Bulk Discounting and Choice of Package Size--论文代写范文精选

不同的消费者会做出不同的选择，取决于收入的边际效用,产品的储存和运输成本,以及未来的消费需求。给定的消费者会做出不同的选择,根据存储成本,耐久性,预期消费等。下面的paper代写范文进行详述。

Abstract
Many grocery items are sold at non-linear prices. Larger package sizes are sold at higher prices but at lower per unit price. For example, Hendel and Nevo (2006a) report that the regular, nonsale, price of a 24-pack of soft drinks cans cost 2.7 times more than a 6-pack. This implies a discount of over 30 percent in the per period unit price. A typical treatment of non-linear prices in the price index is to focus on unit values. This corrects for the difference in the actual price between sizes, as opposed to the difference in the size per unit. However, it does not account for the tradeoffs consumers are making (Triplett, 2003). The tradeoffs facing the consumers are similar to those we discussed in the case of sales. Consider a consumer deciding between purchasing a smaller unit and a larger one at a lower per unit price. Unless she can consume the additional quantity before her next store visit the consumer has to weigh the benefits of the lower price with the costs of storing the product longer and the deprecation in the quality of the product. Different consumers will make different choices depending on their marginal utility from income, the cost of storage and transport of the product, and their future consumption needs. A given consumer will make different choices for different products, depending on storage costs, durability, expected consumption and the price schedule.

How much do households buy in bulk?
As described in section 2 we use quintiles in package size within each food category to measure the extent of bulk discounting. The average household spends 15.8% of their total annual expenditure on the largest package sizes, and 21.2%, 21.3%, 26.8% and 14.9%, on the other sizes from largest to smallest, respectively. There is considerable variation across households in these fractions. For ease of exposition we focus on the two largest quintiles as “bulk” sizes and compare the savings made from purchasing in those quintiles to purchases made in the second largest size group. Figure 3 shows the distribution of the proportion of total household expenditure that was spent of the largest two size quintiles. Households purchase along the entire range of between 0 to nearly 100% of their groceries in large package sizes, spending on average 37% of their budget in these size groups. Unsurprisingly single person households purchase less in bulk than multi person households. Single pensioners make even less use of bulk discounts, spending on average 2.2% less on large pack sizes than single younger households. Households that shop by car buy in bulk more often and the middle income categories make greater use of bulk discounts. Overall, purchasing behaviour of larger package sizes is similar to purchasing on sale, and in fact the two shares are positively correlated at the household level, with a correlation coefficient of 0.23.

How well does a standard price index capture bulk pricing?
The issues in measurement of a price index are very similar to those we saw in the case of sales. First, there is an issue of whether the statistical agencies are correctly sampling all the relevant prices. In the UK, the specification of food items to be collected as part of the basket of goods and services used to calculate inflation rates typically contains an exact size that must be priced such that it is unusual for different sizes of the same product to be sampled. In this case there is no way to account for a change in the relative price of different sizes. As prices change, the tradeoffs between different sizes, and therefore consumer choices, will change. For example, as prices increase consumers might substitute towards larger sizes. Without sampling different sizes statistical agencies will miss this effect and compute an index that over estimates price increases. Occasionally, statistical agencies will sample different package size, for example, because firms change the sizes they offer. A common practice for statistical agencies is to chain the price from different sizes using unit values, i.e., price per ounce. This practice is justified if prices are linear in size, which is rarely the case. As pointed out by Triplett (2004), this will generate an under estimate in the price index. Finally, there is considerable variation across households in the propensity to buy in bulk. This suggests that the biases will vary significantly across households.


Abstract
Many grocery items are sold at non-linear prices. Larger package sizes are sold at higher prices but at lower per unit price. For example, Hendel and Nevo (2006a) report that the regular, nonsale, price of a 24-pack of soft drinks cans cost 2.7 times more than a 6-pack. This implies a discount of over 30 percent in the per period unit price. A typical treatment of non-linear prices in the price index is to focus on unit values. This corrects for the difference in the actual price between sizes, as opposed to the difference in the size per unit. However, it does not account for the tradeoffs consumers are making (Triplett, 2003). The tradeoffs facing the consumers are similar to those we discussed in the case of sales. Consider a consumer deciding between purchasing a smaller unit and a larger one at a lower per unit price. Unless she can consume the additional quantity before her next store visit the consumer has to weigh the benefits of the lower price with the costs of storing the product longer and the deprecation in the quality of the product. Different consumers will make different choices depending on their marginal utility from income, the cost of storage and transport of the product, and their future consumption needs. A given consumer will make different choices for different products, depending on storage costs, durability, expected consumption and the price schedule.

How much do households buy in bulk?
As described in section 2 we use quintiles in package size within each food category to measure the extent of bulk discounting. The average household spends 15.8% of their total annual expenditure on the largest package sizes, and 21.2%, 21.3%, 26.8% and 14.9%, on the other sizes from largest to smallest, respectively. There is considerable variation across households in these fractions. For ease of exposition we focus on the two largest quintiles as “bulk” sizes and compare the savings made from purchasing in those quintiles to purchases made in the second largest size group. Figure 3 shows the distribution of the proportion of total household expenditure that was spent of the largest two size quintiles. Households purchase along the entire range of between 0 to nearly 100% of their groceries in large package sizes, spending on average 37% of their budget in these size groups. Unsurprisingly single person households purchase less in bulk than multi person households. Single pensioners make even less use of bulk discounts, spending on average 2.2% less on large pack sizes than single younger households. Households that shop by car buy in bulk more often and the middle income categories make greater use of bulk discounts. Overall, purchasing behaviour of larger package sizes is similar to purchasing on sale, and in fact the two shares are positively correlated at the household level, with a correlation coefficient of 0.23.

How well does a standard price index capture bulk pricing?
The issues in measurement of a price index are very similar to those we saw in the case of sales. First, there is an issue of whether the statistical agencies are correctly sampling all the relevant prices. In the UK, the specification of food items to be collected as part of the basket of goods and services used to calculate inflation rates typically contains an exact size that must be priced such that it is unusual for different sizes of the same product to be sampled. In this case there is no way to account for a change in the relative price of different sizes. As prices change, the tradeoffs between different sizes, and therefore consumer choices, will change. For example, as prices increase consumers might substitute towards larger sizes. Without sampling different sizes statistical agencies will miss this effect and compute an index that over estimates price increases. Occasionally, statistical agencies will sample different package size, for example, because firms change the sizes they offer. A common practice for statistical agencies is to chain the price from different sizes using unit values, i.e., price per ounce. This practice is justified if prices are linear in size, which is rarely the case. As pointed out by Triplett (2004), this will generate an under estimate in the price index. Finally, there is considerable variation across households in the propensity to buy in bulk. This suggests that the biases will vary significantly across households.(paper代写)

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