Firm Responses to Income Inequality and the Cost of Time

The full cost of shopping includes the cost of the shopper's time.When that cost increases, stores have incentives to respond in waysthat economize on shopper time. One response is to substitute in-storelabor for shopper time. Pooled cross-sectional tests using data fromsuburban and city food stores show that various labor intensitymeasures are higher where the opportunity cost of shopper time ishigher. We distinguish between income and cost of time effects byshowing that store labor intensity depends on the composition ofincome between male and female members of the family, and notonly on the level of family income. We obtain similar results for twoother ways that food stores can economize on shopper time – locatingcloser to the customer and offering more check out stations withina store. We also use a unique shopping time survey to showthat shoppers from higher income households make fewer visitsto food stores, spend less time per visit in the check out line andare more likely to shop at stores with longer hours.     

A model of technology, domestic distribution, and North-South relations

This article describes the macroeconomic model used in an ongoing study of technology policies and North-South relations. Other aspects of the study have been described elsewhere [1,2]. We examine here the rationale for the theoretical structure used in the model, the details of the equations, and a computation of solutions. To illustrate the dynamics of the model, a brief discussion is also given of the preliminary results that indicate critical relationships between domestic technology and income distributions and the North-South terms of trade. The results are based on a calibration of the model using data for Brazil and the United Kingdom.     

Economic Implications of WTO Negotiations on Non‐agricultural Market Access

The economic implications of current WTO negotiations are likely to be far reaching. The World Bank and UNCTAD estimate annual global gains in agriculture and non‐agricultural products (including fish) of about $70−150 billion each under various scenarios and technical assumptions. Liberalising trade in services could be even more important, especially if agreement were reached to facilitate the temporary movement of labour (Mode 4 under the General Agreement on Trade in Services, GATS). Some qualifications, however, are in order. First, gains are likely to be spread unevenly across countries and across sectors; and, second, short‐term adjustment costs might precede long‐term gains. Much depends on how ambitious liberalisation is and on policies to facilitate adjustment. This paper examines the Doha mandate in non‐agricultural market access (NAMA) and the current state of the WTO negotiations, in particular some key proposals being considered at the December 2005 Ministerial Meeting in Hong Kong. We analyse various scenarios and their implications for trade, welfare, output, employment, revenues and preferences, as well as the distributional effects across countries and sectors. We note possible adjustment problems related to balance of payments and structural adjustment, as well as revenue and preference losses. These suggest the need for ‘aid for trade’ to help developing countries realise gains possible from WTO negotiations.     

Bayesian multi-resolution spatial analysis with applications to marketing

Marketing researchers have become increasingly interested in spatial datasets. A main challenge of analyzing spatial data is that researchers must a priori choose the size and make-up of the areal units, hence the resolution of the analysis. Analyzing the data at a resolution that is too high may mask ??macro?? patterns, while analyzing the data at a resolution that is too low may result in aggregation bias. Thus, ideally marketing researchers would want a ??data-driven?? method to determine the ??optimal?? resolution of analysis, and at the same time automatically explore the same dataset under different resolutions, to obtain a full set of empirical insights to help with managerial decision making. In this paper, we propose a new approach for multi-resolution spatial analysis that is based on Bayesian model selection. We demonstrate our method using two recent marketing datasets from published studies: (i) the Netgrocer spatial sales data in Bell and Song (Quantitative Marketing and Economics 5:361?C400, 2007), and (ii) the Pathtracker^? data in Hui et al. (Marketing Science 28:566?C572, 2009b; Journal of Consumer Research 36:478?C493, 2009c) that track shoppers?? in-store movements. In both cases, our method allows researchers to not only automatically select the resolution of the analysis, but also analyze the data under different resolutions to understand the variation in insights and robustness to the level of aggregation.