Note that the fast growing countries, Sweden and UK, where intangiblecapital accounts for 0.8-0.7 percentage points of labour productivitygrowth, are also the most intangible intensive economies (Table 2). On the other hand, Italy and Spain, the slow growing member states are also the less intangible intensive economies. This finding goes in the same direction as van Ark et al (2009) showing a positive association between the GDP share of intangible and hourly labour productivity. Looking at the contribution of each intangible asset, we observe that for Sweden and Finland, R&D is the key source of growth, while for UK, organizational capital is the main driver of growth. Interestingly, in Finland, where intangiblecapital plays a greater role than tangible capital, the largest part of Finnish labour productivitygrowth is accounted for by R&D capital deepening and by a relatively high TFP.
Europeancountries using firm-level data. The paper explores the empirical regularities of firm productivity distribution across countries. In particular, we assess the degree of persistence of firm relative productivity and consider its effect on aggregate productivity improvements. Moreover, the paper analyses the impact of the competitive forces on aggregate productivitygrowth by disentangling the role of firm learning and market selection. Finally, we estimate the relationship between labour productivitygrowth and firm-specific factors such as size, age and capital intensity across countries. The paper uses annual account data over the period 1993-2003 from Amadeus dataset (Bureau van Dijk) for a balanced panel of manufacturing firms. In line with previous evidence, our analysis shows that firm relative productivity levels are both highly heterogeneous across firms and very persistent over time in all the countries in the sample. With reference to aggregate productivitygrowth, we find that both labour productivity and total factor productivity changes are mostly driven by firm learning, i.e. within-firm productivity improvements, in most Europeancountries. Conversely, the reallocation of resources spurred by the competitive selection process is found to play a minor role in fostering aggregate productivitygrowth. Finally, in line with macroeconomic trends, gains in productivity seem to be associated with capital deepening, but also with employment losses.
The recent economic downturn has changed the current debate on economic growth from one that emphasizes the long-run need for productivity and innovation to one that stresses economic recovery, particularly in employment. The focus on job growth is an inevitable aspect of any recession, and the deeper the recession, the greater the concern. This recession, however, is somewhat different because it has unfolded against the backdrop of the job losses and labour force restructuring brought about by the globalization of the world economy. One way to accomplish both short- and long-term objectives is to promote investment where the high-wage economies of Europe and the U.S. have their greatest comparative advantage—the creation of knowledge. As the knowledge-content of the products and services that economies produce gradually increases, investment in knowledge production becomes the key source of economic growth. Moreover, the creation of knowledge acts both raises investment opportunities in the short run while creating the rewards of higher income and productivitygrowth in the future. Knowledge creation is part of a wide-ranging process of investment in intangiblecapital. This investment includes expenditures for human capital, in the form of education and training, public and private scientific research, and business expenditures for product research and development, market development, and organizational and management efficiency. These are strategic
has seen sustained reform momentum across many countries and areas of transition, as measured by the EBRD's transition indicators (EBRD, 2005). A number of countries that had been lagging in reform, such as Bosnia and Herzegovina, the Federal Republic of Yugoslavia, Ukraine and Russia, have made significant progress over the past years as a result of favourable political and economic developments. In 2004, for the first time, Ukraine and Russia became net exporters of grain. Agriculture was the main force behind stellar growth rates in many countries of the region last years: 12 per cent for Ukraine, for example, and 7 per cent for Serbia and Montenegro. The main purpose of this paper is to measure TFP developments in agriculture of transition coun- tries after breakdown of socialism and to compare their TFP growth with other Europeancountries. In the literature, TFP can be measured by using productivity index. The most widely-used produc- tivity index is Malmquist TFP index presented in C AVES et al. (1982) and F ÄRE et al. (1994). This
Overall, the analysis suggests that the labor productivity puzzle is eventually a TFP puzzle. Some authors (Summers 2014) have recently advanced the hypothesis of a secular stagnation which would negatively affect productivitygrowth because of a global slowdown in innovation or, possibly, inadequate spending on the demand side. According to others (Eichengreen, Park and Shin 2016; Acemoglu, 2008) TFP slumps seem to be determined by country specific factors (educational attainment, weak political systems) and global factors (higher risk, higher energy prices). Others (Guarascio et al 2017, Saltari and Travaglini 2008; Vanreenen and Pessoa 2014) have argued that the changes of labor market regulation in Europeancountries, over the last twenty years, have negatively affected the capacity of the firms to sustain over time labor productivity and capital accumulation by means of innovation. In the next Section, we use a simple labor market model in order provide a coherent simple explanation for the above stylized facts.
The agriculture sector in the UK is small and unimportant, but many of the DCs followed suit as political changes of the time led to privatisation and reduced public expenditures. Alston et al. (2008) show that the changes in the US system had similar, but less dramatic results, with R&D, yields, labour productivity and TFP growth rates falling. Alston et al. (1999) show that for all the five countries studied (Australia, the Netherlands, New Zealand, the UK and the US) total R&D expenditures fell in the 1980s, but recovered somewhat in the 1990s. However, the growth of expenditures was much slower than before 1980. Thus, they argue that there was a contraction in the growth of support for public agricultural R&D amongst developed countries. While some countries increased expenditure in the latter half of the 1990s, albeit more slowly than in preceding decades, public agricultural R&D was massively reduced in Japan, and to a lesser degree in several Europeancountries, towards the end of the 1990s, leading to a reduction in the rate of increase in DC spending as a whole for the decade. More recent data, where available, reinforce the longer- term trends. Specifically, support for publicly performed agricultural R&D amongst DCs is being scaled back, or is growing more slowly, and R&D agendas have drifted away from productivity gains in food staples towards concerns for the environmental effects of agriculture, food safety and other aspects of food quality, and the medical, energy, and
A great deal of research has been devoted to the effects of technical change on economic growth. Less attention has been given to the factors driving the growth of the technological innovators themselves. This paper examines the case of one of the central contributors to the IT revolution, the Microsoft Corporation. The company’s sources of growth are estimated using the conventional Solow-Jorgenson- Griliches “residual” model, expanded to include investments in product research and development, sales and marketing, and organizational development (collectively termed the company’s “intangible” capital). The picture of Microsoft that emerges from this analysis is a story about the successful use of knowledge inputs to produce knowledge outputs. It is also a story of the importance of product innovation, rather than process innovation, as a source of total factor productivitygrowth. The theoretical underpinnings of the empirical analysis are also examined, and a model is sketched in which the neoclassical growth accounting framework is linked to the theoretically messier world of the Schumpeterian competitor via the Berndt-Fuss theorem on capital utilization.
the intangible assets recorded by companies in their balance sheets. In this case R&D is treated as an investment which is cumulated in a stock, depreciated and reduced in the same way as investment in a plant or in a piece of tangible equipment. This measure is not available in the firm- level data for the United States. The Financial Accounting Standards Board (FASB, 1974 and 1985), which is the primary body in US that sets reporting standards, mandates that all R&D costs must be immediately expensed (Statements of the Financial Accounting Standards, SFAS No. 2). In contrast, the International Accounting Standards Board (IASB, 2004), which issues international financial reporting standards (IFRS) to over 100 countries including the European Union, allows for the capitalisation of many intangibles (International Accounting Standards, IAS No. 38). 1 Although capitalised assets are available in European firm-level data, the literature analysing productivity in Europeancountries disregarded it. One reason for this may be that Generally Accepted Accounting Principles (GAAP), i.e. the set of rules and practices having substantial authoritative support and used by companies to compile their financial statements, despite being issued by the IASB, leave too much leeway for managerial discretion in deciding what kind of information convey to the investors in the financial markets.
First, we can look at the characteristics of the legal system. Countries with common law systems tend to be attached to the principle of freedom of contracts and have relatively few regulatory provisions concerning labour contracts. In contrast, most civil law systems, and particularly those with a single codified civil code, tend to minutely regulate (see, for example, House of Lords, 2007). One would therefore expect more lenient dismissal regulations in common law countries and more constraining regulations in countries under civil law with a civil code tradition. 24 Scandinavian countries with no consolidated civil codes and a customary law tradition will be a somewhat intermediate case (see Lando, 2001, and Smits, 2007). In fact, from an historical point of view, in Denmark, Finland and Sweden, employment protection rules were introduced first through collective agreements, with a few of them being reflected in legislation only subsequently (Sigeman, 2002). Next, we can look at countries that experienced dictatorships in the 20 th century (excluding during World War II, when most Europeancountries were under puppet pro-Nazi regimes). Due to their paternalistic view of labour relationships, pre-WWII fascist regimes were historically inclined to guarantee workers strong protection against dismissals, albeit within a strict industrial relation system with no voice rights. 25 Stringent regulations generally survived the fall of these political regimes. All these historical and institutional factors pre-date EPL (by more than one century, in the case of legal systems), thereby limiting the risk of reverse causality. True, one can argue that they could also be at the origin of other institutions affecting productivity and/or could have a long-lasting effect on productivity themselves. This is not a problem, however, if we interact these variables with the corresponding indicators of layoff propensity used for EPLR and use the interacted variables as instruments, as we do. In fact, these interacted variables appears to qualify as valid instruments to the extent that we cannot think of any economic mechanism inducing an effect of legal systems or dictatorship spells on productivity that varies across industries as a function of layoff propensity without occurring through their effect on dismissal regulations. Obviously, the validity of our instrumental variable strategy crucially hinges on the validity of this latter statement. 26
This paper tries to calculate some facts for the “knowledge economy”. Building on the work of Corrado, Hulten and Sichel (CHS, 2005,9), using new data sets and a new micro survey, we (1) document UK intangible investment and (2) see how it contributes to economic growth. Regarding investment in knowledge/intangibles, we find (a) this is now greater than tangible investment at, in 2008, £141bn and £104bn respectively; (b) that R&D is about 11% of total intangible investment, software 15%, design 17%, and training and organizational capital 22%; (d) the most intangible-intensive industry is manufacturing (intangible investment is 20% of value added) and (e) treating intangible expenditure as investment raises market sector value added growth in the 1990s due to the ICT investment boom, but slightly reduces it in the 2000s. Regarding the contribution to growth, for 2000-08, (a) intangiblecapital deepening accounts for 23% of labour productivitygrowth, against computer hardware (12%) and TFP (40%); (b) adding intangibles to growth accounting lowers TFP growth by about 15% (c) capitalising R&D adds 0.03% to input growth and reduces lnTFP by 0.03% and (d) manufacturing accounts for just over 40% of intangiblecapital deepening plus TFP.
Second, the relative importance of the factors “explaining” growth changes significantly when intangibles are introduced. In the first period, the portion of top-line growth explained by intangibles goes from 0 percent in the top panel (by definition) to 26 percent with them, and the corresponding numbers for the 1995–2003 period are 0 percent and 27 percent, respectively. Moreover, intangibles moved up to parity with tangible capital in its importance as a source of growth during the second period. Put another way, capital plays a larger role in account- ing for labor productivitygrowth once intangibles are included. In the earlier period, capital accounted for 59 percent of labor productivitygrowth when intan- gibles are included, but only 44 percent when they are excluded. In the latter period, the difference is even greater, with capital accounting for 54 percent of growth when intangibles are included but only 35 percent when they are excluded. Third, a comparison of MFP growth rates reveals that this source of growth declines both in absolute and relative importance when intangibles are included as investment. This is most pronounced for the second period, during which the average annual growth rate of MFP drops from 1.42 percentage points under the “old” view to 1.08 percentage points when intangible investments are included in the analysis. Expressed as a fraction of the rate of growth in output per hour, MFP declines in importance from 51 percent to 35 percent. This result is not particularly surprising in light of Jorgenson and Griliches (1967) and in view of the fact that MFP is measured as a residual.
The data for our analysis cover 10 Europeancountries (listed in table A.2 ) for the period of 1995 to 2007. The data on output, non-ICT tangible capital, ICT and labor input are taken from the EU KLEMS database ( O’Mahony and Timmer ( 2009 )). The sectoral data on intangibles were compiled by the authors within the INDICSER project. The main source for computing sectoral measures of intan- gible investment was the INTAN-Invest database described by Corrado et al. ( 2012 ), which contains data at the level of the aggregate business sector for 7 different intangible assets not included in EU KLEMS: organizational capital, firm-specific human capital, R&D, new architectural and engineering designs, market research and advertising expenditure. Information about the own-account and the purchased component of organizational capital is used from INNODRIVE (see Table A.3 ). We apply sectoral information to the INTAN-Invest data to obtain estimates for investment in individual assets and total intangible investment at the level of 1-digit industries of the NACE rev. 1.1 classification. Table A.1 describes the industry coverage in detail.
Economic reform in the Central and Eastern Europeancountries in the 1980s helped transform the structure and volume of agricultural production, consumption and trade, and resulted in significant agricultural productivity improvements. However, there are large differences among the transition countries in the magnitude and direction of these changes. The main objective of this study is to measure and compare the levels and trends in agricultural productivity in transition countries with those of the European Union (EU) countries making use of the most recent data available from the Food and Agriculture Organization (FAO). This study employs a parametric distance function approach to measure Malmquist productivity index as well as the magnitude and direction of technical change. The Malmquist productivity index is decomposed into technical change (TC), technical efficiency change (TEC) and scale efficiency change (SEC) in which TC is decomposed into input- and output- biased TC. These measures provide insightful information for researchers in designing policies to achieve a high growth rate in transition countries.
where s LN (t) = [P L (t)L N (t)]/[P L (t)L N (t) + P K (t)K N (t) + P R (t)R N (t) ] is labor’s income share in the production of intangible investment, g PN is the growth rate of the price of the intangible good, and the other share and growth rate terms are defined analogously. Using the wage P L (t) as a proxy for P N (t) is equivalent to assuming that the growth rate of output price is g PN (t) is equal to the growth rate of wages, g PL (t). As can be seen in equation (4), this will occur only if labor is the sole input to the production of the intangible and if the rate of multifactor productivitygrowth in the production of intangibles, g AN (t), and the shares of tangible and intangiblecapital, s KN (t) and s RN (t), are all zero. These assumptions are extremely implausible, because, for example, R&D programs require plant and equipment, and knowledge builds on knowledge. Because using a wage deflator tends to give biased results, we adopt the nonfarm business output price deflator as a proxy for P N (t). This proxy can be rationalized by the fact that much R&D and coinvestments in marketing and human competencies are tied to specific product lines. Integrating the cost of productivity-enhancing investments back into the “using” industries is accomplished generally by adopting the nonfarm business output price as the deflator for intangibles. 9
Liu et al. (2006) investigates whether and to what extent the outside of school considerations such as, individual, families, and cultural factors are associated with the students’ TIMSS mathematics achievement for the US and five top performing Asian nations such as, Korea, Taiwan, Japan, Singapore, and Hong Kong. Using scores on TIMSS 2003 grade-8 mathematics tests as the dependent variable, they examine the impact of eight (8) outside of school factors (parent educational level, educational aspiration, students’ self confidence in learning mathematics, students’ valuing mathematics, time students spend doing math homework, extra lessons or tutoring, availability of computer and number of books at home) and fifteen (15) school-associated factors (Student-Principal: school size, good school and class attendance, school climate, low social economic status, high social economic status, grouping instructions, grouping students; Student-Teacher: perception of no or few limitations on instructions due to students, emphasis on homework, class size, covering overall math topics, teaching time, interaction with colleagues, professional development and content related activities) on mathematics achievement. Their results show that outside of school factors are significantly related to students’ mathematics achievement for Korea, Japan, Taiwan, and the US, but less related for Singapore and Hong Kong. It also reveals that none of the school associated factors are related to students’ mathematics achievement for Japan however, some of them are related for other countries. Finally, they conclude that the educational system of a country is not isolated from society and thus the mathematics achievement in their sample countries are related to many (if not all) of these outside of school factors.
The regression provides one new evidence for the discussion on the pattern of inter- national capital flows. The Neo-Classical growth model predicts that one economy with a higher growth rate would receive more inflows of capital. Recently, this implication is supported by the empirical evidence on Alfaro, KalemliOzcan, and Volosovych (2014) for a cross-section sample of both developing and advanced economies. However, for one cross-section sample of Non-OECD economies, Gourinchas and Jeanne (2013) find the evidence that one economy growing faster tends to receive less capital inflows. The authors postulate the result as the allocation puzzle. Therefore, our regression results can be considered as the extension of the allocation puzzle to the panel data sample. On other words, on average across countries, an increase over time of productivitygrowth rate reduces the net total capital inflows. In short, we support the existence of allocation puzzle on one panel sample of about 180 economies from 1980 to 2013.
The literature on economic growth would answer these questions using similar concepts but a different expression: technological progress. In this literature, sustainable growth in the long run is possible only as a result of technological progress. In the productivity literature, a substantial part of technological progress is captured by what is labeled total factor productivity (TFP), which is the “residual” that remains once increases in conventional inputs have been taken account of. However, because TFP is simply the residual, it has also been called the “measure of our ignorance” (Abramovitz, 1956) since we do not know much about what is included in the measure. Nevertheless, as research on productivity and economic growth have continued, there is a growing consensus among scholars that the residual includes the contribution of intangible assets 1 such as R&D activity and human capital. In fact, there is a growing recognition among economists that today, intangible assets are more important than tangible assets as sources of competitiveness, sustainable growth, business success, and so on.
these two components. Yet, a disaggregated inspection shows that a ceteris paribus increase in infrastructure outlays by the federal government would have a negative effect on future productivitygrowth. In contrast, a rise of spending by states and localities, and particularly of O&M outlays, would have a positive effect. Our results coincide with the views expressed by, among others, Hulten and Schwab (1997) who have stressed that state and local under- spending on infrastructure may occur if the presence of spillover benefits from one jurisdiction into another is ignored. The evidence presented here thus sheds some further light in the decentralization question by showing that decentralization of infrastructure spending, and mainly of its O&M component, contributes to future productivitygrowth. In particular, our findings extend those of Kalaitzidakis and Kalyvitis (2005), who have found that Canada overspends on total infrastructure and on maintenance, and Ghosh and Gregoriou (2008) who proxy O&M spending by the component of current expenditures labelled ‘other purchases in goods and services’ in World Bank data, and show in a panel of developing countries that these expenditures have exerted a positive impact on per capita growth. The current paper shares a similar methodology with these studies and finds that overall total spending on public infrastructure is about at the right level. Yet, our results also show that decentralizing infrastructure expenditures from the federal government to states and localities may increase productivitygrowth and that states and localities under-spend on O&M. Our sectoral analysis also indicates that state and local expenditures on transportation should increase and be directed towards O&M. The analysis is in line with the view that infrastructure maintenance is mainly a local task due to greater efficiency in identifying and dealing with maintenance requirements.
European Integration, ProductivityGrowth and Real Convergence
This paper derives a stochastic endogenous growth model that investigates the impact of European Union integration on convergence and productivitygrowth. We deviate from the general strand of literature by not only deriving a theoretical model for the effects of integration on the rate of economic growth, but also by using more appropriate estimation techniques. The outcome of a series of panel and structural break tests examining the accession process of five recent members to the Union generally show improved rates of productivitygrowth and convergence to EU standards. We then draw from the experience of these recent members to derive implications for the first-round EU candidate countries .
One of the main shortcomings of the DEA method is that it does not have any immediate statistical foundation. That is, it is not possible to make an inference, in the classical sense (Kneip, Simar, & Wilson, 2008), about the estimated components of the decomposition of labour productivitygrowth defined by relation (10). In fact, the components for a given sample of countries are only estimates of the true population values (Simar & Wilson, 1998, 1999) and are affected by uncertainty due to sampling variation. However, the bias for each parameter can be corrected using the bootstrap simulation method. Simar and Wilson (1999) employed this technique to estimate the empirical distributions of Malmquist indices of TFP changes. The general principle behind the bootstrap is to simulate the observed sample B times (b=1,2,…,B) and to calculate in each iteration, b, the parameter of interest. Then, the B estimates