Searching and Switching: Empirical estimates of consumer behaviour in regulated markets

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1 Searchng and Swtchng: Emprcal estmates of consumer behavour n regulated markets Catherne Waddams Prce Centre for Competton Polcy, Unversty of East Angla Catherne Webster Centre for Competton Polcy, Unversty of East Angla Mnyan Zhu Centre for Competton Polcy, Unversty of East Angla CCP Workng Paper Abstract Governments and agences ncreasngly ntervene to nfluence consumer decsons, both to beneft ndvdual outcomes and to mprove market functonng. Wth a unque data set drectly ncorporatng consumers own belefs about potental gans and the tme needed to search and swtch across eght markets, we dentfy separately what motvates consumers to search and swtch (or not). Controllng for consumers expectatons of gan and tme needed, ntrnsc markets dfferences and demographc factors, we fnd persstent varatons n consumer responses across ndvduals and markets. Such varatons enable dentfcaton of (n)actve consumers to target, but challenge the wsdom of mposng unform regulatory polces. Overall, we conclude that polces whch emphasse potental gans and reduce antcpated swtchng tme are the most lkely to ncrease consumer actvty, but that polces talored to partcular markets and target groups are necessary to gan maxmum effect. ISSN

2 Searchng and Swtchng: Emprcal estmates of consumer behavour n regulated markets 1 Catherne Waddams Prce 2, Catherne Webster and Mnyan Zhu ESRC Centre for Competton Polcy, Unversty of East Angla December 2013 Abstract Governments and agences ncreasngly ntervene to nfluence consumer decsons, both to beneft ndvdual outcomes and to mprove market functonng. Wth a unque data set drectly ncorporatng consumers own belefs about potental gans and the tme needed to search and swtch across eght markets, we dentfy separately what motvates consumers to search and swtch (or not). Controllng for consumers expectatons of gan and tme needed, ntrnsc markets dfferences and demographc factors, we fnd persstent varatons n consumer responses across ndvduals and markets. Such varatons enable dentfcaton of (n)actve consumers to target, but challenge the wsdom of mposng unform regulatory polces. Overall, we conclude that polces whch emphasse potental gans and reduce antcpated swtchng tme are the most lkely to ncrease consumer actvty, but that polces talored to partcular markets and target groups are necessary to gan maxmum effect. Key words: consumer choce, competton polcy, search and swtchng costs, random parameters (coeffcents), Probt 1 The support of the Economc and Socal Research Councl (ESRC) s gratefully acknowledged. Ths paper s based on prevous analyss of ths data set by Yoonhee Tna Chang. We are very grateful for her contrbuton to organsng the collecton of the data and ntal analyss. The authors thank audences at semnars at the Unversty of Calforna Energy Insttute, the Royal Economc Socety, the Competton Law and Economcs European Network and the ESRC Centre for Competton Polcy for comments on earler versons of ths paper; and Steve Daves, Morten Hvd, Bob Sugden and Chrs Wlson for helpful suggestons. The usual dsclamer apples. 2 Also Centre on Regulaton n Europe; Correspondng author: CCP, Unversty of East Angla, Norwch NR4 7TJ, UK; e: 1

3 Better Choces: Better Deals. Consumers Powerng Growth (Department of Busness, Innovaton and Sklls/Cabnet Offce paper, Aprl 2011) 1. Introducton Publc polcy focuses ncreasngly on the role of consumers decsons n markets. The European Commsson and the UK government are both actvely pursung a strategy of alterng the choce archtecture of consumers to acheve better outcomes both for the ndvduals concerned and for markets as a whole. The Consumer secton of DG Sanco of the European Commsson states that Knowledge of consumer markets, of natonal consumer condtons and consumer behavour n the EU helps make better European polces, and smarter regulaton. (DG Sanco, 2011a 3 ). In the US, and elsewhere, default penson schemes are progressvely desgned to gude employees to choose more benefcal schemes (Bode and Prast, 2011). In the UK, the Cabnet Offce s Behavoural Insghts Team s exertng a profound effect on the approach of mnsters, cvl servants and regulators n ts quest to transform how government thnks about the behavoural aspects of publc polcy, makng t easer for ctzens to make better choces for themselves (Cabnet Offce 4, 2011 p. 4). A smlar advsory group has now been establshed n the US Whte House. Amongst regulators, the UK Competton Commsson has ntroduced consumer remedes n markets even where the man adverse effect s dentfed as a supply rather than demand falure (Garrod et al., 2009). The UK energy regulator has ntroduced radcal restrctons on tarffs to enable consumers to make better choces (Ofgem, 2011). To desgn such nterventons effectvely (and avod unntended consequences) polcy makers need to understand the drvers of consumer behavour. Ths paper nforms such polces by dentfyng how consumers search and swtchng actvty relates to ther expectatons about the potental avalable gan and effort nvolved (and other relevant factors) across eght markets, each subject to sector regulaton. If markets are to work well, consumers need to seek better deals to motvate frms to make such offers avalable (McFadden, 2006). A partcular danger arses f consumers are nactve because they beleve that markets are compettve, whle frms can explot ths belef by rasng prces (Waterson, 2003). We use a specally commssoned survey to relate consumers search and swtchng actvty to ther own expectatons about potental gans 3 (DG Sanco, accessed 27 th November 2011). 4 Cabnet Offce, 2011, Behavoural Insghts Team. Annual update , Update_acc.pdf ; accessed 27th November

4 from swtchng and the tme t wll take them to look around for better deals and change suppler. It s well establshed that consumers are heterogeneous, so the effect of expected gans or search/swtch tme may be dfferent for dfferent consumers. For nstance, Flores and Waddams Prce (2013) dentfy dfferent groups of consumers among whom the effect of varous drvers of searchng and swtchng actvtes dffers n the UK retal electrcty market. In our model, whch s explaned n Secton 3, we take a general approach that allows effects to vary both across demographc groups and for each ndvdual consumer. Our focus on consumers expectatons enables us to abstract from drect ssues of nformaton by utlsng the respondents own belefs as reported to ntervewers. Such separaton nforms the development of approprate polces to address the causes of napproprate choce, beyond poor nformaton ssues. In partcular we focus separately on the searchng and swtchng decson, and fnd sgnfcant dfferences between markets. Search and swtchng behavour are analysed across eght markets, all smlar n that the consumer has an ongong default relatonshp wth a retaler, and therefore needs to take actve steps to change provder. All the markets are also subject to sector specfc regulaton (energy, telecommuncatons, fnancal), as well as general competton provsons, so multple agences have polcy responsblty. We ask (relevant) respondents whether they have looked around for better deals or changed suppler n the last three years. We follow precedng lterature n dentfyng the factors whch determne actvty n these markets, Our model exhbts some smlartes wth earler explanatons and provdes a good ft wth the observatons from the survey. Usng emprcal models to control for underlyng factors, and allowng heterogenety across ndvduals, we explore the effect on searchng and swtchng across markets of three prmary determnants: the expected savng from the actvty, and antcpated tme requred to fnd a better deal and to change supplers. We dentfy robust evdence of sgnfcant dfferences not only across markets and demographc groups, but also more generally, across ndvduals. The next secton brefly dscusses the most relevant lterature, and secton 3 presents the motvaton of the model, the survey and the data. Secton 4 ncludes the man results and a dscusson of selecton ssues, and dentfes persstent dfferences between markets and across ndvduals; secton 5 concludes and dscusses polcy mplcatons. 2. Lterature 3

5 In modellng search and swtchng behavour, we draw on lterature whch focuses on the effect of ether search or swtchng costs or both. Klemperer (1995) derves the nteracton between such costs and market outcomes, and Farrell and Klemperer (2007) revew relevant emprcal studes n a varety of settngs. Bglaser, Crémer and Dobos (2013) show that the presence of a number of nactve consumers gven the number of actve consumers may make the market more attractve to potental entrants, f they can capture some of them, thereby lmtng ncumbency advantages. Wlson (2012) suggests that before startng ther search, consumers may be more deterred by expected search costs than antcpated swtchng costs, partly because any nvestgaton nvolves search costs for certan, but swtchng costs only f a better deal s dscovered durng the search. Moshkn and Shachar (2002) estmate that 71% of consumers behavour (n televson vewng choces) s consstent wth the exstence of search costs. The decson to swtch supplers has often been estmated as a functon of the gans avalable from dong so (objectvely calculated from the researchers nformaton about opportuntes n the market) and a set of demographc and ndvdual varables to proxy search and swtchng costs, though these often explan lttle of the observed swtchng actvty (Chen and Htt, 2002; Kser, 2002). Demographc characterstcs are relatvely easly measured n surveys and Gulett et al. (2005) fnd that searchng cost proxes appear to be the bggest barrers to changng supplers n the newly opened UK gas market, domnatng both swtchng cost proxes and demographc varables. Pomp et al. (2005) use a smlar methodology across a seres of nne dfferent product markets n Holland. Ther approach enables comparson of swtchng behavour across markets whle allowng for unobserved consumer effects, but s lmted by a bnary measure for consumer belefs about gans (hgh/low). Nether of these studes explctly separates the actvtes of searchng and swtchng. Consderng only the energy market, Sturluson (2003) suggests that the probablty of swtchng s over four tmes hgher for those consumers who have actvely searched. Lke Gulett et al. (2005) s fndngs, and Wlson s predctons (2012), Sturluson fnds that swtchng costs exert a larger effect than swtchng costs. However, ths study, too, s lmted by the measure of consumers expectatons of the savngs avalable from swtchng. We follow these studes n nvestgatng consstency (and nconsstences) across ndvdual search and swtchng decsons (Foote et al., 2009), focusng on whether or not to search or 4

6 swtch, based on a subjectve measure of expected gans, rather than whether the choce of producer s objectvely optmal 5. Our data are unque n focusng on the belefs of the consumers themselves about potental gans and tme needed for searchng and swtchng. Use of consumers estmates of gans rather than researcher calculatons from market ntellgence enables the excluson of consumers (ms)nformaton. Drect estmates of antcpated costs enable us to dentfy the nfluence of other factors (such as age and gender) n ther own rght, as well as va any mpact whch they may have on these core expectatons 6. Lke Sturluson (2003), we focus on the factors whch determne search behavour, and then those that nfluence swtchng amongst those who have searched; and lke Pomp et al. (2005), we can compare behavours and decsons of the same group of consumers across a range of dfferent markets. The ablty to dstngush between search and swtchng behavour has drect mplcatons for consumer polcy as we dscuss n our conclusons. Identfyng whch consumers are more lkely to swtch and persstent dfferences between markets also has polcy mplcatons. The growng lterature on behavoural economcs challenges a model of consumer decsons based solely on a model of utlty maxmsaton whch trades off potental gans and losses. DellaVgna (2009) provdes an excellent survey of emprcal evdence of non-standard decson makng; the most relevant for consumer actvty n default relatonshp markets lke those we analyse are choce avodance and status quo bas (see Mehta (2013) for a revew. Huck and Zhou (2011) emphasse the dffculty of explorng the ratonalty of nerta, and Wlson et al. (2013) relate nerta to transactons costs. Recent fndngs from Levav et al (2012) that a sequence of ncreasng choce-set szes trggers deeper search may also be relevant for exploraton across markets contanng dfferent numbers of potental supplers. If consumers were Pror experence may also play a role n justfyng decsons to avod regret n the context of swtchng decsons, as suggested by Inma and Zeelenberg (2001). 5 Several papers provde emprcal evdence of consumers choosng sub optmal: Economdes et al., 2006 and Mravete, 2003 for US telecoms; Agarwal et al. (2006) for US credt cards; Agarwal et al. (2009) for US credt markets; Lambrecht and Skera (2006) for German nternet provson; Wlson and Waddams Prce (2010) for UK electrcty consumers. 6 A recent nterestng stream of emprcal studes uses data on observed search and swtchng behavour n response to market offers, see for example Hortaçsu et al (2011) n electrcty; Honka (2010) n nsurance; and De los Santos et al. (2009) for onlne books; these capture consumer behavour drectly, rather than dependng on consumer report, but tare unable to capture consumer belefs as our data do. 5

7 Our approach s not to dentfy whether ndvdual consumers exhbt non-standard behavour, but rather to understand the pattern of consumers responses across markets. As outlned n the next secton, we employ an underlyng model whch balances antcpated gans aganst expected tme commtment of actvty, dentfyng patterns n and across markets whch can gude governments and agences n developng ther polcy for the market as a whole. In a separate stream of lterature, the mportance of confdence of belefs has been recognsed (Tversky and Kahneman, 1981) and Camerer s survey (2001) provdes several examples of such characterstcs n practce. 3. Modellng and data In ths secton we frst explan the motvaton for the model we use and the flexble approach to account for consumer heterogenety, and then descrbe the survey and the data whch t generated Motvaton for model Consumers maxmsng utlty n a classc economc model would ncrease search and swtchng actvty as antcpated monetary gans rose and the expected hours needed to search and swtch fell. In decdng whether to search, both the tme antcpated for that actvty, and potental tme spent swtchng later (f a better offer s found) wll be relevant; whle n decdng whether to swtch, once search has occurred, only antcpated swtchng tme wll be germane (Wlson, 2012). The trade off between expected monetary gans and the value of the tme vares between consumers accordng to ther crcumstances, n partcular ncome: respondents wth hgher ncome would be less lkely to swtch for gven expected gans and antcpated tme, snce both the value of the monetary gan to them would be lower and the opportunty cost of ther tme would be greater, rasng the dsncentve effect of the actvty. If the gan s expected to be competed away because other frms soon match the lower prce, respondents may be less lkely to make the swtch, because the current value of the accumulated expected gans s lower. Any drect nfluence of more years of formal educaton on expected tme needed to search and swtch should be captured n the drect estmates of antcpated tme, but hgher levels of educaton may render the tme spent less onerous as well as (perhaps) shortenng t. Other demographc varables whch mght affect the trade-off between expected gans and costs nclude age and gender, ether for ntrnsc reasons or as a result of targetng by frms 7. The mportance of 7 Gulett et al. (2005) found that prepayment consumers were less lkely to change supplers n the early days of the gas market because they were less actvely targeted by frms. 6

8 qualty dmensons (whch vares between products) may be captured by whether consumers beleve t s mportant to trust supplers, so that consumers and markets where ths s more mportant exhbt lesssearch and swtchng actvty, ceters parbus. Dfferences n homogenety of products (and the mportance of qualty) across markets may also be captured by the market dummes; electrcty s essentally homogeneous by defnton 8, so we use t as a base case, and antcpate that qualty s more lkely to be pertnent n telecoms and fnancal markets. Consumers wllngness to search and swtch wll also depend on how confdent they are n ther estmates of the potental gans and costs, and n ther ablty to realse them, wth a greater wllngness to act (for gven central expectatons of gan and pan) the less varaton they perceve around ther central estmate. Consumer specfc confdence s lkely to be postvely related to experence of swtchng n other markets. A consumer s atttude to search and swtchng, and to the potental gans avalable, mght vary between markets for several reasons. The searchng and swtchng process may be less psychologcally onerous for some products than for others, ndependently of the tme consumers expect to spend; potental gans whch are a very small proporton of expendture may be regarded as less motvatng than f gans represent a large share of the bll; prces n some markets may be perceved as more changeable; and there may be more knowledge about some markets than others, for example because of sales or nformaton campagns, so that consumers are more confdent n ther estmates. In each of the relatonshp markets whch we study, consumers contnue to receve supply from ther current provder unless they take acton to move away from ths default poston. Applyng a utlty maxmsaton model as n the lterature above, once consumers are aware of a choce n any market they face a two stage decson: frstly whether or not to search; and secondly, dependng on the nformaton obtaned durng such search, whether or not to swtch to a new provder. We can formalse ths through backwards nducton, adaptng the modellng approach n Gulett et al (2005). The monetary value of the tme spent searchng and swtchng depends both on ts opportunty cost and on the ntrnsc (ds)pleasure of the search and swtchng actvtes. As explaned above, the opportunty cost depends on ncome, whle the (ds)pleasure may be nfluenced by educaton, age and gender, and by experence and atttudes. 8 Relablty depends on the monopoly owner of the dstrbuton wres rather than the retaler chosen by the consumers. 7

9 We follow Gulett et al. (2005) p. 954 n usng an expendture functon to derve an approxmaton usng the consumer surplus dfference between beng wth the old and the (potental) new suppler. We analyse the process of decdng whether or not to search and swtch away from the current suppler n each of the eght markets (k=8). In addton to allowng for varatons n behavour across markets, we also allow for varatons n searchng/swtchng decsons across ndvduals. The mportance of allowng for observed and unobserved heterogenety across ndvduals has been demonstrated by Hutchnson et al. (2000), who demonstrate that effects that are sgnfcant n an aggregate analyss may exst separately but not n combnaton at the segment level; smlarly, when effects at segment level are sgnfcant but n opposte drectons they may cancel each other n the aggregate analyss. To allow for heterogenety across ndvduals, we apply a general approach by estmatng a random parameter (mxed) model. These models allow the estmated coeffcents to vary across ndvduals. Note that ths captures not only varatons related to the observed characterstcs of ndvduals but also those related to unobserved characterstcs, or random preference varatons. An early paper to take ths nto account was Hausman and Wse (1978), usng a covarance Probt (as opposed to ndependent Probt or Logt) model to allow for random taste varatons. Wth the development of smulaton methods, a more general framework wth a mxed model of Logt or Probt allows for random taste varatons, based on smulated maxmum lkelhood estmators. Appendx 1 provdes a bref descrpton of the estmaton technques (for technque detals, please refer to Tran 2002; Hensher, Rose and Greene, 2006; Greene 2008.). The models used for the probt estmatons of the probablty of searchng, P(se), of swtchng, P(sw), and of searchng-and-swtchng, P(sesw), were as follows: Pr[ U 1 X, ] where U 1 f ndvdual searched/swtched/searched-and-swtched. Note that we treat a person n each market as dfferent ndvduals to capture the dfference n markets n terms of searchng/swtchng decsons. The ndependent varables are expected gan, expected search tme, expected swtch tme, swtched other, market, ncome, educaton, age, gender. To decde whether each ndependent varable, should have a random or fxed parameter, we start by allowng expected gan, expected search tme, expected swtch tme and swtched other to have random parameters (coeffcents), and test the sgnfcance of the dsperson of each of these random parameters. If the dsperson s sgnfcant we treat t as a random parameter. If the 8

10 dsperson of a parameter s not statstcally sgnfcant, we then treat t as a fxed parameter and re-estmate the model. Ths process results n treatng expected gan, expected swtch tme and swtched other as random parameters n the search model and the search-andswtch model, wth all other ndependent varables havng fxed parameters; n the swtchng model, expected gan and expected swtch tme are treated as random parameters, and all other varables are allocated fxed parameters. Apart from unobserved varatons, we also allow for preference heterogenety around the mean of the random parameter estmate on the bass of the observed covarates (the second term n Equaton A3 n Appendx 1) appled to the ndvdual and market characterstcs: ncome, educaton, age, gender and market. Ths s equvalent to ntroducng nteractve terms n the models (see the Appendx 1 for detals), as n the bvarate probt model above. If the nteracton s not statstcally sgnfcant then we rely only on the standard devaton of the random parameter estmate,.e. unobserved heterogenety or random taste varatons (the thrd term n Equaton A3 n Appendx 1) for sources of preference heterogenety across ndvduals. Therefore our estmates of random parameters nclude three man parts: the mean estmates of the random parameter (the frst term n Equaton A3), the heterogenety around the mean observed from covarates (the second term n Equaton A2), and the standard devaton of the random parameter dstrbuton (related to the sgnfcance of the random varaton- the thrd term n Equaton A3). To assess the effect of allowng varatons across ndvduals from the random parameter model, we also model the observed decson of a consumer to search and swtch as resultng from two seemngly unrelated bvarate latent varables n a bvarate probt model. Detals are gven n appendx The survey and the data The data were generated by a large scale survey admnstered n the summer of 2005, especally commssoned to dentfy consumers own estmates of search and swtchng costs and expected gans from swtchng. The survey was conducted by Market and Opnon Research Internatonal for the ESRC Centre for Competton Polcy, and admnstered to a natonally representatve sample of 2027 adults aged 16 or over, ntervewed face-to-face, nhome, n 167 samplng ponts across Great Brtan. The survey used quota samplng whch followed the Government Offce Regons' set quota on demographcs (age, gender, class etc.). 9

11 Respondents were asked whch products the household consumed and pad for, from a lst comprsng electrcty, moble phone, fxed phone lne rental, natonal and overseas (fxed lne) calls, broadband nternet, car nsurance, mortgage and current bank account. These markets are smlar n that all nvolve a relatonshp between suppler and consumers whch the consumer needs to sever n order to swtch to an alternatve provder, and all are subject to (dfferent forms of) sector regulaton; but they dffer n the degree of homogenety of the product and the nature of regulatory oversght, the transparency of prces and n how long choce had been avalable. Respondents were asked whether they had a choce of suppler for each product (all dd have such a choce); ther responses were ncluded for each market f they were aware that choce was avalable and they were solely or jontly responsble for decsons on who suppled that product to the household. Respondents were asked whether they had searched around for better deals and whether they had swtched suppler n each market durng the prevous three years (other than when movng house). They were also asked how long such search and swtchng had taken and whether ths was more or less than they had expected; or, f they had no experence, how long they would expect to have to spend on each actvty 9. Respondents were asked how much they thought they could save n each market f they shopped around 10, and whether they beleved ther suppler would match cheaper offers n the next few years. Demographc characterstcs, ncludng age and gender, were recorded to dstngush ther drect effect on propenstes to search and swtch from ther role as proxes for these costs, as used n prevous studes. Consumers were asked to report ther current expendture n each market. The questons posed and the constructon of the varables are reported n appendx 2. We analyse each household and market as an ndvdual observaton,.e. we regard our data as a panel (I x K) across households (I) and products (K). Each such household/market observaton was ncluded only f all the relevant varables descrbed above were known for that case. We dscuss the effect of ths selecton process n secton 4.3 below. 4. Results 9 Unfortunately we have been unable to dstngush between any changes n expectatons of swtchng tme whch resulted from the search process tself. We explore how expectatons related to market actvty and the mplcatons for selecton n secton 4.3 below. 10 The queston does not specfy whether ths estmate s of changed expendture (.e. ncorporatng any demand response to a lower prce) or the amount that would be saved f demand were constant. For car nsurance and current bank accounts, only a sngle unt s purchased, though cheaper rates mght prompt an ncreased demand for qualty (e.g. more comprehensve nsurance). In other markets, demand s typcally nelastc, so the ambguty of the queston should not have a substantal nfluence on the results. 10

12 In secton 4.1 we present the results from the random parameter probt model, frst examnng prmary determnants of the decson process, namely expected gan, tme antcpated to search and swtch and experence. We then dentfy the effect of other factors, ncludng demographcs, and analyse how the effects of the prmary varables vary across markets and across ndvduals. In secton 4.2 we test the model for goodness of ft and make bref comparsons wth a correspondng bvarate probt model (whch s presented n Appendx 3, table A4). In secton 4.3 we dscuss ssues of potental selecton bas. 4.1 Man Results Table 1: Average expected savngs, search and swtch tme n each market Market Electrcty Moble phone Fxed phone lne Calls Broadband Car nsurance Mortgage Current bank a/c No. of respondents aware and responsble Expected maxmum gans ( /month) No of resp s Mean (std dev) 9.50 (12.99) 9.95 (12.66) 7.14 (9.53) 8.14 (10.36) 6.86 (8.78) (34.94) (47.79) 5.28 (18.65) Av ge bll Average Expected search tme (hours) No of Mean resp s (std dev) (25.67) (21.42) (23.80) (23.38) (22.63) (23.24) (33.33) (29.40) Expected swtch tme (hours) No of resp s Mean (Std. dev) (29.45) 7.93 (19.52) (25.23) (24.51) (27.21) 7.54 (19.50) (35.08) (32.18) Table 1 presents descrptve statstcs of the prmary ndependent varables, and shows consderable varaton between markets n expected potental savngs and tme for searchng and swtchng, as well as n the number of respondents who could provde such estmates. Descrptve statstcs for other varables are shown n Appendx 2, table A3. 11

13 The man results are reported n Table 2, whch s presented n three parts: the frst part reports coeffcents of varables wth fxed parameters; the second reports coeffcents of varables wth random parameters; and the thrd part reports varous statstcs related to model performance. For the random parameters (part II), the reported means and heterogenety n the means correspond to the frst and second terms n Equaton A3 n Appendx1, whle the standard devatons of the random parameter dstrbuton correspond to the thrd term n the same equaton. All the random parameter models are estmated by throwng dfferent numbers of draws (100, 150, 200, 250, 300) followng the Halton sequence (see Appendx 1) to acheve a stable set of results. 12

14 Table 2: Results from the random parameter models of searchng/swtchng Dependent Varable Probablty of Probablty of Probablty of searchngand-swtchng Independent Searchng swtchng Varable Part I: Fxed Parameters Age n years *** (0.020) *** (0.017) *** (0.019) Age n years squared 0.001*** (0.000) 0.001*** (0.000) 0.001*** (0.000) Gender (1=male, 0=female) *** (0.162) *** (0.156) *** (0.177) Income (gross annual * ** household n 000) (0.004) (0.005) (0.006) Educaton (n years) (0.036) (0.034) (0.041) Expected search tme (hrs) (0.025) (0.017) Swtched other random parameter (0.591) random parameter Constant 1.294* (0.684) (0.603) (0.696) Market (base case electrcty) Moble phone (0.241) 0.423* (0.229) (0.254) Fxed phone lne *** (0.373) *** (0.339) *** (0.741) Natonal and overseas calls ** (0.359) *** (0.552) *** (0.613) Broadband nternet (0.363) *** (0.426) * (0.463) Car nsurance (0.260) (0.235) (0.267) Man mortgage *** (1.142) *** (0.545) *** (2.134) Current bank account (0.395) (0.335) (0.391) Swtched other nteracted wth market (base case electrcty) Moble phone Fxed phone lne Swtched other: random parameter (see below) (0.256) 0.902** (0.358) Swtched other: random parameter (see below) Natonal and overseas calls 1.971*** (0.523) Broadband nternet 1.272*** (0.440) Car nsurance (0.268) Man mortgage 1.313** (0.571) Current bank account (0.406) Age * (0.006) Gender (0.176) Income 0.010* (0.005) Educaton (0.176) 13

15 Table 2-contnued Probablty of Probablty Searchng swtchng Part II: Random Parameters Mean estmates Expected gan per month ( ) 0.105*** 0.081*** (0.032) (0.025) Expected swtch tme (hrs) *** *** (0.060) (0.024) Swtched other (1=yes, 1.130* fxed 0=no) (0.683) parameter Standard Devaton of Random Parameter Dstrbuton Expected gan per month ( ) 0.118*** 0.081*** (0.008) (0.005) Expected swtch tme (hrs) 0.430*** 0.071*** (0.029) (0.006) Swtched other (1=yes, 0.944*** fxed 0=no) (0.080) parameter Heterogenety n the Means of Random Parameters Expected Gan: Income 0.001*** 0.001*** (0.000) (0.000) Expected Gan: Educaton *** ** (0.002) (0.001) Expected Gan: Age 0.002*** (0.000) (0.000) Expected Gan: Gender 0.037*** 0.035*** (0.009) (0.007) Expected Gan: Moble 0.042** phone (0.019) (0.014) Expected Gan: Fxed phone * lne (0.027) (0.020) Expected Gan: Natonal and overseas calls (0.024) (0.021) Expected Gan: Broadband *** ** nternet (0.035) (0.026) Expected Gan: Car ** nsurance (0.018) (0.014) Expected Gan: Man *** *** mortgage (0.018) (0.014) Expected Gan: Current bank ** *** account (0.025) (0.020) of Probablty of searchngand-swtchng 0.080*** (0.030) *** (0.033) (0.652) 0.113*** (0.007) 0.131*** (0.011) 0.521*** (0.062) 0.002*** (0.000) *** (0.002) 0.001*** (0.000) 0.041*** (0.008) (0.017) (0.025) (0.022) *** (0.034) ** (0.016) *** (0.017) *** (0.023) 14

16 Table 2-Conttuned Probablty Searchng Expected swtch tme: Income 0.004*** (0.000) Expected swtch tme: 0.024*** Educaton (0.003) Expected swtch tme: Age *** (0.001) Expected swtch tme: Gender 0.105*** (0.015) Expected swtch tme: Moble 0.088*** phone (0.023) Expected swtch tme: Fxed *** phone lne (0.026) Expected swtch tme: *** Natonal and overseas calls (0.026) Expected swtch tme: 0.067*** Broadband nternet (0.024) Expected swtch tme: Car 0.205*** nsurance (0.029) Expected swtch tme: Man 0.047** mortgage (0.023) Expected swtch tme: Current *** bank account (0.031) of Probablty swtchng (0.000) (0.001) 0.001*** (0.000) (0.006) (0.011) ** (0.017) *** (0.014) 0.052*** (0.012) 0.022* (0.012) 0.029*** (0.010) *** (0.013) of Probablty of searchngand-swtchng 0.001*** (0.000) 0.005** (0.002) (0.000) 0.026*** (0.009) (0.015) *** (0.030) ** (0.019) 0.068*** (0.015) 0.033** (0.016) 0.071*** (0.016) *** (0.018) Swtched other: Income 0.012** (0.006) Swtched other: fxed parameter 0.016*** (0.006) Swtched other: Educaton (0.043) (see above) (0.044) Swtched other: Age *** (0.007) *** (0.007) Swtched other: Gender (0.200) (0.197) Swtched other: Moble phone (0.295) ** (0.284) Swtched other: Fxed phone lne 1.023*** (0.392) 1.962*** (0.724) Swtched other: Natonal and overseas calls 0.726* (0.377) 1.896*** (0.589) Swtched other: Broadband nternet (0.411) (0.479) Swtched other: Car nsurance 0.762** (0.318) (0.295) Swtched other: Man 4.062*** *** mortgage (1.073) (1.987) Swtched other: Current bank account (0.480) (0.451) Part III Log lkelhood Restrcted Log lkelhood Degree of freedom (d.f) ch2 (d.f) Goodness of ft Obs No. of draws

17 *, **,*** represent sgnfcant dfference from zero at the 10, 5 and 1% levels respectvely. % of correctly predcted observatons: If the predcted probablty s greater than 0.5, the observaton s consdered as searched/swtched/searched and swtched. To nterpret Table 2, consder the search model as an example, where expected gan, swtchng tme and swtchng experence n other markets (swtched other) demonstrate random coeffcents n the probablty of searchng 11. The results show that the mean estmator of the coeffcent (.e. the frst part n Part II of the table whch corresponds to the frst term n equaton A3) of expected gan s postvely sgnfcant; the mean estmator of the coeffcent of expected swtchng tme s negatvely sgnfcant; and the mean estmator of the coeffcent of swtched other s postvely sgnfcant at 10%. The terms for heterogenety n the means of these random parameters show, for example, that the nfluence of expected gan s more postve wth hgher ncome, but less postve wth more educaton; expected gan s most postve amongst men and older people, and n the moble phone and electrcty markets, and least so n the mortgage and broadband markets. 12 To consder the results more generally we frst consder the effect of expected gan and antcpated search and swtchng tme on behavour, notng that the mean estmates of expected gan (the frst part n Part II of the table whch corresponds to the frst term n equaton A3) n the random parameter model are statstcally sgnfcant. Fgure 1 shows how expected gans predct the probablty of searchng or swtchng. The search model predcts that when the expected gan s at the sample mean (around 12 per month), the probablty of searchng s about 37% (panel a). If the expected gan s reduced by about half a standard devaton to zero, the probablty of searchng falls to 16.5%, whle an ncrease of about half a standard devaton to 24 rases the probablty of searchng to about 60%. The equvalent probabltes of swtchng are around 18% (for zero expected gan), 40% ( 12 gan) and 66% ( 24 gan) respectvely; and for searchng-and-swtchng model the correspondng fgures are about 8%, 24% and 46%. The postve swtchng rate at zero gan may be explaned by those who swtch for non-fnancal reasons, and s also consstent wth the proporton of pure errors n swtchng dentfed by Wlson and Waddams Prce (2010). 11 A full model was run to nclude expected searchng tme wth random coeffcent but the dsperson of the coeffcent (referrng to the secton of Standard Devaton of Random Parameter Dstrbuton n Table 2) was nsgnfcant, therefore expected search tme s consdered to have a fxed coeffcent. The same approach s taken wth the swtchng model and the search-and-swtchng model. 12 The nteractve terms of expected search tme wth other factors are not reported as they were not sgnfcant. 16

18 Fgure 3: Smulated effect of expected gan on the probablty of a. searchng b. swtchng 17

19 c. searchng-and-swtchng We note that respondents do not seem to be deterred from searchng by longer antcpated search tmes, snce ths varable does not have coeffcents whch are statstcally sgnfcant (though they are of the expected negatve sgn). However we fnd that antcpated swtchng tme does have a statstcally sgnfcant and negatve effect on the probablty of searchng, swtchng and searchng-and-swtchng n the random parameter model. Table 3 shows that the search model predcts the probablty of searchng at 62% and the probablty of swtchng at around 48% when antcpated swtch tme s zero, whle the predcted probablty of searchng-and-swtchng s around 30% when t takes no tme to swtch. Note that the predcted probablty of swtchng goes down faster than the predcted probablty of searchng as expected swtchng tme ncreases, suggestng that antcpated swtchng tme has a greater deterrent effect on searchng than on swtchng. All the predctons above are based on the populaton mean estmates, to provde better general estmates, rather than on the ndvdual-level condtonal estmates whch take account of ndvdual heterogenety The use of the ndvdual-level condtonal estmates means that the predcted outcome s lmted to wthn the sample drawn as part of the study. These ndvdual-specfc estmates are only as good as the data from whch they are estmated and may not be deal for predcton of populaton behavoural reactons to changes n certan attrbutes/polces (such as expected gans) (Hensher et al, 2005, Greene, 2001). Therefore the above predctons are based on the uncondtonal (populaton) mean estmates only. The same problem s also lkely to exst by usng uncondtonal mean estmates obtaned from non-representatve samples. 18

20 Table 3: Smulated effect of expected swtchng tme on the probablty of searchng/swtchng Swtch tme Smulated probablty of searchng Smulated probablty of swtchng Smulated probablty of searchng-andswtchng 0 (no tme at all) (up to an hour) (1-3 hours) (4-8 hours) (about 1 day) Note: The probablty of actvty drops to around zero when expected swtchng tme exceeds one day. Whle the mean estmates (shown n the frst part of Part II n table 2) of swtchng experence n other markets are not sgnfcant n the random parameter model, the effect vares sgnfcantly across markets and ndvduals. For nstance, swtchng experence has a greater effect on actvty n the fxed phone lne, natonal and overseas calls, car nsurance and mortgage markets. Swtchng experence has less effect n other markets, partcularly the moble phone market, and among people who have lower ncome and are older. We dscuss the varatons across ndvduals and markets regardng the effect of expected gan, swtchng tme and swtchng experence n more detal below. Next we analyse the effect of other demographc factors, n partcular age, gender, ncome and educaton, and ther nteractve effect wth expected gan, expected swtchng tme and swtchng experence n other markets. The effect of each of these factors s shown n the fxed parameter part (the frst part of Part I n table 2), whch also shows ther nteractve effect wth swtchng experence n the swtch equaton. Other nteractons, wth parameters found to be random n the relevant equaton, are shown n Part II of Table 2. As n other smlar surveys we found a U-shaped effect of age on searchng/swtchng (see Fgure 2 below for the swtchng model), and are able to separate out the drect effects of age on behavour from ther ndrect effects on expectatons of gan and effort. In terms of the nteractve effects of age wth expected gan, swtchng tme and swtchng experence, we fnd that older people are lkely to value gan more, are more deterred from searchng (but less deterred from swtchng) by longer swtchng tme, and are less affected by ther own 19

21 experence of swtchng n other markets. Swtchng s least lkely around age 56, wth ts probablty ncreasng as respondents reach and pass retrement age. Fgure 2: Smulated effect of age on the probablty of swtchng We also fnd a sgnfcant effect of gender n the random parameter models. Overall males are less lkely to search/swtch than females. And men seem to value gan more and swtchng tme less than women. Income has on average a negatve effect on the probablty of swtchng and of searchngand-swtchng, showng that respondents wth hgher ncome are less lkely to search and swtch, as we mght expect; but the (negatve) effect on the probablty of searchng s not statstcally sgnfcant. The random parameter model shows that the postve effect of expected gan s more sgnfcant wth people of hgher ncome, suggestng that rcher households value a pound of gan more. We also fnd that hgher ncome respondents are less lkely to value swtchng tme (n the sense that swtchng tme s less negatve wth hgher ncome) and more lkely to value swtchng experence (swtchng experence s more postve for hgher ncome). Educaton seems to have no sgnfcant effect on the probablty of searchng or swtchng on average n the random parameter model. However the nteracton terms ndcate that more educated people are less lkely to search or swtch for a gven level of potental gans. People wth more educaton are also less lkely to be deterred from searchng by hgher expected swtchng tme (snce the effect of expected swtchng tme s less negatve wth more educaton). 20

22 Havng shown the effect of prmary varables and ther nteracton wth varous demographc varables, we now focus on dfferences between the markets ncluded n the survey. The market dummes reported n Table 2 (Part I) show large varatons across markets: other thngs beng equal, consumers are less lkely to search/swtch ther fxed lne supplers (phone lne and calls) and ther mortgage provders than they are for electrcty. Such dfferences reflect the descrptve data shown n table 1 and confrm a range of market specfc factors, ncludng the presence of ntermedares such as swtchng web stes, advertsng and sales actvty, concern about qualty ssues whch mght make consumers more reluctant to swtch and how long a market has been open to competton. Even after these market varatons (and other varables) have been taken nto account, dfferences between markets reman n three mportant respects: the margnal nfluence of an addtonal pound s expected gan; the effect of expected swtchng tme; and the experence of swtchng n another market 14 ). Consumers respond dfferently n dfferent markets, even when they hold the same expectatons about expected gans, swtchng tme and swtchng experence, and correctng for aggregate dfferences n actvty between markets. The same monetary expected gan s more lkely to stmulate searchng for an alternatve moble phone provder than n the electrcty, fxed phone and car nsurance markets, but s less lkely to motvate search or swtch actvty for broadband, mortgages or current accounts. Expected swtchng tme seems to provde more of a deterrent to searchng and swtchng n fxed phone provson and current bank accounts, but s less offputtng for moble phones, broadband, car nsurance and mortgages than n electrcty. Table 2 also shows that swtchng experence s partcularly lkely to encourage searchng and swtchng for alternatve mortgages and fxed phone lnes. Table 4 below shows the varaton of estmated random parameters of expected gan, swtch tme and swtched other across markets based on ndvdual-specfc mean estmates 15. Ths enables comparson of the dfferent average effect of each of the prmary varables on consumer behavour across markets, gven the demographc characterstcs of ndvdual respondents n the sample. 14 These are shown n the nteractve terms wth market dummes n the last rows of Parts I and II n table Note that these are means (and standard devatons) of ndvdual-specfc condtonal estmates of coeffcents and should be dstngushed from the margnal effects reported n Table A5 and A6 (whch are assumed to have fxed parameters across ndvduals wthn a market n the bvarate probt model). 21

23 Table 4: means and standard devatons by market of the ndvdual-specfc condtonal estmates of the coeffcents of: a. Expected gan Market Search Model Swtch Model Search-and Swtch-Model mean standard devaton mean standard devaton mean standard devaton Electrcty Moble phone Fxed phone lne Natonal/overseas calls Broadband nternet Car nsurance Mortgage Current account bank All b. Expected swtch tme Market Search Model Swtch Model Search-and Swtch-Model mean standard devaton mean standard devaton mean standard devaton Electrcty Moble phone Fxed phone lne Natonal/overseas calls Broadband nternet Car nsurance Mortgage

24 Current account bank All c. Swtched other Market Search Model Search-and-Swtch Model mean standard devaton mean standard devaton Electrcty Moble phone Fxed phone lne Natonal/overseas calls Broadband nternet Car nsurance Mortgage Current bank account All Table 4 shows consderable dfferences n the smulated condtonal coeffcents averaged across markets, as well as the standard devaton 16. These dfferences suggest that the prmary drvers of actvty, namely antcpated gan and tme needed to swtch, have dfferent effects n each market. Fnally, we dscuss how the estmated effects of expected gan, swtchng tme and experence of swtchng n other markets vary across ndvduals, even after controllng for the observed heterogenety related to markets and to ndvdual characterstcs (the demographcs dscussed above).the observed heterogenety n the mean ( z, n equaton A2) should reduce the role of the resdual mean estmate (the thrd term n equaton A2) for random varables ncludng expected gan, swtch tme and swtched other. However, we stll observe sgnfcant dspersons of ndvdual estmates of these coeffcents (see the secton of Standard Devaton of Random Parameter Dstrbuton n part II of Table 16 Ths s computed from the average of the condtonal varances plus the varance of the condtonal means. 23

25 2), and the condtonal mean estmates exhbt such dfferences across ndvduals. To vsualse the extent of varaton n the effect of the same expected gan on dfferent ndvduals, Fgure 3 shows the dstrbutons 17 of the condtonal coeffcents on the expected gans, showng that dfferent ndvduals respond very dfferently to the prospect of an addtonal pound of gan (further llustratons of heterogenety across consumers are presented n Appendx 4). Fgure 3: Dstrbuton of the random parameter of expected gan (denoted as BEGN) n the search model 4.2 Goodness of Ft and comparson wth bvarate probt model The large Ch2 values n the thrd part of Table 2 show that all three models are statstcally sgnfcant, and the hgh percentage of correctly predcted observatons n the same part of Table 2 demonstrates how closely the predcted probablty matches the actual searchng/swtchng decsons. The extent of such matchng s shown for each market n Table The Ch2 normalty tests do not reject the null hypothess of normal dstrbutons, even though the condtonal dstrbuton may not necessarly be ether symmetrc or normal. 24

26 Table 5: Predcted probabltes of actvty by market [observed ratos n square brackets] (standard devatons of predcted probablty n round brackets) Market Search Swtch Search-and-swtch Electrcty [0.471] [0.417] [0.372] (0.403) (0.337) (0.364) Moble phone [0.534] [0.475] [0.419] (0.392) (0.307) (0.353) Fxed phone lne [0.323] [0.241] [0.194] (0.397) (0.288) (0.298) Natonal/overseas calls [0.383] [0.340] [0.273] (0.406) (0.353) (0.343) Broadband nternet [0.360] [0.324] [0.252] (0.392) (0.320) (0.317) Car nsurance [0.651] [0.509] [0.480] (0.380) (0.332) (0.371) Mortgage [0.419] [0.333] [0.295] (0.486) (0.443) (0.450) Current bank account [0.182] [0.132] [0.132] (0.316) (0.203) (0.229) ALL [0.445] [0.374] [0.328] (0.420) (0.345) (0.364) As an alternatve approach for cross-checkng we have also undertaken a bvarate probt analyss, whose results are reported n Appendx 4. The bvarate probt model confrms the man results from the random parameter model (see the comparsons between the two models n Appendx 4), though wth lower levels of statstcal sgnfcance. In partcular, the random parameter model hghlghts the unobserved heterogeneous preferences among consumers regardng ther searchng/swtchng decsons made n response to expected gan or swtchng tme. Wth regard to swtchng experence, t seems that n the random parameter model, there s more varaton n terms of swtchng experence, resultng n the 25

27 nsgnfcance of the mean estmates. Ths arses because n the random parameter model, the mean estmates of swtched other have much hgher standard devatons (the second part of part II n table 2) than those of the two prmary varables, expected gan and expected swtchng tme. We dscuss the polcy mplcatons of large varatons across ndvduals, both observed and unobserved, n secton 5. It s mportant to note that the assessment based on the smple mean estmator and the observed heterogenety n the mean ( z, n equaton A2) s not complete. Ths s equvalent to an estmate of the common uncondtonal mean as estmated n the bvarate probt model. t s more nformatve to look at the condtonal mean estmates ( n equaton A2) across each ndvdual, snce ndvdual-specfc nformaton s used n the estmate. The fact that we stll observe sgnfcant dspersons n ndvdual estmates of the coeffcents (see the Standard Devaton of Random Parameter Dstrbuton n part II of Table 2) after controllng for the observed heterogenety confrms the mportance of takng nto account unobserved heterogenety or random preferences varatons. Usng random parameter models enables a more realstc assumpton and a more robust model specfcaton whch allows for heterogenety across each ndvdual, arsng from consumer-specfc preferences whch affect searchng or swtchng decsons. Although the two types of models are not nested and the lkelhood rato test s not drectly applcable, we note that the lkelhood s much hgher (around -1000) for the random parameter model than for the bvarate probt (where t s around -1700). For the above reasons, the random parameter model s preferred to the bvarate probt model n our applcaton, wth the latter largely confrmng the man fndngs of the former. 4.3 Selecton ssues Only about half the respondents were able to provde estmates of all the prmary varables, namely how much they expected to gan from changng provder and how long they thought searchng and swtchng would take, and more data were lost 18 through refusal to answer the ncome queston 19. Ths rases concerns about two potental sources of bas: frst, those who 18 We lost 1556 due to mssng expected gan and 4690 due to mssng expected swtch tme; 3425 observatons were lost due to mssng ncome. The combned effect was to lose 7042 out of 8878 potental observatons 19 An earler selecton process ncluded only those who were responsble for choosng the provder and aware of choce of provder (see table A7, appendx 5). However snce we are nterested n the populaton whch s both responsble and aware of choce, these dfferences merely show that the populaton we are samplng dffers from a stratfed sample of all adults. 26

28 could provde estmates may be more lkely to be searchers and swtchers; second, the searchng-and-swtchng process tself may have resulted n dfferent expectatons of potental gan and tme requred to make the change. Table A8 n appendx 5 shows that those who were ncluded n the analyss were ndeed more lkely to have searched and swtched, confrmng the frst potental bas, so that the results apply to a dsproportonately actve subset of the consumers approached, and we take account of ths n drawng polcy conclusons n the next secton 20. The second potental bas arses n dentfyng the causalty of the relatonshp between swtchng and search actvty and expectatons of potental gan and effort. Actvty n the market could have affected respondents estmates of gan and tme taken to search and swtch, rather than vce versa. Cogntve dssonance may also have played a part, wth respondents expressng justfcaton for ther (n)actvty by understatng gans and exaggeratng tme estmates. The raw assocatons shown n table A9 (Appendx 5) naturally rase these questons. We have addressed ths ssue both by askng those who have swtched what they thought they could save beforehand, and by constructng the expectatons of tme as far as possble from pror estmates (see tables A1 and A2 n appendx 2). Amongst those who have swtched, ther mean expectatons of gan before they swtched were sgnfcantly hgher than the gans they thought they could make by swtchng agan, as one would expect f they beleved they had already realsed a substantal porton of any potental gans. Whle our methodology of basng analyss on consumer expectatons rather than market values means that we are unable to dentfy specfc potental gans avalable n all markets to check the realsm of ndvdual expectatons, homogenety n the electrcty market enables some comparsons. Amongst consumers who had searched, those who were stll wth ther electrcty ncumbent expected to be able to save an average of around 4.6 more per month than those who were not. Snce ncumbents were chargng around 10% ( 3.6 per month) more than non ncumbents at ths perod (Ofgem, 2008), average consumer expectatons amongst searchers reflected these market crcumstances reasonably closely. 5. Polcy Dscusson and Conclusons Our model predcts well the factors whch motvate consumers n our sample to be actve. The man fndngs are that antcpated gans are an mportant stmulus, and that both 20 Table A7 shows other varatons n the characterstcs of the ncluded and excluded groups. 27

29 searchng and swtchng are deterred by the expected tme requred for swtchng. Sales tactcs whch emphasse Swtch to us and save 100 a year on your blls, and whch shorten the swtchng process, antcpates just such consumer behavour. Moreover whle the tme to search has lttle deterrent effect, suggestng that t may be ntrnscally more enjoyable or less stressful than the swtchng process, the expected tme to swtch dscourages searchng more than swtchng, so that consumers do not even engage n the ntal stage of lookng around for better deals f they thnk that the process of realsng them s lengthy. Sales actvtes of course am to bypass the search process altogether by presentng potental buyers wth a ready-made offer. Regulators who want to stmulate actvty need to offer the same package as do marketers n terms of confdence n potental gans, and a swtchng process whch s not perceved as tme consumng. In practce such messages may be more successfully conveyed to consumers by frms than by regulators, whose most mportant role may le n ensurng that frms have the ncentve and ablty to stmulate consumers. Polcy makers may have concerns or statutory dutes towards partcular groups of consumers, n partcular the elderly and those wth low ncome, and our results show dfferences n the behavour of these groups. There s a famlar U-shape n the age profle of underlyng propensty to swtch, showng the mddle aged least actve, and the young and old more so. The effects of the man drvers of actvty are exaggerated among older people gans provde a greater ncentve to acton, and swtchng tme a greater deterrent for searchng; whle the experence of prevous experence of swtchng has a smaller effect on older respondents, probably because they have more experence n total. Efforts to emphasse potental gans and mnmse expected tme spent n swtchng would therefore be expected to have a stronger effect amongst older consumers. However the greater actvty amongst older respondents suggests that there s no need for regulatory nterventon on the bass of age alone. Unsurprsngly, lower ncome respondents are more lkely to swtch (but not sgnfcantly more lkely to search) than those wth hgher ncomes, snce ther margnal value of savngs s lkely to be hgher. However lower ncome households are less lkely to search and swtch n response to ncreases n potental gans; and they are more deterred from searchng (but not from swtchng alone) by longer swtchng tme. Those wth lower ncome may be more rsk averse, snce they are less responsve to gans and more responsve to the length of the process, perhaps because they have more at stake (relatvely) than hgher ncome households. The experence of swtchng n other markets s smaller for lower ncome households. These fndngs suggest that polces such as those above whch emphasse 28

30 gans may be less effectve amongst lower ncome households, and may have to be supplemented wth other measures, n partcular reducng swtchng tmes whch partcularly deter these households. Polcy makers may also be concerned to ncrease actvty n the market by those wth lower educatonal achevements. Whle there s no sgnfcant dfference n the underlyng level of searchng and swtchng amongst such households, they are more responsve both to hgher expected gans n encouragng searchng and swtchng, and to antcpated swtchng tme n deterrng searchng. Ths ndcates the mportance of communcatng the avalable gans and any shortenng of swtchng tme to ths group to encourage them to take advantage of potental gans. Polcy makers concerned to ncrease consumer confdence can learn from the role of experence of swtchng n other markets, whch s lkely to ncrease such confdence. The substantal and sgnfcant varaton of ths factor between markets demonstrates that t s not due merely to consumer characterstcs. The strong effect on searchng n the mortgage market suggests that such experence s partcularly valuable n provdng confdence for ths hgh expense fnancal market where consumers may be cautous, and base levels of confdence especally low. Ths may reflect n part the effect of a larger choce-set of potental supplers n the mortgage market, as predcted by Levav et al (2012), or reduced regret (Inma and Zeelenberg (2001). However our results of heterogenety across ndvdual consumers regardng the mportance of pror experence ndcate that such effects vary consderably across consumers. Consstent dfferences between markets n the underlyng propensty to swtch are shown by the market dummes n table 2 and the effects of the prmary varables n table 5. Snce the analyss controls for other nfluences on swtchng, these reflect a more fundamental dfference n swtchng behavour. In ths context we note no sgnfcant underlyng dfference n searchng or swtchng behavour between electrcty, moble phone, broadband, car nsurance and current accounts, despte apparently much hgher actvty by car nsurance consumers and much lower actvty n the current account market shown n the raw data n table A10. Relatve to these markets, the model predcts lower underlyng actvty levels n fxed phone lnes (perhaps because of the relatve novelty of choce at the tme of the survey) and the mortgage market. Such dfferences reflect a varety of dspartes between markets, ncludng the amount of advertsng and marketng, the length of tme for whch choce has been avalable, perceptons of ease of searchng and swtchng (as well as the expected tme nvolved whch 29

31 s drectly measured) and concern about qualty levels whch are common wthn each market but vary between markets. Each of these dfferences between markets provdes evdence for regulators who wsh to ncrease actvty n ther own sector. Increasng (well founded) consumer confdence s lkely to be an mportant element n ther strategy. Authortes who want to ncrease swtchng need to engender the same confdence as does the doorstep salesperson, whle ensurng that t s based on unbased nformaton. Intatves such as the UK energy regulator s takng to smplfy tarffs, to develop ts own prce comparson tool and to admnster the confdence code for commercal prce comparson webstes (Ofgem, 2011) are clearly amed at helpng mprove both the qualty of consumer nformaton and ther (justfed) confdence levels. However snce antcpated savngs wll be strongly nfluenced by the prce offers of compettors, polcy makers need to be careful not to dampen competton and so nadvertently reduce the gans that can be made from swtchng suppler (as for example happened wth reduced swtchng rates after ntroducton of the non-dscrmnaton clause 21 ). In the retal bankng sector, fnancal gans do not appear to be the man drver for swtchng compared to other regulated markets, consstent wth the OFT (2008) s fndng that only 14% of the consumers surveyed swtch current account for better rates, and perhaps reflectng the greater value to consumers of other servces n ths sector compared to other markets. However our results do ndcate that the length of swtchng could be a major deterrent. The sgnfcant heterogenety between consumers makes t more dffcult for regulators to stmulate sgnfcant levels of actvty through unform polces. For example the model predcts that gans have to be as hgh as 100 per month (when the sample mean s about 12 per month) to ensure the majorty of consumers (around 80%) to search and swtch. But the exstence of consumer heterogenety (.e. the co-exstence of both nactve and actve consumers) supported by the emprcal results n ths study may have mportant mplcatons on the pro-compettve role played by market entrants n the presence of swtchng costs. The dstncton between search and subsequent swtchng behavour s mportant n desgnng polcy to affect actvty levels and mprove outcomes for consumers. Both frequency of swtchng and ts qualty n terms of choosng the best deal provde ncentves for compettve behavour among provders. If Ofgem s (2008) categorsaton nto actve 21 See Hvd and Waddams Prce, 2012, Waddams Prce and Zhu, 2013, and for fgures on swtchng from the Department of Energy and Clmate Change (2013) 30

32 searchng consumers, who choose well, and passve consumers, who often choose poorly under the nfluence of drect marketng, extends to other markets, polcy to ncrease the quantty and qualty of swtchng should focus on stmulatng actve searchng. Identfyng the characterstcs of such searchers and ther motvaton enables more effectve targetng, and maxmses the spll-over effects between products. Ths s an area where co-ordnaton of nformaton and campagns across sectors are lkely to be partcularly effectve. Of major polcy relevance s those factors whch have lttle effect on searchng or swtchng actvty. Two partcular expectatons, namely of the tme needed to search, and whether other supplers would match better offers, were both found to be nfluental n prevous studes but not n our models. The tme whch respondents expected to spend lookng around for a better deal had no effect on the probablty of searchng, so reducng such antcpated tme would have lttle mpact on searchng (or swtchng) levels. The mplcatons are that consumers who value comparson webstes do so as much to facltate the swtchng process as to reduce the tme needed for search. The nsgnfcant effect of expectatons about future matchng suggest that regulators should emphasse short-term gans as the man stmulus for consumer actvty n the market. In summary, our fndngs provde a number of polcy mplcatons for regulators. 1. Regulators who want to ncrease actvty n markets should emphasse the potental gans avalable from swtchng, ncrease (well founded) consumer confdence n achevng such gans and reduce the tme whch consumers antcpate t wll take to swtch. 2. To make such gans obvous, regulators may want to facltate comparsons between prces offered by dfferent compettors, partcularly n sectors such as telecoms, energy and fnancal markets where obfuscaton s often rfe. However there are dangers of unntended consequences f ths results n easer co-ordnaton for frms, or napproprate restrctons are placed on offers so that avalable gans are curtaled. 3. Regulators wantng to ncrease the accuracy of swtchng (as compared wth ts frequency) should focus on targetng consumers to encourage them to search, notng that search s deterred by expectatons of longer swtchng processes. In partcular a strategy to encourage searchng among the mddle aged may ncrease actvty n ths group. 31

33 4. Consumer heterogenety,.e. the exstence of a number of nactve consumers gven the number of actve consumers, means that unform polces across the board are unlkely to be very effectve. 5. Vulnerable groups such as those wth low ncome, or wth lower educatonal achevements, react dfferently from others, and may requre specfc focused polces to ncrease ther actvty n the markets. 6. Polces should be talored to partcular markets. In the moble phone market addtonal expected gans wll partcularly encourage searchng, and actvty n the fxed phone lne and current account markets s lkely to be stmulated more than n others by reducng expected swtchng tme. Fnally we note that n the data sample used for estmatons, we have ncluded only the subset who are able to respond to the relevant questons, and that they are more actve than the representatve group whch was ntervewed. Our sample therefore conssts of the most actve half of the populaton. Wthn ths actve group vrtually all the swtchers had looked around for a better deal frst, so are lkely to make better decsons. These selecton ssues suggest that passve consumers who may have swtched wthout frst searchng were excluded from our analyss because of ncomplete answers to the survey, n partcular ther nablty to estmate potental gans. So whle our fndngs can nform polces to ncrease actvty amongst those who are already reasonably well nformed about the market, ther effect may be very dfferent amongst the more dsengaged half of households. If actvty among ths latter group s to be encouraged, research s clearly needed to understand further the drvers of (n)actvty. Feld experments whch capture real tme responses of consumers facng competng demands on ther tme and attenton would provde robust results for ths nactve group; t would also test the recommendatons from ths study, whch whle beneftng from capturng consumers own expectatons and experence are also subject to the lmtatons of partal recall and response. In concluson, we have provded evdence to show varatons n searchng and swtchng behavour whch exhbt dstnct patterns across markets, between types of consumers, and wth further heterogenety between ndvduals. Dfferences n swtchng levels across markets are well documented; usng our emprcal data of consumers own expectatons of potental gans and tme requred, we have shown that searchng and swtchng behavour s stmulated by hgher expected gans and lower expected swtchng tme. The model can nform regulatory polces to empower consumers, ncrease (effectve) actvty n relevant 32

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38 Appendces Appendx 1: Econometrc model The random parameter probt model: Suppose the utlty assocated wth the bnary decson of searchng/swtchng as evaluated by each ndvdual s represented n a bnary choce model by a utlty expresson of the form: U X e, e ~ N[0,1 ]. (A1) X s a vector of (non-stochastc) explanatory varables that are observed. e s ndependent and dentcally dstrbuted. and e are not observed and are treated as stochastc nfluences. The parameter vector ndvduals accordng to: s assumed to be randomly dstrbuted over z v z. (A2) where z s the mean of the dstrbuton (populaton mean or uncondtonal mean). depends on ndvdual characterstcs as well as parameters yet to be estmated, and the random varaton comes from the ndvdual heterogenety, v. Ths random vector s assumed to have mean zero and covarance matrx. Now the utlty functon nvolves terms: X ' ' X ( z ) X X. (A3) As shown n (2) and (3), ntroducng z n (2) to reveal the presence or absence of preference heterogenety around the mean parameter estmate s equvalent to ntroducng nteractve terms n the utlty functon as shown n (3). If the nteracton s not statstcally sgnfcant then we can conclude that there s an absence of preference heterogenety around the mean on the bass of the observed covarates, z. But ths does not mean that there s no preference heterogenety around the mean, but smply that we have faled to reveal ts presence n the second term n (3). Ths means that we rely only on the standard devaton of the parameter estmate,.e. the thrd term n (3) for sources of preference heterogenety across ndvduals. The condtonal densty of the parameters s denoted g( z,,, ) g( v z, ). (A4) The uncondtonal densty for U s obtaned by ntegratng over, 37

39 38 d z g X U f X U f E z X U f ),,, ( ), ( )], ( [ ),,,, (. (A5) The ntegraton wll not exst n closed form. They can be estmated by smulaton. The smulated log lkelhood s )}, ( 1 ln{ ln 1 1 R r n v z X U f R L (A6) Note that the smulaton s over R draws on v through as defned n (2). The maxmum smulated lkelhood estmator s obtaned by maxmsng (A6) over the full set of structural parameters. As we are nterested n estmatng ndvdual-specfc parameters, we compute the posteror estmate as follows based on ndvdual nformaton and the pror estmate, z : ) ˆ, ( 1/ ) ˆ, ( ˆ 1/ ],,,, ˆ[ 1 1 R r R r X U f R X U f R z X E (A7) where ^ ), ( U X f s the smulated probablty of choce and v z ˆ ˆ ˆ In terms of selectng the number of ponts for the smulaton, we follow the Halton sequence suggested by Bhat (2001) and Tran (2003). We try dfferent number of draws to secure a stable set of parameter estmates. Estmatons are obtaned by usng NLOGIT 5 software.

40 Appendx 2: Relevant questons from the survey and constructon of varables Relevant questons from the survey QA. Frstly, could you tell me f you are nvolved solely, jontly or not at all n the decson of whch suppler to use for any of these servces or products? (Solely, Jontly, Not at all) Q1. In your area, do you have a choce of more than one provder for the followng products? (Yes, No, Don t know) Q2. Whch of the followng does your household currently have and pay for? Q4. Usng the words on ths card, how mportant or unmportant s t to trust your provder for the followng products? (Very mportant, Farly mportant, Nether mportant nor unmportant, Farly unmportant, Very unmportant, Don t know) Q5. Have you looked around for a new provder for any of the followng products at any tme n the last three years, that s, snce May 2002? (Yes, No, Don t know) Q11. Apart from when movng home, have you swtched provder of any of these products n the last three years, that s, snce May 2002? (Yes, No, Don t know) Q15. (Ask all who swtched any) Please tell me how much tme you spent searchng around and lookng for the necessary nformaton before you swtched each relevant product area? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) Q17. (Ask f any tme spent searchng at Q15) Would you say t took more tme than expected, less tme than expected or as long as expected to search for nformaton? (More tme than expected, As expected, Less tme than expected, Don t know) Q23. (Ask all who swtched any) How much of your own tme dd t take to swtch PRODUCT AREA after you made a decson? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) Q29. (Ask all not swtched but searched n any area) How much tme dd t take you to search for the necessary nformaton on PRODUCT AREA? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) Q33. (Ask all not swtched but searched n any area) How long do you thnk t would have taken of your own tme to swtch once you had all the necessary nformaton for swtchng? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) 39

41 Q35. (Ask all non-swtchers who have not searched) How much of your own tme dd you thnk t would take you to fnd enough nformaton to decde whether and to whom to swtch PRODUCT AREA? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) Q36. (Ask all non-swtchers who have not searched) Once you have found all the necessary nformaton to choose a new suppler, how much of your own tme do you thnk t would take to swtch PRODUCT AREA? (No tme at all, Up to an hour, 1 to 3 hours, 4 to 8 hours, About 1 day, 2 to 3 days, 4 to 6 days, A week or more, Don t know) Q38. (Ask all relevant swtchers) How much dd you orgnally expect to save per month by swtchng PRODUCT AREA? Q43 Approxmately how much do you pay on average per month for each of these PRODUCT AREAS? Q44. (Ask all f answered Q43) To what extent would you agree or dsagree that you are confdent your estmate for PRODUCT AREAS s accurate? Q46. (Ask all relevant) How much s the most you thnk you could save per month f you shopped around for PRODUCT AREA? 40

42 Constructon of expected tme spent searchng and swtchng and expected gans. The expected search tme (exsetme) and the expected swtchng tme (exswtme) are constructed from dfferent questons for dfferent consumer groups accordng to the table below. Table A1. Constructon of expected search and swtchng tme Consumer group Searched swtched & Searched but not swtched Not searched nor swtched Not searched (Q15=0) and swtched tme spent searchng? Q15 Q29 more or less than expected? Q17 0 from Q15 Q17 expected search tme ex ante? Adjusted Q15 by one scale down or up accordng to Q17. Q35 Adjusted, but not downwards swtchng tme ex post? Q23 Q23 expected swtchng tme ante? Q33 Q36 ex The constructon of the maxmum expected gans from swtchng (exganmax) varable dffers by whether or not the consumer was a swtcher. Table A2 below descrbes how ths varable was constructed. Table A2. Constructon of expected gans Consumer group Swtched Not swtched Expected gans ex ante Q38 Q46 41

43 Table A3: Summary statstcs Varables Obs Mean S.D. Mn Max Expected gan ( per month) Expected search tme (hours) Expected swtch tme (hours) Expected total tme (hours) Age (years) Gender (1=male, 0=female) Educaton (years) Income (household annual gross, 000) Swtched n other markets (1=yes, 0=no) Trust mportant (1=yes, 0=no) Suppler reluctant to match (1=yes, 0=no)

44 Appendx 3: the bvarate probt model The models used for the bvarate probt estmatons of the probablty of searchng, P(se), and of swtchng, P(sw) were as follows: P(se)=f(expected gan, expected gan^2, expected gan*ncome, expected gan*educaton, expected gan*market, expected search cost, expected swtch cost, expected search cost*ncome, expected swtch cost*ncome, expected search cost*educaton, expected swtch cost*educaton, expected search cost* market, expected swtch cost*market, swtched other, swtched other*market, suppler expected to match, suppler expected to match*market, mportant to trust suppler, mportant to trust suppler*market, market, ncome, educaton, age, gender) P(sw)=g(expected gan, expected gan^2, expected gan*ncome, expected gan*educaton, expected gan*market, expected swtch cost, expected swtch cost*ncome, expected swtch cost*educaton, expected swtch cost*market, swtched other, swtched other*market, suppler expected to match, suppler expected to match*market, mportant to trust suppler, mportant to trust suppler*market, market, ncome, educaton, age, gender) Table A4. shows the results from the bvarate probt model descrbed n equatons (A8) and (A9). Importance of trust (trust), suppler reluctant to match (reluctmat) 22 and ther assocated nteractons were subsequently dropped as they dd not sgnfcantly mprove the model ft n the random parameter model. Table A4. Results from a seemngly unrelated bvarate probt model of searchng and swtchng(full model) Varable SEARCH EQUATION SWITCH EQUATION Expected gan per month ( ) 0.036*** (0.012) 0.036*** (0.012) Expected gan per month squared ( ) ** ( ) *** ( ) Expected search tme (hrs) Expected swtch tme (hrs) (0.006) (0.009) (0.009) Age n years *** (0.0160) ** (0.015) Age n years squared *** (0.002) ** (0.0002) Gender (1=male, 0=female) (A8) (A9) 22 In the random parameter model, the factor whether supplers are reluctant to match s not sgnfcant n all cases therefore s dropped from the model. 43

45 (0.086) (0.078) Income (gross annual household n 000) 0.006** (0.003) (0.003) Educaton (n years) 0.050** (0.023) (0.019) Trust mportant (0.170) (0.152) Suppler reluctant to match (0.197) 0.400** (0.196) Swtched other (1=yes, 0=no) 0.370*** (0.144) 0.518*** (0.149) Constant (0.508) (0.417) Market (base case electrcty) Moble phone (0.167) 0.281* (0.162) Fxed phone lne *** (0.199) *** (0.313) Natonal and overseas calls *** (0.207) *** (0.246) Broadband nternet ** (0.254) *** (0.401 Car nsurance (0.187) (0.188) Man mortgage *** (0.299) *** (0.443) Current bank account *** (0.280) ** (0.301) Expected gans nteracted wth market (base case electrcty) Moble phone (0.010) (0.010) Fxed phone lne (0.015) (0.015) Natonal and overseas calls (0.011) 0.021* (0.012) Broadband nternet (0.016) (0.015) Car nsurance (0.008) (0.009) Man mortgage (0.008) (0.009) Current bank account (0.010) (0.010) Expected search tme nteracted wth market (base case electrcty) Moble phone (0.004) Fxed phone lne (0.004) Natonal and overseas calls (0.005) Broadband nternet

46 (0.004) Car nsurance (0.004) Man mortgage (0.004) Current bank account (0.004) Expected swtch tme nteracted wth market (base case electrcty) Moble phone (0.005) Fxed phone lne (0.006) Natonal and overseas calls (0.006) Broadband nternet (0.005) Car nsurance (0.005) Man mortgage (0.005) Current bank account (0.005) Reluctant to match nteracted wth market (base case electrcty) Moble phone (0.263) Fxed phone lne (0.311) Natonal and overseas calls (0.324) Broadband nternet (0.398) Car nsurance (0.296) Man mortgage (0.356) Current bank account (0.396) Swtched other nteracted wth market (base case electrcty) Moble phone (0.176) Fxed phone lne 0.484** (0.209) Natonal and overseas calls 0.426** (0.211) Broadband nternet (0.279) Car nsurance 0.347* (0.188) (0.005) (0.006) (0.008) (0.005) (0.005) (0.005) (0.006) (0.275) * (0.349) (0.414) (0.358) (0.290) (0.349) (0.383) (0.170) 0.742*** (0.287) 1.120*** (0.249) 1.288*** (0.393) (0.186) Man mortgage 0.640** 0.639* 45

47 0 0 Probablty of searchng Probablty of swtchng gven search (0.292) (0.350) Current bank account (0.283) (0.286) Expected gan nteracton wth ncome and educaton Income (gross annual household n 000) (0.0001) ** (0.0001) Educaton (n years) ** (0.001) (0.001) Expected search tme nteracton wth ncome and educaton Income (gross annual household n 000) (0.0001) Educaton (n years) (0.0004) Expected swtch tme nteracton wth ncome and educaton Income (gross annual household n 000) (0.0001) (0.0001) Educaton (n years) (0.001) (0.0006) Arc-hyperbolc tangent of rho 1.324*** (0.077) Rho 0.867*** (0.019) Wald ch2 (100) *** Pseudolkelhood Obs Errors clustered by ndvdual (619 clusters). Robust, cluster-adjusted standard errors n parentheses. *, **,*** represent sgnfcant dfference from zero at the 10, 5 and 1% levels respectvely. From a Wald test of rho=0. Fgure A1: The effect of expected gan on the probablty 23 of: a: searchng b. swtchng-gven-search Expected gan ( /month) Expected gan ( /month) Table A5: Change n probablty of actvty f respondent has swtched n (one or more) other markets Search Swtch Swtch- Search Market gven- search and swtch 23 The probablty s averaged at each observaton 46

48 Electrcty 0.139*** (0.053) 0.185*** (0.052) 0.133** (0.055) Moble phone ** 0.086* (0.052) (0.052) (0.046) Fxed phone lne 0.266*** 0.291*** 0.378*** (0.053) (0.048) (0.135) Natonal/overseas calls 0.274*** 0.424*** 0.531*** (0.061) (0.048) (0.078) Broadband nternet 0.272*** 0.451*** 0.660*** (0.074) (0.057) (0.118) Car nsurance 0.248*** 0.244*** 0.128** (0.055) (0.056) (0.057) Mortgage 0.333*** 0.314*** (0.078) (0.073) (0.165) Current bank account * (0.058) (0.046) (0.107) ALL 0.194*** 0.243*** 0.250*** (0.031) (0.029) (0.038) Standard errors calculated usng delta method n parentheses. ***, **, * represents 1, 5 and 10% sgnfcant dfference from zero respectvely *** (0.047) 0.102** (0.048) 0.250*** (0.040) 0.336*** (0.042) 0.353*** (0.052) 0.253*** (0.053) 0.286*** (0.061) 0.073* (0.038) 0.210*** (0.026) Table A6: Margnal effects of gan (addtonal /month) by market Margnal effect on probablty of: Search Swtch Swtch-gvensearch Market Electrcty 0.006** 0.009*** 0.007*** (0.003) (0.003) (0.002) Moble phone 0.008*** 0.009*** 0.005*** (0.003) (0.003) (0.002) Fxed phone lne 0.007** 0.008*** 0.008** (0.004) (0.003) (0.004) Natonal/ overseas calls 0.008*** 0.012*** 0.012*** (0.003) (0.002) (0.003) Broadband nternet (0.005) (0.004) (0.005) Car nsurance 0.003** 0.008*** 0.008*** (0.002) (0.002) (0.002) Mortgage 0.002** 0.003*** 0.004*** (0.001) (0.001) (0.001) Current bank account 0.004*** 0.003*** (0.002) (0.001) (0.003) Search and swtch 0.008*** (0.003) 0.008*** (0.003) 0.007*** (0.003) 0.009*** (0.002) (0.003) 0.007*** (0.002) 0.002*** (0.001) 0.003*** (0.001) 47

49 Appendx 4: A comparson of results from the random parameter model and the bvarate probt model The mean estmates of the coeffcent on expected gan are more sgnfcant (both economcally and statstcally) n the random parameter model than those n the bvarate probt model. The mean estmates of the coeffcent on expected swtchng tme s negatvely sgnfcant n all three models, an effect whch s not found n the bvarate probt model. In the random parameter model that the postve effect of expected gan s more sgnfcant wth people of hgher ncome, smlar to those found n the bvarate probt model. We fnd smlar effect of expected gan nteracted wth educaton wth both models: more educated people are less lkely to search/swtch for potental gans. In terms of effect of age, whle the sgnfcant U-shaped relatonshp s also found n the bvarate probt model wth searchng (but not swtchng gven searchng), we fnd that n the random parameter model the U-shaped relatonshp s even more sgnfcant, wth both searchng and swtchng. We also fnd sgnfcant effect of gender n the random parameter models whch s not found n the bvarate probt model. The effects of market dummes are smlar comparng the two models: other thngs beng equal consumers are less lkely to search/swtch n fxed lne phones and calls, and mortgage market than they are for electrcty. Smlar nteractve effects of expected gan wth market dummes can also be found n both model estmates. However, unlke the bvarate probt model whch suggests that the effect of an addtonal expected pound s gan on searchng and swthcng dffer weakly across markets, the random parameter suggests that effect of expected gans s lkely to dffer sgnfcantly across markets Swtchng experence n other markets has a greater effect on actvty n the fxed phone lne, natonal and overseas calls, car nsurance and mortgage markets, whch s consstent wth the bvarate model. Swtchng experence has less effect n other markets, partcularly the moble phone market, and among people who have lower ncome and are older, whch are not found n the bvarate probt model. 48

50 Comparng to the bvarate probt model, we fnd large varatons of estmated random parameters of expected gan, swtch tme and swtched other across markets based on ndvdual-specfc mean estmates 24. But the general pattern n terms how one market s dfferent from the other s consstent wth the pattern shown n the bvarate probt model estmates. The followng fgures further llustrate how the coeffcent on expected gan dffers across ndvduals: Fgure A2: Dstrbuton of the random parameter of expected gan (denoted as BEGN) n the search-and-swtch model: Fgure A3: Dstrbuton of the random parameter of expected swtchng tme (denoted as BESWT) n the search-and-swtch model: pped from the model.e means (and standard devatons) of ndvdual-specfc condtonal estmates of coeffcents and should be dfferentated wth margnal effects reported n Table A5 and A6 (whch are assumed to have fxed parameters across ndvduals wthn a market n the bvarate probt model) 49

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