Abstract
This paper presents a new decomposition of the cost of living into price, variety-quality and variety-quantity components. Variety-quantity reflects the value to consumers of an increase in the number of products, while variety-quality measures the average attractiveness of new versus disappearing products. The decomposition is relevant to calculation of the CPI and understanding firms’ product development practices. Our empirical results, using a large US scanner data set, show that variety-quality change is the most important component of variety improvement. This reduced the cost of living by 1.34 percentage points per annum on average, while variety-quantity lowered it by 0.67 percentage points.
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Notes
Hedonic regression methods involve estimating shadow prices for the characteristics which constitute a product. These estimates can be used to value and adjust for changes in a product’s characteristics over time.
The BLS is a standout here with around one-third of the index adjusted using hedonic methods (BLS 2017). This high proportion primarily reflects the use of hedonics for owner-occupier rents which constitutes a significant share of the index. Globally, hedonic adjustments are used in a wide range of areas including used cars, clothing, footwear, household appliances and electronics. Amongst the countries they surveyed, Wells and Restieaux (2014) found that the following used hedonic adjustments: Australia, Canada, New Zealand, the USA, Germany, Sweden, the UK and Switzerland.
Note that the expenditure shares of new and disappearing goods will be influenced by the particular prices that retailers choose to charge in the respective periods. There is some evidence that new and disappearing products are priced differently from existing products (see Melser and Syed 2016, 2017). For example, disappearing products are subject to runout sales. The extent to which these expenditure shares are influenced by such life cycle pricing factors is an important question, but the one that we leave aside in this paper.
The infinite reservation price is a feature of the CES functional form which has raised questions about its ability to accurately value the gains from variety. In the closely related logit discrete choice approach to demand estimation it has been argued that the logit functional form—which has similar properties to the CES—tends to exaggerate the gains from variety (Ackerberg and Rysman 2005).
This index number formula is attractive, while it is not superlative, as defined in Diewert (1976) because the CES preferences structure for which it is exact is not sufficiently flexible, Diewert (1978) argued that the index was pseudo-superlative in that it approximated superlative indexes very closely.
More specifically, price indexes can be distorted by the differential weight given to price rises and falls as a result of sales. The price decline associated with a sale is given a large weight as purchases spike at this time. However, the price rise at the end of a sale is given less weight than the fall. This is because purchases in the period after the sale are diminished due to the stockpiling that occurred during the sale. This process can lead to significant chain index ‘drift’. Additionally, Melser and Webster (2017) document chain drift arising as a result of the large price falls observed in the last period in which a product is available for sale.
There is somewhat less concerned with the correlation of demand and supply shocks in our ‘goods’ demand framework than in alternative frameworks such as the logit discrete choice ‘characteristics’ approach. This is because we are not modelling demand as a function of the characteristics of a product, but are effectively using product fixed effects. This means that, unlike in the discrete choice approach, we do not have to worry about unobserved product characteristics possibly inducing correlation between supply and demand errors. All product characteristics are encompassed in the product-specific effect. Moreover, these characteristics do not change over time as we are considering products at the barcode level—any meaningful change in characteristics necessitates a change in barcode. However, as we note later, concerns about endogeneity remain, particularly with regard to advertising and in-store promotions which are not included in our model, but may change demand independent of price.
We are thankful to a referee for their insightful comments on this issue.
Our data are comprehensively summarized, for each city and product category, in Table A.1 of Online Appendix.
Complete results, by product and city, are reported in Table A.2 of the Online Appendix.
Table A.2 in Online Appendix provides data for all city–product combinations for the statistics shown in Table 2.
The full results, for each city and product combination, are shown in Table A.3 of Online Appendix.
In the bisquare weighting function we used the weights that had the form \(w_{it} = I(|r|<1) ( 1 - r_{it}^2 )^2\), where \(r_{it} = u_{it} / ( 4.685 * \hbox {MAD}/0.6745 )\) and MAD is the mean absolute deviation. The numbers represent various widely used tuning parameters, while \(I(|r|<1)\) is an indicator function which is 1 if the statement is true and 0 otherwise.
Full results, for each product category and city, are given in Table A.4 of Online Appendix.
While using a nested CES approach will likely better fit the data the nature of the nesting structure is somewhat contentious. Broda and Weinstein (2010) used brands as their subgroups. However, it is not clear that brand—as opposed to other characteristics of the product, such as size and flavour—is the most important feature across which consumers substitute. Our data did not include product grouping characteristics. Hence, we did not pursue the nested approach. The nature of consumer substitution amongst product groupings, however, is an important area of future research as it will allow more realistic applications of the CES functional form to expenditure data.
The effects on the variety-quality component are just the mirror image of this because the variety-quality and variety-quantity effects add up to the overall variety effect which is fixed.
The results for a span of 24 months are shown in Table A.6 of Online Appendix and are broadly similar to those using a 13-month span.
This is given in Table A.3 of Online Appendix.
References
Ackerberg D, Rysman M (2005) Unobserved product differentiation in discrete-choice models: estimating price elasticities and welfare effects. RAND J Econ 36(4):771–788
Balk B (2000) On curing the CPI’s substitution and new goods bias, Discussion paper
BLS (2017) Hedonic quality adjustment in the CPI. https://www.bls.gov/cpi/quality-adjustment/home.htm. Accessed 10 Oct 2017
Bresnahan TF (1997) The apple-cinnamon cheerios war: valuing new goods, identifying market power, and economic measurement. www.web.stanford.edu/~tbres/Unpublished_Papers/hausman%20recomment.pdf. Accessed 10 Oct 2017
Bresnahan TE, Gordon RJ (eds) (1996) The economics of new goods NBER studies in income and wealth, vol 58. he University of Chicago Press, Chicago
Broda C, Weinstein DE (2006) Globalization and the gains from variety. Q J Econ 121(2):541–585
Broda C, Weinstein DE (2010) Product creation and destruction: evidence and price implications. Am Econ Rev 100(3):691–723
Bronnenberg BJ, Kruger M, Mela CF (2008) The IRI marketing data set. Market Sci 27(4):745–748
de Haan J, van der Grient HA (2011) Eliminating chain drift in price indexes based on scanner data. J Economet 161(1):36–46
Diewert WE (1976) Exact and superlative index numbers. J Economet 4(2):115–145
Diewert WE (1978) Superlative index numbers and consistency in aggregation. Econometrica 46(4):883–900
Feenstra R (1994) New product varieties and the measurement of international prices. Am Econ Rev 84(1):157–177
Feenstra RC (2010) Product variety and the gains from international trade. MIT Press, Cambridge
Feenstra RC, Markusen JR (1994) Accounting for growth with new inputs. Int Econ Rev 35(2):429–447
Gini C (1931) On the circular test of index numbers. Metron 9(9):3–24
Groshen EL, Moyer BC, Aizcorbe AM, Bradley R, Friedman DM (2017) How government statistics adjust for potential biases from quality change and new goods in an age of digital technologies: a view from the trenches. J Econ Perspect 31(2):187–210
Hardy GH, Littlewood JE, Polya G (1934) Inequalities. Cambridge University Press, Cambridge
ILO (2004) Consumer Price Index manual: theory and practice, Produced by: ILO/IMF/OECD/UNECE/Eurostat/The World Bank, Geneva
Ivancic L, Diewert EW, Fox KJ (2011) Scanner data, time aggregation and the construction of price indexes. J Economet 161(1):24–35
Melser D (2006) Accounting for the effects of new and disappearing goods using scanner data. Rev Income Wealth 52(4):547–568
Melser D (2017) Scanner data price indexes: addressing some unresolved issues. J Bus Econ Stat 36(3):516–522
Melser D, Syed IA (2016) Life cycle price trends and product replacement: implications for the measurement of inflation. Rev Income Wealth 62(3):509–533
Melser D, Syed IA (2017) The product life cycle and sample representativity bias in price indexes. Appl Econ 49(6):573–586
Melser D, Webster M (2017) Multilateral methods, substitution bias and chain drift: an empirical note, Discussion paper
Nielsen (2015) Breakthrough innovation report 2015—Europe edition. http://innovation.nielsen.com/breakthrough2015EU
Rao DSP (2001) Weighted EKS and generalized CPD methods for aggregation at the basic heading level and above basic heading level, In: Joint World Bank - OECD Seminar on Purchasing Power Parities, Recent Advances in Methods and Applications, Washington D.C
Redding SJ, Weinstein DE (2017) A unified approach to estimating demand and welfare, NBER working paper no. 22479
Sato K (1976) The ideal log-change index number. Rev Econ Stat 58(2):223–28
Schultze CL, Mackie C (eds) (2002) At what price?: conceptualizing and measuring cost-of-living and price indexes. National Academy Press, Washington, D.C
Sheu G (2014) Price, quality and variety: measuring the gains from trade in differentiated products. Am Econ J Appl Econ 6(4):66–89
Szulc B (1964) Indices for multiregional comparisons. Przeglad Statystyczny 3:239254 [in Polish]
Vartia Y (1976) Ideal log-change index numbers. Scand J Stat 3(3):121–26
Wells J, Restieaux A (2014) Review of hedonic quality adjustment in UK consumer price statistics and internationally, Office for National Statistics, UK
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The author is grateful to Mike Krueger and IRI for making available the scanner data used in this study. The author is also grateful to the editor and for insightful comments received from two anonymous referees.
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Melser, D. Valuing the quantity and quality of product variety to consumers. Empir Econ 57, 2107–2128 (2019). https://doi.org/10.1007/s00181-018-1532-6
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DOI: https://doi.org/10.1007/s00181-018-1532-6