بررسی اثرات ساختار بازار بر قیمت های لباس و مبلمان خانگی

In this paper prices of clothing and household furnishings are explained using commuting variables and market concentration of department stores. The concentration variable has a strong effect on prices of both product types. The commuting variables only affect furnishings prices, a result that is in keeping with expectations.

A growing literature exists linking structure to prices, previous studies dealing heavily with banking practices and grocery retailing but also with a collection of other industries.1 In this paper, retail prices for household furnishings and clothing are explained using a measure of department store concentration and commuting variables. The commuting variables were first used in Claycombe and Mahan (1993)and price and cost data are similar to those used in Lamm (1981). The study of both furnishings and clothing prices presents a unique opportunity to test the effects of commuting behavior on structure and retail prices. As shown in Claycombe and Mahan (1993)and Claycombe (1995), retail markets for food are most competitive when commuters travel long distances. In these circumstances, commuters pass many stores, enlarging the narrow geographic market in which they shop. Commuting distances only matter, however, if commuters shop along their commutes and this need not be true for all products. Clothing would seem to be a product that is not often bought along a commute since much clothing is bought by consumers who do not commute (e.g. students) or it is bought with a child or spouse along, after the commute is over. Shopping for household furnishings does not, however, require the presence of a child and furnishings are not often bought by students. For this product category, commuting variables should perform as they did in the grocery studies. This study is also the first to examine the effect of concentration of department stores on prices. Results in this paper for both furnishings and clothing price regressions show department store concentration to have a strong effect on price. Results in these regressions also confirm the expectations for the commuting variables, as described in the preceding paragraph. The regression methodology follows that developed in Lamm (1981).

The regressions in this paper show department store concentration to have a strong effect on furnishings and clothing prices. The strongest effects are found in the pooled data models, but fixed effect models also show effects of some strength. The commuting effects can only be estimated with the pooled data, due to data limitations. As expected, the commuting variables only affect furnishings prices. It may be that the effects of the commuting variables in the clothing price regressions are obscured by differences in the product bases between cities. But it may also be that the commuting variables perform poorly in the clothing models because much clothing is not bought along a commute. Furnishings prices appear to be affected by the number of stores along a commute and by the proportion of commuters who ride mass transit or car pool. Cities where commuters pass many stores and where many commuters travel alone seem to have more competitive markets and lower prices for household furnishings. These findings have implications for policy of various types beyond the ones involving simple concentration. Current US merger guidelines allow consideration of factors other than concentration in challenges to mergers. Considering that mergers of major department stores have occurred recently and that concentration effects of such mergers may be substantial in some MSAs, it is of interest to consider the effects of commuting patterns. Not only might public policy makers have an interest in the competitive implications of commuting patterns, pricing strategists of retail firms should also be interested. Firms that ignore the effect of commuting patterns on demand elasticities may misinterpret their markets.