New Ventures’ influence on the Growth of SME Employment, Production and Exports in India: Does Institutional Credit matter?

New ventures are considered to be a major source of small firm growth. In Indian context the contribution of new ventures in terms of new employment, production and exports has largely remained unexplored. It is equally important and unexplored, the significance of the contribution of bank credit to the growth of new ventures in India. This paper is an attempt to throw light on these two aspects. The research is based on secondary data of the liberalized period provided by Ministry of Micro, Small and Medium Enterprises, Government of India and Reserve Bank of India. To analyze the influence of bank credit growth on new ventures and the influence of new ventures on growth of additional employment, additional production and additional exports, we used a Bi-Variate Vector Auto Regression. Based on the model generated, Granger causality tests are conducted to obtain the results. The study found that rate of growth of bank credit causes the number of new ventures, implying any increase in the rate of growth of bank credit will be beneficial to the growth of new ventures. The study also concluded that new ventures are not causing the growth of additional employment or additional production. However new ventures cause the growth of additional exports. This is reasonable as entrepreneurs start their new ventures with minimum possible employment and relatively low rate of capacity utilization and they come up to take advantage of the process of globalization by catering to the international market.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.