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Seasonality, consumer heterogeneity and price indexes: the case of prepackaged software

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Abstract

This paper measures constant-quality price change for prepackaged software in the US using detailed and comprehensive scanner data. Because there is a large sales surge over the winter-holiday, it is important to account for seasonal variation. Using a novel approach to constructing a seasonally-adjusted cost-of-living price index that explicitly accounts for consumer heterogeneity, I find that from 1997 to 2003 constant-quality software prices declined at an average 15.9% at an annual rate. As a point of comparison, the Bureau of Labor Statistics reports average annual price declines of only 7.7% for prepackaged software.

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Notes

  1. Jorgenson (2001) emphasizes that information gaps remain about understanding software pricing trends.

  2. See Parker and Grimm (2000) for details on the high rate of growth of prepackaged software.

  3. See Diewert (1998, 1999) and Nesmith (2007).

  4. For example, Oliner and Sichel (1994) and McCahill (1997) study price movements of word processors, spreadsheet, and database software applications. Abel et al. (2003) examine price movements of Microsoft’s personal computer software products, and Gandal (1994) analyzes prices of spreadsheets.

  5. For information on this marketing and consumer research firm go to http://www.npd.com.

  6. These definitions follow those used to measure prepackaged software in the US national income and product accounts. See Parker and Grimm (2000) for more details.

  7. In the Appendix, we report the coefficient estimates and associated standard errors.

  8. The US Census Bureau publishes retail sales seasonal factors which show the large surge in sales in December for most kinds of businesses (see http://www.census.gov/svsd/www/adseries.html).

  9. An example of a subcategory is “Foreign Language” software within the “Education” category.

  10. See http://www.census.gov/srd/www/x12a/ for more information.

  11. I define entry as the first month a product appears in the data and exit as the last month a product appears in the data.

  12. The large amount of entry and exit in March is partly driven by income-tax preparation software.

  13. In addition to the removing the suppressed observations, I also removed four subcategories in which the percentage of suppressed observations accounted for over 60% of units sold. These subcategories are Data Center Management, Drivers/Spoolers, Engineering, and Network Resource Sharing, and together they make up an insignificant portion of all units sold.

  14. The main results of the paper are robust to only dropping monthly price ratios that are in the top and bottom 1%. In this case, however, some of the price indexes for Finance software are implausibly high.

  15. “Casual Fans Are Driving Growth of Video Games,” Seth Schiesel, The New York Times, September 11, 2007.

  16. Bils (1989) discusses how these results would extend to a version of the model with monopolistic competition.

  17. The fact the maximum-overlap Fisher, Mudgett–Stone and Heterogenous price indexes are in the same ballpark, despite the large December seasonality, reflects the smooth price decline of prepackaged software over its product cycle along with the average software’s product life lasting more than a year.

  18. The BLS index is the US city average for Computer Software and Accessories series. The BLS only publishes a non-seasonally adjusted version of this price index.

  19. See Feenstra and Shapiro (2003) for a collection of articles concerning the promise and challenges of using scanner data to produce economic statistics.

  20. For a recent and thorough overview of accounting for seasonality in price indexes, see Diewert et al. (2009).

  21. For example, when looking at System Utilities, a category with little seasonality, the Heterogenous and regular maximum-overlap price indexes are quite close. Further, for Business software, which also typically exhibits little seasonality, the Heterogenous and regular maximum-overlap price indexes are also close, except for 2001. In that year, there was some unusual seasonality in Business software.

  22. Further, for software with minimal seasonality in December, the year-over-year sub-index for the Heterogenous approach will receive a small weight.

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Authors and Affiliations

  1. Money and Payments Studies, Federal Reserve Bank of New York, 33 Liberty St., New York, NY, 10045, USA

    Adam Copeland

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  1. Adam Copeland
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Correspondence to Adam Copeland.

Additional information

I would like to thank Ana Aizcorbe, Dennis Fixler and Marshall Reinsdorf for helpful comments. I also thank Andrew Miller for substantive help as a research assistant. The views expressed herein are my own and not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.

Appendix

Appendix

Table 9 reports the results from the regressions of the log of sales (prices) on product cycle dummies, with fixed effects for each software product and using revenue weights. The coefficients were plotted in Fig. 1.

Table 9 Regression coefficient estimates and standard errors
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Copeland, A. Seasonality, consumer heterogeneity and price indexes: the case of prepackaged software. J Prod Anal 39, 47–59 (2013). https://doi.org/10.1007/s11123-012-0266-2

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