UK Letter Mail Demand: a Content Based Time Series Analysis using Overlapping Market Survey Statistical Techniques

Transcription

1 Research Group: Econometrcs and Statstcs 2010 UK Letter Mal Demand: a Content Based Tme Seres nalyss usng Overlappng Market Survey Statstcal Technques CTHERINE CZLS, JEN-PIERRE FLORENS, LETICI VERUETE-MCKY, FRNK RODRIGUEZ ND SOTERIOS SOTERI

2 UK letter mal demand: a content based tme seres analyss usng overlappng market survey statstcal technques* Catherne Cazals 1, Jean-Perre Florens 1, Letca Veruete-McKay 2, Frank Rodrguez 3 and Soteros Soter 2 Unversty of Toulouse Captole (Gremaq, TSE and IDEI) 1, Royal Mal Group 2 and Oxera 3 1. INTRODUCTION The postal market n the UK s experencng a perod of prolonged structural change. The key factors drvng such change can be vewed to orgnate from two qute dfferent sources: those related to the regulatory and polcy makng framework; and advances n technology that nfluence customer communcaton channels. Ths, combned wth the deep recesson of and slow economc recovery n 2010, has created a hgh level of uncertanty wth respect to both the short and long term demand for letter mal 1. Uncertanty s a factor confronted by busnesses, consumers and polcymakers on a day-to-day bass. Busnesses and organsatons that have a better understandng of why customers demand ther products and what factors underpn that demand are more lkely to successfully manage ths uncertanty. However, postal operators nformaton systems manly focus on the products they sell, whch n the UK prmarly relate to speed of delvery and presortaton dscounts, and tend to contan lttle nformaton on the types of letter communcatons customers are purchasng. Whle letter traffc nformaton based on speed and presortaton attrbutes s essental for many reasons ncludng a range of operatonal, fnancal, marketng and regulatory requrements, t s less helpful n provdng nsghts nto the reasons for sendng letters and for assessng the lkelhood that customers wll contnue to send mal n the future. Prevous UK studes have focused on the demand for letters by speed of delvery and presortaton levels 2. In an envronment of contnung and evolvng structural change t may be as approprate also to assess the demand for sendng dfferent types of letter communcatons. Unfortunately, letter traffc data by content type s not readly avalable n the UK. The absence of letter traffc volumes by content type was overcome by Veruete-McKay et al (2010) (henceforth, VM) by combnng Royal Mal total traffc data on addressed nland mal and survey nformaton. That study provded mportant new nsghts nto the nfluence of the economc cycle, prces, technology and other factors on the demand for UK letter traffc by content type. In partcular, t estmated the prce elastcty of addressed drect mal (or advertsng) letters to be substantally hgher than for other types of letter communcatons and provded quanttatve estmates of the extent to whch technology has drven a wedge between the rates of growth of economc actvty and letter traffc (referred to by some n the postal ndustry as the "technology wedge") 3.

3 The ncomplete and overlappng coverage of the survey data used by VM to derve letter volumes by content type led to the generaton of two content traffc data seres. In general, the econometrc results usng these seres were broadly smlar, but generated some dfferences. In partcular, the magntude of the estmated prce elastcty for drect mal dffered markedly when usng the two data sets. The two sets of estmated elastctes reported by VM therefore provde a range of values that can be used to nform busness and polcymakng decsons wthn the postal ndustry n the UK. Ths paper bulds on the approach adopted by VM to further our understandng of the demand for letters by content type n the UK and provdes two mportant developments to the lterature on the demand for mal. Frst, the paper shows how maxmum lkelhood statstcal technques can be used to make more effcent use of nformaton and create a unfed framework wthn whch to derve tme seres content traffc data usng ncomplete but overlappng survey data. Second, based on ths t provdes addtonal nsghts nto the demand for letters n the UK usng econometrc tme seres technques. Secton 2 descrbes the maxmum lkelhood statstcal methodology used to derve a sngle tme seres of letter traffc by content type usng lmted and partal nformaton sets. Secton 3 descrbes the econometrcs methodology adopted to estmate the letter traffc content models and reports the results of the econometrcs analyss. Secton 4 provdes a summary and conclusons. 2. DERIVING LETTER TRFFIC CONTENT TIME SERIES USING INCOMPLETE SURVEY DT Letter traffc data by content type for socal, commercal (manly transactonal) and drect mal were derved n two stages. In the frst stage, nformaton on letter contents was obtaned from two Royal Mal surveys by sender and recever segments. However, as nether of the surveys on ther own provded full coverage of addressed nland letter traffc volumes for the whole tme seres, a maxmum lkelhood statstcal technque was used to derve content share estmates for each of the three content types by usng partal and overlappng nformaton from the two surveys. The second stage then used the content share estmates from stage one and Royal Mal total addressed nland letter traffc data to generate tme seres data for each of the three content types. Ths secton contans nformaton on the two surveys sources, the maxmum lkelhood estmaton methodology adopted to estmate content shares and the resultng letter content tme seres data. 2.1 Letter Content Survey Data Informaton on letter traffc contents was obtaned from two Royal Mal surveys, the Mal Characterstcs Survey (MCS) and the Consumer Panel Survey (CPS). The MCS contaned nformaton on the content of letters delvered by Royal Mal on an endto-end (E2E) bass (that s, collected, sorted and delvered by Royal Mal) for UK 2

4 fnancal years 1980/81 to 2007/08 and the CPS on mal sent and receved by households from 1997/98 onwards 4. Nether of the two surveys provded a fully comprehensve survey of total letter traffc delvered by Royal Mal. For example, the MCS survey excluded downstream access traffc handled by compettors upstream (that s, mal collected and sorted by compettors pror to handng t back to Royal Mal for delvery) whch developed from 2004/05, whle the CPS covered all types of Royal Mal delvered volumes except busness-to-busness traffc. Tme seres estmates for letter traffc volumes by content type for socal, commercal (manly transactonal) and drect mal were derved from 1980/81 to 2001/02 usng nformaton contaned n the MCS. For the perod 2002/03 to 2007/08 nformaton on each content type for up to four sender-recever segments was obtaned from the two surveys to estmate letter traffc by content shares for Royal Mal E2E traffc and access volumes. Table 1 contans a summary of the coverage of the survey nformaton and data on total traffc that were used to nform the maxmum lkelhood estmates for letter traffc content shares. Note that both surveys contaned some nformaton on E2E letter content sender-recever segments, that s person to person (P2P), person to busness (P2B), busness to person (B2P) and busness to busness (B2B), but only the CPS contaned nformaton on access traffc. Table 1. Data used to estmate letter traffc content shares Mal Characterstcs Survey (MCS) 1 Consumer Panel Survey (CPS) 1,4 End-to-end (E2E) mal End-to-end (E2E) mal Sender-to-recever segments Sender-to-recever segments Contents P2P P2B B2P B2B P2P P2B B2P 5 B2B Socal - - Drect mal Commercal - ccess mal 3 ccess mal Contents P2P P2B B2P B2B P2P P2B B2P B2B Socal Drect mal Commercal Data on total traffc 6 Total E2E Total access Note: (1) ndcates survey provded nformaton to maxmum lkelhood letter content share estmates. - ndcates that no survey nformaton was provded to nform maxmum lkelhood content share estmates. (2) Sample responses from both surveys suggested a very small quantty of drect mal was sent by prvate ndvduals. Ths was constraned to equal zero. (3) The MCS survey does not cover access mal (4) The CPS dd not cover B2B mal (5) Pror to 2006/07 the CPS survey dd not adequately dstngush between B2P socal and commercal letters. Informaton from the MCS suggested that socal B2P mal volumes were very small and therefore ths segment of CPS traffc was reallocated to the commercal B2P segment. (6) Refers to Royal Mal addressed nland letter traffc mal. The methodology used by VM to derve letter content traffc data was to estmate two traffc seres for each of the three content groups. In partcular, VM used each of the two surveys n turn as the prmary source of nformaton and then used nformaton from the other survey to complete the data set 5. Ths paper follows a smlar approach to VM ntally, n that t derves letter content traffc tme seres 3

5 data by combnng survey data and Royal Mal traffc volume data. However, a key development of ths paper s that t adopts a maxmum lkelhood approach to estmate a sngle set of letter content volume shares usng ncomplete and overlappng survey data from dfferent sources Dervng Letter Content Share Estmates Usng Incomplete and Overlappng Survey Data Ths secton descrbes the methodology used to estmate letter traffc content shares for the sx year perod 2002/03 to 2007/08 usng the two survey sources outlned n secton 2.1 that provde partal and overlappng nformaton on the populaton of letter traffc 6. In the frst nstance, t descrbes the methodology usng a standard sngle survey case wth three characterstcs (letter content types) and generalses ths to the case wth up to k characterstcs (letter content types dsaggregated further by sender-recever segments). It then proceeds to explan how the standard sngle survey case can be extended to cover the case where nformaton s avalable from two partal samples. Fnally, ths secton summarses how ths methodology was appled to estmate letter traffc content shares for UK addressed nland letter traffc The standard case usng a sngle sample In the standard case t s assumed that we obtan a sngle sample n whch we observe letter traffc by content type characterstcs. For example, consder three possble content types denoted by, B and C, for the letter traffc populaton where the actual proportons of these content types n the letter traffc populaton are denoted by α, α, α and are the parameters to be estmated. B C The sum of the three populaton proportons must sum to one, that s, α + αb + αc = 1. Ths dentty mposes an addng up restrcton whch means that only two parameters can be ndependently estmated subject to the restrcton holdng. For example, re-arrangng the addng-up constrant yelds the relatonshp αc = 1 ( α + αb) and therefore once estmates for α, and α B are derved subject to the addng up constrant, α C s also dentfed. To estmate the parameters, α, α B, and α C t s assumed that a sample of n letters s observed and each has one of the three content types, B, C, such that n +n B +n C =n and n k are the observed number of letter traffc tems of random varables N k, k=, B, C. The jont dstrbuton of (N, N B, N C ) s a multnomal dstrbuton, where the probablty densty functon for the multnomal dstrbuton (see Mood et al, 1974) s gven by: n! pn (, n n) = n n n α α α (1) n nb n C B, C B C! B! C! and the log-lkelhood functon can be expressed as: 4

6 l = n Lnα + n Lnα + n Ln(1 α α ) (2) B B C B For smplcty, the constant term Ln n! ( Ln n! + Ln nb! + Ln nc!) s omtted snce t does not nclude any of the parameters to be estmated n the log-lkelhood maxmzaton. The terms α k, k=, B, C, n the log-lkelhood functon (2) represent the letter traffc contrbutons to the lkelhood, that s the probablty that a letter s of content type k. The correspondng maxmum lkelhood estmates of the proportons are: n nb nc ˆ α ˆ ˆ = αb = αc = (3) n n n where the estmated proportons are smply equal to the number of observed letter traffc tems of each content type n the sample dvded by the total sample sze. In the general case where we deal wth a set of k letter traffc content types dfferentated by sender-recever segments denoted by K={1,2,,k}, where k has an nteger value, the log-lkelhood functon (2) can be expressed as: l = k = 1 n Lnα (2) where n represents the number of tems of traffc n the observed sample wth letter traffc content types, and α s the unknown actual proporton of letter traffc volume wth content types n the whole populaton, for =1,, k. Obvously, n ths case agan, the addng up restrcton holds (that s, α = 1) and one parameter s lnearly dependent on the others and the maxmum lkelhood estmates for the proportons wth content types are: n α =, = 1,...,k (3) n k = The case of two partal and overlappng samples ssume that we have two dfferent samples correspondng to two survey data sets. The frst survey, wth sze n, s lmted only to the two categores and B. So we have nformaton about the number of tems of traffc n the sample wth letter traffc content types, denoted n, and the number of tems of traffc n the sample wth content type B, denoted n B, wth n + nb = n. The second survey, wth sze m, s lmted to the categores B and C. So we have nformaton about the number of tems of traffc n the sample wth content type B, denoted m B, and the number of tems n the sample wth content type C, denoted m C, wth mb + mc = m. That s, we have a case where each survey partally covers the letter traffc populaton by content type (, B and C) and the survey nformaton overlap one another (that s, both contan nformaton on content type B). The objectve s to estmate the actual proportons of the three letter traffc content types n the whole populaton from the nformaton contaned n the two ncomplete 5

7 surveys. The log-lkelhood functon n ths case (see sano, 1965) can be expressed n the followng way: ( α α ) α 1 αb α + B B l = nln + nbln + mbln + mcln α + αb α + αb 1 α 1 α contrbuton of frst sample contrbuton of second sample (4) The thrd parameter, α C, can be deduced from the parameters α and α B, usng the addng up constrant αc = 1 ( α + αb). In ths case, we are dealng wth condtonal probabltes. For example, let us consder the contrbuton of the frst α sample n the log-lkelhood n equaton (4): the term represents the α + αb probablty that an tem of letter traffc s of content type gven that t comes from a sample whch only contans content types or B; a smlar nterpretaton can be αb ascrbed to the second term, whch refers to letter traffc content type B n α + αb ths sample. Equally, a smlar nterpretaton apples to the contrbuton of the second sample, where we use the relaton between α and, α and α (that s, αc = 1 ( α + αb) ). The maxmzaton of log-lkelhood functon such as (4) has no analytcal soluton but estmates can be obtaned by numercal computaton. The log-lkelhood functon for the three letter traffc content types case can be generalzed to deal wth a set of K={1,2,,k} content types dsaggregated further to nclude sender-recever segments. Here we consder that we have a frst sample wth sze n for whch we observe a sub-set of letter traffc by content type senderrecever segments denoted K 1, where K1 K, and a second sample wth sze m, for whch we observe only a sub-set of content types by sender-recever segments denoted K 2, where K2 K, and we have K1I K2 and K1U K2 = K. Ths more general log-lkelhood functon can be expressed as follows: l = n Ln( α ) + α m Ln( K1 K 2 K1 K 2 α ) α where n represents the number of tems of letter traffc n the sample wth content types, K1, n the frst observed sample, and m represents the number of tems of letter traffc wth content types, K 2,n the second observed sample. gan, one parameter s lnearly dependent on the others due to the addng up k constrant α 1. = 1 = Estmatng letter traffc by content shares usng nformaton from two partal and overlappng surveys We appled ths methodology to obtan estmates of the actual proportons for three content categores (socal, commercal and drect mal) by sender-recever flows C B (4) 6

8 (person to person (P2P), person to busness (P2B), busness to person (B2P) and busness to busness (B2B)) and the proporton of access mal for total addressed nland letter traffc volumes delvered by Royal Mal. The drect mal sender-recever letter traffc flows n the survey orgnatng from persons were very small n number and ther respectve proportons n the maxmum lkelhood estmaton were assumed to be equal to zero. Therefore, n total the two surveys provded partal nformaton on up to 12 mal categores that covered the populaton of total addressed nland letter traffc excludng B2B access mal. More precsely and by reference to Table 1, the MCS data set provded nformaton on 10 sender-recever mal categores but no nformaton on access mal 7, whle the CPS provded nformaton on 8 mal categores (2 access traffc segments and 6 E2E traffc sender-recever segments) 8. In addton, snce nformaton on the actual proporton of total access volumes was observed ths was drectly ncorporated nto the maxmum lkelhood estmaton functon to nform the estmated content shares for access mal. The maxmum lkelhood methodology was appled to nformaton on the senderrecever segments from the two surveys for each of the sx years 2002/2003 to 2007/2008 to estmate proportons of mal for 12 mal categores. These are reported n Table 1 and are gven by each sender-recever category where there was nformaton from at least one of the surveys. Wth no overlappng nformaton on access mal volumes from B2B from the two surveys, t was necessary to use data on access mal volumes from B2P. It was assumed that the rato of drect mal access mal volumes by B2P and commercal mal busness access volumes by B2P was the same as the correspondng rato for B2B. Ths allowed the shares of access of drect mal and commercal mal volumes send by B2B to be derved. 2.3 ddressed Inland Letter Traffc Trends by Content Type Once traffc share estmates for the perod 2002/03 to 2007/08 were obtaned usng the maxmum lkelhood estmaton technques descrbed above, a contnuous tme seres was derved by lnkng them to those estmated for the perod 1980/81 to 2001/02 usng MCS nformaton 9. Estmates of letter volumes by content type ( V^ ) were then derved from Royal Mal total addressed nland letter traffc volume ndex data (V) usng expresson (5) V = α s V (5) where α denotes the estmated share of total addressed nland letter traffc beng of content type; refers to socal, commercal and drect mal; and s denotes a scalar to generate ndex numbers equal to 100 n 2005/06. Note that by defnton = 3 α V = V and α = 1. = 1 = 3 = 1 Fgure 1 contans a plot of the annual rates of growth for the estmated addressed nland letter traffc data by content type generated by expresson (5) and also the 7

9 correspondng seres reported n VM. Two ponts to note about the dfferent estmates for each content type contaned n fgure 1 from 2002/03 onwards are: frstly, the estmates nformed by the maxmum lkelhood methodology that used overlappng nformaton from both surveys seem to be less volatle than those derved by VM; and secondly, the maxmum lkelhood estmates are not equal to the average of the two seres derved by VM. In fact, as we assumed that nformaton from the two surveys was equally relable, the maxmum lkelhood content share estmates for each year broadly reflect the relatve sze of the number of tems of letter traffc covered by each survey. Fgure 1: Estmated Letter Volume Growth Rates by Content Type Socal mal Drect mal % on fnancal year per workng days % on fnancal year per workng days /82 83/84 85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02 03/04 05/06 07/08 81/82 83/84 85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02 03/04 05/06 07/08 Commercal (manly transactonal) mal Total mal % on fnancal year per workng % on fnancal year per workng days /82 83/84 85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02 03/04 05/06 07/08 81/82 83/84 85/86 87/88 89/90 91/92 93/94 95/96 97/98 99/00 01/02 03/04 05/06 07/08 VM estmated content growth rate usng data method VM estmated content growth rate usng data method 2 Maxmum lkelhood estmated growth rates usng overlappng market survey data Note: Data refers to year-on-year growth n UK Fnancal years (FY). For example, UK FY 2007/08 refers to the data perod prl 2007 to March The two tme seres data sets derved by VM are denoted n the paper as those derved usng Method 1 and Method 2. Method 1 was based prmarly on nformaton avalable from the MCS and then used the CPS to complete the data set where data were not avalable from the MCS (for example, on access). In contrast, Method 2 prmarly focused on nformaton avalable from the CPS and then used nformaton avalable from the MCS to complete the data set where not avalable from the CPS (for example, busness-to-busness segments). In terms of drectonal trends, fgure 1 shows the annual rate of growth of total UK letter traffc per workng day to have been negatve between 2005/06 and 2007/08. The three estmated traffc seres for commercal (manly transactonal) mal began to declne a year later than total traffc, whle socal letter communcatons were estmated to have started two, or possbly three years earler. Note also that drect mal volumes are estmated to have declned n at least 8

10 three out of the four years between 2004/05 to 2007/08, despte the UK economy recordng robust rates of growth over ths perod. The three qute dfferent estmated rates of growth for drect mal n 2006/07 reported n fgure 1 suggest there s some uncertanty assocated wth data for ndvdual years for letter traffc by content type. However, greater confdence can be attrbuted to the broad drectonal movements n the estmates. Bearng ths n mnd, and concentratng on the annual growth rates of the tmes seres usng the maxmum lkelhood estmates reported n fgure 1 whch are less volatle than those contaned n VM, a number of nterestng trends emerge. Frst, t s estmated that socal letter communcatons ncreased, on average, by around 2% per annum over the two decades coverng 1981/82 to 2000/01, but that between 2001/02 and 2007/08 socal mal declned, on average, by almost 4% per annum. Second, drect mal letter communcatons have hstorcally exhbted hgh rates of growth and fluctuated wth economc actvty. Thrd, commercal (manly transactonal) letter traffc volumes have, smlar to drect mal, hstorcally exhbted postve rates of growth and fluctuated n lne wth economc condtons. However, the demand for commercal letter communcatons has fluctuated less than that for drect mal. 3. N ECONOMETRIC NLYSIS OF UK LETTER TRFFIC DEMND BY CONTENT TYPE 3.1 Estmaton Methodology n econometrc analyss of the demand for addressed UK nland letter traffc by content type was undertaken usng a smlar estmaton methodology to Veruete- McKay et al (2010) (denoted as VM n the remander of ths secton). In summary, the modellng comprsed three relatonshps: one for socal mal letter traffc; a second for commercal (manly transactonal) letters; and a thrd for drect mal letter communcatons 10. The demand relatonshps were estmated usng UK fnancal year data and sngle equaton statc ordnary least squares (OLS) tme seres models of the followng form 11 : q t = D + Π x + η (6) ' t ' t t where lower case letters for Q t and X t denote logarthms of varables n tme perod t. The varable Q denotes the volume of traffc per household per workng day for content type. The varable X t denotes a vector of explanatory varables correspondng to each traffc stream. The vector of explanatory varables X t ncluded economc actvty, real letter tarff prce ndces 12 for content type, the qualty of letter servce delvery, and the proporton of nternet advertsng expendture relatve to total advertsng expendture 13. lso, ntally ncluded n X t was the real prce of tradtonal non-mal advertsng meda substtutes and a number of varables lnked to technology trends, such as the proporton of households wth access to the nternet and broadband and real telecommuncaton prces. D t s a vector of determnstc varables whch ncludes a constant, dummy 9

11 varables and a number of tme trends. Π s a vector of long-run coeffcents; s a vector of estmated coeffcents; and η t s a random dsturbance term. 3.2 Estmated Models of Letter Traffc by Content Type The estmated equatons for addressed nland letter traffc by content types, after elmnatng nsgnfcant varables, are reported n table 2. The estmated parameters have reasonably hgh t-statstcs and the reported dagnostc tests suggest that the model s statstcally sound 14. The estmated elastctes for the three content types are broadly smlar to those estmated by VM. However, there are also a number of dfferences, partcularly wth respect to the estmated mpact of prces and nternet advertsng on drect mal and the estmated tme trend mpacts across all three content types. The estmated long run elastctes and tme trends for total addressed nland letter traffc volumes were calculated by weghtng the long-run parameters for each of the three content types by ther respectve share of total traffc. The estmated elastctes for economc actvty, qualty of servce and prce of telecommuncaton reported n table 2 are very smlar to those contaned n VM. In partcular, commercal (manly transactonal) letter traffc s estmated to have a near unt elastcty wth respect to economc actvty, whle drect mal s estmated to be hghly pro-cyclcal and posses an elastcty of about two. In contrast, socal letter traffc s estmated to be ndependent of economc actvty. Furthermore, the estmated elastctes for total letter demand wth respect to qualty of servce and the prce of telecommuncaton are almost dentcal to those estmated by VM (0.3 and 0.1 respectvely). Note n the case of the estmated commercal prce and telecommuncaton prce elastctes that the hypothess that they are equal n magntude and opposte n sgn was tested and could not be statstcally rejected. The adopton of ths hypothess n the model leads to consderably hgher t- statstcs than those estmated for each of the varables ndvdually (as a comparson of the estmated parameters n the restrcted and unrestrcted columns for commercal traffc show). n mportant pont to note wth respect to the estmated prce elastctes for letter traffc by content type s that, agan as n VM, they dffer substantally by communcaton type. In partcular, the econometrc models ndcated that commercal (manly transactonal) letter communcatons has the lowest prce elastcty (of around -0.1 to -0.2) and socal letter mal has a hgher prce elastcty of demand than transactonal mal but t s stll qute nelastc (around -0.5). The drect mal estmated prce elastcty reported n table 2 of a lttle under unty (-0.9) les towards the mddle of the range of the two estmates contaned n VM (that s, -0.7 and -1.4) 15. Ths fndng s as expected, snce the maxmum lkelhood methodology used to derve the content traffc data combned nformaton from the two survey sources used by VM. The hgh estmated prce elastcty of drect mal 10

12 relatve to other types of letter communcatons s consstent wth the fndngs of Thress (2006) and Santos and Lagao (2001). Table 2: ddressed Inland Letter Content Traffc Per Household Model: Estmated Long Run Elastctes and Tme Trends 1 Socal Commercal mal 4 Drect mal Total Traffc 5 Unrestrcted Restrcted Unrestrcted Restrcted Economc actvty 2 ns 0.98 (7.7) 1.02 (25.7) 1.98 (4.9) Own prce tarff ndex (-4.3) (-1.2) (-3.9) (-2.2) Qualty of servce 0.44 (5.4) 0.34 (5.5) 0.33 (6.4) ns Prce of telecommuncatons na 0.11 (2.1) 0.12 (-3.9) ns ndex 3 Proporton of nternet advertsng expendture na na na (-6.8) Tme trend estmates Net mpact of unexplaned tme trends per annum at end of estmaton perod Pre 2003 ns Post % (-9.3) Pre 2002 ns Post % (-8.6) Pre 2002 ns Post % (-9.0) Pre % (2.3) Post % (-4.2) Pre % Post % Pre % Post % -4.1% -2.6% -2.6% -1.8% -2.6% -2.6% Rsq adjusted Reg. SE DW Dagnostc tests (p-values) Seral Correlaton Heteroscedastcty Normalty Reset Chow test Note: (1) T-statstcs reported n brackets. (2) Refers to Gross Domestc Product (GDP). (3) Deflated by the all tems retal prces ndex. (4) The hypothess that the own-prce elastcty and the telecommuncaton prce elastctes were equal and opposte n sgn n the commercal equaton was tested and could not be rejected. The commercal estmates mposng ths hypothess are reported n the restrcted column of results and the freely estmated parameters not mposng ths hypothess s reported n the unrestrcted column. (5) The total traffc estmated long run elastctes and parameters were calculated by weghtng the estmated coeffcents of each traffc content stream by ther respectve volume share n 2007/08. (6) Ths s a test for 1 st order autocorrelaton. (7) Refers to Whte s test for heteroscedastcty. (8) Refers to the Jarque-Bera test for normalty n the resduals. (9) Refers to Ramsey s RESET test of functonal form msspecfcaton. (10) Refers to Chow s predctve falure test from 2005/06 onwards. Note that for the drect mal equaton used to undertake the Chow test the dummy varable assocated wth 2006/07 was not consdered n the regresson. In order to examne the extent to whch the results reported n table 2 depend on the mportance placed on the two surveys used to derve tme seres for letter traffc by content type, two senstvty tests were undertaken. lthough both surveys are consdered to be of a hgh standard, and purely to test the possble mpact f one survey were to be consdered superor to the other, n the frst senstvty, t was assumed that the nformaton provded by the CPS was ncorporated as f t was twce as relable as that provded by the MCS and the second senstvty assumed the opposte 16. The estmated drect mal prce elastctes resultng from these two senstvtes were -0.9 and -1.1 respectvely. Ths suggests that, wth both survey sets consdered to be a hgh standard, a reasonable estmate of the prce elastcty for total drect mal over the estmaton perod s around

13 The estmated tme trend mpacts reported n table 2 suggest that there was a declne n the trend rate of socal and commercal traffc from around 2003/04 and 2002/03 respectvely and that ths slowdown was of the scale of about 4% and 2½% per annum respectvely 17. The tmng concdes wth the sharp ncrease n the number of frms and ndvduals wth broadband connectons n the UK. It s lkely that ths combned wth advances n nternet enabled technology has resulted n contnung substtuton of socal and commercal letter traffc. The mpact of esubsttuton on drect mal resultng from the emergence of the nternet and, n partcular, pad-for-search advertsng can be estmated usng the coeffcent of the proporton of nternet advertsng expendture reported n table 2. For example, multplyng the average change n the proporton of nternet advertsng expendture over the perod 2005/06 to 2007/08 (4 percentage ponts per annum) by the long-run coeffcent reported n table 2 suggests that nternet related esubsttuton could have reduced drect mal traffc volumes by an average of around 9% per annum durng ths perod 18. However, ndependent of the nfluence of nternet advertsng, t s lkely that the hgh rates of drect mal growth experenced n the UK n the 1980s and 1990s would have eventually slowed down n order to stablse ts share of advertsng spend wthn overall marketng budgets. The declne n the post 1997 drect mal tme trend term effects reported n table 2 s consstent wth ths hypothess. 3.3 Usng the Letter Traffc by Content Model to ssess Prospects for Mal Trends The estmated parameters reported n table 2 can be used as a startng pont to assess the prospects for traffc growth n the UK n the near future f esubsttuton effects are projected to be broadly n lne wth those n the recent past. s a frst step n such an exercse t s necessary to make assumptons regardng the future values for all of the explanatory varables n the model ncludng those for esubsttuton. s an llustratve example, assume GDP growth n the UK were to be equal to 2% per annum; household growth were to be 1% per annum; the share of nternet advertsng expendture were to contnue to ncrease by 4 percentage ponts per annum; real telecommuncaton prces were to declne by around 5% per annum; real letter prces and QoS were to be unchanged; and tme trend terms were to contnue n lne wth the estmates n table 2. These assumptons together wth the estmated parameters reported n table 2 would mply that socal letter communcatons would declne by around 3% per annum; commercal (manly transactonal) letter traffc communcatons would be broadly flat; and drect mal traffc would declne by around 6% per annum. In total, ths would suggest that f the mpact of esubsttuton as well as the other coeffcents n the model were to reman broadly stable at around 2007/08 levels, then letter traffc n the UK would declne by around 2% per annum n the near to medum term for the assumptons n ths llustratve example. However, the ncrease n the share of nternet advertsng expendture wll decrease at some pont n the future. If ths ntally were to ncrease by, say, between only 1% and 2% per annum, whle the assumptons for all the other factors remaned as above, then 12

14 drect mal demand would be expected to recover from a heavly negatve growth rate. In ths llustratve example, ths would reduce the rate of declne n the demand for letter traffc overall n the UK, and possbly even stablse t. Unfortunately, the mpact of esubsttuton n the medum to long term future on the demand for letter traffc s uncertan and clearly may change from the effects experenced over the recent past. s dscussed by Nkal (2008), esubsttuton s not a sngle process but reflects the effects of many technologes each wth ts own s- shaped dffuson curve. s some technologes mature others, perhaps as yet unknown, may mpact on letter traffc n the future such that "the curve for substtuton s remnscent of a large corrugated s-curve" (Nkal, 2008, p91). For example, pressures to reduce busness costs and concerns wth the envronment are exertng downward pressure on the demand for transactonal letters (whch s the largest category of letter mal n the UK and many other countres) and perhaps also on the extent to whch drect mal wll recover as the ncrease n nternet advertsng expendture slows down. If, the declne n transactonal letter mal over the future were to take place at a faster rate than n the past, then the esubsttuton mpacts contaned n the model reported n table 2 would underestmate the extent to whch transactonal mal letter communcaton wll be substtuted by electronc and dgtal forms of communcaton. It s mportant that projectons of mal volumes attempt to reflect these uncertantes regardng the quanttatve mpact of esubsttuton n future years through rsk and senstvty analyss as well as adjustments to projectons for factors that, as yet, have not entered tme seres of past trends. It s lkely that the factors affectng future demand for mal may be better understood through usng a model of letter demand that s based on letter content types than models that focus on the demand for mal dfferentated by speed of delvery or presortaton levels. Therefore, whle there may be some uncertanty assocated wth ndvdual year estmates for letter traffc volumes by content type, model based projectons that are augmented wth off-model addtonal net trend adjustments (NTs) based on an analyss of demand by content type may provde a more nformed framework for forecastng mal volume trends nto the future. Ths approach s consstent wth that developed n Feve et al (2010) where t s recommended that nformaton prors (such as NTs) should be used to augment econometrc models when forecastng the demand for letter mal n a changng and evolvng market envronment. 13

15 4. SUMMRY ND CONCLUSIONS Structural changes n the communcatons market are havng dfferent mpacts on dfferent types of letter communcatons. However, many postal operators nformaton systems, ncludng those n the UK, tend to focus on the types of products sold, whch manly reflect speed of delvery and presortaton dscounts, and not the type of communcaton purchased by customers. In order to better understand the factors nfluencng the total demand for letters and mprove forecastng n an evolvng market envronment, t s mportant to understand the key drvers underpnnng the demand for dfferent types of letter communcatons. However, there s a lack of good qualty data on letter traffc by content type n most countres. Ths paper attempts to brdge the nformaton gap n the UK by developng the framework set out n Veruete-McKay et al (2010) (denoted as VM) and combnng actual total traffc data wth survey nformaton to derve estmates for letter traffc volumes by content type. Estmates for three letter content types (socal, commercal (manly transactonal) and drect mal) were derved usng two sources of ncomplete but overlappng survey data. In order to extend the analyss n VM, ths paper has used maxmum lkelhood estmaton technques to combne nformaton from dfferent survey sources to obtan a sngle data set for UK delvered traffc segmented nto the three content types. Ths data set was then used to estmate econometrc tme seres models of the demand for letter traffc by content type. The results were consstent wth those n VM and the econometrc estmates n ths paper can be vewed as a set of best central estmates that le wthn a range of potental estmates. The elastctes contaned n secton 3 of the paper provde nsghts nto the relatve mportance of the key drvers for dfferent types of letter communcatons n the UK. In partcular, t s estmated that the elastcty of demand for commercal (manly transactonal) letter traffc wth respect to economc actvty s, approxmately, unty and that drect mal s twce as senstve to the economc cycle as commercal letter traffc. In contrast socal letter traffc was found to be nvarant to the economc cycle. Drect mal was estmated to be the most prce senstve segment of letter traffc, wth an estmated prce elastcty of around -1. Both commercal and socal mal prce elastctes were found to be consderably less senstve to prce changes. Esubsttuton mpacts were estmated to affect all three content types. For example, over the three years between 2005/06 to 2007/08 the econometrc models estmate that esubsttuton was reducng the demand for: drect mal by at least 9% per annum 19 ; socal letter mal by around 4% per annum; and commercal (manly transactonal) letter traffc by around 2½% per annum. The econometrc results reported n ths paper can be vewed as a startng pont n consderng future prospects for letter traffc volumes n the UK. Whle the mpact of economc factors (ncludng prces) appears to be comparatvely stable, the sgnfcant mpacts of esubsttuton on mal volumes reman uncertan. In such an envronment, t s mportant that projectons of mal volumes attempt to reflect these uncertantes through rsk and senstvty analyss as well as adjustments to 14

16 projectons for factors that are, as yet, not reflected n tme seres of past trends. Over tme, as new outcome data on the evolvng mal market envronment becomes avalable off-model senstvty analyss of ths knd can be updated and rsks reassessed. Such an approach s set out formally n Feve et al (2010) and the model set out n the current paper n terms of traffc by content type can be vewed as beng fully consstent wth that wder framework. NOTES * The analyss contaned n ths paper reflects the vews of the authors and not necessarly those of Royal Mal Group. 1 Except where otherwse stated, the analyss n ths paper refers solely to addressed nland mal volumes and does not consder developments n unaddressed or nternatonal mal volumes. 2 See Nankervs et al (2002) and Soter et al (2009). 3 See Hooper et al (2008 p 43-44). 4 See appendx for further detals on the two surveys. Note that the UK fnancal year runs from prl to March of the followng year. 5 Veruete-McKay et al (2010) used nformaton from the MCS and CPS to derve two alternatve estmates for content traffc data for socal, commercal (manly transactonal) and drect mal traffc. They refer to the two dfferent traffc estmates as those obtaned usng Method 1 and Method 2. Method 1 was based prmarly on nformaton avalable from the MCS and then used the CPS to complete the data set where data were not avalable from the MCS (for example, on access). In contrast, Method 2 prmarly focused on nformaton avalable from the CPS and then used nformaton avalable from the MCS to complete the data set where data were not avalable from the CPS (for example, busness-to-busness segments). 6 Royal Mal data on addressed nland letter traffc covers both Royal Mal traffc that s delvered end-to-end and access traffc that s handled by compettors who gve ths mal back to Royal Mal to delver. There s a small amount of addressed nland letter traffc that s not ncluded n the surveys but t s deemed to be too small to materally affect our results. 7 The 10 sender-recever segments that the MCS provdes nformaton on (as shown n Table 1) are: 4 for socal mal (P2P, P2B, B2P, B2B); 2 for drect mal (B2P, B2B); 4 for commercal mal (P2P, P2B, B2C, B2B). 8 The 8 categores of mal that the CPS provdes nformaton on (as shown n Table 1) are: 2 for socal mal sent by persons (P2P, P2B); 1 for drect mal ((B2P); 3 for commercal mal (P2P, P2B, B2P); and 2 for access mal (drect mal B2P and commercal B2P). 9 In partcular, changes n MCS content shares were lnked to the 2002/03 maxmum lkelhood estmated content shares to derve tme seres data gong back to 1980/ The model was estmated usng EVIEWS5. 11 More general specfcatons ncludng lags and leads were tested usng dynamc OLS estmaton methods suggested by Sakkonen (1991) and popularsed by Stock and Watson (1993). However, specfcatons ncludng no leads and no lags were preferred on the bass of statstcal crtera. 12 Where real prces n the paper refer to nomnal prces deflated by the all tems retal prces ndex. 13 The data on nternet advertsng expendture are consstent wth the data sources and defntons contaned n WRC(2008). 14 ll the dagnostc tests are passed at the 5% level of sgnfcance. However, t should be noted that the Heteroskedastcty and Ramsey Reset tests are strctly not vald when I(1) varables (such as economc actvty) are ncluded n regresson models of ths type (see Gerrard and Godfrey, 1998). In addton to the Chow forecast test for parameter stablty, CUSUMQ and CUSUMQ Squared tests were also undertaken and they dd not ndcate any clear evdence of parameter nstablty. 15 The estmated drect mal prce elastcty n Veruete-McKay et al (2010) usng Method 1 (whch prmarly used nformaton from the MCS) was -1.4 and that usng Method 2 (whch prmarly used nformaton from the CPS) was The senstvtes for estmatng content traffc data followed the same methodology as that outlned n secton 2 except that the sample szes used to combne the two surveys were amended to reflect the desred weghts. 17 number of tme trends were tested around ths tme perod. The adopton of the 2002/03 and 2003/04 tme trends were nformed by the kake nformaton crteron (IC) and the Schwarz crteron (SC). 18 Ths estmate s consstent wth results reported n Soter et al (2009). 19 The esubsttuton mpact of 9% per annum s based only on the mpact of the share of nternet advertsng varable. If the tme trend varable n the drect mal equaton s also consdered to be a proxy for esubsttuton ths would add a further negatve mpact of around 2% per annum. However, f the drect mal tme trend mpact to some extent reflects factors relatng to the slow down n drect mal growth from 1997 onwards due to market saturaton factors, t can be argued that ths should not be fully consdered to be an esubsttuton mpact. 15

17 REFERENCES sano, C. (1965), On estmatng multnomal probabltes by poolng ncomplete samples, nnals of the Insttute of Statstcal Mathematcs, 17, 1-13 Cazals, C., J.P. Florens, F. Rodrguez and S. Soter (2008), Forecast uncertanty n dynamc models: an applcaton to the demand for mal, n M.. Crew and P.R. Klendorfer (eds), Competton and Regulaton n the Postal and Delvery Sector, Edward Elgar publshers, Fève, F., J.P. Florens, F. Rodrguez and S. Soter (2010), Forecastng mal volumes n an evolvng market envronment, n M.. Crew and P.R. Klendorfer (eds), Heghtenng Competton n the Postal and Delvery Sector, Edward Elgar. Gerrard, W.J., and L.G. Godfrey (1998), Dagnostc checks for sngle-equaton errorcorrecton and autoregressve dstrbuted lag models, The Manchester School, Vol. 66, No.2, pp Hooper, R., D. Hutton and I.R. Smth (2008), Modernse or declne: polces to mantan the unversal postal servce n the Unted Kngdom. Mood,.M., F.. Graybll and D.C.Boes (1974), Introducton to the Theory of Statstcs, McGraw-Hll, New York. Nankervs, J., S. Rchard, S. Soter and F. Rodrguez (2002), Dsaggregated letter traffc demand n the UK, n M.. Crew and P.R. Klendorfer (eds), Postal and Delvery Servces, Boston, M: Kluwer cademc Publshers, pp Nkal, H (2008), Substtuton of letter mal for dfferent sender-recever segments, n M.. Crew and P.R. Klendorfer (eds), Competton and Regulaton n the Postal and Delvery Sector, Edward Elgar publshers, pp Sakkonen, P. (1991), symptotcally Effcent Estmaton of Contegraton Regressons Econometrc Theory: 7:1-21. Santos, R.G., and S.C. Lagao (2001), The demand for drect mal n Portugal, n M.. Crew and P.R. Klendorfer (eds), Future Drectons n Postal Reform, M: Kluwer cademc publshers, pp Soter, S., F. Fève, J.P. Florens and F. Rodrguez (2009). Internet advertsng and drect mal: trends and analyss for the UK, n M.. Crew and P.R. Klendorfer (eds), Progress n the Compettve genda n the Postal and Delvery Sector, Edward Elgar publshers, pp Stock, J.H, and M.W. Watson (1993), Smple Estmator of Contegratng Vectors n Hgher Order Integrated Systems. Econometrca: 61: Thress, T.E. (2006), Drect Testmony of Thomas E. Thress on behalf of the Unted States Postal Servce, Postal Rate and Fee Changes Docket No. R Veruete-McKay, L., S. Soter, J. Nankervs and F. Rodrguez (2010), Letter traffc demand n the UK: an analyss by product and envelope content type. Presented at the Insttut d Econome Industrelle (IDEI) Sxth Conference on Regulaton, competton and unversal servce n the postal sector, Toulouse, March WRC (2008), The dvertsng Forecast, volume 32, number 34, January Publshed n assocaton wth the dvertsng ssocaton, Nelsen Meda Research, World dvertsng Research Center. 16

18 PPENDIX: ROYL MIL SURVEY DT Informaton on the contents of Royal Mal letter traffc s avalable from two dfferent surveys: the Mal Characterstcs Survey (MCS) and the Consumer Panel Survey (CPS). The MCS s a random unclustered survey of around 0.7 mllon consumers and busnesses. Ths survey attaches a questonnare card to randomly selected envelopes and has a response rate of around one n sx. Data collecton takes place at all mal centres (MC) and dstrbuton centres (DC). Ths means that end to end (E2E) mal traffc s covered n the MCS, wth the excepton of products such as Response Mal, Specal Delvery and Cleanmal dvance (3% of total E2E mal n 2007/08). However, the MCS excludes nformaton on downstream access volumes. Traffc data by type of contents s avalable from 1980/81 from the MCS. lso ths survey records up to fve dfferent detaled content types for a specfc envelope and allocated a prortsaton routne to dentfy the prmary content. Ths elmnates double countng of contents wthn the envelope. The CPS s a weekly survey dary and covers a panel of around 1,000 households wth a natonal representaton. Ths survey s weghted by socoeconomc group, household sze and age. The weghts are updated every two years. The CPS has the mportant feature that provdes some nformaton on downstream access traffc. However, t does not capture nformaton on busnessto-busness traffc. Snce nether of the two surveys s fully comprehensve of total letter traffc, nformaton from both was used to derve letter content tme seres data for addressed nland mal volumes. Traffc data by content types (and dsaggregated by flows of senders and recevers) from the MCS and CPS on a fnancal year bass from 2002/03 to 2007/08 were used to estmate shares of content types of total nland addressed traffc. Due to the longer tme span of data avalable from the MCS ts content category defntons were adopted to derve content shares for total UK letter traffc. 17