Municipal Bonds: One Market or Fifty?

Transcription

1 Municipal Bonds: One Market or Fifty? Paul Schultz * October, 2012 Preliminary, do not quote. * Mendoza College of Business Administration, University of Notre Dame. I am grateful to Paul Gao, Chuck Trzcinka, and to workshop participants at e University of Notre Dame and e Federal Reserve Board for comments and suggestions. 1

2 1. Introduction The municipal bond market is large. According to e Securities Industry and Financial Market Association, ere were $3.7 trillion wor of municipal bonds outstanding as of Cities, states, counties, school districts and oer government entities issued $294 billion in 1 municipal bonds in 2011, down from $433 billion in It is not clear ough, wheer municipals represent one large integrated market, or wheer it is more accurately characterized as a set of separate markets for investors from different geographic areas. There are a number of reasons why e municipal bond market may be geographically segmented. First and foremost, most states exempt interest income from municipal bonds issued in e state from state income tax, but do not extend e same tax benefit to bonds from out-ofstate issuers. Differences in tax treatments of in-state and out-of-state bonds mean at significant differences in before tax yields across states are possible wiout giving investors an incentive to purchase out-of-state bonds. For example, if investors in a state face a marginal tax rate of 5%, out-of-state bonds must yield more an 5.25% before investors will prefer em over in-state bonds at yield 5%. This is a major reason for segmentation of e municipal bond market, and e one at is studied here. There are, however, oer factors at may contribute to segmentation. Municipal bond issuers are, of course, associated wi a particular location. This is different from corporate bond issuers who will sell products all over e country and may have plants, retail locations or oer facilities in numerous locations. Investors only have first-hand knowledge of municipal issuers at are located nearby. Municipal issuers are notorious for providing financial information irregularly - often only when anoer bond issue is contemplated. Hence investors may prefer local bonds because e soft information about a bond issuer at is available to nearby investors may be e best information available. In addition, some underwriters of municipal bonds do all or a majority of eir business in one state or a few neighboring states. For example, Ameritas Investment was lead underwriter for 1 2

3 1,901 municipal bond issues over 1999 rough June ,893 of ese bonds were from Nebraska. First Midwest underwrote 956 Illinois municipal bond offerings over June 2010, and one offering from anoer state. This geographic specialization of municipal bond 2 underwriting may be bo a cause and consequence of market segmentation. Finally, e Securities and Exchange Commission (SEC) has no direct auority over e municipal bond market. It can indirectly influence e municipal market to e extent at it has regulatory auority over municipal bond dealers. Regulatory responsibility rests primarily wi e states. In is paper, I test wheer e municipal bond market is segmented by state. A comparison of trade times for bonds by state of issue suggests at in-state investors provide a disproportionate amount of e trading of municipal bonds. What is of most interest of course is not wheer ownership or trading of e bonds is segmented, but wheer bond prices or yields differ as a result os e segmentation. I examine differences in yields to maturity across states over two periods. The first, , represents a normal period for municipal bonds. The second period, June, 2010 includes e financial crisis and e collapse of e bond insurers. I use bo characteristic matching and regressions to adjust for differences in bond characteristics across states. During e first period, ere are significant and persistent differences in yields to maturity across states. There are handful of states at offer no tax advantage to home state issuers. These include states like Texas at do not tax interest on eier in-state or out-of-state bonds, and states like Illinois at tax bo. Bonds issued in states wi no home state tax advantage cannot offer yields less an out of state bonds or investors would forego em for e bonds issued in oer states. Consistent wi is, I find at bonds issued in states wi no home state tax advantage consistently offer higher yields an bonds from oer states. The difference in yields was approximately 10 basis points over Municipal bond defaults were very unusual before e financial crisis and risk was ought to be minimal. More an half of municipal bonds were insured and ought to carry 2 Butler (2008) compares local and national underwriters for municipal bonds and finds evidence at local underwriters have informational advantages. 3

4 virtually no risk. It is difficult to attribute differences in yields across states to differences in risk over , especially since I control for differences in Moody s and Standard and Poor s ratings. Default risk took on new importance from 2007 rough In 2005, an uninsured bond at carried e lowest investment grade rating from Moody s and Standard and Poor s had a yield to maturity approximately 75 basis points greater an a similar bond hat carried e highest rating from bo agencies. By e first quarter of 2009, e difference in yields had grown to 279 basis points. During e June, 2010 period, differences in yields were often much larger. Yields on bonds from Michigan, Louisiana, and particularly California were high. At times during 2009, even after adjusting for differences in bond ratings, bonds from ese states had yields at were 50 basis points higher an yields of similar bonds from Massachusetts, Maryland, and some of e smaller states. It seems likely at some of at difference in yields is due to differences in default risk at is not reflected in ratings. Nevereless, during e crisis period, bonds from states wi no home-state tax advantage continued to carry higher yields an bonds from oer states. The market segmentation described here offers several disadvantages over a fully integrated market. Some bond issuers will be forced to pay higher interest rates an ey would in an integrated market. Investors may concentrate all of eir municipal bond holdings in one state and lose any benefits of geographic diversification, alough ese benefits seemed small until recently. Finally, investors may be forced to buy bonds wi features (maturity, rating, insurance, etc.) at are less desirable an could be found in bonds in a fully integrated market. This paper complements a recent study by Pirinsky and Wang (2011). They employ a sample of municipal bond offerings from e Thomson Financial SDC Platinum database. They use investment income per capita in a state as a measure of in-state demand for municipal bonds. They use new debt per capita in a state, measured as e municipal bonds issued in e state at year, as a measure of e supply of municipal bonds. Regression estimates suggest at ese measures of supply and demand affect yields of municipal bonds in e expected ways if e state only exempts in-state bonds from state taxes. If e state also exempts 4

5 out-of-state bonds from state income tax, eir measures of in-state municipal bond supply and demand do not have significant affects on yields. Pirinsky and Wang also observe at taxable municipal bonds are less likely to trade in a segmented market an tax exempt bonds at are likely to be purchased mainly by in-state investors. Consistent wi is, ey find at tax free municipal bond yields are inversely related to a state s investment income per capita (eir measure of demand for bonds) while yields on taxable municipal bonds do not decrease wi an increase in in-state investment income per capita. Analogous results are obtained when ey use e municipal bonds per capita issued in a state as a measure of e supply of bonds. For tax-free municipal bonds, yields increase wi in-state new bonds per capita. Yields of taxable bonds are not affected by recent issues of in-state bonds. The data used in is paper allow me to do ings at cannot be done wi Pirinsky and Wang s data. Their sample is much smaller an e one used here, and consists only of bond offerings. More an ree-quarters of e offerings cannot be used because of inadequate information on yields. Their smaller sample size makes it difficult to examine yields over time or across individual states. The rest of e paper is organized as follows. The data used here is described in Section 2. Evidence of segmentation from intraday trading volume is provided in Section 3. The next section provides evidence of market segmentation over Municipal bond yields during e financial crisis are examined in Section 5. Section 6 summarizes and concludes. 2. Data The data used in is paper comes from ree sources. The Municipal Securities Rulemaking Board (MSRB) supplies data on all municipal bond trades from 1999 ough June, Each trade record contains e time and date of e transaction, e CUSIP number of e bond, e price and yield to maturity of e bond, e par value of e bonds in e trade, and wheer e trade was a purchase of bonds buy an investor from a dealer, a sale of bonds from an investor to a dealer, or an inter dealer trade. The MSRB municipal bond trade data has been used 5

6 in a number of recent studies, including Harris and Piwowar (2006), Green, Hollifield and Schürhoff (2007a), Green, Hollifield and Schürhoff (2007a), and Schultz (2012). Bond characteristics are obtained from e Mergent/FISD fixed income securities 3 database. Municipal bond issues usually consist of a number of bonds wi different maturities. Mergent/FISD provides e offering date, underwriter, issuer state, and total amount offered for issues. For individual bonds, Mergent/FISD provides e coupon, maturity date, offering price, debt type (general obligation, revenue, etc.), codes for wheer e bond is putable or callable, e insurance provider (if any), and Moody s, Standard and Poor s, and Fitch ratings and rating dates. These ratings are e most recent ratings as of e time e Mergent/FISD data was purchased in The ird data set consists of all changes in Moody s ratings of municipal bonds over June, This is combined wi e Moody s ratings from Mergent/FISD to provide e Moody s rating in effect for a bond at e time of each trade. The ree data sets are combined using CUSIP numbers. Firm quotes, or even indicative quotes, are unavailable for municipal bonds. Hence to study municipal bond yields, I must use e yields implied by trade prices. To minimize issues related to trading costs or intermediation, I use only purchases of bonds by investors from dealers to compare yields. Municipal bonds are infrequently traded. A large portion of e trades are associated wi e original sale of e bonds. In total, ere are 36.2 million purchases of tax-exempt fixed-rate bonds by investors from dealers in e data set. Panel A of Table 1 provides some summary statistics for e trades for each year from June, The mean time to maturity at e time of e trades ranges from a low of years in 2006 to years in The median size trade is $25,000 par value most years, but reaches $30,000 in 2006 and In most years e 25 percentile of trade size is $10,000 and e 75 percentile is $50,000. The 25 and 75 percentiles of trade size are somewhat larger over Most purchases are small purchases by retail investors. The median yield to maturity ranges from 3.67% in 2005 to 5.25% in The 3 See Schultz (2012). 6

7 interquartile range is as small as 87 basis points in 2000 and as large as 202 basis points in This range depends on e variation of yields across bonds on a specific date, and e variation of yields over e year. The next line shows e proportion of trades at are in insured bonds. These bonds are ought to be more liquid an uninsured bonds, so it is likely at ey trade more frequently. Hence e proportion of trades in insured bonds is likely to be slightly larger an e proportion of bonds at are insured. Insurance purchases dropped of dramatically for new issues of bonds after 2007, so it is not surprising at e proportion of trades in insured bonds falls sharply toward e end of e sample period. General obligation bonds make up between 26.6% and 37.5% of e trades. The majority of e bonds at trade in any year are callable. A sizeable proportion of e bond trades were in new issues, bonds at were issued no more an two mons before. In 2000, e lowest proportion of trades, 23.5%, were in new issues. The highest proportion, 43.2%, occurred in Panel B provides e average number of trades per day over June, 2010 for e 25 most populous states, and for all oers. The average is 1,762.8 for California, 1,307.2 for New York, 832 for Texas, and 1,955.3 for all states outside of e 25 most populous. Wi 2,883 trading days over June, 2010, There are over 5,000,000 trades of California bonds, 3.8 million trades of New York bonds and 2.4 million trades of Texas bonds. 3. Segmentation: Evidence from Intraday Trading Volume If municipal bonds are traded exclusively in state, we would expect intraday trading patterns to be affected by e time zone of e state. When it is 9:30 am on e East Coast, we would expect more trading volume in bonds issued by Eastern states an in bonds issued by Pacific Coast states where e time is only 6:30 am. Conversely, when it is 12:30 Eastern Time, we would expect little trading in bonds issued on e east coast as traders and investors break for lunch. It would be only 9:30 on e West Coast ough, and we would expect heavier volume in bonds issued by cities in e Pacific time zone. On e oer hand, if all municipal bonds were 7

8 equally likely to be traded all over e country, we would expect to see similar intraday trade patterns regardless of e geographic origin of e bond. I calculate e proportion of all buy orders for municipal bonds for each state at take place during every half-hour interval from 8 am to 5 pm Eastern Time over June, Figure 1a depicts e proportions for ree Eastern states: New Jersey, New York, and Pennsylvania. Intraday patterns are similar for bonds from each state. Volume rises steadily from 8 am to 11 am. It declines sharply from 11:30 to 12:30 as investors and bond dealers head to lunch, and en rises again until 3 pm. Volume en falls until e close. Figure 1b shows e proportion if intraday volume in each Eastern Time half hour period for bonds from ree Pacific states: California, Oregon, and Washington. Intraday trading patterns are very similar for bonds from each of e Pacific states, but very different from patterns for bonds from Eastern states. These bonds have a smaller portion of eir volume in in e early morning an do bonds from e Eastern states. Their trading volume peaks when it is 1pm on e East Coast and investors ere are still at lunch. Volume spikes near 5pm for West Coast bonds, while it declines for East Coast bonds. Figure 1c contrasts e patterns of intraday trading for New York, Texas, and California. When plotted togeer, it is obvious at e patterns are very different. It is interesting at e proportion of trades of Texas municipal bonds is similar to at of New York bonds one hour earlier. Almost all of Texas is in e central time zone, one hour earlier an New York s eastern time zone. These differences in intraday trading patterns suggest at municipal bonds are traded primarily by investors in e time zone of e issuer. It seems likely at bonds are traded primarily in e state in which ey are issued, and perhaps in e municipality at issues em, but ese intraday trading patterns do not provide direct evidence on is issue. Of course, even if e great majority of municipal bonds are purchased by in-state investors, e municipal bond market need not be segmented by state. A small number of investors, operating across state lines, can keep yields in line. I now turn to testing wheer bond yields differ across states. 8

9 4. Market Segmentation Over I split e sample period into two separate subperiods to examine market segmentation in yields. The first, , represents a normal period. During is time, default risk was ought to be minimal for municipal bonds. About half of bonds were insured by AAA rated insurers. The second subperiod, June, 2010, includes e financial crisis. Bond insurance, which was ought to make e market more liquid, was purchased by few issuers as e insurers emselves were downgraded. 4.1 Evidence from Bond Yields of Matched Trades Even if most trading in municipal bonds is concentrated in e state of eir issue, a few trades by sophisticated investors can keep bond yields e same across states. On e oer hand, if taxes and oer market frictions provide significant advantages to investing in-state, municipal bond yields may differ significantly across states. I first employ a matching procedure to see if bonds issued in different states have different yields. Each day I match purchases of bonds from each of e ten largest population states wi purchases of bonds from e oer 40 states, Washington D.C., and Puerto Rico. The ten largest population states (California, New York, Texas, Florida, Illinois, Pennsylvania, Ohio, Michigan, Georgia, Nor Carolina) are compared wi e oers because each of e ten has enough trades to ensure a large number of matches. For a large state bond trade to be matched wi e trade of a bond from anoer state, e two trades 1) must be purchases of bonds by investors from dealers, 2) must bo occur on e same day, 3) must be purchases of e same par value of bonds, 4) must mature wi 0.04 years (two weeks) of each oer, 4) must have coupon yields at differ by 25 basis points or less, 5) must be of bonds at or bo callable or not callable, 6) must be of bonds at are bo ranked as investment grade by Moody s. Each trade is used in only one match. So, if a purchase of a small state bond is matched wi e trade of a New York bond, it will not be matched wi any oer New York bond trades or any California bond trades eier. This meodology results in 996,496 matched bond trade pairs, or on average, 9

10 a little less an 50 per large state per day. While e total of almost 2,000,000 trades is a large sample, it is derived from e much larger total of over 23,000,000 trades during e same period. In matching, I only require at bo bonds have investment grade ratings because an exact match of Moody s ratings reduces e number of matched trades by about 75%. Defaults of municipal bonds are far less common an defaults of corporate bonds, and ratings differences are associated wi smaller differences in yields. Nevereless, for each day, I regress e differences between bond yields of large states and eir small state matches on e differences in eir Moody s rankings. Differences in Moody s ratings are e number of notches between em. So, for example, e difference between an Aa3 and an Aa1 ranking is two. I use e intercepts of e regression as measures of e excess bond yield for e large state on at day. The excess bond yields for California bonds for are graphed in Figure AA. The yields are expressed in percentages, so an excess yield of 0.1 represents a yield at is ten basis points above e yield of matching bonds. The excess yields of California bonds are consistently negative in , and less an negative 20 basis points for extended periods. They are positive and generally larger an 10 basis points in 2003 and The excess yields of Texas bonds are graphed in Figure BB. These excess yields are consistently positive and typically greater an 10 basis points. Investors in Texas bonds receive higher yields an investors in small state bonds. These results are consistent wi a segmented market for municipal bonds. Alternatively, it could be argued at ese results reflect deficiencies of e matching procedure. The matching meodology controls very well for some of e important determinants of municipal bond yields, particularly trade size and time to maturity. On e oer hand, in order to maintain a reasonable sample size, I do not control for some oer variables associated wi yield. It seems unlikely at differences, for example, in e use of bond insurance or differences in e proportion of general obligation bonds across states could be so large and so consistent as to explain e excess yields of ese bonds. Nevereless, I adjust for a number of oer yield determinants using a regression meodology later in e paper. I use a Fama-MacBe meodology to test wheer e excess yields of e bonds from e ten largest states are different from zero. Recall at for each large state each day, I regress 10

11 e difference between e yield on e large state bond trade and e yield on e matching trade on e difference in e Moodys rankings of e two bonds. The intercept term is a measure of e excess yield of municipal bonds of at state. Then, I assume e excess yields follow and AR(1) process and estimate e constant, e AR(1) parameter and eir standard errors. The constant term is an estimate of e average excess yield of bonds issued in at state. I estimate e average excess returns separately for e two halves of e sample period: and These estimates are reported in Table 2. In e first subperiod, all ten of e large states have excess yields at are significantly different from zero at a one percent confidence level. In e second subperiod, excess returns are significantly different from zero at e one percent level for eight of e ten large states. Over four of e large states have municipal bond yields at are smaller an e yields of e small states, while six of e ten have municipal bond yields at exceed small state yields. Over , six of e states bonds have yields at are smaller an e small state controls, while four have larger yields. Bo Texas and Illinois bond yields are about ten basis points more an e yields of small state bonds in bo periods. There are no state tax advantages to buying in state bonds in eier Texas or Illinois. Texas has no state income tax, and Illinois taxes interest on bo in-state and out-of-state municipal bonds. Hence Issuers in bo of ese states cannot offer low yields and expect in-state investors to purchase em to avoid state taxes. If yields on Texas or Illinois bonds are low relative to yields of bonds from oer states, investors will find it worwhile to instead purchase out-of-state bonds. California is unusual in at excess yields on California bonds are, on average over but switch signs to over These averages conceal a lot of variation in California bond yields over e subperiods. Figure AA shows at California bond yields were more an 20 basis points below small state yields for extended parts of , and more an 10 basis points above small state yields for long stretches of Table 2 provides time series means of e intercept coefficients from e daily regressions of e difference in yield to maturity for matched bond trades on differences in Moody s ratings. Table 3 reports e proportion of individual day intercepts at are more an two standard deviation above or below zero for each state each year. For California, a large portion of e 11

12 individual day regression intercepts are significantly less at zero over In 2000, e intercept coefficient had a t-statistic less an -2 in more an 96% of e individual day regressions. On a specific day it may be possible to argue at e lower yields of California bonds are due to an inadequate matching procedure. When California bonds offer significantly lower yields on 96% of e trading days, while e specific bonds traded change daily, it seems clear at e state of issue affects e yields. In California, e low yields on bonds eventually disappeared. In 2003 and 2004, t- statistics of more an 2 occurred on many days for California bonds. Yields on California municipals were frequently significantly greater an yields on matched bonds. Results for Texas are shown in e last two rows of Table 3. In each year, 0.4% or fewer of e intercept coefficients have t-statistics less an -2. In every year, at least 30%, and in two years more an 70% of e daily intercept coefficients have t-statistics > 2. The yields on Texas bonds are consistently higher an yields on matched bonds. The results from matched trades lead to two conclusions. First, e municipal bond market does appear to be segmented by state. Bonds from some large states offer yields at are consistently higher an yields on small state bonds. Bonds from oer large states offer yields at are consistently lower an similar bonds from small states. Second, bonds from Texas and Illinois, e states at offer no tax advantage to in-state bonds, offer yields at are consistently about ten basis points higher an yields on small state bonds. Most states offer a state tax advantage to in-state municipal bond issuers. An investor in, say, New York, does not pay state income taxes on a interest on bond issued by a municipality in e state, but does pay state tax on interest from out-of-state bonds. So, unless e yield on e out-of-state bonds is much higher, e New York investor will prefer to invest in New York bonds. Texas and Illinois are two states at do not provide a home-state tax advantage to municipal issuers. There are several oers as well. Alaska, Nevada, Sou Dakota, Washington and Wyoming, like Texas, have no state income tax and hence no tax advantage for in-state bonds. Wisconsin, like Illinois, taxes interest on bo in-state and out-of-state bonds. Hence, for ese states ere is again no tax advantages to holding in-state bonds. Indiana and Washington 12

13 D.C. do not tax interest on eier eir own bonds or e bonds of out-of-state issuers. These nine states, plus Washington D.C. make up e no home-state tax advantage states. 4 Because ere are no home state tax advantages for bonds issued in ese states, yields cannot be lower an investors could get elsewhere. If yields were low, investors in at state would be happy to buy out-of-state bonds wi higher yields. Hence it is not surprising at yields of bonds from Illinois and Texas are, on average, higher an yields of matching bonds. The tax treatment of bonds from issuers in U.S. Territories - Puerto Rico, e Virgin Islands, and Guam - provide an interesting contrast. They are exempt from state income tax in every state. If ese bonds offered abnormally high yields, ey would be purchased by investors all over e country. Therefore, bonds from U.S. Territories should not have high yields, and may, on average, have lower yields an similar bonds from most states. I next test wheer yields of bonds from states wi no home-state tax advantage and bonds from U.S. Territories provide different yields an bonds from e majority of states wi a home-state tax advantage. The matching procedure used up to is point has limitations. Most trades are not matched. In addition, bonds from U.S. Territories have relatively few trades. It is difficult to estimate a yield premium for bonds from e Territories when most of e trades are not matched. So, to test wheer bonds from states wiout a home-state tax advantage have higher yields, and bonds from issuers in U.S. Territories have lower yields, I use regressions to estimate excess yields after adjusting for oer factors. 4.2 Using Regressions to Determine e Yield to Maturity on a Purchased Bond For e rest of e paper, I use cross-sectional regressions wi bond characteristics and dummy variables for states to test wheer e state of issue is a determinant of yield after adjusting for oer bond characteristics. This is similar to e approach taken by Bergstrasser, Cohen, and Shenai (2011). They look at e impact of fractionalization of county population 4 Florida did not have a state income tax, but until January 2007 ey had an intangible personal property tax on e value of asset holdings at ey applied to out-of-state but not instate bonds. 13

14 makeup and its impact on bond yields in offerings. Fractionalization is measured as 1 minus e Herfindahl index for religious or enic groups. Bond characteristics at Bergstrasser, Cohen, and Shenai find significant in explaining muni yields include log of issue size and log of bond size, dummies for competitive and negotiated issues, bond insurance, callable and puttable dummies, and a GO bond dummy. They use separate dummy variables for each maturity mon. For every day from 1999 rough 2006, I run cross-sectional regressions wi e yield to maturity as e dependent variable. Observations consist of every trade at day in which an investor purchased at least $25,000 and less an $10,000,000 (par value) of tax exempt municipal bonds from a dealer. Yields differ significantly for different maturities, so I include only trades of bonds wi ree to 25 years until maturity. The regression for e yield on purchase i is Table 4 reports e mean, 25 percentile, and 75 percentile of each coefficient and t- statistic from e daily cross-sectional regressions. The second and ird variables are No Home State Adv and U.S. Territory. These are e variables of primary interest. The No Home State Adv variable takes a value of one for bonds issued in states wi no state tax advantage for instate bonds. The mean coefficient across days is , indicating at bonds from issuers in ese states had yields to maturity at were 10.7 basis points greater an yields on bonds issued in states wi a tax advantage for in-state bonds. Bo e 25 and 75 percentile of coefficients are positive wi t-statistics at are statistically significant at e 5% level. This result is not 14

15 surprising. If ere is no tax advantage to buying in-state municipal bonds, e in-state bonds will not attract in-state investors if yields are low. The next variable is a dummy variable at takes a value of one if e bond issuer is from a U.S. territory - Puerto Rico, e Virgin Islands, or Guam. These bonds are exempt from state taxes in all U.S. states. Hence, if ey carried high yields, investors from all over e country would find it worwhile to invest in ese bonds. It is erefore not surprising at e mean, 25 percentile, and 75 percentile of e coefficients are negative. The mean and 75 percentile of e t-statistics indicate at e coefficients on individual day regressions are usually not significant. This is not surprising. There are just not at many bonds issued from ese territories. The next ree variables are time to maturity, inverse of time to maturity, and e natural logarim of time to maturity. I include all ree to capture any non-linearities in e relation between time to maturity and yield. Typically, e coefficient on time to maturity is negative, while e coefficient on e inverse of time to maturity is positive and e coefficient natural logarim of e time to maturity is positive and significant. When e time to maturity is used wiout its transformations, e coefficient is typically positive and significant. I also use bo trade size and log of trade size and issue size and log of issue size to capture non-linearities in e relation between yield to maturity and ese variables. Trade size is included to capture fixed costs of trading and negotiating power and ability of larger investors. Coefficients on trade size are generally positive, while coefficients on e natural log of trade size are consistently negative and significant. Issue size is a proxy for liquidity, wi larger issues associated wi greater liquidity. Longstaff (2011) suggests at much of e municipal puzzle, at is e finding at marginal tax rates implied by municipals are low, may be due to e liquidity premium embedded in municipal prices. Wang, Wu, and Zhang (2008) examine e impact of liquidity risk on municipal bond yields using bond transactions from e MSRB database for July 2000 rough June Their results suggest at liquidity risk affects returns, particularly for low rated bonds and bonds wi a long-time to maturity. Coefficients on bo issue size and e natural log of issue size are usually negative, which is consistent wi e joint hypoesis at size proxies for liquidity and greater liquidity is associated wi lower yields. The next four variables included in e regressions are dummies for four bond types - 15

16 general obligation bonds, tobacco bonds, loan agreements, and doubled barreled bonds. General obligation (GO) bonds are backed by e full taxing power of e state, county, or municipality raer an e being paid from e revenues of a specific bridge, parking ramp, or oer project. Traditionally, ey are ought to be among e safest municipal bonds. The mean coefficient on e GO bond dummy is , wi a t-statistic of The 25 and 75 percentiles of e GO coefficient are also negative and significant at e 5% level. Tobacco bonds are paid wi revenues from tobacco settlements. In general, e coefficients on e dummy variables for tobacco bonds are positive. The coefficients on e loan agreement dummy variables are typically positive. Double barreled bonds are backed by more an one entity - say a school district and a county. Coefficients on on e double barreled bond dummy are usually negative. Many municipal bonds, particularly ose wi more an ten years to maturity when issued, are callable. We would expect ese bonds to carry higher yields to compensate for e risk of being called if interest rates fall. The mean coefficient on e dummy variable for callable bonds is , suggesting at ese bonds have yields at are 22 basis points higher an non- callable bonds, all else equal. The 25 and 75 percentiles of e callable dummy variable are negative and significant at e 5% level. Putable bonds can be put to e issuer at e bond s par value. They are somewhat unusual. Fewer an 2% of sample purchases are of putable bonds. The ability to put e bonds if interest rates rise is valuable to investors, and we would expect em to have a lower yield. The mean coefficient is , indicating at putable bonds have yields at are more an 124 basis points less an similar bonds. Bo e 25 and 75 percentile coefficients are negative and statistically significant at e 1% level. The premium dummy takes a value of one if e bond is selling for a higher price en its issue price. The mean coefficient is positive and but insignificantly different from zero. The impact of call provisions on a bond price is complex, and depends on e likelihood e bond will be called, e existence of a no call period and oer factors. Clearly, using just a dummy variable for a call provision cannot completely capture its effect on yields. I also include in e regressions an interaction between e dummy variable for premium and e call provision dummy variable. 16

17 This coefficient on is variable is generally negative and highly significant. Insured is a dummy variable at has a value of one if e bond is insured. Insurance is purchased by e issuer when e bonds is offered to e public. The issue pay a single fee at e time of issuance, and e insurance company agrees to make any future principle and interest payments at would oerwise be missed. The issuer expects to make up e up front costs of e insurance wi lower interest payments. The mean coefficient for insured is , implying at insured bonds offer yields to maturity at are 47 ½ basis points lower an similar uninsured bonds. Bo e 25 and 75 percentiles of e coefficients on insured are negative. The 25 and 75 percentiles of e t-statistics a high degree of statistical significance. On e great majority of days, insured bonds provide lower yields. The next two variables are interaction terms between e insured dummy variable and dummy variables for an investment grade rating by Moody s and an investment grade rating by Standard and Poor s. The coefficients on e interaction between Insured and an investment grade rating from Moody s are generally positive and significant. Much of e benefit from insurance is lost when bonds are already highly rated. The interactions between Insured and an investment grade rating from Standard and Poor s are generally insignificant. These two interaction terms are highly correlated however. The next variable in e regressions is New Issue, a dummy variable at takes a value of one if e bond was issued less an two mons before. The mean of e daily coefficients is , indicating at newly issued bonds carry yields at are about 25 basis points greater an more seasoned bonds. Bo e 25 and 75 percentiles of coefficients are positive. More an 75 percent of e New Issue coefficients in e daily regressions are significant at e one percent level. The variable Deminimus is a dummy variable at takes a value of one if e trade price of e bond is more an 25 basis points times e number of years to maturity less an e offer price of e bond. Coupon payments on municipal bonds are free from federal income tax, as are capital gains from original issue discount bonds. If, however, a bond is purchased in e secondary market for a price below e reoffering price, at additional gain is taxable. Far bonds issued at par, e gain is ignored for tax purposes if e discount to e reoffering price is less 17

18 an deminimus level of 25 basis points times e number of years to maturity. The rule is similar for original issue discount bonds. These tax issues are described oroughly in Ang, Bhansali, and Xing (2010). The dummy variable is intended to capture e existence of gains at are not exempt from federal taxes. We would expect such bonds e have higher yields an oers. The mean coefficient of indicates at ese bonds do indeed have higher yields to maturity. On more an 75 percent of e days in e sample period, e Deminimus coefficient is significant at e one percent level. I next include dummy variables for each of e Moody s investment grade bond ratings, a dummy variable for Moody s ratings at are below investment grade, and a dummy variable for no rating from Moody s. These dummy variables provide a comparison of yields wi Moody s Aaa rated bonds. The Mergent data includes e most recent rating by Moody s as of e date e Mergent data was cut, in e summer of Bonds at traded, say in 2000, may well have had ratings changes by 2010, or when ey matured. I obtain a data set from Moody s of all ratings changes in all municipal bonds for The data set contains CUSIP numbers of bonds wi ratings changes, dates of changes, ratings on e underlying security, on e insured security, and enhanced ratings before and after e ratings change. This allows me to construct a contemporaneous rating for each underlying bond at e time of each trade. If e bond rating did not change over , I use e last rating from Mergent to determine e bond rating at e time of e trade. I use a similar dummy variables for Standard and Poor s ratings. S&P ratings are from Mergent and are e most recent rating as of July Unlike e Moody s rating, is rating may not have been in effect at e time of a trade. Mean coefficients on all of e ratings dummies are positive. This is not surprising. It just indicated at all bond wiout top ratings (AAA or Aaa) have higher yields. For bonds wi lower investment grade ratings from Moody s, mean yields are more an 30 basis points greater an yields of Aaa bonds. Likewise, bonds wi S&P ratings of BBB or BBB- had mean yields at were 35 basis points lower an AAA bonds. Note at bonds wi e lowest investment grade rating from bo Moody s and Standard and Poor s had mean yields at were 65 basis points greater an mean yields on bonds at received e highest ratings from bo agencies. 18

19 Inevitably, ere are omitted variables in e regression. All of e determinants of municipal bond yields are not included. Likewise, experimentation would almost certainly allow me to improve e form of e current variables. For example, incorporating features of e call and put provisions raer an just using dummy variables would probably provide more accurate descriptions of yields. Additional interaction terms between, say, ratings and time to maturity might also be useful. The issue ough, is wheer ere are remaining systematic differences between bonds from different states at would explain differences in yields A Comparison of Yields from Bonds Wi and Wiout a Home State Tax Advantage Every day from 1999 rough 2006, I run cross-sectional regressions of yield to maturity on a dummy variable at takes a value of one if e bond is issued in a state wi no home- state tax advantage, a dummy variable at equals one if e bond is issued from a U.S. Territory, and oer variables as described above. When measured eier over e entire period or over individual years e time series of coefficients on e no home-state tax advantage variable tends to have a significant, positive first order autocorrelation and slowly declining autocorrelations for longer lags. So, to test wheer e average daily coefficient is different from zero I assume at e time series of coefficients follows an ARMA(1,1) process. Results are reported in Panel A of Table 5. The first column reveals at e average coefficient on no home-state tax advantage calculated using all days of is All else equal, yields of bonds from states wiout a home-state tax advantage are more an ten basis points higher an yields of bonds from oer states. The z-statistic of indicates at e yield difference for states wiout a home tax advantage is highly significant. This makes sense. If yields were higher in oer states, investors in states like Texas, where ere is no home state tax advantage, will buy out-of-state bonds. On e oer hand, e higher yields will not attract investors from states where ere is a home state tax advantage. The extra ten basis point yield would be attractive, but would be cancelled out by e higher state income taxes. The oer rows in Panel A of Table 5 report mean coefficients for each separate year from 19

20 There is some year-to-year variation in e yield premium paid by issuers from states wi no home-state tax advantage, but every year e mean coefficient is positive and significant. Bonds issued in states wiout a home state tax advantage have consistently higher yields. It seems unlikely at e higher yields of bonds from states wiout a home state tax advantage are due to greater risk. Nevereless, I rerun e regressions using only insured bonds. Before e 2008 financial crisis, investors had confidence in e companies at insured ese bonds. The ratings wi e insurance would be Aaa, and e yields reflect e bonds perceived safety. The second column of Panel A reports e average of e daily coefficients on yields of bonds wiout a home state tax advantage. When e sample is restricted to insured bonds, e average coefficient for e entire period changes only sightly from to The ird column reports results when e sample is restricted to trades of uninsured bonds. Over e entire period, uninsured bonds wiout a home state tax advantage provide yields at are basis points higher an bonds wi a home state tax advantage. These results suggest at differences in yields between bonds wi and wiout a home state tax advantage are not due to differences in risk. The next two columns of Panel A provide excess yields for bonds wi no home-state tax advantage separately for large ( $100,000 par value) and small (< $100,000 par value trades. It is plausible at large investors are more likely to buy out-of-state bonds to get higher yields. The excess yields, however, are positive and significant for bo trade size categories for each year. The last two columns report mean coefficients and z-scores for e no home-state advantage variable for bonds wi ree to ten years to maturity, and for bonds wi more an ten years to maturity. Coefficients on e no home-state advantage dummy are positive and significant for e entire period and each individual year for bo short and long-term bonds. The magnitudes of e coefficients are very similar across bonds wi short and long maturities. Panel B is similar to Panel A but reports coefficients for e U.S. Territories dummy variable. As before, an ARMA (1,1) process is fitted to e U.S. Territories coefficients, and e intercept and corresponding z-statistics are reported. When all all bonds issued in U.S. Territories 20

21 are included, e intercept coefficient for e entire period is negative and significant as are e coefficients for each individual year. Municipal bonds issued in U.S. Territories provide lower yields an municipal bonds issued in states. This is not surprising. Since ey are exempt from taxes in all U.S. states, ey would be purchased by investors oughout e U.S. if ey had high yields. The next two columns report coefficients when only insured and when only uninsured bonds are used. Intercept coefficients from e ARMA (1,1) process are negative and significant in bo cases, but tend to be of greater magnitude for e uninsured bonds. A way to interpret is is at uninsured bonds from U.S. states tend to have high yields, while uninsured bonds from U.S. territories do not have particularly high yields. It is possible at only e safest and most liquid bonds from U.S. Territories are uninsured. The next two columns report results for trade of more an $100,000, and for trades of less an $100,000 (par value) of bonds. Yields for bonds from U.S. Territories are significantly lower an yields for U.S. bonds bo for large and small trades. The difference in yields is generally lower for trades of more an $100,000 ough. Results for bonds wi ree to ten years to maturity are shown in e penultimate column, while results for bonds wi more an ten years to maturity are shown in e last column. It appears at e low yields for bonds from U.S. Territories are concentrated in e bonds wi longer maturities. For e entire period, yields on bonds from U.S. Territories wi more an ten years to maturity are about 24 basis points less an similar bonds from U. S. States wi a home-state advantage. The yield differential for e entire period, for bonds wi ree to ten years to maturity, is indistinguishable from zero. The regressions have us far lumped togeer bonds from all states wiout a home state tax advantage. It is worwhile to see if ese results are driven by bonds from specific states. I rerun e daily cross-sectional regressions including only e states wi a home state tax advantage and one of e states wi no advantage. I do is for each of e ten states wi no home state tax advantage. I report results for e excess yields of bonds from each of e no advantage states for e entire period in Table 6. There are a total of 2,007 trading days over e period, but ere are not enough trades to estimate e excess yield for each state each day. This is an especially 21

22 significant problem for Wyoming, where an estimate of e excess yield can only be obtained on 1,392 out of 2,007 days. The ird column of e table reports e percentage of days wi a premium, at is e percentage of days when bonds from e state have a higher yield an bonds from states wi a home-state tax advantage. The proportion is greater an 70% for Alaska, e District of Columbia, Illinois, Indiana, Nevada, Sou Dakota, Texas and Washington. No state has a proportion less an 50%. Wi e exception of Wisconsin and Wyoming, all of e states wi no home state tax advantage report a positive time-series mean of e excess yield at is significantly different from zero. Bonds from Alaska yield basis points more an bonds from states wi a homestate tax advantage all else equal. For Sou Dakota, e mean excess yield is basis points. Wisconsin bonds yield half a basis point more an states wi a home state tax advantage, a difference at is not statistically significant. The time series mean of e Wyoming excess yield is basis points. It is not clear why Wyoming residents continued to buy eir in-state bonds during is period. This is e least populous state, so it is likely at e yields reflect e decisions of a small number of investors and institutions. The last two columns report e proportion of e daily regressions in which e t-statistic for e excess yield is less an negative two and more an two. For each state, ere are far more days wi excess yields at are significantly greater an zero an days when excess yields are significantly less an zero. For Indiana, for example, e t-statistic on excess yield is less an negative two on 0.3% of e days, and greater an positive two on 26.5% of e days. For Alaska, t-statistics for e excess yield are less an negative two for 0.4% of e days and more an two for 47.6% of e trading days. Alough e results for Wyoming are anomalous, e clear result seems to be at yields are higher for bonds issued in states wiout a home state tax advantage. To summarize, we have learned ree ings from examining yields to maturity of municipal bond from e normal period of First, yields do differ based on e state of e issuer. Second, e differences in yields of similar bonds across states are of reasonable magnitude. Yield differences are seldom consistently greater an 20 basis points. Third, tax 22

23 treatment of in-state versus out-of-state bonds is a major reason for differences in yields across states. Bonds from states at do not offer a tax advantage for home-state bond have higher yields. Residents of ose states can always buy out-of-state bonds if yields on e in-state bonds are low. 4.4 Discussion: The Costs of Market Segmentation By restricting eir municipal bond investments to one state, investors forego many of e benefits of geographical diversification. For e period examined here, ese benefits were small. Municipal bond defaults were unusual, and were almost always confined to one municipality or project. In more recent years, e benefits of geographic diversification have increased. The likelihood of default has increased significantly for a number of cities in California, Michigan, and Illinois. Diversifying into out of state bonds can be valuable for residents of ese states. When municipal bond investments are restricted to one state, investors may also have to compromise on oer bond characteristics. An investor who wants a 12-year general obligation bond at is not callable and is insured may have a difficult time locating e bond if he restricts his search to, say, Connecticut bonds. Municipal investors are not, of course, prevented from buying out-of-state bonds. They will buy out-of-state bonds for diversification purposes or to obtain bonds wi specific characteristics. Taxes just make it more costly for em to do so. If all states exempted interest from out-of-state bonds from state taxation, investors would be better off. It may, however, mean higher interest rates for issuers from states wi high demand for and little supply of bonds. As we have seen, yields are high on bonds issued in states wi no home state tax advantage. For issuers in ese states, yields may decrease if e states start to tax interest on outof-state bonds. This will create a captive clientele for ese bonds at would oerwise invest in out-of-state bonds. 23