Note that all NPVs are deflated to January 2000 to allow for a fair comparison

Note that all npvs are deflated to january 2000 to

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denotes the end of its life cycle, and all other terms are defined as earlier. Note that all NPVs are deflated to January 2000 to allow for a fair comparison. Analysis at the Product Level We compare first the NPVs of individual products based on their estimated price-cost margins multiplied by the actually realized demand in the past. While this analysis provides im- portant retrospective insights into differences in profitability for the analyzed sample of products, we cannot claim that these differences arise from strategy choice unless we test the counterfac- tual. Consequently, we also conduct 663 separate counterfactual pricing experiments, in which we switch the pricing strategy for each camera from skimming (penetration) to their penetration (skimming) counterpart. Specifically, we switch from monotonic skimming to monotonic pene- tration, from non-monotonic skimming to non-monotonic penetration, etc. An initial skimming conduct parameter (to measure the relative price at launch) that exceeds 1 by x%/100 is con-
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28 verted to 1-x%/100 for the corresponding penetration strategy and vice versa. For the shape pa- rameters, a 1 kis and a 2 kis , we adopt the averages across cameras from each of the four pricing strategies. Note that demand and costs are endogenously defined by the chosen pricing strategy in the policy simulations (for details of the simulation see the Web appendix). The NPV measure in Equation (7) includes both profits to the manufacturer and retailer because we cannot estimate retail margins separately. Specifically, the profit margin Bertrand kist kist m λ in (7) equals the manufacturer profit margin multiplied by the retail margin 1+ r kis . Since we consider relative profit changes in the counterfactual experiments, the retail margin cancels out and is not relevant. It may, however, matter in the comparison of estimated absolute actual profits across cameras. NPV estimates might be affected to the extent that retail margins vary with manufacturer and retailer brand strengths, product age, and price tier. To control for these potential sources of profit variance, we subject our estimates to an ANCOVA within each price tier. Here, pricing strategy serves as a quasi-experimental factor and brand dummies (brand variance), average weighted distribution (retailer variance), and the length of the camera’s life cycle (age variance) are controls. We also add mean total segment sales over the camera’s life cycle to control for time effects from evolving primary demand. Table 8 displays estimated marginal means and F-values associated with pricing strategy. Overall, we find significant differences between pricing strategies in terms of NPV. It turns out that the average NPV for a camera that followed a skimming strategy in the observation period is significantly higher than for a penetration strategy. This finding is especially strong in the me- dium (F 3,258 =5.05, p < .01) and the high-price (F 3,160 =7.84, p < .01) segment. It is less strong in the low-price segment as the F-value is only marginally significant (F 3,233 =2.47, p < .10). Al-
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29 though NPVs of monotonic and non-monotonic strategies also differ within a strategy type, this difference is not statistically significant (test statistics not shown in Table 8).
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