Are Indian Firms too Small? A Nonparametric Analysis of Cost Efficiency and Industry Structure of Indian Manufacturing

In this paper we use the 2004-05 Annual Survey of Industries data to estimate the levels of cost efficiency of Indian manufacturing firms in the various states and also get state level measures of industrial organization (IO) efficiency. The empirical results show the presence of considerable cost inefficiency in a majority of the states. Further, we also find that, on average, Indian firms are too small. Consolidating them to attain the optimal scale would further enhance efficiency and lower average cost

Efficiency in Managing the Environment and the Opportunity Cost of Pollution Abatement

Using the directional distance function we study a cross section of 110 countries to examine the efficiency of management of the tradeoffs between pollution and income. The DEA model is reformulated to permit \u27reverse disposability\u27 of the bad output. Further, we interpret the optimal solution of the multiplier form of the DEA model as an iso-inefficiency line. This permits us to measure the shadow cost of the bad output for a country that is in the interior, rather than on the frontier of the production possibilities set. We also compare the relative environmental performance of countries in terms of emission intensity adjusted for technical efficiency. Only 10% of the countries are found to be on the frontier. Also, there is considerable inter-country variation in the imputed opportunity cost of CO2 reduction. Further, differences in technical efficiency contribute substantially to differences in the observed levels of CO2 intensity

The commonly used eye-specific sev-GAL4 and GMR-GAL4 drivers in Drosophila melanogaster are expressed in tissues other than eyes also

The binary GAL4-UAS system of conditional gene expression is widely used by Drosophila geneticists to target expression of the desired transgene in tissue of interest. In many studies, a preferred target tissue is the Drosophila eye, for which the sev-GAL4 and GMR-GAL4 drivers are most widely used since they are believed to be expressed exclusively in the developing eye cells. However, several reports have noted lethality following expression of certain transgenes under these GAL4 drivers notwithstanding the fact that eye is not essential for survival of the fly. Therefore, to explore the possibility that these drivers may also be active in tissues other than eye, we examined the expression of UAS-GFP reporter driven by the sev-GAL4 or GMR-GAL4 drivers. We found that both these drivers are indeed expressed in additional tissues, including a common set of specific neuronal cells in larval and pupal ventral and cerebral ganglia. Neither sev nor glass gene has so far been reported to be expressed in these neuronal cells. Expression pattern of sev-GAL4 driver parallels that of the endogenous Sevenless protein. In addition to cells in which sev-GAL4 is expressed, the GMR-GAL4 is expressed in several other larval cell types also. Further, two different GMR-GAL4 lines also show some specific differences in their expression domains outside the eye discs. These findings emphasize the need for a careful confirmation of the expression domains of a GAL4 driver being used in a given study, rather than relying only on the empirically claimed expression domains

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe