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The Indian pharmaceutical industry currently faces multiple challenges, including rising costs and slowing export growth, which in turn have limited its ability to expand presence in global canvas. Given the nature of sectoral dynamics, a pharmaceutical firm must undertake huge investments in R&D to introduce product innovation, in turn enhancing market share and sustaining profit streams. The development of novel medicines, confirmed by the granting of patent rights, provides a pharma company edge over its competitors. In addition, presence of innovator firms within the industry invigorates the sectoral value chain and raises efficiency. Hence, it is important to analyze whether granting patent rights enhances the exports of pharmaceutical products in the Indian context.

The current study explored this question using a simultaneous-equation framework. Specifically, the authors use the methods developed by Davidson and MacKinnon (1993) and Greene (2003) to obtain heteroscedasticity-consistent estimates. The time-series properties of the data were further probed, and robust estimates were used to test the theory. Methods developed by Baltagi (1981) have been used further to refine the authors’ estimations.

After controlling for relevant variables, it is observed that granting of patents caused a significantly positive impact on pharmaceutical exports. Furthermore, the change in the patent administration regime had a significant impact on patent fillings, which further impacted their exportability. Compared to patents granted patents filed had a higher impact on pharmaceutical exports.

This study attempts to apply the framework developed by Goldstein and Khan (1978) with necessary modifications to suit the context of a developing country. The application of the 3SLS method to estimate the export supply equation for pharmaceutical products is a novel approach to the research question in general and to the Indian context in particular. System autocorrelation and heteroscedasticity tests were performed to refine the results further.

Trade can be severely impacted by economic crisis whatever might have been the trigger. Protectionist tendencies are also normally on the rise during such times having further impact on trade and associated variables. In this chapter, an attempt has been made to assess the impact of economic crisis on international trade. Three episodes of crisis have been picked up for the same, the oil crisis of early 1970s, the global financial crisis (GFC) of 2007–2008, and the COVID-19 crisis of 2019. Each episode is triggered by different factors. Do the differing triggers ultimately lead to different impact on trade? This chapter first tries to answer this question at aggregate trade level and then across country groups and product groups. Interesting trends are observed. Across product groups, both agricultural and manufacturing goods exports were more impacted during the GFC, whereas for services exports, the impact was severe during the COVID-19 pandemic. Across country groups, the high-income countries were more impacted during the first two episodes, whereas the less-developed countries (LDCs) were more impacted during the pandemic. Further, a gravity model estimation was attempted to empirically measure the effects of crisis for the world as a whole and for India and China separately. All country bilateral trades were significantly impacted by the three episodes. After controlling for the relevant factors for India and China, certain differences in impact are observed. China seems to be less impacted by destination country factors.

India's manufacturing exports are heavily tilted toward primary and resource-based products, while its Asian peers have a significant proportion of high and mid-tech products in their export baskets. An attempt is thus made to understand the hurdles faced by technology-intensive exports by India, using gravity panel estimates on its high, mid, and low-tech exports, using data across 130 countries from 2001 to 2019. In line with the Knowledge Theories of trade, which postulate that technical and scientific knowledge and innovation provide trade advantages, this chapter also tries to understand how India fares on this front. We use Principal Component Analysis to construct an index which provides a relative understanding of India's technical and scientific knowledge base.

We conclude that nontariff measures (NTMs) are a stringent hurdle faced by Indian exports, especially in the European Union. Tariffs have the most debilitating effect on its mid-tech exports and the least on high-tech exports. Regional Trade Agreements (RTAs) are most effective in creating trade for mid-tech exports and least in case of low-tech exports. The index for ascertaining India's relative knowledge base shows that while India ranked 6th in 2017, much higher than its Asian peers, its high and mid-tech exports lagged behind these countries. This puzzle is explained by the fact that scientific research in India has very little industry collaboration and thus is out of sync with market needs. Hence, the prevalent scientific and technical knowledge in India does not have the expected favorable impact on its technology-intensive exports.

Political parties are continuously interested to gain knowledge about the factors that influence the voter to select political candidate of his/her choice. The purpose of this paper is to examine cumulative impact of sources of associations on voters’ preference of the political party and to investigate the type of causal relationship that exists among those sources.

The authors have proposed five key sources of associations of the overall political party, namely, campaign effectiveness, image of its leaders, intensity of anti-incumbency effect, meaning and trust attached with the party. Here the authors have considered four important political parties relevant to the voters of West Bengal. Those are Congress, Bharatiya Janata Party, Communist Party of India (Marxist) and All India Trinamool Congress. The authors have used SEM method for estimating the model as the same is widely used for estimating a system of equations with latent variables.

Out of the eight path coefficients six are found to be statistically significant. Political campaign impacts brand trust positively and brand trust in turn impacts party preference positively. Again political campaign’s direct impact on political party preference is found to be positive. However, the impact of political campaign on party preference also runs through brand meaning. Both the path coefficients are significantly negative showing that more the voters develop understanding about political parties through different independent sources lesser are the impact of political campaigns as they highlight positive aspects of the party and the candidate only, ignoring facts. Interestingly leadership is impacting party preference negatively. Thus individual leadership traits have negatively impacted party preference in the sample.

In the paper, the authors have identified factors impacting political brand choice in an emerging country like India. This research explores the factors that need to be considered by the political parties to influence preference of voters for political brand. As far as the authors’ knowledge goes no such studies have been carried out in the Indian context and certainly not in the context of a regime change after three decades. Additionally, the theoretical model proposed is firmly grounded in theory and its estimation is based on well-developed scales. The approach is thus unique in this area of enquiry. Finally, application of SEM in political branding context is a significant contribution of this work.

The purpose of this study is to attempt to address the effect of country of origin (COO) from three different dimensions, namely, country image (CI) effect, COO image effect and awareness level about the COO of the brand and its resultant effect on product evaluation (PrEva). Further, the brand awareness, level of involvement and the COO interaction effect on consumer PrEva has also been studied to indicate the intensity of COO impact. Due to rapid globalization and reduction in trade barriers, major emerging economics from Asia witnessed entry of cross-border brands into their markets. India, as an emerging economy, has also followed the trend. In this backdrop, the present study is proposed for a better understanding of the effect of COO on PrEva.

The study has been conducted in India, and the mobile phone market has been taken as the hub of the study because of the presence of maximum cross-border companies in India. The study is built on existing literature on influence of COO, brand image (BI), awareness about brands, CI and product image on consumer’s evaluation of mobile phones. Seven theoretical constructs have been used to test the relationship between the COO and consumer PrEva for the selected product with the help of structural equation modeling technique.

The study shows that brand awareness for the class of mobile phones selected does not affect PrEva whereas CI, which is a general perception about the country from where the product originates, significantly affects consumers’ PrEva. Results also show that COO does not act as a mediator between CI and PrEva even though COO has a significant negative direct effect on PrEva. Thus, from this study, the learning for marketers in this price segment for mobile phones in emerging markets is that consumers are more sensitive to features per price than any other influencers. So, BI and/or COO are not sufficient conditions for product success in emerging market like India.

The findings are outcomes of an empirical study conducted in mobile handset industry of India based on the sample set of urban consumers in the city of Kolkata, India. It is quite possible that there may be other issues relevant to other parts of the country. This may influence the degree of association studied herein. Hence, the results may be treated with caution in terms of the generalizations of the same to other regions and countries. The study may, in future, be done by including questions related to price sensitivity and perception about features of the mobile phone hand-sets. This may give a clearer picture about the influences of these factors on PrEva of mobile phones in emerging markets. Finally, this study should be repeated in other major emerging markets to test the general applicability of the theoretical model and the empirical results introduced in this paper.

For the low-end mobile handset market, general country characteristics from where the product originates reduce the importance of specific product attributes in evaluating the product. We may further conclude from the present study that the country’s overall image can be an effective tool to influence the consumer PrEva process. To be competitive in an emerging country like India, multinational firms should promote the overall country’s image to cement relationship with target consumers.

Moreover, Ahmed and d’Astous (1996) viewed that several empirical research have independently focused on the impact of COO on the country, product or brand level, but there is lack of an integrated approach in this regard to capture different propensity of effects of COO at different levels. To our knowledge, it is one of the first attempts to analyze consumers’ PrEva of mobile phones in an emerging market.