Tracking Consumer Energy Price Change: An Overview of Federal Data Sources and Methodologies

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1 Tracking Consumer Energy Price Change: An Overview of Federal Data Sources and Methodologies Janice Lent 1, Joseh Ayoub 2 1 Energy Information Administration, EI-70, 0 Indeendence Ave., S.W., Washington, DC Energy Information Administration, EI-64, 0 Indeendence Ave., S.W., Washington, DC Abstract The Energy Information Administration (EIA) is researching estimation methods with the goal of develoing an exerimental Energy Consumer Price (ECPI), based almost entirely on EIA data. For some major energy sources, EIA collects universe or large-samle rice and sales data, which can be used to comute rice indexes with very low samling error. Also, EIA s model-based rojections of future energy rices and consumtion levels can be used to develo CPI forecasts for some energy comonents. Because the exerimental indexes are being comuted in a research environment rather than in a large-scale roduction environment, the rocess of incororating data from new energy surveys will be streamlined. This aer rovides background information and reliminary results of EIA s rice index estimation research. Key Words: rice index, Fisher, Törnvist 1. Data Sources for Consumer Energy Prices and Consumtion 1.1 Introduction Price index estimation reuires data on both rices aid and uantities of goods and services urchased by consumers. With regard to energy rices, a vast array of data sources is ublicly available. Price data aroriate for use in a consumer rice index, i.e., data on rices aid by ultimate consumers, including all alicable taxes and distribution costs, must be distinguished from other tyes of rice data. Moreover, household energy consumtion (e.g., number of gallons of gasoline urchased by households) is often difficult to estimate from the available data sources. umerous government and non-government entities collect and ublish information on consumer energy rices and consumtion. The American Automobile Association, for examle, makes gasoline and on-highway diesel fuel rices available on its daily Fuel Gauge Reort website. Here we focus rimarily on estimates ublished by the Energy Information Administration (EIA) and the Bureau of Labor Statistics (BLS), the rincial federal ublishers of energy and rice index data, resectively. 1.2 Energy Price and Exenditure Data from on-eia Sources The BLS uses data from three sources for its Consumer Price (CPI) rograms. The Consumer Exenditure Survey (CEX) and the Telehone Point of Purchase Survey (TPOPS) are household surveys conducted by the Census Bureau and sonsored by BLS. In addition, BLS field economists collect rice data from a samle of retail outlets each month for use in the CPI. Data from all three surveys are used in estimating consumer exenditures and exenditure shares for individual items or item categories; the estimated shares are used as weights in the CPI. The BLS all-items CPI (or CPI-U) is designed to reflect inflation exerienced by all urban consumers in the U.S. The samle of retail outlets from which rices are collected for the CPI is constructed using data from the TPOPS. Although the CPI samling frame covers the entire country, the outlets identified through the TPOPS are those from which metroolitan and non-metroolitan urban households urchase items. For certain categories of goods and services, however, the TPOPS samling frame is not used. Electricity and ied utility natural gas are two of the non- TPOPS categories in the CPI. For these categories, the BLS regional offices collect rice data from utility comanies in samled geograhic areas. For further details on the samling and estimation methods used for the BLS CPI, see the BLS Handbook of Methods (2005). The target index formulas used are described in the next subsection. 1239

2 1.3 Background on Alternative Price Formulas In general, a rice index measures inflation/deflation or change in the urchasing ower of a currency. In addition to broad inflation measures, rice indexes for secific collections of goods and services are freuently comuted, either as inuts to an aggregate index or as economic analysis tools in their own right. The economics literature contains a wide variety of rice index formulas that may be acceted as estimation targets. The textbook Laseyres formula, for examle, is given by L where is the number of items in the target oulation, and, for 1, 2 i1 i1 t, i, t and i, t denote the rice and uantity urchased, resectively, of item i in time eriod t, i 1, 2,...,. ote that the index reresents a comarison between rices in two arbitrary but discrete time eriods 1 and 2 (e.g., months, years). The classical index formulas also rely on the imlicit assumtion that the collection of items remains the same for the two reference eriods. We may also write L wi, 1, i1 i, 1 where i, t wi, t, the exenditure share associated with item i in time t 1,2. i, t i 1 i, t i, t The ratio is often called the rice relative for item i. The Laseyres index is an arithmetic mean of the rice relatives, weighted by the first eriod exenditure shares. The Paasche index is similar to the Laseyres, but it is based on uantity measures from the second eriod. We define it as P i1 i1 i1 w 1 i i,2,1 1, the harmonic mean of the rice relatives with exenditure share weights from the second eriod. In the economics literature, certain rice index formulas have come to be known as suerlative indexes, because they aroximate a true cost of living index under relatively weak assumtions regarding consumer buying behavior. (See Fisher 1922 and Diewert 1976.) The suerlative index formulas include the Fisher index, defined as and the Törnvist index, defined as F LP, T i1 i, 1 w i, where 1240

3 w i wi, 1 w. 2 Although the Fisher and Törnvist formulas share some roerties in common, they differ in their algebraic forms and in regard to sensitivity to extreme rice values (see Lent 2004). In many ractical alications, estimating either of the suerlative indexes is imossible, because data on the second-eriod uantities i, 2 and exenditure shares w i, 2 are unavailable at the time of index ublication. In such cases, the Laseyres index or the weighted Geometric Mean (or geomean ) index, w i,1 G, i1 i, 1 may be used. The Laseyres index aroximates the Fisher index when uantities urchased remain fairly consistent over time, i.e., when consumers buy a fixed market basket of items. In the case i, 1 i, 2 for all i, we have L P F. Similarly, the geomean index aroximates the Törnvist when the exenditure shares remain similar over time. When wi, 1 wi, 2 for all i, we have G T. A simler index formula is often used for aggregating rices within narrowly-defined categories of very similar items t 1,2 let measured in the same units. For such a category c and for c, t i, t. ic The unit value index for category c is defined as u c i c ic / / c,2 c,1. In words, the unit value index is the average rice aid for an item in category c during time eriod 2 divided by the average rice aid for an item in category c during time eriod 1. For more on the use of the unit value index in rice index estimation, see Balk (1999). Due to data availability and other considerations, government statistical agencies often comute national-level rice indexes by imlementing multile stages of aggregation (see, for examle, Dorfman et al or Balk 2003). The formulas and estimation methods used for the different aggregation levels often differ. In the BLS CPI rogram, for examle, the geomean index is used for aggregating rice ratios within narrowly defined categories; for higher level aggregation, the Laseyres and Törvist formulas are used. In the research described below, we used the unit value index formula to comute regional-level rice indexes for the various energy comonents. We then used L, P, F, T, and G to aggregate the unit value subindexes to the national level. (The unit value subindexes take the lace of the rice relatives in the formulas L, P, F, T, and G.) We reasoned that, if L or G rovided a close aroximation to F and T, they would be referable, due to ease of comutation. 2. Emirical Research Methods and Preliminary Results 2.1 Energy Price and Sales Data Available from EIA The EIA Regional Short-term Energy Modeling (RSTEM) system roduces estimates of residential-sector average rices by region, including alicable taxes and distribution costs, for the following energy roducts: 1241

4 Electricity Pied (utility) natural gas Heating oil Proane Gasoline, all grades Gasoline, regular grade On-highway diesel fuel The regions for which average rices are comuted differ by commodity: electricity, natural gas, and heating oil rices are ublished by Census region and division, while gasoline and on-highway diesel rices are ublished by Petroleum Allocation for Defence Districts (PADD s). 1 ly residential consumtion estimates are also available, from RSTEM or other EIA sources, for the following energy roducts: Electricity Pied (utility) natural gas Heating oil Renewable sources (in BTU s), including geothermal, biofuels and biomass, and solar Household utility consumtion estimates are obtained through EIA s surveys of residential energy roviders. Household vehicle fuel consumtion, however, is more difficult to estimate, because gas stations normally do not know the ercentage of their gasoline that is urchased by household vs. commercial vehicles (e.g., taxis and family cars may be refueled at the same stations, their drivers often aying the same rices at the um). Our method of estimating household vehicle fuel consumtion from the available data is described below. Conversely, renewable energy consumtion estimates are available in BTU s, but the rice er renewable-source BTU cannot be directly estimated from current data. EIA does collect data on rices of renewable energy euiment sold to customers in the residential sector, e.g., solar thermal collector systems (EIA-63A), hotovoltaic modules and systems (EIA-63B), geothermal heat ums (EIA-902), and alternative fueled vehicles (EIA-886). The roblem of translating euiment rices into actual energy rices, however, is beyond the scoe of the research described here. 2.2 Methods and Assumtions Used in Estimating Price Weights As discussed above, estimated exenditures (rices multilied by uantities urchased) for commodities urchased are used as weights in the commonly used rice index formulas. Because of the data gas discussed above, some assumtions were used in estimating levels of residential urchases of gasoline and of #2 distillate fuel oil. For each fuel tye, we estimated adjustment factors that could be alied to the EIA estimates of fuel sales by rime suliers 2 (including sales to non-residential customers) to reduce them to levels reresentative of household sales levels. In the case of gasoline, the most recent available household consumtion estimates were from the 2001 ational Household Transortation Survey (HTS). 3 For each PADD, we calculated an adjustment factor based entirely on annual data from Because the HTS consumtion estimates were ublished by Census Division rather than by PADD, we comuted PADD-level factors as weighted averages of Census Division-level factors. We used state-level annual (2001) gasoline sales estimates to weight the Census Division-level factors in the calculations. Secifically, for each PADD P, we comuted a household sales adjustment factor for gasoline, ˆ, as follows: f g, P 1 Definitions of these geograhic areas are available at htt:// and htt:// 2 Prime suliers are dealers who sell to other dealers, end-use customers, or both. They include roducers, imorters, and wholesalers. The rime sulier sales volumes are used here as a roxy for total sales. EIA also ublishes estimates of roduct sulied, which is often used as a roxy for total consumtion. 3 The HTS motor fuel volume estimates were given in gasoline euivalents, i.e., volumes of other fuels used had been converted into gasoline euivalents. Because no data were available on the volume roortions for gasoline, diesel, and other fuels, we treated the gasoline euivalents as gallons of gasoline in this research. The values of the adjustment factors comuted are clearly higher than they would have been if estimates of gasoline consumtion alone had been used. For more information on the HTS, see htt://nhts.ornl.gov/. 1242

5 gˆ h, D, sd gˆ s sp gˆ D sd fˆ, g, P, gˆ s sp where gˆ s estimated gallons of gasoline sold by rime suliers in state s; gˆ h, D, sd s gˆ D, sd estimated gallons of gasoline euivalents sold by rime suliers in Census Division D containing state s. estimated gallons of gasoline euivalents urchased by households in Census Division D containing state ; The estimates ĝ s and gˆ D, sd were obtained from EIA data, while gˆ D, sd was based on HTS data. Total residential urchases for PADD P was then estimated as ˆ gˆ h, P f g, P gˆ P, where ĝ P estimated gallons of gasoline sold by rime suliers in PADD P, based on EIA data. Thus we assumed that the adjustment factor f ˆ g, P, calculated from 2001 annual data, could be alied to subseuent months and years a strong assumtion. The adjustment factors varied by PADD and generally ran between 0.75 and For #2 distillate fuel oil, EIA ublishes PADD-level annual estimates of volumes urchased by households and monthly estimates of total volumes sold by rime suliers. We used the annual estimates to comute annual household adjustment factors and then alied these to the monthly total sales estimates to comute monthly estimates of household urchases. Secifically, for each month m in year y, we estimated the household sales of #2 distillate fuel oil within each PADD P as follows: dˆ P y h dˆ P m h dˆ,,,, P, m, dˆ P, y where ˆ = gallons of #2 distillate fuel oil sold to households in PADD P during month m in year y; d P, m, h ˆ = total gallons of #2 distillate fuel oil sold by rime suliers in PADD P during month m in year y; d P, m ˆ = gallons of #2 distillate fuel oil sold to households in PADD P during year y; and d P, y, h ˆ = total gallons of #2 distillate fuel oil sold by rime suliers in PADD P during year y; d P, y The imlicit assumtion is that the roortion of fuel oil sales accounted for my household consumtion remains constant over the year. In fact, household fuel use tends to be more seasonal than commercial or industrial use. Estimating ˆ by this method may therefore damen the seasonality in the resulting index series. d P, m, h 2.3 Preliminary Results of Emirical Research Figure 1 shows the electricity comonent comuted with the five alternative index formulas based to January The index rojections shown run through 2009 and are based on rice and sales data from EIA s Regional Short-term Energy Models (RSTEM). The exerimental Fisher and Törnvist series run very close together; the Laseyres index runs above them, while the Paasche and geomean run low. Figure 1 also shows the BLS electricity CPI, which runs fairly close to our exerimental Fisher and Törnvist series. Initially, the BLS CPI runs below our Fisher and Törnvist, but it increases more uickly and eventually starts to run above our series. (Projections for the BLS CPI are not available.) For natural gas, we did find some fairly ronounced differences between the series comuted by the different formulas and between the BLS CPI and our exerimental indexes. The alternative series for natural gas are 1243

6 shown in Figure 2. The BLS natural gas CPI (not seasonally adjusted) clearly doesn t show the same seasonal attern that is reflected in the EIA data. The differences between these series indicates that natural gas rices follow different atterns of change in different arts of the country. The weights we aly to the rice ratios can therefore make a substantial difference in the attern of rice change indicated. Here again, however, we see the exerimental Fisher and Törnvist series running close together. Motor fuel is the largest comonent for which we faced challenges due to data gas. As described above, we relied on some assumtions in deriving the exenditure share weights. For this comonent, however, the weights had very little imact on the attern of rice change indicated by the series. Figure 3 shows all of the exerimental EIA series running very closely together, regardless of the rice index formula or the weights used. Moreover, the BLS CPI runs ractically on to of our exerimental series. This henomenon indicates that the rice movements during this ten-year eriod were very similar in different arts of the country. The rice levels, of course, were different. We know, for examle, that gas rices in the Midwest were considerably lower than rices in California. But, in terms of ercentage change from month to month, the rices were moving in tandem, essentially resonding to changes in global oil rices. In this tye of economic situation, the use of different formulas and weights has little imact on the general movements of the series. 160 Figure 1. EIA Preliminary and BLS CPI Comonent Series for Electricity January 1998 = Projected Jan-98 May-98 Se-98 Jan-99 May-99 Se-99 Jan-00 May-00 Se-00 Jan-01 May-01 Se-01 Jan-02 May-02 Se-02 Jan-03 May-03 Se-03 Jan-04 May-04 Se-04 Jan-05 May-05 Se-05 Jan-06 May-06 Se-06 Jan-07 May-07 Se-07 Jan-08 May-08 Se-08 Jan-09 May-09 Se-09 Laseyres Paasche Fisher Törnvist Geomean BLS CPI Jan-98 Figure 2. EIA Preliminary and BLS CPI Comonent Series for atural Gas January 1998 = May-98 Se-98 Jan-99 May-99 Se-99 Jan-00 May-00 Se-00 Jan-01 May-01 Se-01 Jan-02 May-02 Se-02 Jan-03 May-03 Se-03 Jan-04 May-04 Se-04 Jan-05 May-05 Se-05 Jan-06 May-06 Se-06 Jan-07 May-07 Se-07 Jan-08 May-08 Se-08 Jan-09 May-09 Se-09 Projected Laseyres Paasche Fisher Törnvist Geomean BLS CPI 1244

7 350 Figure 3. EIA Preliminary and BLS CPI Comonent Series for Motor Gasoline January 1998 = Jan-98 May-98 Se-98 Jan-99 May-99 Se-99 Jan-00 May-00 Se-00 Jan-01 May-01 Se-01 Jan-02 May-02 Se-02 Jan-03 May-03 Se-03 Jan-04 May-04 Se-04 Jan-05 May-05 Se-05 Jan-06 May-06 Se-06 Jan-07 May-07 Se-07 Jan-08 May-08 Laseyres Paasche Fisher Törnvist Geomean BLS CPI Figure 4 shows the exerimental series for heating oil (#2 distillate). In some arts of the ortheast, this comonent accounts for a fairly large share of household energy exenditures, although this is not the case at the national level. Although the attern of rice change for distillate differed somewhat from the attern for gasoline, the results for this comonent were similar in that all of the index series followed very similar aths. Prices across the country were essentially moving together. Also, for this category, the ortheast regions account for most of the exenditures, so rice movements in the ortheast regions dominate the national index movements. For distillate, as for gasoline, the BLS CPI comonent was very similar to our exerimental CPI s. 450 Figure 4. EIA Preliminary and BLS CPI Comonent Series for #2 Distillate Fuel Oil January 1998 = Jan-98 May-98 Se-98 Jan-99 May-99 Se-99 Jan-00 May-00 Se-00 Jan-01 May-01 Se-01 Jan-02 May-02 Se-02 Jan-03 May-03 Se-03 Jan-04 May-04 Se-04 Jan-05 May-05 Se-05 Jan-06 May-06 Se-06 Jan-07 May-07 Se-07 Jan-08 May-08 Laseyres Paasche Fisher Törnvist Geomean BLS CPI We aggregated the Fisher and Törnvist indexes for the four dominant energy comonents to get the exerimental CPI s shown in Figure 5. This figure shows the exerimental EIA Fisher and Törnvist series as well as the two energy CPI s that BLS ublishes. For the all energy comonent, BLS ublishes one index series (the CPI-U) based on the Laseyres formula and another (the Chained CPI-U) based on the Törnvist. Here we see the BLS Törnvist series running slightly below the BLS Laseyres for the later years of the series. The EIA Fisher and Törnvist series run together, just slightly below the BLS Tornvist. Although the reasons for the differences are not entirely clear, the BLS and EIA series are based on different data sources and different estimation methods. The fact that they track each other so closely was actually unexected. 1245

8 Figure 5. EIA Preliminary and BLS CPI Series for All Energy December 1999= Dec-99 Ar-00 Aug-00 Dec-00 Ar-01 Aug-01 Dec-01 Ar-02 Aug-02 Dec-02 Ar-03 Aug-03 Dec-03 Ar-04 Aug-04 Dec-04 Ar-05 Aug-05 Dec-05 Ar-06 Aug-06 Dec-06 Ar-07 Aug-07 Dec-07 BLS (Laseyres) BLS Chained (Törnvist) E IA Fisher E IA Törnvist 3. Conclusions and Plans for Future Research In conclusion, we have a roof of concet for the idea of estimating an ECPI based on EIA data. We can also see that the use of the Fisher or the Tornvist formula is necessary in this alication. The other indexes (Laseyres, Paasche, geomean.) ran too high or too low for some of the energy comonents we examined. In our future research, we d like to enhance our exerimental index series by adding some new comonents such as roane and ossibly wood, kerosene, or coal, though these three energy sources account for very small exenditure shares in the residential sector. We d also like to research additional forecasting ossibilities, esecially for the etroleum-based roducts. Because the weights had very little imact on the indexes, we might be able to roject fixed the indexes forward using fixed weights. In the future, we would like to include data on renewables in our index. Because most renewable energy is currently used for electric ower generation, most household consumtion of renewables in the U.S. is indirect, making rices and uantities for renewables difficult to estimate. Residential use of alternative fuels is still relatively limited in the U.S., and EIA does not yet have reliable data on the uantities of such fuels urchased by households. In the future, however, as the use of alternative motor fuels increases, better estimates of uantities and rices may become available. Similarly, direct household use of renewable energy sources may become more common in future decades. EIA s small-scale index estimation system should allow timely introduction of data from new sources in the index estimates. References Balk, B. (1999), On the Use of Unit Values as Consumer Price Subindices, Proceedings of the Fourth International Working Grou on Price Indices, BLS, Washington, D.C. Balk, B. (2003), Price es for Elementary Aggregates: The Samling Aroach, Proceedings of the Seventh Meeting of the International Working Grou on Price Indices (Ottawa Grou), Paris. BLS Handbook of Methods (2005), htt://stats.bls.gov/bls/descritions.htm Diewert, W. E. (1976). Exact and Suerlative umbers, Journal of Econometrics, 4, Dorfman, A.H., Leaver, S.G., and Lent, J. (1999), Some Observations on Price Estimators, Proceedings of the Federal Committee on Statistical Methodology Research Conference, Monday B Sessions, Fisher, I. (1922). The Making of umbers. Lent, J. (2004). Effects of Extreme Values on Price es: The Case of the Air Travel Price, Journal of Transortation and Statistics, Volume 7, umbers 2/3, U.S. Deartment of Transortation. Disclaimer: Oinions exressed in this aer are those of the authors and do not constitute olicy of the Energy Information Administration. 1246