Paying a Premium on your Premium? Consolidation in the U.S. Health Insurance Industry. Leemore Dafny Northwestern University and NBER

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1 Paying a Preiu on your Preiu? Consolidaion in he U.S. Healh Insurance Indusry Leeore Dafny Norhwesern Universiy and NBER Mark Duggan Universiy of Maryland and NBER Subraania Raanarayanan Universiy of California a Los Angeles Sepeber 2008 Preliinary: Please Do No Cie or Circulae Absrac We exaine wheher and o wha exen consolidaion in he U.S. healh insurance indusry is leading o higher privae insurance preius. Using a proprieary, panel daase of healhplans enrolling over 10 illion Aericans annually beween 1998 and 2006, we esiae he ipac of changes in local arke concenraion on preius. We pursue wo disinc approaches o accoun for he endogeneiy of local arke concenraion wih respec o preius: (1) focusing on srucural changes in concenraion induced by enry and exi of insurance carriers; (2) exploiing he differenial ipac of a large naional erger beween wo insurance firs across local arkes. We find ha increased consolidaion in he healh insurance indusry can a bes explain only a sall porion of he growh in preius seen over he las decade. e-ail: l-dafny@kellogg.norhwesern.edu, duggan@econ.ud.edu, subbu@anderson.ucla.edu We are graeful for helpful coens by Julie Cullen, Alan Sorensen, and paricipans of he Inernaional Indusrial Organizaion Conference, he Aerican Sociey of Healh Econoiss Conference, and he Universiy of Briish Colubia Suer Indusrial Organizaion Conference. Dafny acknowledges funding fro The Searle Cener on Law, Regulaion, and Econoic Growh a he Norhwesern Universiy School of Law.

2 Alhough he vas ajoriy of healhcare expendiures in he U.S. are funneled hrough he healh insurance indusry, few researchers have exained wheher he indusry iself is conribuing o rising healh insurance preius. This possibiliy has becoe ever ore salien as consolidaions coninue in his highly-concenraed secor. In 2001, he Aerican Medical Associaion (AMA) repored nearly half of he 40 larges Meropolian Saisical Areas were highly concenraed, using he Horizonal Merger Guidelines cuoff of HHI > 1,800. By 2007, he AMA expanded is annual repor o include 313 MSAs, 96 percen of which were found o be highly concenraed. 1 During he sae period, he average preiu for a faily of four receiving coverage hrough an eployer rose 72%, reaching $ 12,106 in This sudy exaines wheher here is a causal link beween changes in arke concenraion and growh in healh insurance preius. Fro a heoreical sandpoin, boh he sign and he agniude of he effec of concenraion on insurance preius are abiguous. On he one hand, increases in arke concenraion ay allow healh insurers o raise heir arkups, leading o higher preius. On he oher hand, increases in arke share ay srenghen insurers bargaining posiions vis a vis healhcare providers, leading o reduced oulays and lower preius. Alhough increased bargaining power is ofen cied as a key efficiency fro insurance ergers, 3 here are any oher sources of econoies (or diseconoies) of scale ha ay also affec opial preius (e.g. invesens in inforaion echnology and disease anageen progras). Which of hese effecs doinae is uliaely an epirical quesion. The key challenges o epirically esiaing such a link are adequae daa and exogenous variaion in arke concenraion. Coprehensive daa on healhplans is exreely difficul o obain because conracs are cusoized for each buyer across any differen diensions, renegoiaed annually, and considered highly confidenial. Moreover, because price varies depending on he risk profile of he insured populaion, calculaing a sandardized price for insurance requires no only a sandardized produc bu also a sandardized insured populaion. Our sudy explois a rich, proprieary panel daase of healhplans offered by a large saple of U.S. firs. The daa, which we call he Large Eployer Healh Insurance Daase 1 Copeiion in Healh Insurance: A Coprehensive Sudy of U.S. Markes, Aerican Medical Associaion, Preius include boh eployer and eployee conribuions. Source: Eployer Healh Benefis Survey, 2007 Suary of Findings, Kaiser Faily Foundaion/Healh Research and Educaional Trus Survey. 3 Here efficiency refers o he direc consuer benefi. Ren ransfers do no increase econoic efficiency, ceeris paribus. 1

3 or LEHID, span he period fro 1998 o 2006 (inclusive), represening coverage for over 10 illion nonelderly Aericans each year. By focusing on preiu growh for he sae healhplans over ie (where a healhplan is defined by a unique cobinaion of eployer, arke, insurance carrier, and plan ype like HMO,) we ake significan progress oward adjusing for unobservable populaion and plan characerisics (a he expense of offering conclusions wih regard o preiu levels). The daase conains inforaion on boh fullyinsured and self-insured healhplans. In he forer, he insurance carrier pays all clais and preius reflec a risk preiu. In he laer, he eployer pays realized clais plus adinisraive fees (and preius for sop-loss insurance, if purchased). We focus on fullyinsured healhplans in he ajoriy of our analyses, since repored preius for self-insured plans are projeced expenses, and no acual payens ade by he eployer. We use he LEHID daa o esiae local insurance arke concenraion (i.e. he HHI). We hen esiae odels ha explore he relaionship beween changes in healhplan preius and changes in HHI wihin he relevan arke. Changes in HHI include any facors ha are arguably no exogenous o preiu growh. These include changes in consuer preferences, changes in produc offerings and pricing sraegies, and changes in he healhcare provider landscape. 4 We ake wo approaches o isolaing variaion in he HHI ha is plausibly orhogonal o oher deerinans of healh insurance preius. Firs, we use he enry and exi of insurance carriers as an insruen for changes in HHI. Focusing on srucurally-induced changes in HHI ay help o isolae he effec of decreased copeiion on preius, under he (parially esable) assupion ha enry and exi of carriers is relaed o preiu changes only hrough heir effec on he copeiive landscape Second, we exploi sharp and varying increases in local arke concenraion generaed by he 1999 erger of wo indusry gians, Aena and Prudenial Healhcare. Boh were naional firs, acive in os local insurance arkes, and hus he erger had widespread ipac. However, pre-erger arke shares of he wo firs varied significanly across local arkes, resuling in very differen shocks o pos-erger copeiion. For exaple, in our saple he pre-erger arke shares of Aena and Prudenial in Newark were 39 and 16 percen, respecively, versus 3 and 5 percen, respecively, in Orange Couny, California. Holding all else consan, his iplies an increase in pos-erger HHI of 1,228 poins in Newark, bu only 30 in 4 Using InerSudy daa, Scanlon e al (2006) esiae ha 35-39% of he variaion in HHI is aribuable o enrollen changes, wih he reainder due o srucural changes (exi/enry). Scanlon e al use he MSA o define geographic arkes and focus exclusively on HMOs because Inersudy does no reliably repor PPO daa for he enire sudy period,

4 Orange Couny. Focusing on he years iediaely surrounding his erger, we exaine he relaionship beween preiu growh and HHI changes using hese prediced changes as insruens for acual changes. Boh proposed insruens perfor well in he firs sage, explaining a significan aoun of he underlying variaion in he arke concenraion easure (HHI). Using he firs approach, we find soe evidence ha increases in concenraion caused by exi (or consolidaion) of insurance carriers leads o sall increases in preius. Using he resuls fro his analysis, we esiae he edian enrollee in our daase faced a preiu increase of 1.2 percen due o consolidaion in her local arke beween 1998 and When we exaine he effec of he erger-induced change in concenraion on preius, we find evidence suggesing ha preius acually declined. In fuure drafs, we inend o explore wheher hese declines are due o increased bargaining power vis-à-vis healhcare providers (and hence lower negoiaed reiburseens). Taken ogeher, however, he resuls sugges ha consolidaion in he privae healh insurance secor can explain a bes only a sall porion of he oal growh in privae healh insurance preius over he las decade. The paper is organized as follows. Secion 1 discusses prior relaed research and offers background on he healh insurance indusry. Secion 2 describes he daa in deail. We hen analyze he relaionship beween concenraion and preius using variaion in HHI induced by enry and exi of all insurance carriers (Secion 3) and he Aena-Prudenial erger (Secion 4). Secion 5 discusses he resuls and offers preliinary conclusions. I. Background A. Prior Research This sudy draws fro wo sreas of research: sudies of he relaionship beween arke concenraion and copeiive oucoes in he epirical indusrial organizaion lieraure, and sudies of he healh insurance indusry, ainly fro he healh services lieraure. In his secion, we suarize boh, highlighing our conribuions a he end. Price-Concenraion Sudies in Indusrial Organizaion The srucure-conduc-perforance paradig in indusrial organizaion gave birh o a nuber of sudies exaining he relaionship beween arke concenraion and profiabiliy. 3

5 Many of hese sudies eployed cross-secional daa across indusries, and found evidence of a posiive relaionship beween profis and concenraion. This approach was faously criiqued by Harold Desez (1973), who argued ha he observed relaionship could also be explained by differences in efficiency across firs. 5 The focus in he lieraure hen shifed o using price as he oucoe variable of ineres, based on he arguen ha i was no subjec o he efficiency criique. Recen sudies in his lieraure focus on wihin-indusry variaion in concenraion and price, e.g. by using observaions on differen geographic arkes. Mos docuen higher prices in ore concenraed arkes. Exaples include Morrison and Winson (1990) in airlines, Hannan (1992) in banking, and (Coerill 1986) in grocery reailing. However, uch of his work assues arke srucure is exogenously deerined wih respec o price. Since unobserved shocks o cos and deand ay affec boh prices and arke srucure, a siple regression of price on concenraion could lead o biased esiaes. Recen sudies have pursued wo key approaches o suroun his endogeneiy issue. 6 One soluion relies on a wo-sep esiaion procedure. In he firs sep, he auhors esiae an equilibriu odel predicing he nuber of copeing firs in a arke. They use his odel o generae a correcion er o include in he second-sage regression of price on concenraion, uch in he sae way selecion correcion ers are included in wage regressions (Heckan 1979). Soe recen exaples include Manuszak and Moul (2008), who use his ehod o evaluae he prospecive ipac of he Saples-Office Depo erger, and Singh and Zhu (2006), who sudy auo renal arkes. Mazzeo (2002) exends his approach o accoun for he ipac of produc differeniaion by specifically allowing for differences in he copeiive effecs of firs wih differen produc characerisics. This approach lends iself o esiaing welfare changes and perforing counerfacual experiens (like esiaing he effecs of a erger, for exaple), bu i enails aking srong assupions abou he behavior of firs o enable an accurae characerizaion of arke srucure in he firs-sage equaion. The second soluion requires variables ha can serve as insruens for arke srucure, i.e. easures ha are correlaed wih arke srucure bu uncorrelaed wih unobservable facors 5 This approach was also criicized on oher frons, paricularly on he failure o conrol for differences in econoic facors across indusries, and on he use of accouning easures for profiabiliy. 6 In a panel daa seing (siilar o he seing we use), one can include arke fixed effecs o conrol for ieinvarian facors ha are specific o each arke. However, o he exen ha unobservable changes in cos or deand condiions affec changes in price and arke srucure, esiaes fro fixed effecs specificaions will sill be biased. 4

6 affecing price. Two of he bes-known sudies in his vein use lagged arke srucure as an insruen for curren arke srucure, e.g. Evans, Froeb and Werden (1993) (airlines) and Davis (2004) (ovie heaers). For exaple, Davis explores he relaionship beween wihinheaer variaion in pricing and geographic arke srucure, using lagged couns of ovie screens owned by own and rival chains wihin various disances as insruens for heir curren levels. He finds ownership srucure has a saisically significan bu econoically sall effec on adission prices charged o consuers. Unforunaely, using lags of an endogenous variable as an insruen is only valid under srenuous assupions. 7 Sudies Focusing on he Healh Insurance Indusry Several sudies published in healh econoics or healh services journals aep o exaine he relaionship beween indusry srucure and insurance price (i.e., preius). Robinson (2004) uses a daabase of sae regulaory filings o sudy he sae-level arke srucure of coercial insurance carriers (HMOs and PPOs) in He finds he larges fir conrols a leas a hird of he arke in alos 40 saes in The op 3 insurance firs conrol over 50% of oal enrollen in alos all saes. Using a variey of oher sources, Robinson also docuens a sharp increase in insurer revenues and profis over he ie period There is, however, no aep o esablish a causal relaionship beween hese wo phenoena. Wholey, Feldan and Chrisianson (1995) exaine he effecs of HMO arke srucure (easured by he nuber of HMOs) on HMO preius fro 1988 o Their uni of observaion is he HMO, hus preius are esiaed as average preiu revenue per eber, and he arke srucure facing each HMO is a weighed average of he arke srucures he HMO faces naionwide. The resuls sugges preius decrease when enry occurs. However, since each HMO is observed only a couple of ies in heir daa, heir specificaions do no include HMO fixed effecs and he resuls are subjec o he usual biases arising fro crosssecional sources of idenificaion. 7 Alhough our discussion focuses on sudies of horizonal consolidaion, researchers have also invesigaed he ipac of verical consolidaion on price. Recen exaples of such sudies include Cuellar and Gerler (2005) on physician-hospial inegraion and Gilber and Hasings (2001) on gasoline refinery-reailing inergraion. 5

7 Conribuions This sudy esiaes he ipac of arke concenraion on price in an iporan indusry: he fully-insured, large group privae healh insurance arke in he U.S. The privae insurance indusry provides priary insurance o over 160 illion non-elderly Aericans. 87 percen of hese enrollees are covered hrough eployer-sponsored group plans, and 45 percen in fully-insured plans. 8 Thus, our resuls are relevan o 63 illion consuers a a iniu, and likely ore as os non-elderly Aericans have soe privae coverage, and any of he sae insurance carriers paricipae in boh he fully-insured and self-insured arkes. Key o our sudy design is a unique, proprieary daase conaining deailed inforaion on he healhplans of roughly 10 illion Aericans in every year fro 1998 o 2006, inclusive. This daase affords us several advanages over oher sudies of he indusry. I includes he acual preiu charged o every sapled eployer for each healhplan hey offer. Several deails are available for each healhplan as well, including he ideniy of he insurance carrier, he plan ype, and he deographics of he populaion enrolled in he plan. We are also able o perfor our analyses using geographic arke definiions supplied o us by he indusry, as well as unis of observaion ha do no require aggregaion assupions and he noise and error hese enail. We ake wo separae approaches o isolae exogenous changes in arke concenraion. Firs, we use he enry and exi of insurance carriers as an insruen for changes in HHI. This approach is siilar in spiri o ha of Goolsbee and Syverson (2008), who sudy he reacion of incuben airlines o he hrea of enry. Second, we use changes in arke concenraion ha arise fro a naional erger beween wo large privae healh insurance firs as an insruen for local arke concenraion. This idenificaion sraegy is siilar o ha of Gilber and Hasings (2005), who use an acquisiion of a Wes Coas refinery as a source of exogenous variaion in he degree of verical inegraion across reail gasoline arkes in 13 Wes Coas eropolian areas. However, as we describe below, we have a larger nuber of affeced arkes, as well as a large nuber of unaffeced conrol arkes and a uch larger saple of icro daa. Finally, our research copleens recen work by Dafny (2008). Using he sae daase eployed here, Dafny evaluaes wheher healh insurance arkes are copeiive by exaining wheher healh insurers engage in direc price discriinaion, charging higher preius o firs wih deeper pockes, as easured by operaing profis. She finds hey do, and his resul is 8 Source: EBRI Issue Brief, Ocober 2007; Kaiser/HRET Survey of Eployer Sponsored Healh Benefis, 2007, Exhibi

8 no driven by cross-secional differences across firs or plans: firs wih posiive profi shocks subsequenly face higher preiu growh, even for he sae healhplans. Moreover, his relaionship is sronges in geographic arkes served by a sall nuber of insurance carriers. This evidence of price discriinaion by healh insurers iplies insurers possess and exercise arke power in soe local arkes. Here, we focus on wheher insurers use heir arke power o raise preius overall, and by how uch. B. The Healh Insurance Indusry To be wrien. II. Daa The Large Eployer Healh Insurance Daase The Large Eployer Healh Insurance Daase (LEHID) conains inforaion on all of he healhplans offered by a large and non-rando saple of eployers beween 1998 and 2006, inclusive. Descripive saisics for each year of daa are presened in Table 1. LEHID is gahered and ainained by a leading benefis consuling fir, and he eployers included in he daase have soe pas or presen affiliaion wih he fir. The uni of observaion is he healhplan-year. A healhplan is defined as a unique cobinaion of eployer, arke, insurance carrier, and planype, e.g. Copany X s Chicago-area Aena HMO. We now discuss each of he coponens ha joinly idenify his uni of observaion in urn. The full daase includes observaions fro 813 eployers. Eployers ay ener or exi he saple a any ie. The edian nuber of years an eployer is presen in he saple is wo. One-quarer of eployers appear in he saple for for 4 or ore years. A sall nuber of eployers reappear afer exiing. Mos eployers are large, uli-sie, publicly-raded firs, such as hose included on he Forune 1000 lis. The leading indusries represened include anufacuring (110 eployers), finance (101), and consuer producs (73), alhough nonprofi and governen secors are also represened (43 in he governen/educaion caegory). Geographic arkes are defined by he source using 3-digi zipcodes. The 139 arkes reflec he geographic boundaries used by insurance carriers when quoing prices, and collecively cover all of he coninenal U.S., wih he excepion of a few rural areas. Large eropolian areas are separae arkes, and non-eropolian areas are luped ogeher wihin 7

9 sae boundaries, e.g. New Mexico Albuquerque and New Mexico excep Albuquerque. 9 To ach couny-level daa o hese arkes, we allocae zipcodes wihin he arkes o counies, and use zipcode populaion daa o weigh he couny daa appropriaely when aggregaing o he arke level. The wo couny-year easures we use are he uneployen rae (fro he Bureau of Labor Saiscs), and he average Medicare coss per capia (or AAPCC, fro he Cener for Medicare and Medicaid Services). We also calculae he general, acue-care hospial HHI a he arke-year level using hospial-year level daa on he nuber of beds for all general hospials included in he Aerican Hospial Associaion Annual Surveys of Hospials. To creae his easure, we assign hospials wih he sae syse ID o a coon owner. Each fir ha adinisers any plan in he daa is labeled an insurance carrier. 10 During he enire sudy period, here are 357 carriers ha serve a leas one eployer, and 195 ha serve 5 or ore. The saller carriers end o be local or regional firs, or soeies hird pary adinisraors who pay clais and conrac wih anoher fir o ren is nework of providers and associaed discouns. The indusry is exreely concenraed even when all carriers are couned. Figure 1 presens he four-fir concenraion raio for he naion as a whole. This easure increased fro an ipressive 58% in 1998 o 71% in 2005, and of course concenraion raios wihin local arkes are uch larger. The arke srucure for he fullyinsured subsaple will be discussed in grea deail in he following secion. The plan ypes, ordered fro os o leas resricive in ers of provider choice, are Healh Mainenance Organizaion (HMO), Poin of Service (POS), Preferred Provider Organizaion (PPO), and Indeniy. HMOs and POS plans conrol uilizaion of care hrough priary-care physicians ( gaekeepers ). Only in-nework providers are covered by HMOs, while POS plans provide soe coverage for ou-of-nework providers (once he gaekeeper has approved he service in quesion). PPOs engage in less uilizaion anageen, and like POS plans, ypically cover ou-of-nework care a a reduced rae. Finally, indeniy plans are radiional fee-for-service arrangeens in which benefis do no depend on he nework saus of he provider. As Table 1 reveals, he coposiion of plan ypes flucuaed during he sudy period, wih a clear resurgence of PPOs oward he end of he sudy period. 9 There is only one arke ha crosses sae boundaries, Massachuses Souhern and Rhode Island. A ap of he arkes is available in Dafny (2008). 10 Blue Cross and Blue Shield affiliaes are all assigned he sae carrier ID. Given we calculae concenraion wihin each arke, and here are only a handful of arkes in which uliple Blue Cross/Blue Shield affiliaes coplee, he unifor coding of hese affliaes is unlikely o be consequenial for our analysis. 8

10 In addiion o he eleens ha joinly define a plan, we have he following variables: preiu, deographic facor, plan design facor, and nuber of enrollees. Preiu is expressed as an average aoun per enrollee; i herefore increases wih he average faily size of enrollees in a given plan. Preiu cobines eployer and eployee conribuions, and for self-insured plans i is a projecion of expeced coss per enrollee (including esiaed adinisraive fees paid o an insurance carrier). These projecions are used for budgeing purposes and o deerine enrollee conribuions. They are no direcly coparable o preius for fully-insured plans, which are se prospecively and always include risk preius. (Selfinsured projecions ay include a parial risk preiu if he eployer purchases sop-loss coverage). Deographic facor is a easure ha reflecs faily size, age, and gender coposiion of enrollees in a given plan. Plan design facor capures he generosiy of benefis wihin a paricular carrier-plan ype, including copays. Boh facors are calculaed by he source, and he forulae were no disclosed. The nuber of enrollees in LEHID plans averages 4.7 illion per year. Given an average faily size above 2, his iplies roughly 10 illion Aericans are represened in he saple in a ypical year. Table 2 presens descripive saisics for fully-insured plans only. Fully-insured plans are far ore likely o be HMOs han are self-insured plans, in par because, hisorically, insurance carriers did no offer self-insured HMOs owing o he difficuly of racking acual expendiures associaed wih a paricular se of eployees. Also noable in Table 2 is he seep decline in he nuber of fully-insured plans fro This decline irrors declines observed in daa on siilar-sized firs fro he Medical Expendiure Panel Survey-Insurance Coponen, which conducs sei-regular annual surveys of eployers of sapled individuals. 11 There are several possible reasons fully-insured plans have becoe less aracive for large firs, including he diinishing disincion beween non-hmos and HMOs (soeies available only as a fully-insured produc), and he poenial ha insurance carriers are charging increasing risk preia (perhaps due o arke power). This is an iporan subjec for fuure research. For he presen sudy, here are wo key iplicaions: (1) we will es he robusness of he resuls o dropping he daa; (2) we will exaine he relaionship beween a fir s choice of insurance ype and changes in arke concenraion [in a laer draf]. Before proceeding o he analyses, we suarize he discussion in Dafny (2008) on he represenaiveness of he saple. She copares preiu growh in he LEHID FI saple wih preiu growh for fully-insured plans surveyed by he annual Eployer Healh Benefis 11 We are graeful o Kosali Sion for abulaing he MEPS-IC daa o invesigae his rend. 9

11 Survey, sponsored joinly by he Kaiser Faily Foundaion (KFF) and he Healh Research and Educaional Trus (HRET). 12 The growh rends in boh saples are very siilar over ie. She also repors he raio of sapled enrollees o oal insured lives (available a he couny-level fro he US Census of 2000) varies lile across geographic arkes. III. Evaluaing he Effecs of Horizonal Consolidaion on Preius using Enry and Exi-Induced Changes in Marke Srucure In his secion, we exaine he relaionship beween preiu growh for fully-insured healhplans and changes in local arke concenraion. We begin by describing he disribuion of arke-level HHI and how his has changed over ie. Nex, we esiae OLS regressions relaing preiu growh a he plan level o changes in he corresponding arke HHI. These regressions answer he baseline quesion: are preius growing ore quickly in arkes ha are becoing ore concenraed? The answer o his quesion does no indicae wheher such an associaion is causal, however, as changes in concenraion are no likely o be exogenous o preiu growh. To esiae a causal link, we use he nuber of insurance carriers in a given arke-year o insruen for HHI. Wih arke fixed effecs in all odels, he resul is ha ne carrier exi (or enry) is used o isolae (hopefully) exogenous variaion in HHI. We presen firs-sage, reduced-for, and insruenal variables esiaes using his insruen. Throughou his analysis, we consider carefully he challenges o his idenificaion sraegy. A. Marke Srucure of he Fully-Insured Group Insurance Marke, During he 9-year sudy period, he average arke-level HHI (esiaed using our saple, on a scale fro 0 o 1) increased fro.34 o.47. As illusraed by he hisogras presened in Figure 2, he bigges increases occurred during he second half of he sudy period, bu sizeable increases are presen in he firs half as well. Beween , 60 percen of arkes experienced increases in HHI of.10 or ore. The corresponding figure is 74 percen for Iporanly, here is wide variaion in he agniude of changes in HHI across arkes, nowihsanding he fac ha os are posiive. Figure 3 conains analogous hisogras for changes in he nuber of carriers. Whereas in he odal ne loss is 0 12 The KFF/HRET survey randoly selecs public and privae eployers o obain naional daa abou eployersponsored healh insurance; approxiaely 2000 eployers respond each year. The daa are no publicly available, nor is he saple designed o provide esiaes a he arke level 10

12 or 1 carriers, beween 2002 and 2006 he odal ne loss is 2 or 3 carriers. The ean nuber of carriers per arke declined fro 8.4 in 1998 o 5.2 in Given he naure of our saple, here is easureen error in hese variables. Our epirical sraegy relies on changes in hese variables. So long as easureen error in he change in HHI is no syseaically correlaed wih easureen error in he change in he nuber of carriers, easureen error in he forer should lead o noisier esiaes and in he laer o aenuaion bias, i.e. conservaive esiaes of he ipac of enry/exi on average HHI. Furherore, he use of weighs in our regressions (described below) iniizes he influence of arkes wih sall nubers of enrollees, where easureen error is likely o be os severe. We reurn o hese poins laer in he secion on robusness. To confir here is a relaionship beween HHI and carrier enry/exi, we aggregae he daa o he arke-year level and esiae he following equaion: [ X β] + [ λ year] + (1) HHI = α + β# carriers + κ + τ + ε. The baseline specificaion (1A, Table 3) relaes HHI o he nuber of carriers in a arke, conrolling for arke and year fixed effecs. The second specificaion (2A) adds he following arke-year covariaes: he uneployen rae (o capure local econoic condiions), ln(percapia Medicare coss) (o capure rends in healhcare uilizaion), and he general, acue-care hospial HHI (o capure concenraion in he provider arke). These covariaes are included priarily because hey play a role in he second-sage preiu regressions, bu he resuls also confir he nuber of carriers has an independen associaion wih HHI even when hese facors are included. Specificaions 3A and 4A add linear rends o 1A and 2A. All odels are esiaed by weighed leas squares, using he nuber of enrollees in each arke-year as weighs. The weighs help us o obain a represenaive esiae of he relaionship beween concenraion and arke srucure for he ypical enrollee in he saple, and also ac o reduce he influence of saller arkes in which easureen error ay be ore severe. Finally, we exclude 2006 daa o be consisen wih subsequen analyses. (As we describe below, we use lagged HHI in he second-sage odel, so 2006 daa on HHI canno be included.) The resuls reveal a robus, saisically-significan relaionship beween he nuber of carriers and HHI. A single exi is associaed wih an increase of in HHI, on average. As an illusraion of such a change, consider a arke wih 5 carriers, each wih arke share of 40%, 25%, 15%, 10%, and 5%, respecively (yielding an HHI of 0.26, slighly below he ean a 11

13 he sar of he sudy period). If he salles carrier exied by erging wih he secondsalles carrier, he arke-level HHI would increase by Using carrier enry/exi as an insruen for changes in HHI eliinaes non-srucural changes in HHI, i.e. changes induced by unobserved changes in consuer preferences and/or carrier acions (e.g. arkeing). Such changes ay have independen effecs on preiu apar fro ha induced hrough heir ipac on HHI. For exaple, a arke wih poor econoic prospecs ay see increases in he arke share of a low-priced carrier, leading o increases in concenraion and decreases in preiu growh. Of course, srucural sources of HHI variaion (i.e. carrier enry/exi) are no iune o endogeneiy concerns. However, his variaion is arguably less likely o be endogenously relaed o preiu growh because of he high fixed coss associaed wih enry/exi decisions. While eployers ay change plans as ofen as once per year, upgrading or downgrading heir choices o reflec heir iediae circusances, enry and exi enail significan, sunk invesens. For exaple, a carrier choosing o exi a arke severs relaionships wih insureds, providers, and regulaors. Re-enry in he sae arke can be quie difficul and cosly, as all of hese paries ay be loah o inerac wih a fly by nigh carrier. Thus, while slow preiu growh (for exaple) ay be associaed wih exi, his is likelier o be a long-run phenoenon, in which case linear arke rends ay address he endogeneiy concerns. Afer presening he reduced-for and IV esiaes, we es his assupion by exaining wheher here is an associaion beween pas preiu growh and changes in he nuber of carriers, as would be expeced if srucural changes are endogenous o preiu growh. B. The Relaionship Beween Marke Srucure and Preiu Growh To analyze he relaionship beween arke srucure and preiu growh, we use he plan-year level daa for fully-insured plans, described in Table 2. All specificaions include plan fixed effecs. As noed above, plan fixed effecs reove cross-secional idenificaion of he relaionship of ineres. Thus, we assess wheher an especially high price for a given plan is correlaed wih an especially high HHI for he arke in which ha plan is offered. The inclusion of fixed effecs also alleviaes concerns abou changes in he ideniy and coposiion of eployers in he saple over ie. These coposiional changes in he saple will no lead 12

14 o biased esiaes unless hey are syseaically correlaed wih wihin-plan preiu changes and wihin-arke changes in HHI. 13 The specificaion of ineres akes he following for: (2) ln(preiu) p( ) = α + γhhi + τ + 1 [ X β] + [ λ year] + ε p() 1 + ϕdeographics p( ) + ϑplan design p( ) + ρ p( ). The subscrip p() refers o a plan (which is defined wihin a paricular arke) and year. ρ and τ represen plan and year fixed effecs, respecively. Because preius for p() year are deerined in -1, HHI and he arke-year covariaes (X) are lagged by one year. Marke fixed effecs are subsued wihin he plan fixed effecs. Deographics and plan design are ie-varying plan-specific easures, as discussed in Secion 3. Specificaions are esiaed wih and wihou arke-year covariaes and linear arke rends. A logical alernaive o equaion 1 is a firs-differenced odel, in which year-o-year changes in ln(preiu) (i.e. preiu growh) are regressed on year-o-year changes in lagged HHI. We presen resuls fro such a odel in he robusness secion ha follows. However, he levels specificaion is our preferred odel, as i allows for he response o a change in HHI o ake place wih soe lag. We esiae equaion 1 by weighed leas squares, using he ean nuber of enrollees in a given plan (over ie) as he weighs. 14 Sandard errors are clusered by he arke-year o allow for correlaion in he error ers aong plans in he sae arke-year. The esiaion saple excludes 1998 because lagged HHI canno be calculaed for (Of course, he 1998 daa are used o consruc he lagged HHI assigned o observaions for 1999). The resuls are presened in Table 4, Panel A. Colun 1A repors resuls of he baseline specificaion. Colun 2A adds he hree arke-year covariaes: lagged ln(aapcc), lagged uneployen rae, and lagged hospial-arke HHI. If changes in arke srucure are 13 To see how his assupion could be violaed, consider he following hypoheical scenario. Suppose anufacuring firs are beer-represened in one year han anoher, and ha preiu growh for hese firs is generally higher. If he arkes in which anufacuring firs are becoing beer-represened are also hose arkes in which HHI is growing ore quickly, he coefficien esiae on HHI will be oversae due o changes in he saple of eployers. 14 When esiaing cross-secional regressions, Saa does no peri he weigh o vary over ie wihin a given uni of observaion. 13

15 correlaed wih changes in local-area edical uilizaion or arke condiions, hese facors should affec he coefficien on HHI. Colun 3A ineracs HHI wih a caegorical variable represening he level of HHI in =0, or Using he caegorizaion fro he Horizonal Merger Guidelines is no an opion, as 129 of he 139 arkes fall ino he op caegory in 1998 (HHI >.18, or highly concenraed ). Insead, we use erciles of he iniial HHI disribuion, where he cuoff for ercile 1 is.26 and ercile 2 is.36. Coluns 4A hrough 6A repea 1A hrough 3A wih he addiion of linear arke rends. The specificaions wihou rends indicae increases in HHI are associaed wih increases in preius. The coefficien on HHI is affeced lile by he inclusion of arke-year covariaes, and is greaes in arkes ha are os concenraed o begin wih. However, he agniude is raher sall (an increase in HHI of 0.1 is associaed wih an increase of roughly 0.4 percen in preiu), and he resuls disappear upon inclusion of linear arke rends. Panel B presens resuls fro reduced-for odels. These are siply regressions of preius on he lagged nuber of carriers, which will serve as our insruen for lagged HHI. The specificaions are analogous o hose described above, alhough he ineracions wih HHI erciles (coluns 3 and 6) are eliinaed. The specificaions wihou linear rends do no sugges a relaionship beween preius and he lagged nuber of carriers. However, once hese rends are included, here appears o be a arginally-significan negaive relaionship (p=.08 wihou arke covariaes, and p=.13 wih covariaes). Panel C conains he insruenal variables esiae of he effec of HHI on lagged preiu. Given he equaion is exacly idenified, his esiae is equal o he raio of he reduced-for esiae o he firs-sage esiae for he corresponding specificaion. (The firssage esiaes are presened in Panel B of Table 3. They are quie siilar o he resuls in Panel A of Table 3, which uses he arke-year as he uni of observaion.) As expeced, he IV esiaes fro odels wih arke rends sugges a posiive, saisically-significan relaionship beween lagged HHI and preius. 15 An increase in HHI of 0.1 is associaed wih an increase of 1.7 o 1.9 percen in preius. Increases in healhcare uilizaion (beyond a arke-specific rend) are also associaed wih higher preius, as are higher uneployen raes. Our easure of provider concenraion, hospial HHI, is no significanly associaed wih preius 15 A presen, we are unable o correc he IV sandard errors for clusering a he arke-year level. The precision of he esiaes ay be affeced by such a correcion. 14

16 in any specificaion. One possible explanaion is he geographic arke definiion we use. Research suggess hospial arkes are significanly saller han healh insurance arkes. 16 C. Endogeneiy Tess and Robusness Checks We begin wih a es of he key idenifying assupion for our odel: ha enry and exi is no correlaed wih fuure preius excep hrough is ipac on he copeiive landscape. To es his assupion, we explore wheher he nuber of carriers in a arke appears o respond o changes in preius, raher han vice versa. Pu differenly, we exaine wheher carriers are differenially likely o ener (or no o exi) arkes wih above- (or below-) average preiu growh. To answer his quesion, we ransfor he daa ino firs differences and esiae odels of he following for: (3) Δ# carriers, 1 = α + πδln(preiu) + ϑδplan design p( ) 1, 2 p( ) 1, 2 + τ + ϕδdeographics + [ ΔX β] + [ λ ] + Δε p(),-1 1, 2 p( ) 1, 2. Noe linear arke rends becoe arke fixed effecs in he firs-differenced odel. A posiive πˆ would iply ha enry is ore likely o occur in arkes wih seeper preiu increases (or seeper increases han expeced afer conrolling for a arke-specific rend, if arke fixed effecs are included). Resuls fro he four specificaions represened by equaion 3 (wih and wihou arke-year covariaes, wih and wihou rends) are given in Panel A of Table 5. Each observaion is weighed by he nuber of eployees in plan p() (averaged over all years), and sandard errors are again clusered by arke-year. The poin esiaes fro all four specificaions are noisy, posiive and sall. The agniudes decrease as addiional covariaes are included. The esiaes iply ha any role of endogenous enry/exi is likely o be sall, especially in he riches odel. For exaple, he 95% confidence inerval for πˆ (using he esiae fro 4A) is o The ean value for Δln(preiu) p ( ) 2, 1 is.10, iplying he effec of ypical aoun of preiu growh on he nuber of carriers ranges beween and.063. This is a very sall effec relaive o he enry and exi paerns docuened in Secion A above. 16 For exaple, a recen sudy of hospial ergers finds evidence suggesing ha circular arkes wih 10-ile diaeers are reasonable geographic arke definiions in urban areas (Dafny 2009). 15

17 By conras, he resuls in Panel A sugges ha carrier enry and exi is sensiive o changes in healhcare uilizaion. The negaive coefficien on ln(aapcc) is boh saisically and econoically significan, and changes lile upon he inclusion of arke rends. Using he sae approach oulined above, we find he ean increase in uilizaion of is associaed wih a decrease of o in he nuber of carriers. As a robusness check, we also re-esiae our specificaion of ineres in firs-differences: (4) Δln preiu p( ), 1 = α + γδhhi + τ + 1, 2 [ ΔX β] + [ λ ] + Δε p(),-1 1, 2 + ϕδdeographics p( ), 1 + ϑδplan design p( ), 1 IV resuls for his odel (using Δ # carriers 1, 2 as he insruen for ΔHHI 1, 2 ) are repored in Panel B of Table 5. The coefficien esiaes are siilar in agniude across all specificaions, hough again hey achieve saisical significance only in he specificaions wih arke rends. The poin esiaes are also a bi larger: an increase of 0.1 in HHI is associaed wih an increase of 2.4 o 2.7 percen in preius. As addiional robusness checks of our ain odel, we considered he following: (1) oiing weighs; (2) using he four-fir concenraion raio as he endogenous regressor in place of HHI; (3) dropping arke-years wih fewer han 20 eployers (o reduce he influence of easureen error on he resuls). The resuls (available upon reques) are rearkably siilar across all odels. To see wheher he decline in FI plans during presens selecion probles, we also re-esiae our ain odel using daa fro Alhough he precision of he esiaes suffers, he coefficien agniudes reain siilar. IV. Evaluaing he Effecs of Horizonal Consolidaion on Preius using he 1999 Aena-Prudenial Merger In his secion, we exploi shocks o arke concenraion produced by he 1999 egaerger of Aena and Prudenial o esiae he ipac of arke concenraion on healh insurance preius. Pos-erger, he new fir (known as Aena ) was widely repored o be he naional s larges insurer, covering 21 illion individuals. 17 Iporanly, and as we describe in deail below, here was subsanial overlap in he local arke paricipaion of Aena and 17 Sanders, Alain L., Will he Aena-Prudenial Merger Hur he Paien? TIME agazine, June 22,

18 Prudenial prior o he erger, generaing he poenial for sizeable pos-erger changes in local arke concenraion. Alhough we considered he coplee se of large ergers during our sudy period as candidaes for siilar analyses, we were forced o rule ou all ohers on he grounds of insufficien pre or pos periods (e.g. Aena and NYLCare in 1998), few overlapping arkes or very sall shares for one of he erging paries (e.g. Unied Healhcare and MAMSI), and os frequenly - a paricular regional focus of he erger, which hreaens he exogeneiy of he shock (e.g. Excellus and Univera in New York Sae). A. The Effec of he Aena-Prudenial Merger on Marke Concenraion According o our daa on enrollen by healh insurer in 1999, Aena and Prudenial were he second and fourh larges insurers in ers of he nuber of enrollees in fully-insured plans. There was considerable overlap beween he wo insurers in a large nuber of arkes. In our saple fro 1999, 58 of 139 arkes included plans offered by boh firs. There was also significan variaion across arkes in he pre-erger shares of each fir. We hypohesize ha arkes served by boh firs experienced a rapid and significan increase in arke concenraion iediaely following he erger of Aena and Prudenial, and ha his increase varied by he pre-erger shares of he wo firs. Specifically, for every arke we calculae siulaed HHI change ( si Δ HHI ) as follows: 2 [ ] 2 2 [ Aena 1999 share + Pru 1999 share ] ( Aena 1999 share ) ( Pru 1999 ) ( 5) si ΔHHI = + share. si ΔHHI represens he erger-induced increase in arke s HHI ha would have occurred fro 1999 o 2000 absen any oher changes in carriers arke shares. For exaple, if Aena and Prudenial were wo of four firs in a arke in 1999 and each of he four firs had a 25 percen share, he value of si ΔHHI would equal.125 (= (.5) 2 ((.25) 2 + (.25) 2 )). Appendix Table 1 provides deail on he disribuion of si Δ HHI for he 58 arkes in which i does no ake a value of zero. We propose o use si ΔHHI, ineraced wih a pos indicaor for he period afer he erger was consuaed, as an insruen for HHI. One concern wih his approach is ha 17

19 he erger ay no have been an exogenous even. Markes served differenially by boh Aena and Prudenial ay be differen fro oher arkes. To he exen ha hese differences are no changing over ie, including arke fixed effecs in all specificaions will address his concern. However, he agniude of hese underlying differences which could exer an independen effec on healh insurance preius - ay have been changing in he period leading up o he erger. We ake wo approaches o address his possible concern. Firs, we invesigae wheher eiher arke concenraion or healh insurance preius were rending differenly jus prior o he erger in hose arkes prediced o be os affeced by he erger. Second, we exploi he fac ha he U.S. Deparen of Jusice aeped o iigae he ipac of he erger hroughou uch of he sae of Texas by requiring Aena o dives all Houson and Dallas-area plans acquired in 1998 fro anoher insurer, NYLCare. The raionale for his inervenion was sraighforward he governen feared ha, absen he NYLCare divesiure, he cobined arke share of he wo insurers in his sae would lead o deleerious effecs for consuers hrough an increase in preius. Wihin hese arkes, he Anirus Division of he Deparen of Jusice alleged ha afer he erger, Aena would have a arke share for fullyinsured HMOs of 63 percen (in Houson), and 42 percen (in Dallas). Our daa corroboraes hese concerns. In our saple (ainly large firs) and produc arke definiion (which includes all plan ypes), we esiae he cobined arke share of Aena and Prudenial for plans in effec during 1999 was 50 percen (in Houson) and 59 percen (in Dallas/For Worh). We begin by esiaing he following specificaion on arke-year level daa fro all non-texas arkes: [ X ] + (6) HHI = α + λ + τ + βsi ΔHHI τ + ε As in he previous secion, λ and τ represen a full se of year and arke fixed effecs, respecively, and [ X ] a vecor of arke-year covariaes included in soe specificaions. By ineracing si ΔHHI wih separae duies for each year (excep 1998, he oied caegory), his odel invesigaes he possibiliy ha arke concenraion ay have been rending differenly prior o he erger in arkes differenially served by he erging firs. The erger was effecively cleared in July 1999, when he Deparen of Jusice subied is Proposed Final Judgen. Given insurance preius are se a few onhs prior o he sar of he calendar year, he ipac of he erger should becoe apparen in 2000 or laer. 18

20 The resuls are suarized in Panel A, Table 6. In he firs specificaion (1A), we include daa fro (2 pre and 2 pos years). Relaive o he oied ineracion er, si ΔHHI ( year == 1998), only he wo pos-period ineracion ers are saisically significan. The coefficien esiae of for β in 1999 is posiive bu subsanially saller in agniude han he corresponding esiae for β in 2000 (.869) and 2001 (.714). Specificaions 2A and 3A expand he saple o and , respecively. The coefficien esiaes on β in 2002 are 2003 are boh noisy and negaive. These specificaions reveal ha he effec of he erger on arke concenraion declined sharply afer This finding is consisen wih repors fro indusry expers. According o a 2004 Healh Affairs aricle by Jaes Robinson, [G]ossip speculaes [Aena] would be lucky o sill have 30,000 of he 5 illion i acquired fro Prudenial. Robinson clais NYLCare and Prudenial were resraining heir preiu increases o axiize ebership in anicipaion of a sale. Once Aena rese preius on NYLCare and Pru accouns a susainable levels, he wries, os forer cusoers siply wen elsewhere. We revisi hese observaions laer (in Secion 5). Finally, he las specificaion in Panel A confirs he resuls are robus o he inclusion of arke-year covariaes. Given he resuls in Panel A, we focus our aenion on he period fro Panel B repors resuls of specificaions ha ake he following for: (7) HHI = α + λ [ + β siδhhi I( year >= 2000)* Texas + νi( year >= 2000)* Texas ] + X 1 + τ ϑ + ε + β siδhhi 0 I( year >= 2000) A single ineracion beween si ΔHHI and a pos duy replaces he individual year ineracions in his parsionious specificaion. Afer esiaing he baseline odel (which excludes he ers in brackes), we add he Texas arkes o he saple and include a ripleineracion, si ΔHHI I( year >= 2000)* Texas, o explore wheher he pos-erger ipac of siδhhi differs in hese arkes. We also add he er I ( year >= 2000) * Texas o conrol for average changes in Texas as copared o oher saes during he pos-period. As in prior specificaions, we es he sensiiviy of he resuls o he inclusion of arke-year covariaes. 19

21 Specificaion 1B confirs he resuls in Panel A: hose arkes prediced o be os affeced by he Aena-Prudenial erger did experience significanly greaer increases in arke concenraion during he firs 2 pos-erger years. The increase in he HHI is no quie one-forone wih he prediced increase (which is no surprising given eployers ay swich carriers a any ie), bu neiher is i saisically disinguishable fro 1. In Colun 2B, we see a nearly equal, bu opposie-signed ineracion er for he Texas arke. We easily accep he null hypohesis of βˆ + βˆ 0 agains he wo-sided alernaive (p < xxx). I herefore appears ha he 0 1 = federal governen achieved is objecive of eliinaing he erger s effec on arke concenraion in Texas arkes. These arkes ay herefore consiue a useful coparison group for our laer analyses involving healh insurance preius and relaed oucoes of ineres. Boh ses of resuls are no aerially affeced by he addiion of arke-year covariaes (coluns 3B and 4B). Taken ogeher, he resuls in his secion deonsrae ha he erger of Aena and Prudenial, wo of he naion s larges healh insurers, resuled in subsanial increases in arke concenraion in arkes differenially served by boh firs. The effec of he erger dissipaed quickly, however, wih no lingering effec on arke concenraion by More specifically, a arke prediced o have an increase of.05 in is HHI as a resul of he erger experienced an average increase of.033 during he firs wo years following he erger. We also find no effec of he erger on concenraion wihin Texas arkes, where he federal governen required he divesiure of plans affiliaed wih an insurer previously acquired by Aena (NYLCare) as a precondiion of he erger. B. The Effec of he Aena-Prudenial Merger on Healh Insurance Preius To invesigae he effec of he erger-induced increases in local arke concenraion on plan preius, we begin by esiaing specificaions of he following for: (8) ln(preiu) p( ) = α + βsi ΔHHI + ϑplan design p( ) τ + ρ + ϕdeographics p( ) + τ + p( ) [ X 1ϑ] + ε p(). As in he preiu regressions presened in he Secion 2, his odel includes a full se of plan and year fixed effecs, as well as conrols for he deographic characerisics of a plan s enrollees and he generosiy of plan design. Boh of hese conrol variables could heselves be affeced by he erger and we discuss his issue in soe deail below [in he nex draf]. We 20

22 esiae equaion 8 by weighed leas squares, using he ean nuber of enrollees in a given plan (over ie) as he weighs. Sandard errors are clusered by he arke-year o allow for correlaion in he error ers aong plans in he sae arke-year. Before urning o he resuls, i is iporan o noe ha a plan observed in year will no necessarily appear in year +1 and vice-versa. However, because our specificaions include plan fixed effecs, our resuls will be driven by he variaion across arke areas in wihin-plan preiu changes. The coefficien esiaes are presened in Table 7, Panel A. The specificaions irror hose in Table 7, Panel A: we begin wih in colun 1, and hen exend he saple o (colun 2) and (colun 3). We again find no significan relaionship beween he dependen variable and si ΔHHI ( year == 1999) - in fac he poin esiaes are near zero - relaive of course o he relaionship beween he dependen variable and si ΔHHI ( year == 1998). Beginning in 2000, he coefficiens on he ineracion ers becoe negaive and saisically significan. In conras o he resuls in he firs sage, he effec of si ΔHHI on healh insurance preius does no appear o decline over ie. For exaple, in he final specificaion, he esiae for β in 2003 is acually larger (in absolue ers) han he corresponding esiae for β in However, here is no seady increasing rend in hese coefficien esiaes, so i appears he erger affeced he level raher han he growh rae of healh insurance preius. Panel B focuses on he years 1998 o The regressions are based on equaion 9 below: (9) ln(preiu) p( ) = α + β osiδhhi + ϑplan design + + p( ) I( year >= 2000) + ϕdeographics + ρ + τ p( ) p( ) [ β1siδhhi I( year >= 2000)* Texas + νi( year >= 2000)* Texas ] [ X -1ϑ] + ε p() As in Table 6, we presen resuls wih and wihou Texas arkes (and he accopanying ineracion ers), and wih and wihou arke-year covariaes. Owing o he iing of preiu-seing, arke-year covariaes in his odel (as in all our preiu regressions) are lagged. Thus, all odels wih arke-year covariaes exclude 1998, one of he wo pre-period years. Unsurprisingly, he resuls sugges significan, negaive effecs of si ΔHHI on preiu growh. The ineracion ers for Texas arkes are posiive and saisically 21