Variation In Sales Research Articles

Sales Persistence and the Reductions in GDP Volatility

A number of explanations for the observed decline in GDP volatility since the mid-1980s have been offered. Valerie Ramey and Daniel Vine (2003a, 2003b) in a couple of recent papers offer the hypothesis that a decline in the persistence of sales is an explanation for the decline in GDP volatility. Their models show that a decrease in sales persistence leads to a decline in the variance of production relative to the variance of sales. They provide econometric evidence that the persistence of unit automobile sales has declined at both the aggregate and model level. This paper explores reasons why sales persistence may have declined and then tests the Ramey-Vine hypothesis with monthly chain-weighted sales data from 2- and 3-digit manufacturing and trade industries. The estimates confirm the Ramey-Vine findings for motor vehicle retailers, wholesalers, and manufacturers. For a number of industries outside of motor vehicles, especially those in wholesaling and nondurable manufacturing, considerable evidence is found of declines in sales persistence. These declines seem to be consistent with changes in supply and distribution chains that have occurred as the result of the introduction of new information, inventory, and production control systems. However, in equations estimated for aggregate manufacturing, wholesaling, and retail sector sales, declines in sales persistence are not found.

Hotels and restaurants—are the risks rewarded? Evidence from Norway

The purpose of this paper is to apply accounting-based risk measures to describe and compare the risk involved in accommodation and restaurants to three other industries. It is found that the variation in earnings is particularly high in construction, but also significant in accommodation and restaurants. The volatility in construction is caused by variance in sales. Construction companies experience low profits when the economy is depressed, while earnings boom when the economy progresses. On the other hand, earnings in retail are stable. It appears that retailing provides low risk and steady profits. The sales variance in accommodation and restaurants is fairly low. The high variability in earnings is mainly caused by operational and financial leverage. Despite the high total risk, hotels and restaurants earn less profit than the other industries examined. Apparently, the market does not reward self-inflicted risk induced by high leverage. Perhaps Norwegian accommodation providers and restaurant keepers have concerns other than maximising profit and minimising risk, and owners’ lifestyles are reflected in the financial accounts. An alternative explanation might be that the accommodation/catering sector has had unrealistic expectations in the period covered by the research.

Ownership Structure as Corporate Governance Mechanism: Evidence from Chinese Listed Companies

This paper examines the cross-sectional relation between ownership structure and corporate performance of a sample of 434 manufacturing firms listed on the Chinese stock exchange. Following the agency theory and taking other influential factors into account, such as firm size, leverage ratio, variance of sales, growth of sale and firm age, the results suggest that there is a strong relation between ownership concentration and corporate performance, measured by Tobin's Q. A further classification of owners reveals that while shares held by state play a negative role in corporate governance, domestic institutional and managerial shareholdings improve the firms' performance.

A New 2-Variate Method for Sales Variance Analysis in a Mathematical Way

A New 2-Variate Method for Sales Variance Analysis in a Mathematical Way

Serial Correlation in Demand, Backlogging and Production Volatility

This paper explores the idea that stockout avoidance motives together with serial correlation in demand and/or the backlogging of excess demand is sufficient to induce firms to behave so that the variance of production exceeds the variance of sales. The authors show that the idea holds under a wide range of circumstances facing firms, including additive as well as multiplicative demand uncertainty, a general form of serial correlation in demand, whether inventory and shortage costs apply, and whether payment occurs at order or delivery. The results greatly enhance the empirical relevance of the idea. Copyright 1996 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Uncertainty in Sales and Inventory Behaviour in the U.S. Trade Sectors

This paper investigates uncertainty in sales and inventory behavior for the U.S. wholesale and retail trade sectors. First, using a vector error correction model with GARCH-M, the authors find that the uncertainty measured by forecast error variance in sales does not affect inventory behavior in both trade sectors. Second, using forecast error variance decomposition and estimated permanent components, they observe that the uncertainty may be attributed more to demand shocks than to cost shocks.

Analysing sales variances: a microeconomic approach

Sales variance is a performance measure commonly reported by management accountants: it is the difference between the actual and the expected sales revenues. Given additional information, it can be decomposed into component variances arising from changes in price, sales volume, market size and market share. However, little guidance is provided to managers and marketers on the proper interpretation of these quantities. Further, the standard textbook analysis neglects the effect of price on sales volume and therefore may be misleading. Before an unambiguous interpretation of the causes of variance can be made, price-volume relationships must be either known or assumed. We present decompositions of the sales volume variance that incorporate the price-volume relationship and discuss their use, in order to help measure performance and formulate market strategy.

Simulating the inventory cycle

Simulations of the inventory cycle utilizing a model based on 84 interacting firms in 21 industries reveals that the ratio of the variance of sales to the variance of output is an unreliable indicator of how seriously firms attempt to smooth production or whether such efforts, in the aggregate, even out economic fluctuations. It is also shown that the ‘two slow’ estimates of the speed of adjustment reported in many econometric studies of United States data may result in part from aggregation bias rather than from limitations of the flexible accelerator model itself.

The Variability of Aggregate Demand with (S, s) Inventory Policies

This paper develops a general theory of the aggregate implications of (S, s) inventory policies. It is shown that (S, s) policies add to the variability of demand, with the variance of orders exceeding the variance of sales. Overall, the (S, s) theory contradicts the widely held notion that retail inventories act as a buffer, protecting manufacturers from fluctuating sales. In 1951, Arrow, Harris, and Marschak [3] introduced the (S, s) form of inventory policy. The policies are designed for retailers of finished goods, who face economies of scale when placing orders with their suppliers. To pursue an (S, s) inventory policy, the retailer establishes a lower stock point s, and an upper stock point S. No order is placed until inventories fall to s or below, whereupon they are restored to the maximum of S. A general proof of the optimality of these (S, s) inventory policies was provided by Scarf [13]. At the microeconomic level, the model has been extensively investigated. Formulae are available to compute optimal policies (e.g., Ehrhardt [6]), and these policies are xidely used in industry (e.g., Schwartz (ed.) [14]). In addition, the model has been extended to increasingly complex demand environments (e.g., Karlin and Fabens [11]). In contrast, little is known about the macroeconomic implications of (S, s) policies. Several recent papers have begun to correct this deficiency. Akerlof has suggested that pursuit of constant threshold money holding policies of the (S, s) variety might be responsible for the observed low short-run income elasticity of the demand for money (Akerlof [1] and Akerlof and Milbourne [2]). In the operations research literature, Ehrhardt, Schultz, and Wagner [7] analyzed the demand environment of a wholesaler supplying several retailers. They required that the distinct retailers have independent sales, ruling out the analysis of common factors in sales. Finally, simulation results of Blinder [4] suggested a role for the (S, s) model in understanding retail sector inventories. However the theoretical difficulties with the model remained unresolved. Blinder commented: If firms have a technology that makes the S, s rule optimal, aggregation across firms is inherently difficult. Indeed it is precisely this difficulty which has prevented the S, s model from being used in empirical work to date (Blinder [4, p. 459]). In this paper we present a general theory of the aggregate implications of (S, s) policies. Our central finding is that (S, s) policies add to the variability of demand, with the variance of orders exceeding the variance of sales. This result holds even in the presence of common factors in retail sales. In addition, a close connection

A Simple Durable Goods Model

The durability of a good has two implications. First, it can be stored in inventories by producers. Second, if it provides a stream of services to consumers, consumers may wish to defer purchases to take advantage of price fluctuations. The most significant conclusion is that the stockpiling of demand that results when consumers defer purchases explains why the variance of output exceeds the variance of sales even if demand shocks are serially independent.

The Production and Inventory Behavior of the American Automobile Industry

Understanding inventory movements is central to an understanding of business cycles. This paper presents an empirical study of the behavior of inventories in the automobile industry. It finds that inventory behavior is well explained by the assumption of intertemporal optimization with rational expectations. The underlying cost structure appears to have substantial costs of changing production as well as substantial costs of being away from target inventory, thelatter being a function of current sales. Given this cost structure, whether inventory behavior is stabilizing or destabilizing depends on the characteristics of the demand process. In the automobile industry, inventory behavior is destabilizing: the variance of production is larger than the variance of sales.

The dependency of the prices of farm estates on arable land and forest land areas in Finland in the years 1961, 1962 and 1966

As general conclusions of the performed regression analysis it can be established that the increase of both agricultural and forest land areas increases the sales price less than proportionally (elasticity < 1) and that the area of arable land, the forest land and the dummy variables explain, in 1961, about 43 %, in 1962 29 % and in 1966 about 39 % of the changes of the sales prices of farm estates in the whole country. From the provincial models it can be observed that the variance of sales prices explained is highest in the field cultivation territory, i.e. the provinces Turun ja Porin lääni, Hämeen lääni and Kymen lääni, in which it varies from 42—58 %. On the other hand, in the ’’forest region” of Finland, i.e. in the provinces Keski-Suomen lääni and Pohjois-Karjalan lääni, the variance explained was also high, being 33—52 %. The lowest R2‘s were found in the provinces Mikkelin lääni, Vaasan lääni and Oulun lääni. Thus many other factors, such as the quality of the land, the location of the estate with regard to population centers, the buildings, roads and environmental factors, which could not be included in this research, are evidently important arguments of the sales price. It would thus be necessary to expand the research in suchwise that the explanatory efficiency of the mentioned variables would be tested, which would, again, mean a considerable decrease of the size of the sample, because it would be necessary to obtain the data by means of expensive field research and interviews (BRIGHAM 1965). As it is difficult to find out the movements of the price level of farm estates by means of the regression analysis, here will be given the sales prices of estates in the whole country by averages, according to size categories, which averages are compared with the development of the indicators of the general price level (the basic year being the same in all series, 1961 = 100). 1961, 1962, 1966; The wholesale price index: the general index of home market goods (1935 = 100) 100 102 122; The producer price index for agricultural products (1937—39 = 100) 100 102 132; The cost of living index (X 1951 = 100) 100 104 131; The sales prices of farm estates in the whole country according to the total area: area 2—5 hectares 100 116 144; „ 5—10 „ 100 113 157; „ 10—20 „ 100 110 138; „ 20—50 „ 100 104 151; „ 50— „ 100 127 148; The sales prices of farm estates in the whole country according to the area of arable land: area 2—5 hectares 100 110 136; „ 5—10 „ 100 106 149; „ 10-20 „ 100 101 156; „ 20—50 „ 100 135 160; The sales prices of farm estates in the whole country according to the forest land area: area 2—5 hectares 100 122 154; „ 5-10 „ 100 109 156; „ 10—20 „ 100 100 140; „ 20—50 „ 100 113 163; „ 50— „ 100 125 131. As can be observed from the above table and figure 1 the sales prices of farm estates have risen considerably more in the years 1961—1966 than the general price level as measured by the Wholesale Price Index or the Cost of Living Index. Relatively the greatest price increase has taken place in farm estates with 5–10 hectares, and thereafter in sequence farm estates with 20—50 hectares, more than 50 hectares, 2—5 hectares and 10—20 hectares. It is also to be observed that the sales prices of all farm estates have risen more than the Producer Price Index for Agricultural Products.