Productivity and convergence in India: A state-level analysis

Total factor productivity plays an important role in the growth of the Indian economy. Using state-level data from 1993 to 2005 that were recently made available, we find widespread regional variation in productivity changes. In the years immediately following economic liberalization, productivity growth improved technical efficiency; however, in subsequent years, productivity growth was propelled by technological progress. We find a tendency toward convergence with regard to productivity growth among states; however, the states that were technically efficient when the economic reforms were instituted remained innovative in later years.     

THE RELATIVE INCOME HYPOTHESIS-A CROSS COUNTRY ANALYSIS

This paper presents an attempt to examine the applicability of the relative income hypothesis (RIH) in terms of its various specifications proposed by Duesenberry, Duesenberry, Eckstein and Fromm (DEF), Davis and the authors (MD). Using the time series data for 1951 through 1968 the analysis has been carried out for Canada, Finland, Guatemala, Honduras, India, Japan, Philippines, Sweden, United Kingdom and the United States. It is found that RIH provides a fairly good representation of the consumption behaviour of all the countries included in the study. All specifications, however, do not perform equally well. DEF and Davis functions score the maximum points; MD comes at par with DEF in case of Finland, Guatemala, and India. The original Duesenberry specification performs very poorly. This leads us to conclude that the process of habit formation is continuous contrary to what is implied by Duesenberry's original specification and that consumption is a better indicator of the standard of living than income is. Estimates of the long-run marginal propensities to consume are essentially the same as those computed from the permanent income hypothesis by Singh and Drost [1970]. This lends support to the view that the two hypotheses have essentially the same long-run implications.     

ECONOMIC IMPORTANCE OF THE SUGAR INDUSTRY FOR FIJI

For more than a century, the sugar industry has been perceived as the backbone of the Fijian economy, given its contributions to gross domestic product (GDP) and employment generation. However, because of the non-renewal of land leases and the gradual withdrawal of preferential prices by the European Union, the industry is on the verge of collapse. We use the Fiji computable general equilibrium model to simulate the economy-wide impact of a 30% reduction in sugar production. Among our key results, we find that in the long run a 30% reduction in sugar production leads to a 2.1% fall in exports, and government expenditure and real consumption fall by 1.9% and 1.6%, respectively. These declines in the aggregate demand components are reflected in a fall of approximately 1.8% in Fiji's GDP. The negative repercussion of declining economic growth is reflected in a 1.5% decline in real national welfare.     

A polynomial goal programming approach to a class of quality control problems

The traditional quality control approach based on statistical tools has been very useful and effective when output and input qualities can be denned in terms of a single characteristic. However, in process industries such as paper, the output quality is denned in terms of two or more distinct characteristics; hence, reducing the deviation of one output characteristic from its permissible limits could result in forcing other output and/or input characteristics to deviate from their respective limits. Compounding this phenomenon is the fact that most of these industries produce substantial amounts of pollutants whose characteristics are a function of the input and output characteristics. Thus, with increasing costs of waste treatment and stringent pollution standards, there arises a notion of a trade-off between attaining market specified output characteristics and meeting federally regulated pollution standards.In this article a general process quality control problem has been formulated that reflects the above trade-off both in terms of a linear and a polynomial goal programming problem. Major advantages and differences between the two formulations are highlighted and illustrated with a practical example drawn from the paper industry.Three separate cases each with different priorities assigned to the output, pollutant and input characteristics are developed and solved under both formulations. Based on the analysis it is observed that the different solutions that result are contingent on the assumptions concerning the priorities associated with each goal and the manner by which one chooses to incorporate tradeoffs between goals in the objective function. Additionally, it is found that the solutions obtained under polynomial goal programming formulation are more conducive for implementation in practical quality control contexts.     

Demographic trends and the impact of family welfare programmes in India

The authors examine population growth trends and evaluate family planning programs in India. They note that despite intensified efforts and government claims of increased acceptance of family planning, the birth rate has remained stationary since 1977.     

A model for risk classification of banks

This paper deals with designing a bank risk classification scheme based on readily available performance data. This risk rating is referred to as ‘Risk rating’. Due to non-availability of data on CAMEL rating (C rating), R risk rating has potential for studying risk-based premiums insurance policy and for determining optimal frequencies for variable frequency on-site examination policy. A composite non-performance measure is developed to estimate probability of failure of a bank based on performance data available in bank call reports by fitting a Logit curve and estimating its parameters using maximum likelihood method. Division of banks into healthy and watchful types is based on critical dividing value of probability of failure.     

Modeling the relationship between firm and user generated content and the stages of the marketing funnel

While research has successfully linked social media to separate customer metrics, an in-depth conceptual and empirical understanding of how social media affects the stages of the marketing funnel is currently lacking. We draw on extant theories of consumer information processing and source credibility to conceptually link and contrast the relationships between firm generated content (FGC) dimensions of neutral valence, positive valence and vividness, user generated content (UGC) dimensions of volume and valence and the marketing funnel stages of awareness, consideration, purchase intent and satisfaction. Using daily aggregate brand-level data for 19 brands across seven industries, our analysis shows that UGC dimensions have a stronger relationship with awareness and satisfaction while FGC dimensions are more effective for consideration and purchase intent. Specifically, we observe that FGC vividness has the strongest relationship with consideration and purchase intent, while UGC valence dominates UGC volume for these stages. Our results also show that brands with higher corporate reputation have stronger relationships between dimensions of FGC and the marketing funnel stages. Findings by consumption category show that UGC and FGC dimensions have larger positive relationships with awareness for durables and non-durables, and with consideration, purchase intent, and satisfaction for services. Thus, overall, our study offers critical managerial insights into social media marketing regarding how to leverage both FGC and UGC in managing the marketing funnel and brand reputation.     

Identifying Customer Loyalty Supporting Factors in the Retail Banking Context: An Empirical Examination

This article aims to identify factors which support customer loyalty, either through dedication or by imposing constraints, and subsequently empirically analyzes the relationship between identified factors and the attitudinal and behavioral component of loyalty in the context of retail banking. Data collected from a sample of 293 retail banking customers have been analyzed through structural equation modeling. The study results suggested two constraint promoting factors and one dedication promoting factor which supported loyalty. The study contributes to knowledge by empirically testing the qualitative model and coming out with empirically proven dedication-promoting and constraint-promoting loyalty supporting factors.     

Do market characteristics impact the relationship between retailer characteristics and online prices?

We propose that market characteristics interact with retailer characteristics to determine online prices. The retailer characteristics examined include—service quality of a retailer, channels of transaction provided by a retailer and the size of a retailer. The market characteristics capture the level and nature of competition, and the price level of a product. We utilize a Hierarchical Linear Model (HLM) framework for capturing and testing the proposed interactions. The better fit between the model and the online market structure is reflected by a twenty-five percent increase in explainable price dispersion over results from comparable studies. Our study demonstrates that while retailer characteristics do impact online prices, this influence is significantly enhanced or diminished by the accompanying market characteristics.