performance effects fell within wide confi dence intervals and were sensitive to seemingly reasonable modifi cations to variable and model specifi cations. Diagnostic tests revealed standard estimating equations to be mis-specifi ed.
Infrastructures and New Technologies
The paper revises the impact of infrastructures and Information and Communications Technologies (ICT) on Spanish economic growth. It makes use of the Fbbva/Ivie capital services database recently released (Mas, Pérez and Uriel (2005)) which follows closely OECD (2001a, b) recommendations. The paper also addresses the problem posed by the presence of publicly owned assets, especially when implementing the endogenous approach to the internal rate of return determination. After offering an alternative to the standard approach, it carries out a growth accounting exercise considering explicitly three types of ICT capital assets (software, hardware and communications) and six different types of infrastructures (roads, ports, railways, airports, and water and urban infrastructures).
New Technologies and the Growth of Capital Services
The outstanding progress in Information and Communication Technology (ICT) witnessed in the past decade seems to have had a remarkable role in fostering economic growth both in developed and developing countries (Vu, 2004). However measuring and assessing the impact of ICT on economic growth is still a challenging task for most economies. The developments of the new economy have raised many essential questions about the measurement of intangible assets and high-technology capital. Indeed the answer to these questions can lead to better assessment of the economy’s long run pace of economic growth and rate of technological advance.
Productivity Measurement at Statistics Netherlands
In 2007 the National Accounts of the Netherlands have been expanded with a set of multi factor productivity (MFP) statistics. There are two guiding principles. The fi rst is to construct a system of productivity statistics at the industry branch and macro level that is, to the extent possible, consistent with National Accounts statistics. By doing this we are joining Australia, Canada, New Zealand, Switzerland, and the United States; see OECD (2006) for a summary of all these systems.
Sectoral Productivity in the United States
As the step-up in U.S. productivity growth in the mid-1990s became evident, research on productivity surged. Initially, the new work concentrated on estimating the contribution of information technology (IT) to the productivity pickup, with similar results obtained using industry-level or broad macroeconomic time-series data (Jorgenson and Stiroh 2000, Oliner and Sichel 2000, respectively). Later, studies exploited more detailed data and showed that, while multi-factor productivity (MFP) growth in the IT-producing industries was very high, many services industries also had substantial MFP growth in the late 1990s (Triplett and Bosworth 2004; Jorgenson, Ho, and Stiroh 2005a, 2005b).
Estimates of Industry Level Multifactor Productivity in Australia
The ABS has been producing productivity estimates for approximately 20 years. Considerable development work took place during the 1980s leading to the publication of the fi rst estimates of multi-factor productivity (MFP) in 1985. Since then MFP estimates for the market sector have been produced each year and released in conjunction with the annual national accounts.
Shopping With Friends Give More Fun; How Competition, Innovation and Productivity Relate in Dutch Retail Trade
This study focuses on the relation between competition, innovation and productivity in the Dutch retail trade. Everyone is very familiar with the retail trade.299 Sometimes, we do our shopping alone, now and then together with friends. But each of us has frequently or even daily contact with this part of the economy. In fact, the retail trade acts as an intermediate between producers and consumers. The industry is responsible for a considerable part of output and employment of industrialised countries, including the Netherlands. In fact, the share of nominal value added and employment was approximately 4 and 7 percent respectively in 2000 for the Netherlands.
Economic Growth in Sweden, New Measurements
Almost 50 years ago Robert Solow324 started up a new era in growth measurement by publishing his article on economic growth and technological development in the US economy. He used the technique of Growth Accounting to break down growth in US labour productivity into components. His results indicated that almost all growth in the US economy was due to technological developments and very little to capital deepening. This inspired Zwi Griliches and Dave W. Jorgensen325 to try to improve the capital measurements. Another important contribution was made by Denison326 who tried to incorporate a measurement of the improvement in labour quality. This period of rapid development of the neoclassical growth theory and use of the Growth Accounting technique lost momentum due to researchers’ increasing interest in short term questions, a lack of adequate data and the fact that growth was treated as exogenous in the neoclassical word, so these theories could not explain growth in itself.
Estimates of Labor and Total Factor Productivity by 72 Industries in Korea (1970–2003)
In recent years, especially since the 1997 economic crisis in the East Asian countries including Korea, considerable changes have taken place in the Korean economy, such as investment stagnation (see e.g. Pyo (2006) Pyo and Ha (2005)), changes in production input patterns, and so on. One of the most important changes is the demand for high productivity, which would compensate the recent slowdowns of growth rates in capital and labor inputs. As Krugman (1994), Young (1994), and Lau and Kim (1994) showed, the East Asian economic miracle may be summarized as `input-led’ growth. Korea was no exception in this respect of growth pattern.
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