Learning across policy regimes: The impact of protection vis-à-vis competition in the Indian automotive industry

Learning has been recognized as an important factor in explaining the growth of firms in both industrial organization theory and literature. However, few models have attempted to relate the learning and growth literature with the industrial policy regime, especially in economies heavily regulated by government policies. The present study attempts to apply one such model of growth and learning of firms across three different industrial policy regimes in the Indian automotive industry. It tries to analyze whether learning is promoted by a competitive or a protective policy regime. It also tries to decompose learning into several types to understand the mechanism underlying the growth process. In doing so, it relies on the growth-size distribution literature.Learning; growth; policy regime; automobile Industry; India; Asia

Export Competitiveness in the Indian auto-component industry : Does Low Wage Cost matter?

This paper analyzes the differences in the export behavior of domestic and multinational firms in the Indian auto component industry. Three types of firms are identified according to ownership : purely domestic and licensees, domestic joint ventures and joint ventures with majority stakes by the multinationals. Although all the types of firms face the same labor costs, any difference in export performances could arise because of higher productivity of labor. The paper tests whether this is true for the domestic firms and the multinational firms in the Industry. It finds that only in the case of the multinational firms, it is not just cheap labor in terms of low wage rate per worker, but low wage in relation to productivity of that labor which leads to comparative cost advantage in exports. The domestic firms are competing based on low wage cost more than the productivity of the labor. Among other factors discussed, of the reasons is the low value added nature of the components that are being exported. The role of other factors like, size, import- intensity and distribution expenses is also examined, followed by an analysis of the scope for domestic firms to become a part of the global supply chain.Export intensity, labor costs, productivity, competitiveness, import intensity, auto component industry, India

Analysis of solution trajectories of linear fractional order systems

The behavior of solution trajectories usually changes if we replace the classical derivative in a system by a fractional one. In this article, we throw a light on the relation between two trajectories $X(t)$ and $Y(t)$ of such a system, where the initial point $Y(0)$ is at some point $X(t_1)$ of trajectory $X(t)$. In contrast with classical systems, trajectories $X$ and $Y$ do not follow the same path. Further, we provide a Frenet apparatus of both trajectories in various cases and discuss their effect.Comment: 19 pages, 17 figure

Automatic Classification of Fish in Underwater Video; Pattern Matching - Affine Invariance and Beyond

Underwater video is used by marine biologists to observe, identify, and quantify living marine resources. Video sequences are typically analyzed manually, which is a time consuming and laborious process. Automating this process will significantly save time and cost. This work proposes a technique for automatic fish classification in underwater video. The steps involved are background subtracting, fish region tracking and classification using features. The background processing is used to separate moving objects from their surrounding environment. Tracking associates multiple views of the same fish in consecutive frames. This step is especially important since recognizing and classifying one or a few of the views as a species of interest may allow labeling the sequence as that particular species. Shape features are extracted using Fourier descriptors from each object and are presented to nearest neighbor classifier for classification. Finally, the nearest neighbor classifier results are combined using a probabilistic-like framework to classify an entire sequence. The majority of the existing pattern matching techniques focus on affine invariance, mainly because rotation, scale, translation and shear are common image transformations. However, in some situations, other transformations may be modeled as a small deformation on top of an affine transformation. The proposed algorithm complements the existing Fourier transform-based pattern matching methods in such a situation. First, the spatial domain pattern is decomposed into non-overlapping concentric circular rings with centers at the middle of the pattern. The Fourier transforms of the rings are computed, and are then mapped to polar domain. The algorithm assumes that the individual rings are rotated with respect to each other. The variable angles of rotation provide information about the directional features of the pattern. This angle of rotation is determined starting from the Fourier transform of the outermost ring and moving inwards to the innermost ring. Two different approaches, one using dynamic programming algorithm and second using a greedy algorithm, are used to determine the directional features of the pattern