Financial Integration and Scope Efficiency in U.S. Financial Services Post Gramm-Leach-Bliley

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1 Fnancal Integraton and Scope Effcency n U.S. Fnancal Servces Post Gramm-Leach-Blley Yuan Yuan a,*, Rchard D. Phllps b a Unversty of Wsconsn Whtewater, Whtewater, WI USA b Georga State Unversty, Atlanta, GA USA Ths verson: June 8, 2008 Abstract The Gramm-Leach-Blley (GLB) Act of 1999 largely removed the barrers that forced separaton between commercal bans, nvestment bans, and nsurance companes n the U.S. and promsed the most fundamental reform of U.S. fnancal servces regulaton n more than half a century. In ths study we construct a unque dataset that lns the U.S. banng and nsurance regulatory datasets. Dong so allows us to dentfy newly formed domestc assurbans (nsurers ownng bans), bancassurers (bans ownng nsurers), and all the unque subsdares lcensed ether as commercal bans, thrfts, or nsurance companes. We use ths data set to nvestgate effcency effects from possble economes of scope across the two formally separate sectors by estmatng mult-product costs, revenue, and proft functons. The emprcal evdence suggests that a sgnfcant number of cost scope dseconomes, revenue scope economes, and wea proft scope economes exst n the post-glb U.S. ntegrated banng and nsurance sectors. The scope economes are varant among frms, and certan frm characterstcs are the determnants of scope economes. JEL Classfcaton: G21; G22; L25 Key Words: Fnancal servces ntegraton; Gramm-Leach-Blley Act; Economy of scope Correspondng author. Tel.: ; fax: E-mal addresses: yuany@uww.edu (Y. Yuan), rphllps@gsu.edu (R. Phllps).

2 Abstract Fnancal Integraton and Scope Effcency n U.S. Fnancal Servces Post Gramm-Leach-Blley The Gramm-Leach-Blley (GLB) Act of 1999 largely removed the barrers that forced separaton between commercal bans, nvestment bans, and nsurance companes n the U.S. and promsed the most fundamental reform of U.S. fnancal servces regulaton n more than half a century. In ths study we construct a unque dataset that lns the U.S. banng and nsurance regulatory datasets. Dong so allows us to dentfy newly formed domestc assurbans (nsurers ownng bans), bancassurers (bans ownng nsurers), and all the unque subsdares lcensed ether as commercal bans, thrfts, or nsurance companes. We use ths data set to nvestgate effcency effects from possble economes of scope across the two formally separate sectors by estmatng mult-product costs, revenue, and proft functons. The emprcal evdence suggests that a sgnfcant number of cost scope dseconomes, revenue scope economes, and wea proft scope economes exst n the post-glb U.S. ntegrated banng and nsurance sectors. The scope economes are varant among frms, and certan frm characterstcs are the determnants of scope economes. 1

3 1. Introducton and motvaton When the landmar fnancal servce reform legslaton, the Fnancal Servce Modernzaton Act 1 (nown as the Gramm-Leach-Blley Act), was passed n 1999, t was vewed as the culmnaton of years of effort to reform the U.S. fnancal servce ndustry. The Gramm- Leach-Blley Act (GLB) repealed the 66-year old Glass-Steagall Act (GSA) of and elmnated the restrctons on afflaton between commercal bans and nvestment bans. GLB also substantally modfed the 43-year old Ban Holdng Company Act (BHCA) of and removed barrers whch forced separaton between commercal bans and nsurance companes. The potental for GLB to have a profound mpact on fnancal servces provders are great and, under the new regulatons, the 600+ companes that were operatng as Fnancal Holdng Companes (FHC) by the end of 2003 represented 78 percent of the total assets of all Ban Holdng Companes (BHC) n the Unted States. In addton, by 2003 more than 1,300 FHCs/BHCs were engaged n nsurance agency or underwrtng actvtes under the Act, and more than 2,500 nsurance companes (ether through agents or through rs bearng underwrters) were afflated wth banng nsttutons. 4 Today, consumers can choose from a broadenng array of fnancal tools and from companes ether amng to serve ther multple needs or companes specalzng n one or two types of fnancal products. Taen at face value, the early results of GLB suggest the U.S. fnancal ndustry was headed towards greater and greater consoldaton both wthn sector and across sectors. However, debate exsts amongst practtoners and researchers ale regardng both the motves and benefts for fnancal ntegraton. In addton, a number of organzatons that ntally too advantage of 1 Pub. L , 113 Stat (1999). 2 Pub. L. 66, 48 Stat. 162 (1933). 3 Ch. 240, 70 Stat. 133 (1956). 4 BHC Statutory Fnancal Report (multple years); Federal Reserve Report to Congress,

4 the new rules under GLB have, over tme, returned to ther core busnesses and gven up on the ntegraton strateges. 5 One of the mportant elements n the debate regardng the benefts of offerng fnancal products va an ntegrated fnancal servce provder s whether any productvty benefts exst for a sngle frm to manufacture dsparate products. Ths s a bt surprsng snce substantal research has been devoted to measurng the productve effcency of fnancal nsttutons over the post twenty years - partcularly n commercal bans where lterally hundreds of studes have been conducted to estmate varous measures of effcency of fnancal nsttutons located n more than two-dozen countres. 6 That sad, there s lttle research on the effcency effects of ntegratng provders of dfferent categores of fnancal servces nto unversal-type organzatons. The scant evdence that does appear n the lterature s extrapolated ether from scope effcency wthn one sector of the fnancal ndustry, e.g., commercal banng (Berger, et al. 1993; Berger, et al. 1996) or nsurance ndustry (Grace and Tmme, 1992; Berger, et al. 2000), or from smulatons of rs dversfcaton benefts n the absence of synergstc gans (Boyd, et al. 1993; Allen and Jagtan, 2000). 7 The purpose of ths paper s to remedy ths defcency n the lterature and provde the frst set of evdence regardng the productve effcency effects of formng an ntegrated fnancal servce company. To do so we estmate economes of scope of the post-glb U.S. fnancal servces ndustry across costs, revenue and proft measures. The most commonly quoted source of potental gans from cross-sector ntegraton s the explotaton of scope economes. Scope 5 The most notable example of ths trend was Ctgroup whch was formed n the late 1990 s through the merger of the largest commercal ban n the Unted States, Ctban, and The Travelers one of the largest mult-lne nsurance companes n the Unted States. However, begnnng n 2002, Ctgroup began to dsband and sold the property-lablty nsurance companes to a rval non-lfe nsurer, St. Paul, n 2003 and subsequently sold the subsdares underwrtng lfe nsurance to MetLfe n Berger and Humphrey (1997) crtcally revewed more than 130 studes and summarzed emprcal effcency estmates of fnancal nsttutons n 21 countres. 7 There are a few exceptons to ths broad-based statement that are dscussed later n the paper. 2

5 effcency gans can be created by sharng such physcal nputs as dstrbuton systems, reusng manageral expertse, and provdng consumpton complementartes. However, scope dseconomes may also arse at the same tme because of hgher admnstraton and coordnaton expenses, organzatonal dseconomes, cross-subsdzaton n nternal captal marets, or the loss of specalzed expertse for customers needng talored products. A lely reason for the scarce emprcal research on the ntegraton of banng and nsurance sectors s because the regulatory data sets avalable are product specfc, and there s no convenent way to dentfy companes afflated wth one another across sectors. Thus, the frst effort n our study s to construct a unque dataset whch lns the nsurance and the ban regulatory data sets. The combned data set exsts for fnancal conglomerates, specalst bans, and specalst nsurers n the U.S. banng and nsurance ndustry durng 2003, 2004, and We utlze a two-stage econometrc method to nvestgate economes of scope. We frst estmate mult-product cost, revenue, and proft producton functons. We estmate separate functons for banng (commercal bans and savng and loans thrfts) and nsurance (lfe-health nsurance vs. property-lablty nsurance) subsdares, of specalsts frms and jont producers whch are frms jontly producng both banng and lfe nsurance, banng and property-lablty nsurance, and frms partcpatng n all three sectors. We then use the estmated functons to calculate scope economy scores for all fnancal nsttutons n our database. In the second stage, the scope economy scores are regressed upon a set of varables descrbng frm characterstcs. The second stage regressons allows us to test varous hypotheses whether scope economes exst for frms jontly producng fnancal products across multple sectors and to explan the varaton of scope economy estmatons. 3

6 Ths paper s organzed as follows: Secton 2 dscusses the concept of scope economes and revews the lterature. Secton 3 descrbes the dataset constructon. In Secton 4, we develop the testable hypotheses. Secton 5 explans the estmaton methodologes. Secton 6 dscusses the scope economes estmaton results and the regresson results. Secton 7 offers conclusons. 2. Scope economes n fnancal servces: A survey of the evdence 2.1. Cost scope economes Cost scope economes refer to the reducton of the average total producton costs through the producton of a wder varety of goods or servces. For cross-sector ntegraton, cost scope economes may be realzed from the sharng of physcal nputs; reusng manageral expertse and experence; rasng external captal at lower costs because of reduced rss; employng common servce functons, such as advertsement or nvestment; cross-sellng products; and usng nformaton at a lower cost. However, cost scope dseconomes may also arse because of hgher coordnaton and admnstratve costs and organzatonal dseconomes when producng a broad range of products. Some studes exst estmatng the cost scope effcences of provdng multple products wthn a sngle fnancal nsttuton. These studes evaluate cost scope economes of wthn-sector products (e.g., commercal loans and consumer loans) rather than cross-sector products (e.g., commercal loans and lfe nsurance polces). Wthn the U.S. banng sector, the evdence has been mxed and most studes found no substantal evdence of cost scope economes n commercal bans or n savngs and loans (e.g., Mester, 1987, 1993; Berger and Humphrey, 1991; Pulley and Humphrey, 1993; and Rogers, 1998). Two papers found evdence of cost scope dseconomes arsng from the coordnaton and admnstratve costs (Cebenoyan, 1990; Wnton, 1999). 4

7 Wthn the nsurance sector, Grace and Tmme (1992) report no sgnfcant cost scope economes for the largest 423 U.S. lfe nsurance companes. Berger, et al. (2000) nstead found cost scope economes for nsurers jontly offered lfe and property-lablty nsurance durng However, a more recent paper by Cummns, et al. (2003) extended Berger et al. (2000) and found no evdence of cost scope effcences for nsurance groups durng the perod Although studes have nvestgated scope economes across lnes of busness wthn the same sector n the U.S. fnancal ndustry, no evdence exsts on whether scope economes exst for offerng a wder varety of products across dfferent fnancal sectors n the U.S Revenue scope economes Scope economes n revenue refer to the ncreases of total revenue resultng from the producton of dfferent categores of servces or products. Revenue scope economes may occur because of consumpton complementartes, otherwse called the demand super-addtvty. By offerng one-stop shoppng convenence, fnancal conglomerates can reduce consumers search and transactons costs. Some customers may be wllng to pay more for the convenence of supermaret shoppng for ther banng and nsurance needs. Demand sde scope effcency gans may arse by cross-sellng a broad range of fnancal products or ntegratng dstrbuton systems. Reputaton recognton can also lead to revenue scope economes when the ntegraton or consoldaton assocates tself wth a strong brand name, whch customers recognze and prefer. By dversfyng across products, fnancal conglomerates may lower ther rs f net cash flows are negatvely correlated. In ths sense, conglomerates may realze revenue scope economes by chargng hgher prces because of lower banruptcy and fnancal dstress costs. 5

8 However, the exstence of revenue scope dseconomes cannot be dsmssed, especally f the ntegraton creates or enhances conflcts of nterest. The conglomerate form of organzaton can create nternal captal marets, whch may somehow worsen neffcent cross-subsdes and nvestment neffcences arsng from manageral agency problems (e.g., Shn and Stulz, 1998; Scharfsten and Sten, 2000). Mester (1992) argued that the maret would vew problems n one subsdary as sgnalng problems n other subsdares and emphaszed that BHCs have tended to rescue ther falng afflates. In a fnancal conglomerate, catastrophc losses of one busness lne (e.g., property-lablty nsurance) could be subsdzed by other busness lnes (e.g., commercal banng). Thus, commercal banng customers would worry about ther savngs and change to unafflated bans. In ths sense, revenue scope dseconomes are more lely to occur. Revenue scope dseconomes may also arse f specalsts from dfferent types of fnancal servces have better nowledge and expertse n ther areas and can provde better-talored products, and, thereby, charge hgher prces than jont producers. There exsts lttle research on revenue scope effcency effects of fnancal ntegraton. The studes that do exst, agan, are based on wthn-sector data, and the results are nconclusve. Berger, et al. (1996) and Noulas, et al. (1993) found lttle or no evdence of revenue scope effcency for provdng deposts and loans. In addton, both studes showed non-ncreasng revenue for chargng customers for jont consumpton benefts. Berger, et al. (2000) found revenue scope dseconomes from provdng lfe and property-lablty nsurance products by ntegrated nsurers. However, usng a more sophstcated estmaton technque, Cummns, et al. (2003) found wea evdence of revenue scope economes n the U.S. nsurance ndustry Proft scope economes 6

9 Proft scope economes generally refer to the ncreased profts from producng a broader range of products or servces. Proft scope economes smultaneously consder both costs and revenues and, therefore, reflect dfferences n product or servce qualty, whch may not be measurable by consderng cost or revenue scope effcency n solaton. For example, customers show ther preference for one-stop shoppng by payng more for such consumpton convenence, whch leads to revenue scope economes. However, fnancal conglomerates may ncur addtonal expenses n jont producton that leads to cost scope dseconomes. So, revenue scope economes or cost scope dseconomes alone cannot explan the net scope effcency gan/loss from ntegratng dfferent categores of fnancal products. In ths sense, proft scope economes domnate the more commonly used concept of cost and revenue scope economes. Studes of proft scope effcences generally do not fnd consstent benefts of ether jont producton or specalzaton wthn ether the banng or the nsurance ndustry. Studes typcally fnd that jont producton was more effcent for some frms and specalzaton was more effcent for others wthn ether U.S. banng or nsurance sector (Berger, et al. 1993; Berger, et al. 2000; Cummns, et al. 2003). Few studes have found meanngful proft scope economes among tradtonal depost and loan outputs (Humphrey and Pulley, 1997; Rogers, 1998). 3. Constructon of the banng/nsurance database The regulatory data sets n the U.S. coverng fnancal servce frms are product specfc and there s no convenent way to dentfy afflatons among companes across dfferent product lnes. In ths study we construct a lnng varable that allows us to match the unque company dentfers between the nsurance and ban regulatory data sets. The varable we develop lns the unque company dentfers n the Natonal Assocaton of Insurance Commssoners (NAIC) nsurance data sets together wth the dentfers n the Ban Holdng Company Fnancal Report 7

10 (BHCFR), the Commercal Bans Call Report (CALL), and the Thrft Fnancal Report (TFR). These four data sets contan fnancal and domcle nformaton for almost all nsurers, BHCs, FHCs, commercal bans, and thrfts operatng n the U.S. for our research perod over The procedure we followed to develop the lnng varable was as follows. Frst, We dentfy the lst of nsurers afflated wth bans, f any, by usng ey word search on the Federal Reserve Structure Report (FEDSR) and the Thrft Holdng Company Structure Report (THCFR), and by usng self-reported answers to queres posed by nsurance regulators as part of the annual oversght process as reported on the annual regulatory statements. Supplementary nformaton from the Natonal Informaton Center Herarchy Report (NICHR) allows us to then te the BHCFR, TFR and CALL data to FEDSR and THCFR by the core lnage code assgned by the FRB. In the fnal step, we match the banng data to the nsurance regulatory data manually usng company name, locaton and other domcle nformaton requred to perform the merge. Frms under common ownershp are aggregated to the group level. We separately aggregate the data of each group s lfe, property-lablty, commercal ban, and thrft subsdares to obtan dvsonal totals. A frm s treated as a sngle producer wth up to four busness dvsons Inactve frms wth non-postve total assets, labltes, or net worth are elmnated. Snce almost all jont producers n our data are large, we then focus on large fnancal nsttutons lcensed as nsurers or bans n the U.S. Followng the lterature (e.g., Berger and Mester, 1997, 2003), we further exclude specalst bans wth less than $1 bllon assets, and specalst nsurers wth less than $600 mllon assets. The fnal data sample conssts of 260 observatons of dversfed frms jontly producng banng and nsurance products, 613 nsurance specalst observatons and 1,450 ban specalst observatons over the perod Table 1 shows the statstcs of 8

11 the data sample. The frms ncluded account for 98 percent lfe nsurance ndustry assets, 94 percent property-lablty nsurance assets, 88 percent commercal bans assets, and 81 percent thrfts assets. 4. Hypotheses development Although GLB largely elmnates the barrers and restrctons on afflatons across fnancal sectors, t does not necessarly follow that fnancal "supermarets" wll become domnant. Furthermore, we observe the coexstence of conglomeraton and specalzaton busness strateges by U.S. bans and nsurers. Whether scope effcency benefts exst for jont producton remans an open queston for cross-sector fnancal conglomeraton. These suggest the frst two general hypotheses regardng the exstence of scope economes, stated n null form: Hypothess 1: Nether scope economes nor dseconomes exst n the post-glb ntegrated banng and nsurance ndustres. Hypothess 2: Economes of scope are nvarant among fnancal conglomerates jontly producng banng and nsurance products. Economes of scope can arse from varety cost complementartes and/or revenue complementartes. Frm-specfc characterstcs, e.g., frm sze, product mx, dstrbuton networ, and regulatory regme, may affect those complementartes and thus contrbute to assorted scope economes or dseconomes. Therefore, some bans or nsurers could be n a better poston to beneft from dversfed manufacturng. There s a consensus that frm sze may be assocated wth greater or lesser scope economes, and t s typcally found that large bans or nsurance companes are better postoned to explot hgher scope economes (Berger, et al. 1996; Mlbourn, et al. 1999; Berger, et al., 2000; Cummns, et al. 2003) and large bans benefted more from the passage of GLB than small bans (Ahgbe and Whyte, 2001). Fnancal conglomerates may gan a compettve 9

12 advantage through mplct government guarantee too bg to fal. If consumers perceve that the mplct guarantees reduce potental losses, the conglomerates may gan a reputatonal advantage that allow them to borrow funds at lower costs (Kane, 1999; Carow, 2001). Cost scope economes may exst on a small scale, from sharng some nputs or fxed resources, but for larger conglomerates these cost scope economy gans could be exhausted or offset by coordnaton or management dseconomes. Nevertheless, large scale may be needed to generate revenue scope economes from consumpton complementartes because of the need to mantan a large dstrbuton networ. Gven ths dscusson, our thrd hypothess s as follows: Hypothess 3: Larger fnancal conglomerates are more lely to explot economes of scope n jontly producng banng and nsurance products. The busness opportuntes offered by GLB may beneft some lnes of nsurance busness more than others. Carow (2001) found that lfe nsurance companes benefted more from the passage of GLB than dd property-lablty nsurers. A GAO (1990) study concluded that synergstc gans are greater for the combnaton of bans and lfe nsurers than for the combnaton of bans and property-casualty nsurers. Gven regulatons precludng bans from manufacturng nsurance, U.S. bans have long entered dstrbuton allances wth nsurers and have been engagng n the nsurance busness through the sellng of annutes and the credt related nsurance. Snce lfe nsurance products have more smlartes wth banng products and functon as complementartes to banng products, bans may be more nterested to cross-sell lfe nsurance products than property-lablty products (Johnston and Madura, 2000). On a unvarate bass, our data sample supports ths nference as ban-owned nsurers accounted for 7.3 percent lfe nsurance ndustry assets but only 3 percent property-lablty ndustry assets. A 1996 Lfe Offce Management Assocaton survey of bans and thrfts nvolved n nsurance dstrbuton showed that bans were most lely to sell annutes followed by term lfe 10

13 and whole lfe, and were least lely to dstrbute property-lablty products. As a result, many bans and thrft nsttutons have accumulated sgnfcant experence dstrbutng lfe nsurance. Hence, bans could gan greater scope economes when combnng wth lfe nsurers than wth property-lablty nsurers. The fourth null hypothess s stated as follows: Hypothess 4: Economes of scope are more lely to occur n jontly producng lfe and banng products than n jontly producng property-lablty and banng products. Economes that arse from maretng, dstrbuton, admnstraton, and other functons could be more prevalent n retal product lnes than n commercal lnes. The prmary argument n favor of ths poston s that maretng, dstrbuton, admnstraton, and other functons tend to be more homogeneous n the retal lnes and more heterogeneous n the commercal lnes. Retal customers may be wllng to pay more for such one-stop shoppng convenence; however, commercal customers face relatvely trval search or transacton costs and prefer talored products. These arguments suggest the null hypothess n relaton to the product portfolo: Hypothess 5: Economes of scope are more lely to occur n the retal fnancal product lnes than n the commercal lnes. Although a consoldated ban and nsurer may lower total costs or ncrease expected revenue by cross-sellng, scope economes may vary wth dstrbuton systems. Insurers usng vertcally ntegrated dstrbuton systems dstrbute ther products through exclusve agents, drect maretng, or mass maretng. Insurers usng horzontally ntegrated dstrbuton systems dstrbute through broers or ndependent agents who sell the products of multple nsurers. Bans are expected to sell ther nsurance afflates products through ther branches or offces. Bans afflated wth vertcally ntegrated nsurers can reuse nsurers relatvely large nvestments n advertsng and maretng, whch more lely create cost scope economes. However, revenue scope economes may be less lely because such afflatons may ncrease 11

14 competton and conflcts wthn the group, whch s especally serous when ban-sold nsurance products are smlar to the nsurance products offered by ther nsurer afflates, especally annutes. In ths sense, nsurers usng horzontal dstrbuton systems may be n a better poston to gan revenue scope economes. Berger, et al. (1997) report that vertcally ntegrated dstrbutors are more lely to realze both cost and revenue scope economes n the lfe nsurance ndustry. However, Cummns, et al. (2003) dd not fnd evdence that scope economes were more lely for nsurers usng vertcally ntegrated dstrbutons than those usng horzontal dstrbuton systems. These arguments nform the next hypothess: Hypothess 6: Economes of scope are more lely to be present for the combnaton of bans and nsurers usng vertcally ntegrated dstrbutons than for the combnaton of bans and nsurers usng horzontally ntegrated dstrbutons. X-effcences capture ndvdual frm s devatons from the effcency fronter. Snce effcent managers are already outperformng ther compettors, wth the ablty to acheve hgh levels of X-effcency, they can use and extend ther manageral talent, experence, and expertse nto the conglomerate. Thus, they can better realze economes of scope, yeldng a postve relatonshp between effcency and scope economes. The last hypothess s as follows: Hypothess 7: X-effcent frms are more lely to enjoy economes of scope n the jont producton of banng and nsurance products. 5. Methodology and data 5.1. Econometrc approach We use the composte producton functon frst proposed by Pulley and Braunsten (1992) to estmate the cost, revenue and proft functons used n ths study. The PB composte functon s deal for our settng as t not only allows zero output for some products, but t also allows for 12

15 negatve values for the dependent varable. Thus, ths functonal form s attractve for the scope economes analyss, especally for estmatng proft scope effcences. 8 It s well recognzed that specalst frms and dversfed frms may operate at dfferent producton fronters due to dfferent busness strateges and technologes (Berger et al. 2000). Thus, we estmate cost, revenue, and proft composte functons for specalst and dversfed frms, respectvely, usng non-lnear least squares. A total of 24 functons are estmated: two types of frms (specalsts and dversfed) across four sectors (lfe nsurance, property-lablty nsurance, commercal banng, and thrfts) by three types of functons (cost, revenue, and proft) Functon form The composte Pulley and Braunsten (P-B) model combnes a quadratc structure for outputs and a log-quadratc specfcaton for nput prces. It s gven by: C = [ α + 0 exp[ β + 0 α q β ln r + (1 2) + (1/ α j 2) q q β l j + ln r ln r δ q ln r l + ] μ q ln r ] + ε (1) where C s total costs; q s the th output, =1,,n; r s the th nput prce, =1,,m; α, β, δ, μ are coeffcent vectors to be estmated; ε denotes a random error term. The theoretcal requrement that the cost functon be homogeneous of degree one n nput prces s met by mposng the followng restrctons (Brown, Caves and Chrstensen, 1979): = 1 β and β = δ = 0 ( = 1,,m) l β = 0 (, l = 1,,m ) l l 8 P-B model has been used to estmate economes of scope n banng (e.g., Pulley and Humphrey, 1993; McKllop, Glass, and Morawa, 1996; Berger, Humphrey, and Pulley, 1996; Humphrey and Pulley, 1997) and n nsurance (e.g., Berger et al., 2000; Hrao and Inoue, 2004). 13

16 The symmetry restrctons mply α j = α j and β l = β l. The constant terms β 0 and the nteracton term μ q ln r are omtted because of the techncal dffculty n estmatng both constant terms and both nteracton terms smultaneously (Pulley and Braunsten, 1992; McKllop, et al., 1996; and Berger, et al., 2000). Hence, the P-B composte model used s specfed as C = α + exp[ [ 0 α q β ln r + (1 2) + (1/ 2) α q q β l j ln r j + ln r ] + ε l δ q ln r ] (2) Consstent wth the lterature, we adopt a modfed verson of the composte cost functon form by normalzng the dependent varable and the output and nput varables as follows: C r m exp[ = α + [ 0 β ln s α y + (1 2) + (1/ 2) α y y β l j ln s j + ln s ] + ε l δ y ln s ] (3) where the dependent varable s normalzed by the prce of the last nput (r m ). Output terms are scaled by the sample mean (y =q /mean(q )) and nput prces are normalzed by the last nput prce (s = r / r m ). The revenue and proft functons estmated n our study are dentcal to the composte cost functon (3), except that the dependent varable, cost, s replaced by revenue and proft respectvely. 9 Thus, revenue (R) and proft functons (P) are R r m exp[ P r m exp[ = α + [ 0 β ln s = α + [ 0 β ln s α y α y + (1 2) + (1/ 2) + (1 2) + (1/ 2) α y y β β l j ln s ln s α y y l j j + ln s ] + ε j + l ln s ] + ε l δ y ln s δ y ln s ] ] (4) (5) The modfed composte cost, revenue, proft functons are estmated by non-lnear least squares. 9 Followng Berger et al. (2000), we adopt the alternatve proft effcency concept, whch use the same form and ndependent varables for the cost, revenue, and proft functons to avod the mpact of specfcaton dfferences on the cost, revenue, and proft scope economes estmatons (Berger and Mester, 1997, 2003). 14

17 Measurement of scope economes Cost economes of scope (CSCOPE) are measured as the percentage of costs that frms could save by producng multple products jontly nstead of producng each product separately n specalst frms (Panzar and Wllg, 1975, 1981). The measure of CSCOPE n the case of a frm producng m product categores, Q 1,, Q m, s gven by CSCOPE m C S = 1 = m ( q ; r ) = 1 C J m = 1 C J ( q ; r ) ( q ; r ) (6) where C S (q ; r ) s the cost functon for the specalst frm S producng Q ; and C J (q ; r ) s the cost functon for the dvson J (producng Q only) of conglomerates. Cost scope economes are beleved to be present f CSCOPE> 0, and dseconomes are present f CSCOPE< 0. Revenue and proft scope economes are generally measured as the percentage ncrease n revenue or proft when dfferent categores of products are provded jontly nstead of beng provded separately. The revenue scope economes score (RSCOPE) s gven by RSCOPE m R J = 1 = m ( q ; r ) = 1 R J m = 1 R S ( q ; r ) ( q ; r ) (7) and proft scope economes score (PSCOPE) s PSCOPE m P ( q ; r ) J = 1 = m = 1 J m = 1 S P ( q ; r ) P ( q ; r ) (8) where R S (. ) and P S (. ) are revenue and proft functons for specalst frms S ; R J (. ) and P J (. ) are revenue and proft functons for dvsons J of conglomerates. Smlarly, revenue or proft 15

18 scope economes are beleved to be present f RSCOPE> 0 or PSCOPE> 0, and scope dseconomes are present f RSCOPE< 0 or PSCOPE< Defnng outputs, nputs, and prces Le other servce ndustres, t s dffcult to defne what fnancal nsttutons produce and how the servces are prced. The lterature has evolved over tme and three alternatve methods have been used to measure outputs n the fnancal servces ndustry the asset (fnancal ntermedaton) approach (Sealey and Lndley, 1977), the user-cost approach (Donovan, 1978), and the value-added (producton) approach (Berger and Humphrey, 1992). Ths secton brefly dscusses the measurement of outputs, nputs, and prces for nsurers and bans, respectvely Insurance outputs Consstent wth the recent lterature on nsurance effcency, we measure nsurance outputs usng the value-added approach (Cummns and Wess 2001). We dentfy three prncpal servces provded by nsurance companes: rs poolng and rs-bearng, real fnancal servces, and fnancal ntermedaton. 10 Property-Lablty Insurance. For property-lablty nsurers, the present value of real losses ncurred (PV(L)) s used as a output proxy. Losses ncurred are generally defned as the total losses that are expected to be pad arsng from busness wrtten n prevous years; specfcally they are calculated as the sum of losses pad plus the net change n loss reserves. We dscount the expected loss payments to reflect clam settlement lags that may have a sgnfcant 10 The actuaral, underwrtng, clam settlement, and assocated expenses ncurred n operatng rs pools are the man components of value added related to rs-poolng and rs-bearng. Real fnancal servces nclude personal fnancal plannng, commercal beneft plan admnstraton, coverage program desgn, and servces related to nsured losses. For fnancal ntermedaton, nterest credted to lfe nsurance polces and premum dscounts appled to property-lablty nsurance polces represent the value added of the nsurers ntermedaton functon. See Berger, et al. (1997), and Cummns and Wess (2001) for detals on nsurance outputs and the measurement 16

19 mpact on certan product lnes (most notably the lablty lnes of nsurance). Four output lnes of busness are defned: personal property, personal lablty, commercal property, and commercal lablty products. The output for the ntermedaton functon s measured by the annual average real nvested assets. Because of lmtatons due to sample sze, we use three aggregate nsurance outputs for property-lablty nsurers: (1) personal lnes combnng personal property and lablty products; (2) commercal lnes combnng commercal property and lablty products; (3) ntermedary output (nvested assets). Lfe Insurance. Unfortunately lmtatons n nformaton reported by lfe nsurers, the present value of benefts ncurred for lfe nsurers s not avalable. Thus, followng accepted practce (e.g., Yuengert 1993; Berger, et al. 2000; Cummns, et al. 2003) we use ncurred benefts (payments receved by polcyholders n a year) plus addtons to reserves as a proxy of lfe nsurance outputs. We defne fve busness lnes: personal lfe nsurance, personal annutes, group lfe nsurance, group annutes, and accdent and health nsurance. We use three aggregate outputs for lfe nsurers: (1) lfe nsurance ncludng personal and group lfe nsurance; (2) annutes ncludng personal and group annutes; (3) accdent and health nsurance Banng outputs Consstent wth the majorty of the lterature on ban effcency, we assume the servces provded by bans are tradtonal fnancal servces, new fnancal servces, and off-balance sheet (OBS) actvtes (see Berger, et al, 1997). The tradtonal fnancal servces nclude deposts (demand, tme and savngs) and loans (real estate, commercal, nstallment), whch contnue to represent the majorty of ban revenue. Fee-based servces and OBS actvtes have been recognzed as a growng category of ban assets and now account for a substantal porton of ban revenues (Stroh, 2000). New fnancal servces nclude portfolo management, mutual or 17

20 penson fund dstrbutons, and safeeepng servces. The major OBS actvtes are loan commtments, credt dervatves, letters of credt, and loan orgnatons, sales, and servcng. 11 Followng the lterature, we dentfy three categores of outputs consumer loans, busness loans, and other assets. 12 Consumer loans nvolve ntermedaton and loan servces and are calculated as the sum of the dollar value of resdental loans, credt card loans and other nstallment loans. Busness loans nclude the dollar value of real estate loans, commercal and ndustral loans, farm loans and other loans and leases. Other assets nclude a ban's new fnancal servces and OBS busness, and are defned as the sum dollar value of securtes and tradng assets held and rs-weghted (based on Basle Accord rs weghts) OBS actvtes Insurance nputs and prces Unle output defntons, there s a general agreement n the lterature regardng the measurement of nputs n the fnancal servce ndustry. Generally, the nputs to nsurance fall nto four prncpal categores home offce admnstratve labor, agent labor, materal and physcal captal, and fnancal equty and debt captal. Snce nsurers are not requred to report detaled nformaton about the number of employees and quantty of materals used n busness, the prce of the home offce labor s obtaned from average weely wage rates for lfe nsurers (NAICS categores ) and property-lablty nsurers (NAICS ) n ther domcle state avalable from the U.S. Department of Labor. Smlarly, the prce of agent labor s defned as the premum-weghted average weely wage rates for nsurance agents (NAICS ) n states where the nsurer operates. The prce of the materals and busness servces s defned as the U.S. Department of Labor average weely wage rates for busness servces (NAICS 5614). 11 See Pulley and Humphrey (1993), Berger et al. (1997) and Kashyap, Rajan, and Sten (2002) for detals. 12 It would be deal f the ban outputs were dsaggregated nto more categores. However, because of the trade-off between the degree of aggregaton and the degree of freedom, our data cannot afford losng the degree of freedom. 18

21 We nclude two proxes for the fnancal captal: equty captal and debt captal. Fnancal equty captal plays an mportant role n reducng the nsolvency rs and s vewed as one of the mportant nputs as conveyed n the lterature (Berger, et al., 1997; Hughes, et al. 2001). An nsurer s fnancal equty captal s defned as the statutory polcyholders surplus measured as the average of begnnng and end-of-year equty captal. To measure the prce of fnancal equty captal, we use the estmated cost of equty captal usng 3-Factor Fama-French CAPM wth ndustry specfc beta obtaned from Ibbotson Assocates Cost of Captal Yearboo. 13 The debt captal s defned as the funds borrowed from polcyholders, whch are comprsed of loss reserves and unearned premums reserves. The prce of debt captal s calculated as total expected nvestment ncome mnus expected nvestment ncome attrbuted to equty captal dvded by average debt captal (Berger, et al., 1997; Cummns and Wess, 2001) Banng nputs and prces The four nputs for bans (commercal bans and thrft nsttutons) are wdely recognzed as deposts, labor, physcal captal, and purchased funds 14. Deposts nclude demand deposts, tme and savng deposts. The prce of deposts s calculated as total nterest expense on the deposts dvded by the dollar value of deposts. The prce of labor s calculated as the salary, wage and welfare per employee. The prce of physcal captal s obtaned as occupancy and fxed asset expendtures dvded by the dollar value of net premses and fxed assets avalable n the bans regulatory report. Purchased funds nclude federal funds purchased, large CDs, foregn deposts, demand notes, and other labltes for borrowed money. The prce of purchased funds s calculated as nterest pad on these funds dvded by the total dollar value of these funds. 13 Cummns and Wess (2001) proposed a boo-value approach, whch assumed that nsurers held equty portfolos wth a maret beta coeffcent of 1.0; therefore, they use a constant cost of equty across all nsurers n the ndustry. 14 See Berger and Humphrey (1992), Berger and Mester (2003), Berger, et al. (2005) for detals. 19

22 6. Emprcal results 6.1. Summary Statstcs and Scope Economy Estmates Tables 2, 3, 4 and 5 show the descrptve statstcs of the varables used to estmate the cost, revenue and proft functons separately for bans, thrft, lfe nsurers and for property-lablty nsurers. A revew of the tables suggests the subsdares of conglomerate organzatons are, on average, larger than the specalst frms operatng n the same ndustry. It s also nterestng to note the nput prce for labor tends to be hgher for the subsdares of dversfed frms than for specalst frms consstent wth the hypothess greater manageral expertse s needed to successfully operate a conglomerate organzaton. After estmatng the twenty-four composte cost, revenue and proft functons (avalable from the authors), we estmate the scope economy scores usng the coeffcents estmated from the composte functons. The scope economy estmatons are presented n several dfferent ways. Frst, the cost, revenue, and proft scope economes are evaluated for all the frms n the sample and for the actual jont producers only. The scope economy scores, then, are evaluated at the 25 th (Q1), 50 th (medan), and 75 th (Q3) percentle of the data. 15, 16 Table 6 summarzes the estmates of costs, revenue, and proft scope economy scores. Panel A shows consstent negatve cost scope economy estmates regardless of evaluaton ponts. The evdence ndcates sgnfcant cost scope dseconomes on jontly producng banng and nsurance products. For example, at medan level of outputs and nput prces, the data show that 15 One common approach n nferrng scope economes s to evaluate at a sngle pont, e.g., the mean or medan of the data. Nevertheless, ths pont estmaton method has been crtczed for ts wea representaton, as t may not provde a good approxmaton for the whole sample (Hrao and Inoue, 2004; Berger et al., 2000). 16 To chec for robustness, we also estmated scope economy scores where we adjusted the amount of output produced across the quantles but held nput prces fxed at the medan level. The results are smlar to what we report n the paper. For example, the proft scope economy scores for all frms ranges from 2.67 percent at the 25 th percentle of output, 3.98 percent at the 50 th percentle of output, and 7.67 percent at the 75 th percentle. For the jont producers the same scope economy scores were percent 2.12 percent 11.8 percent, respectvely 20

23 costs ncrease by 11.4 percent for all the frms and 24.1 percent for actual jont producers. The estmates at Q1 and Q3 suggest the same concluson, and the cost scope dseconomes are statstcally sgnfcant. Panel A also shows an nverse relatonshp between frm sze and cost scope dseconomes. As dscussed earler, cost scope economes may exst on a small scale from sharng nputs or sharng fxed resources, but such gans could be exhausted n larger scale. Panel B of Table 6 lsts the revenue scope economes estmates. The postve and sgnfcant revenue scope economy scores ndcate revenue scope economes n jont producton, suggestng that offerng both banng and nsurance products can generate hgher sales revenue than producng the products separately n specalsts frms. Consstent wth Hypothess 3, we see that small sze frms are less capable of beneftng from such jont producton strategy. As dscussed earler, large scale operatons may be needed to generate revenue scope economes from consumpton complementartes because of the need to mantan a large dstrbuton networ. Ths fndng further suggests that large frms are more effcent n utlzng cross-sellng and explotng benefts from offerng one-stop shoppng convenence. Panel C of Table 6 provdes the summary of proft scope economy score estmatons. The data generally show sgnfcant proft scope economes. However, proft scope economes are small and less sgnfcant than cost and revenue scope economes. For example, at sample medan, jont producers could beneft from jont producton by ncreasng 2 percent net ncome. Smlar to the revenue scope economes, large frms show hgher proft scope economes when producng both banng and nsurance products. The fndngs reject Hypothess 1 and suggest that sgnfcant cost scope dseconomes, sgnfcant revenue scope economes, and wea proft scope economes exst n the post-glb ntegrated banng and nsurance ndustres. The evdence of cost scope dseconomes cannot 21

24 support producton complementartes and ndcates that cost savngs from sharng resources do not offset the extra costs possbly ncurred n jont producton and conglomeraton. The fndngs of revenue scope economes support consumpton complementartes, whch show the demand sde scope effcency gans by cross-sellng. The fndngs of proft scope economes ndcate that revenue scope economes slghtly domnate cost scope dseconomes on jont productons, whch leads to the fnal net proft scope economes, whch, however, are small Regresson analyss We next nvestgate the types of frms are more lely to beneft from conglomeraton. To do so we regress the cost, revenue, and proft scope economy scores on a set of frm characterstc varables, respectvely. We estmate the models usng Tobt methodology after elmnatng observatons wth extreme scores, e.g., greater than 1 or less than -1. The fnal data sample contans 214 jont producer observatons. Table 7 summarzes descrptve statstcs of regresson varables. Table 8 shows the regresson results. In Table 7 we see the average conglomerate frm had negatve cost scope economes, postve revenue scope economes, and slghtly postve proft scope economes although not statstcally dfferent from zero. Table 7 also dsplays summary statstcs of a set of ndcator varables we created desgned to capture broad dfferences n the busness strateges employed by the frms n our sample. Fnancal nsttutons wth more than 75 percent of group assets n banng dvson are defned as bancassurers; frms are categorzed as assurbans f banng dvson assets account for less than 25 percent of group assets; and the remanng frms are conglomerates. We further segmented the bancassurers and conglomerates by the range of nsurance busness n whch they engage: n lfe nsurance only, n property-lablty nsurance only, and n both lfe and property-lablty nsurance. Assurbans were also segregated by the 22

25 range of banng busness n whch they engage: owng commercal bans only, ownng thrfts only, and ownng both commercal bans and thrfts. Accordng to ths segmentaton, the most popular conglomeraton strategy among U.S. fnancal nsttutons over ths tme perod was nsurance companes movng nto the banng busness usng the thrft organzatons (over 40 percent of our observatons). The second most popular strategy was bans movng nto nsurance through the purchase of lfe nsurance subsdares. Table 8 dsplays two sets of regressons where the only dfference between the models s varables used to capture frm s busness portfolo and product mx. Models <1>, <2>, and <3> use the total personal busness share and overall frm categores, whle models <4>, <5>, and <6> further segregate personal busness share and frm categores. To test Hypothess 3 that frm sze s related to scope economes, we use the natural log of gross total assets as a proxy for sze. The coeffcent s negatve n the cost regresson and postve n both revenue and proft regressons. All of the estmated coeffcents are statstcally sgnfcant and the results are consstent wth the unvarate fndng reported n Table 6 that small frms are more lely to beneft from cost savng by sharng mportant or costly resources n conglomeraton, whle large frms are more lely to beneft from revenue and proft ncreases when jontly producng banng and nsurance products. Hypotheses 4 and 5 are nvestgated usng the frm s busness portfolo and product mx varables. In Models 1 through 3, we use the aggregated frm-type ndcators dscussed earler and a varable equal to the percentage of the frm s revenue comng from retal marets. In Model 4 through 6, we use the more granular versons of these same varables. As shown n Table 8, the personal product share varables all have negatve coeffcents n the cost scope regresson and postve coeffcents n the revenue and proft scope regresson. The results 23

26 suggest that conglomerate frms emphaszng retal busness are less lely to realze cost savngs but more lely to beneft from ncreases n revenue and profts. Thus, proft scope economes are more lely to occur n personal product lnes than n commercal lnes consstent wth the hypothess that retal banng and nsurance products are more homogeneous and jont-producers appear to have modest success cross-sellng. The result s also consstent wth the hypothess that commercal products are more heterogeneous and commercal customers may prefer expertse and talored products to meet ther banng and/or nsurance needs. The estmated coeffcents on busness portfolo ndcator varables ndcates that bans appear more proft scope effcent when partcpatng n both lfe and property-lablty nsurance busness (.e., bancassurers), that assurbans are proft scope neffcent when partcpatng n both commercal and thrft banng busness, and that conglomerate frms wth balanced banng and nsurance busnesses are more scope effcent when offerng property-lablty nsurance products. We nclude two varables to control for the product dversfcaton: the nsurance product Herfndahl ndex (HHI) and banng product HHI. The maxmum possble value for the Herfndahl ndex s 1.0 and ndcates the frm only offers a sngle product. As such, hgher ndex values ndcate a decrease n product dversfcaton. The results suggest frms offerng a more narrow set of products from ther nsurance dvson were less lely to explot cost scope economes but the more lely to explot revenue and proft scope economes. Product dversfcaton n the banng dvson does not appear to have a sgnfcant mpact n jont producton. Thus, jont producers wth more focused nsurance operatons appears to be have been the domnate strategy at ths tme. 24

27 Two varables are used to control for the geographc dversfcaton and the strateges of natonal or local operaton: the geographc busness HHI n nsurance dvson and the number of domestc ban branches. 17 Frms are less geographcally dversfed wth hgher values of the nsurance geographc HHI and wth a lower number of ban branches. Both measures present a consstent mpact. Jont frms wth more geographcally dversfed nsurance or banng busnesses appear more lely to realze proft scope economes. The results suggest that conglomerate frms had to operate at the natonal level (or at least super-regonal level) level n order to enjoy the postve net effects on proftablty. The next set of varables were ncluded to test our hypotheses whether the type of dstrbuton system used by the nsurance subsdares are related to scope economes. We defned three varable based upon the dstrbuton strateges of the frm. The frst ndcator was set equal to one for frms that exclusvely employed ndependent agents and broers to dstrbute ther product (.e., horzontal nsurance dstrbuton). The second ndcator was set equal to one for frms that use employees or some drect channel mechansm to dstrbute ther products (.e., vertcal nsurance dstrbuton). Fnally, we defned a thrd category for nsurers that use both systems. 18 To avod perfect colnearty n the regressons, we elmnated the horzontal dstrbuton varable to estmate the regressons. The results shown n Table 8 suggest the type of dstrbuton strategy employed by the nsurer had no sgnfcant mpact on scope economes. The remanng varables shown n Table 8 were ncluded as controls for varous nfluences the prevous lterature suggests are mportant determnants of scope economes. The sgnfcant 17 HHI for a frm producng n types of products s measured as (X X X n 2 )/(X 1 + X X n ) 2, where X s the th product output. HHI for nsurers operatng n m states s measured as (Y Y Y m 2 )/(Y 1 + Y Y m ) 2, where Y s the drect premum wrtten n the th state. 18 Vertcally ntegrated dstrbuton channels nclude exclusve/captve agents, drect response, nternet, affnty group maretng, worste maretng. Horzontally ntegrated dstrbuton channels nclude ndependent agents, broers, general agents, career agents, and bans. 25