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The telecommunications act of 1996 and Medicaid Health Maintenance Organizations

University of Florida Institutional Repository
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THE TELECOMMUNICATIONS ACT OF 1996 AND MEDICAID HEALTH MAINTENANCE ORGANIZATIONS By TROY CLARENCE QUAST A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Troy Clarence Quast

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To my parents, Clarence and Elaine Quast; and to my son, Andres Quast.

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iv ACKNOWLEDGMENTS I thank my advisor, David Sappington, and committee members Chunrong Ai, David Figlio, and Justin Brown. I also th ank Betsy Shenkman, David Gabel, Donald Stockdale, Richard Romano, Mark Jamison, Sanford Berg, Jonathan Hamilton, Laura Braden, Damon Clark, Roger Clemmons, Mirc ea Marcu, Burcin Unel, Richard Gentry, and Vanessa Cruz for their helpful advice and words of encouragement. I am grateful for financial support from the University of Florida Department of Economics, the University of Florida Public Policy Res earch Center, the Telecommunications Policy Research Conference, and the University of Florida Public Utilities Research Center. I am also grateful for technical and research support provided by the University of Florida Department of Epidemiology and Health Po licy Research. Finall y, I thank my wife, Daniela Quast, for her unwavering support and he r ability to feign interest in the notion of endogeneity.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 2 HOW STATE GOVERNMENTS IMPLEM ENT FEDERAL POLICIES: THE TELECOMMUNICATIONS ACT OF 1996...............................................................3 Introduction...................................................................................................................3 Background Information on Unbundled Network Element (UNE) Rate Proceedings...............................................................................................................7 Steps Involved in Setting UNE Rates....................................................................7 Influence of Neighboring States............................................................................9 Hypothesized UNE Rate Determinants......................................................................12 Model Specification and Data Used...........................................................................15 Model Specification.............................................................................................15 Data Used............................................................................................................16 Estimation Results......................................................................................................20 Coefficient Estimates...........................................................................................20 Economic Effects.................................................................................................22 Conclusion..................................................................................................................24 3 THE EXTENT AND MEANS OF ENTRY INTO LOCAL TELECOMMUNICATIONS MARKETS.................................................................37 Introduction.................................................................................................................37 Background Information on UNE-Based Entry.........................................................42 Loop-Based Versus Platform-Based Entry.........................................................42 History of Platform-Based Entry Regulation......................................................43 Hypothesized Determinants of Loopand Platform-Based Entry..............................45 Model Specification and Data Used...........................................................................48

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vi Estimation Results......................................................................................................51 Estimates without Popu lation Interactions..........................................................51 Coefficient estimates....................................................................................51 Economic effects..........................................................................................52 Estimates with Population Interactions...............................................................54 Coefficient estimates....................................................................................54 Economic effects..........................................................................................55 Conclusion..................................................................................................................58 4 DOES THE FORM OF DOCTOR COMP ENSATION AFFECT THE QUALITY OF CARE IN MEDICAID HMOS?...........................................................................67 Introduction.................................................................................................................67 Background Information.............................................................................................69 Data and Empirical Specification...............................................................................71 Findings......................................................................................................................75 Conclusions.................................................................................................................77 5 CONCLUSIONS........................................................................................................82 APPENDIX A DATA NOTES AND ADDI TIONAL RESULTS.....................................................83 Data Notes..................................................................................................................83 UNE Rates...........................................................................................................83 Cost Estimate.......................................................................................................83 PUC Characteristics.............................................................................................84 State Political Variables......................................................................................85 Section 271 Status...............................................................................................86 Level of Competitive UNE Entry........................................................................86 Tests of Instrument Validity and the E xogeneity of the Average Retail Rate Variable...................................................................................................................87 Tests of Instruments Orthogona lity to the Error Process...................................87 Tests of Instrument Relevance............................................................................88 Test of the Exogeneity of th e Lagged Average Retail Rate................................88 Additional Results......................................................................................................89 B ADDITONAL RESULTS..........................................................................................95 LIST OF REFERENCES...................................................................................................98 BIOGRAPHICAL SKETCH...........................................................................................105

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vii LIST OF TABLES Table page 2-1 Summary Statistics...................................................................................................31 2-2 Correlation Matrix of Non-UNE Rate Variables.....................................................32 2-3 Coefficient Estimates from One-Ye ar and Two-Year Lag Specifications...............34 2-4 Effects of Changes in Explanatory Va riables on UNE Rates Two Years Later......36 3-1 Summary Statistics...................................................................................................62 3-2 Correlation Matrix of Explanatory Variables...........................................................62 3-3 Coefficient Estimates wit hout Population Interactions............................................63 3-4 Economic Effects without Population Interactions..................................................64 3-5 Coefficient Estimates with Population Interactions.................................................65 3-6 Economic Effects for the 25th a nd 75th Populatio n Percentiles..............................66 4-1 HMO Attributes........................................................................................................79 4-2 HEDIS Success Rates by HMO Attribute................................................................79 4-3 Demographic Characteristics By Population...........................................................80 4-4 HEDIS Success Rates by Population.......................................................................80 4-5 OLS Regression Estimates.......................................................................................81 A-1 Acronyms Used........................................................................................................90 A-2 Tests of Overidentifying Restrictions......................................................................91 A-3 Tests of Instrument Relevance.................................................................................92 A-4 Effects of Changes in Explanatory Variables on UNE Rates Two Years Later Using One-Year Lag Specification..........................................................................93

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viii A-5 Effects of Changes in Explanatory Va riables on UNE Rates in the Long-Run.......94 B-1 Marginal Effects from Probit Regressions...............................................................96 B-2 OLS Regression Estimates with HMO Fixed Effects..............................................97

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ix LIST OF FIGURES Figure page 2-1 Comparison of UNE Rates. A) New Hampshire and Vermont. B)Kentucky and Tennessee. C) Wyoming and Utah...........................................................................26 2-2 HCPM Estimate versus UNE Rate. A) Ameritech. B) BellSouth. C) Pacific Telesis. D) Qwest. E) Southw estern Bell. F) Verizon.............................................28 2-3 Cumulative effect of $1 in crease in the leaders ra te on the followers rate............30 3-1 Fraction of Incumbent Lines Leased by Entrants (National Average).....................60 3-2 Fraction of Incumbent Lines Leased by Entrants by Means of Entry (National Average)...................................................................................................................60 3-3 Loop Share of Incumbent Lines, July 2001 and July 2004......................................61 3-4 Platform Share of Incumbent Lines, July 2001 and July 2004................................61

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x Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THE TELECOMMUNICATIONS ACT OF 1996 AND MEDICAID HEALTH MAINTENANCE ORGANIZATIONS By Troy Clarence Quast August 2006 Chair: David Sappington Major Department: Economics We analyzed the effects of the landma rk Telecommunications Act of 1996 (TA96). We also investigated whether the method of provider compensation affects the level of care provided by Medicaid health ma intenance organizations (HMOs). We examined the rates set by state pub lic utility commissions (PUCs) that competitors must pay to lease parts of the lo cal network from the largest incumbent U.S. telecommunications suppliers (RBOCs). The re sults indicate that rates in the smaller states in each RBOC region are strongly in fluenced by the largest state in the region. Rates are lower where the level of competitive entry is lower, while they are higher in states where the governor is a Republican. The an alysis suggests that di fferent states have employed different methodologi es in implementing TA96. We investigated entry in local telecommuni cations markets. Panel data are used to analyze the number of lines that competitiv e local exchange carrier s (CLECs) lease from RBOCs using two alternative arrangements : leasing only the wi res that connect a

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xi customers premises to the phone network (loop-based entry), or leasing all of the network elements that are needed to provide phone service (platf orm-based entry). The estimates suggest that while the two types of entry are generally affected by different market factors, there appears to be cost-based substitution between them. Also, loopbased entry is more responsive to changes in economic conditions in smaller states, while platform-based entry is more responsive in larger states. Using data for all of the Medicaid HMO enrollees in a large state, my coauthors and I found that enrollees in HMOs that pay their doctors exclusively via fee-for-service arrangements are more likely to receive se rvices for which the HMOs doctors receive additional compensation. Further, these enrollees are less likely to receive services for which the HMOs doctors do not receive additional compensation. These findings suggest that financial incentives may influe nce the behavior of doctors in Medicaid HMOs, and thus the health care received by Medicaid participants enrolled in HMOs.

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1 CHAPTER 1 INTRODUCTION The federal government in the United States often attempts to harness competitive market forces to achieve policy outcomes. Politicians and analysts frequently claim that policies that are based largely on market ince ntives are best suited at resolving observed inefficiencies in the economy. Two important areas in which this approach has been attempted are telecommunications and hea lth care. One of the hallmarks of the Telecommunications Act of 1996 (TA96) wa s its focus on introducing competition into the industry, which its supporte rs claimed would lower pric es and result in greater deployment of technology. Meanwhile, a prom inent component of Medicaid reform has been to move enrollees into health main tenance organizations (HMOs). Many believe that when HMOs assume responsibility for the health care of Medicaid participants, costs are lowered and quality incr eases. This study analyzes how these policies have been implemented and whether they have been successful. First, we investigate how state governme nts have implemented TA96. State public utility commissions have been given somewhat vague guidelines as to how to set the rate at which entrants can lease portions of th e incumbent telephone companys network. We find that the commissions may look to neighbor ing states for guidance as to how these rates. Further, the commissions may be influenced by the level of existing entry when determining the rates. Second, we explore the factors that de termine where entry occurs in local telecommunications markets and the means by wh ich entrants choose to enter. Beginning

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2 in late 2000, entrants could ei ther lease part or all of th e incumbent firms network to provide service. Regression resu lts indicate that the relative co st of the two forms of entry may influence the means of entry chosen by firm s. Further, the factors that influence the level of entry vary by the size of the market. Finally, we analyze how the means by which primary care physicians are paid may influence the quality of care they provide. Using data from Medicaid HMOs, we compare the quality of care provided by physicians who are paid a flat rate per enrollee versus the quality of care provided by physicians who ar e paid per service provide. The results suggest that doctors who are paid a flat ra te per enrollee may be less likely to provide check-up visits to children than docto rs who are paid per service provided.

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3 CHAPTER 2 HOW STATE GOVERNMENTS IMP LEMENT FEDERAL POLICIES: THE TELECOMMUNICATIONS ACT OF 1996 Introduction A primary goal of the TA96 (Table A-1) was to encourage competitive entry into the local telecommunications market, under the presumption that such entry would lower prices and increase social welfare. One of the means by which entrants1 were to enter this market was by leasing unbundled network elemen ts (UNEs). The legislation decreed that entrants could lease certain segments of the incumbents2 network3, thus allowing for competition where it would otherwise be unprofit able or infeasible. The rates at which entrants could lease the UNEs could play an important role in the degree of entry observed.4 The only rate-setting guidance offered by TA96 was that the rates were to be priced at cost plus possibly a reasonable profit, determined without reference to a rate-ofreturn proceeding, and set by the state public utility commissions.5 1 Entrants into local telecommunication markets are re ferred to as Competitive Local Exchange Carriers, or CLECs. 2 Specifically, this paper analyzes the rates at wh ich Regional Bell Operating Companies (RBOCs) lease UNEs to entrants. RBOCs are the regional monopolie s that were created in the split-up of AT&T in 1984. 3 The question as to which segments the incumbent should be forced to lease has been litigated extensively (see Lichtman and Picker, 2003). 4 As discussed in Lichtman and Picker (2003, pp 22-23), excessively low rates can discourage entrant investment in their own networks and foster excessive reliance on UNEs. Conversely, exceptionally high rates may encourage inefficient investment by entrants The effect of the rates on incumbent investment is ambiguous. 5 47, USC 252(d).

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4 Given the ambiguity in how the UNE rates were to be set, the Federal Communications Commission (FCC) interpreted TA96 as giving it the authority to proscribe the methodology the states should use in determining the rates. The FCC decided that the rates should be based on total element long-run incremental costs (TELRIC), the hypothetical costs of impleme nting the least-cost network given the current locations of the incumbents wire centers.6 The state commissions were left with th e unenviable task of operationalizing the vague TELRIC notion. Figure 2-1 depicts UNE loop rates for three pairs of arguably comparable states: New Hampshire and Vermont; Kentucky and Tennessee; and Wyoming and Utah. The charts suggest that the state commissions operationalized the TELRIC concept differently ove r time. For example, when Wyoming and Utah first set rates in 1997, the UNE rate in Utah was only $2.00 less than the rate in Wyoming. By 2003 that difference had increased five-fold. Th is type of variability across states and over time is representative of the experiences in many st ates and suggests the states varied a great deal as to how th ey implemented the TELRIC methodology.7 Understanding how the state public u tility commissions set UNE rates can provide valuable lessons, both in telecommuni cations regulation and in the formation of federal policies. Any evaluation of the succe ss of TA96 at promoti ng competitive entry 6 The FCCs decision was challenged numerous times in the courts, both regarding whether the commission had the authority to dictate the rate-s etting methodology and also whether the TELRIC methodology was consistent with TA96. The Supreme Co urt eventually decided both issues in favor of the FCC. (AT&T v. Iowa Utilities Board (1999), Verizon, et al v. FCC, et al (2002)) More recently, the Washington, D.C. Court of Appeals recently ruled that incumbents should not be forced to lease switching equipment to entrants at TELRIC rates, and the FCC subsequently revised their rules to adhere to that decision (FCC (2005)). However, local loops, the focus of this paper, continue to be included by the FCC as a UNE that the incumben ts must lease at TELRIC rates. 7 Further, the Department of Justice recommended rejecting applications by some incumbents for permission to sell interLATA service on the grounds that the state commissions had not correctly calculated TELRIC rates (e.g., see Department of Justice (1997, 1998, and 2000)).

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5 must account for how states implemented the Act. Given the widespread criticism that TA96 did not spur the level of entry that was anticipated, it is important to disentangle effects due to the execution of TA96 versus the Act itse lf. Beyond telecommunications, the UNE rate experience provides an opportunity to analyze the behavior of state officials charged with implementing federal policy. A ccording to TA96, the state commissions are to base the rates only on state-specific co st factors. However, given the vague TELRIC definition of cost and the discretion given to the states, it is possible that additional factors influence how these rates are set. An analysis of these factors can provide insight as to how state officials impl ement federal policies, and may apply to other policy areas such as education or environmental regulation. Previous studies have analyzed the dete rminants of UNE rates. De Figueiredo and Edwards (2004) examine the UNE rate in pla ce in the three two-year election cycles from 1998 through 2002. The authors find that states with Democratic st ate legislatures and relatively large political contri butions by entrants have lower UNE rates. Further, elected commissions are found to set high er rates, while states in which the utility commission has imposed retail price caps tend to have lower UNE rates. Lehman and Weisman (2000) explore UNE rates set immediately following the implemen tation of TA96. They also find that elected commissions set higher ra tes while price caps lead to lower rates. Beard and Ford (2004) test whether the UNE rates for certain combinations of network elements in 2002 are correlated with the rates proposed by in cumbents or entrants. They find that the rates set by state commissions can be explained as splitting the difference between the preferred rates of the two parties. Eisenach a nd Mrozek (2003) regress the observed UNE rate against a cost estimate produced by the FCC Hybrid Cost Proxy

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6 Model (see further description of this model in Section 4.2 ) and find that costs explain only half of the variation in UNE rates.8 This paper extends these studies in severa l important directions. First, the present analysis is based on a unique data set that contains the UNE loop rates for each state since TA96 was enacted and when those rates were ordered. Further, unlike previous studies, it is known whether the rate was th e result of a voluntary reduction by the incumbent. Second, the analysis examines the potential for information spillovers across states.9 Given the ambiguity of the TELRIC notion and the lack of experience state utility commissions had with the concept, it is possi ble if not likely that many states looked to other states for guidance. Speci fically, the influence of the rates set by the commission in the largest state in each incumbent region (the leader) on the other states in the region (the followers) is tested. Third, whereas in previous papers the form of retail rate regulation was assumed to be exogenous, the analysis corrects for the potential endogeneity of this variable. Fourth, the imp act of the level of entry on UNE rates is measured. Four primary conclusions emerge from th is research. First, less than 50% of a change in the estimated cost of providing a UNE is reflected in the UNE rate two years later. Second, a $1 increase in the leader states rate results in a roughly $0.75 increase in 8 Another area in which state utility commission tel ecommunications regulation has been analyzed is how retail rates are set. Donald and Sappington (1995) find that state commissions are more likely to choose incentive regulation where the incumbent can gain mo re, rates are especially high, and elected state leaders are churned by voters. Smart (1994) observes that retail rates are lower in states where the commissioners are elected and that co ntrol of the governorship and state legislature only has an impact on prices when the offices are held by different political parties. 9 There is a large literature regarding strategic interac tion between governments, especially in the areas of environmental, welfare, and tax policy (see Brueckner (2003) for a survey). However, the information spillovers in UNE rates do not appear to be strategi c, i.e., the state commissions do not appear to take into consideration the reactions of other co mmissions when setting their own UNE rates.

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7 the rates of the follower states two years later. Conversely, a states rate does not appear to be affected by changes in the rates of the follower states. Third, applications for permission to sell long-distance services and lo wer levels of competitive entry tend to put downward pressure on rates. Fourth, Republican leadership in a state is associated with higher UNE rates. These conclusions suggest that smaller st ate utility commissions rely on the work of larger state commissions to help determ ine the regulated rates that they choose for their state. Further, non-cost factors such as the market and political environment may influence the rates set by the commissions These results indicate that information spillovers and factors beyond those specifi ed in TA96 may influence how state governments implemented this federal policy. The paper is organized as follows. Section 2 presents background information on how UNE rates are set. Section 3 describe s the hypothesized UNE rate determinants. Section 4 details the econometric methodology a nd the data used. Section 5 discusses the estimation results, while Section 6 provides c onclusions and areas for further research. Background Information on Unbundled Ne twork Element (UNE) Rate Proceedings Steps Involved in Setting UNE Rates Before reviewing the formal analysis, consider how UNE rates have been determined by the states. Immediately following the passage of TA96, state public utility commissions were often forced to arbitrate interconnection ag reements between incumbents and entrants without having the luxury of completing a fo rmal cost study. To prevent the delay of interconnection agreements, the commissions of ten decided on a UNE rate based solely on the proposed cost studies and testimony submitted by the two sides. When the

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8 commission completed its own cost study, the rates from the study replaced the temporary placeholder rates in the existing interconnection agreements.10 Following the initial cost study, the states had complete discretion as to when to review the UNE rates, if at all. Most interconnec tion agreements betw een the incumbents and entrants last three years, so the states often re vise the UNE rates to coincide with the expiration of the agreements. However, some times commissions revise the rates before the end of this three-year cycle, while on other occasions they review the rates less frequently. While practices differ to some extent, most states follow the same procedures when modifying UNE rates. First, the comm ission announces that it will review the UNE rates and that hearings will be held. Before these hearings take place, the incumbent and the entrant submit their own cost models a nd expert testimony. Following the hearings, the commission reviews the material and testimony and sometimes will ask for further information from the parties. After this stage, the commi ssion announces its decision on a cost model and the proper inputs for it. This de cision will often include what it thinks the resulting UNE rates are, but it will ask the in cumbent or entrant to run the model chosen by the commission with the proscribed inputs and report the resulting rates. The initial commission decision regarding the model and in puts may also be appealed. (The state typically has discretion as to whether it will allow an appeal to be heard.) This process can last as long as two years. The cost studies performed by the stat e commissions involve decisions on many parameters, ranging from labor costs to the co sts of telephone poles. In its most general 10 Note that some states were able to complete a TELRIC cost study quickly en ough so that they did not have to implement the placeholder rates.

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9 form, the TELRIC studies consider three type s of costs: operating costs, depreciation costs, and the cost of capital. Influence of Neighboring States As described above, the TELRIC me thodology that the FCC ordered the commissions to follow to set UNE rates is both complicated and vague. The states are forced to estimate the costs that a provider of the loops would incur if they were to build the network today using an efficient tec hnology. Specifying the efficient technology is a daunting task in itself. In addition, the co mmission must determine the hypothetical cost of installing the efficient technology throughout the incumbents service area. Such an analysis requires considerable resources, a nd can be particularly burdensome for states that have relatively small staffs and budgets. Given these challenges, it is not surprising that smaller states look to larger stat es for guidance in setting UNE rates. The state commissions in an incumbents region share a working relationship that is conducive to collaboration in setting UNE ra tes. For example, regi onal associations of state commissions that are carved almost exactly on incumbent regional lines meet annually or semiannually.11 The meetings typically include working sessions where commission staff members that work in tel ecommunications discuss current issues and listen to incumbent and entrant represen tatives present thei r views on upcoming regulatory matters. The potential for inform ation spillovers among states was further highlighted in a recent survey by the Nationa l Association of Regul atory Commissioners of state commissions. When the state commission s were asked if they would be interested 11 The Qwest utility commissions comprise of the Qwest Regional Oversight Committee, the BellSouth commissions are part of the Southeastern Associa tion of Regulatory Utility Commissioners, the Verizon commissions are members of either the New England Conference of Public Utility Commissioners or the Mid-Atlantic Conference of Regulat ory Utilities Commissioners, while all but two of the SBC states are members of the Mid-Americ a Regulatory Conference.

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10 in working with other states in a matter cl osely related to UNE rates, many of them indicated that they believed su ch coordination would be useful and has been useful in the past (NARUC TRIP Task Force, 2003).12 Figure 2-2 provides additional evidence that information spillovers may be present within incumbent regions. The charts show for each state in January 2001 the UNE rate set by the state and an estimate of the TELRIC loop cost using a cost model developed by the FCC known as the Hybrid Cost Proxy Model. While the ability of the this model to accurately calculate TELRIC costs has b een hotly disputed, the model provides a consistent measure across states and over time of the relevant TELRIC cost, under specified assumptions. For most states, the UNE rate set by the state is lower than the model estimate. However, the difference varies considerably by incumbent. For instance, the UNE rates in the Ameritech states are rough ly half of the model cost estimate, while in the Qwest states the UNE rates are typica lly roughly equal to the cost estimate. These inter-regional differences are consistent with the notion that the state commissions within an incumbent region base their UNE rates in part on the rates of their neighbor states. It is unlikely that all states within an incumbents region have an equal influence on each other. Within each region ther e appears to be a leader state that other states in that region look to for guidance in their UNE rate proceedings. The lead er state not only has the most resources available in that region to conduct a UNE rate study13, but it may also 12 The Kansas utility commission replied, The Commissi on believes it would be especially beneficial and cost effective for the five original Southwestern Bell Telephone Company states to coordinate efforts. The Rhode Island commission stated, RI has a very small staff and would be interested in coordinating logistics with other states in Verizons territory Wyomings response included, The Commission is considering the possible benefits of a regional approach such as participating in a coordinated effort of the Qwest ROC (Qwest Regional Oversight Committee). 13 An appropriate measure of the resources available would be the number of commission employees or the commissions budget. However, state utility commissions are organized differently across states and state comparisons of these measures are problematic For example, Arizonas is part of the states

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11 have been the first state in which the in cumbent applied for permission to sell longdistance services in its territory14. The FCC encouraged smaller states to use the information provided by leader states in their UNE rate cost studies: We recognize that many states lack the ex tensive resources that were dedicated to the process by New York and Texas, as de tailed in our orders in those states We encourage states with limited resources to take advantage of the efforts devoted by New York and Texas in establishi ng TELRIC-compliant prices, by relying where appropriate on the existing work product of those states. (FCC, 2001, p 40) There is considerable evidence that followe r states look to the leader states for guidance. For example, in the BellSouth region, it was reported that the Kentucky commission closely monitored the UNE rate proceeding in Florida and even discussed Floridas findings with the Florida commission and its sta ff (Caldwell, 2002, paragraph 131). In the SBC region, the Kansas commissi on suggested that they delay their UNE proceeding in order to wait until the ongoing Texas study was completed (Kansas Corporation Commission, 2000, p 2). In Nevada, the commission approved a stipulation between the incumbent and the entrants that set UNE rates based on the proceedings of the California commission (Public Utility Co mmission of Nevada, 2002). According to a recent trade press article, follower states in Verizons region halted their UNE rate cases to see how the New York would decide its rate ( State Telephone Regulation Report 2004). Corporation Commission, which also handles issues regarding securities and insurance. Data on the number of employees and budget for the Arizona commission are available only for the state corporation commission. As a proxy for state commission resources the number of telephone access lines is used. 14 Under Section 271 of TA96, in order for an in cumbent to receive permission to sell long-distance services in a state, it had to file an application with both the FCC and the state that demonstrated that the local telephone service market was open to competition. One of the criteria by which the application was judged was whether the UNE rates were based on TELRIC estimates.

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12 The influence of a leader states rate on ra tes in follower states may be influenced by incumbent behavior. To illustrate, incumbents have encouraged the influence of leader state rates by benchmarking follower states ra tes to them when applying for permission from the FCC to sell long-distance servic es. For instance, in 2002 Qwest voluntarily lowered their UNE rates in eight states in order to benchmark them to the rates set by Colorado (FCC, 2002).15 The incumbents also urge followe r states to base their rulings on decisions by the leader states. For example, in an Alabama UNE rate case BellSouth urged the Alabama commission to adopt the Florida commissions position regarding the timing of UNE rates, while in Georgia BellS outh argued for the approach that Florida adopted to allocate costs between regula r telephone service a nd data service. Hypothesized UNE Rate Determinants If the states followed the TELRIC me thodology to the letter, the TELRIC cost would perfectly determine the UNE rate. However, as described above and demonstrated in Figures 1-1 and 1-2, it does not appear that costs alone explain the UNE rates. Therefore, other variables are included in the model to account for the variation in rates. To control for information spillovers across states, the rates of the other states in the same incumbent region are included in the model.16,17 To test for the effect of the leader state on the follower states, the mode l includes the rate of the leader state 15 Specifically, Qwest voluntarily lowered the rates to th e level in Colorado adjusted for the difference in average costs according to the FCCs cost model. 16 The regions are muddled somewhat by the merger s that have taken place among the incumbents. For instance, Pacific Telesis and Amer itech were acquired by SBC in 1 998 and 1999, respectively, while Verizon (formerly Bell Atlantic) acquired NYNEX in 1997. Given their geographic locations and the timing of the acquisitions, Pacific Telesis and Amer itech are treated separately from SBC while the former Bell Atlantic and NYNEX are treated as one entity. 17 The identification strategy employed to capture information spillovers is closely related on the strategic interaction literature cited above. Specifically, the lagged rates of neighboring states are interacted with dummy variables in order to isolate the effects of interest. See Fredriksson and Millimet (2002), Fredriksson, List, and Millimet (2004), and Hayashi and Boadway (2001).

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13 interacted with a dummy variab le that equals one if the state is a follower state. This variable will capture how the follower states re spond to changes in the leaders rate. If the coefficient on this variable is statistically significant, one can conclude that the rates in the follower states are influen ced by the leader states rate. However, it may be the case that the follower states also are influenced by the rates in the other follower states. Therefore, also included in the regressions is the weighted average18 of the follower states rates interacted w ith a dummy variable that equals one if the state is a follower state. If there are information spillovers between the follower states, the coefficient on this variable will also be statistically significant. It may also be the case that the leader stat es are influenced by the rates in the other states in the incumbents region. To test fo r this, the weighted average of the follower states rates is interacted with a dummy variable that equals one if the state is a leader. Thus, any influence of the follower states on the leader states is captured in this variable.19 Characteristics of the state commissions may also influence UNE rates. Under the theory of regulatory capture, the longer a commissioner has served, the more amenable s/he might be to setting a rate favorable to the incumbent. Applied to UNE rates, this theory suggests that the longe r the commissioners have serv ed the higher the UNE rates will be. There may also be an influence due to the political affiliation of the 18 The weights are based on the number of switched access lines per state. The results do not change when weights based on population are used. 19 The analysis in this paper focuses on the influence of neighboring states within the incumbents region. It is possible that some commissions might have influence that extends beyond the incumbents region. However, given the limited degrees of freedom available, it is not possible to simultaneously control for nation-wide influences of all of the leader states. Initial estimates that test for the influence of one national leader state at a time suggest that such effects may be present. However, the qualitative conclusions reported below persist in settings that ad mit national leadership patterns. As stated in the conclusion, further analysis of this issue is warranted.

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14 commissioners. However, it is unclear as to the direction of this influence. One may surmise that Republicans generally favor less regulation and less aggressive (i.e., higher) UNE rates. Conversely, it may be the case that Republicans are sympathetic towards small businesses, and therefore may favor lo wer UNE rates to benefit both small-business consumers of telecommunications services and entrants. Prior st udies have debated whether states that enact incentive-based re tail rate regulation enact lower UNE rates20. Further, the incumbents retail rates may in fluence the level of UNE rates, as state commissioners may regard the retail rate as an upper bound on what an entrant could afford to pay for a UNE. Finally, UNE rates may systematically differ if they were set in an arbitration case immediately following the enactment of TA96. Beyond the state commissions, there may be state-specific influences that vary by time. For instance, the political affiliation of the governor or the state legislature may influence how the state sets UNE rates. No t only can the governor and state legislature have a direct influence on the state ut ility commission through appointments and budgetary powers, but their political affiliati on may be a proxy for the political sentiment of the citizens and reflect the general regulator y environment in the state. As described in the previous paragraph, the direc tion of this effect is ambiguous. The incumbents federal regulatory status may play an important role in how UNE rates are set. As noted above the FCC and state commission s had to certify that the incumbents UNE rates were TELRIC-based be fore the incumbent was allowed to sell long-distance phone service. Therefore, one mi ght expect that the UNE rates in the period immediately prior to the FCCs decision were lower than they would have otherwise 20 Lehman and Weisman (2000) argue that state commissions can unfairly shift risk to incumbent firms by enacting a retail price cap and setting a relatively low UNE rate.

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15 been. Some incumbents also voluntarily lo wered their UNE rates during the application process in the hope of securi ng permission to provide long -distance service. If the incumbents were in fact lowering their UNE rates below the rates that would have prevailed otherwise, one would expect these voluntary reductions to result in lower UNE rates. However, the incumbent might have made voluntary reductions that were not as drastic as would otherwise have been ordere d by the state during the application process. If so, the incumbent might have been able to secure a more favorable UNE rate by preempting action by the state. In such a case, th e marginal effect of the voluntary reduction could be positive. Lastly, the level of observed UNE entr y may affect the UNE rates. If the commission views the level of entry as relati vely low, certes paribus, it may be inclined to lower UNE rates to encourage additional entry. Thus, one would expect a positive coefficient on this variable. Model Specification and Data Used Model Specification As noted above, UNE rates changed infre quently in some states. Consequently, UNE rates often exhibit a high degree of statio narity. However, relati vely frequent data are required to capture the exact timing of the rate decisions. To allow for frequent data and the stationarity of the lagged dependent va riable, the lagged rate is included as an explanatory variable.21 Including the lagged dependent variable as an explanatory variab le in a panel data regression complicates the econometric analysis When an OLS fixed-effects estimator is 21 In the econometrics literature this is referred to a dynamic panel data model.

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16 used, a negative bias of order 1/T is introdu ced in the coefficient on the lagged dependent variable (Nickell, 1981). OLS estimation of the model in first differences partially corrects the bias, but does not entirely alleviate the endogeneity of the lagged dependent variable. Arellano and Bover (1995) and Blundell and Bond ( 1998) derive a generalized methods of moments estimator (known as sy stem GMM) that simultaneously estimates the model in levels and first differences. Blundell and Bond (1998) perform Monte Carlo simulations that demonstrate the system G MM estimator is superior to both the OLS fixed effects and GMM estimations using firs t differences only. Further lagged values of the levels and first difference of the dependent variables are used as instruments for the lagged dependent variable.22 The system GMM estimator is appropriate when the coefficient on the lagged dependent variable is 0.8 or greater. For this estimator to be valid, the lagged dependent variable must ha ve a constant correlation with the state effects and be uncorrelated with present and pa st values of the error term. Further, it is assumed that the error terms have a mean of zero and are not serially correlated. Robust standard errors that are c onsistent in the presence of heteroskedasticity and autocorrelation within states are used in calculating t-statistics. Data Used Summary statistics of the data used are provided in Table 2-1. The sample is comprised of quarterly data. The date in whic h a state enters the sample is determined by the date the utility commission in that state and the corresponding leader state ordered its initial UNE rates. That date ranges from Ap ril 1997 to October 1997. Data for all states 22 In the estimations that follow, the previous four qu arters of the level of the lagged dependent variable are used as instruments in the first differences equation, while one lag of the first difference of the lagged dependent variable is used as an instrument in the levels equation.

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17 included in the analysis run through the end of 2003. Correlation coefficients between the non-UNE rate explanatory variable s are provided in Table 2-2. The dependent variable in the analysis is the statewide aver age recurring rate for the local loop in a quarter.23,24,25 The local loop consists of the wires that connect a customers premises to the incumbents wi re center. The local loop is the network element that is the most costly to replicate, and so is the element that entrants would most likely lease from the incumbent.26 The rates used in the study were obtained primarily from state commission orders and incumbent do cuments. Unlike data sets used in earlier studies, the exact date on which the rate wa s ordered is known and will be integral in examining the issue of interstate interdependence The cost variable is a measure of the monthly cost of the loop. Beginning in 2000 the FCC published annual TELRIC estimates. As noted above, while parties have debated whether the model overor under-estimates co sts, it does provide a reference over time and across states. However, these data are no t available prior to 2000. Therefore the cost variable used here is constructed from two different data series. For the period prior to 2000 the cost variable equals the embedded, or historical, cost of the loop as reported by the National Exchange Carrier Association re garding universal serv ice funding. For 2000 23 Specifically, it is weighted average of the monthly rate for the 2-wire copper in the various density zones in the state. The weights are the num ber of lines in each density zone. 24 Another option is to use the rate set for the loop in the densest areas of the stat e, known as the urban zone rate. However, many states did not de-average rates by zone until 2000 and did so at differing times. Thus, to be able to model rates since TA96 was im plemented, statewide average rates must be used. 25 Data for Alaska and Hawaii are not included as they are not part of any of the major incumbent regions. Arkansas is also not included because the commission in that state ruled that it did not have the authority to conduct a cost study to set UNE rates. Rates in the District of Columbia are not included, as the focus is on rate setting by state utility commissions. Rates for Connecticut are not included as it is geographically surrounded by Veri zon states but the incumbent in the state is owned by SBC. 26 Some entrants serving large business customers built their own networks and therefore did not have to rent any network elements from the incumbent.

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18 forward, the cost variable is the estimate from the FCCs model. While the variable is not ideal, it should capture the factors that accoun t for the discrepancies in costs, such as population density, wire center loca tions, and local cost levels.27 As explained in Section 2, UNE rate pr oceedings are often lengthy. As such, it takes time for commissions to incorporate new information in their cost studies. Further, state commissions may not learn the results of proceedings in other states for some time or may be in an earlier stage of their UNE rate study. Therefore, it is appropriate to model with a lag the influence of the rates of the ot her states. However, as the number of lags included in the model increases, the number of explanatory variables increases quickly28, thus limiting degrees of freedom. With this constraint in mind, the model is estimated using two different lag structur es. The first includes the neig hbor states rates over each of the last four quarters, thus spanning one year. The second structure includes the neighbor states rates from each of the last four half-years, thus spanning two years.29 As described above, characteristics of the state utility commissions may also influence UNE rates. To control for the le ngth of time commissione rs have served, the average tenure of the co mmissioners is included. The political ideology of the commission is captured in the fraction of commissioners that describe themselves as Republican. The effect on UNE rates of the form of retail rate regula tion is captured in a dummy variable that equals one if the state employs rate of return regulation on either 27 The results do not change substantially when embedded costs are used for the entire period. 28 For each lag of other states rates that is included in the estimation, three explanatory variables are added (the weighted average of the neighbor rates interact ed with the leader and fo llower dummy variables and the leaders rate interacted with the follower dummy variable). 29 Given the rates of other states enter the model w ith a lag, they are treated as exogenous from the perspective of the commission setting current rates.

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19 residential or business services.30 However, this variable may be endogenous, as unobserved factors may be jointly determining the form of retail ra te regulation and the UNE rate. To correct for this endogeneity, vari ables that measure cust omer satisfaction as reported by the FCCs ARMIS database are used as instruments.31 The average retail rate for the incumbent is calculated using FCC ARMI S data and is lagged one year to account for the information delays in UNE rate proceedings outlined above.32 Finally, a dummy variable is also included that takes the value of one if the ra te was set during an arbitration case immediately following the passage of TA96, and zero otherwise.33 Two explanatory variables are employed to account for the political sentiment of the states elected officials and its citizens. A dummy variable is included that equals one if the governor is a Republican and zero ot herwise. Another variable is included that equals the percent of the state legislators th at are Republican. To determine the potential interactive effects between th e ideology of the commission and the governor, the product of the two variables is included as an e xplanatory variable. 30 Retail rate regulation schemes are quite complex, as some plans can have price caps on some services and another form of incentive regulation on other products. By using a dummy variable that reflects whether the state employs rate-of-return regulation on either residential or business basic services, the complications posed by the idiosyncrasies of the various incentive-based regulation plans are avoided. As this variable takes a value of one when rate-ofreturn regulation is used, the expected sign of the coefficient of this variable is positive given Lehman and Weismans (2000) analysis. 31 These variables are used as instruments because wh ile UNE rate proceedings typically pit incumbents against entrants, retail rate regulation proceedings tend to be disputes between the incumbent and consumer groups. Therefore, while th ese satisfaction variables affect the form of retail rate regulation, they do not affect the level of UNE rates. The econome tric evidence supports this logic, as the tests of joint significance indicate that the in struments are correlated with the form of retail rate regulation while the C-statistic values indicate they are not correl ated with UNE rates. See Appendix A for details. 32 Given the variable enters the estimation with a lag, endogeneity is not expected to be a concern. A C-test confirms that the variable is not endogenous. See Appendix A for details. 33 Specifically, the arbitration rates used in this study are those from arbitrations between AT&T and the local incumbent.

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20 Dummy variables are also used to capt ure the effects of the status of the incumbents application to sell long-distance service in that state. A dummy variable is included that equals one if the rate was se t during the year prior to the incumbents application up to the date of the FCC's deci sion. To measure the marginal impact of the incumbent making a voluntary reduction, a dummy variable is included that equals one if the rate was the result of a voluntary reduction by the incumbent in conjunction with its application. The level of competitive entry in the stat e is controlled for by the number of UNE lines leased by entrants. To account for delays in commissions incorporating information into their decisions, the variab le is lagged one year. Endogeneity may be present, as the level of entry is likely determined in part by the future UNE rate, which in turn (given the stationarity of UNE rates) is likely co rrelated with the present rate. The state unemployment rate lagged one year is used as an instrument for this variable.34 To allow for meaningful comparisons across states, the number of leased lines is divided by the standard deviation of the variable for that state. Finally, dummy variables for each calendar year are included in the analysis to account for shocks common to all states in a given year not captured by the other explanatory variables. Estimation Results Coefficient Estimates Table 2-3 summarizes the coefficient estim ates from the basic model. Column (1) lists the estimates from the lag structur e spanning one year, while the estimates 34 The logic behind the use of the variable is that the level of economic activity affects the level of UNE entry but not UNE rates. The econometric results sugg est that these instrument s are valid. See Appendix A for details.

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21 corresponding to a lag struct ure spanning two years are li sted in Column (2). The estimation diagnostic tests indicate that the syst em GMM approach is valid with this data set. The tests for autocorrelation demonstrate that there is first-order autocorrelation in first differences, but no autocorr elation of higher order. This indicates that the error terms are not serially correlated. Further, the specification pa sses the Hansen tests of overidentification, which tests whether the moment conditions beyond those needed to identify the parameters are valid.35 Finally, the coefficient on the lagged dependent variable is over 0.8 in both specifications, which conf irms that the system GMM estimator is appropriate. The estimates from both columns indicate th at the cost variable is statistically significant, at either a 90% or 95% stat istical confidence level depending on the specification. Of the neighbor rates, the leade rs rate lagged six mont hs has a statistically significant impact on the followers rates, while the followers rates do not have a statistically significant impact on the ra tes of other followers or leaders. The dummy variables that capture the effect of the incumbents application to sell long-distance services are negative and stat istically significant. The negative coefficient on the variable that indicates whether the incumbent made a voluntary reduction in the application process suggests that the incumben ts were not engaging in strategic behavior. In the two-year lag specifi cation, the dummy variable that captures the effect of a Republican governor is positive and statistica lly significant. Finally, the level of UNE entry appears to have a statistically signi ficant effect at a 90% confidence level. 35 See Appendix A for further details.

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22 Somewhat surprisingly, none of the variables that capture commission characteristics are statistically significant. Economic Effects The presence of the lagged dependent va riable as an explanatory variable implies that the coefficient estimates only measure sh ort-run effects. Furthermore, the coefficient estimates do not clearly convey the economic significance of the expl anatory variables. Table 2-4 details the effects on UNE rates from a change in an explanatory variable that lasts two years.36 The effects reported in Table 2-4 are based on the coefficient estimates in the specification that includes two years of lagged neighbor UNE rates37. According to the estimates, a one-dollar increase in the cost variable on average leads to a $0.40 increase in the UNE rate two years later. This increase is highly statistically significant. However, an F-test as to whether a $1 increase in the cost variable leads to $1 increase in UNE rates is rejected at over a 99% confidence level. Thus, UNE rates do not perfectly reflect changes in cost (a s measured by the available variable).38 The estimates suggest that UNE rates are significantly affected by changes in the leaders UNE rate. Figure 2-3 depicts the eff ect over two years on followers rates after a one dollar increase in the lead ers rate and the correspondi ng 95% confidence interval. As the figure shows, the average effect after one year is to increase the followers rate by 36 Long-run effects that are based on permanent changes in the explan atory variable are reported in Appendix A. 37 The corresponding effects based on the specification that includes one year of lagged neighbor rates are very similar and are contained in Appendix A. 38 It is worth repeating that the cost variable used here (based mostly on the FCCs cost model) is not universally considered a valid cost proxy. Thus, while a $1 increase in the cost vari able is not realized in the UNE rate, one could argue that the cost variab le overstates TELRIC costs to such a degree that changes in costs are in fact fully realized in changes in UNE rates.

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23 roughly $0.60, while after two years, the eff ect reaches almost $0.80. Table 2-4 reports that a one standard deviation ($3.37) in the leaders rate leads to a $2.47 increase in the followers rates. Conversely, the estimates sugge st that the effect of followers rates on other followers is not statisti cally significant. Thus, the re sults suggest that the leader states do in fact have a significant impact on the other states in the incumbents region.39 Of the variables that capture characteris tics of the state utility commission, the effect of average tenure has the expected si gn, but is not statistic ally significant. In regards to political ideology, ne ither the effects of the ideology of the commissioners nor the ideology of the commissioners interact ed with the ideology of the governor are statistically significant. This may be due to the potentially conflic ting effects described above. The effect of the rate -of-return retail rate regula tion variable is positive as expected but statistically insignificant and onl y a third of the eff ect found by Lehman and Weisman (2000). The lack of statistical signifi cance and smaller estimated effect may be due in part to the evolving nature of incen tive regulation plans and the difficulty in classifying them (as noted above). Furthermore, the average retail rate also is statistically insignificant. Finally, while the AT&T arbitration rate has the expected sign, it too is not statistically significant. However, the election of a Republican gover nor is associated with a $1.15 increase in UNE rates two years later that is statistica lly significant. One may attribute this effect 39 The estimate of the effect of the followers rate on the leaders rate is somewhat surprising. While it is only roughly two-fifths of the size of the leaders effect on the followers, it is statistically significant at a 90% confidence level. From the coefficient estimates, it appears this effect exists four quarters after the followers change their rates. A possible explanation is that there is a feedback effect. For example, if rates are generally falling, the following pattern may be present: The leader lowers its rate, which is followed six months later by the followers. One year after the followers rate change, the leader again lowers its rate again, which causes the leaders rate to appear to be influen ced by the followers rate change. Also, given that the coeffici ent estimates of the effect of the followers rates on the leaders are not statistically significant, the sta tistical significance of the overall ef fect is being driven largely by covariance between the coefficients on the followers rates and the lagged dependent variable.

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24 to two, perhaps not mutually ex clusive effects: a preference for less-aggressive regulation and favorable treatment for large corporations.40 The effects of both the percent of Republican state legislators and the inte raction of the Republican governor and commission political affiliation are statistically insignificant. The variables that capture the long-dista nce application status of the incumbent indicate that those ap plications had a strong influence on the UNE rates. On average, UNE rates set during the period prior to applica tions to enter the long-distance market are roughly $2.00 lower two years later. When the incumbent voluntarily lowered the UNE rate this effect more than doubled. Finally, the observed level of entrant use of incumbent UNE lines is both statistically and economically significant. A one standard deviation decrease in UNE lines is associated with UNE rates fall ing roughly $4.00. As UNE rates are generally falling during this period, th e positive coefficient may also reflect that UNE rate reductions are more modest when UNE entry is relatively strong. Conclusion This paper analyzes the factors that determine the rates that state utility commissions set for access to the incumbents telecommunications network. The results suggest that factors other than cost influence the rates. The ra te set by the largest state in each incumbent region appears to influence th e rates set by other st ates in that region. Further, rates tend to be highe r in states where the governor is a Republican and lower in the period prior to an incumbent applying fo r permission to sell long-distance services and when observed competitive entry is lower. 40 One concern with this result could be that Republican governors tend to be elected in less urban, and thus higher UNE-cost, states. However, the average UNE rate in states with Republican governors is virtually identical to the average in stat es with Democratic governors.

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25 Beyond providing insights in to telecommunications regul ation, these results may have implications for other areas in which the federal government delegates implementation of policies to state agencies While state agencies may possess valuable local knowledge, it is possible that the intent of the federal po licy will not be perfectly realized through the acti ons of the states. Information spil lovers across states and factors outside the scope of the federal mandate may affect how the policy is implemented. Other policy areas in which this type of analys is may provide some insight include state implementation of the No Child Left Behind Act and environmental policies as dictated by the EPA. The findings in this paper could be exte nded in several directions. For example, the rates used in this paper are the monthl y rates for the local loop. The commissions also set one-time connection fees for these loops that are al so important to entrants considering entering a state. Also, while the local loop is arguably the most important network element, the states set rates for ma ny other network elements. Analyses of these additional rates could shed furt her light on the factors that influence state commissions. Finally, as noted in footnote 20, initial tests for nation-wide information spillovers suggested that states may have influence outside the incumben ts region. Further analysis into this issue could provide additional insight into how state agencies implement federal regulations.

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26 15 20 25 30Dollars per Month Jan97 Jan98 Jan99 Jan00 Jan01 Jan02 Jan03 Jan04 New HampshireVermont A 15 16 17 18 19 20Dollars per Month Jan97 Jan98 Jan99 Jan00 Jan01 Jan02 Jan03 Jan04 KentuckyTennessee B Figure 2-1 Comparison of UNE Rates. A) New Hampshire and Vermont. B)Kentucky and Tennessee. C) Wyoming and Utah.

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27 10 15 20 25Dollars per Month Jan97 Jan98 Jan99 Jan00 Jan01 Jan02 Jan03 Jan04 WyomingUtah C Figure 2-1 Continued

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28 B $0 $5 $10 $15 $20 $25 $30 $35 $40 ALFLGAKYLAMSNCSCTNDollars per Mont h HCPM estimate UNE rate A $0 $5 $10 $15 $20 $25 ILINMIOHWIDollars per Mont h HCPM estimate UNE rate C $0 $5 $10 $15 $20 $25 CANVDollars per Mont h HCPM estimate UNE rate Figure 2-2 HCPM Estimate versus UNE Rate. A) Ameritech. B) BellSouth. C) Pacific Telesis. D) Qwest. E) Southwestern Bell. F) Verizon.

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29 F $0 $5 $10 $15 $20 $25 $30 $35 DEMAMDMENHNJNYPARIVAVTWVDollars per Mont h HCPM estimate UNE rate E $0 $5 $10 $15 $20 $25 KSMOOKTXDollars per Mont h HCPM estimate UNE rate D $0 $5 $10 $15 $20 $25 $30 $35 AZCOIAIDMNMTNDNENMORSDUTWAWYDollars per Mont h HCPM estimate UNE rate Figure 2-2 Continued.

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30 $0.00 $0.20 $0.40 $0.60 $0.80 $1.00 $1.20 $1.40 $1.60 12345678 Number of Quarters Since Increase Figure 2-3 Cumulative effect of $1 increase in the leaders rate on the followers rate.

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31 Table 2-1 Summary Statistics Standard VariableMeanMinimumMaximumDeviaion Lagging Neighbor States' Rates One Year (n = 1103) UNE Rate (All States)$16.62$7.01$28.824.59 UNE Rate (Leader States)$15.30$9.81$20.653.41 UNE Rate (Weighted Average of$15.80$8.13$27.223.14 Follower States) Cost Variable$21.27$13.97$41.784.38 Percent of Commissioners Republican46.3%0.0%100.0%30.7 Average Tenure of Commissioners (yea r 5.2022.53.1 Rate of Return Retail Rate Regulation0.18010.39 Governor Republican0.6010.49 Percent of Legislature Republican0.50.130.890.14 AT&T Arbitration0.16010.37 Period Prior to Section 271 Decision0.25010.43 Voluntary Reduction0.07010.26 UNE Entry (Lagged One Year, Divided0.5402.770.69 by Standard Deviation) Average Retail Rate$29.16$14.45$44.445.16 Lagging Neighbor States' Rates Two Years (n = 919) UNE Rate (All States)$16.44$7.01$28.294.49 UNE Rate (Leader States)$15.11$9.81$20.653.37 UNE Rate (Weighted Average of$15.65$8.13$20.513.13 Follower States) Cost Variable$21.24$13.97$35.864.22 Percent of Commissioners Republican45.8%0.0%100.0%30.8 Average Tenure of Commissioners (yea r 5.20.122.53.0 Rate of Return Retail Rate Regulation0.17010.37 Governor Republican0.6010.49 Percent of Legislature Republican0.50.130.890.14 AT&T Arbitration0.11010.31 Period Prior to Section 271 Decision0.29010.45 Voluntary Reduction0.09010.28 UNE Entry (Lagged One Year, Divided0.6402.770.71 by Standard Deviation) Average Retail Rate$29.53$14.45$44.445.22

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32 Table 2-2 Correlation Matrix of Non-UNE Rate Variables Avg % Comms.Tenure ofROR Retail% Legis. Cost Var.Rep.Comms.Rat e Reg.Gov. Rep.Rep. Cost Var.1.00 % Comms.-0.231.00 Rep. Avg0.240.001.00 Tenure of Comms. ROR Retail0.23-0.08-0.021.00 Rate Reg. Gov. Rep.-0.070.440.01-0.041.00 % Legis.0.040.200.110.270.121.00 Rep. AT&T Arb. 0.17-0.140.000.100.140.10 Long-Dist. 0.000.03-0.01-0.26-0.060.04 App. Vol. Reduct. 0.010.100.06-0.120.08-0.01 By Incumb. Gov. Rep. -0.180.80-0.010.00-0.080.78 % Comms. Rep. UNE Entry -0.110.07-0.07-0.29-0.12-0.05 Avg. Retail 0.430.010.15-0.09-0.12-0.06 Rate

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33 Table 2-2 Continued Gov. Rep. Long-Dist.Vol. Reduct.% Comms.Avg. Retail AT&T Arb.App.By Incumb.Rep.UNE EntryRate AT&T Arb.1.00 Long-Dist.-0.061.00 App. Vol. Reduct.0.130.491.00 By Incumb. Gov. Rep. *0.190.000.051.00 % Comms. Rep. UNE Entry0.040.500.37-0.011.00 Avg. Retail-0.190.210.08-0.040.121.00 Rate

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34 Table 2-3 Coefficient Estimates from OneYear and Two-Year Lag Specifications One-YearTwo-Year Explanatory VariableSpecificationSpecification 0.876***0.815*** (17.18)(12.37) Effect of Leader's Rate on Followers -0.012 (-0.15) 0.164*0.281*** (1.73)(3.02) -0.016 (-0.22) -0.058-0.081 (-1.64)(-0.89) -0.054 (-0.90) 0.038 (1.16) Effect of Followers' Rates on Other Followers 0.001 (0.20) 0.076-0.112 (0.54)(-1.27) -0.080 (-0.73) -0.0160.009 (-0.33)(0.89) 0.026 (0.34) 0.012 (0.15) Effect of Followers' Rates on Leaders 0.048 (0.62) -0.0600.028 (-0.70)(0.37) 0.003 (0.04) 0.0650.045 (1.69)(1.06) 0.008 (0.21) 0.018 (0.46) Lagged Two Quarters Lagged Three Quarters UNE Rate, Lagged One Quarter Lagged Five Quarters Lagged Six Quarters Lagged One Quarter Lagged Two Quarters Lagged Three Quarters Lagged Five Quarters Lagged Four Quarters Lagged One Quarter Lagged Eight Quarters Lagged Four Quarters Lagged Six Quarters Lagged Seven Quarters Lagged One Quarter Lagged Two Quarters Lagged Three Quarters Lagged Four Quarters Lagged Seven Quarters Lagged Eight Quarters Lagged Seven Quarters Lagged Eight Quarters Lagged Five Quarters Lagged Six Quarters

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35 Table 2-3 Continued One-YearTwo-Year Explanatory VariableSpecificationSpecification 0.061*0.096** (1.91)(2.38) 0.000-0.002 (-0.12)(-0.97) 0.0170.016 (1.10)(0.93) 0.1870.154 (0.28)(0.20) 0.2110.263* (1.53)(1.85) -0.244-0.375 (-0.53)(-0.69) -0.002-0.002 (-0.98)(-0.85) 0.0830.074 (0.30)(0.30) -0.481 ** -0.479 *** (-2.65)(-2.48) -0.532 ** -0.636 ** (-2.45)(-2.16) 0.770 0.999 (1.72)(1.92) 0.0080.011 (0.77)(0.79) Overall F-Statistic18941412 Number of Observations1103919 Arellano-Bond Test -2.85***-2.57*** (0.00)(0.01) -0.22-0.8 (0.82)(0.42) 1.762.59 (0.94)(0.86) Notes Year dummy variables are included a nd are generally statistically significant. Unless otherwise noted, t-statis tics are reported in parentheses. *** statistically significant at 99% confidence level ** statistically significant at 95% confidence level statistically significant at 90% confidence level Hansen Test of Overidentified Restrictions (p-value) For AR(1) in First Differences For AR(2) in First Differences Average Tenure of Commissioners Rate of Return Retail Rate Regulation Cost Percent of Commissioners Republican Average Retail Rate (lagged 1 year) Governor Republican UNE Entry (lagged 1 year, divided b y standard deviation Voluntary Reduction by Incumbent Percent of State Legislature Re p ublican Percent of PUC Republican Interacted with Governor Period Prior to Decision on Lon g -Distance A pp lication AT&T Arbitration

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36 Table 2-4 Effects of Changes in Explanator y Variables on UNE Rates Two Years Later Effect of a One Explanatory VariableUnit Increase Economic Effect1Effect of Neighbors' Rates 0.73**2.47** (2.57)(2.57) -0.35-1.10 (-1.03)(-1.03) 0.31*0.97* (1.91)(1.91) Effect of Other Explanatory Variables 0.40***1.68*** (3.65)(3.65) -0.01-0.31 (-1.10)(-1.10) 0.070.21 (0.96)(0.96) 0.670.67 (0.20)(0.20) 1.15*1.15* (1.89)(1.89) -1.63-0.23 (-0.74)(-0.74) -0.01-0.37 (-0.81)(-0.81) 0.320.32 (0.30)(0.30) -2.08**-2.08** (-2.27)(-2.27) -2.77**-2.77** (-2.19)(-2.19) 4.35**4.35** (2.42)(2.42) 0.050.24 (0.81)(0.81) Notes (B) binary explanatory variable, (C) continuous explanatory variable, t-statistics are reported in parentheses. *** statistically significant at 99% confidence level ** statistically significant at 95% confidence level statistically significant at 90% confidence level Period Prior to Decision on LongDistance Application (B) Voluntary Reduction by Incumbent (B) UNE Entry (lagged 1 year, divided by state standard deviation) (C) 2Average Retail Rate (lagged 1 year) (C) Percent of Commissioners Republican (C) Average Tenure of Commissioners (C) Rate of Return Retail Rate Regulation (B) Governor Republican (B) Leader's Rate on Followers (C) Followers' Rates on Followers (C) Followers' Rates on Leaders (C) Cost (C) Percent of State Legislature Republican (C) Percent of PUC Republican Interacted with Governor Republican AT&T Arbitration (B)1 Economic effects for continuous explanatory variables are based on a one standard deviation increase in that variable, while economic effects for binary explanatory variables are based on a change in the variable from zero to one.2 This variable is already scaled by dividing by the standard deviation, so the economic effect is based on a one-unit increase.

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37 CHAPTER 3 THE EXTENT AND MEANS OF ENTR Y INTO LOCAL TELECOMMUNICATIONS MARKETS Introduction A primary goal of the Telecommunicati ons Act of 1996 (TA96) is to facilitate competition in the local telecommunications ma rket. TA96 includes provisions that allow entrants1 to lease parts of the incumbents2 network, known as unbundled network elements (UNEs), at relatively low rates determined by state public utility commissions. There are two ways in which entrants can provide phone service by leasing UNEs. Under a loop arrangement3, the entrant rents from the incumbent the phone line that connects a customers residence or premises to the loca l wire center. However, the entrant provides the equipment that connects the customers line to the broader telephone network. The other means by which entrants can offer se rvice is by leasing UNEs through a platform arrangement, which became widely available in 2000.4,5 The key difference between loopand platform-based entry is that in plat form arrangements, the entrant leases all of the UNEs needed to provide telephone servi ce. In other words, the entrant simply 1 Entrants into local telecommunications markets are known as Competitive Local Exchange Carriers, or CLECs. 2 Incumbents in local telecommunica tions markets are known as Incumb ent Local Exchange Carriers, or ILECs. 3 This is also referred to as UNE-L entry. 4 This is also referred to as UNE-P entry. 5 As described below, recent court rulings and revi sions to FCC regulations have altered the regulatory treatment of platform-based entry.

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38 rebundles the UNEs that are requi red to provide service and does not have to own any of the necessary equipment.6 A sufficiently long time series of data is now available to inve stigate the factors that determine the extent of entry via th ese two alternative a rrangements. Figure 3-1 indicates that from July 2001 to July 2004 the national average of the fraction of incumbent7 lines leased to entrants increased steadily. The average increased from just over 5% in July 2001 to almost 20% three year s later. However, Figur e 3-2 indicates that the increases in entry differ greatly by means of entry. During the 2001 2004 period, while the fraction of lines leased through a loop arrangement ranged between 2% and 4%, the fraction leased through a platform arra ngement tripled from less than 4% to over 12%. Figures 2-3 and 2-4 suggest that the evolution of entry has also differed significantly across states. Each dot in Figure 3-3 represents for a given state the fraction of lines leased through a loop arrangement in July 2001 and in July 2004. Dots near the 45-degree line represent states where the sh are of loop-based entry changed little between the two dates, while dots above the 45-degree line indicate that th e share increased. As the figure indicates, while there are some stat es in which the share changed little, there 6 Incumbents have been quick to point out that there is no actual difference between platform-based entry and reselling the incumbents services, another option provided for under TA96. Given the cost of platform arrangements and the resale discount rates, reselling the incumbents services is typically more costly for entrants than providing service under a pl atform arrangement. However, platform-based entry is more risky for entrants as they are not guarantee d a positive gross profit margin as they are under a resale arrangement. This risk would be realized if the retail price fell below the platform cost, in which case the entrant would face a negative gross profit unde r a platform arrangement whereas under a resale arrangement the entrants cost would fall by the same percentage as the retail price. 7 The largest incumbents in each st ate are the regional monopolies that were created in the court-ordered split-up of AT&T in 1984 and are commonly refe rred to as RBOCs (Regional Bell Operating Companies). While entrants can rent UNEs from othe r incumbents in areas not served by an RBOC, the vast majority of UNE entry has occurred in RBOC regions. For the remainder of this paper, the term incumbent will refer exclusively to RBOCs.

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39 are also a number of states where the shar e increased substantiall y. Figure 3-4 displays the corresponding data for platform-based entry. While most states are above the 45degree line, the extent to which they line above the line varies substantially. Figures 2-1 through 2-4 raise important questions regarding telecommunications market dynamics, including the following two: Wh at market factors in fluence the level of competitive entry via the two alternative m eans? What effects did state regulatory policies or the political environment ha ve on the observed level of entry? The answers to these questions are of interest to both policymakers and researchers. On one level, the answers shed light on the effectiveness of TA96 and its implementation. Not only can these answer s inform the ongoing debate regarding regulation of U.S. telecommunications market s, but they may also provide guidance to regulators in other countries attempting to fashion policies. Further, the results supplement the existing body of literature rega rding competitive entry. There has been scant prior research regarding how entrants choose whether to lease all of the inputs from their competitors (as in a platform arrangeme nt) or buy some of the inputs themselves (as in a loop arrangement). The choices of entran ts could have importa nt implications for long-term competition. Several authors have analyzed the determinants of competitive entry into local telecommunications markets.8 These papers often use cross-se ctional data sets to explain the level of competitive entry, as measured by the number of entrants or the number of telephone lines entrants have acquired. Typically these pa pers find that variables associated with higher demand correspond to higher entry, while va riables associated 8 Examples include Abel (2002), Abel and Clemments (2001), Alexander and Feinberg (2004), Brown and Zimmerman (2004), Jamison (2004), Lehman (2002), Lehman (2003), Roycroft (2005), and Zolnierek, Eisner, and Burton (2001).

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40 with higher costs are correlated with a lower degree of entry. In regards to the effects of regulation, areas where retail price caps are used are generally associated with lower levels of entry, while the effects of the political composition of state public utility commissions differ across papers. This paper is closely related to Bear d and Ford (2002) and Beard, Ford, and Koutsky (2005). Beard and Ford (2002) use a po oled data set to analyze the determinants of loopand platform-based entry. Among thei r findings is that, for both types of entry, as the cost of leasing the UNEs used in that type of entry increas es, the level of entry falls. Their estimates of the cross-price elasti city of demand also suggest that entry via loop and platform arrangements are not subs titutes. Beard, Ford, and Koutsky (2005) examine a cross-sectional data set to examine the deploymen t of equipment by entrants. Using proprietary data, they come to the conc lusion that higher UNE leasing costs lead to decreased entrant equipment investment. Sappington (2005) constructs a theoretical model that suggests the pr ice at which an entrant can lease an input may have little effect on its decision to lease the input from the in cumbent supplier or make the input itself. This conclusion arises because the lease pr ice influences the intensity of downstream competition such that the incumbent tends to price less aggressively when the lease price paid by the competitor is higher. This paper improves upon the existing literat ure in at least four important ways. First, panel data are used rather than crosssectional data. Thus, the data will allow for a more precise estimate of the effects of dyna mic changes in the market environment on the level of entry observed. Second, the influence of the political composition of state public utility commissions is estimated. Third, the e ffect of the connection charge that loop

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41 entrants must pay whenever they gain a new customer is measured. The potentially dampening effect of the connection charge on loop-based entry was cited by the Federal Communication Commission as an important basis for it s regulatory treatment of platform-based entry.9 Fourth, the empirical specificati on allows for the estimation of differential effects of the explanatory vari ables for varying leve ls of market size. Four significant conclusions are offered. First, while the two types of entry are generally affected by different market factors, there appears to be cost-based substitution between them. Second, changes in the own mont hly costs of the two forms of entry have limited effects, but loop-based entry decrease s in response to increas es in the connection charges entrants must pay incumbents when a customer is acquired. Third, loop-based entry tends to be more pronounced relative to platform-based entry as the degree of Republican representation on stat e public utility commission s increases. Fourth, loopbased entry is more responsive to changes in market conditions in smaller states, while platform-based entry is more responsive to market conditions in larger states. The paper is organized as follows. Section 2 presents background information on the functional and regulatory differences between loopand platform-based entry. Section 3 presents the determinants that ar e hypothesized to affect the level of entry. Section 4 details the econometric methodology a nd the data used. Section 5 presents the estimation results, while Section 6 provides c onclusions and areas for further research. 9 For example, see FCC (2003), 295-298.

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42 Background Information on UNE-Based Entry Loop-Based Versus Platform-Based Entry To properly interpret the results be low, it is important to understand the differences between loop-based and platform-based entry. As stated above, when an entrant acqui res a customer under a loop arrangement, the entrant leases only the UNEs associated w ith the wire that connects the customers premises to the incumbents wire center. This wire is referred to as the local loop. In order to provide service usi ng a loop strategy, an entrant must provide its own switching equipment10 and pay the incumbent for the space it rents and the power it uses in the incumbents buildings where the switch ing and related equipment are housed.11,12 Conversely, under a platform arrangement, th e entrant does not have to own any of the network equipment needed to provide phone service. The entrant in effect rebundles all of the UNEs it n eeds to provide service. An important difference between loopand platform-based entry is how the incumbents customers are switched to the entrants service. Lines that are used to serve customers under a loop arrangement must be physically disconnected from the incumbents switching equipment and reconnect ed to the entrants equipment. This transfer, known as a hot-cut, requires that bo th an incumbent and entrant technician be 10 While switches can have many functions, the key role that they serve for entrants to provide telephone service is that they connect the incumb ents loop to the entrants network. 11 Entrant switches require economies of scale to be co st-effective. Thus, entrants may install equipment in remote locations that allows them to aggregate traffi c before reaching a switch that it owns (this process is known as back-hauling). However, the entrant then not only has to pay for the aggregation equipment, but also must pay the incumbent to move its traffic to its switch. 12 This practice of renting space in an incumbent telephone fac ility is known as collocation.

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43 present in order to perform a seamless migration13 of the customer to the entrants network. For each hot-cut that is performed, the entrant must pay the incumbent a connection charge to compensate the incu mbent for the labor involved. Conversely, to transfer an incumbents customer to an entrant who employs a platform strategy, computer software is used that allows for the process to be fully automated. The entrant must only pay a nominal administrative fee to the incumbent to transfer the customer. History of Platform-Based Entry Regulation While there has been widespr ead agreement that allowing en trants to lease the local loop is beneficial to long-run competition, ther e has been heated disagreement as to the effects of allowing entrants to follow a platform strategy. Advocates of allowing platform-based entry typically point to the technical and financial difficulties entrants face when using loop arrangements to serve residential customers.14 Those critical of forcing incumbents to provide platform a rrangements argue that the option discourages entrant investment in telecommunications eq uipment, which is one of the goals of TA96.15 Among the FCC commissioners, this di sagreement typically splits along party lines, with Democratic commi ssioners in support of the pl atform-based entry option and Republicans opposed.16 13 A seamless migration is an industry term that describes a hot-cut where the customer does not lose phone service for any noticeable le ngth of time. 14 The connection charges and labor co sts involved in a hot cut are clai med to be prohibitively expensive for entrants, especially as the churn rate among their customers is relatively high (WorldCom (2002)). In addition, when the hot cut is performed, the customer may lose service for a brief period of time. Entrants complain that customers associate the dela y with the entrant to whom they are transferring service, and thus the customer immediately perceives a lower quality of service with the entrant (FCC (2003), 290-291). 15 See, for example, Crandall, Ingraham, and Singer (2004). 16 A notable exception to this generalization is the current Republican FCC Chairman Kevin Martin, who sided with the two Democratic commissioners in the 2003 Triennial Review Order (FCC (2003)) to continue the availability of platform-based entry.

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44 Given the enormous financial stakes i nvolved in the local telecommunications market, perhaps it is not surprising that ther e has been a great deal of regulation and litigation concerning platform-based entry. There are two main regulatory requirements for platform-based entry to be feasible: entrants must be able to re-bundle UNEs and all of the necessary network elements must be unbundled by the FCC. Entrants eventually realized that for certain customers they could realize greater profits if they simply leased all of the UNE s necessary for them to provide phone service, rather than providing any of the necessary equipment themselves.17 While incumbents began allowing entrants to lease the UNEs nece ssary in a platform arrangement in some states in 1999, the incumbents often char ged additional fees for re-bundling the network elements. The FCC then ruled that the incumbents could not charge these rebundling fees. Litigation soon followed, culmin ating with the Supreme Court ruling in 2002 in Verizon vs FCC that entrants can legally re-bundle UNEs at no additional charge from the incumbents. In terms of the availability of UNEs, the network element that has been at the heart of the platform-based entry debate is the in cumbent switches, specifically those that are used to service residential and small business customers.18 While the debate among the FCC commissioners has often been very contentious, until recently most incumbent switches19 have been available to entrants as UNEs at relatively low rates20. However, in 17 To be precise, the entrant must still provide equipm ent necessary for billing, marketing, and customer service functions. 18 The FCC defines this classification as any customer with four access lines or fewer. 19 In its 1999 UNE Remand Order (FCC (1999)), the FCC ordered that incumbents did not have to lease certain switches located in the 50 largest MSAs. 20 These rates are set by state public utility commissions and based on a TELRIC (total element long-run incremental cost) methodology. In essence, the UNE cost is to be based on the costs a hypothetical

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45 March 2004, the U.S. Court of Appeals in Washington, D.C. ruled that the FCC rules did not comply with TA96. In June 2004, the Bush administration announced that it would not appeal the decision by the U.S. Court of Appeals, thus ringi ng the death bell of platform-based entry as k nown by market participants.21 Partly in response to these developments, AT&T and MCI subsequently announced that they were exiting the residential market (Young (2004)). Finally, in February 2005, the FCC (2005) ruled that as of February 2006 incumbent switches would not be available to entrants at the low rates.22 Hypothesized Determinants of Loopand Platform-Based Entry This section outlines the likely determin ants of loopand platform-based entry. The determinants can be classified as descri bing the revenue potential, regulated costs, and political effects. Measures of revenue potential reflect the profits an entrant would expect to earn. An obvious candidate would be the current reta il price in that market, in so far as, ceteris paribus the higher the retail price the more attractive a market is to a potential entrant.23 The incumbents average net revenue per line in that state is used as a proxy for these prices. Another variable of potential interest to entrants is the gr owth prospects of the market. One may expect that entrants will focu s their efforts in areas where they expect incumbent would incur using current technology and is not to be based on the actual historical investment by the incumbent. See Quast (2005) for further details. 21 Rather than abruptly make incumbent switches unavailable as UNEs, the FCC ordered a 12-month transition phase during which switches would be availa ble at slightly higher costs. However, after the 12-month transition the parties are to negotiate the rates at which switches can be leased. 22 Many entrants have negotiated agr eements with incumbents to conti nue leasing switches after they are no longer available at TELRIC rates. For instance, Qwest has negotiated such agreements with more than 60 entrants, while Verizon and BellSouth have negotiated such agreements with over 50 and 45 CLECs, respectively. (See BellSouth (2004); State Telephone Regulation Report (2005); and Telecom A.M. (2005).) 23 Unfortunately, within a state an d across customer types, several retail prices may be charged.

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46 increases in the number of potential customers. To control for the effects of changes in the growth level in a state, the change in the unemployment rate is included as an explanatory variable. The level of entry is also likely to be affected by the costs an entrant expects to incur in providing service. A main source of co sts for entrants is the payments they must pay to incumbents to lease UNEs. As noted above, during the sample period those rates are determined by state public utility comm issions and do not vary by entrant within a given state. For loop-based entr y, there are two UNE rates that are especially important to entrants: (1) the monthly price they must pay to incumbents to rent the local loop and a connection device known as a line port; and (2) the fee charged by the incumbent to transfer one of its customers line to the entr ants equipment (i.e., to perform a hot cut). One would expect both rates to have a negative effect on the level of loop-based entry. In terms of platform-based entry, the process used to transfer a custom er to an entrants network is fully automated and incurs only a nominal charge. However, under a platform arrangement the entrant must pay the incumb ent to lease its switching equipment and to transport calls over the long dist ance network, as well as to le ase the local loop. This total charge is referred to as the platform rate and, as it increases, platform-based entry is expected to decline. Political factors also may influence entry in ways that are not entirely clear. A potential influence is the po litical composition of the stat e public utility commission. Among the ways state public utility commissi ons affect entry (beyond setting UNE rates) is by forcing the incumbent to adjust thei r hot-cut processes and computer systems

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47 through which entrants order and are billed for services for newly acquired customers.24 Also, state commissions set right -of-way access regulations that can either help or hinder entrants who install their own equipment. On e could imagine that a commission that is majority Republican would oppose governmental involvement in the market and thus limit their efforts at facilita ting a relatively more involve d regulatory design such as platform. However, it may also be the cas e that Republican allegiances to small businesses may predispose them to treat entr ants favorably. Likewi se, the effect of a Republican governor on competitive entry is not clear.25,26 While the factors described above may be expected to have certain effects on entry, these effects may differ substantially by the size of th e market. For instance, a $1 increase in revenue per customer may lead to entrants attaining a greater additional market share in larger markets than in sma ller markets, as entrants may already have a presence in larger markets and the incremental cost of acquiring an additional customer is relatively low. Conversely, changes in market conditions may have a greater impact in smaller markets, as entry in these markets is less certain and changes in potential profits can determine whether entrants choose to enter the market. To capture these potential differences across states, the model includes interaction terms between the factors listed above and the population of the state. 24 For example, states have implemented various approaches in forcing incumbents to implement procedures to migrate customers to entrants (FCC (2003), 309-310). In the former Ameritech region, entrants accused SBC of not having adequate computer systems to allow for platform-based entry (see Kovacs (2002)). 25 Note that while the discussion that follows refers to measurements of Republican influence, given the virtual binary political party environment, the results can be cast in terms of Democratic influence merely by replacing the reported result with its opposite. 26 Another regulatory variable that could have a sign ificant effect on entry is whether the incumbent is under price or rate of return regulation for their retail prices. During the sample period the type of retail rate regulation did not change for any of the incumben ts. Therefore, this effect is captured in the fixed effects analysis.

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48 One of the advantages of a panel data anal ysis is the ability to control for effects specific to a state that are not captured else where. State fixed effects control for timeinvariant effects that are not captured in th e other explanatory va riables and that are specific to a given state (e .g., operating cost differences).27 In addition, time fixed effects are included to control for national effects specific to a given period.28 Model Specification and Data Used Given that the levels of loopand pl atform-based entry are highly related,29 it is appropriate to estimate them simultaneously. Thus, a seeming unrelated regression (SUR) approach is employed.30 Summary statistics of the data used are provided in Tabl e 3-1. The sample is based on semi-annual data by state for the period January 2001 to July 2004.31,32,33 Correlation coefficients between the explanatory variables are provi ded in Table 3-2. 27 Given that in the sample no state is served by more than one incumbent, state fixed effects also control for incumbent fixed effects. 28 The model was also estimated without state and time fixed effects. Compared with the results reported below that include these fixed effects, the estimate s without fixed effects indicate a stronger negative relationship between the level of entry and the monthl y cost of that form of entry. These results are similar to those in Beard and Ford (2002), who do no t include state fixed effect s in their regressions. However, in the regressions without state and time fi xed effects, the monthly cost of entry may reflect the attractiveness of entry into that state. For example, a high loop rate may be associated with lower entry, but high loop rates exist in areas where loop entry is rela tively unattractive (e.g., Wyoming). 29 Insofar as when an entrant decides to enter a mark et by leasing unbundled el ements, the entrant must choose either one of these entry methods. 30 As specified, the SUR model results in the same coe fficient estimates as when each dependent variable is regressed separately. However, the SUR estimator is more efficient (assuming the model is correctly specified) and thus results in lower standa rd errors of the coefficient estimates. 31 Alaska, Hawaii, and Washington, DC are not includ ed in the analysis because of their particular geographic circumstances. Arkansas is not included due to difficulties in obtaining connection cost data, while for New Mexico those data could only be obtained for January 2003 forward. The fraction of lines leased in a platform arrangement by Verizon are not available for Maine and Vermont for January and July 2002, and for Delaware, New Hampshire, and West Virginia for July 2002 only. The resulting data set contains 271 observations. 32 The data regarding the level of entry, UNE rates, and the incumbents average revenue are based on the incumbents service territory within a state, rather than on the entire state. Typically the two differ in that the incumbent service area ma y not include some of the relatively rura l areas within a state. However, as

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49 The dependent variables in the estimations are the fraction of incumbent lines leased by entrants using a l oop arrangement and the fracti on of lines leased using a platform arrangement. These variables are ba sed on data reported by incumbents to the FCC in their Form 477 data submissions (http://www.fcc.gov/wcb/iatd/comp.html ).34 In regards to revenue poten tial, the incumbents revenue per line for a state is obtained from ARMIS data submitted by the incumbents to the FCC (http://www.fcc.gov/wcb/armis/ ).35 The change in the unemployment rate is based on seasonally adjusted data reported by U.S. Bureau of Labor Statistics (http://www.bls.gov/data/home.htm ). As the level of entry may affect the av erage revenue, the average revenue may be endogenous to the level of competitive entry. To attempt to control for this potential endogeneity, three alternative strategies were also employe d: using the lagged value of average revenue as the explanatory variable, us ing the lagged value of average revenue as an instrumental variable, and using as an instrumental variable the percent of the incumbents lines that are used by particip ants in a low-income assistance program known as Lifeline.36 The estimates from each of these sp ecifications are very similar to those reported below.37 most of the entry occurred in non-rural areas, this discrepancy between the data sets should not materially affect the results. 33 The incumbents revenue per line is reported annually. The missing data points are calculated by linearly interpolating the data. 34 As the UNE cost data are not ava ilable, the service areas of Verizo n that were formerly GTE service areas are not included in the analysis. 35 Specifically, the average revenue is calculated by dividing net revenue (ARMIS Report 43-01, Line 1090) by the number of lines (ARMIS Report 43-08). While a variable that more precisely measures the retail rate would be preferred, th is variable should approximate th e potential revenue an entrant could obtain and has been used by other authors, e.g., Ai and Sappington (2005) and Abel (2002). 36 The Lifeline program is an effort coordinated by the FCC and state public utility commissions that helps pay for basic phone service for low-income consum ers. Incumbents are oblig ated to publicize the

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50 The monthly loop and platform UNE costs are obtained from surveys of state public utility commissions by Billy Jack Gr egg of West Virginias Office of the Consumer Advocate (http://www.cad.state.wv.us/Une Page.htm ).38 During the sample period examined in this paper, most state public utility commi ssions set rates that varied by different density zones within the state. For the loop monthly cost, the monthly loop and port cost in the urban zone used39, as these are the areas most likely to be entered by entrants who utilize a UNE-L entry strategy. For the platform monthly cost, the variable used is the incremental cost of providing serv ice to customers via a platform-based entry strategy in the suburban zone, as entrants typically serve residential customers via a availability of this service and are encouraged to sign up as many participants as possible, which therefore influences their customer mix. Further, one can imagine th at low-income customers are more likely to participate in the Lifeline program the higher the retail price charged by the incumbent. As such, it is likely that the extent to which the incumbents customers participate in this program is correlated with the in cumbents average revenue. The first-stage Fstatistic confirms that the variable is sufficiently correlated with the incumbents average revenue (or, in the language of the instrumental variable literature, it is sufficiently relevant). Conv ersely, entrants typically do not participate in this program. Also, differences in how state utility commissions administer the Lifeline program result in the fraction of participants being uncorrelated with state income. Thus, the effect on entry of the fraction of the incumbents customers that are Lifeline subscribers should be minimal and the variable should be exogenous to the level of entry. Wh ile these arguments suggest that the variable is exogenous, there is potential correlation between how state utility commissions administer the Lifeline program and how they treat competitive entry. Further, there does not exist a test to determine if an instrument is exogenous in an exactly identified model such as this. Thus, it is not clear that this candidate instrument is sufficiently exogenous to be valid. 37 While none of these three strategies is perhaps ideal, only one existing telecommunications entry paper reviewed by the author has attempted to address this issue (Abel (2002)), and it employs the first strategy listed above. Further, concerns about endogeneity are mitigated by the fact that often many of the incumbents retail rates are either regulated or publishe d in tariffs that must be approved by state utility commissions. Thus, the response in average revenue to changes in entry may be delayed or diminished. 38 Some participants in legal proceedings have disput ed the way in which platfo rm arrangement costs are calculated in these data (see Willig, et al, 2002). Switc hing rates are typically based on minutes of use, and the rates reported by Gregg are based on 1000 mi nutes of use. Criticisms of the Gregg rates center on whether 1000 minutes of use is an appropriate benchmark. However, there is no other source of consistently reported UNE rates over this period Perhaps more importantly, as the fixed effects estimator is based on deviations from the state mean for each explanatory variable, mismeasurement bias is unlikely. Specifically, if the mi nutes of use used to calculate th e platform arrangement cost is consistently either less or more than the actual minutes of use, the coefficient estimates will not be affected. 39 Data on collocation costs are not available.

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51 platform-based entry strategy.40 The loop connection charge s are obtained by reviewing state public utility commission and FCC docum ents and trade pre ss, and by contacting the state public utility commissions directly.41 The percent of Republicans serving on state public utili ty commissions is derived by reviewing membership di rectories of the National Association of Regulatory Commissioners,42 while the political affiliation of th e Governor in a state is obtained from the U.S. Statistical Abstract (http://www.census.gov/prod/ www/statistical-abstract04.html ) and the National Gover nors Association website (http://www.nga.org) Estimation Results Estimates without Population Interactions Coefficient estimates Table 3-3 summarizes the coefficient estimat es and t-statistics from estimating the fraction of lines entrants acquired via loop and platform arrangements. The estimations are first performed including each group of expl anatory variables individually, and then including all of the explanatory variables. The table indicates that the estimates do not vary substantially according to whether th e other groups of explanatory variables are included in the regression. 40 Due to difficulty in comparing these costs across st ates, costs for transporting calls between switches are not included in the Greg g UNE-P cost estimate. 41 A concern regarding the endogeneity of the UNE rates could arise due to the potential for reverse causation. In particular, one could surmise that low levels of entry may persuade state public utility commissions to lower UNE rates. However, any such response by state commissions could only occur with a substantial lag, as the commissions would only learn of the level of entry with a lag and can only revise UNE rates after a lengthy set of proceedings. Quast (2005) finds that it takes approximately one year for the level of UNE entry to affect UNE ra tes set by state commissions. Thus, UNE rates can be treated as exogenous. 42 The dates of the membership directories used to construct this variable are February 1999, February 2002, February 2003, July 2003, and March 2004

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52 Of the variables that measure revenu e potential, the average revenue has a positive and statistically signifi cant effect on the platform sh are, while its effect on loop share is statistically insignificant. The lack of significance in th e loop share equation may reflect that this variable is a better indicat or of the average resi dential price than the average business price. The change in the unemp loyment rate is statistically insignificant for both types of entry. In regards to the variables that measure costs, those that measure the own monthly cost have negative coefficients. Specifically, the coefficient on the loop monthly cost in the loop share equation and the coefficient on the incremental plat form cost in the platform share equation are negative. However, neither is statistic ally significant. In contrast, the loop connection charge is nega tive and statistically significant. In the platform share equation, the coefficient on the loop monthly cost is positive, suggesting that there may be substitution to platform-based entry as the monthly loop cost increases. The coefficients on the regulatory variable s indicate that Republican state public utility commissions are associated with highe r shares of loop-based entry and lower shares of platform-based entry.43 Conversely, Republican governors are associated with less loop-based entry. Economic effects To obtain a sense of the rela tive importance of the explan atory variables, Table 3-4 details the effect of a one-sta ndard deviation increase in e ach explanatory variable on the share of loop and platform-based entry.44 43 Conceivably, the Republication state commission variable could simply reflect that Republican commissions set lower loop rates and higher platform rates. However, the very low correlation between the UNE rates and commission political affiliati on (see Table 3-2) mitigates this concern. 44 For binary explanatory variables, th e economic effect is calculated as the effect from a change in the

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53 The largest effect in either equation is associated with average revenue in the platform share equation. A one-standard devi ation increase in the average revenue ($9.60) is associated with an increase of ove r 2 percentage points in the share of the incumbents lines leased in a platform a rrangement, which transl ates to a roughly 33% increase in the platform share wh en measured at the mean of 6.1%. Of the cost variables, a one-standard deviation incr ease in the loop connection charge ($25.80) decreases the level of entry by almost 0.2 percentage points. Measured at the mean of 2.6%, this effect translates to an approxima tely 6% decrease in the loop share, which validates to some extent the FCCs concern regarding the dampening effect of this charge on loop-based entry.45 The economic effect of the monthly loop cost on platform-based entry is nontrivial, which suggests that for some customers entrants utilize a platform strategy when the monthl y loop cost increases. Surprisingly, the owncost economic effects are not only statistically insignificant but al so relatively small. The political affiliation of the state utility commissions has divergent effects on the two means of entry. When adjusted for the diffe rence in means, the absolute value of the economic effects is roughly equal for loop and platform-based entry. As mentioned above, this may reflect a preference on the pa rt of Republican comm issioners to promote the entry option that entails less regulat ory intervention. The negative effect of Republican governors on loop-based entry is in teresting, given the estimated preference of Republican commissioners and the absence of an effect on platform-based entry. This effect may reflect a concern on the part of en trants that Republican governors discourage explanatory variable from zero to one. 45 Note that this effect does not capture the effe cts of logistic difficulties entrants may face when attempting to connect a customer to its network via a loop arrangement.

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54 regulation and are thus more likely to advocat e policies that are favorable to incumbents. The lack of effect on platform-based entry ma y reflect the low fixed costs that platformbased entry requires, and the re sulting ability of an entrant to exit a market relatively quickly. Estimates with Population Interactions Coefficient estimates As described above, the estimates in Table 3-3 and Table 3-4 and estimates in prior studies assume that the effects of the expl anatory variables do not vary across states. However, the effects of market and political factors may well differ by the size of the state. To account for such potential variation, the model is also estimated such that each explanatory variable is intera cted with the state population. Table 3-5 details the coefficient estimates and t-statistics that result from including population interactions. For each explanatory va riable, two coefficients are reported: the coefficient on that variable and the coefficient of the variable multiplied by the state population. As such, one can inte rpret the second coefficient as the additional effect of the explanatory variable as the population is increased. For the variables that measure revenue pot ential, the strong effect of the average revenue on the platform share persists when population interactions are included. However, whereas previously average revenue did not have a stat istically significant effect in the loop share equa tion, the average revenue intera cted with the population does. Further, the coefficient on the uninteracted av erage revenue variable is larger than the estimate in Table 3-3 and falls just short of a statistical signi ficance level of 90%. Market size effects also appear to be im portant in measuring the effects of costs on entry share. For the loop connection charge in the loop share e quation, the negative

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55 coefficient on the uninteracted variable and the positive coefficient on the interacted variable suggest that the connection charge has a negative effect on entry in smaller markets, but the effect diminishes in larger markets. Conversely, th e negative effect of the monthly incremental platform cost on plat form-based entry tends to increase as the population increases. The effect s of the own monthly costs also appear to differ by market size. Of the political variables, in contrast to the results in Table 3-3 and Table 3-4, it appears that there may be an effect on plat form-based entry associated with Republican governors when population effects are taken in to account. Specifically, in small states Republican governors are associated with greater loop-based entry, but the effect diminishes as the states population increases. Economic effects To estimate the economic effects implied by the estimates in Table 3-5, Table 3-6 calculates the effect on entry share of a one-standard deviation in each explanatory variable for two representa tive population sizes: the 25th percentile and the 75th percentile (2.2 and 7.4 million, respectively).46 The first two columns detail the economic effects on the loop share while the last two columns detail the econom ic effects on the platform share. The economic effects on the loop share di ffer significantly by population. None of the statistically significant explanatory variables for the 25th percentile of population has a statistically significant effect for the 75th percentile of population. Specifically, the effects of the loop connection charge and the political affiliation of the utility commission 46 The economic effect is calculated as the standard de viation of the explanatory variable times the sum of the coefficient on the uninteracted variable plus the population times the coefficient on the interacted variable.

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56 and governor become statistically insignificant as the size of the state increases. Further, the effect of the monthly loop cost is n early statistically si gnificant at the 25th percentile but not at the 75th percentile. The effects on loop share may be diminished in larger states because loop-based entry is less responsive to ch anges in revenue and costs in larger markets. Loop-based entrants may already be present in larger ma rkets because they contain the most potential customers, and thus even a small profit margin per customer translates to large profits in that market. Further, incumbent prices to bus iness customers have historically been well above cost in order to cross-subsidize lower prices to residential customers. Loop-based entrants may have previously entered thes e larger markets to undercut the inflated incumbent business prices and may therefore be less affected by changes in revenue and costs. The differential effect of Republican comm issioners on loop share in smaller states may also be due in part to differences in entry conditions. In large markets loop-based entry may be sufficiently profitable that subs tantial entry will occur regardless of state regulatory policy. In contrast, given the relative difficulty of attracting loop-based entry in small states, Republican state commissions may adopt policies that are especially favorable to loop-based entry and thus the co mmissions have a larger effect on the level of entry. However, the effects of the explanatory va riables on platform share offer more of a mixed picture. The positive effect of the l oop monthly cost is quite similar in the two market sizes, but the effects of the other expl anatory variable differ. In contrast to the effects on loop share, the economic effects on platform share tend to become more

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57 pronounced as the population increases. For inst ance, the effect of the average revenue per line is over 20% larger at the 75th population percentile than at the 25th percentile. Also, the monthly incremental platform cost ha s a statistically significant effect in larger states whereas in smaller states it does not. These findings may reflect in part how platform-based entrants attract customers. Platform-based entrants tend to target resi dential customers in broad geographic markets (such as an MSA or state) and use mass marke ting to acquire customers. In smaller states like Wyoming, an incremental increase in pe r customer profit may not justify the fixed marketing costs of entering the state due to the limited number of potential customers. Conversely, when per customer pr ofit levels increase in a larg e state such as California, the same increase in marketing efforts can reach a much larger population and thus may justify the additional expense. The negative effect of Repub lican commissioners on platform-based entry may also be due to a difference in the ability of small and large states to attract entry. Republican commissions may have a prefer ence for loop-based entry over platform-based entry, but in small states it may be difficult to attract loop-based entry. Thus, th e desire to have any form of entry may offset whatever prefer ences the commissioners possess. In larger states, commissioners can perhaps be more selective as to which type of entry to encourage, thus Republican commissi oners may discourage platform entry. The economic effect of the loop connecti on charge on platform-based entry also differs by market size. The positive effect in small states can be e xplained by substitution from loop-based to platform-based entry as the cost of loop-based entry increases. However, the negative effect in large states implies that increase s in the loop connection

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58 charge deter both types of entry. A possible explanation is that some platform-based entrants in larger states enter those markets w ith the intent to in the long-term to convert to a loop-based arrangement. When the cost of loop-based entry increases, some of these entrants may decide that it is less profitable to pursue such a strategy and they may elect to curtail their platform-based operations. Conclusion This paper analyzes the factors that determine the level of entry in local telecommunications markets given two alternative entry strategies. The estimates suggest that while generally the two types of entry ar e affected by different market factors, there appears to be cost-based subs titution between them. Changes in the own monthly costs of the two forms of entry have limited effects, but loop-based entry decrea ses in response to increases in the connection charges entrants must pay incumbents when a customer is acquired. Loop-based entry tends to be more pronounced relative to platform-based entry as the degree of Republican representation on state public utility commissions increases. Finally, loop-based entry is more respons ive to changes in economic conditions in smaller states, while platform-based entry is more responsive to market conditions in larger states. The results in this paper may suggest so me potential lessons regarding the effects of TA96 and potential revisions to it. Proponents of platform-b ased entry can point to the negative effect of loop connection charges as evidence of the need for an alternative to loop-based entry. On the other hand, critics of platform-based entry can argue that, given the positive effect of the monthly loop cost on the platform share, loop-based entry is hindered by the existence of the platform-bas ed entry option. Also, the results suggest that the local interests of state regulators need to be taken into account when they are

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59 charged with implementing federal policies. Finally, given the differing effects across states of different sizes, policymakers need to consider how to fashion policies that achieve national goals but recognize local market conditions. The findings in this paper could be extended in several directions. First, if the data become available, a more disaggregated an alysis of the entry decision could provide more precise results. Market conditions can va ry greatly within a st ate, but the available data do not allow for an analysis of that granularity. Also, as not ed above, the monthly loop cost variable used in the regressions does not include collocation costs, while the estimate of the incremental cost of platform-b ased service does not include costs related to transporting calls between switches. Includ ing these additional costs would allow for a more complete analysis. Additionally, a more precise measure of the retail price may uncover a more important role for it in determ ining the level of entry. Finally, a complete analysis would simultaneously estimate the e ffects of other types of telecommunications entry, such as cellular phones and the emer ging presence of new technologies such as voice-over-internet-protocol.

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60 6 8 10 12 14 16Percent Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 Figure 3-1 Fraction of Incumbent Lines L eased by Entrants (National Average). 2 4 6 8 10 12Percent Jul01 Jan02 Jul02 Jan03 Jul03 Jan04 Jul04 LoopPlatform Figure 3-2 Fraction of Incumbent Lines Leased by Entrants by Means of Entry (National Average).

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61 0 5 10 15 051015 Share in July 2001 (Percent)Share in July 2004 (Percent) Figure 3-3 Loop Share of Incumbent Lines, July 2001 and July 2004. 0 5 10 15 20 25 30 051015202530 Share in July 2001 (Percent)Share in July 2004 (Percent) Figure 3-4 Platform Share of Incumb ent Lines, July 2001 and July 2004.

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62 Table 3-1 Summary Statistics. Standard Variable MeanMinimumMaximumDeviation Dependent Variables Share of Entrant Loop Lines2.60.0411.92.0 Share of Entrant Platform Lines6.10.0024.45.2 Explanatory Variables Incumbent Average Revenue per Line$61.12$45.16$101.19$9.60 Change in Unemployment Rate0.13-1.32.20.61 Loop Monthly Cost$13.90$4.77$28.14$3.80 Loop Connection Charge$45.69$3.33$159.76$25.80 Platform Incremental Monthly Cost$7.25-$0.17$24.81$4.70 Percent of Commission Republican53.20.0100.033.3 Governor Republican0.560.001.000.5 Interaction Variable Population (millions)6.30.535.96.5 Table 3-2 Correlation Matrix of Explanatory Variables. UnemployLoop MthlyLoop ConPlat Incr% PUC Avg RevRateCostChrgMthly CostRepGov Rep Avg Rev 1.00 Unemploy Rate -0.141.00 Loop Mthly Cost 0.250.101.00 Loop Con Chrg 0.160.140.371.00 Plat Incr Mthly 0.310.080.250.151.00 Cost % PUC Rep 0.230.070.050.02-0.011.00 Gov Rep 0.130.050.07-0.040.040.411.00

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63 Table 3-3 Coefficient Estimates without Population Interactions. Specification #1Specification #2Specification #3Specification #4 Explanatory VariableLoopPlatformLoopPlatformLoopPlatformLoopPlatform Revenue Potential 0.0100.185***0.0010.217*** (0.48)(3.05)(0.04)(3.58) 0.115-0.0510.164-0.157 (1.10)(-0.17)(1.56)(-0.54) UNE Rates -0.0320.194***-0.0330.186*** (-1.32)(2.87)(-1.37)(2.79) -0.006**0.002-0.007***0.008 (-2.10)(0.31)(-2.72)(1.04) 0.010-0.0230.014-0.028 (0.51)(-0.44)(0.76)(-0.53) Political 0.009 ** -0.026 ** 0.009 ** -0.026 ** (2.24)(-2.36)(2.34)(-2.40) -0.225*0.001-0.306**0.004 (-1.72)(0.00)(-2.33)(0.01) Within R-Squared0.340.710.350.710.350.700.370.72 Number of Observations314314314314314314314314 Notes t-statistics are reported in parentheses, coefficient es timates for state and time fixed effects and constant term omit ted for brevity, *** statistically significant at 99% confidence level, ** statistically significant at 95% confidence level, s tatistically significant at 90% confidence level Governor Republican (Binary) Percent State Utility Commission Change in Unemployment Rate Loop Monthly Cost Average Revenue per Line Platform Incremental Monthly Cost Loop Connection Charge

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64 Table 3-4 Economic Effects wit hout Population Interactions. Exp l anatory Var i a bl e LoopP l at f orm Revenue Potential 0.012.08 *** (0.04)(3.58) 0.10-0.10 (1.56)(-0.54) Costs -0.130.71*** (-1.37)(2.79) -0.18***0.21 (-2.72)(1.04) 0.03-0.13 (0.76)(-0.53) Political 0.30 ** -0.87 ** (2.34)(-2.40) -0.31**0.00 (-2.33)(0.01) Notes Economic effects for continuous explanatory variables are based on a one standard deviation increase in that variable, economic effects fo r binary explanatory variables are based on a change in the variable from zero to one. t-statistics are reporte d in parentheses, *** statistically significant at 99% confidence level, ** statistically significant at 95% confidence level, statistically significant at 90% confidence level Governor Republican (Binary ) Percent State Utility Commission Re p ublican Loop Monthly Cost Average Revenue per Line Change in Unemployment Rate Platform Incremental Monthl y Cost Loop Connection Charge

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65 Table 3-5 Coefficient Estimates with Population Interactions. Exp l anatory Var i a bl eLoopP l at f orm Revenue Potential 0.0360.225*** (1.50)(3.42) -0.010***0.015** (-3.82)(2.03) 0.0320.269 (0.27)(0.81) 0.011-0.047 (0.73)(-1.14) Costs -0.0590.186* (-1.58)(1.80) 0.0060.002 (1.04)(0.09) -0.016***0.037*** (-3.86)(3.17) 0.003***-0.008*** (3.33)(-3.73) 0.0350.099 (1.18)(1.23) -0.004-0.033** (-0.64)(-2.13) Political 0.011 ** -0.010 (2.03)(-0.63) -0.001-0.010 (-1.25)(-0.75) -0.342*0.895* (-1.86)(1.76) 0.019-0.176*** (0.99)(-3.32) Within R-Squared0.440.76 Number of Observations314314 Notes t-statistics are reported in parentheses, coefficient estimates for state fixed effects and constant term omitted for brevity, *** statistically significant at 99% confidence level, ** statistically significant at 95% confidence level, statistically significant at 90% confidence level Change in Unemployment Rate Loop Monthly Cost Average Revenue per Line Interacted with Population Interacted with Population Interacted with Population Interacted with Population Interacted with Population Interacted with Population Interacted with Population Platform Incremental Monthly Cost Loop Connection Charge Governor Republican (Binary ) Percent State Utility Commission Republican

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66 Table 3-6 Economic Effects for the 25t h and 75th Population Percentiles. LoopPlatform 25th75th25th75thExp l anatory Var i a bl ePercent il ePercent il ePercent il ePercent il e Revenue Potential 0.13-0.362.47***3.20*** (0.65)(-1.65)(4.24)(5.25) 0.030.070.10-0.05 (0.52)(1.02)(0.57)(-0.25) Costs -0.17-0.050.72**0.75** (-1.62)(-0.42)(2.44)(2.55) -0.28***0.080.52**-0.59** (-3.47)(0.83)(2.31)(-2.29) 0.130.040.14-0.67 ** (1.28)(0.33)(0.49)(-2.04) Political 0.33 ** 0.20-0.40-0.63 (2.06)(1.54)(-0.95)(-1.79) -0.30*-0.200.51-0.41 (-1.92)(-1.56)(1.19)(-1.15) Notes t-statistics are reported in parentheses, coefficient estimates for state fixed effects and constant term omitted for brevity, *** statis tically significant at 99% confidence level, ** statistically significant at 95% confidence level, statistically significant at 90% confidence level Change in Unemployment Rate Loop Monthly Cost Average Revenue per Line Platform Incremental Monthl y Cost Loop Connection Charge Governor Republican (Binary) Percent State Utility Commission Republican

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67 CHAPTER 4 DOES THE FORM OF DOCTOR COMPEN SATION AFFECT THE QUALITY OF CARE IN MEDICAID HMOS?1 Introduction The Medicaid program, already one of th e largest social programs in the United States, is growing rapidly. Between 1996 a nd 2004, the number of Medicaid enrollees increased by roughly one-third, from 33 milli on to 44 million (U.S. HHS (2004), 3). In an attempt to control the cost of the Medicaid program and to improve the quality of care provided to enrollees, many states have m oved enrollees into HMOs (also known as managed care organizations). During the same 1996 to 2004 time period, the fraction of Medicaid enrollees in manage d care organizations increase d by approximately one-half, from 40% to over 60% (U.S. HHS (2004), 3) The trend towards managed care does not appear to be slowing. For instance, in D ecember 2005 the governor of Florida signed a bill that requires all of the states Medicai d enrollees eventually be enrolled in HMOs (Farrington (2005)). State Medicaid programs often contract w ith HMOs to care for the health care needs of Medicaid participants. The HMOs, in turn, contract with doctors and other health care providers to deliver necessary he alth care services. HMOs can differ in many ways, including the manner in which they pay their doctors. FFS and capitated payment arrangements are common. Under a FFS arrangeme nt, the doctor is paid according to the 1 This paper is co-authored with Betsy Shenkman (D epartment of Epidemiology, Health Policy Research and Pediatrics, University of Florida) and David Sappington (Department of Economics, University of Florida).

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68 services she provides to an enrollee. Under a capitated payment arrangement, the doctor is paid a fixed amount per enrollee regardle ss of the health care services actually provided to the enrollee. Consequently, a doc tor paid via FFS can increase her revenue by providing additional services. In contrast, the revenue a capitated doctor receives is not affected by the health care services she provides. This paper investigates whether the means by which HMOs compensate their doctors influence the quality of care the HMOs enrollees receive. Using data for all of the Medicaid HMO enrollees in a large state,2 we find that enrollees in HMOs that pay their doctors exclusiv ely via FFS arrangements are more likely to receive services for which the HMOs doctors receive additional compensation. Further, these enrollees are less likely to receive services for which the HMOs doctors do not receive additional compensation. These findings suggest that financial incentives may influence the behavior of doctors in Medica id HMOs, and thus the health care received by Medicaid participants enrolled in HMOs. Numerous studies have anal yzed whether the form of physician pay influences the level of care provided to HMO enrollees. The papers suggest such a link often is present. For example, Hillman, Pauly, and Kerstein (1989) report that hosp italization rates are higher for enrollees whose physicians are paid via FFS rather than capitation. Stearns, Wolfe, and Kindig (1992) find that specialist referrals, hospital admissions, and hospital length of stays fell when an HMO switche d from FFS physician payment to capitation payment. Ransom et al. (1996) report that gynecologists tend to pr ovide fewer elective surgical procedures when their payment method is changed from FFS to capitation. 2 The state is not identified, to preserve confidentiality of key data.

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69 Shrank et al. (2005) find that fewer cataract procedures are performed when physicians are moved to capitation. In contrast, Conr ad et al. (1998) do not find any significant effects of physician payment meth od on health care utilization. Our analysis enhances the literature in th ree important respects. First, we examine differences in the behavior of FFS and capitated doctors for services that have different effects on the doctors expected profits. As noted, we find that FFS doctors deliver more services that increase thei r profit than capitated doct ors. However, corresponding differences are not detected on services that do not increase physician profit. Second, we consider services for which there are clea rly stated and widely accepted norms for the proper level of care. Consequently, we are able to assess whether financial incentives affect the extent to which actual care depart s from the most appropriate level of care. Third, we examine the health care services delivered to Medicaid enrollees, who often are especially at risk of not obta ining the proper level of care. The paper is organized as follows. Section 2 describes the services we analyze and explains how the profits of FFS and capitated doctors are aff ected by the delivery of these services. Section 3 describes our data and em pirical specification. Section 4 presents our findings. Section 5 provides conclusions and directions for future research. Background Information Preventive care is a major focus of the Medicaid program, especially for children. Preventative care can both reduce treatment costs and avoid painful and debilitating illness. Routine well-child visits and the provision of asthma medications are two important forms of preventive care. Annual we ll-child visits allo w doctors to monitor enrollees health and to deliver essential, routine health care services such as immunization shots. Asthma is considered to be an epidemic and affects over 4 million

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70 children in the U.S. (US HHS (2000), 2). Pr oper treatment of asthma conditions can reduce asthma attacks, emergenc y room visits, and morbidity. While both well-child visits and asthma medi cations can be beneficial to enrollees, these two forms of preventive care can affect th e profits of doctors differently. In the state studied here, the cost of a well-child visit is borne by the doctor th at performs the wellchild. In contrast, the Medicaid program pa ys for prescribed asthma medications. Consequently, the amount of asthma medicati on prescribed has no direct financial impact on either the HMO or its doctors. This differe nce in the incidence of the costs of wellchild visits and asthma medi cations is an important elem ent of the ensuing analysis. Because they are reimbursed for each service they provide, FFS doctors increase their revenue with every well-child visit they perform. In contrast, FFS doctors do not receive greater revenue when they prescribe ad ditional asthma medications. In fact, to the extent that the prescribed asthma medicat ions control the symptoms of asthma and thereby reduce future office visits, additional prescriptions can reduce the doctors revenue. Capitated doctors receive no additional revenue when they perform a well-child visit, but do incur the cost of providing the visit. The well-child visit may reduce future costs by allowing the doctor to detect an ailment and treat the enrollee before complications arise and the requisite care becomes more costly. However, Medicaid enrollees often have limited spells in the program, which reduces the likelihood that the capitated doctor would bear the costs of later treatment. As noted above, capitated doctors do not bear the costs of prescrib ed asthma medications. Furthermore, the

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71 medications can reduce the costs of the capit ated doctor by limiting the need for office visits. This paper compares the extent to wh ich FFS and capitated doctors in Medicaid HMOs provide well-child visits and asthma medications. Given the differences in the compensation structures the FFS and capitated doctors face, our findings may provide some useful evidence about how financial incentives affect the quality of care received by HMO enrollees. Data and Empirical Specification Our data is of two types: enrollment and encounter data and data from interviews. The enrollment and encounter data is for every healthy3 Medicaid enrollee in an HMO in the state in question in 2004. This enrollment data contains demographic information for each enrollee; the encounter data records the enrollees usage of medical services, including office visits, medical treatmen ts, and pharmaceutical prescriptions. The encounter data also documents diagnoses made by the physician when treating the enrollee. The interview data is derived from inte rviews with personnel from the HMOs. The HMOs in our sample are required to answ er questions posed by the state regarding various characteristics of their organization. Among the questions asked is how the HMO compensates its doctors.4 The dependent variables in the analysis are based on measures established in the Health Plan Employer Data and Informa tion Set (HEDIS) developed by the National 3 Specifically, the sample is limited to enrollees with a clinical risk grouping (CRG) score of one. 4 Unfortunately, data for each HMO are aggreg ate data, not data on how the HMO compensates each individual provider.

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72 Committee for Quality Assurance (NCQA) (N CQA, 2002). HEDIS measures are used to evaluate the care received by HMO enro llees and are widely used by industry participants. These measures are based on di agnosis codes and treatment codes found in enrollee encounter data and the ages of the enrollees.5 The measures are binary: one indicates that the proper care was provided; ze ro indicates otherwise. The measures are based on treatment received over a twelve mont h period and specify the age ranges of the enrollees to be included. The treatment period examined in the analysis below is January 2004 December 2004. The first HEDIS measure analyzed is whet her the enrollee received at least one well-child visit during the treatment period. Two age cohorts are analyzed: children between 3 and 6 years of age; and adol escents between the ages of 12 and 21.6 The success rates for the younger and older age c ohorts are 50% and 32%, respectively, in our sample. The second HEDIS measure we analyze is wh ether children with persistent asthma were prescribed appropriate medications.7 This measure is based on two years of data. Data from 2003 are examined for evidence of persistent asth ma. Data for 2004 are examined for evidence of appropriate medi cation. The age cohorts employed for this analysis are 5 through 9 and 10 through 18. Th e success rates for these cohorts on this measure in our sample are 51% and 54%, respectively. 5 The measures used in this paper are based on the administrative specification of the measures. 6 The two measures are named, Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years of Life (page 177) and Adolescent Well-Care Visits (page 180). 7 The measure is named Use of Appropriate Medications for People with Asthma (page 104).

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73 The explanatory variables reflect HMO operating characteristics and enrollee demographics. The HMO operating characteristic of primary interest is whether the HMO compensates all of its doctors via FFS arrangements. During the HMO interviews, each HMO is asked to report the percent of its doctors that are paid via FFS arrangements. The distribution of answers was bimodal, with five of the eight HMOs in our sample stating that they pay roughl y 100% of their doctors via FFS8 and three HMOs stating that they pay 85%9 of their doctors via FFS. This bi-modal distribu tion underlies our treatment of this va riable as binary. Two other variables are included to contro l for the practices of the HMO: whether HMO case managers work directly with the primary care physicians10 and whether the HMO makes reminder calls to enrollees immediat ely prior to their well-child visits. (This latter variable is included onl y in the analyses of the HEDI S well-child visit measure.) The other variables included to control for characteristics of the HMO are the forprofit/non-profit status of the HMO, the number of enro llees (Medicaid and otherwise), the percent of the HMOs enrollees that ar e in Medicaid, and the number of years the HMO has been operating in the state in any capacity. Table 4-1 lists the HMO attributes in cluded in the estimation. As the table indicates, there is signifi cant heterogeneity among the HM Os for each of the variables employed in the analysis. The smallest HMO has roughly 35,000 enrollees while the largest has approximately nine times that num ber. The HMOs also vary significantly in 8 Specifically, the reported percentages were 100%, 100%, 100%, 99%, and 99%. 9 Specifically, the reported percentages were 84%, 85%, and 85%. 10 HMO case managers are responsib le for ensuring that children with chronic conditions receive appropriate care.

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74 their operating practices and in the extent to which their enro llment is limited to Medicaid enrollees. Table 4-2 presents the success rate for th e two HEDIS measures for each value of the binary HMO attributes. Children in HMOs that paid all doctors via FFS arrangements had higher well-child visit success rates than HMOs that paid only some of their doctors via FFS. In addition, children in HMOs wher e case managers worked directly with primary care physicians had higher as thma medication success rates. The demographic variables we employed ar e gender, race, age, and whether the enrollee resides in a rural area. Table 4-3 presents the values of the demographic variables in our sample. Hispanics outnumber blacks and whites, while the vast majority of enrollees reside in non-ru ral areas. The data in Table 4-4 indicate that, relative to whites and blacks, Hispanic children had a higher success rate for well-child visits and lower success rate for asthma medications Also, non-rural children had higher success rates for well-child visits and lower success rates for asthma medications. The two equations estimated are: j i j i j i j i j i j iZ X ASTHMA Z X WCHILD, 2 1 , 2 1 ,) 2 ( ) 1 ( where j iWCHILD, is the HEDIS well-child measure for enrollee i in HMO j j iASTHMA, is the HEDIS asthma medication measure for enrollee i in HMO j iX are enrollee demographic variables jZ are HMO attribute variables

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75 Although the dependent variab les are binary, the large number of observations in our sample ensures that regression via ordinary least squares is consistent.11 To account for unobserved HMO-level effects (see Moult on (1990)), the observa tions are clustered by HMO and location.12 Findings Table 4-5 presents the regression estimat es. The first two columns in Table 4-5 contain the estimates for the well-child vis it measure for the two age cohorts. The last two columns contain the estimates for the asthma medication measure for the two age cohorts. The first row of data in Table 4-5 presents the coefficient estimates for the variable that indicates whether the HM O pays all of its doctors via a FFS arrangement. For both age cohorts, the well-child visit success rate fo r enrollees in HMOs that pay all of their doctors via FFS is six percentage points higher than for those enrolled in HMOs that pay some of their doctors via capitation. Given th e mean success rates, th is difference implies that the average probability that an enrollee receives a well-child vi sit is 10-20% higher in an HMO that pays all of its doctors via FFS. The opposite conclusion arises with regard to the asthma medication measure. For both age cohorts, the success rate is lower fo r HMOs that pay all of their doctors via FFS. The effect is statistically significant for the 5-9 year old cohort. The estimates imply that 11 As a specification test, the model was also estimated via probit. The results are largely unchanged and are reported in Appendix Table B-1. 12 By clustering the observations, the estimates of the co efficient standard errors are adjusted to allow for the possibility that the observations within each gr oup are not independent. The enrollees are grouped here by the HMO in which they are enrolled and the metropolitan area in which they reside.

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76 the probability that the recommended asthma me dications are prescribed is approximately 5% lower in HMOs that pay all of th eir doctors via FFS than in other HMOs.13 These findings suggest that financial incentives may affect the services doctors provide to Medicaid enrollees. In particular, the services th at increase the revenue of FFS doctors (well-child visits) ar e provided more frequently in HMOs where all doctors are compensated via FFS. Furthermore, the services that could reduce the future revenues of FFS doctors (asthma medication prescriptions ) are provided less frequently in HMOs where all of the doctors are paid via FFS. The other variables that measure HMO attr ibutes generally are not statistically significant in our regressions. A larger number of enrollees is associated with a higher success rate on the well-child visit measure. Ho wever, the effect is of limited statistical and economic significance. Th e probability that an enro llee in the 5-9 year cohort receives the recommended asthma medica tions is higher in HMOs where the case manager works directly with the primary care physician. This effect may be due to case managers working with children with severe asthma to ensure that they receive the appropriate medications. The fi nding that the probability that an enrollee in the 10-18 year cohort receives the recommended asthma medication declines as the percentage of HMO enrollees in Medicaid increases may re flect practice style effects in HMOs that serve both commercial and Me dicaid populations. HMOs may tend to provide relatively high service quality to commerc ial populations in an effort to retain these profitable 13 To control for the possibility th at the effects are being driven by spurious interaction between the variables measuring HMO attributes, the m odel was also estimated where the other HMO attributes were replaced by HMO dummy variabl es. The results, reported in Appendix Table B-2, are largely unaffected. (The HMO attributes and HMO dummy variables cannot be included simultaneously due to perfect multico llinearity among the two sets of variables.)

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77 clients. To the extent that Medicaid and commercial enrollees receive the same basic health care services within an HMO, HMOs with a larger concentration of commercial enrollees may provide higher quality care to their Medicaid enrollees. Hispanic children in our sample have a higher likelihood of receiving an annual well-child visit and a lower likelihood of receiving asthma medications than black children and white children. This result is inte resting in light of th e Hispanic paradox that suggests Hispanics tend to have better health outcomes than non-Hispanics of similar socio-economic status (Franzini, Ribble, and Keddie (2001)). Within age cohorts, younger children generally are more likely than their older counterparts to receive wellchild visits and asthma medications. (The ex ception is for asthma medications for the 5-9 year cohort.) The finding that 4-year-olds re ceive well-child visits with relatively high frequency likely reflects the fact that pare nts often take their ch ildren to the doctor to obtain the immunizations required to enter kind ergarten. Rural reside nce is associated with reduced (but statistically insignifican t) performance on the well-child measure and increased (and statistically significant) pe rformance on the asthma medication measure for the younger cohort. Families that live in rural regions likely have to travel farther for well-child visits, which may reduce the likelihoo d of such visits. However, because they tend to live farther from the doctors office or the emergency room, parents of rural families may take particular precautions to be sure their asthmatic children do not develop serious conditions that would require long trips to receive immediate care. Conclusions We have examined whether the form of doctor compensation affects the quality of care received by Medicaid HMO enrollees. Our findings suggest that financial incentives may influence the services that doctors deliver to enrollees.

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78 Further research is required to determ ine whether our findings persist in other settings. The HEDIS measures we employed requ ire that an enrollee be a member of the HMO for almost the entire period in question.14 Therefore, our findings pertain only to enrollees with relatively stable enroll ment. These enrollees may not be entirely representative, as many Medica id enrollees move in and out of Medicaid frequently. It would be ideal to be able to identify exactly which doctors are paid via FFS and which are paid via capitation. This informa tion would permit more precise measurement of the effects of doctor compensation arrangements on the quality of care they provide. Finally, time series data for each enrollee w ould allow for the inclusion of enrollee fixed effects. Such effects would control fo r time-invariant, unobservable characteristics of each enrollee, and would thereby imp rove the precision of the analysis. 14 For the well-child visit measure the enrollee must be enrolled in the same HMO for 11 of the 12 months in question. For the asthma medication measure, the minimum enrollment in the same HMO is 22 of the previous 24 months.

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79 Table 4-1 HMO Attributes. AttributeHMO #1HMO #2HMO #3HMO #4HMO #5HMO #6HMO #7HMO #8 Providers Paid Only via FFSnonoyesyesyesyesnoyes Markets Served41411131 Total Enrollees292,09197,60636,31134,016115,23070,503116,853100,710 Years in State685417567 Fraction of Enrollees in Medicaid76%37%100%46%10%100%80%24% For-Profityesnononoyesnoyesno Check-Up Visit Reminder Callsyesnonononoyesnoyes Case Manager Works with PCPnoyesnoyesnoyesnono Table 4-2 HEDIS Success Rates by HMO Attribute. Check-Up VisitsAsthma Medications Ages 3-6Ages 12-21Ages 5-9Ages 10-18 Total50%32%51%54% Are All Providers Paid on a Fee-For-Service Basis? Yes51%35%50%54% No48%30%52%53% Does HMO Place Reminder Calls? Yes51%33%n/an/a No47%31%n/an/a Does Case Manager Work with Primary Care Physician? Yes50%33%55%57% No48%32%50%53% Is HMO a For-Profit HMO? Yes51%32%50%52% No48%32%52%57%

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80 Table 4-3 Demographic Characteristics By Population. Check-Up VisitsAsthma Medications Ages 3-6Ages 12-21Ages 5-9Ages 10-18 Total82,22762,4751,9711,756 Gender Male41,32030,8991,155985 Female40,95731,576816771 Race White10,2909,153288291 Black15,69919,684585707 Hispanic54,31531,8001,038727 Other1,9731,8386031 Rural Non-Rural79,28360,0261,8901,693 Rural2,9942,4498163 Table 4-4 HEDIS Success Rates by Population. Check-Up VisitsAsthma Medications Ages 3-6Ages 12-21Ages 5-9Ages 10-18 Total50%32%51%54% Gender Male50%32%51%52% Female50%33%51%56% Race White45%30%56%59% Black47%32%54%54% Hispanic52%33%48%52% Other49%29%57%39% Rural Non-Rural50%32%50%54% Rural45%30%70%57%

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81 Table 4-5 OLS Regression Estimates. Dep. Var.: Check-Up VisitDep. Var.: Asthma Medication Explanatory VariableAged 3-6Aged 12-21Aged 5-9Aged 10-18 HMO Operating Characteristics 0.06***0.06***-0.03**-0.02 (3.50)(3.29)(2.12)(-0.54) 0.04*0.02-0.02-0.01 (1.80)(0.81)(0.82)(-0.87) 0.04-0.01-0.04-0.22** (0.96)(0.20)(1.24)(-3.93) -0.010.020.04-0.02 (0.18)(0.43)(0.75)(-0.37) -0.02-0.02 (1.16)(0.77) 0.0050.030.08***0.02 (0.24)(1.46)(3.57)(0.51) 0.0040.0030.02***-0.01 (0.76)(0.49)(3.95)(-1.31) Enrollee Characteristics 0.10***-0.03* (13.37)(1.77) 0.17***0.11** (38.55)(3.59) 0.06*** (14.80) 0.22*** (22.07) 0.10*** (11.27) 0.001-0.01**0.01-0.04* (0.21)(2.49)(0.76)(1.86) 0.010.010.03-0.04 (1.40)(1.30)(0.61)(1.15) 0.07***0.03**-0.06-0.09** (5.86)(2.41)(1.23)(-2.34) 0.03**-0.02*0.07-0.22* (2.10)(1.81)(0.82)(-1.98) -0.03-0.030.14**-0.01 (1.62)(1.10)(2.41)(-0.19) # Observations822776247519711756 R-squared0.020.020.020.02 Notes The t-statistics reported in parentheses are based on H uber-White robust standard errors clustered at the HMO-market level. The age dummy variables differ across specifications: In the check-up visit regressions, age dummy 1 corresponds 3 years, age dummy 2 corresponds to 4 years, age dummy 3 corresponds to 5 years, age dummy 4 corresponds to 12-15 years, and age dummy 5 corresponds to 16-18 years. In the asthma medications regressions, age dummy 1 corresponds to 5-7 years and age dummy 2 corresponds to 10-14 years. *** 99% confidence level, ** 95% confidence level, 90% confidence level Check-Up Visit Reminder Calls Total Enrollees (Medicaid & Commercial, hundred thousands) For-Profit Rural Male Black Hispanic Providers Paid Only via FFS Years Operating in State Percent of Enrollees in Medicaid Other Case Manager works with PCP Age Dummy 1 Age Dummy 2 Age Dummy 3 Age Dummy 4 Age Dummy 5

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82 CHAPTER 5 CONCLUSIONS This dissertation analyzed government attemp ts at using market forces to achieve policy outcomes in two important areas, telecommunications and health care. The chapters analyzed how state governments im plemented TA96 and the effects of marketoriented polices in the telecommuni cations and health care sector. A number of significant results emerge. First, the manner in which the policies are executed may be inconsistent with the goals of the policy. Our anal ysis of TA96 suggests that state public utility comm issions may have been influenced by decisions in larger states and by factors outside of the po licys guidelines. Second, firms may respond to incentives in ways that policymakers may not foresee. In the investigation of local telecommunications markets, it was shown that responses by entrants to market factors may vary by the size of the market. Thus, national policies may not be well-suited to heterogeneous markets. Third, the details of how firms operate may have substantial effects on the level of quality they provide. Our results demonstrate that the way in which physicians in Medicaid HMOs are paid may in fluence the level of care that they provide to enrollees.

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83 APPENDIX A DATA NOTES AND ADDITIONAL RESULTS This appendix describes some of the detail s of the data and spec ification tests used in the analysis in Chapter 2. Data Notes UNE Rates For 18 of the 919 observations de-average d rates were reporte d and a statewide average was neither reported nor could be cal culated based on the available data. In those instances a simple average of the de-average d rates was used as the statewide average rate. In December 1997 the Texas PUC set both a statewide rate that was effective immediately and de-averaged rates that were to take effect a month later. In this analysis the initial statewide rate is ignored. The former Pacific Telesis region includes only two states, California (the leader) and Nevada. Thus, Nevada is the only follower state and the rates of other follower states in the region do not exist. To keep Neva da in the sample and given that SBC now controls the former Pacific Telesis and Amerit ech regions, the rates of the follower states in the former Ameritech region are used as a proxy for the other follower rates for Nevada. Cost Estimate The HCPM data were received via email from the FCC.

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84 Both the embedded and HCPM cost estim ates are reported annually. Quarterly values are obtained via linear interpolation of the data. For each year except 2000, the HCPM cost estimate is provided only for the cost of the entire line, which includes, in addition to the loop cost, the cost for line port, EO usage, signaling, transport, billing/bill i nquiries, and directory listing. To obtain an estimate of the loop cost for those years, the fract ion of the line cost th at is attributable to the loop in 2000 is applie d to the total line cost s in the other years. PUC Characteristics Data regarding PUC commissi oners were derived from Profiles of Regulatory Agencies of the United Stat es & Canada: Yearbook 1995-1996 (NARUC) and NARUC membership directories (specifically, di rectories dated January 1998, February 1999, February 2002, February 2003, July 2003, and March 2004). Besides being reported as either a Democr at or Republican, a commissioner could also be listed as independent or have no re ported political affiliati on. For the purposes of this analysis, those commissioners who were reported as independe nt of for whom a political affiliation was not reported are equa lly Democrat and Republican. (For example, if a states PUC is composed entirely of independents and/or commissioners for whom their political affiliation is not reported, the value of the variable percent of commissioners that are Republican for that PUC would be 0.5.) The type of retail rate regul ation employed in each state is derived from reports in the State Telephone Regulation Report (1/25/96, 2/8/96, 3/20/ 97, 4/3/97, 4/3/98, 4/17/98, 8/20/99, 9/3/99, 9/29/00, 10/13/ 00, 10/27/00, 2/15/02, 3/1/ 02, 3/15/02, 5/9/03, 5/23/03, 6/6/03, 7/30/04, 8/13/04, and 8/27/04). For some of the descriptions of the regulatory

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85 plans, only a year was given for the beginning or the end of the plans duration. In those instances, the exact dates were inferred from the prior or succeeding plan. The three residential customer satisfacti on variables used as instruments for the form of retail rate regulation come from the annual FCC ARMIS Report 43-06. Specifically, they are the percent of customer s surveyed that are dissatisfied with the RBOCs installation, repair, and billing servic es. Quarterly values are obtained via linear interpolation of the data. Estimates of retail rates are somewhat problematic as retail rates can vary across customers and regions within a state. The proxy used in this analysis is the RBOCs local network services revenue (Row 520 from AR MIS report 43-03) divided by the number of switched access lines. State Political Variables The gubernatorial data are obtained from the Book of the States (The Council of State Governments, 1996-1997, 1998-1999, 2001-2002, 2002, 2003) and the CNN.com web page Election Results (http://www.cnn.com/ELEC TION/2004/pages/results /governor/ full.list/ ). The state legislature data are obtained from Statistical Abstracts of the United States (U.S. Census Bureau, 2002, 2003, 2004-2005) and the National Conference of State Legislatures website (2005 Partisan Composition of State Legislatures, http://www.ncsl.org/ncsldb/elect 98/ partcomp.cfm?yearsel=2005 ). As Nebraskas legislature is non-partisan, for this analysis the percent of state legislatures that are Republi can is assumed to be 50%.

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86 Section 271 Status Data regarding RBOC applications to pr ovide long-distance service are obtained from the FCC web page RBOC Applications to Provide In -region, InterLATA Services Under 271 (http://www.fcc.gov/Bureaus/Common_C arrier/in-region_ applications/ ). Level of Competitive UNE Entry The data reported are from two series of (non-overlapping) reports of RBOC survey responses. The 1997 and 1998 data ar e from voluntary surveys completed by the RBOCs and are reported in the December 1998 and August 1999 FCC Local Competition reports produced by the Industry Analysis Division in the Common Carrier Bureau. The data for 1999 forward are based on RBOC res ponses to the mandatory Form 477 survey and are obtained fr om reports entitled Selected RBOC Local Telephone Data. Both data series can be found at http://www.fcc. gov/wcb/iatd/comp.html The UNE line count used in this analys is includes both UNE-L and UNE-P lines. UNE-L lines are where the CLEC leases only the local loop, whereas UNE-P lines involve the CLEC leasing sw itching unbundled elements in addition to the local loop. For some states the first report of the numb er of UNE lines is made some time (at most one year) after the CLECs are able to begin leasing the lines. The data for the period prior to this first report is linearly inter polated by assuming that zero UNE lines were being leased prior to a rate being set. Finally, the data are reported on a qu arterly, annual, or semi-annual basis depending on the time period. Quarterly valu es are obtained when necessary via linear interpolation of the data.

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87 Tests of Instrument Validity and the Exogene ity of the Average Retail Rate Variable This section outlines how the validity of the instruments used in the UNE rates regressions is tested. The firs t sub-section describes the tests performed to determine if the instruments are not correlated with th e error term, while the second sub-section details the test used to determine if the instruments are sufficiently correlated with the endogenous explanatory variables. The third sub-section explains the econometric test used to confirm that the lagged average retail rate is exogenous to the determination of UNE rates. (This section borrows heavily from Ba um, Schaffer, and Stillman (2004).) Tests of Instruments Orthogon ality to the Error Process When there are more instruments than endogenous variables (i.e., the model is overidentified), one can test whether all of the instrument s are orthogonal to the error term. In GMM estimation the overidentifying rest rictions can be tested with Hansens J statistic, which if found to be greater than a threshold value indicates that the instruments are not exogenous or that they should be included as explanatory (rather than instrumental) variables in the regression. The J statistics are reporte d in Table A-2. The J statistics for the base model suggest that the instrument set as a w hole is orthogonal to the error term. One can also test whether subsets of instruments are orthogonal to the error process. The test statistic (referred to as the C statistic) is the diffe rence in J statistics between the specification that includes all th e instruments and the specification that excludes the instruments to be tested. If the C statistics exceeds a threshold value, there is cause for concern that the tested instrument s are not valid. Table A-2 also details the C statistics for the instrument sets used for each of the three endogenous explanatory

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88 variables. The C-statistics i ndicate that the instrument sets used for the three endogenous variables are neither correlated to UNE rates nor endogenous. Tests of Instrument Relevance In addition to being orth ogonal to the error term, instruments also must be sufficiently correlated with the endogenous explanatory variables. To test for this, each endogenous explanatory variable is regressed on all of the exogenous and instrumental variables in the model. The coefficients on the instruments are then tested for whether they are jointly equal to zero. To be valid, th e coefficients should not jointly equal zero. This test is complicated by the use of the system GMM estimator. By definition, the system GMM estimator estimates two equa tions simultaneously, one in levels and the other in first differences. The equation in le vels is estimated along with the equation in first differences because estimations in first differences with highly persistent dependent variables result in weak instruments (Bond, 2002, p 154). Table A-3 reports the results of the tests of instrument relevance for each of the three endogenous explanatory variables for the equations in both levels and first differences. The chi-square values suggest th at the instruments eas ily pass the threshold test for relevance except for the form of retail rate regulation variable in the first differences equation. As noted above, this result is not worrisome as it is addressed by the system GMM estimation. Test of the Exogeneity of th e Lagged Average Retail Rate One may be concerned that the average retail rate variable is endogenous, even though it is lagged one year. To test for this the C statistic can be used where the J statistic from the model assuming the variab le is endogenous is subtracted from the J

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89 statistic assuming the variable is exogenous. The results of this test are shown at the bottom of Table A-2 and confirm th at the variable is exogenous. Additional Results This section describes additional results from the model. Table A-4 contains the economic effects two years after a change in an explanatory variable based on the coefficient estimate s the one-year lag specification. Table A-5 details the long-run economic effects based on the coefficient estimates from both the two-year and one-year lag specifications.

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90 Table A-1 Acronyms Used AcronmymFull TermDefinition TA96Telecommunications Act of 1996 Legislation that provided framework for competitive entry into local telecommunications markets RBOCRegional Bell Operating Company Incumbent telecommunications companies mandated by TA96 to lease parts of their networks to entrants CLECCompetitive Local Exchange Company Competitive entrants in local telecommunications markets UNEUnbundled Network Element A portion of the incumbent's network that CLECs can lease to provide phone service PUCPublic Utility Commission State commissions charged with state telecommunicatoins regulation and implementing parts of TA96 FCCFederal Communications Commission Federal agency charged with implementing parts of TA96 TELRICTotal Element LongRun Incremental Cost Methodology for calculating costs based on the longrun costs of an efficient provider using current technology HCPMHybrid Cost Proxy Model A cost model developed by the FCC to estimate TELRIC costs

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91 Table A-2 Tests of Over identifying Restrictions One-Year LagTwo-Year LagDegrees of Equation /Test StatisticSpecificationSpecificationFreedom Base Model 1.762.59 (0.940)(0.858) Excluding Instruments for Lagged Dependent Variable 0.250.34 (0.617)(0.561) 1.512.25 (0.912)(0.814) Excluding All ROR & Entry Instruments 0.670.51 (0.714)(0.777) 1.092.08 (0.896)(0.721) Excluding ROR Instruments 0.911.27 (0.824)(0.737) 0.851.32 (0.837)(0.724) Excluding Entry Instruments 1.211.045 (0.944)(0.960) 0.551.55 (0.458)(0.213) Assuming Average Retail Rate Endogenous 1.421.335 (0.992)(0.932) 0.341.26 (0.560)(0.262) Notes C-Statistics are based on compar isons to base mode l, p-values are reported in parentheses 1 1 Hansen's J Statistic C-Statistic Hansen's J Statistic C-Statistic 5 2 Hansen's J Statistic C-Statistic 4 3 3 Hansen's J Statistic Hansen's J Statistic Hansen's J Statistic C-Statistic C-Statistic 6 1

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92 Table A-3 Tests of Instrument Relevance One-Year LagTwo-Year Lag Endogenous Regressor / Equatio nSpecificationSpecification Lagged Dependent Variable 160.23133.20 (0.000)(0.000) 2449.491814.71 (0.000)(0.000) Form of Retail Rate Regulation 25.1623.39 (0.000)(0.000) 6.716.37 (0.568)(0.606) Level of CLEC UNE Entry 57.2456.65 (0.000)(0.000) 67.1362.85 (0.000)(0.000) Notes Chi-square test statistics are reported with p-values in parentheses. Chi-square test statistics are from joint-significance tests of the instruments in the first-stage regression. Levels Equation Levels Equation Levels Equation First Differences Equation First Differences Equation First Differences Equation

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93 Table A-4 Effects of Changes in Explanator y Variables on UNE Rates Two Years Later Using One-Year Lag Specification Effect of a OneExplanatory VariableUnit Increase Economic Effect1Effect of Neighbors' Rates 0.441**1.50** (2.16)(2.16) 0.0150.07 (0.08)(0.08) 0.237*0.61* (1.68)(1.68) Effect of Other Explanatory Variables 0.319**1.40** (2.58)(2.58) -0.001-0.03 (-0.13)(-0.13) 0.0890.28 (1.13)(1.13) 0.9850.99 (0.28)(0.28) 1.110.54 (1.61)(1.61) -1.29-0.18 (-0.55)(-0.55) -0.012-0.48 (-0.89)(-0.89) 0.4390.44 (0.30)(0.30) -2.35***-2.35*** (-2.84)(-2.84) -2.80**-2.80** (-2.32)(-2.32) 4.06**4.06** (2.01)(2.01) 0.0410.21 (0.78)(0.78) Notes (B) binary explanatory variable, (C) continuous explanatory variable, t-statistics are reported in parentheses. *** statistically significant at 99% confidence level ** statistically significant at 95% confidence level statistically significant at 90% confidence level1 Economic effects for continuous explanatory variables are based on a one standard deviation increase in that variable, while economic effects for binary explanatory variables are based on a change in the variable from zero to one.2 This variable is already scaled by dividing by the standard deviation, so the economic effect is based on a one-unit increase. Period Prior to Decision on LongDistance Application (B) Voluntary Reduction by Incumbent (B) UNE Entry (lagged 1 year, divided by state standard deviation) (C) 2Average Retail Rate (lagged 1 year) (C) Governor Republican (B) Percent of State Legislature Republican (C) Percent of PUC Republican Interacted with Governor Republican AT&T Arbitration (B) Cost (C) Percent of Commissioners Republican (C) Average Tenure of Commissioners (C) Rate of Return Retail Rate Regulation (B) Leader's Rate on Followers (C) Followers' Rates on Followers (C) Followers' Rates on Leaders (C)

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94 Table A-5 Effects of Changes in Explanator y Variables on UNE Rates in the Long-Run One-Year Lag SpecifiationTwo-Year Lag Specifiation Effect of a 1-Economic Effect of a 1-Economic VariableUnit Increase Effect1Unit Increase Effect1Effect of Neighbors' Rates 0.632.161.00**3.36** (1.67)(1.67)(2.47)(2.47) -0.067-0.31-0.345-1.08 (-0.21)(-0.21)(-0.98)(-0.98) 0.467***1.21***0.534***1.67*** (3.38)(3.38)(4.79)(4.79) Effect of Other Explanatory Variables 0.489***2.14***0.521***2.20*** (3.93)(3.93)(5.17)(5.17) -0.002-0.07-0.011-0.38 (-0.13)(-0.13)(-1.20)(-1.20) 0.1360.420.0870.26 (1.11)(1.11)(0.96)(0.96) 1.5101.510.8340.83 (0.29)(0.29)(0.20)(0.20) 1.710.841.42*1.42* (1.53)(1.53)(1.75)(1.75) -1.97-0.28-2.03-0.28 (-0.57)(-0.57)(-0.77)(-0.77) -0.019-0.77-0.011-0.45 (-0.78)(-0.78)(-0.77)(-0.77) 0.6720.670.4010.40 (0.30)(0.30)(0.30)(0.30) -3.890**-3.89**-2.590*-2.59* (-2.24)(-2.24)(-1.90)(-1.90) -4.29*-4.29*-3.44*-3.44* (-1.78)(-1.78)(-1.96)(-1.96) 6.22**6.22**5.40**5.40** (2.18)(2.18)(2.60)(2.60) 0.0630.330.0580.30 (0.77)(0.77)(0.80)(0.80) Notes (B) binary explanatory va riable, (C) continuous explanatory variable, t-statis tics are reported in reported in parentheses *** statistically significant at 99% confidence level, ** statistically signific ant at 95% confidence level, statistically significant at 90% confidence level1 Economic effects for continuous explan atory variables are base d on a one standard de viation increase in that variable, while economic effects for binary explanatory variables are based on a change in the variable from zero to one.2 This variable is already scaled by dividing by the standard deviation, so the economic effect is based on a one-unit increase. Period Prior to Decision on LongDistance Application (B) Voluntary Reduction by Incumbent (B) UNE Entry (lagged 1 year, divided by state standard deviation) (C) 2Average Retail Rate (lagged 1 year) (C) Rate of Return Retail Rate Regulation (B) Governor Republican (B) Percent of State Legislature Republican (C) Percent of PUC Republican Interacted with Governor Followers' Rates on Leaders (C) Cost (C) Percent of Commissioners Republican (C) Average Tenure of Commissioners (C) Leader's Rate on Followers (C) Followers' Rates on Followers (C) AT&T Arbitration (B)

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95 APPENDIX B ADDITONAL RESULTS This appendix contains additional regression results described in Chapter 4.

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96 Table B-1 Marginal Effect s from Probit Regressions. Dep. Var.: Check-Up VisitDep. Var.: Asthma Medication Explanatory VariableAged 3-6Aged 12-21Aged 5-9Aged 10-18 HMO Operating Characteristics 0.05***0.06***-0.03**-0.02 (3.51)(3.27)(2.00)(0.53) 0.04*0.02-0.02-0.01 (1.80)(0.79)(0.84)(0.88) 0.04-0.01-0.04-0.22** (0.96)(0.19)(1.21)(3.90) -0.010.020.04-0.02 (0.18)(0.38)(1.21)(0.37) -0.02-0.02 (1.16)(0.75) 0.0050.030.09***0.02 (0.24)(1.43)(3.59)(0.51) 0.0040.0030.04-0.01 (0.76)(0.49)(0.79)(1.32) Enrollee Characteristics 0.10***-0.03* (13.08)(1.79) 0.17***0.11** (38.56)(3.58) 0.06*** (14.63) 0.23*** (19.93) 0.12*** (11.20) 0.001-0.01**0.01-0.04* (0.20)(2.48)(0.76)(1.85) 0.010.010.03-0.05 (1.39)(1.37)(0.61)(1.15) 0.07***0.03**-0.06-0.09** (5.85)(2.46)(1.21)(2.33) 0.03**-0.02*0.07-0.22* (2.10)(1.75)(0.82)(1.93) -0.03-0.030.15**-0.01 (1.62)(1.08)(2.32)(0.19) # Observations822776247519711756 R-squared0.020.020.020.02 Notes The t-statistics reported in parentheses are based on Huber-White robust standard errors clustered at the HMO-market level. The age dummy variables differ across specifications: In the check-up visit regressions, age dummy 1 co rresponds 3 years, age dummy 2 corresponds to 4 years, age dummy 3 corresponds to 5 years, age dummy 4 corresponds to 12-15 years, and age dummy 5 corresponds to 16-18 years. In the asthma medications regressions, age dum my 1 corresponds to 5-7 years and age dummy 2 corresponds to 10-14 years. *** 99% confidence level, ** 95% confidence level, 90% confidence level Check-Up Visit Reminder Calls Total Enrollees (Medicaid & Commercial, hundred thousands) For-Profit Rural Male Black Hispanic Providers Paid Only via FFS Years Operating in State Percent of Enrollees in Medicaid Other Case Manager works with PCP Age Dummy 1 Age Dummy 2 Age Dummy 3 Age Dummy 4 Age Dummy 5

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97 Table B-2 OLS Regression Estimat es with HMO Fixed Effects. Dep. Var.: Check-Up VisitDep. Var.: Asthma Medication Explanatory VariableAged 3-6Aged 12-21Aged 5-9Aged 10-18 HMO Operating Characteristics 0.03**0.03*-0.04-0.07** (2.14)(1.66)(1.32)(2.82) Enrollee Characteristics 0.10***-0.03* (13.45)(1.76) 0.18***0.11** (39.12)(3.48) 0.06*** (14.77) 0.22*** (22.01) 0.10*** (11.31) 0.001-0.01**0.01-0.04* (0.22)(2.55)(0.68)(1.89) 0.02*0.02**0.03-0.04 (1.83)(1.99)(0.64)(1.12) 0.07***0.03***-0.06-0.07** (6.20)(2.90)(1.20)(2.00) 0.03**-0.02*0.08-0.21* (2.23)(1.66)(0.81)(1.84) -0.03*-0.030.14**-0.01 (1.93)(1.48)(2.51)(0.19) # Observations823806251819741758 R-squared0.020.030.020.03 Notes HMO fixed effect coefficients omitted for brevity. The t-statistics reported in parentheses are based on Huber-White robust standard errors clustered at the HMO-market level. The age dummy variables differ across specifications: In the check-up visit regressions, age dummy 1 corre sponds 3 years, age dummy 2 corresponds to 4 years, age dummy 3 corresponds to 5 years, age dummy 4 corresponds to 12-15 years, and age dummy 5 corresponds to 16-18 years. In the asthma medications regressions, age dummy 1 corresponds to 5-7 years and age dummy 2 corresponds to 10-14 years. *** 99% confidence level, ** 95% confidence level, 90% confidence level Rural Male Black Hispanic Providers Paid Only via FFS Other Age Dummy 1 Age Dummy 2 Age Dummy 3 Age Dummy 4 Age Dummy 5

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98 LIST OF REFERENCES Abel, Jaison. Entry into Regulated Monopoly Markets: The Development of a Competitive Fringe in the Local Telephone Industry. Journal of Law and Economics. 2002, 45(2), pp. 289-316. Abel, Jaison and Michael Clements. E ntry Under Asymmetric Regulation. Review of Industrial Economics. 2001, 19(2), pp. 227-242. Ai, Chunrong and David Sappington. The Imp act of State Incentiv e Regulation on the U.S. Telecommunications Industry. Journal of Regulatory Economics. 2002, 22(2), pp. 133-160. Alexander, Donald and Robert Feinberg. E ntry in Local Telecommunications Markets. Review of Indus trial Economics. 2004, 25(2), pp. 107-127. Andrews, Edmund. "Communications Bill Si gned and the Battles Begin Anew." The New York Times, February 8, 1996, A1. Arellano, Manual and Olympia Bover. "Anothe r Look at the Instrumental Variable Estimation of Error Component Models." Journal of Econometrics, 1995, 68(1), pp. 29-51. AT&T. Petition of AT&T Corp. for Limited R econsideration. Federal Communications Commission WC Docket No. 03-109. July 21, 2004. Baum, Christopher, Mark Schaffer and Steve Stillman. "Instrumental Variables and GMM: Estimation and Testing." The Stata Journal, 2003, 3(1), pp. 1-31. Beard, Randolph and George Ford. Make or Buy? Unbundled Elements as Substitutes for Competitive Facilities in the Local Exchange Network. Phoenix Center Policy Paper No. 14. 2002, downloaded on September 23, 2003 from http://www.phoenixcenter.org/pcpp/PCPP14 Final.pdf Beard, T. Randolph and George Ford. "Splitting the Baby: An Empirical Test of Rules of Thumb in Regulatory Price Setting." A pplied Economic Studies Working Paper. 2004. downloaded on October 11, 2004 from http://www.aestudies.com/library/uneprice.pdf Beard, Randolph, George For d, and Thomas Koutsky. Mandated Access and the Makeor-Buy Decision: The Case of Local Telecommunications Competition. Quarterly Review of Economics and Finance. 2005, 45(1), pp. 28-47.

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100 Department of Justice. Evaluation of the Department of Justice: In the Matter of Application of SBC Communications Inc. et al. Pursuant to Section 271 of the ) Telecommunications Act of 1996 to Provide In-Region, InterLATA Services in the State of Oklahoma. CC Docket No. 97-121. May 16, 1997. Department of Justice. Evaluation of the Department of Justice: In the Matter of Second Application by BellSouth Corporation, BellSouth Telecommunications, Inc., and BellSouth Long Distance, Inc., for Provision of In-Region, InterLATA Services in Louisiana. CC Docket No. 98-121. August 19, 1998. Department of Justice. Evaluation of the Department of Justice: In the Matter of Joint Application by SBC Communications In c., Southwestern Bell Telephone Company, and Southwestern Bell Communications Serv ices, Inc. d/b/a Southwestern Bell Long Distance for Provision of In-Regi on, InterLATA Services in Kansas and Oklahoma. CC Docket No. 00-217. December 4, 2000. Donald, Stephen and David. E.M Sappingt on. Explaining the Choice Among Regulatory Plans in the U.S. Telecommunications Industry. Journal of Economics and Management Strategy, 1995, 4(2), pp. 237-265. Eisenach, Jeffrey and Janusz Mrozek. Do UNE Rates Reflect Underlying Costs? CapAnalysis Working Paper, December 16, 2003. Farrington, Brendan. Bush Signs Medicaid Overhaul into Law. Associated Press Newswires. December 16, 2005. Federal Communications Commission. Third Report and Order and Fourth Further Notice of Proposed Rulemaking: In the Ma tter of Implementation of the Local Competition Provisions of the Telecommunications Act of 1996. FCC 99-238. November 5, 1999. Federal Communications Commission. Memorandum Opinion and Order: In the Matter of Joint Application by SBC Communicat ions Inc., Southwestern Bell Telephone Company, and Southwestern Bell Comm unications Services, Inc. d/b/a Southwestern Bell Long Distance for Provision of In-Region, InterLATA Services in Kansas and Oklahoma. FCC 01-29. January 22, 2001. Federal Communications Commission. Memorandum Opinion and Order: In the Matter of Joint Application by Qwest Comm unications International, Inc. for Authorization to Provide In-Region, InterL ATA Services in the States of Colorado, Idaho, Iowa, Montana, Nebraska, North Dakota, Utah, Washington, and Wyoming. FCC 02-332. December 23, 2002. Federal Communications Commission. Report and Order and Order on Remand and Further Notice of Proposed Rulemaking: In the Matter of Review of the Section 251 Unbundling Obligations of Incumbent Local Exchange Carriers. FCC 03-36. August 21, 2003.

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101 Federal Communications Commission. Report and Order and Further Notice of Proposed Rulemaking: In the Ma tter of Lifeline and Link-Up. FCC 04-87. April 29, 2004. Federal Communications Commission. Order on Remand: In the Matter of Unbundled Access to Network Elements Review of the Section 251 Unbundling Obligation of Incumbent Local Exchange Carriers. FCC 04-313. February 4, 2005. Franzini, L., J.C. Ribble, and A.M. Keddi e. Understanding the Hispanic Paradox. Ethnicity and Disease, 2001, 11(3), pp. 496-518. Fredriksson, Per, John List, and Daniel Mi llimet. Chasing the Sm okestack: Strategic Policymaking with Multiple Instruments. Regional Science and Urban Economics, 2004, 34(4), pp. 387-410. Fredriksson, Per and Daniel Millimet. I s there a Californi a Effect in U.S. Environmental Policymaking. Regional Science and Urban Economics, 2002, 32(6), pp. 737-764. Greene, William. Econometric Analysis. New York: Prentice Hall, 2003.. Hayashi, Masayoshi and Robin Boadway. An Empirical Analysis of Intergovernmental Tax Interaction: The Case of Business Income Taxes in Canada. Canadian Journal of Economics, 2001, 34(2), pp. 481-503. Hillman, A. L., M. V. Pauly, and J. J. Kerstein. How do Financial Incentives Affect Physicians Clinical Decisions and th e Financial Performance of Health Maintenance Organizations? New England Journal of Medicine, 1989, 321(2), pp. 86-92. Jamison, Mark. Effects of Prices for Lo cal Network Interconnection on Market Structure in the U.S. In E. Bohlin, et al eds.: Global Economy and Digital Society. New York: Elsevier, 2004. Kansas Corporation Commission. Order Opening Docket, Assessing Costs and Soliciting Comments. In the Matter of a General Investigation to Establish a Successor Standard Agreement to the Kans as 271 Interconnection Agreement Also Known as the K2A (Docket No. 04-SWBT-763-GIT). March 5, 2004. Kovacs, Anna Maria. The Status of 271 and UNE-Platform in the Regional Bells Territories. Commerce Capital Markets Regulatory Update. May 1, 2002. Lehman, Dale. The Courts Divide. Review of Network Economics. 2002, 1(1), pp. 106-118. Lehman, Dale. (How) Do Regulated Prices Affect Competitive Entry? Info, 2003, 5(1), pp. 20-27.

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102 Lehman, Dale and Dennis Weisman. The Po litical Economy of Price Cap Regulation. Review of Industrial Organization, 2000, 16(4), pp. 343-356. Lichtman, Douglas and Randal C. Picker. E ntry Policy in Local Telecommunications: Iowa Utilities and Verizon. Supreme Court Review, 2002, pp. 41-53. McConnell, Brian. An Introduction to Cent rex. HelloDirect.com Customer Support Document. downloaded on August 9, 2004 from http://telecom.hellodirect.com/doc s/Tutorials/CentrexIntro.1.032999.asp Moulton, Brent. An Illustrati on of a Pitfall in Estimati ng the Effects of Aggregate Variables on Micro Units. Review of Economics and Statistics, 1990, 72(2), pp. 334-338. NARUC Triennial Review Impl ementation Process (TRIP) Task Force. Summary of State Responses to TRIP Questions. May 2003. downloaded on February 11, 2004 from http://www.naruc.org/associat ions/1773/files/ questions.pdf National Committee for Quality Assurance (NCQA). HEDIS 2003 Technical Specifications. NCQA: Washington, DC, 2002. Nickell, Stephen. Biases in Dynamic Model with Fixed Effects. Econometrica, 1981, 49(6), pp. 1417-1426. Quast, Troy. How State Governments Implement Federal Policies: The Telecommunications Act of 1996. University of Florida Public Utility Research Center Working Paper, 2005, downloaded on June 6, 2005 from http://bear.cba.ufl.edu/centers/purc/pub lications/documents/ how_state_govts_ implement_fed_policies_000.pdf. Ransom, Scott, et al. The Effect of Capitated and Fee-for-Service Remuneration on Physician Decision Making in Gynecology. Obstetrics and Gynecology, 1996, 87(5), pp. 707-710. Rice, Thomas. Physician Payment Polic ies: Impacts and Implications. Annual Review of Public Health, 1997, 18(1), pp. 549-565. Roycroft, Trevor. Empirical Analysis of En try in the Local Exchange Market: The Case of Pacific Bell. Contemporary Economic Policy. 2005, 23(1), pp. 107-115. Sappington, David. E.M. (2005). On the Irreleva nce of Input Prices for Make or Buy Decisions. American Economic Review. Forthcoming, downloaded on September 9, 2005 from http://bear.cba.ufl.edu/ sappington/PDF/Irrelevance of Input Prices 3-11-04.pdf

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103 SBC. Press release entitled, SBC Ca lls Unbundling Rules and UNE-Platform Devastating. July 17, 2002, dow nloaded on June 22, 2004 from http://www.sbc.com/gen/pressroom?pid=4800&cdvn=news &newsarticleid=20189 Shrank, William, et al. Effect of Physic ian Reimbursement Methodology on the Rate and Cost of Cataract Surgery. Archives of Ophthalmology, 2005, 123(12), pp. 1733-1738. Smart, Susan. The Consequences of A ppointment Methods and Party Control for Telecommunications Pricing. Journal of Economics and Management Strategy, 1994, 3(2), pp. 301-323. State Telephone Regulation Report. States Weigh in at FCC UNE Order Implementation Deadline. February 15, 2005. Stearns, S. C., B. L. Wolfe, and D. A. Kindig. Physician Responses to Fee-for-Service and Capitation Payment. Inquiry, 1992, 29(4), pp. 416-425. Supreme Court of the United States. AT&T v. Iowa Utilities Board. Case No. 0366. January 25, 1999. Supreme Court of the United States. Verizon et al v. FCC et al. Case No. 0051. May 13, 2002. Telecom A.M.. Qwest-AT&T in Pact to Replace UNE-P. February 15, 2005. United States Department of Health and Human Services (US HHS). Action Against Asthma: A Strategic Plan for the Depart ment of Health and Human Services. 2000, downloaded on March 23, 2006 from http://aspe.hhs.gov/sp/asthma/ United States Department of Health and Human Services (US HHS). Medicaid Managed Care Enrollment Report. 2004, downloaded on March 18, 2006 from http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/04_MdManCrEnrllRep.asp United States Senate Co mmittee on Commerce, Scienc e, and Transportation. Telecommunications Competition a nd Deregulation Act of 1995. Senate Report 104-23. March 30, 1995. Wawro, Gregory. Estimating Dynamic Panel Data Models in Political Science. Political Analysis, 2002, 10(1), pp. 25-48. Willig, Robert, et al. Stimulating Investme nt and the Telecommunications Act of 1996. Ex Parte (filed by AT&T Corporation). Fe deral Communications Commission, CC Docket Nos. 01-338, 96-98, 98-147. October 11, 2002.

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104 WorldCom. Ex Parte. Federal Communications Commission, CC Docket Nos. 01-338, 96-98, 98-147. November 15, 2002. Young, Shawn. MCI Reports Loss, Plans Dividend. The Wall Street Journal. August 6, 2004. Zolnierek, James, James Eisner, and Ellen Bu rton. An Empirical Examination of Entry Patterns in Local Telephone Markets. Journal of Regulatory Economics. 2001, 19(2), pp. 143-159.

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105 BIOGRAPHICAL SKETCH I was born in Waverly, Minnesota, in Augus t 1973 and moved to Ocala, Florida, in 1980. In 1995 I received a Bachelor of Science degree in economics from the George Washington University in Washington, DC. After working for two years for National Economic Research Associates, I began gradua te study at the University of Texas at Austin. I received my Master of Science in Economics in 1997. I then returned to National Economics Research Associates for two additional years, after which I began my studies at the University of Florida. I am married to the former Daniela Portar o of Ocala, Florida. We have one son, Andres.


Permanent Link: http://ufdc.ufl.edu/UFE0014440/00001

Material Information

Title: The telecommunications act of 1996 and Medicaid Health Maintenance Organizations
Physical Description: Mixed Material
Language: English
Creator: Quast, Troy Clarence ( Dissertant )
Sappington, David ( Thesis advisor )
Ai, Chunrong ( Reviewer )
Figlio, David ( Reviewer )
Brown, Justin ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Economics Thesis, Ph.D.
Dissertations, Academic -- UF -- Economics

Notes

Abstract: We analyzed the effects of the landmark Telecommunications Act of 1996 (TA96). We also investigated whether the method of provider compensation affects the level of care provided by Medicaid health maintenance organizations (HMOs). We examined the rates set by state public utility commissions (PUCs) that competitors must pay to lease parts of the local network from the largest incumbent U.S. telecommunications suppliers (RBOCs). The results indicate that rates in the smaller states in each RBOC region are strongly influenced by the largest state in the region. Rates are lower where the level of competitive entry is lower, while they are higher in states where the governor is a Republican. The analysis suggests that different states have employed different methodologies in implementing TA96. We investigated entry in local telecommunications markets. Panel data are used to analyze the number of lines that competitive local exchange carriers (CLECs) lease from RBOCs using two alternative arrangements: leasing only the wires that connect a customer’s premises to the phone network (loop-based entry), or leasing all of the network elements that are needed to provide phone service (platform-based entry). The estimates suggest that while the two types of entry are generally affected by different market factors, there appears to be cost-based substitution between them. Also, loopbased entry is more responsive to changes in economic conditions in smaller states, while platform-based entry is more responsive in larger states. Using data for all of the Medicaid HMO enrollees in a large state, my coauthors and I found that enrollees in HMOs that pay their doctors exclusively via fee-for-service arrangements are more likely to receive services for which the HMO’s doctors receive additional compensation. Further, these enrollees are less likely to receive services for which the HMO’s doctors do not receive additional compensation. These findings suggest that financial incentives may influence the behavior of doctors in Medicaid HMOs, and thus the health care received by Medicaid participants enrolled in HMOs.
Thesis: Thesis (Ph.D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi, 105 p, also contains graphics.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014440:00001

Permanent Link: http://ufdc.ufl.edu/UFE0014440/00001

Material Information

Title: The telecommunications act of 1996 and Medicaid Health Maintenance Organizations
Physical Description: Mixed Material
Language: English
Creator: Quast, Troy Clarence ( Dissertant )
Sappington, David ( Thesis advisor )
Ai, Chunrong ( Reviewer )
Figlio, David ( Reviewer )
Brown, Justin ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Economics Thesis, Ph.D.
Dissertations, Academic -- UF -- Economics

Notes

Abstract: We analyzed the effects of the landmark Telecommunications Act of 1996 (TA96). We also investigated whether the method of provider compensation affects the level of care provided by Medicaid health maintenance organizations (HMOs). We examined the rates set by state public utility commissions (PUCs) that competitors must pay to lease parts of the local network from the largest incumbent U.S. telecommunications suppliers (RBOCs). The results indicate that rates in the smaller states in each RBOC region are strongly influenced by the largest state in the region. Rates are lower where the level of competitive entry is lower, while they are higher in states where the governor is a Republican. The analysis suggests that different states have employed different methodologies in implementing TA96. We investigated entry in local telecommunications markets. Panel data are used to analyze the number of lines that competitive local exchange carriers (CLECs) lease from RBOCs using two alternative arrangements: leasing only the wires that connect a customer’s premises to the phone network (loop-based entry), or leasing all of the network elements that are needed to provide phone service (platform-based entry). The estimates suggest that while the two types of entry are generally affected by different market factors, there appears to be cost-based substitution between them. Also, loopbased entry is more responsive to changes in economic conditions in smaller states, while platform-based entry is more responsive in larger states. Using data for all of the Medicaid HMO enrollees in a large state, my coauthors and I found that enrollees in HMOs that pay their doctors exclusively via fee-for-service arrangements are more likely to receive services for which the HMO’s doctors receive additional compensation. Further, these enrollees are less likely to receive services for which the HMO’s doctors do not receive additional compensation. These findings suggest that financial incentives may influence the behavior of doctors in Medicaid HMOs, and thus the health care received by Medicaid participants enrolled in HMOs.
Thesis: Thesis (Ph.D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi, 105 p, also contains graphics.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014440:00001


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THE TELECOMMUNICATIONS ACT OF 1996 AND MEDICAID HEALTH
MAINTENANCE ORGANIZATIONS
















By

TROY CLARENCE QUAST


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Troy Clarence Quast

































To my parents, Clarence and Elaine Quast; and to my son, Andres Quast.















ACKNOWLEDGMENTS

I thank my advisor, David Sappington, and committee members Chunrong Ai,

David Figlio, and Justin Brown. I also thank Betsy Shenkman, David Gabel, Donald

Stockdale, Richard Romano, Mark Jamison, Sanford Berg, Jonathan Hamilton, Laura

Braden, Damon Clark, Roger Clemmons, Mircea Marcu, Burcin Unel, Richard Gentry,

and Vanessa Cruz for their helpful advice and words of encouragement. I am grateful for

financial support from the University of Florida Department of Economics, the

University of Florida Public Policy Research Center, the Telecommunications Policy

Research Conference, and the University of Florida Public Utilities Research Center. I

am also grateful for technical and research support provided by the University of Florida

Department of Epidemiology and Health Policy Research. Finally, I thank my wife,

Daniela Quast, for her unwavering support and her ability to feign interest in the notion

of endogeneity.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES .............. ................. ........... ................ .......... vii

LIST OF FIGURES ......... ......................... ...... ........ ............ ix

A B STR A C T ................................................. ..................................... .. x

CHAPTER

1 IN T R O D U C T IO N ............................................................................. .............. ...

2 HOW STATE GOVERNMENTS IMPLEMENT FEDERAL POLICIES: THE
TELECOM M UNICATIONS ACT OF 1996 ........................................ ....................3

In tro d u ctio n ........................................................... ... ... .................................3
Background Information on Unbundled Network Element (UNE) Rate
Proceedings ....................................... ..... ..... 7
Steps Involved in Setting UNE Rates......................................... ............... 7
Influence of N neighboring States....... ........................................... ................. 9
Hypothesized UNE Rate Determinants ........................... ................................... 12
M odel Specification and Data U sed ..................................................................... 15
M odel Specification................. ........................ .. .... ........ .. ......15
D ata U se d ................................................................16
E stim action R results ........................ .................... .. .. .... ........ ......... 20
C oefficient E stim ates........... .............................................. ........ .............20
E conom ic E ffects............ ........................................................ .. .... ..... .. 22
C conclusion ....................................................... ...........................24

3 THE EXTENT AND MEANS OF ENTRY INTO LOCAL
TELECOM M UNICATIONS M ARKETS ........................................ .....................37

Introduction ................................................................. .........................37
Background Information on UNE-Based Entry ............................... ...............42
Loop-Based Versus Platform-Based Entry ............................... ............... 42
History of Platform-Based Entry Regulation .............................. ... ..................43
Hypothesized Determinants of Loop- and Platform-Based Entry ..............................45
M odel Specification and D ata U sed ........................................ ....................... 48









E stim ation R results ....................................................................... ........ 51
Estimates without Population Interactions.......................................................51
Coefficient estim ates .................. ...................... ...... .. ..................... ..51
E conom ic effects ........................................................ .. ....... ........... 52
Estim ates with Population Interactions .................................... ............... 54
Coefficient estim ates ............................................................... .. ........ 54
E conom ic effects ........................................................ .. ....... ........... 55
C conclusion .................................. ........................... .... ..... ........ 58

4 DOES THE FORM OF DOCTOR COMPENSATION AFFECT THE QUALITY
OF CARE IN M EDICAID HM OS? ........................................ ....................... 67

In tro d u ctio n .......................................................................................6 7
B background Inform ation................................................................. ............... 69
D ata and Em pirical Specification ................................................................... ......71
F in d in g s ..............................................................................7 5
C o n c lu sio n s........................................................................................................... 7 7

5 CON CLU SION S .................................. .. .......... .. .............82

APPENDIX

A DATA NOTES AND ADDITIONAL RESULTS ............... .................... ..........83

D ata N o te s ......................................................................... 8 3
UNE Rates ..................................... ................................ .........83
C o st E stim ate ................................................................................... 83
PUC Characteristics.................. ..... ...... .... .. .. ...............84
State P political V ariab les ........................................................... .....................85
Section 271 Status ................................................. ... ............. ............ 86
Level of Com petitive UNE Entry ........................................... .........................86
Tests of Instrument Validity and the Exogeneity of the Average Retail Rate
V variable .............................................. ............................................... 87
Tests of Instruments' Orthogonality to the Error Process.............. ................ 87
Tests of Instrum ent Relevance .................................................. .... .........88
Test of the Exogeneity of the Lagged Average Retail Rate ...............................88
A additional R results ............................................ .. .. .... ......... ......... 89

B ADDITONAL RESULTS ..................................... ................................ 95

L IST O F R E FE R E N C E S ............................................................................. .............. 98

BIOGRAPHICAL SKETCH ............................................................. ............... 105
















LIST OF TABLES


Table pge

2 -1 Su m m ary Statistics ............................................. ......................... ......................... 3 1

2-2 Correlation Matrix of Non-UNE Rate Variables ........................................... 32

2-3 Coefficient Estimates from One-Year and Two-Year Lag Specifications...............34

2-4 Effects of Changes in Explanatory Variables on UNE Rates Two Years Later ......36

3-1 Sum m ary Statistics. ......................................... .. .. .... ........ ......... 62

3-2 Correlation Matrix of Explanatory Variables.............. .... .................62

3-3 Coefficient Estimates without Population Interactions..................... ..............63

3-4 Economic Effects without Population Interactions ............................................64

3-5 Coefficient Estimates with Population Interactions..........................................65

3-6 Economic Effects for the 25th and 75th Population Percentiles.............................66

4-1 H M O A ttributes.......... .................................................................... ........ .. ....... .. 79

4-2 HEDIS Success Rates by HM O Attribute..................................... ............... 79

4-3 Demographic Characteristics By Population. .................................. ...............80

4-4 HEDIS Success Rates by Population. ........................................... ............... 80

4-5 OLS Regression Estim ates. ...... ........................... ....................................... 81

A-i Acronym s U sed .................. ............................... ........ ... .......... 90

A-2 Tests of Overidentifying Restrictions ........................................... ............... 91

A-3 Tests of Instrum ent Relevance ..................................................................92

A-4 Effects of Changes in Explanatory Variables on UNE Rates Two Years Later
U sing One-Y ear Lag Specification ........................................ ....... ............... 93









A-5 Effects of Changes in Explanatory Variables on UNE Rates in the Long-Run.......94

B-l Marginal Effects from Probit Regressions. ............. ........................................96

B-2 OLS Regression Estimates with HMO Fixed Effects...................... ...............97
















LIST OF FIGURES


Figure page

2-1 Comparison of UNE Rates. A) New Hampshire and Vermont. B)Kentucky and
Tennessee. C) W yom ing and Utah........................................................................ 26

2-2 HCPM Estimate versus UNE Rate. A) Ameritech. B) BellSouth. C) Pacific
Telesis. D) Qwest. E) Southwestern Bell. F) Verizon. .........................................28

2-3 Cumulative effect of $1 increase in the leader's rate on the follower's rate............30

3-1 Fraction of Incumbent Lines Leased by Entrants (National Average)...................60

3-2 Fraction of Incumbent Lines Leased by Entrants by Means of Entry (National
A average .............................................................................60

3-3 Loop Share of Incumbent Lines, July 2001 and July 2004............................... 61

3-4 Platform Share of Incumbent Lines, July 2001 and July 2004. ............................61















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

THE TELECOMMUNICATIONS ACT OF 1996 AND MEDICAID HEALTH
MAINTENANCE ORGANIZATIONS

By

Troy Clarence Quast

August 2006

Chair: David Sappington
Major Department: Economics

We analyzed the effects of the landmark Telecommunications Act of 1996 (TA96).

We also investigated whether the method of provider compensation affects the level of

care provided by Medicaid health maintenance organizations (HMOs).

We examined the rates set by state public utility commissions (PUCs) that

competitors must pay to lease parts of the local network from the largest incumbent U.S.

telecommunications suppliers (RBOCs). The results indicate that rates in the smaller

states in each RBOC region are strongly influenced by the largest state in the region.

Rates are lower where the level of competitive entry is lower, while they are higher in

states where the governor is a Republican. The analysis suggests that different states have

employed different methodologies in implementing TA96.

We investigated entry in local telecommunications markets. Panel data are used to

analyze the number of lines that competitive local exchange carriers (CLECs) lease from

RBOCs using two alternative arrangements: leasing only the wires that connect a






xi


customer's premises to the phone network (loop-based entry), or leasing all of the

network elements that are needed to provide phone service (platform-based entry). The

estimates suggest that while the two types of entry are generally affected by different

market factors, there appears to be cost-based substitution between them. Also, loop-

based entry is more responsive to changes in economic conditions in smaller states, while

platform-based entry is more responsive in larger states.

Using data for all of the Medicaid HMO enrollees in a large state, my coauthors

and I found that enrollees in HMOs that pay their doctors exclusively via fee-for-service

arrangements are more likely to receive services for which the HMO's doctors receive

additional compensation. Further, these enrollees are less likely to receive services for

which the HMO's doctors do not receive additional compensation. These findings

suggest that financial incentives may influence the behavior of doctors in Medicaid

HMOs, and thus the health care received by Medicaid participants enrolled in HMOs.














CHAPTER 1
INTRODUCTION

The federal government in the United States often attempts to harness competitive

market forces to achieve policy outcomes. Politicians and analysts frequently claim that

policies that are based largely on market incentives are best suited at resolving observed

inefficiencies in the economy. Two important areas in which this approach has been

attempted are telecommunications and health care. One of the hallmarks of the

Telecommunications Act of 1996 (TA96) was its focus on introducing competition into

the industry, which its supporters claimed would lower prices and result in greater

deployment of technology. Meanwhile, a prominent component of Medicaid reform has

been to move enrollees into health maintenance organizations (HMOs). Many believe

that when HMOs assume responsibility for the health care of Medicaid participants, costs

are lowered and quality increases. This study analyzes how these policies have been

implemented and whether they have been successful.

First, we investigate how state governments have implemented TA96. State public

utility commissions have been given somewhat vague guidelines as to how to set the rate

at which entrants can lease portions of the incumbent telephone company's network. We

find that the commissions may look to neighboring states for guidance as to how these

rates. Further, the commissions may be influenced by the level of existing entry when

determining the rates.

Second, we explore the factors that determine where entry occurs in local

telecommunications markets and the means by which entrants choose to enter. Beginning









in late 2000, entrants could either lease part or all of the incumbent firm's network to

provide service. Regression results indicate that the relative cost of the two forms of entry

may influence the means of entry chosen by firms. Further, the factors that influence the

level of entry vary by the size of the market.

Finally, we analyze how the means by which primary care physicians are paid may

influence the quality of care they provide. Using data from Medicaid HMOs, we compare

the quality of care provided by physicians who are paid a flat rate per enrollee versus the

quality of care provided by physicians who are paid per service provide. The results

suggest that doctors who are paid a flat rate per enrollee may be less likely to provide

check-up visits to children than doctors who are paid per service provided.















CHAPTER 2
HOW STATE GOVERNMENTS IMPLEMENT FEDERAL POLICIES:
THE TELECOMMUNICATIONS ACT OF 1996

Introduction

A primary goal of the TA96 (Table A-i) was to encourage competitive entry into

the local telecommunications market, under the presumption that such entry would lower

prices and increase social welfare. One of the means by which entrants1 were to enter this

market was by leasing unbundled network elements (UNEs). The legislation decreed that

entrants could lease certain segments of the incumbent' s2 network3, thus allowing for

competition where it would otherwise be unprofitable or infeasible. The rates at which

entrants could lease the UNEs could play an important role in the degree of entry

observed.4 The only rate-setting guidance offered by TA96 was that the rates were to be

priced at cost plus possibly a reasonable profit, determined without reference to a rate-of-

return proceeding, and set by the state public utility commissions.5






SEntrants into local telecommunication markets are referred to as Competitive Local Exchange Carriers,
or CLECs.
2 Specifically, this paper analyzes the rates at which Regional Bell Operating Companies (RBOCs) lease
UNEs to entrants. RBOCs are the regional monopolies that were created in the split-up of AT&T in
1984.
3 The question as to which segments the incumbent should be forced to lease has been litigated
extensively (see Lichtman and Picker, 2003).
4 As discussed in Lichtman and Picker (2003, pp 22-23), excessively low rates can discourage entrant
investment in their own networks and foster excessive reliance on UNEs. Conversely, exceptionally high
rates may encourage inefficient investment by entrants. The effect of the rates on incumbent investment
is ambiguous.
5 47, USC 252(d).









Given the ambiguity in how the UNE rates were to be set, the Federal

Communications Commission (FCC) interpreted TA96 as giving it the authority to

proscribe the methodology the states should use in determining the rates. The FCC

decided that the rates should be based on total element long-run incremental costs

(TELRIC), the hypothetical costs of implementing the least-cost network given the

current locations of the incumbent's wire centers.6

The state commissions were left with the unenviable task of operationalizing the

vague "TELRIC" notion. Figure 2-1 depicts UNE loop rates for three pairs of arguably

comparable states: New Hampshire and Vermont; Kentucky and Tennessee; and

Wyoming and Utah. The charts suggest that the state commissions operationalized the

TELRIC concept differently over time. For example, when Wyoming and Utah first set

rates in 1997, the UNE rate in Utah was only $2.00 less than the rate in Wyoming. By

2003 that difference had increased five-fold. This type of variability across states and

over time is representative of the experiences in many states and suggests the states

varied a great deal as to how they implemented the TELRIC methodology.7

Understanding how the state public utility commissions set UNE rates can

provide valuable lessons, both in telecommunications regulation and in the formation of

federal policies. Any evaluation of the success of TA96 at promoting competitive entry

6 The FCC's decision was challenged numerous times in the courts, both regarding whether the
commission had the authority to dictate the rate-setting methodology and also whether the TELRIC
methodology was consistent with TA96. The Supreme Court eventually decided both issues in favor of
the FCC. (AT&T v. Iowa Utilities Board (1999), Verizon, et al v. FCC, et al (2002)) More recently, the
Washington, D.C. Court of Appeals recently ruled that incumbents should not be forced to lease
switching equipment to entrants at TELRIC rates, and the FCC subsequently revised their rules to adhere
to that decision (FCC (2005)). However, local loops, the focus of this paper, continue to be included by
the FCC as a UNE that the incumbents must lease at TELRIC rates.
7Further, the Department of Justice recommended rejecting applications by some incumbents for
permission to sell interLATA service on the grounds that the state commissions had not correctly
calculated TELRIC rates (e.g., see Department of Justice (1997, 1998, and 2000)).









must account for how states implemented the Act. Given the widespread criticism that

TA96 did not spur the level of entry that was anticipated, it is important to disentangle

effects due to the execution of TA96 versus the Act itself. Beyond telecommunications,

the UNE rate experience provides an opportunity to analyze the behavior of state officials

charged with implementing federal policy. According to TA96, the state commissions are

to base the rates only on state-specific cost factors. However, given the vague TELRIC

definition of cost and the discretion given to the states, it is possible that additional

factors influence how these rates are set. An analysis of these factors can provide insight

as to how state officials implement federal policies, and may apply to other policy areas

such as education or environmental regulation.

Previous studies have analyzed the determinants of UNE rates. De Figueiredo and

Edwards (2004) examine the UNE rate in place in the three two-year election cycles from

1998 through 2002. The authors find that states with Democratic state legislatures and

relatively large political contributions by entrants have lower UNE rates. Further, elected

commissions are found to set higher rates, while states in which the utility commission

has imposed retail price caps tend to have lower UNE rates. Lehman and Weisman

(2000) explore UNE rates set immediately following the implementation of TA96. They

also find that elected commissions set higher rates while price caps lead to lower rates.

Beard and Ford (2004) test whether the UNE rates for certain combinations of network

elements in 2002 are correlated with the rates proposed by incumbents or entrants. They

find that the rates set by state commissions can be explained as splitting the difference

between the preferred rates of the two parties. Eisenach and Mrozek (2003) regress the

observed UNE rate against a cost estimate produced by the FCC Hybrid Cost Proxy










Model (see further description of this model in Section 4.2) and find that costs explain

only half of the variation in UNE rates.8

This paper extends these studies in several important directions. First, the present

analysis is based on a unique data set that contains the UNE loop rates for each state

since TA96 was enacted and when those rates were ordered. Further, unlike previous

studies, it is known whether the rate was the result of a voluntary reduction by the

incumbent. Second, the analysis examines the potential for information spillovers across

states.9 Given the ambiguity of the TELRIC notion and the lack of experience state utility

commissions had with the concept, it is possible if not likely that many states looked to

other states for guidance. Specifically, the influence of the rates set by the commission in

the largest state in each incumbent region (the "leader") on the other states in the region

(the "followers") is tested. Third, whereas in previous papers the form of retail rate

regulation was assumed to be exogenous, the analysis corrects for the potential

endogeneity of this variable. Fourth, the impact of the level of entry on UNE rates is

measured.

Four primary conclusions emerge from this research. First, less than 50% of a

change in the estimated cost of providing a UNE is reflected in the UNE rate two years

later. Second, a $1 increase in the leader state's rate results in a roughly $0.75 increase in



8 Another area in which state utility commission telecommunications regulation has been analyzed is how
retail rates are set. Donald and Sappington (1995) find that state commissions are more likely to choose
incentive regulation where the incumbent can gain more, rates are especially high, and elected state
leaders are churned by voters. Smart (1994) observes that retail rates are lower in states where the
commissioners are elected and that control of the governorship and state legislature only has an impact
on prices when the offices are held by different political parties.
9 There is a large literature regarding strategic interaction between governments, especially in the areas of
environmental, welfare, and tax policy (see Brueckner (2003) for a survey). However, the information
spillovers in UNE rates do not appear to be strategic, i.e., the state commissions do not appear to take
into consideration the reactions of other commissions when setting their own UNE rates.









the rates of the follower states two years later. Conversely, a state's rate does not appear

to be affected by changes in the rates of the follower states. Third, applications for

permission to sell long-distance services and lower levels of competitive entry tend to put

downward pressure on rates. Fourth, Republican leadership in a state is associated with

higher UNE rates.

These conclusions suggest that smaller state utility commissions rely on the work

of larger state commissions to help determine the regulated rates that they choose for

their state. Further, non-cost factors such as the market and political environment may

influence the rates set by the commissions. These results indicate that information

spillovers and factors beyond those specified in TA96 may influence how state

governments implemented this federal policy.

The paper is organized as follows. Section 2 presents background information on

how UNE rates are set. Section 3 describes the hypothesized UNE rate determinants.

Section 4 details the econometric methodology and the data used. Section 5 discusses the

estimation results, while Section 6 provides conclusions and areas for further research.

Background Information on Unbundled Network Element (UNE) Rate Proceedings

Steps Involved in Setting UNE Rates

Before reviewing the formal analysis, consider how UNE rates have been

determined by the states.

Immediately following the passage of TA96, state public utility commissions were

often forced to arbitrate interconnection agreements between incumbents and entrants

without having the luxury of completing a formal cost study. To prevent the delay of

interconnection agreements, the commissions often decided on a UNE rate based solely

on the proposed cost studies and testimony submitted by the two sides. When the









commission completed its own cost study, the rates from the study replaced the

temporary placeholder rates in the existing interconnection agreements.10

Following the initial cost study, the states had complete discretion as to when to

review the UNE rates, if at all. Most interconnection agreements between the incumbents

and entrants last three years, so the states often revise the UNE rates to coincide with the

expiration of the agreements. However, sometimes commissions revise the rates before

the end of this three-year cycle, while on other occasions they review the rates less

frequently.

While practices differ to some extent, most states follow the same procedures

when modifying UNE rates. First, the commission announces that it will review the UNE

rates and that hearings will be held. Before these hearings take place, the incumbent and

the entrant submit their own cost models and expert testimony. Following the hearings,

the commission reviews the material and testimony and sometimes will ask for further

information from the parties. After this stage, the commission announces its decision on a

cost model and the proper inputs for it. This decision will often include what it thinks the

resulting UNE rates are, but it will ask the incumbent or entrant to run the model chosen

by the commission with the proscribed inputs and report the resulting rates. The initial

commission decision regarding the model and inputs may also be appealed. (The state

typically has discretion as to whether it will allow an appeal to be heard.) This process

can last as long as two years.

The cost studies performed by the state commissions involve decisions on many

parameters, ranging from labor costs to the costs of telephone poles. In its most general


10 Note that some states were able to complete a TELRIC cost study quickly enough so that they did not
have to implement the placeholder rates.









form, the TELRIC studies consider three types of costs: operating costs, depreciation

costs, and the cost of capital.

Influence of Neighboring States

As described above, the TELRIC methodology that the FCC ordered the

commissions to follow to set UNE rates is both complicated and vague. The states are

forced to estimate the costs that a provider of the loops would incur if they were to build

the network today using an efficient technology. Specifying the efficient technology is a

daunting task in itself. In addition, the commission must determine the hypothetical cost

of installing the efficient technology throughout the incumbent's service area. Such an

analysis requires considerable resources, and can be particularly burdensome for states

that have relatively small staffs and budgets. Given these challenges, it is not surprising

that smaller states look to larger states for guidance in setting UNE rates.

The state commissions in an incumbent's region share a working relationship that

is conducive to collaboration in setting UNE rates. For example, regional associations of

state commissions that are carved almost exactly on incumbent regional lines meet

annually or semiannually.11 The meetings typically include working sessions where

commission staff members that work in telecommunications discuss current issues and

listen to incumbent and entrant representatives present their views on upcoming

regulatory matters. The potential for information spillovers among states was further

highlighted in a recent survey by the National Association of Regulatory Commissioners

of state commissions. When the state commissions were asked if they would be interested

1 The Qwest utility commissions comprise of the Qwest Regional Oversight Committee, the BellSouth
commissions are part of the Southeastern Association of Regulatory Utility Commissioners, the Verizon
commissions are members of either the New England Conference of Public Utility Commissioners or the
Mid-Atlantic Conference of Regulatory Utilities Commissioners, while all but two of the SBC states are
members of the Mid-America Regulatory Conference.









in working with other states in a matter closely related to UNE rates, many of them

indicated that they believed such coordination would be useful and has been useful in the

past (NARUC TRIP Task Force, 2003). 12

Figure 2-2 provides additional evidence that information spillovers may be present

within incumbent regions. The charts show for each state in January 2001 the UNE rate

set by the state and an estimate of the TELRIC loop cost using a cost model developed by

the FCC known as the Hybrid Cost Proxy Model. While the ability of the this model to

accurately calculate TELRIC costs has been hotly disputed, the model provides a

consistent measure across states and over time of the relevant TELRIC cost, under

specified assumptions. For most states, the UNE rate set by the state is lower than the

model estimate. However, the difference varies considerably by incumbent. For instance,

the UNE rates in the Ameritech states are roughly half of the model cost estimate, while

in the Qwest states the UNE rates are typically roughly equal to the cost estimate. These

inter-regional differences are consistent with the notion that the state commissions within

an incumbent region base their UNE rates in part on the rates of their neighbor states.

It is unlikely that all states within an incumbent's region have an equal influence on

each other. Within each region there appears to be a leader state that other states in that

region look to for guidance in their UNE rate proceedings. The leader state not only has

the most resources available in that region to conduct a UNE rate study13, but it may also


12 The Kansas utility commission replied, "The Commission believes it would be especially beneficial and
cost effective for the five original Southwestern Bell Telephone Company states to coordinate efforts."
The Rhode Island commission stated, "RI has a very small staff and would be interested in coordinating
logistics with other states in Verizon's territory..." Wyoming's response included, "The Commission is
considering the possible benefits of a regional approach such as participating in a coordinated effort of
the Qwest ROC (Qwest Regional Oversight Committee)."
13 An appropriate measure of the resources available would be the number of commission employees or the
commission's budget. However, state utility commissions are organized differently across states and
state comparisons of these measures are problematic. For example, Arizona's is part of the state's









have been the first state in which the incumbent applied for permission to sell long-

distance services in its territory14. The FCC encouraged smaller states to use the

information provided by leader states in their UNE rate cost studies:

We recognize that many states lack the extensive resources that were dedicated to
the process by New York and Texas, as detailed in our orders in those states...

We encourage states with limited resources to take advantage of the efforts devoted
by New York and Texas in establishing TELRIC-compliant prices, by relying
where appropriate on the existing work product of those states. (FCC, 2001, p 40)

There is considerable evidence that follower states look to the leader states for

guidance. For example, in the BellSouth region, it was reported that the Kentucky

commission closely monitored the UNE rate proceeding in Florida and even discussed

Florida's findings with the Florida commission and its staff (Caldwell, 2002, paragraph

131). In the SBC region, the Kansas commission suggested that they delay their UNE

proceeding in order to wait until the ongoing Texas study was completed (Kansas

Corporation Commission, 2000, p 2). In Nevada, the commission approved a stipulation

between the incumbent and the entrants that set UNE rates based on the proceedings of

the California commission (Public Utility Commission of Nevada, 2002). According to a

recent trade press article, follower states in Verizon's region halted their UNE rate cases

to see how the New York would decide its rate (State Telephone Regulation Report,

2004).





Corporation Commission, which also handles issues regarding securities and insurance. Data on the
number of employees and budget for the Arizona commission are available only for the state corporation
commission. As a proxy for state commission resources, the number of telephone access lines is used.
14 Under Section 271 of TA96, in order for an incumbent to receive permission to sell long-distance
services in a state, it had to file an application with both the FCC and the state that demonstrated that the
local telephone service market was open to competition. One of the criteria by which the application was
judged was whether the UNE rates were based on TELRIC estimates.









The influence of a leader state's rate on rates in follower states may be influenced

by incumbent behavior. To illustrate, incumbents have encouraged the influence of leader

state rates by benchmarking follower states' rates to them when applying for permission

from the FCC to sell long-distance services. For instance, in 2002 Qwest voluntarily

lowered their UNE rates in eight states in order to benchmark them to the rates set by

Colorado (FCC, 2002). 15 The incumbents also urge follower states to base their rulings

on decisions by the leader states. For example, in an Alabama UNE rate case BellSouth

urged the Alabama commission to adopt the Florida commission's position regarding the

timing of UNE rates, while in Georgia BellSouth argued for the approach that Florida

adopted to allocate costs between regular telephone service and data service.

Hypothesized UNE Rate Determinants

If the states followed the TELRIC methodology to the letter, the TELRIC cost

would perfectly determine the UNE rate. However, as described above and demonstrated

in Figures 1-1 and 1-2, it does not appear that costs alone explain the UNE rates.

Therefore, other variables are included in the model to account for the variation in rates.

To control for information spillovers across states, the rates of the other states in

the same incumbent region are included in the model.16,17 To test for the effect of the

leader state on the follower states, the model includes the rate of the leader state

15 Specifically, Qwest voluntarily lowered the rates to the level in Colorado adjusted for the difference in
average costs according to the FCC's cost model.
16 The regions are muddled somewhat by the mergers that have taken place among the incumbents. For
instance, Pacific Telesis and Ameritech were acquired by SBC in 1998 and 1999, respectively, while
Verizon (formerly Bell Atlantic) acquired NYNEX in 1997. Given their geographic locations and the
timing of the acquisitions, Pacific Telesis and Ameritech are treated separately from SBC while the
former Bell Atlantic and NYNEX are treated as one entity.
7 The identification strategy employed to capture information spillovers is closely related on the strategic
interaction literature cited above. Specifically, the lagged rates of neighboring states are interacted with
dummy variables in order to isolate the effects of interest. See Fredriksson and Millimet (2002),
Fredriksson, List, and Millimet (I 21114, and Hayashi and Boadway (2001).









interacted with a dummy variable that equals one if the state is a follower state. This

variable will capture how the follower states respond to changes in the leader's rate. If the

coefficient on this variable is statistically significant, one can conclude that the rates in

the follower states are influenced by the leader state's rate.

However, it may be the case that the follower states also are influenced by the rates

in the other follower states. Therefore, also included in the regressions is the weighted

average18 of the follower states' rates interacted with a dummy variable that equals one if

the state is a follower state. If there are information spillovers between the follower

states, the coefficient on this variable will also be statistically significant.

It may also be the case that the leader states are influenced by the rates in the other

states in the incumbent's region. To test for this, the weighted average of the follower

states' rates is interacted with a dummy variable that equals one if the state is a leader.

Thus, any influence of the follower states on the leader states is captured in this

variable.19

Characteristics of the state commissions may also influence UNE rates. Under the

theory of regulatory capture, the longer a commissioner has served, the more amenable

s/he might be to setting a rate favorable to the incumbent. Applied to UNE rates, this

theory suggests that the longer the commissioners have served the higher the UNE rates

will be. There may also be an influence due to the political affiliation of the


18 The weights are based on the number of switched access lines per state. The results do not change when
weights based on population are used.
19 The analysis in this paper focuses on the influence of neighboring states within the incumbent's region. It
is possible that some commissions might have influence that extends beyond the incumbent's region.
However, given the limited degrees of freedom available, it is not possible to simultaneously control for
nation-wide influences of all of the leader states. Initial estimates that test for the influence of one
national leader state at a time suggest that such effects may be present. However, the qualitative
conclusions reported below persist in settings that admit national leadership patterns. As stated in the
conclusion, further analysis of this issue is warranted.









commissioners. However, it is unclear as to the direction of this influence. One may

surmise that Republicans generally favor less regulation and less aggressive (i.e., higher)

UNE rates. Conversely, it may be the case that Republicans are sympathetic towards

small businesses, and therefore may favor lower UNE rates to benefit both small-business

consumers of telecommunications services and entrants. Prior studies have debated

whether states that enact incentive-based retail rate regulation enact lower UNE rates20

Further, the incumbent's retail rates may influence the level of UNE rates, as state

commissioners may regard the retail rate as an upper bound on what an entrant could

afford to pay for a UNE. Finally, UNE rates may systematically differ if they were set in

an arbitration case immediately following the enactment of TA96.

Beyond the state commissions, there may be state-specific influences that vary by

time. For instance, the political affiliation of the governor or the state legislature may

influence how the state sets UNE rates. Not only can the governor and state legislature

have a direct influence on the state utility commission through appointments and

budgetary powers, but their political affiliation may be a proxy for the political sentiment

of the citizens and reflect the general regulatory environment in the state. As described in

the previous paragraph, the direction of this effect is ambiguous.

The incumbent's federal regulatory status may play an important role in how UNE

rates are set. As noted above, the FCC and state commissions had to certify that the

incumbent's UNE rates were TELRIC-based before the incumbent was allowed to sell

long-distance phone service. Therefore, one might expect that the UNE rates in the period

immediately prior to the FCC's decision were lower than they would have otherwise


20 Lehman and Weisman (2000) argue that state commissions can unfairly shift risk to incumbent firms by
enacting a retail price cap and setting a relatively low UNE rate.









been. Some incumbents also voluntarily lowered their UNE rates during the application

process in the hope of securing permission to provide long-distance service. If the

incumbents were in fact lowering their UNE rates below the rates that would have

prevailed otherwise, one would expect these voluntary reductions to result in lower UNE

rates. However, the incumbent might have made voluntary reductions that were not as

drastic as would otherwise have been ordered by the state during the application process.

If so, the incumbent might have been able to secure a more favorable UNE rate by pre-

empting action by the state. In such a case, the marginal effect of the voluntary reduction

could be positive.

Lastly, the level of observed UNE entry may affect the UNE rates. If the

commission views the level of entry as relatively low, certes paribus, it may be inclined

to lower UNE rates to encourage additional entry. Thus, one would expect a positive

coefficient on this variable.

Model Specification and Data Used

Model Specification

As noted above, UNE rates changed infrequently in some states. Consequently,

UNE rates often exhibit a high degree of stationarity. However, relatively frequent data

are required to capture the exact timing of the rate decisions. To allow for frequent data

and the stationarity of the lagged dependent variable, the lagged rate is included as an

explanatory variable.21

Including the lagged dependent variable as an explanatory variable in a panel data

regression complicates the econometric analysis. When an OLS fixed-effects estimator is


21 In the econometrics literature this is referred to a dynamic panel data model.









used, a negative bias of order 1/T is introduced in the coefficient on the lagged dependent

variable (Nickell, 1981). OLS estimation of the model in first differences partially

corrects the bias, but does not entirely alleviate the endogeneity of the lagged dependent

variable. Arellano and Bover (1995) and Blundell and Bond (1998) derive a generalized

methods of moments estimator (known as system GMM) that simultaneously estimates

the model in levels and first differences. Blundell and Bond (1998) perform Monte Carlo

simulations that demonstrate the system GMM estimator is superior to both the OLS

fixed effects and GMM estimations using first differences only. Further lagged values of

the levels and first difference of the dependent variables are used as instruments for the

lagged dependent variable.22 The system GMM estimator is appropriate when the

coefficient on the lagged dependent variable is 0.8 or greater. For this estimator to be

valid, the lagged dependent variable must have a constant correlation with the state

effects and be uncorrelated with present and past values of the error term. Further, it is

assumed that the error terms have a mean of zero and are not serially correlated. Robust

standard errors that are consistent in the presence of heteroskedasticity and

autocorrelation within states are used in calculating t-statistics.

Data Used

Summary statistics of the data used are provided in Table 2-1. The sample is

comprised of quarterly data. The date in which a state enters the sample is determined by

the date the utility commission in that state and the corresponding leader state ordered its

initial UNE rates. That date ranges from April 1997 to October 1997. Data for all states



22 In the estimations that follow, the previous four quarters of the level of the lagged dependent variable are
used as instruments in the first differences equation, while one lag of the first difference of the lagged
dependent variable is used as an instrument in the levels equation.










included in the analysis run through the end of 2003. Correlation coefficients between the

non-UNE rate explanatory variables are provided in Table 2-2.

The dependent variable in the analysis is the statewide average recurring rate for

the local loop in a quarter.23,24,25 The local loop consists of the wires that connect a

customer's premises to the incumbent's wire center. The local loop is the network

element that is the most costly to replicate, and so is the element that entrants would most

likely lease from the incumbent.26 The rates used in the study were obtained primarily

from state commission orders and incumbent documents. Unlike data sets used in earlier

studies, the exact date on which the rate was ordered is known and will be integral in

examining the issue of interstate interdependence

The cost variable is a measure of the monthly cost of the loop. Beginning in 2000

the FCC published annual TELRIC estimates. As noted above, while parties have debated

whether the model over- or under-estimates costs, it does provide a reference over time

and across states. However, these data are not available prior to 2000. Therefore the cost

variable used here is constructed from two different data series. For the period prior to

2000 the cost variable equals the embedded, or historical, cost of the loop as reported by

the National Exchange Carrier Association regarding universal service funding. For 2000


23 Specifically, it is weighted average of the monthly rate for the 2-wire copper in the various density zones
in the state. The weights are the number of lines in each density zone.
24 Another option is to use the rate set for the loop in the densest areas of the state, known as the urban zone
rate. However, many states did not de-average rates by zone until 2000 and did so at differing times.
Thus, to be able to model rates since TA96 was implemented, statewide average rates must be used.
25 Data for Alaska and Hawaii are not included as they are not part of any of the major incumbent regions.
Arkansas is also not included because the commission in that state ruled that it did not have the authority
to conduct a cost study to set UNE rates. Rates in the District of Columbia are not included, as the focus
is on rate setting by state utility commissions. Rates for Connecticut are not included as it is
geographically surrounded by Verizon states but the incumbent in the state is owned by SBC.
26 Some entrants serving large business customers built their own networks and therefore did not have to
rent any network elements from the incumbent.









forward, the cost variable is the estimate from the FCC's model. While the variable is not

ideal, it should capture the factors that account for the discrepancies in costs, such as

population density, wire center locations, and local cost levels.27

As explained in Section 2, UNE rate proceedings are often lengthy. As such, it

takes time for commissions to incorporate new information in their cost studies. Further,

state commissions may not learn the results of proceedings in other states for some time

or may be in an earlier stage of their UNE rate study. Therefore, it is appropriate to model

with a lag the influence of the rates of the other states. However, as the number of lags

included in the model increases, the number of explanatory variables increases quickly28,

thus limiting degrees of freedom. With this constraint in mind, the model is estimated

using two different lag structures. The first includes the neighbor states' rates over each

of the last four quarters, thus spanning one year. The second structure includes the

neighbor states' rates from each of the last four half-years, thus spanning two years.29

As described above, characteristics of the state utility commissions may also

influence UNE rates. To control for the length of time commissioners have served, the

average tenure of the commissioners is included. The political ideology of the

commission is captured in the fraction of commissioners that describe themselves as

Republican. The effect on UNE rates of the form of retail rate regulation is captured in a

dummy variable that equals one if the state employs rate of return regulation on either




27 The results do not change substantially when embedded costs are used for the entire period.
28 For each lag of other states' rates that is included in the estimation, three explanatory variables are added
(the weighted average of the neighbor rates interacted with the leader and follower dummy variables and
the leader's rate interacted with the follower dummy variable).
29 Given the rates of other states enter the model with a lag, they are treated as exogenous from the
perspective of the commission setting current rates.










residential or business services.30 However, this variable may be endogenous, as

unobserved factors may be jointly determining the form of retail rate regulation and the

UNE rate. To correct for this endogeneity, variables that measure customer satisfaction as

reported by the FCC's ARMIS database are used as instruments.31 The average retail rate

for the incumbent is calculated using FCC ARMIS data and is lagged one year to account

for the information delays in UNE rate proceedings outlined above.32 Finally, a dummy

variable is also included that takes the value of one if the rate was set during an

arbitration case immediately following the passage of TA96, and zero otherwise.33

Two explanatory variables are employed to account for the political sentiment of

the state's elected officials and its citizens. A dummy variable is included that equals one

if the governor is a Republican and zero otherwise. Another variable is included that

equals the percent of the state legislators that are Republican. To determine the potential

interactive effects between the ideology of the commission and the governor, the product

of the two variables is included as an explanatory variable.






30 Retail rate regulation schemes are quite complex, as some plans can have price caps on some services
and another form of incentive regulation on other products. By using a dummy variable that reflects
whether the state employs rate-of-return regulation on either residential or business basic services, the
complications posed by the idiosyncrasies of the various incentive-based regulation plans are avoided.
As this variable takes a value of one when rate-of-return regulation is used, the expected sign of the
coefficient of this variable is positive given Lehman and Weisman's (2000) analysis.
31 These variables are used as instruments because while UNE rate proceedings typically pit incumbents
against entrants, retail rate regulation proceedings tend to be disputes between the incumbent and
consumer groups. Therefore, while these satisfaction variables affect the form of retail rate regulation,
they do not affect the level of UNE rates. The econometric evidence supports this logic, as the tests of
joint significance indicate that the instruments are correlated with the form of retail rate regulation while
the C-statistic values indicate they are not correlated with UNE rates. See Appendix A for details.
32 Given the variable enters the estimation with a lag, endogeneity is not expected to be a concern. A C-test
confirms that the variable is not endogenous. See Appendix A for details.
33 Specifically, the arbitration rates used in this study are those from arbitrations between AT&T and the
local incumbent.









Dummy variables are also used to capture the effects of the status of the

incumbent's application to sell long-distance service in that state. A dummy variable is

included that equals one if the rate was set during the year prior to the incumbent's

application up to the date of the FCC's decision. To measure the marginal impact of the

incumbent making a voluntary reduction, a dummy variable is included that equals one if

the rate was the result of a voluntary reduction by the incumbent in conjunction with its

application.

The level of competitive entry in the state is controlled for by the number of UNE

lines leased by entrants. To account for delays in commissions incorporating information

into their decisions, the variable is lagged one year. Endogeneity may be present, as the

level of entry is likely determined in part by the future UNE rate, which in turn (given the

stationarity of UNE rates) is likely correlated with the present rate. The state

unemployment rate lagged one year is used as an instrument for this variable.34 To allow

for meaningful comparisons across states, the number of leased lines is divided by the

standard deviation of the variable for that state.

Finally, dummy variables for each calendar year are included in the analysis to

account for shocks common to all states in a given year not captured by the other

explanatory variables.

Estimation Results

Coefficient Estimates

Table 2-3 summarizes the coefficient estimates from the basic model. Column (1)

lists the estimates from the lag structure spanning one year, while the estimates

34 The logic behind the use of the variable is that the level of economic activity affects the level of UNE
entry but not UNE rates. The econometric results suggest that these instruments are valid. See Appendix
A for details.









corresponding to a lag structure spanning two years are listed in Column (2). The

estimation diagnostic tests indicate that the system GMM approach is valid with this data

set. The tests for autocorrelation demonstrate that there is first-order autocorrelation in

first differences, but no autocorrelation of higher order. This indicates that the error terms

are not serially correlated. Further, the specification passes the Hansen tests of

overidentification, which tests whether the moment conditions beyond those needed to

identify the parameters are valid.35 Finally, the coefficient on the lagged dependent

variable is over 0.8 in both specifications, which confirms that the system GMM

estimator is appropriate.

The estimates from both columns indicate that the cost variable is statistically

significant, at either a 90% or 95% statistical confidence level depending on the

specification. Of the neighbor rates, the leader's rate lagged six months has a statistically

significant impact on the followers' rates, while the followers' rates do not have a

statistically significant impact on the rates of other followers or leaders.

The dummy variables that capture the effect of the incumbent's application to sell

long-distance services are negative and statistically significant. The negative coefficient

on the variable that indicates whether the incumbent made a voluntary reduction in the

application process suggests that the incumbents were not engaging in strategic behavior.

In the two-year lag specification, the dummy variable that captures the effect of a

Republican governor is positive and statistically significant. Finally, the level of UNE

entry appears to have a statistically significant effect at a 90% confidence level.


35 See Appendix A for further details.









Somewhat surprisingly, none of the variables that capture commission characteristics are

statistically significant.

Economic Effects

The presence of the lagged dependent variable as an explanatory variable implies

that the coefficient estimates only measure short-run effects. Furthermore, the coefficient

estimates do not clearly convey the economic significance of the explanatory variables.

Table 2-4 details the effects on UNE rates from a change in an explanatory variable that

lasts two years.36 The effects reported in Table 2-4 are based on the coefficient estimates

in the specification that includes two years of lagged neighbor UNE rates37

According to the estimates, a one-dollar increase in the cost variable on average

leads to a $0.40 increase in the UNE rate two years later. This increase is highly

statistically significant. However, an F-test as to whether a $1 increase in the cost

variable leads to $1 increase in UNE rates is rejected at over a 99% confidence level.

Thus, UNE rates do not perfectly reflect changes in cost (as measured by the available

variable).38

The estimates suggest that UNE rates are significantly affected by changes in the

leader's UNE rate. Figure 2-3 depicts the effect over two years on followers' rates after a

one dollar increase in the leaders' rate and the corresponding 95% confidence interval. As

the figure shows, the average effect after one year is to increase the followers' rate by


36 Long-run effects that are based on permanent changes in the explanatory variable are reported in
Appendix A.
7 The corresponding effects based on the specification that includes one year of lagged neighbor rates are
very similar and are contained in Appendix A.
38 It is worth repeating that the cost variable used here (based mostly on the FCC's cost model) is not
universally considered a valid cost proxy. Thus, while a $1 increase in the cost variable is not realized in
the UNE rate, one could argue that the cost variable overstates TELRIC costs to such a degree that
changes in costs are in fact fully realized in changes in UNE rates.









roughly $0.60, while after two years, the effect reaches almost $0.80. Table 2-4 reports

that a one standard deviation ($3.37) in the leader's rate leads to a $2.47 increase in the

followers' rates. Conversely, the estimates suggest that the effect of followers' rates on

other followers is not statistically significant. Thus, the results suggest that the leader

states do in fact have a significant impact on the other states in the incumbent's region.39

Of the variables that capture characteristics of the state utility commission, the

effect of average tenure has the expected sign, but is not statistically significant. In

regards to political ideology, neither the effects of the ideology of the commissioners nor

the ideology of the commissioners interacted with the ideology of the governor are

statistically significant. This may be due to the potentially conflicting effects described

above. The effect of the rate-of-return retail rate regulation variable is positive as

expected but statistically insignificant and only a third of the effect found by Lehman and

Weisman (2000). The lack of statistical significance and smaller estimated effect may be

due in part to the evolving nature of incentive regulation plans and the difficulty in

classifying them (as noted above). Furthermore, the average retail rate also is statistically

insignificant. Finally, while the AT&T arbitration rate has the expected sign, it too is not

statistically significant.

However, the election of a Republican governor is associated with a $1.15 increase

in UNE rates two years later that is statistically significant. One may attribute this effect

39 The estimate of the effect of the followers' rate on the leader's rate is somewhat surprising. While it is
only roughly two-fifths of the size of the leader's effect on the followers, it is statistically significant at a
90% confidence level. From the coefficient estimates, it appears this effect exists four quarters after the
followers change their rates. A possible explanation is that there is a feedback effect. For example, if
rates are generally falling, the following pattern may be present: The leader lowers its rate, which is
followed six months later by the followers. One year after the followers' rate change, the leader again
lowers its rate again, which causes the leader's rate to appear to be influenced by the followers' rate
change. Also, given that the coefficient estimates of the effect of the followers' rates on the leaders are
not statistically significant, the statistical significance of the overall effect is being driven largely by
covariance between the coefficients on the followers' rates and the lagged dependent variable.









to two, perhaps not mutually exclusive effects: a preference for less-aggressive regulation

and favorable treatment for large corporations.40 The effects of both the percent of

Republican state legislators and the interaction of the Republican governor and

commission political affiliation are statistically insignificant.

The variables that capture the long-distance application status of the incumbent

indicate that those applications had a strong influence on the UNE rates. On average,

UNE rates set during the period prior to applications to enter the long-distance market are

roughly $2.00 lower two years later. When the incumbent voluntarily lowered the UNE

rate this effect more than doubled.

Finally, the observed level of entrant use of incumbent UNE lines is both

statistically and economically significant. A one standard deviation decrease in UNE

lines is associated with UNE rates falling roughly $4.00. As UNE rates are generally

falling during this period, the positive coefficient may also reflect that UNE rate

reductions are more modest when UNE entry is relatively strong.

Conclusion

This paper analyzes the factors that determine the rates that state utility

commissions set for access to the incumbent's telecommunications network. The results

suggest that factors other than cost influence the rates. The rate set by the largest state in

each incumbent region appears to influence the rates set by other states in that region.

Further, rates tend to be higher in states where the governor is a Republican and lower in

the period prior to an incumbent applying for permission to sell long-distance services

and when observed competitive entry is lower.

40 One concern with this result could be that Republican governors tend to be elected in less urban, and thus
higher UNE-cost, states. However, the average UNE rate in states with Republican governors is virtually
identical to the average in states with Democratic governors.









Beyond providing insights into telecommunications regulation, these results may

have implications for other areas in which the federal government delegates

implementation of policies to state agencies. While state agencies may possess valuable

local knowledge, it is possible that the intent of the federal policy will not be perfectly

realized through the actions of the states. Information spillovers across states and factors

outside the scope of the federal mandate may affect how the policy is implemented. Other

policy areas in which this type of analysis may provide some insight include state

implementation of the No Child Left Behind Act and environmental policies as dictated

by the EPA.

The findings in this paper could be extended in several directions. For example,

the rates used in this paper are the monthly rates for the local loop. The commissions also

set one-time connection fees for these loops that are also important to entrants

considering entering a state. Also, while the local loop is arguably the most important

network element, the states set rates for many other network elements. Analyses of these

additional rates could shed further light on the factors that influence state commissions.

Finally, as noted in footnote 20, initial tests for nation-wide information spillovers

suggested that states may have influence outside the incumbent's region. Further analysis

into this issue could provide additional insight into how state agencies implement federal

regulations.














30-


--
S25-
o
0


E -
. 20-
o


15-


20-


19-

18-


17-


16-

15-

J,


an97 Jan98 Jan99 JanOO Jan01 Jan02 Jan03 Jan04

----- New Hampshire -Vermont





B



i-------- -----------


an97 Jan98


Jan99


JanOO


Jan01


Jan02


Jan03 Jan04


----- Kentucky Tennessee


Figure 2-1 Comparison of UNE Rates. A) New Hampshire and Vermont. B)Kentucky
and Tennessee. C) Wyoming and Utah.


I --~~-----


J,











C


25- -


t---
O 20











Wyoming Utah


Figure 2-1 Continued
















k


VI


IN MI OH WI

U HCPM estimate U UNE rate


$40
S$35
S$30
$25
S$20
S$15
E $10
$5
$0


AL FL GA KY LA MS NC SC TN


* HCPM estimate U UNE rate


C


$25
$20
$15
$10
$5
$0


* HCPM estimate U UNE rate


Figure 2-2 HCPM Estimate versus UNE Rate. A) Ameritech. B) BellSouth. C) Pacific
Telesis. D) Qwest. E) Southwestern Bell. F) Verizon.


$25
$20
$15
$10
$5
$0


I
















AZ CO IA ID MN MT ND NE NM OR SD UT WA WY
U HCPM estimate U UNE rate


$25
$20
$15
$10
$5
$0


MO OK
* HCPM estimate U UNE rate

F


$35
S$30
$25
S$20
S$15
o $10
$5
$0


Figure 2-2 Continued.


DE MA MD ME NH NJ NY PA RI VA VT WV
HCPM estimate U UNE rate


t~HHHHtt~


I b


I










$1.60


$1.40


$1.20


$1.00


$0.80


$0.60


$0.40


$0.20


$0.00


1 2 3 4 5 6 7 8
Number of Quarters Since Increase


Figure 2-3 Cumulative effect of $1 increase in the leader's rate on the follower's rate.










Table 2-1 Summary Statistics
Standard
Variable Mean Minimum Maximum Deviaion

Laging Neighbor States' Rates One Year (n = 1103)
UNE Rate (All States) $16.62 $7.01 $28.82 4.59
UNE Rate (Leader States) $15.30 $9.81 $20.65 3.41
UNE Rate (Weighted Average of $15.80 $8.13 $27.22 3.14
Follower States)
Cost Variable $21.27 $13.97 $41.78 4.38
Percent of Commissioners Republican 46.3% 0.0% 100.0% 30.7
Average Tenure of Commissioners (yeai 5.2 0 22.5 3.1
Rate of Return Retail Rate Regulation 0.18 0 1 0.39
Governor Republican 0.6 0 1 0.49
Percent of Legislature Republican 0.5 0.13 0.89 0.14
AT&T Arbitration 0.16 0 1 0.37
Period Prior to Section 271 Decision 0.25 0 1 0.43
Voluntary Reduction 0.07 0 1 0.26
UNE Entry (Lagged One Year, Divided 0.54 0 2.77 0.69
by Standard Deviation)
Average Retail Rate $29.16 $14.45 $44.44 5.16

Laging Neighbor States' Rates Two Years (n = 919)
UNE Rate (All States) $16.44 $7.01 $28.29 4.49
UNE Rate (Leader States) $15.11 $9.81 $20.65 3.37
UNE Rate (Weighted Average of $15.65 $8.13 $20.51 3.13
Follower States)
Cost Variable $21.24 $13.97 $35.86 4.22
Percent of Commissioners Republican 45.8% 0.0% 100.0% 30.8
Average Tenure of Commissioners (yeai 5.2 0.1 22.5 3.0
Rate of Return Retail Rate Regulation 0.17 0 1 0.37
Governor Republican 0.6 0 1 0.49
Percent of Legislature Republican 0.5 0.13 0.89 0.14
AT&T Arbitration 0.11 0 1 0.31
Period Prior to Section 271 Decision 0.29 0 1 0.45
Voluntary Reduction 0.09 0 1 0.28
UNE Entry (Lagged One Year, Divided 0.64 0 2.77 0.71
by Standard Deviation)
Average Retail Rate $29.53 $14.45 $44.44 5.22










Table 2-2 Correlation Matrix of Non-UNE Rate Variables
Avg
% Comms. Tenure of ROR Retail % Legis.
Cost Var. Rep. Comms. Rate Reg. Gov. Rep. Rep.
Cost Var. 1.00
% Comms. -0.23 1.00
Rep.
Avg 0.24 0.00 1.00
Tenure of
Comms.
ROR Retail 0.23 -0.08 -0.02 1.00
Rate Reg.
Gov. Rep. -0.07 0.44 0.01 -0.04 1.00
% Legis. 0.04 0.20 0.11 0.27 0.12 1.00
Rep.
AT&T Arb. 0.17 -0.14 0.00 0.10 0.14 0.10
Long-Dist. 0.00 0.03 -0.01 -0.26 -0.06 0.04
App.
Vol. Reduct. 0.01 0.10 0.06 -0.12 0.08 -0.01
By Incumb.
Gov. Rep. -0.18 0.80 -0.01 0.00 -0.08 0.78
% Comms.
Rep.
UNE Entry -0.11 0.07 -0.07 -0.29 -0.12 -0.05
Avg. Retail 0.43 0.01 0.15 -0.09 -0.12 -0.06
Rate










Table 2-2 Continued
Gov. Rep. *
Long-Dist. Vol. Reduct. % Comms. Avg. Retail
AT&T Arb. App. By Incumb. Rep. UNE Entry Rate
AT&T Arb. 1.00
Long-Dist. -0.06 1.00
App.
Vol. Reduct. 0.13 0.49 1.00
By Incumb.
Gov. Rep. 0.19 0.00 0.05 1.00
% Comms.
Rep.
UNE Entry 0.04 0.50 0.37 -0.01 1.00
Avg. Retail -0.19 0.21 0.08 -0.04 0.12 1.00
Rate











Table 2-3 Coefficient Estimates from One-Year and Two-Year Lag Specifications
One-Year Two-Year
Explanatory Variable Specification Specification
0.876 *** 0.815 ***
UNE Rate, Lagged One Quarter
(17.18) (12.37)
Effect of Leader's Rate on Followers
-0.012
Lagged One Quarter
(-0.15)
0.164 0.281 ***
Lagged Two Quarters
(1.73) (3.02)
-0.016
Lagged Three Quarters
(-0.22)
-0.058 -0.081
Lagged Four Quarters -
(-1.64) (-0.89)

Lagged Five Quarters

-0.054
Lagged Six Quarters-0.
(-0.90)
Lagged Seven Quarters

0.038
Lagged Eight Quarters (1.16
(1.16)
Effect of Followers' Rates on Other Followers
0.001
Lagged One Quarter
(0.20)
0.076 -0.112
Lagged Two Quarters 0
(0.54) (-1.27)
-0.080
Lagged Three Quarters
(-0.73)
-0.016 0.009
Lagged Four Quarters-0
(-0.33) (0.89)
Lagged Five Quarters

0.026
Lagged Six Quarters0.0
(0.34)
Lagged Seven Quarters

0.012
Lagged Eight Quarters 0.1
(0.15)
Effect of Followers' Rates on Leaders
0.048
Lagged One Quarter
(0.62)
-0.060 0.028
Lagged Two Quarters-0
(-0.70) (0.37)
0.003
Lagged Three Quarters
(0.04)
0.065 0.045
Lagged Four Quarters 0
(1.69) (1.06)

Lagged Five Quarters

0.008
Lagged Six Quarters0.0
(0.21)
Lagged Seven Quarters

0.018
Lagged Eight Quarters 0.018
(0.46)












Table 2-3 Continued


Explanatory Variable


Cost

Percent of Commissioners
Republican
Average Tenure of
Commissioners
Rate of Return Retail Rate
Regulation

Governor Republican

Percent of State Legislature
Republican
Percent of PUC Republican
Interacted with Governor

AT&T Arbitration


One-Year
Specification
0.061 *
(1.91)
0.000
(-0.12)
0.017
(1.10)
0.187
(0.28)
0.211
(1.53)
-0.244
(-0.53)
-0.002
(-0.98)
0.083
(0.30)


Two-Year
Specification
0.096 **
(2.38)
-0.002
(-0.97)
0.016
(0.93)
0.154
(0.20)
0.263 *
(1.85)
-0.375
(-0.69)
-0.002
(-0.85)
0.074
(0.30)


Period Prior to Decision on -0.481 ** -0.479 ***
Long-Distance Application (-2.65) (-2.48)
Voluntary Reduction by -0.532 ** -0.636 **
Incumbent (-2.45) (-2.16)
UNE Entry (lagged 1 year, 0.770 0.999 *
divided by standard deviation (1.72) (1.92)
Average Retail Rate (lagged 1 0.008 0.011
year) (0.77) (0.79)
Overall F-Statistic 1894 1412
Number of Observations 1103 919
Arellano-Bond Test
For AR(1) in First -2.85 *** -2.57 ***
Differences (0.00) (0.01)
For AR(2) in First -0.22 -0.8
Differences (0.82) (0.42)
Hansen Test of Overidentified 1.76 2.59
Restrictions (p-value) (0.94) (0.86)
Notes Year dummy variables are included and are generally statistically significant.
Unless otherwise noted, t-statistics are reported in parentheses.
*** statistically significant at 99% confidence level
** statistically significant at 95% confidence level
* statistically significant at 90% confidence level









Table 2-4 Effects of Changes in Explanatory Variables on UNE Rates Two Years Later
Effect of a One
Explanatory Variable Unit Increase Economic Effect1

Effect of Neighbors' Rates
0.73 ** 2.47 **
Leader's Rate on Followers (C) .72.7
(2.57) (2.57)
-0.35 -1.10
Followers' Rates on Followers (C)-.031.
(-1.03) (-1.03)97 *
Followers' Rates on Leaders (C) 1..91
(1.91) (1.91)
Effect of Other Explanatory Variables
0.40 *** 1.68 ***
Cost (C) 0.40 1.68
(3.65) (3.65)
Percent of Commissioners -0.01 -0.31
Republican (C) (-1.10) (-1.10)
Average Tenure of Commissioners 0.07 0.21
(C) (0.96) (0.96)
Rate of Return Retail Rate 0.67 0.67
Regulation (B) (0.20) (0.20)
1.15 1.15 *
Governor Republican (B)1.
(1.89) (1.89)
Percent of State Legislature -1.63 -0.23
Republican (C) (-0.74) (-0.74)
Percent of PUC Republican -0.01 -0.37
Interacted with Governor Republican (-0.81) (-0.81)
0.32 0.32
AT&T Arbitration (B)3
(0.30) (0.30)
Period Prior to Decision on Long- -2.08 ** -2.08 **
Distance Application (B) (-2.27) (-2.27)
Voluntary Reduction by Incumbent -2.77 ** -2.77 **
(B) (-2.19) (-2.19)
UNE Entry (lagged 1 year, divided by 4.35 ** 4.35 **
state standard deviation) (C) 2 (2.42) (2.42)
Average Retail Rate (lagged 1 year) 0.05 0.24
(C) (0.81) (0.81)
Economic effects for continuous explanatory variables are based on a one standard
deviation increase in that variable, while economic effects for binary explanatory variables
are based on a change in the variable from zero to one.
2 This variable is already scaled by dividing by the standard deviation, so the economic
effect is based on a one-unit increase.
Notes (B) binary explanatory variable, (C) continuous explanatory variable,
t-statistics are reported in parentheses.
*** statistically significant at 99% confidence level
** statistically significant at 95% confidence level
statistically significant at 90% confidence level















CHAPTER 3
THE EXTENT AND MEANS OF ENTRY INTO LOCAL TELECOMMUNICATIONS
MARKETS

Introduction

A primary goal of the Telecommunications Act of 1996 (TA96) is to facilitate

competition in the local telecommunications market. TA96 includes provisions that allow

entrants1 to lease parts of the incumbent' s2 network, known as unbundled network

elements (UNEs), at relatively low rates determined by state public utility commissions.

There are two ways in which entrants can provide phone service by leasing UNEs. Under

a loop arrangement3, the entrant rents from the incumbent the phone line that connects a

customer's residence or premises to the local wire center. However, the entrant provides

the equipment that connects the customer's line to the broader telephone network. The

other means by which entrants can offer service is by leasing UNEs through a platform

arrangement, which became widely available in 2000.4,5 The key difference between

loop- and platform-based entry is that in platform arrangements, the entrant leases all of

the UNEs needed to provide telephone service. In other words, the entrant simply





SEntrants into local telecommunications markets are known as Competitive Local Exchange Carriers, or
CLECs.
2 Incumbents in local telecommunications markets are known as Incumbent Local Exchange Carriers, or
ILECs.
3 This is also referred to as UNE-L entry.
4 This is also referred to as UNE-P entry.
5 As described below, recent court rulings and revisions to FCC regulations have altered the regulatory
treatment of platform-based entry.










rebundles the UNEs that are required to provide service and does not have to own any of

the necessary equipment.6

A sufficiently long time series of data is now available to investigate the factors

that determine the extent of entry via these two alternative arrangements. Figure 3-1

indicates that from July 2001 to July 2004 the national average of the fraction of

incumbent7 lines leased to entrants increased steadily. The average increased from just

over 5% in July 2001 to almost 20% three years later. However, Figure 3-2 indicates that

the increases in entry differ greatly by means of entry. During the 2001 2004 period,

while the fraction of lines leased through a loop arrangement ranged between 2% and

4%, the fraction leased through a platform arrangement tripled from less than 4% to over

12%.

Figures 2-3 and 2-4 suggest that the evolution of entry has also differed

significantly across states. Each dot in Figure 3-3 represents for a given state the fraction

of lines leased through a loop arrangement in July 2001 and in July 2004. Dots near the

45-degree line represent states where the share of loop-based entry changed little between

the two dates, while dots above the 45-degree line indicate that the share increased. As

the figure indicates, while there are some states in which the share changed little, there


6 Incumbents have been quick to point out that there is no actual difference between platform-based entry
and reselling the incumbents' services, another option provided for under TA96. Given the cost of
platform arrangements and the resale discount rates, reselling the incumbents' services is typically more
costly for entrants than providing service under a platform arrangement. However, platform-based entry
is more risky for entrants as they are not guaranteed a positive gross profit margin as they are under a
resale arrangement. This risk would be realized if the retail price fell below the platform cost, in which
case the entrant would face a negative gross profit under a platform arrangement whereas under a resale
arrangement the entrant's cost would fall by the same percentage as the retail price.
The largest incumbents in each state are the regional monopolies that were created in the court-ordered
split-up of AT&T in 1984 and are commonly referred to as RBOCs (Regional Bell Operating
Companies). While entrants can rent UNEs from other incumbents in areas not served by an RBOC, the
vast majority of UNE entry has occurred in RBOC regions. For the remainder of this paper, the term
incumbent will refer exclusively to RBOCs.









are also a number of states where the share increased substantially. Figure 3-4 displays

the corresponding data for platform-based entry. While most states are above the 45-

degree line, the extent to which they line above the line varies substantially.

Figures 2-1 through 2-4 raise important questions regarding telecommunications

market dynamics, including the following two: What market factors influence the level of

competitive entry via the two alternative means? What effects did state regulatory

policies or the political environment have on the observed level of entry?

The answers to these questions are of interest to both policymakers and

researchers. On one level, the answers shed light on the effectiveness of TA96 and its

implementation. Not only can these answers inform the ongoing debate regarding

regulation of U.S. telecommunications markets, but they may also provide guidance to

regulators in other countries attempting to fashion policies. Further, the results

supplement the existing body of literature regarding competitive entry. There has been

scant prior research regarding how entrants choose whether to lease all of the inputs from

their competitors (as in a platform arrangement) or buy some of the inputs themselves (as

in a loop arrangement). The choices of entrants could have important implications for

long-term competition.

Several authors have analyzed the determinants of competitive entry into local

telecommunications markets.8 These papers often use cross-sectional data sets to explain

the level of competitive entry, as measured by the number of entrants or the number of

telephone lines entrants have acquired. Typically these papers find that variables

associated with higher demand correspond to higher entry, while variables associated

8 Examples include Abel (2002), Abel and Clemments (2001), Alexander and Feinberg 21i '14), Brown and
Zimmerman (2"'114), Jamison (2"'114), Lehman (2002), Lehman (2003), Roycroft (2005), and Zolnierek,
Eisner, and Burton (2001).









with higher costs are correlated with a lower degree of entry. In regards to the effects of

regulation, areas where retail price caps are used are generally associated with lower

levels of entry, while the effects of the political composition of state public utility

commissions differ across papers.

This paper is closely related to Beard and Ford (2002) and Beard, Ford, and

Koutsky (2005). Beard and Ford (2002) use a pooled data set to analyze the determinants

of loop- and platform-based entry. Among their findings is that, for both types of entry,

as the cost of leasing the UNEs used in that type of entry increases, the level of entry

falls. Their estimates of the cross-price elasticity of demand also suggest that entry via

loop and platform arrangements are not substitutes. Beard, Ford, and Koutsky (2005)

examine a cross-sectional data set to examine the deployment of equipment by entrants.

Using proprietary data, they come to the conclusion that higher UNE leasing costs lead to

decreased entrant equipment investment. Sappington (2005) constructs a theoretical

model that suggests the price at which an entrant can lease an input may have little effect

on its decision to lease the input from the incumbent supplier or make the input itself.

This conclusion arises because the lease price influences the intensity of downstream

competition such that the incumbent tends to price less aggressively when the lease price

paid by the competitor is higher.

This paper improves upon the existing literature in at least four important ways.

First, panel data are used rather than cross-sectional data. Thus, the data will allow for a

more precise estimate of the effects of dynamic changes in the market environment on the

level of entry observed. Second, the influence of the political composition of state public

utility commissions is estimated. Third, the effect of the connection charge that loop









entrants must pay whenever they gain a new customer is measured. The potentially

dampening effect of the connection charge on loop-based entry was cited by the Federal

Communication Commission as an important basis for its regulatory treatment of

platform-based entry.9 Fourth, the empirical specification allows for the estimation of

differential effects of the explanatory variables for varying levels of market size.

Four significant conclusions are offered. First, while the two types of entry are

generally affected by different market factors, there appears to be cost-based substitution

between them. Second, changes in the own monthly costs of the two forms of entry have

limited effects, but loop-based entry decreases in response to increases in the connection

charges entrants must pay incumbents when a customer is acquired. Third, loop-based

entry tends to be more pronounced relative to platform-based entry as the degree of

Republican representation on state public utility commissions increases. Fourth, loop-

based entry is more responsive to changes in market conditions in smaller states, while

platform-based entry is more responsive to market conditions in larger states.

The paper is organized as follows. Section 2 presents background information on

the functional and regulatory differences between loop- and platform-based entry.

Section 3 presents the determinants that are hypothesized to affect the level of entry.

Section 4 details the econometric methodology and the data used. Section 5 presents the

estimation results, while Section 6 provides conclusions and areas for further research.


9 For example, see FCC (2003), 295-298.









Background Information on UNE-Based Entry

Loop-Based Versus Platform-Based Entry

To properly interpret the results below, it is important to understand the

differences between loop-based and platform-based entry.

As stated above, when an entrant acquires a customer under a loop arrangement,

the entrant leases only the UNEs associated with the wire that connects the customer's

premises to the incumbent's wire center. This wire is referred to as the local loop. In

order to provide service using a loop strategy, an entrant must provide its own switching

equipment10 and pay the incumbent for the space it rents and the power it uses in the

incumbent's buildings where the switching and related equipment are housed.11,12

Conversely, under a platform arrangement, the entrant does not have to own any of

the network equipment needed to provide phone service. The entrant in effect "re-

bundles" all of the UNEs it needs to provide service.

An important difference between loop- and platform-based entry is how the

incumbent's customers are switched to the entrant's service. Lines that are used to serve

customers under a loop arrangement must be physically disconnected from the

incumbent's switching equipment and reconnected to the entrant's equipment. This

transfer, known as a hot-cut, requires that both an incumbent and entrant technician be






10 While switches can have many functions, the key role that they serve for entrants to provide telephone
service is that they connect the incumbent's loop to the entrant's network.
1 Entrant switches require economies of scale to be cost-effective. Thus, entrants may install equipment in
remote locations that allows them to aggregate traffic before reaching a switch that it owns (this process
is known as back-hauling). However, the entrant then not only has to pay for the aggregation equipment,
but also must pay the incumbent to move its traffic to its switch.
12 This practice of renting space in an incumbent telephone facility is known as collocation.










present in order to perform a "seamless migration"13 of the customer to the entrant's

network. For each hot-cut that is performed, the entrant must pay the incumbent a

connection charge to compensate the incumbent for the labor involved. Conversely, to

transfer an incumbent's customer to an entrant who employs a platform strategy,

computer software is used that allows for the process to be fully automated. The entrant

must only pay a nominal administrative fee to the incumbent to transfer the customer.

History of Platform-Based Entry Regulation

While there has been widespread agreement that allowing entrants to lease the local

loop is beneficial to long-run competition, there has been heated disagreement as to the

effects of allowing entrants to follow a platform strategy. Advocates of allowing

platform-based entry typically point to the technical and financial difficulties entrants

face when using loop arrangements to serve residential customers.14 Those critical of

forcing incumbents to provide platform arrangements argue that the option discourages

entrant investment in telecommunications equipment, which is one of the goals of

TA96.15 Among the FCC commissioners, this disagreement typically splits along party

lines, with Democratic commissioners in support of the platform-based entry option and

Republicans opposed. 16


13 A seamless migration is an industry term that describes a hot-cut where the customer does not lose
phone service for any noticeable length of time.
14 The connection charges and labor costs involved in a hot cut are claimed to be prohibitively expensive
for entrants, especially as the chur rate among their customers is relatively high (WorldCom (2002)). In
addition, when the hot cut is performed, the customer may lose service for a brief period of time.
Entrants complain that customers associate the delay with the entrant to whom they are transferring
service, and thus the customer immediately perceives a lower quality of service with the entrant (FCC
(2003), 290-291).
15 See, for example, Crandall, Ingraham, and Singer (2i 1'4).
16 A notable exception to this generalization is the current Republican FCC Chairman Kevin Martin, who
sided with the two Democratic commissioners in the 2003 Triennial Review Order (FCC (2003)) to
continue the availability of platform-based entry.









Given the enormous financial stakes involved in the local telecommunications

market, perhaps it is not surprising that there has been a great deal of regulation and

litigation concerning platform-based entry. There are two main regulatory requirements

for platform-based entry to be feasible: entrants must be able to "re-bundle" UNEs and all

of the necessary network elements must be unbundled by the FCC.

Entrants eventually realized that for certain customers they could realize greater

profits if they simply leased all of the UNEs necessary for them to provide phone service,

rather than providing any of the necessary equipment themselves. 17 While incumbents

began allowing entrants to lease the UNEs necessary in a platform arrangement in some

states in 1999, the incumbents often charged additional fees for "re-bundling" the

network elements. The FCC then ruled that the incumbents could not charge these re-

bundling fees. Litigation soon followed, culminating with the Supreme Court ruling in

2002 in Verizon vs FCC that entrants can legally re-bundle UNEs at no additional charge

from the incumbents.

In terms of the availability of UNEs, the network element that has been at the heart

of the platform-based entry debate is the incumbent switches, specifically those that are

used to service residential and small business customers.18 While the debate among the

FCC commissioners has often been very contentious, until recently most incumbent

switches19 have been available to entrants as UNEs at relatively low rates20. However, in



17 To be precise, the entrant must still provide equipment necessary for billing, marketing, and customer
service functions.
18 The FCC defines this classification as any customer with four access lines or fewer.
19 In its 1999 UNE Remand Order (FCC (1999)), the FCC ordered that incumbents did not have to lease
certain switches located in the 50 largest MSAs.
20 These rates are set by state public utility commissions and based on a TELRIC (total element long-run
incremental cost) methodology. In essence, the UNE cost is to be based on the costs a hypothetical









March 2004, the U.S. Court of Appeals in Washington, D.C. ruled that the FCC rules did

not comply with TA96. In June 2004, the Bush administration announced that it would

not appeal the decision by the U.S. Court of Appeals, thus ringing the death bell of

platform-based entry as known by market participants.21 Partly in response to these

developments, AT&T and MCI subsequently announced that they were exiting the

residential market (Young (2004)). Finally, in February 2005, the FCC (2005) ruled that

as of February 2006 incumbent switches would not be available to entrants at the low

rates.22

Hypothesized Determinants of Loop- and Platform-Based Entry

This section outlines the likely determinants of loop- and platform-based entry.

The determinants can be classified as describing the revenue potential, regulated costs,

and political effects.

Measures of revenue potential reflect the profits an entrant would expect to earn.

An obvious candidate would be the current retail price in that market, in so far as, ceteris

paribus, the higher the retail price the more attractive a market is to a potential entrant.23

The incumbent's average net revenue per line in that state is used as a proxy for these

prices. Another variable of potential interest to entrants is the growth prospects of the

market. One may expect that entrants will focus their efforts in areas where they expect

incumbent would incur using current technology and is not to be based on the actual historical
investment by the incumbent. See Quast (2005) for further details.
21 Rather than abruptly make incumbent switches unavailable as UNEs, the FCC ordered a 12-month
transition phase during which switches would be available at slightly higher costs. However, after the
12-month transition the parties are to negotiate the rates at which switches can be leased.
22 Many entrants have negotiated agreements with incumbents to continue leasing switches after they are
no longer available at TELRIC rates. For instance, Qwest has negotiated such agreements with more
than 60 entrants, while Verizon and BellSouth have negotiated such agreements with over 50 and 45
CLECs, respectively. (See BellSouth (t' "4); State Telephone Regulation Report (2005); and Telecom
A.M. (2005).)
23 Unfortunately, within a state and across customer types, several retail prices may be charged.









increases in the number of potential customers. To control for the effects of changes in

the growth level in a state, the change in the unemployment rate is included as an

explanatory variable.

The level of entry is also likely to be affected by the costs an entrant expects to

incur in providing service. A main source of costs for entrants is the payments they must

pay to incumbents to lease UNEs. As noted above, during the sample period those rates

are determined by state public utility commissions and do not vary by entrant within a

given state. For loop-based entry, there are two UNE rates that are especially important to

entrants: (1) the monthly price they must pay to incumbents to rent the local loop and a

connection device known as a line port; and (2) the fee charged by the incumbent to

transfer one of its customers line to the entrant's equipment (i.e., to perform a hot cut).

One would expect both rates to have a negative effect on the level of loop-based entry. In

terms of platform-based entry, the process used to transfer a customer to an entrant's

network is fully automated and incurs only a nominal charge. However, under a platform

arrangement the entrant must pay the incumbent to lease its switching equipment and to

transport calls over the long distance network, as well as to lease the local loop. This total

charge is referred to as the platform rate and, as it increases, platform-based entry is

expected to decline.

Political factors also may influence entry in ways that are not entirely clear. A

potential influence is the political composition of the state public utility commission.

Among the ways state public utility commissions affect entry (beyond setting UNE rates)

is by forcing the incumbent to adjust their hot-cut processes and computer systems










through which entrants order and are billed for services for newly acquired customers.24

Also, state commissions set right-of-way access regulations that can either help or hinder

entrants who install their own equipment. One could imagine that a commission that is

majority Republican would oppose governmental involvement in the market and thus

limit their efforts at facilitating a relatively more involved regulatory design such as

platform. However, it may also be the case that Republican allegiance to small

businesses may predispose them to treat entrants favorably. Likewise, the effect of a

Republican governor on competitive entry is not clear.25,26

While the factors described above may be expected to have certain effects on

entry, these effects may differ substantially by the size of the market. For instance, a $1

increase in revenue per customer may lead to entrants attaining a greater additional

market share in larger markets than in smaller markets, as entrants may already have a

presence in larger markets and the incremental cost of acquiring an additional customer is

relatively low. Conversely, changes in market conditions may have a greater impact in

smaller markets, as entry in these markets is less certain and changes in potential profits

can determine whether entrants choose to enter the market. To capture these potential

differences across states, the model includes interaction terms between the factors listed

above and the population of the state.

24 For example, states have implemented various approaches in forcing incumbents to implement
procedures to migrate customers to entrants (FCC (2003), 309-310). In the former Ameritech region,
entrants accused SBC of not having adequate computer systems to allow for platform-based entry (see
Kovacs (2002)).
25 Note that while the discussion that follows refers to measurements of Republican influence, given the
virtual binary political party environment, the results can be cast in terms of Democratic influence
merely by replacing the reported result with its opposite.
26 Another regulatory variable that could have a significant effect on entry is whether the incumbent is
under price or rate of return regulation for their retail prices. During the sample period the type of retail
rate regulation did not change for any of the incumbents. Therefore, this effect is captured in the fixed
effects analysis.










One of the advantages of a panel data analysis is the ability to control for effects

specific to a state that are not captured elsewhere. State fixed effects control for time-

invariant effects that are not captured in the other explanatory variables and that are

specific to a given state (e.g., operating cost differences).27 In addition, time fixed effects

are included to control for national effects specific to a given period.28

Model Specification and Data Used

Given that the levels of loop- and platform-based entry are highly related,29 it is

appropriate to estimate them simultaneously. Thus, a seeming unrelated regression (SUR)

approach is employed.30

Summary statistics of the data used are provided in Table 3-1. The sample is based

on semi-annual data by state for the period January 2001 to July 2004.31'32'33 Correlation

coefficients between the explanatory variables are provided in Table 3-2.



27 Given that in the sample no state is served by more than one incumbent, state fixed effects also control
for incumbent fixed effects.
28 The model was also estimated without state and time fixed effects. Compared with the results reported
below that include these fixed effects, the estimates without fixed effects indicate a stronger negative
relationship between the level of entry and the monthly cost of that form of entry. These results are
similar to those in Beard and Ford (2002), who do not include state fixed effects in their regressions.
However, in the regressions without state and time fixed effects, the monthly cost of entry may reflect
the attractiveness of entry into that state. For example, a high loop rate may be associated with lower
entry, but high loop rates exist in areas where loop entry is relatively unattractive (e.g., Wyoming).
29 Insofar as when an entrant decides to enter a market by leasing unbundled elements, the entrant must
choose either one of these entry methods.
30 As specified, the SUR model results in the same coefficient estimates as when each dependent variable
is regressed separately. However, the SUR estimator is more efficient (assuming the model is correctly
specified) and thus results in lower standard errors of the coefficient estimates.
31 Alaska, Hawaii, and Washington, DC are not included in the analysis because of their particular
geographic circumstances. Arkansas is not included due to difficulties in obtaining connection cost data,
while for New Mexico those data could only be obtained for January 2003 forward. The fraction of lines
leased in a platform arrangement by Verizon are not available for Maine and Vermont for January and
July 2002, and for Delaware, New Hampshire, and West Virginia for July 2002 only. The resulting data
set contains 271 observations.
32 The data regarding the level of entry, UNE rates, and the incumbent's average revenue are based on the
incumbent's service territory within a state, rather than on the entire state. Typically the two differ in that
the incumbent service area may not include some of the relatively rural areas within a state. However, as










The dependent variables in the estimations are the fraction of incumbent lines

leased by entrants using a loop arrangement and the fraction of lines leased using a

platform arrangement. These variables are based on data reported by incumbents to the

FCC in their Form 477 data submissions (http://www.fcc.gov/wcb/iatd/comp.html).34

In regards to revenue potential, the incumbent's revenue per line for a state is

obtained from ARMIS data submitted by the incumbents to the FCC

(http://www.fcc.gov/wcb/armis/).35 The change in the unemployment rate is based on

seasonally adjusted data reported by U.S. Bureau of Labor Statistics

(http://www.bls.gov/data/home.htm).

As the level of entry may affect the average revenue, the average revenue may be

endogenous to the level of competitive entry. To attempt to control for this potential

endogeneity, three alternative strategies were also employed: using the lagged value of

average revenue as the explanatory variable, using the lagged value of average revenue as

an instrumental variable, and using as an instrumental variable the percent of the

incumbent's lines that are used by participants in a low-income assistance program

known as Lifeline.36 The estimates from each of these specifications are very similar to

those reported below.37


most of the entry occurred in non-rural areas, this discrepancy between the data sets should not
materially affect the results.
33 The incumbent's revenue per line is reported annually. The missing data points are calculated by linearly
interpolating the data.
34 As the UNE cost data are not available, the service areas of Verizon that were formerly GTE service
areas are not included in the analysis.
35 Specifically, the average revenue is calculated by dividing net revenue (ARMIS Report 43-01, Line
1090) by the number of lines (ARMIS Report 43-08). While a variable that more precisely measures the
retail rate would be preferred, this variable should approximate the potential revenue an entrant could
obtain and has been used by other authors, e.g., Ai and Sappington (2005) and Abel (2002).
36 The Lifeline program is an effort coordinated by the FCC and state public utility commissions that helps
pay for basic phone service for low-income consumers. Incumbents are obligated to publicize the










The monthly loop and platform UNE costs are obtained from surveys of state

public utility commissions by Billy Jack Gregg of West Virginia's Office of the

Consumer Advocate (http://www.cad.state.wv.us/Une Page.htm).38 During the sample

period examined in this paper, most state public utility commissions set rates that varied

by different density zones within the state. For the loop monthly cost, the monthly loop

and port cost in the urban zone used39, as these are the areas most likely to be entered by

entrants who utilize a UNE-L entry strategy. For the platform monthly cost, the variable

used is the incremental cost of providing service to customers via a platform-based entry

strategy in the suburban zone, as entrants typically serve residential customers via a




availability of this service and are encouraged to sign up as many participants as possible, which
therefore influences their customer mix. Further, one can imagine that low-income customers are more
likely to participate in the Lifeline program the higher the retail price charged by the incumbent. As
such, it is likely that the extent to which the incumbent's customers participate in this program is
correlated with the incumbent's average revenue. The first-stage F-statistic confirms that the variable is
sufficiently correlated with the incumbent's average revenue (or, in the language of the instrumental
variable literature, it is sufficiently relevant). Conversely, entrants typically do not participate in this
program. Also, differences in how state utility commissions administer the Lifeline program result in the
fraction of participants being uncorrelated with state income. Thus, the effect on entry of the fraction of
the incumbent's customers that are Lifeline subscribers should be minimal and the variable should be
exogenous to the level of entry. While these arguments suggest that the variable is exogenous, there is
potential correlation between how state utility commissions administer the Lifeline program and how
they treat competitive entry. Further, there does not exist a test to determine if an instrument is
exogenous in an exactly identified model such as this. Thus, it is not clear that this candidate instrument
is sufficiently exogenous to be valid.
7 While none of these three strategies is perhaps ideal, only one existing telecommunications entry paper
reviewed by the author has attempted to address this issue (Abel (2'' i' I?', and it employs the first strategy
listed above. Further, concerns about endogeneity are mitigated by the fact that often many of the
incumbent's retail rates are either regulated or published in tariffs that must be approved by state utility
commissions. Thus, the response in average revenue to changes in entry may be delayed or diminished.
38 Some participants in legal proceedings have disputed the way in which platform arrangement costs are
calculated in these data (see Willig, et al, 2002). Switching rates are typically based on minutes of use,
and the rates reported by Gregg are based on 1000 minutes of use. Criticisms of the Gregg rates center
on whether 1000 minutes of use is an appropriate benchmark. However, there is no other source of
consistently reported UNE rates over this period. Perhaps more importantly, as the fixed effects
estimator is based on deviations from the state mean for each explanatory variable, mismeasurement bias
is unlikely. Specifically, if the minutes of use used to calculate the platform arrangement cost is
consistently either less or more than the actual minutes of use, the coefficient estimates will not be
affected.
39 Data on collocation costs are not available.










platform-based entry strategy.40 The loop connection charges are obtained by reviewing

state public utility commission and FCC documents and trade press, and by contacting

the state public utility commissions directly.41

The percent of Republicans serving on state public utility commissions is derived

by reviewing membership directories of the National Association of Regulatory

Commissioners,42 while the political affiliation of the Governor in a state is obtained

from the U.S. Statistical Abstract (http://www.census.gov/prod/www/statistical-abstract-

04.html) and the National Governors Association website (http://www.nga.org).

Estimation Results

Estimates without Population Interactions

Coefficient estimates

Table 3-3 summarizes the coefficient estimates and t-statistics from estimating the

fraction of lines entrants acquired via loop and platform arrangements. The estimations

are first performed including each group of explanatory variables individually, and then

including all of the explanatory variables. The table indicates that the estimates do not

vary substantially according to whether the other groups of explanatory variables are

included in the regression.




40 Due to difficulty in comparing these costs across states, costs for transporting calls between switches are
not included in the Gregg UNE-P cost estimate.
41 A concern regarding the endogeneity of the UNE rates could arise due to the potential for reverse
causation. In particular, one could surmise that low levels of entry may persuade state public utility
commissions to lower UNE rates. However, any such response by state commissions could only occur
with a substantial lag, as the commissions would only learn of the level of entry with a lag and can only
revise UNE rates after a lengthy set of proceedings. Quast (2005) finds that it takes approximately one
year for the level of UNE entry to affect UNE rates set by state commissions. Thus, UNE rates can be
treated as exogenous.
42 The dates of the membership directories used to construct this variable are February 1999, February
2002, February 2003, July 2003, and March 2004









Of the variables that measure revenue potential, the average revenue has a

positive and statistically significant effect on the platform share, while its effect on loop

share is statistically insignificant. The lack of significance in the loop share equation may

reflect that this variable is a better indicator of the average residential price than the

average business price. The change in the unemployment rate is statistically insignificant

for both types of entry.

In regards to the variables that measure costs, those that measure the own monthly

cost have negative coefficients. Specifically, the coefficient on the loop monthly cost in

the loop share equation and the coefficient on the incremental platform cost in the

platform share equation are negative. However, neither is statistically significant. In

contrast, the loop connection charge is negative and statistically significant. In the

platform share equation, the coefficient on the loop monthly cost is positive, suggesting

that there may be substitution to platform-based entry as the monthly loop cost increases.

The coefficients on the regulatory variables indicate that Republican state public

utility commissions are associated with higher shares of loop-based entry and lower

shares of platform-based entry.43 Conversely, Republican governors are associated with

less loop-based entry.

Economic effects

To obtain a sense of the relative importance of the explanatory variables, Table 3-4

details the effect of a one-standard deviation increase in each explanatory variable on the

share of loop and platform-based entry.44


43 Conceivably, the Republication state commission variable could simply reflect that Republican
commissions set lower loop rates and higher platform rates. However, the very low correlation between
the UNE rates and commission political affiliation (see Table 3-2) mitigates this concern.
44 For binary explanatory variables, the economic effect is calculated as the effect from a change in the









The largest effect in either equation is associated with average revenue in the

platform share equation. A one-standard deviation increase in the average revenue

($9.60) is associated with an increase of over 2 percentage points in the share of the

incumbent's lines leased in a platform arrangement, which translates to a roughly 33%

increase in the platform share when measured at the mean of 6.1%.

Of the cost variables, a one-standard deviation increase in the loop connection

charge ($25.80) decreases the level of entry by almost 0.2 percentage points. Measured at

the mean of 2.6%, this effect translates to an approximately 6% decrease in the loop

share, which validates to some extent the FCC's concern regarding the dampening effect

of this charge on loop-based entry.45 The economic effect of the monthly loop cost on

platform-based entry is non-trivial, which suggests that for some customers entrants

utilize a platform strategy when the monthly loop cost increases. Surprisingly, the own-

cost economic effects are not only statistically insignificant but also relatively small.

The political affiliation of the state utility commissions has divergent effects on the

two means of entry. When adjusted for the difference in means, the absolute value of the

economic effects is roughly equal for loop and platform-based entry. As mentioned

above, this may reflect a preference on the part of Republican commissioners to promote

the entry option that entails less regulatory intervention. The negative effect of

Republican governors on loop-based entry is interesting, given the estimated preference

of Republican commissioners and the absence of an effect on platform-based entry. This

effect may reflect a concern on the part of entrants that Republican governors discourage



explanatory variable from zero to one.
45 Note that this effect does not capture the effects of logistic difficulties entrants may face when
attempting to connect a customer to its network via a loop arrangement.









regulation and are thus more likely to advocate policies that are favorable to incumbents.

The lack of effect on platform-based entry may reflect the low fixed costs that platform-

based entry requires, and the resulting ability of an entrant to exit a market relatively

quickly.

Estimates with Population Interactions

Coefficient estimates

As described above, the estimates in Table 3-3 and Table 3-4 and estimates in prior

studies assume that the effects of the explanatory variables do not vary across states.

However, the effects of market and political factors may well differ by the size of the

state. To account for such potential variation, the model is also estimated such that each

explanatory variable is interacted with the state population.

Table 3-5 details the coefficient estimates and t-statistics that result from including

population interactions. For each explanatory variable, two coefficients are reported: the

coefficient on that variable and the coefficient of the variable multiplied by the state

population. As such, one can interpret the second coefficient as the additional effect of

the explanatory variable as the population is increased.

For the variables that measure revenue potential, the strong effect of the average

revenue on the platform share persists when population interactions are included.

However, whereas previously average revenue did not have a statistically significant

effect in the loop share equation, the average revenue interacted with the population does.

Further, the coefficient on the uninteracted average revenue variable is larger than the

estimate in Table 3-3 and falls just short of a statistical significance level of 90%.

Market size effects also appear to be important in measuring the effects of costs on

entry share. For the loop connection charge in the loop share equation, the negative









coefficient on the uninteracted variable and the positive coefficient on the interacted

variable suggest that the connection charge has a negative effect on entry in smaller

markets, but the effect diminishes in larger markets. Conversely, the negative effect of

the monthly incremental platform cost on platform-based entry tends to increase as the

population increases. The effects of the own monthly costs also appear to differ by

market size.

Of the political variables, in contrast to the results in Table 3-3 and Table 3-4, it

appears that there may be an effect on platform-based entry associated with Republican

governors when population effects are taken into account. Specifically, in small states

Republican governors are associated with greater loop-based entry, but the effect

diminishes as the state's population increases.

Economic effects

To estimate the economic effects implied by the estimates in Table 3-5, Table 3-6

calculates the effect on entry share of a one-standard deviation in each explanatory

variable for two representative population sizes: the 25th percentile and the 75th percentile

(2.2 and 7.4 million, respectively).46 The first two columns detail the economic effects on

the loop share while the last two columns detail the economic effects on the platform

share.

The economic effects on the loop share differ significantly by population. None of

the statistically significant explanatory variables for the 25th percentile of population has

a statistically significant effect for the 75th percentile of population. Specifically, the

effects of the loop connection charge and the political affiliation of the utility commission

46 The economic effect is calculated as the standard deviation of the explanatory variable times the sum of
the coefficient on the uninteracted variable plus the population times the coefficient on the interacted
variable.









and governor become statistically insignificant as the size of the state increases. Further,

the effect of the monthly loop cost is nearly statistically significant at the 25th percentile

but not at the 75th percentile.

The effects on loop share may be diminished in larger states because loop-based

entry is less responsive to changes in revenue and costs in larger markets. Loop-based

entrants may already be present in larger markets because they contain the most potential

customers, and thus even a small profit margin per customer translates to large profits in

that market. Further, incumbent prices to business customers have historically been well

above cost in order to cross-subsidize lower prices to residential customers. Loop-based

entrants may have previously entered these larger markets to undercut the inflated

incumbent business prices and may therefore be less affected by changes in revenue and

costs.

The differential effect of Republican commissioners on loop share in smaller states

may also be due in part to differences in entry conditions. In large markets loop-based

entry may be sufficiently profitable that substantial entry will occur regardless of state

regulatory policy. In contrast, given the relative difficulty of attracting loop-based entry

in small states, Republican state commissions may adopt policies that are especially

favorable to loop-based entry and thus the commissions have a larger effect on the level

of entry.

However, the effects of the explanatory variables on platform share offer more of a

mixed picture. The positive effect of the loop monthly cost is quite similar in the two

market sizes, but the effects of the other explanatory variable differ. In contrast to the

effects on loop share, the economic effects on platform share tend to become more









pronounced as the population increases. For instance, the effect of the average revenue

per line is over 20% larger at the 75th population percentile than at the 25th percentile.

Also, the monthly incremental platform cost has a statistically significant effect in larger

states whereas in smaller states it does not.

These findings may reflect in part how platform-based entrants attract customers.

Platform-based entrants tend to target residential customers in broad geographic markets

(such as an MSA or state) and use mass marketing to acquire customers. In smaller states

like Wyoming, an incremental increase in per customer profit may not justify the fixed

marketing costs of entering the state due to the limited number of potential customers.

Conversely, when per customer profit levels increase in a large state such as California,

the same increase in marketing efforts can reach a much larger population and thus may

justify the additional expense.

The negative effect of Republican commissioners on platform-based entry may also

be due to a difference in the ability of small and large states to attract entry. Republican

commissions may have a preference for loop-based entry over platform-based entry, but

in small states it may be difficult to attract loop-based entry. Thus, the desire to have any

form of entry may offset whatever preferences the commissioners possess. In larger

states, commissioners can perhaps be more selective as to which type of entry to

encourage, thus Republican commissioners may discourage platform entry.

The economic effect of the loop connection charge on platform-based entry also

differs by market size. The positive effect in small states can be explained by substitution

from loop-based to platform-based entry as the cost of loop-based entry increases.

However, the negative effect in large states implies that increases in the loop connection









charge deter both types of entry. A possible explanation is that some platform-based

entrants in larger states enter those markets with the intent to in the long-term to convert

to a loop-based arrangement. When the cost of loop-based entry increases, some of these

entrants may decide that it is less profitable to pursue such a strategy and they may elect

to curtail their platform-based operations.

Conclusion

This paper analyzes the factors that determine the level of entry in local

telecommunications markets given two alternative entry strategies. The estimates suggest

that while generally the two types of entry are affected by different market factors, there

appears to be cost-based substitution between them. Changes in the own monthly costs of

the two forms of entry have limited effects, but loop-based entry decreases in response to

increases in the connection charges entrants must pay incumbents when a customer is

acquired. Loop-based entry tends to be more pronounced relative to platform-based entry

as the degree of Republican representation on state public utility commissions increases.

Finally, loop-based entry is more responsive to changes in economic conditions in

smaller states, while platform-based entry is more responsive to market conditions in

larger states.

The results in this paper may suggest some potential lessons regarding the effects

of TA96 and potential revisions to it. Proponents of platform-based entry can point to the

negative effect of loop connection charges as evidence of the need for an alternative to

loop-based entry. On the other hand, critics of platform-based entry can argue that, given

the positive effect of the monthly loop cost on the platform share, loop-based entry is

hindered by the existence of the platform-based entry option. Also, the results suggest

that the local interests of state regulators need to be taken into account when they are









charged with implementing federal policies. Finally, given the differing effects across

states of different sizes, policymakers need to consider how to fashion policies that

achieve national goals but recognize local market conditions.

The findings in this paper could be extended in several directions. First, if the data

become available, a more disaggregated analysis of the entry decision could provide

more precise results. Market conditions can vary greatly within a state, but the available

data do not allow for an analysis of that granularity. Also, as noted above, the monthly

loop cost variable used in the regressions does not include collocation costs, while the

estimate of the incremental cost of platform-based service does not include costs related

to transporting calls between switches. Including these additional costs would allow for a

more complete analysis. Additionally, a more precise measure of the retail price may

uncover a more important role for it in determining the level of entry. Finally, a complete

analysis would simultaneously estimate the effects of other types of telecommunications

entry, such as cellular phones and the emerging presence of new technologies such as

voice-over-internet-protocol.


























-*-
0
_ 10-


8-


6-


Jul01


Jan02 Jul02 Jan03 Jul03 Jan04


Jul04


Figure 3-1 Fraction of Incumbent Lines Leased by Entrants (National Average).


Jul01


Jan02 Jul02 Jan03 Jul03 Jan04


Jul04


Loop Platform


Figure 3-2 Fraction of Incumbent Lines Leased by Entrants by Means of Entry (National
Average).

































0 5 10 15
Share in July 2001 (Percent)


Figure 3-3 Loop Share of Incumbent Lines, July 2001 and July 2004.


0 5 10 15 20 25 30
Share in July 2001 (Percent)


Figure 3-4 Platform Share of Incumbent Lines, July 2001 and July 2004.










Table 3-1 Summary Statistics.
Standard
Variable Mean Minimum Maximum Deviation

Dependent Variables
Share of Entrant Loop Lines 2.6 0.04 11.9 2.0
Share of Entrant Platform Lines 6.1 0.00 24.4 5.2

Explanatory Variables
Incumbent Average Revenue per Line $61.12 $45.16 $101.19 $9.60
Change in Unemployment Rate 0.13 -1.3 2.2 0.61
Loop Monthly Cost $13.90 $4.77 $28.14 $3.80
Loop Connection Charge $45.69 $3.33 $159.76 $25.80
Platform Incremental Monthly Cost $7.25 -$0.17 $24.81 $4.70
Percent of Commission Republican 53.2 0.0 100.0 33.3
Governor Republican 0.56 0.00 1.00 0.5

Interaction Variable
Population (millions) 6.3 0.5 35.9 6.5




Table 3-2 Correlation Matrix of Explanatory Variables.
Unemploy Loop Mthly Loop Con Plat Incr % PUC
Avg Rev Rate Cost Chrg Mthly Cost Rep Gov Rep
Avg Rev 1.00
Unemploy Rate -0.14 1.00
0.25 0.10 1.00
Loop Mthly Cost 0.25 0.10 1.00
Loop Con Chrg 0.16 0.14 0.37 1.00
Plat Incr Mthly 0.31 0.08 0.25 0.15 1.00
Cost
% PUC Rep 0.23 0.07 0.05 0.02 -0.01 1.00
Gov Rep 0.13 0.05 0.07 -0.04 0.04 0.41 1.00








63



Table 3-3 Coefficient Estimates without Population Interactions.
Specification #1 Specification #2 Specification #3 Specification #4
Explanatory Variable Loop Platform Loop Platform Loop Platform Loop Platform

Revenue Potential
Average Revenue per 0.010 0.185 *** 0.001 0.217 ***
Line (0.48) (3.05) (0.04) (3.58)
Change in 0.115 -0.051 0.164 -0.157
Unemployment Rate (1.10) (-0.17) (1.56) (-0.54)
UNE Rates
p M C -0.032 0.194 *** -0.033 0.186 ***
Loop Monthly Cost
(-1.32) (2.87) (-1.37) (2.79)
Loop Connection -0.006 ** 0.002 -0.007 *** 0.008
Charge (-2.10) (0.31) (-2.72) (1.04)
Platform Incremental 0.010 -0.023 0.014 -0.028
Monthly Cost (0.51) (-0.44) (0.76) (-0.53)
Political
Percent State Utility 0.009 ** -0.026 ** 0.009 ** -0.026 **
Commission (2.24) (-2.36) (2.34) (-2.40)
Governor Republican -0.225 0.001 -0.306 ** 0.004
(Binary) (-1.72) (0.00) (-2.33) (0.01)
Within R-Squared 0.34 0.71 0.35 0.71 0.35 0.70 0.37 0.72
Number of Observations 314 314 314 314 314 314 314 314
Notes t-statistics are reported in parentheses, coefficient estimates for state and time fixed effects and constant term omitted for
brevity, *** statistically significant at 99% confidence level, ** statistically significant at 95% confidence level, statistically
significant at 90% confidence level









Table 3-4 Economic Effects without Population Interactions.
Explanatory Variable Loop Platform


Revenue Potential

Average Revenue per Line
Change in Unemployment
Rate
Costs

Loop Monthly Cost

Loop Connection Charge

Platform Incremental
Monthly Cost
Political


0.01
(0.04)
0.10
(1.56)

-0.13
(-1.37)
-0.18 ***
(-2.72)
0.03
(0.76)


2.08 ***
(3.58)
-0.10
(-0.54)

0.71 ***
(2.79)
0.21
(1.04)
-0.13
(-0.53)


Percent State Utility 0.30 ** -0.87 **
Commission Republican (2.34) (-2.40)
-0.31 ** 0.00
Governor Republican (Binary .
(-2.33) (0.01)
Notes Economic effects for continuous explanatory variables are based
on a one standard deviation increase in that variable, economic effects for
binary explanatory variables are based on a change in the variable from
zero to one. t-statistics are reported in parentheses, *** statistically
significant at 99% confidence level, ** statistically significant at 95%
confidence level, statistically significant at 90% confidence level










Table 3-5 Coefficient Estimates with Population Interactions.
Explanatory Vanable Loop Platform


Revenue Potential


Average Revenue per Line

Interacted with
Population
Change in Unemployment
Rate
Interacted with
Population
Costs

Loop Monthly Cost

Interacted with
Population

Loop Connection Charge

Interacted with
Population
Platform Incremental
Monthly Cost
Interacted with
Population


0.036
(1.50)
-0.010 ***
(-3.82)
0.032
(0.27)
0.011
(0.73)


-0.059
(-1.58)
0.006
(1.04)
-0.016 ***
(-3.86)
0.003 ***
(3.33)
0.035
(1.18)
-0.004
(-0.64)


0.225 ***
(3.42)
0.015 **
(2.03)
0.269
(0.81)
-0.047
(-1.14)


0.186 *
(1.80)
0.002
(0.09)
0.037 ***
(3.17)
-0.008 ***
(-3.73)
0.099
(1.23)
-0.033 **
(-2.13)


Political
Percent State Utility 0.011 ** -0.010
Commission Republican (2.03) (-0.63)
Interacted with -0.001 -0.010
Population (-1.25) (-0.75)
-0.342 0.895 *
Governor Republican (Binary .7
(-1.86) (1.76)
Interacted with 0.019 -0.176 ***
Population (0.99) (-3.32)
Within R-Squared 0.44 0.76
Number of Observations 314 314
Notes t-statistics are reported in parentheses, coefficient estimates for
state fixed effects and constant term omitted for brevity,
*** statistically significant at 99% confidence level, ** statistically
significant at 95% confidence level, statistically significant at 90%
confidence level










Table 3-6 Economic Effects for the 25th and 75th Population Percentiles.
Loop Platform
25th 75th 25th 75th
Explanatory Variable Percentile Percentile Percentile Percentile

Revenue Potential
0.13 -0.36 2.47 *** 3.20 ***
Average Revenue per Line
(0.65) (-1.65) (4.24) (5.25)
Change in Unemployment 0.03 0.07 0.10 -0.05
Rate (0.52) (1.02) (0.57) (-0.25)
Costs
p M C -0.17 -0.05 0.72 ** 0.75 **
Loop Monthly Cost
(-1.62) (-0.42) (2.44) (2.55)
-0.28 *** 0.08 0.52 ** -0.59 **
Loop Connection Charge
(-3.47) (0.83) (2.31) (-2.29)
Platform Incremental 0.13 0.04 0.14 -0.67 **
Monthly Cost (1.28) (0.33) (0.49) (-2.04)
Political
Percent State Utility 0.33 ** 0.20 -0.40 -0.63
Commission Republican (2.06) (1.54) (-0.95) (-1.79)
Governor Republican -0.30 -0.20 0.51 -0.41
(Binary) (-1.92) (-1.56) (1.19) (-1.15)
Notes t-statistics are reported in parentheses, coefficient estimates for state fixed effects and
constant term omitted for brevity, statistically significant at 99% confidence level,
** statistically significant at 95% confidence level, statistically significant at 90%
confidence level















CHAPTER 4
DOES THE FORM OF DOCTOR COMPENSATION AFFECT THE QUALITY OF
CARE IN MEDICAID HMOS?1

Introduction

The Medicaid program, already one of the largest social programs in the United

States, is growing rapidly. Between 1996 and 2004, the number of Medicaid enrollees

increased by roughly one-third, from 33 million to 44 million (U.S. HHS (2004), 3). In an

attempt to control the cost of the Medicaid program and to improve the quality of care

provided to enrollees, many states have moved enrollees into HMOs (also known as

managed care organizations). During the same 1996 to 2004 time period, the fraction of

Medicaid enrollees in managed care organizations increased by approximately one-half,

from 40% to over 60% (U.S. HHS (2004), 3). The trend towards managed care does not

appear to be slowing. For instance, in December 2005 the governor of Florida signed a

bill that requires all of the state's Medicaid enrollees eventually be enrolled in HMOs

(Farrington (2005)).

State Medicaid programs often contract with HMOs to care for the health care

needs of Medicaid participants. The HMOs, in turn, contract with doctors and other

health care providers to deliver necessary health care services. HMOs can differ in many

ways, including the manner in which they pay their doctors. FFS and capitated payment

arrangements are common. Under a FFS arrangement, the doctor is paid according to the



1 This paper is co-authored with Betsy Shenkman (Department of Epidemiology, Health Policy Research
and Pediatrics, University of Florida) and David Sappington (Department of Economics, University of
Florida).









services she provides to an enrollee. Under a capitated payment arrangement, the doctor

is paid a fixed amount per enrollee regardless of the health care services actually

provided to the enrollee. Consequently, a doctor paid via FFS can increase her revenue by

providing additional services. In contrast, the revenue a capitated doctor receives is not

affected by the health care services she provides.

This paper investigates whether the means by which HMOs compensate their

doctors influence the quality of care the HMO's enrollees receive. Using data for all of

the Medicaid HMO enrollees in a large state,2 we find that enrollees in HMOs that pay

their doctors exclusively via FFS arrangements are more likely to receive services for

which the HMO's doctors receive additional compensation. Further, these enrollees are

less likely to receive services for which the HMO's doctors do not receive additional

compensation. These findings suggest that financial incentives may influence the

behavior of doctors in Medicaid HMOs, and thus the health care received by Medicaid

participants enrolled in HMOs.

Numerous studies have analyzed whether the form of physician pay influences the

level of care provided to HMO enrollees. The papers suggest such a link often is present.

For example, Hillman, Pauly, and Kerstein (1989) report that hospitalization rates are

higher for enrollees whose physicians are paid via FFS rather than capitation. Steams,

Wolfe, and Kindig (1992) find that specialist referrals, hospital admissions, and hospital

length of stays fell when an HMO switched from FFS physician payment to capitation

payment. Ransom et al. (1996) report that gynecologists tend to provide fewer elective

surgical procedures when their payment method is changed from FFS to capitation.


2 The state is not identified, to preserve confidentiality of key data.









Shrank et al. (2005) find that fewer cataract procedures are performed when physicians

are moved to capitation. In contrast, Conrad et al. (1998) do not find any significant

effects of physician payment method on health care utilization.

Our analysis enhances the literature in three important respects. First, we examine

differences in the behavior of FFS and capitated doctors for services that have different

effects on the doctors' expected profits. As noted, we find that FFS doctors deliver more

services that increase their profit than capitated doctors. However, corresponding

differences are not detected on services that do not increase physician profit. Second, we

consider services for which there are clearly stated and widely accepted norms for the

proper level of care. Consequently, we are able to assess whether financial incentives

affect the extent to which actual care departs from the most appropriate level of care.

Third, we examine the health care services delivered to Medicaid enrollees, who often are

especially at risk of not obtaining the proper level of care.

The paper is organized as follows. Section 2 describes the services we analyze and

explains how the profits of FFS and capitated doctors are affected by the delivery of these

services. Section 3 describes our data and empirical specification. Section 4 presents our

findings. Section 5 provides conclusions and directions for future research.

Background Information

Preventive care is a major focus of the Medicaid program, especially for children.

Preventative care can both reduce treatment costs and avoid painful and debilitating

illness. Routine well-child visits and the provision of asthma medications are two

important forms of preventive care. Annual well-child visits allow doctors to monitor

enrollees' health and to deliver essential, routine health care services such as

immunization shots. Asthma is considered to be an epidemic and affects over 4 million









children in the U.S. (US HHS (2000), 2). Proper treatment of asthma conditions can

reduce asthma attacks, emergency room visits, and morbidity.

While both well-child visits and asthma medications can be beneficial to enrollees,

these two forms of preventive care can affect the profits of doctors differently. In the state

studied here, the cost of a well-child visit is borne by the doctor that performs the well-

child. In contrast, the Medicaid program pays for prescribed asthma medications.

Consequently, the amount of asthma medication prescribed has no direct financial impact

on either the HMO or its doctors. This difference in the incidence of the costs of well-

child visits and asthma medications is an important element of the ensuing analysis.

Because they are reimbursed for each service they provide, FFS doctors increase

their revenue with every well-child visit they perform. In contrast, FFS doctors do not

receive greater revenue when they prescribe additional asthma medications. In fact, to the

extent that the prescribed asthma medications control the symptoms of asthma and

thereby reduce future office visits, additional prescriptions can reduce the doctor's

revenue.

Capitated doctors receive no additional revenue when they perform a well-child

visit, but do incur the cost of providing the visit. The well-child visit may reduce future

costs by allowing the doctor to detect an ailment and treat the enrollee before

complications arise and the requisite care becomes more costly. However, Medicaid

enrollees often have limited spells in the program, which reduces the likelihood that the

capitated doctor would bear the costs of later treatment. As noted above, capitated

doctors do not bear the costs of prescribed asthma medications. Furthermore, the









medications can reduce the costs of the capitated doctor by limiting the need for office

visits.

This paper compares the extent to which FFS and capitated doctors in Medicaid

HMOs provide well-child visits and asthma medications. Given the differences in the

compensation structures the FFS and capitated doctors face, our findings may provide

some useful evidence about how financial incentives affect the quality of care received

by HMO enrollees.

Data and Empirical Specification

Our data is of two types: enrollment and encounter data and data from interviews.

The enrollment and encounter data is for every healthy3 Medicaid enrollee in an HMO in

the state in question in 2004. This enrollment data contains demographic information for

each enrollee; the encounter data records the enrollee's usage of medical services,

including office visits, medical treatments, and pharmaceutical prescriptions. The

encounter data also documents diagnoses made by the physician when treating the

enrollee.

The interview data is derived from interviews with personnel from the HMOs. The

HMOs in our sample are required to answer questions posed by the state regarding

various characteristics of their organization. Among the questions asked is how the HMO

compensates its doctors.4

The dependent variables in the analysis are based on measures established in the

Health Plan Employer Data and Information Set (HEDIS) developed by the National


3 Specifically, the sample is limited to enrollees with a clinical risk grouping (CRG) score of
one.
4 Unfortunately, data for each HMO are aggregate data, not data on how the HMO compensates
each individual provider.









Committee for Quality Assurance (NCQA) (NCQA, 2002). HEDIS measures are used to

evaluate the care received by HMO enrollees and are widely used by industry

participants. These measures are based on diagnosis codes and treatment codes found in

enrollee encounter data and the ages of the enrollees.5 The measures are binary: one

indicates that the proper care was provided; zero indicates otherwise. The measures are

based on treatment received over a twelve month period and specify the age ranges of the

enrollees to be included. The treatment period examined in the analysis below is January

2004 December 2004.

The first HEDIS measure analyzed is whether the enrollee received at least one

well-child visit during the treatment period. Two age cohorts are analyzed: children

between 3 and 6 years of age; and adolescents between the ages of 12 and 21.6 The

success rates for the younger and older age cohorts are 50% and 32%, respectively, in our

sample.

The second HEDIS measure we analyze is whether children with persistent asthma

were prescribed appropriate medications.7 This measure is based on two years of data.

Data from 2003 are examined for evidence of persistent asthma. Data for 2004 are

examined for evidence of appropriate medication. The age cohorts employed for this

analysis are 5 through 9 and 10 through 18. The success rates for these cohorts on this

measure in our sample are 51% and 54%, respectively.





5 The measures used in this paper are based on the administrative specification of the measures.
6 The two measures are named, "Well-Child Visits in the Third, Fourth, Fifth, and Sixth Years
of Life" (page 177) and "Adolescent Well-Care Visits" (page 180).
7 The measure is named "Use of Appropriate Medications for People with Asthma" (page 104).









The explanatory variables reflect HMO operating characteristics and enrollee

demographics. The HMO operating characteristic of primary interest is whether the HMO

compensates all of its doctors via FFS arrangements. During the HMO interviews, each

HMO is asked to report the percent of its doctors that are paid via FFS arrangements.

The distribution of answers was bimodal, with five of the eight HMOs in our sample

stating that they pay roughly 100% of their doctors via FFS8 and three HMOs stating that

they pay 85%9 of their doctors via FFS. This bi-modal distribution underlies our

treatment of this variable as binary.

Two other variables are included to control for the practices of the HMO: whether

HMO case managers work directly with the primary care physicians10 and whether the

HMO makes reminder calls to enrollees immediately prior to their well-child visits. (This

latter variable is included only in the analyses of the HEDIS well-child visit measure.)

The other variables included to control for characteristics of the HMO are the for-

profit/non-profit status of the HMO, the number of enrollees (Medicaid and otherwise),

the percent of the HMO's enrollees that are in Medicaid, and the number of years the

HMO has been operating in the state in any capacity.

Table 4-1 lists the HMO attributes included in the estimation. As the table

indicates, there is significant heterogeneity among the HMOs for each of the variables

employed in the analysis. The smallest HMO has roughly 35,000 enrollees while the

largest has approximately nine times that number. The HMOs also vary significantly in



8 Specifically, the reported percentages were 100%, 100%, 100%, 99%, and 99%.
9 Specifically, the reported percentages were 84%, 85%, and 85%.
10 HMO case managers are responsible for ensuring that children with chronic conditions receive
appropriate care.









their operating practices and in the extent to which their enrollment is limited to Medicaid

enrollees.

Table 4-2 presents the success rate for the two HEDIS measures for each value of

the binary HMO attributes. Children in HMOs that paid all doctors via FFS arrangements

had higher well-child visit success rates than HMOs that paid only some of their doctors

via FFS. In addition, children in HMOs where case managers worked directly with

primary care physicians had higher asthma medication success rates.

The demographic variables we employed are gender, race, age, and whether the

enrollee resides in a rural area. Table 4-3 presents the values of the demographic

variables in our sample. Hispanics outnumber blacks and whites, while the vast majority

of enrollees reside in non-rural areas. The data in Table 4-4 indicate that, relative to

whites and blacks, Hispanic children had a higher success rate for well-child visits and

lower success rate for asthma medications. Also, non-rural children had higher success

rates for well-child visits and lower success rates for asthma medications.

The two equations estimated are:

(1) WCHILD,, = a + ,X,+ /2Z, + E,
(2) ASTHMA,, = 3 + yX, + y,2Z + ,J

where

WCHILD, is the HEDIS well-child measure for enrollee i in HMOj

ASTHMA, is the HEDIS asthma medication measure for enrollee i in HMOj

X, are enrollee demographic variables


Z are HMO attribute variables









Although the dependent variables are binary, the large number of observations in

our sample ensures that regression via ordinary least squares is consistent.11 To account

for unobserved HMO-level effects (see Moulton (1990)), the observations are clustered

by HMO and location.12

Findings

Table 4-5 presents the regression estimates. The first two columns in Table 4-5

contain the estimates for the well-child visit measure for the two age cohorts. The last

two columns contain the estimates for the asthma medication measure for the two age

cohorts.

The first row of data in Table 4-5 presents the coefficient estimates for the variable

that indicates whether the HMO pays all of its doctors via a FFS arrangement. For both

age cohorts, the well-child visit success rate for enrollees in HMOs that pay all of their

doctors via FFS is six percentage points higher than for those enrolled in HMOs that pay

some of their doctors via capitation. Given the mean success rates, this difference implies

that the average probability that an enrollee receives a well-child visit is 10-20% higher

in an HMO that pays all of its doctors via FFS.

The opposite conclusion arises with regard to the asthma medication measure. For

both age cohorts, the success rate is lower for HMOs that pay all of their doctors via FFS.

The effect is statistically significant for the 5-9 year old cohort. The estimates imply that





1 As a specification test, the model was also estimated via probit. The results are largely
unchanged and are reported in Appendix Table B-1.
12 By clustering the observations, the estimates of the coefficient standard errors are adjusted to allow for
the possibility that the observations within each group are not independent. The enrollees are grouped
here by the HMO in which they are enrolled and the metropolitan area in which they reside.









the probability that the recommended asthma medications are prescribed is approximately

5% lower in HMOs that pay all of their doctors via FFS than in other HMOs.13

These findings suggest that financial incentives may affect the services doctors

provide to Medicaid enrollees. In particular, the services that increase the revenue of FFS

doctors (well-child visits) are provided more frequently in HMOs where all doctors are

compensated via FFS. Furthermore, the services that could reduce the future revenues of

FFS doctors (asthma medication prescriptions) are provided less frequently in HMOs

where all of the doctors are paid via FFS.

The other variables that measure HMO attributes generally are not statistically

significant in our regressions. A larger number of enrollees is associated with a higher

success rate on the well-child visit measure. However, the effect is of limited statistical

and economic significance. The probability that an enrollee in the 5-9 year cohort

receives the recommended asthma medications is higher in HMOs where the case

manager works directly with the primary care physician. This effect may be due to case

managers working with children with severe asthma to ensure that they receive the

appropriate medications. The finding that the probability that an enrollee in the 10-18

year cohort receives the recommended asthma medication declines as the percentage of

HMO enrollees in Medicaid increases may reflect practice style effects in HMOs that

serve both commercial and Medicaid populations. HMOs may tend to provide relatively

high service quality to commercial populations in an effort to retain these profitable



13 To control for the possibility that the effects are being driven by spurious interaction between
the variables measuring HMO attributes, the model was also estimated where the other HMO
attributes were replaced by HMO dummy variables. The results, reported in Appendix Table
B-2, are largely unaffected. (The HMO attributes and HMO dummy variables cannot be
included simultaneously due to perfect multicollinearity among the two sets of variables.)









clients. To the extent that Medicaid and commercial enrollees receive the same basic

health care services within an HMO, HMOs with a larger concentration of commercial

enrollees may provide higher quality care to their Medicaid enrollees.

Hispanic children in our sample have a higher likelihood of receiving an annual

well-child visit and a lower likelihood of receiving asthma medications than black

children and white children. This result is interesting in light of the "Hispanic paradox"

that suggests Hispanics tend to have better health outcomes than non-Hispanics of similar

socio-economic status (Franzini, Ribble, and Keddie (2001)). Within age cohorts,

younger children generally are more likely than their older counterparts to receive well-

child visits and asthma medications. (The exception is for asthma medications for the 5-9

year cohort.) The finding that 4-year-olds receive well-child visits with relatively high

frequency likely reflects the fact that parents often take their children to the doctor to

obtain the immunizations required to enter kindergarten. Rural residence is associated

with reduced (but statistically insignificant) performance on the well-child measure and

increased (and statistically significant) performance on the asthma medication measure

for the younger cohort. Families that live in rural regions likely have to travel farther for

well-child visits, which may reduce the likelihood of such visits. However, because they

tend to live farther from the doctor's office or the emergency room, parents of rural

families may take particular precautions to be sure their asthmatic children do not

develop serious conditions that would require long trips to receive immediate care.

Conclusions

We have examined whether the form of doctor compensation affects the quality of

care received by Medicaid HMO enrollees. Our findings suggest that financial incentives

may influence the services that doctors deliver to enrollees.









Further research is required to determine whether our findings persist in other

settings. The HEDIS measures we employed require that an enrollee be a member of the

HMO for almost the entire period in question. 14 Therefore, our findings pertain only to

enrollees with relatively stable enrollment. These enrollees may not be entirely

representative, as many Medicaid enrollees move in and out of Medicaid frequently.

It would be ideal to be able to identify exactly which doctors are paid via FFS and

which are paid via capitation. This information would permit more precise measurement

of the effects of doctor compensation arrangements on the quality of care they provide.

Finally, time series data for each enrollee would allow for the inclusion of enrollee

fixed effects. Such effects would control for time-invariant, unobservable characteristics

of each enrollee, and would thereby improve the precision of the analysis.

























14 For the well-child visit measure, the enrollee must be enrolled in the same HMO for 11 of the
12 months in question. For the asthma medication measure, the minimum enrollment in the
same HMO is 22 of the previous 24 months.










Table 4-1 HMO Attributes.
Attribute HMO#1 HMO#2 HMO#3 HMO#4 HMO#5 HMO#6 HMO#7 HMO#8
Providers Paid Only via FFS no no yes yes yes yes no yes
Markets Served 4 1 4 1 1 1 3 1
Total Enrollees 292,091 97,606 36,311 34,016 115,230 70,503 116,853 100,710
Years in State 6 8 5 4 17 5 6 7
Fraction of Enrollees in Medicaid 76% 37% 100% 46% 10% 100% 80% 24%
For-Profit yes no no no yes no yes no
Check-Up Visit Reminder Calls yes no no no no yes no yes
Case Manager Works with PCP no yes no yes no yes no no


Table 4-2 HEDIS Success Rates by HMO Attribute.
Check-Up Visits Asthma Medications
Ages 3-6 Ages 12-21 Ages 5-9 Ages 10-18
Total 50% 32% 51% 54%
Are All Providers Paid on a Fee-For-Service Basis?
Yes 51% 35% 50% 54%
No 48% 30% 52% 53%
Does HMO Place Reminder Calls?
Yes 51% 33% n/a n/a
No 47% 31% n/a n/a
Does Case Manager Work with Primary Care Physician?
Yes 50% 33% 55% 57%
No 48% 32% 50% 53%
Is HMO a For-Profit HMO?
Yes 51% 32% 50% 52%
No 48% 32% 52% 57%










Table 4-3 Demographic Characteristics By Population.
Check-Up Visits Asthma Medications
Ages 3-6 Ages 12-21 Ages 5-9 Ages 10-18
Total 82,227 62,475 1,971 1,756


Gender
Male
Female


Race
White
Black
Hispanic
Other
Rural
Non-Rural
Rural


41,320
40,957


10,290
15,699
54,315
1,973


79,283
2,994


30,899
31,576


9,153
19,684
31,800
1,838


60,026
2,449


1,155
816


288
585
1,038
60


1,890
81


985
771


291
707
727
31


1,693
63


Table 4-4 HEDIS Success Rates by Population.
Check-Up Visits
Ages 3-6 Ages 12-21
Total 50% 32%


Gender
Male
Female


Race
White
Black
Hispanic
Other
Rural
Non-Rural
Rural


50%
50%


45%
47%
52%
49%


50%
45%


32%
33%


30%
32%
33%
29%


32%
30%


Asthma Medications
Ages 5-9 Ages 10-18
51% 54%


51%
51%


56%
54%
48%
57%


50%
70%


52%
56%


59%
54%
52%
39%


54%
57%












Table 4-5 OLS Regression Estimates.


Explanatory Variable
HMO Operating Characteristics


Dep. Var.: Check-Up Visit
Aged 3-6 Aaed 12-21


Dep. Var.: Asthma Medication
Aged 5-9 Aaed 10-18


Providers Paid Only via FFS

Total Enrollees (Medicaid &
Commercial, hundred thousands)

Percent of Enrollees in Medicaid


For-Profit


Check-Up Visit Reminder Calls


Case Manager works with PCP


Years Operating in State

Enrollee Characteristics


Age Dummy 1


Age Dummy 2


Age Dummy 3


Age Dummy 4


Age Dummy 5


Male


Black


Hispanic


Other


Rural

# Observations
R-squared


0.06 ***
(3.50)

0.04 *
(1.80)

0.04
(0.96)

-0.01
(0.18)

-0.02
(1.16)

0.005
(0.24)

0.004
(0.76)


0.10 ***
(13.37)
0.17 ***
(38.55)
0.06 ***
(14.80)


0.001
(0.21)

0.01
(1.40)

0.07 ***
(5.86)

0.03 **
(2.10)

-0.03
(1.62)
82277
0.02


0.06 ***
(3.29)

0.02
(0.81)

-0.01
(0.20)

0.02
(0.43)

-0.02
(0.77)

0.03
(1.46)

0.003
(0.49)


-0.03
(2.12)

-0.02
(0.82)

-0.04
(1.24)

0.04
(0.75)




0.08
(3.57)

0.02
(3.95)


-0.03
(1.77)


-0.02
(-0.54)

-0.01
(-0.87)

-0.22 **
(-3.93)

-0.02
(-0.37)


0.02
(0.51)

-0.01
(-1.31)


0.11 **
(3.59)


0.22 ***
(22.07)
0.10 ***
(11.27)
-0.01 **
(2.49)

0.01
(1.30)

0.03 **
(2.41)

-0.02 *
(1.81)

-0.03
(1.10)
62475
0.02


0.01
(0.76)

0.03
(0.61)

-0.06
(1.23)

0.07
(0.82)

0.14
(2.41)
1971
0.02


-0.04
(1.86)

-0.04
(1.15)

-0.09 **
(-2.34)

-0.22
(-1.98)

-0.01
(-0.19)
1756
0.02


Notes -
The t-statistics reported in parentheses are based on Huber-White robust standard errors clustered at the
HMO-market level.
The age dummy variables differ across specifications:
In the check-up visit regressions, age dummy 1 corresponds 3 years, age dummy 2 corresponds to 4
years, age dummy 3 corresponds to 5 years, age dummy 4 corresponds to 12-15 years, and age dummy 5
corresponds to 16-18 years.
In the asthma medications regressions, age dummy 1 corresponds to 5-7 years and age dummy 2
corresponds to 10-14 years.
*** 99% confidence level, ** 95% confidence level, 90% confidence level














CHAPTER 5
CONCLUSIONS

This dissertation analyzed government attempts at using market forces to achieve

policy outcomes in two important areas, telecommunications and health care. The

chapters analyzed how state governments implemented TA96 and the effects of market-

oriented polices in the telecommunications and health care sector.

A number of significant results emerge. First, the manner in which the policies are

executed may be inconsistent with the goals of the policy. Our analysis of TA96 suggests

that state public utility commissions may have been influenced by decisions in larger

states and by factors outside of the policy's guidelines. Second, firms may respond to

incentives in ways that policymakers may not foresee. In the investigation of local

telecommunications markets, it was shown that responses by entrants to market factors

may vary by the size of the market. Thus, national policies may not be well-suited to

heterogeneous markets. Third, the details of how firms operate may have substantial

effects on the level of quality they provide. Our results demonstrate that the way in which

physicians in Medicaid HMOs are paid may influence the level of care that they provide

to enrollees.















APPENDIX A
DATA NOTES AND ADDITIONAL RESULTS

This appendix describes some of the details of the data and specification tests used

in the analysis in Chapter 2.

Data Notes

UNE Rates

For 18 of the 919 observations de-averaged rates were reported and a statewide

average was neither reported nor could be calculated based on the available data. In those

instances a simple average of the de-averaged rates was used as the statewide average

rate.

In December 1997 the Texas PUC set both a statewide rate that was effective

immediately and de-averaged rates that were to take effect a month later. In this analysis

the initial statewide rate is ignored.

The former Pacific Telesis region includes only two states, California (the leader)

and Nevada. Thus, Nevada is the only follower state and the rates of other follower states

in the region do not exist. To keep Nevada in the sample and given that SBC now

controls the former Pacific Telesis and Ameritech regions, the rates of the follower states

in the former Ameritech region are used as a proxy for the other follower rates for

Nevada.

Cost Estimate

The HCPM data were received via email from the FCC.









Both the embedded and HCPM cost estimates are reported annually. Quarterly

values are obtained via linear interpolation of the data.

For each year except 2000, the HCPM cost estimate is provided only for the cost of

the entire line, which includes, in addition to the loop cost, the cost for line port, EO

usage, signaling, transport, billing/bill inquiries, and directory listing. To obtain an

estimate of the loop cost for those years, the fraction of the line cost that is attributable to

the loop in 2000 is applied to the total line costs in the other years.

PUC Characteristics

Data regarding PUC commissioners were derived from Profiles ofRegulatory

Agencies of the United States & Canada: Yearbook 1995-1996 (NARUC) and NARUC

membership directories (specifically, directories dated January 1998, February 1999,

February 2002, February 2003, July 2003, and March 2004).

Besides being reported as either a Democrat or Republican, a commissioner could

also be listed as independent or have no reported political affiliation. For the purposes of

this analysis, those commissioners who were reported as independent of for whom a

political affiliation was not reported are equally Democrat and Republican. (For example,

if a state's PUC is composed entirely of independents and/or commissioners for whom

their political affiliation is not reported, the value of the variable percent of

commissioners that are Republican for that PUC would be 0.5.)

The type of retail rate regulation employed in each state is derived from reports in

the State Telephone Regulation Report (1/25/96, 2/8/96, 3/20/97, 4/3/97, 4/3/98, 4/17/98,

8/20/99, 9/3/99, 9/29/00, 10/13/00, 10/27/00, 2/15/02, 3/1/02, 3/15/02, 5/9/03, 5/23/03,

6/6/03, 7/30/04, 8/13/04, and 8/27/04). For some of the descriptions of the regulatory









plans, only a year was given for the beginning or the end of the plan's duration. In those

instances, the exact dates were inferred from the prior or succeeding plan.

The three residential customer satisfaction variables used as instruments for the

form of retail rate regulation come from the annual FCC ARMIS Report 43-06.

Specifically, they are the percent of customers surveyed that are dissatisfied with the

RBOC's installation, repair, and billing services. Quarterly values are obtained via linear

interpolation of the data.

Estimates of retail rates are somewhat problematic as retail rates can vary across

customers and regions within a state. The proxy used in this analysis is the RBOC's local

network services revenue (Row 520 from ARMIS report 43-03) divided by the number of

switched access lines.

State Political Variables

The gubernatorial data are obtained from the Book of the States (The Council of

State Governments, 1996-1997, 1998-1999, 2001-2002, 2002, 2003) and the CNN.com

web page "2004 Election Results"

(http://www.cnn.com/ELECTION/2004/pages/results/governor/ full.list/). The state

legislature data are obtained from Statistical Abstracts of the United States (U.S. Census

Bureau, 2002, 2003, 2004-2005) and the National Conference of State Legislatures

website (2005 Partisan Composition of State Legislatures,

http://www.ncsl.org/ncsldb/elect98/ partcomp.cfm?yearsel=2005).

As Nebraska's legislature is non-partisan, for this analysis the percent of state

legislatures that are Republican is assumed to be 50%.









Section 271 Status

Data regarding RBOC applications to provide long-distance service are obtained

from the FCC web page "RBOC Applications to Provide In-region, InterLATA Services

Under 271" (http://www.fcc.gov/Bureaus/Common_Carrier/in-region_applications/).

Level of Competitive UNE Entry

The data reported are from two series of (non-overlapping) reports of RBOC

survey responses. The 1997 and 1998 data are from voluntary surveys completed by the

RBOCs and are reported in the December 1998 and August 1999 FCC Local Competition

reports produced by the Industry Analysis Division in the Common Carrier Bureau. The

data for 1999 forward are based on RBOC responses to the mandatory Form 477 survey

and are obtained from reports entitled SelectedRBOC Local Telephone Data. Both data

series can be found at http://www.fcc. gov/wcb/iatd/comp.html.

The UNE line count used in this analysis includes both UNE-L and UNE-P lines.

UNE-L lines are where the CLEC leases only the local loop, whereas UNE-P lines

involve the CLEC leasing switching unbundled elements in addition to the local loop.

For some states the first report of the number of UNE lines is made some time (at

most one year) after the CLECs are able to begin leasing the lines. The data for the period

prior to this first report is linearly interpolated by assuming that zero UNE lines were

being leased prior to a rate being set.

Finally, the data are reported on a quarterly, annual, or semi-annual basis

depending on the time period. Quarterly values are obtained when necessary via linear

interpolation of the data.









Tests of Instrument Validity and the Exogeneity of the Average Retail Rate Variable

This section outlines how the validity of the instruments used in the UNE rates

regressions is tested. The first sub-section describes the tests performed to determine if

the instruments are not correlated with the error term, while the second sub-section

details the test used to determine if the instruments are sufficiently correlated with the

endogenous explanatory variables.

The third sub-section explains the econometric test used to confirm that the lagged

average retail rate is exogenous to the determination of UNE rates.

(This section borrows heavily from Baum, Schaffer, and Stillman (2004).)

Tests of Instruments' Orthogonality to the Error Process

When there are more instruments than endogenous variables (i.e., the model is

overidentified), one can test whether all of the instruments are orthogonal to the error

term. In GMM estimation the overidentifying restrictions can be tested with Hansen's J

statistic, which if found to be greater than a threshold value indicates that the instruments

are not exogenous or that they should be included as explanatory (rather than

instrumental) variables in the regression. The J statistics are reported in Table A-2. The J

statistics for the base model suggest that the instrument set as a whole is orthogonal to the

error term.

One can also test whether subsets of instruments are orthogonal to the error

process. The test statistic (referred to as the C statistic) is the difference in J statistics

between the specification that includes all the instruments and the specification that

excludes the instruments to be tested. If the C statistics exceeds a threshold value, there is

cause for concern that the tested instruments are not valid. Table A-2 also details the C

statistics for the instrument sets used for each of the three endogenous explanatory









variables. The C-statistics indicate that the instrument sets used for the three endogenous

variables are neither correlated to UNE rates nor endogenous.

Tests of Instrument Relevance

In addition to being orthogonal to the error term, instruments also must be

sufficiently correlated with the endogenous explanatory variables. To test for this, each

endogenous explanatory variable is regressed on all of the exogenous and instrumental

variables in the model. The coefficients on the instruments are then tested for whether

they are jointly equal to zero. To be valid, the coefficients should not jointly equal zero.

This test is complicated by the use of the system GMM estimator. By definition,

the system GMM estimator estimates two equations simultaneously, one in levels and the

other in first differences. The equation in levels is estimated along with the equation in

first differences because estimations in first differences with highly persistent dependent

variables result in weak instruments (Bond, 2002, p 154).

Table A-3 reports the results of the tests of instrument relevance for each of the

three endogenous explanatory variables for the equations in both levels and first

differences. The chi-square values suggest that the instruments easily pass the threshold

test for relevance except for the form of retail rate regulation variable in the first

differences equation. As noted above, this result is not worrisome as it is addressed by the

system GMM estimation.

Test of the Exogeneity of the Lagged Average Retail Rate

One may be concerned that the average retail rate variable is endogenous, even

though it is lagged one year. To test for this the C statistic can be used where the J

statistic from the model assuming the variable is endogenous is subtracted from the J






89


statistic assuming the variable is exogenous. The results of this test are shown at the

bottom of Table A-2 and confirm that the variable is exogenous.

Additional Results

This section describes additional results from the model.

Table A-4 contains the economic effects two years after a change in an explanatory

variable based on the coefficient estimates the one-year lag specification. Table A-5

details the long-run economic effects based on the coefficient estimates from both the

two-year and one-year lag specifications.