the internet is that it is a (very large) piece of communications capital equipment, building on older telecoms capital and being augmented by broadband and mobile technologies. If one thinks of such equipment as building networks it is very natural to ask if there is any evidence of spillovers from telecoms equipment. As Corrado (2010) points out, there are many channels through which communications capital deepening may have contributed to improved growth in TFP e.g. improved opportunity and ability for collaboration; more effective communication and increased quality of information communicated both within and between organisations; improved access to freely available knowledge via the internet; and improved organisational and business processes, including within supply chains, derived from each of the previous effects described. Such spillovers could be even more substantial in more knowledge-oriented economies. In fact, recent studies (Adams, Black, Clemmons and Stephan 2005; Ding, Levin, Stephan and Winkler 2010) have shown a positive impact from the internet on academic collaboration and productivity.
Figure 8 shows the changes in capital stocks as a result of this investment, along with changes in R&D capital and telecoms equipment. The latter bears some comment. One way of thinking about the internet is that it is a (very large) piece of communications capital equipment, building on older telecoms capital and being augmented by broadband and mobile technologies. If one thinks of such equipment as building networks it is very natural to ask if there is any evidence of spillovers from telecoms equipment. As Corrado (2010) points out, there are many channels through which communications capital deepening may have contributed to improved growth in TFP e.g. improved opportunity and ability for collaboration; more effective communication and increased quality of information communicated both within and between organisations; improved access to freely available knowledge via the internet; and improved organisational and business processes, including within supply chains, derived from each of the previous effects described. Such spillovers could be even more substantial in more knowledge-oriented economies. In fact, recent studies (Adams, Black, Clemmons and Stephan 2005; Ding, Levin, Stephan and Winkler 2010) have shown a positive impact from the internet on academic collaboration and productivity.
There is also suggestive evidence consistent with the idea that the U.K. was experiencing a surge of supply-driven growth in the period 1995-2000. Note that over this period the U.K. was going from a deep slump to a boom. The average growth rate of output grew by 1 percentage point, and of investment by 7.2 percentage points, while unemployment fell a full 2.8 percentage points to 6.7 percent, a level not seen for decades. And in the midst of this boom, the inflation rate also fell, by 1.4 percentage points to 2.5 percent. Admittedly, it is not clear that embedding our story in a short-run macro model must lead to this result, since we claim that TFP growth was higher than recorded because output growth was also higher than recorded, which should have put extra upward pressure on prices. But it is suggestive, in part because it is difficult to see how else one might reconcile the full set of facts.
As noted in the previous section, another candidate for the decline in labour productivity which has attracted a great deal of attention is capital shallowing, that is, the decline in the capital- labour ratio. This occurs when there are substantial shifts in the relative price of factor inputs, as happened with real wages in the UK. The UK has experienced one of the lowest rates of growth in hourly labour costs through recession: according to Eurostat, in 2013 they stood at 20.9 Euros per hour, compared to the EU28 average of 24.2 Euros (Eurostat, 2015). The UK's hourly labour costs were static between 2008 and 2013, rising more slowly than all but three of the EU's 28 countries. 16 At the same time, the cost of capital has risen, despite low interest rates, due to banks' reluctance to lend (Broadbent, 2012; Pessoa and Van Reenen, 2013). These trends create incentives for firms to reduce levels of capital investment and increase their labour usage. The increase in new hires since 2008 is striking and is consistent with "capital shallowing" (Broadbent, 2012: Chart 4). When uncertainty is rife, firms may feel more
There is increasing interest in studying the asset pricing implications of investment in physical capital. I complement the existing literature by investigating the joint effects of investment in physical and human capital on asset pricing. I produce a general equilibrium model with endogenous growth where human capital risk explains the Value Premium. Thus the Fama-French HmL factor is a proxy for some macro and financial variables reflecting human capital risk. The model allows us to identify some of these variables as the covariance between Human capital, or labor income growth with the growth rate of firm assets. More specifically, in the model the Value premium arises from the fact that Value firms are associated with more risk to human capital. This is because Value firms have relatively more firm-specific human capital and are hence more burdened by their wage bill and also because human capital (both aggregate and firm-level) is more dependent on the fate of Value firms and hence covary more positively with the assets of Value firms. As a result, a negative productivity shock to Value firms’ assets implies a negative productivity shock to human capital. Consequently, these firms are less valuable, have greater Book-to-Market Equity and greater expected equity returns. I provide empirical evidence in support of these model implications.
The magnitude of the improvement in explanatory power is a modest 4% when including intangibles in column 2. In column 2, organization capital is on average insignificantly related to market value. Therefore, the estimations in columns 3-7 include the interaction with man- ufacturing dummy. Column 3 shows that intangible investments have contributed to market value especially in firms in manufacturing since the interaction term is positive. Organization capital is also interacted with ICT personnel assets, which has a negative and significant coef- ficient for the whole sample in column 3. Last column 7 shows that especially in services, organization capital does not improve market value when combined with ICT personnel as- sets. Bresnahan, Brynjolfsson, and Hitt (2000) found certain organizational practices com- bined with investments in information technology to have been associated with significant increases in productivity in the late 1980s and early 1990s. Here we do not find evidence for this. It can be concluded that organization capital investment increases market value in man- ufacturing and in services that are not very intensive in ICT personel assets.
There is increasing interest in studying the asset pricing implications of investment in physical capital. I complement the existing literature by investigating the joint eﬀects of investment in physical and human capital on asset pricing. I produce a general equilibrium model with endogenous growth where human capital risk explains the Value Premium. Thus the Fama-French HmL factor is a proxy for some macro and financial variables reflecting human capital risk. The model allows us to identify some of these variables as the covariance between Human capital, or labor income growth with the growth rate of firm assets. More specifically, in the model the Value premium arises from the fact that Value firms are associated with more risk to human capital. This is because Value firms have relatively more firm-specific human capital and are hence more burdened by their wage bill and also because human capital (both aggregate and firm-level) is more dependent on the fate of Value firms and hence covary more positively with the assets of Value firms. As a result, a negative productivity shock to Value firms’ assets implies a negative productivity shock to human capital. Consequently, these firms are less valuable, have greater Book-to-Market Equity and greater expected equity returns. I provide empirical evidence in support of these model implications.
Depreciation rates for ICT tangible capital are as in the EUKLEMS, which in turn follows Jorgenson et al. (2005). Depreciation is assumed to be geometric at rates for vehicles, buildings, plant and computer equipment of 0.25, 0.025, 0.13, and 0.40 respectively. As for intangible assets, they are assumed to be the same for all industries. We discuss depreciation in the context of intangible assets in more detail below, but the asset-specific depreciation rates for intangibles are as follows: 33% for software, 60% for advertising and market research, 40% for training and organisational investments, 20% for R&D (broadly defined, thus including Design, Mineral Exploration, Financial Innovation, Artistic Originals and non-scientific R&D). Given that the EU KLEMS database does not provide data on capital tax rates by country, industry and year, and that Timmer et al. (2010) point out that evidence for major European countries shows that their inclusion has only a very minor effect on growth rates of capital services and TFP, we did not introduce a tax adjustment.
Productivity is a multi-faceted concept; no single definition can holistically describe it. However, in the simplest form, productivity signifies the ratio between the input and output. In achieving sustained economic growth of a country, increased productivity remains as the key component. Productivity signifies a continual striving towards the economically most efficient mode of production of goods, commodities, and services needed by a society. Prior to the mid-1980s, labour productivity growth was a useful barometer of the world economy: it was low when the economy was depressed and high when it was booming. In many larger advanced economies like UK labour productivity growth slowed sharply and remained subdued for years after the credit crisis of 2007/08. After the early 1980s productivity issues were considered as a priority area for action in Bangladesh: a small economic country, but Productivity has slowed down again significantly during the last decade (2001-2012) because of some reasons. In this paper, we tried to find out the reasons behind for productivity puzzling in UK as well as in Bangladesh. For UKproductivitypuzzle, this study considered that workforce composition, lower business investment, flexibility of labour market, Impaired resource allocation, and public sector productivity were the major factors that might have caused productivity to fall and in Bangladesh, insignificant role of allocative efficiency of resources within industry, poor performing public sector, inadequate public sector investment, labor union, firm size and productivity are inversely related were the major factors that might had caused low productivity. In light of these findings, it is found that there is a similarity between productivity puzzling factor of UK and Bangladesh. Performance and investment in public sector and improper resource allocation are common productivity puzzling factor for both the countries.
The insurance costs can make the terms onerous. If borrowers want to take more than 60% of the loan in the first year as a lump sum, they must pay 2.5% of the property value as an initial mortgage insurance premium instead of the usual 0.5%. (That’s separate from the annual insurance premium of 1.25% of the loan balance.) If the borrower has difficulty qualifying, part of the home equity can be set aside for future taxes and insurance. All of these costs can be financed, but if they are, they can substantially reduce the available capital. Certain borrowers may, in practice, be able to access only about a quarter of their home equity in the first year of the loan.
In Table 4 for the subsample of state-owned listed companies, the relation be- tween equity investment and operating performance is not significantly corre- lated, fixed assets investment and intangibleinvestment are so, and the Pearson correlation coefficient of equity investment and operating performance is nega- tive, H2-1, H2-2, H2-3 are not verified. In state-owned listed companies, there is no significant linear relationship between capital allocation and operating per- formance, which shows that the increase of capital does not bring the increase of operating performance, and capital allocation do not agree with operating per- formance. State-owned enterprises are the mixtures of administration manage- ment and market. Besides the pursuit of performance, they take into account tax, employment and social stability, and many other targets. Corresponding to these, senior executives are appointed and appraised by economic factors, polit- ical factors, social responsibility and other factors. Under multiple targets of state-owned enterprises, operating performance is not the sole decision criteria for selecting an investment project. Therefore, investment surplus or underin- vestment occurs, and the investment efficiency is not high.
Nowadays, the efficiency of the productive and physical investment depends on the intangibleinvestment that is associated him. This last takes a very important place in activities and form a distinctive advantage for the competitiveness of enterprises. But it is again very complex and difficult to be able to follow it respectable. The objective of our work is therefore to look for the method according to which we are going to try to follow them in enterprises. The follow-up of operations of the enterprise is not content any more the analysis of costs it is rather oriented toward the analysis of the value and the performance. It will be made by logic of piloting with the help of a certain number of specific indicators that must be in scorecard of the enterprise. The result of our research in the Tunisian context shows that, in spite, our enterprises are conscious of the importance of the immaterial in their activities and well they invest more and more in the intangible, they remain even incapable to follow efforts and the intangible assets appropriately.
Abstract: Chinese research institutions and universities used to have to face many problems when they invested with their intangible technical assets, such as complicated examination and approval procedures for the disposal of scientific and technological achievements, unreasonable design of income tax system for technology investment, the difficulty in executing equity incentives, and the lack of motivation to researchers . The newly revised Law of the People's Republic of China on Promoting the Transformation of Scientific and Technological Achievements in 2015, and the supporting policies and measures subsequently introduced improved the systems regarding the management of scientific and technological achievements and technology investment. However, after a large number of interviews and literature research, the authors find that now there are still some obstacles for value-based investment, such as the mandatory technology investment evaluation does not conform to the law of the transformation of scientific and technological achievements, corporate capital increase and exit mechanism are not smooth after technology investment, ownership of state-owned influence the enthusiasm of the transformation of scientific and technological achievements. At the same time, get the conclusion that duties, obligations, and interests of researchers, technology transfer personnel and institutions, legal entities, and government departments are important factors that influence the transfer of technology conducted by research institutions and universities; As a special commodity, scientific and technological achievement can realize their real value only after it enters the market. Its industrialization process may be successful or failed, as there are lots of uncertainties and risks; Existing state-owned asset management policy equates technical intangible assets with tangible assets management, and stipulates that public research institutions and universities must evaluate the value of scientific and technological achievements they possess or enterprises invested when investing with technology, increasing capital or withdrawing shares, to fulfill the obligation to maintain and increase the value of state-owned assets of scientific and technological achievements. Formalistic assessment, long review cycle and the lack of evaluation standards are obstacles for investment with technical intangible assets by research institutions. For this, the author suggests exploring the mixed ownership for scientific and technological achievements, establishing special intangible assets evaluation criteria based on The General Rules for Science and Technology Research Projects Evaluation, and a system combining mandatory assessment evaluation and selective evaluation.
It was seen above that the basic Balassa-Samuelson model canexplain the stylized fact of an emerging and growing relative price effect if one assumes that the bias of productivity shocks toward the traded sector increases over time, or if one assumes that the traded sector shrinks over time. Recent theories in trade offer some interesting suggestions for why such a result might be expected to arise endogenously. It has been documented in recent work on trade that there is a good deal of heterogeneity in terms of productivity among firms, even within the same sector. It is only a relatively small number of firms that participate in international trade, and these firms systematically tend to be large and have high levels of productivity. Trade theory has offered models to replicate this pattern, by assuming a continuum of firms with heterogeneous draws from a productivity
number of possible cases where intelligent policy formulation should consider not only the marginal contribution of input use to the mean of output, but also the marginal reduction in the variance of output (Koundouri et al, 2006). Ignoring the impact of risk in an adoption study can provide misleading guidance to policy makers. This study is motivated by a hypothesis that other than access to credit, risk preferences related to variance of profit and attitude to downside-risk may be responsible for the low adoption of such technologies. Specifically, we hypothesize that that farmers that exercise risk- aversion towards fertilizer-use are less likely to adopt hybrid maize. The general objective of this paper is, therefore, to assess the determinants of the adoption of hybrid maize among farmers that vary in their risk preferences towards the use of fertilizer.
canexplain the productivity advantages of …rms located near other highly productive …rms. I relate my …ndings to the existing evidence on the pro- ductivity advantages of agglomeration, focusing in particular on the study performed in Greenstone, Hornbeck and Moretti (2010, henceforth GHM). The authors …nd that after the opening of a large manufacturing establish- ment, total factor productivity (TFP) of incumbent plants in US counties that were able to attract one of these large plants increases signi…cantly rela- tive to the TFP of incumbent plants in counties that survived a long selection process but narrowly lost the competition. The observed e¤ect on TFP is larger if incumbent plants are in the same industry as the large plant, and increases over time. These two facts are consistent with the presence of in- tellectual externalities that are embodied in workers who move from …rm to …rm. However, data limitations prevent GHM from drawing de…nitive con- clusions regarding the driving mechanism. I evaluate the extent to which worker ‡ows explain empirical evidence on the productivity advantages of agglomeration, by simulating an event similar to that studied by GHM but within the worker mobility framework described above. The change in pro- ductivity predicted within this framework equals 10-15 percent of the overall e¤ect found in GHM, indicating that knowledge transfer through worker ‡ows explain a signi…cant portion of the productivity advantages through agglomeration.
If your students have a working knowledge of these terms they will get much more out of their visit to the Sandia Mountain Natural History Center. These terms are the foundation from which we will teach all day. You may need to explain to students than an organism is a living thing.
Importantly, the key findings in the previous section are not overturned by the inclusion of the control variables. Consider first the estimates of the productivity growth equation in the upper half of Table 5. The coefficients of the growth in R&D expenditures or the number of R&D workers are significantly positive in most cases. Furthermore, the coefficients of research intensity are highly significant in all cases, suggesting that the significance of R&D growth is not implying that growth is semi-endogenous but rather that the estimates have been influenced by transitional dynamics. This conclusion is reinforced by the fact that the coefficients of levels R&D are insignificant in the regressions of ideas production functions (panel B in Table 5). The estimates of ideas production functions give even stronger support in favor of the Schumpeterian growth theory. All coefficients of research intensity are highly significant and the coefficients of knowledge production are also very close to one. The null hypothesis of the presence of scale effects in ideas production cannot be rejected at the conventional levels, as indicated by the Wald test results in the table.
Figure 1 shows a clear negative correlation between productivity performance (TFP in this case) and effective tax rate (ETR). The latter is computed as the share of corporate tax over gross profits. This preliminary evidence supports our initial argument that higher tax rates decrease working capital and thus impede market expansion and investment. Figure 2 is an initial indication regarding returns to R&D. The positive correlation illustrated in the graph is clear and shows that there are positive private returns to R&D as the evidence is at company level (not industry). Our empirical evidence enriches this point with the regressions later in the paper emphasising the importance of R&D in productivity growth. In this line of argument, Figure 3 supports the idea that R&D active firms as well as exporting firms tend to be closer to the frontier. For example, the number 0.72 for R&D active firms indicate that on average an R&D active firm’s TFP is equal to 72% TFP of the frontier’s while for the R&D inactive firm the distance is bigger, currently 67%. One could argue that the difference in the GAP between R&D and non-R&D firms (or exporting and non- exporting firms) is not large enough. Because the time span is relatively small, the dynamics of convergence process cannot be fully captured. Given that time series in firm level data are always shorter, 5 percentage points distance from the frontier between R&D and non-R&D active firms is still a considerable difference.