The effect of birth order on schooling in India

Using large nationally representative data, I estimate the effect of birth order on educational outcomes of children in India. To establish causality, endogeneity of family size is addressed by approaching an instrumental variable method. Employing a district fixed effects model and proportion of boys in the family as the instrument for number of children, I show that later-born children attain higher education compared to earlier-born children. Results are robust to inclusion of child, parents and household characteristics.     

A Time‐series Approach to the Feldstein–Horioka Puzzle with Panel Data from the OECD Countries

This paper estimates the Feldstein–Horioka equation from 1960–2007 with a panel of 13 OECD countries with the Pedroni method. It is found that the Feldstein–Horioka puzzle exists in a weaker form. Structural break tests indicated that there was a break in the mid‐1970s or in the early 1990s. These break dates seem to capture the effects of the last decade of the Bretton Woods agreement and the early years of the Maastricht agreement. In the post‐break periods, this relationship is weaker and the saving retention coefficient has declined, implying that capital mobility has increased between these OECD countries. It is likely that these two agreements may have decreased investor uncertainty and improved capital mobility. However, this conclusion should be interpreted cautiously because alternative explanations for the observed correlation between the saving–investment ratios are possible. For example, Byrne et al. (2009) have argued that the observed correlation between investment–saving ratios could be due to common global factors and therefore may not be useful for testing whether capital mobility has decreased or increased.     

Getting the most out of all your customers

Companies spend billions of dollars on direct marketing, targeting individual customers with ever more accuracy. Yet despite the power of the myriad data-collecting and analytical tools at their disposal, they're still having trouble optimizing their direct-marketing investments. Many marketers try to minimize costs by pursuing only those customers who are cheap to find and cheap to keep. Others try to get the most customers they possibly can and keep all of them for as long as they can. But a customer need not be loyal to be highly profitable, and many loyal customers turn out to be highly unprofitable. Companies can get more out of direct marketing if they see it as a single system for generating profits than if they try to maximize performance measures at each stage of the process. This article describes a tool for doing just that. Called ARPRO (Allocating Resources for Profits), the tool is essentially a complex regression analysis that can estimate the impact of a company's direct-marketing investments on the profitability of its customer pool. With data that companies already gather, the tool can show managers how much to spend on acquisition versus retention and even what percentage of their funds they should allocate to the different direct-marketing channels. Using the model, companies can easily see that even small deviations from the optimal levels of customer profitability are expensive. Applying it to one catalog retailer showed, for instance, that a 10% reduction in marketing costs would lead to a 1.8 million dollar drop in long-term customer profits. Conversely, spending 69% less on marketing would actually increase average customer profitability at one B2B service provider by 42%. What's more, the tool can show that finding the optimal balance between investments in acquisition and retention can be more important than finding the optimum amount to invest overall.     

Kill a brand, keep a customer

Most brands don't make much money. Year after year, businesses generate 80% to 90% of their profits from less than 20% of their brands. Yet most companies tend to ignore loss-making brands, unaware of the hidden costs they incur. That's because executives believe it's easy to erase a brand; they have only to stop investing in it, they assume, and it will die a natural death. But they're wrong. When companies drop brands clumsily, they antagonize loyal customers: Research shows that seven times out of eight, when firms merge two brands, the market share of the new brand never reaches the combined share of the two original ones. It doesn't have to be that way. Smart companies use a four-step process to kill brands methodically. First, CEOs make the case for rationalization by getting groups of senior executives to conduct joint audits of the brand portfolio. These audits make the need to prune brands apparent throughout the organization. In the next stage, executives need to decide how many brands will be retained, which they do either by setting broad parameters that all brands must meet or by identifying the brands they need in order to cater to all the customer segments in their markets. Third, executives must dispose of the brands they've decided to drop, deciding in each case whether it is appropriate to merge, sell, milk, or just eliminate the brand outright. Finally, it's critical that executives invest the resources they've freed to grow the brands they've retained. Done right, dropping brands will result in a company poised for new growth from the source where it's likely to be found--its profitable brands.     

Sibling brands, multiple objectives, and response to entry: The case of the marion retail coffee market

A new brand entering a market often finds itself in competition with sibling brands (those owned by the same parent company). In a case study of a retail coffee market, the authors examine how these brand relationships might influence the sibling and competitor brands' responses to entry. Using an empirically validated brand-share attraction model, the authors compare the actual responses to entry with the optimal responses under different incumbent objectives. The authors find that the responses by sibling brand are more accommodating than those of unrelated brands whose responses are consistent with the preservation of preentry levels of sales. Thomas S. Gruca (Ph.D., University of Illinois) is a Lloyd J. and Thelma W. Palmer Research Fellow and an associate professor of marketing in the Tippie College of Business at the University of Iowa. His research on defensive marketing strategy has appeared in the International Journal of Research in Marketing, the Journal of Marketing, the Journal of Marketing Research, Marketing Management, and Marketing Science. His research on health care has appeared in Contemporary Accounting Research, Health Care Management Science, and Strategic Management Journal. He is currently working on electronic prediction markets and modeling hospital network formation. He is a member of the editorial board of Marketing Letters and a reviewer for a number of management science journals. D. Sudharshan (Ph.D., University of Pittsburgh) is a professor of business administration in the College of Commerce and Business Administration at the University of Illinois at Urbana-Champaign. His research interests lie in the areas of marketing strategy, new product and service development, and marketing technology management. He serves on the editorial boards of the Journal of Marketing and the Journal of the Academy of Marketing Science. His articles have appeared in various journals including Marketing Science, Management Science, the Journal of Marketing, the Journal of Marketing Research, the Strategic Management Journal, the European Journal of Operational Research, the Journal of Service Research, and the Journal of Market Focused Management. K. Ravi Kumar (Ph.D., Northwestern University) is a professor in the Department of Information and Operations Management, Marshall School of Business, at the University of Southern California. His current research interests include the embedding of information systems within global physical operation and the development of sustainable information technology industries in developing countries. He is the author or coauthor of articles appearing in Management Science, Marketing Science, the Journal of Economic Theory, Production and Operations Management, and the Journal of Operations Management. He serves on the advisory boards of Production and Operations Management and Technology and Operations Review.     

Entry, reforms, complementarity and performance: A tale of two Indian manufacturing sectors

In this paper, we use plant-level data from two Indian industries, namely, electrical machinery and textiles, to examine the empirical relationship between structural reforms like abandonment of entry restrictions to the product market, competition and firm-level productivity and efficiency. These industries have faced different sets of policies since Independence but both were restricted in the adoption of technology and in the development of optimal scales of production. They also belonged to the first set of industries that benefited from the liberalization process started in the 1980s. Our results suggest that both the industries have improved their efficiency and scales of operation by the turn of the century. However, the process of adjustment seems to have been worked out more fully for electrical machinery. We also find evidence of spatial fragmentation of the market as late as 2000–2001. Gains in labour productivity were much more evident in states that either have a strong history of industrial activity or those that have experienced significant improvements in business environment since 1991.     

Contemporary Accounting Research: A Retrospective between 1984 and 2021 using Bibliometric Analysis*

This study critically evaluates research published by Contemporary Accounting Research (CAR) between 1984 and 2021 using bibliometric analysis. We examine the following: (i) CAR's publication quality and the factors associated with its citations and (ii) CAR's scope regarding research diversity, methods, authors geographical dispersion, and collaborative networks. The methodology permits observation of finer collaboration details and research patterns not apparent by simply categorizing the data. We use tools such as performance analysis, coauthorship analysis, bibliographic coupling, and regression analysis. The bibliometric analysis shows improvement in CAR's CiteScore and source-normalized impact per paper over time, consistent with publishing high-quality research. Our analysis reveals that authors' geographical affiliations, research subject areas, and research methods are not systematically associated with citations across our various subsamples. A notable exception is that research on audit topics generates more citations than studies examining financial accounting topics. Other factors significantly and positively associated with citations include article age, article length, number of authors, order of author names, and number of references. We also show that CAR has become more diverse regarding author affiliations, subject areas, and research methods than most leading accounting journals. Only Accounting, Organizations and Society emerges as more diverse, thereby serving as a benchmark for CAR in the future. CAR should consider focusing on high-interest areas to boost citations and tightening its acceptance criteria.     

A global food–energy–water nexus with heterogeneity,non-stationarity and cross-sectional dependence

We estimate the Food–Energy–Water (FEW) nexus for 21 countries worldwide, with data available from year 1990–2000 in order to investigate the relationship between food production and two scarce resources: energy and water. Food production is proxied by four alternative variables: The index of agricultural production, the index of crops production, the index of livestock production and the value added from agriculture. Water and energy as independent variables are controlled by methane and nitrogen emissions, capital, labor and five versions of fertilizer proxy: pesticides, insecticides, fungicides, herbicide and other. For robust estimation, we have perused a number of standard and novel panel estimators such as the common correlated effects mean group estimator and the augmented mean group estimator (AMG). These estimators can account both for non-stationarity and the cross-dependence problems. Based on standard estimators such as the generalized least squares estimator or the Arellano-Bond generalized method of moments GMM, they reveal the existence of a significant FEW nexus while the mean group estimator, the group mean DOLS estimator, the common correlated effects and the augmented mean group estimator (AMG) do not yield significant coefficients for water and energy. In the latter models only labor and pesticides are significant at 5%. Also, the unobserved total factor productivity appears significant at 5% under the AMG estimation. When significant, energy and water elasticities ranged from ? 0.001 to ? 0.256 and from ? 0.014 to ? 0.084 respectively.     

Destination brand experience and visitor behavior: the mediating role of destination brand identification

ABSTRACT

Destination branding has emerged as a critical tool for achieving competitive advantage through various conceptualizations, focusing on various aspects of branding. This research examines the role of destination brand experience (DBE), a new conceptualization, in assessing the holistic and unified view of tourism destinations. The conceptual model proposed in this research has been validated using structural equation modeling, based on the primary responses collected from 312 and 262 foreign visitors for study 1 and study 2 respectively, conducted at two different tourism destinations in India. Findings of both the studies suggest that various dimensions of DBE have a varied influence on destination brand identification (DBI), which subsequently affects both tourists’ trust and loyalty toward tourism destinations. In addition, DBI emerges as an important mediator for the relationship between DBE and destination trust (DT) as well as DBE and destination loyalty (DL). The study provides several implications for destination marketers about building trust and loyalty among tourists using DBE and DBI.     

A wavelet analysis for exploring the relationship between economic policy uncertainty and tourist footfalls in the USA

We attempt to establish the relationship between Economic Policy Uncertainty (EPU) and international tourist footfalls in the USA. In the first stage, we investigate the influence of country-specific EPU and global EPU on tourist footfalls. Since, these two are overlapping in nature, in the second stage, we study the isolated influence of country-specific EPU on footfalls by eliminating the influence of global EPU and vice versa. We consider a study period spanning over January 1997 to April 2017. To capture the variations in the relationship at different time dimensions, we apply wavelet-based techniques. We observe the following: (a) the impact of policy uncertainty shock has a little immediate impact on tourist footfalls, (b) medium to long-run shocks persist due to occurrence of major undesirable economic events, and (c) the influence of domestic (country-specific) EPU is dominant in comparison to global EPU for the USA.